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0.02179896645114448, + "repetitive@256@64,all": 0.06614583333333333, + "repetitive@256@64_stderr,all": 0.02179896645114448, + "repetitive@512@64,all": 0.06614583333333333, + "repetitive@512@64_stderr,all": 0.02179896645114448, + "repetitive@1024@64,all": 0.06614583333333333, + "repetitive@1024@64_stderr,all": 0.02179896645114448, + "repetitive@2048@64,all": 0.06614583333333333, + "repetitive@2048@64_stderr,all": 0.02179896645114448, + "repetitive@4096@64,all": 0.06614583333333333, + "repetitive@4096@64_stderr,all": 0.02179896645114448, + "repetitive@8192@64,all": 0.06614583333333333, + "repetitive@8192@64_stderr,all": 0.02179896645114448, + "repetitive@16384@64,all": 0.06614583333333333, + "repetitive@16384@64_stderr,all": 0.02179896645114448, + "repetitive@32768@64,all": 0.06614583333333333, + "repetitive@32768@64_stderr,all": 0.02179896645114448, + "repetitive@65536@64,all": 0.06614583333333333, + "repetitive@65536@64_stderr,all": 0.02179896645114448, + "repetitive@131072@64,all": 0.06614583333333333, + "repetitive@131072@64_stderr,all": 0.02179896645114448, + "repetitive@262144@64,all": 0.06614583333333333, + "repetitive@262144@64_stderr,all": 0.02179896645114448 + } + }, + "group_subtasks": { + "aime25_nofigures_tok262144_agg64": [] + }, + "configs": { + "aime25_nofigures_tok262144_agg64": { + "task": "aime25_nofigures_tok262144_agg64", + "tag": [ + "math_word_problems" + ], + "dataset_path": "simplescaling/aime25_nofigures", + "dataset_name": "default", + "test_split": "train", + "process_docs": "def process_docs(dataset: Dataset) -> Dataset:\n shard_rank = int(os.getenv(\"SHARD_RANK\", \"0\"))\n shard_world_size = int(os.getenv(\"SHARD_WORLD_SIZE\", \"1\"))\n\n def _process_doc(doc: dict, idx: int) -> dict:\n out_doc = {\n \"problem\": doc.get(\"problem\", doc.get(\"question\")),\n \"answer\": doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\"))),\n \"doc_id\": idx,\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n dataset = dataset.map(_process_doc, with_indices=True)\n if shard_world_size > 1:\n dataset = dataset.filter(\n lambda doc: doc[\"doc_id\"] % shard_world_size == shard_rank\n )\n return dataset\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc.get(\"problem\", doc.get(\"question\")))\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n tokenizer=None,\n max_len=32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n\n SEP = os.getenv(\"SEP\", \"\")\n VERIFYFN = os.getenv(\"VERIFYFN\", \"verify_math\")\n\n for i, a in enumerate(results, start=1):\n if not a:\n a = \"\"\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n if len(toks) >= repeat_window:\n w = toks[-repeat_window:]\n metrics[\"repetitive\"].append(len(set(w)) / len(w) < unique_thresh)\n else:\n metrics[\"repetitive\"].append(False)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n if VERIFYFN == \"verify_math\":\n match, x, y = verify_math(a, gt, sep=SEP)[0]\n elif VERIFYFN == \"verify_generic\":\n match, x, y = verify_generic(a, gt, sep=SEP, m='gpt-4.1-mini-2025-04-14')[0]\n\n metrics[\"extracted_answers\"].append(gt if match else x)\n # if not(match): # Optional logging\n # print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = match\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(match)\n elif i > 1:\n metrics[\"exact_matches\"].append(match)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n \n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(gt == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + 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true, + "tok_ans@32768@16": true, + "tok_ans@32768@8": true, + "tok_ans@32768@4": true, + "tok_ans@32768@2": true, + "tok_ans@32768@1": true, + "tok@32768@64": true, + "tok@32768@32": true, + "tok@32768@16": true, + "tok@32768@8": true, + "tok@32768@4": true, + "tok@32768@2": true, + "tok@32768@1": true, + "too_long@32768@64": true, + "too_long@32768@32": true, + "too_long@32768@16": true, + "too_long@32768@8": true, + "too_long@32768@4": true, + "too_long@32768@2": true, + "too_long@32768@1": true, + "repetitive@32768@64": true, + "repetitive@32768@32": true, + "repetitive@32768@16": true, + "repetitive@32768@8": true, + "repetitive@32768@4": true, + "repetitive@32768@2": true, + "repetitive@32768@1": true, + "extracted_answers@32768": true, + "tok@32768": true, + "tok_think@32768": true, + "tok_ans@32768": true, + "too_long@32768": true, + "repetitive@32768": true, + "exact_matches@32768": null, + "exact_match@65536": true, + "avg@65536@64": true, + "avg@65536@32": true, + "avg@65536@16": true, + "avg@65536@8": true, + "avg@65536@4": true, + "avg@65536@2": true, + "cov@65536@64": true, + "cov@65536@32": true, + "cov@65536@16": true, + "cov@65536@8": true, + "cov@65536@4": true, + "cov@65536@2": true, + "maj@65536@64": true, + "maj@65536@32": true, + "maj@65536@16": true, + "maj@65536@8": true, + "maj@65536@4": true, + "maj@65536@2": true, + "tok_think@65536@64": true, + "tok_think@65536@32": true, + "tok_think@65536@16": true, + "tok_think@65536@8": true, + "tok_think@65536@4": true, + "tok_think@65536@2": true, + "tok_think@65536@1": true, + "tok_ans@65536@64": true, + "tok_ans@65536@32": true, + "tok_ans@65536@16": true, + "tok_ans@65536@8": true, + "tok_ans@65536@4": true, + "tok_ans@65536@2": true, + "tok_ans@65536@1": true, + "tok@65536@64": true, + "tok@65536@32": true, + "tok@65536@16": true, + "tok@65536@8": true, + "tok@65536@4": true, + "tok@65536@2": true, + "tok@65536@1": true, + "too_long@65536@64": true, + "too_long@65536@32": true, + "too_long@65536@16": true, + "too_long@65536@8": true, + "too_long@65536@4": true, + "too_long@65536@2": true, + "too_long@65536@1": true, + "repetitive@65536@64": true, + "repetitive@65536@32": true, + "repetitive@65536@16": true, + "repetitive@65536@8": true, + "repetitive@65536@4": true, + "repetitive@65536@2": true, + "repetitive@65536@1": true, + "extracted_answers@65536": true, + "tok@65536": true, + "tok_think@65536": true, + "tok_ans@65536": true, + "too_long@65536": true, + "repetitive@65536": true, + "exact_matches@65536": null, + "exact_match@131072": true, + "avg@131072@64": true, + "avg@131072@32": true, + "avg@131072@16": true, + "avg@131072@8": true, + "avg@131072@4": true, + "avg@131072@2": true, + "cov@131072@64": true, + "cov@131072@32": true, + "cov@131072@16": true, + "cov@131072@8": true, + "cov@131072@4": true, + "cov@131072@2": true, + "maj@131072@64": true, + "maj@131072@32": true, + "maj@131072@16": true, + "maj@131072@8": true, + "maj@131072@4": true, + "maj@131072@2": true, + "tok_think@131072@64": true, + "tok_think@131072@32": true, + "tok_think@131072@16": true, + "tok_think@131072@8": true, + "tok_think@131072@4": true, + "tok_think@131072@2": true, + "tok_think@131072@1": true, + "tok_ans@131072@64": true, + "tok_ans@131072@32": true, + "tok_ans@131072@16": true, + "tok_ans@131072@8": true, + "tok_ans@131072@4": true, + "tok_ans@131072@2": true, + "tok_ans@131072@1": true, + "tok@131072@64": true, + "tok@131072@32": true, + "tok@131072@16": true, + "tok@131072@8": true, + "tok@131072@4": true, + "tok@131072@2": true, + "tok@131072@1": true, + "too_long@131072@64": true, + "too_long@131072@32": true, + "too_long@131072@16": true, + "too_long@131072@8": true, + "too_long@131072@4": true, + "too_long@131072@2": true, + "too_long@131072@1": true, + "repetitive@131072@64": true, + "repetitive@131072@32": true, + "repetitive@131072@16": true, + "repetitive@131072@8": true, + "repetitive@131072@4": true, + "repetitive@131072@2": true, 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true, + "tok_ans@262144@8": true, + "tok_ans@262144@4": true, + "tok_ans@262144@2": true, + "tok_ans@262144@1": true, + "tok@262144@64": true, + "tok@262144@32": true, + "tok@262144@16": true, + "tok@262144@8": true, + "tok@262144@4": true, + "tok@262144@2": true, + "tok@262144@1": true, + "too_long@262144@64": true, + "too_long@262144@32": true, + "too_long@262144@16": true, + "too_long@262144@8": true, + "too_long@262144@4": true, + "too_long@262144@2": true, + "too_long@262144@1": true, + "repetitive@262144@64": true, + "repetitive@262144@32": true, + "repetitive@262144@16": true, + "repetitive@262144@8": true, + "repetitive@262144@4": true, + "repetitive@262144@2": true, + "repetitive@262144@1": true, + "extracted_answers@262144": true, + "tok@262144": true, + "tok_think@262144": true, + "tok_ans@262144": true, + "too_long@262144": true, + "repetitive@262144": true, + "exact_matches@262144": null + } + }, + "n-samples": { + "aime25_nofigures_tok262144_agg64": { + "original": 30, + "effective": 30 + } + }, + "config": { + "model": "vllm-tool", + "model_args": { + "pretrained": "Qwen/Qwen3-1.7B", + "tensor_parallel_size": 1, + "data_parallel_size": 1, + "dtype": "auto", + "hf_overrides": { + "max_position_embeddings": 270000 + } + }, + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 262144, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "3b87dedd", + "date": 1787528857.7491658, + "pretty_env_info": "PyTorch version: 2.9.1+cu128\nIs debug build: False\nCUDA used to build PyTorch: 12.8\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 24.04.4 LTS (x86_64)\nGCC version: (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.39\n\nPython version: 3.12.13 | packaged by Anaconda, Inc. | (main, Jul 9 2026, 14:38:16) [GCC 14.3.0] (64-bit runtime)\nPython platform: Linux-6.17.0-1020-gcp-x86_64-with-glibc2.39\nIs CUDA available: True\nCUDA runtime version: 12.8.93\nCUDA_MODULE_LOADING set to: \nGPU models and configuration: \nGPU 0: NVIDIA H100 80GB HBM3\nGPU 1: NVIDIA H100 80GB HBM3\nGPU 2: NVIDIA H100 80GB HBM3\nGPU 3: NVIDIA H100 80GB HBM3\nGPU 4: NVIDIA H100 80GB HBM3\nGPU 5: NVIDIA H100 80GB HBM3\nGPU 6: NVIDIA H100 80GB HBM3\nGPU 7: NVIDIA H100 80GB HBM3\n\nNvidia driver version: 595.71.05\ncuDNN version: Could not collect\nIs XPU available: False\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 208\nOn-line CPU(s) list: 0-207\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 52\nSocket(s): 2\nStepping: 8\nBogoMIPS: 5399.99\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rtm avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 arat avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.9 MiB (104 instances)\nL1i cache: 3.3 MiB (104 instances)\nL2 cache: 208 MiB (104 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-51,104-155\nNUMA node1 CPU(s): 52-103,156-207\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Old microcode: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\nVersions of relevant libraries:\n[pip3] mypy_extensions==1.1.0\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cudnn-frontend==1.27.0\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pip3] nvidia-curand-cu12==10.3.9.90\n[pip3] nvidia-cusolver-cu12==11.7.3.90\n[pip3] nvidia-cusparse-cu12==12.5.8.93\n[pip3] nvidia-cusparselt-cu12==0.7.1\n[pip3] nvidia-nccl-cu12==2.27.5\n[pip3] nvidia-nvjitlink-cu12==12.8.93\n[pip3] nvidia-nvtx-cu12==12.8.90\n[pip3] torch==2.9.1\n[pip3] torchaudio==2.9.1\n[pip3] torchvision==0.24.1\n[pip3] triton==3.5.1\n[conda] cuda-cudart 12.8.90 0 nvidia\n[conda] cuda-cudart-dev 12.8.90 0 nvidia\n[conda] cuda-cudart-dev_linux-64 12.8.90 0 nvidia\n[conda] cuda-cudart-static 12.8.90 0 nvidia\n[conda] cuda-cudart-static_linux-64 12.8.90 0 nvidia\n[conda] cuda-cudart_linux-64 12.8.90 0 nvidia\n[conda] cuda-cupti 12.8.90 0 nvidia\n[conda] cuda-cupti-dev 12.8.90 0 nvidia\n[conda] cuda-libraries 12.8.2 0 nvidia\n[conda] cuda-libraries-dev 12.8.2 0 nvidia\n[conda] cuda-nvrtc 12.8.93 0 nvidia\n[conda] cuda-nvrtc-dev 12.8.93 0 nvidia\n[conda] cuda-nvtx 12.8.90 0 nvidia\n[conda] cuda-opencl 12.8.90 0 nvidia\n[conda] cuda-opencl-dev 12.8.90 0 nvidia\n[conda] libcublas 12.8.5.5 h2bcd275_0 nvidia\n[conda] libcublas-dev 12.8.5.5 h2bcd275_0 nvidia\n[conda] libcufft 11.3.3.83 0 nvidia\n[conda] libcufft-dev 11.3.3.83 0 nvidia\n[conda] libcurand 10.3.9.90 0 nvidia\n[conda] libcurand-dev 10.3.9.90 0 nvidia\n[conda] libcusolver 11.7.3.90 0 nvidia\n[conda] libcusolver-dev 11.7.3.90 0 nvidia\n[conda] libcusparse 12.5.8.93 0 nvidia\n[conda] libcusparse-dev 12.5.8.93 0 nvidia\n[conda] libnvjitlink 12.8.93 1 nvidia\n[conda] libnvjitlink-dev 12.8.93 1 nvidia\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.8.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.27.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.3.83 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.9.90 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.3.90 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.8.93 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.7.1 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.27.5 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.8.90 pypi_0 pypi\n[conda] torch 2.9.1 pypi_0 pypi\n[conda] torchaudio 2.9.1 pypi_0 pypi\n[conda] torchvision 0.24.1 pypi_0 pypi\n[conda] triton 3.5.1 pypi_0 pypi", + "transformers_version": "4.57.6", + "lm_eval_version": "0.4.8", + "upper_git_hash": null, + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 270000, + "task_hashes": { + "aime25_nofigures_tok262144_agg64": "894a3709f96f30244251ac51dc749939ee86662d410567e0de887d6ff7a321d0" + }, + "model_source": "vllm-tool", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": 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"repetitive@262144@64,all": 0.0015625, + "repetitive@262144@64_stderr,all": 0.0008704468977049306 + } + }, + "group_subtasks": { + "aime25_nofigures_tok262144_agg64": [] + }, + "configs": { + "aime25_nofigures_tok262144_agg64": { + "task": "aime25_nofigures_tok262144_agg64", + "tag": [ + "math_word_problems" + ], + "dataset_path": "simplescaling/aime25_nofigures", + "dataset_name": "default", + "test_split": "train", + "process_docs": "def process_docs(dataset: Dataset) -> Dataset:\n shard_rank = int(os.getenv(\"SHARD_RANK\", \"0\"))\n shard_world_size = int(os.getenv(\"SHARD_WORLD_SIZE\", \"1\"))\n\n def _process_doc(doc: dict, idx: int) -> dict:\n out_doc = {\n \"problem\": doc.get(\"problem\", doc.get(\"question\")),\n \"answer\": doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\"))),\n \"doc_id\": idx,\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n dataset = dataset.map(_process_doc, with_indices=True)\n if shard_world_size > 1:\n dataset = dataset.filter(\n lambda doc: doc[\"doc_id\"] % shard_world_size == shard_rank\n )\n return dataset\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc.get(\"problem\", doc.get(\"question\")))\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n tokenizer=None,\n max_len=32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n\n SEP = os.getenv(\"SEP\", \"\")\n VERIFYFN = os.getenv(\"VERIFYFN\", \"verify_math\")\n\n for i, a in enumerate(results, start=1):\n if not a:\n a = \"\"\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n if len(toks) >= repeat_window:\n w = toks[-repeat_window:]\n metrics[\"repetitive\"].append(len(set(w)) / len(w) < unique_thresh)\n else:\n metrics[\"repetitive\"].append(False)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n if VERIFYFN == \"verify_math\":\n match, x, y = verify_math(a, gt, sep=SEP)[0]\n elif VERIFYFN == \"verify_generic\":\n match, x, y = verify_generic(a, gt, sep=SEP, m='gpt-4.1-mini-2025-04-14')[0]\n\n metrics[\"extracted_answers\"].append(gt if match else x)\n # if not(match): # Optional logging\n # print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = match\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(match)\n elif i > 1:\n metrics[\"exact_matches\"].append(match)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n \n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(gt == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@64", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@64", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@128", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@128", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@128", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@256", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@32", + "aggregation": 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"cov@65536@4": true, + "cov@65536@2": true, + "maj@65536@64": true, + "maj@65536@32": true, + "maj@65536@16": true, + "maj@65536@8": true, + "maj@65536@4": true, + "maj@65536@2": true, + "tok_think@65536@64": true, + "tok_think@65536@32": true, + "tok_think@65536@16": true, + "tok_think@65536@8": true, + "tok_think@65536@4": true, + "tok_think@65536@2": true, + "tok_think@65536@1": true, + "tok_ans@65536@64": true, + "tok_ans@65536@32": true, + "tok_ans@65536@16": true, + "tok_ans@65536@8": true, + "tok_ans@65536@4": true, + "tok_ans@65536@2": true, + "tok_ans@65536@1": true, + "tok@65536@64": true, + "tok@65536@32": true, + "tok@65536@16": true, + "tok@65536@8": true, + "tok@65536@4": true, + "tok@65536@2": true, + "tok@65536@1": true, + "too_long@65536@64": true, + "too_long@65536@32": true, + "too_long@65536@16": true, + "too_long@65536@8": true, + "too_long@65536@4": true, + "too_long@65536@2": true, + "too_long@65536@1": true, + "repetitive@65536@64": true, + "repetitive@65536@32": true, + "repetitive@65536@16": true, + "repetitive@65536@8": true, + "repetitive@65536@4": true, + "repetitive@65536@2": true, + "repetitive@65536@1": true, + "extracted_answers@65536": true, + "tok@65536": true, + "tok_think@65536": true, + "tok_ans@65536": true, + "too_long@65536": true, + "repetitive@65536": true, + "exact_matches@65536": null, + "exact_match@131072": true, + "avg@131072@64": true, + "avg@131072@32": true, + "avg@131072@16": true, + "avg@131072@8": true, + "avg@131072@4": true, + "avg@131072@2": true, + "cov@131072@64": true, + "cov@131072@32": true, + "cov@131072@16": true, + "cov@131072@8": true, + "cov@131072@4": true, + "cov@131072@2": true, + "maj@131072@64": true, + "maj@131072@32": true, + "maj@131072@16": true, + "maj@131072@8": true, + "maj@131072@4": true, + "maj@131072@2": true, + "tok_think@131072@64": true, + "tok_think@131072@32": true, + "tok_think@131072@16": true, + "tok_think@131072@8": true, + "tok_think@131072@4": true, + "tok_think@131072@2": true, + "tok_think@131072@1": true, + "tok_ans@131072@64": true, + "tok_ans@131072@32": true, + "tok_ans@131072@16": true, + "tok_ans@131072@8": true, + "tok_ans@131072@4": true, + "tok_ans@131072@2": true, + "tok_ans@131072@1": true, + "tok@131072@64": true, + "tok@131072@32": true, + "tok@131072@16": true, + "tok@131072@8": true, + "tok@131072@4": true, + "tok@131072@2": true, + "tok@131072@1": true, + "too_long@131072@64": true, + "too_long@131072@32": true, + "too_long@131072@16": true, + "too_long@131072@8": true, + "too_long@131072@4": true, + "too_long@131072@2": true, + "too_long@131072@1": true, + "repetitive@131072@64": true, + "repetitive@131072@32": true, + "repetitive@131072@16": true, + "repetitive@131072@8": true, + "repetitive@131072@4": true, + "repetitive@131072@2": true, + "repetitive@131072@1": true, + "extracted_answers@131072": true, + "tok@131072": true, + "tok_think@131072": true, + "tok_ans@131072": true, + "too_long@131072": true, + "repetitive@131072": true, + "exact_matches@131072": null, + "exact_match@262144": true, + "avg@262144@64": true, + "avg@262144@32": true, + "avg@262144@16": true, + "avg@262144@8": true, + "avg@262144@4": true, + "avg@262144@2": true, + "cov@262144@64": true, + "cov@262144@32": true, + "cov@262144@16": true, + "cov@262144@8": true, + "cov@262144@4": true, + "cov@262144@2": true, + "maj@262144@64": true, + "maj@262144@32": true, + "maj@262144@16": true, + "maj@262144@8": true, + "maj@262144@4": true, + "maj@262144@2": true, + "tok_think@262144@64": true, + "tok_think@262144@32": true, + "tok_think@262144@16": true, + "tok_think@262144@8": true, + "tok_think@262144@4": true, + "tok_think@262144@2": true, + "tok_think@262144@1": true, + "tok_ans@262144@64": true, + "tok_ans@262144@32": true, + "tok_ans@262144@16": true, + "tok_ans@262144@8": true, + "tok_ans@262144@4": true, + "tok_ans@262144@2": true, + "tok_ans@262144@1": true, + "tok@262144@64": true, + "tok@262144@32": true, + "tok@262144@16": true, + "tok@262144@8": true, + "tok@262144@4": true, + "tok@262144@2": true, + "tok@262144@1": true, + "too_long@262144@64": true, + "too_long@262144@32": true, + "too_long@262144@16": true, + "too_long@262144@8": true, + "too_long@262144@4": true, + "too_long@262144@2": true, + "too_long@262144@1": true, + "repetitive@262144@64": true, + "repetitive@262144@32": true, + "repetitive@262144@16": true, + "repetitive@262144@8": true, + "repetitive@262144@4": true, + "repetitive@262144@2": true, + "repetitive@262144@1": true, + "extracted_answers@262144": true, + "tok@262144": true, + "tok_think@262144": true, + "tok_ans@262144": true, + "too_long@262144": true, + "repetitive@262144": true, + "exact_matches@262144": null + } + }, + "n-samples": { + "aime25_nofigures_tok262144_agg64": { + "original": 30, + "effective": 30 + } + }, + "config": { + "model": "vllm-tool", + "model_args": { + "pretrained": "Qwen/Qwen3-1.7B", + "tensor_parallel_size": 1, + "data_parallel_size": 1, + "dtype": "auto", + "hf_overrides": { + "max_position_embeddings": 270000 + } + }, + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 262144, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "3b87dedd", + "date": 1787529689.3881524, + "pretty_env_info": "PyTorch version: 2.9.1+cu128\nIs debug build: False\nCUDA used to build PyTorch: 12.8\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 24.04.4 LTS (x86_64)\nGCC version: (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.39\n\nPython version: 3.12.13 | packaged by Anaconda, Inc. | (main, Jul 9 2026, 14:38:16) [GCC 14.3.0] (64-bit runtime)\nPython platform: Linux-6.17.0-1020-gcp-x86_64-with-glibc2.39\nIs CUDA available: True\nCUDA runtime version: 12.8.93\nCUDA_MODULE_LOADING set to: \nGPU models and configuration: \nGPU 0: NVIDIA H100 80GB HBM3\nGPU 1: NVIDIA H100 80GB HBM3\nGPU 2: NVIDIA H100 80GB HBM3\nGPU 3: NVIDIA H100 80GB HBM3\nGPU 4: NVIDIA H100 80GB HBM3\nGPU 5: NVIDIA H100 80GB HBM3\nGPU 6: NVIDIA H100 80GB HBM3\nGPU 7: NVIDIA H100 80GB HBM3\n\nNvidia driver version: 595.71.05\ncuDNN version: Could not collect\nIs XPU available: False\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 208\nOn-line CPU(s) list: 0-207\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 52\nSocket(s): 2\nStepping: 8\nBogoMIPS: 5399.99\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rtm avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 arat avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.9 MiB (104 instances)\nL1i cache: 3.3 MiB (104 instances)\nL2 cache: 208 MiB (104 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-51,104-155\nNUMA node1 CPU(s): 52-103,156-207\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Old microcode: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\nVersions of relevant libraries:\n[pip3] mypy_extensions==1.1.0\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cudnn-frontend==1.27.0\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pip3] nvidia-curand-cu12==10.3.9.90\n[pip3] nvidia-cusolver-cu12==11.7.3.90\n[pip3] nvidia-cusparse-cu12==12.5.8.93\n[pip3] nvidia-cusparselt-cu12==0.7.1\n[pip3] nvidia-nccl-cu12==2.27.5\n[pip3] nvidia-nvjitlink-cu12==12.8.93\n[pip3] nvidia-nvtx-cu12==12.8.90\n[pip3] torch==2.9.1\n[pip3] torchaudio==2.9.1\n[pip3] torchvision==0.24.1\n[pip3] triton==3.5.1\n[conda] cuda-cudart 12.8.90 0 nvidia\n[conda] cuda-cudart-dev 12.8.90 0 nvidia\n[conda] cuda-cudart-dev_linux-64 12.8.90 0 nvidia\n[conda] cuda-cudart-static 12.8.90 0 nvidia\n[conda] cuda-cudart-static_linux-64 12.8.90 0 nvidia\n[conda] cuda-cudart_linux-64 12.8.90 0 nvidia\n[conda] cuda-cupti 12.8.90 0 nvidia\n[conda] cuda-cupti-dev 12.8.90 0 nvidia\n[conda] cuda-libraries 12.8.2 0 nvidia\n[conda] cuda-libraries-dev 12.8.2 0 nvidia\n[conda] cuda-nvrtc 12.8.93 0 nvidia\n[conda] cuda-nvrtc-dev 12.8.93 0 nvidia\n[conda] cuda-nvtx 12.8.90 0 nvidia\n[conda] cuda-opencl 12.8.90 0 nvidia\n[conda] cuda-opencl-dev 12.8.90 0 nvidia\n[conda] libcublas 12.8.5.5 h2bcd275_0 nvidia\n[conda] libcublas-dev 12.8.5.5 h2bcd275_0 nvidia\n[conda] libcufft 11.3.3.83 0 nvidia\n[conda] libcufft-dev 11.3.3.83 0 nvidia\n[conda] libcurand 10.3.9.90 0 nvidia\n[conda] libcurand-dev 10.3.9.90 0 nvidia\n[conda] libcusolver 11.7.3.90 0 nvidia\n[conda] libcusolver-dev 11.7.3.90 0 nvidia\n[conda] libcusparse 12.5.8.93 0 nvidia\n[conda] libcusparse-dev 12.5.8.93 0 nvidia\n[conda] libnvjitlink 12.8.93 1 nvidia\n[conda] libnvjitlink-dev 12.8.93 1 nvidia\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.8.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.27.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.3.83 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.9.90 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.3.90 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.8.93 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.7.1 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.27.5 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.8.90 pypi_0 pypi\n[conda] torch 2.9.1 pypi_0 pypi\n[conda] torchaudio 2.9.1 pypi_0 pypi\n[conda] torchvision 0.24.1 pypi_0 pypi\n[conda] triton 3.5.1 pypi_0 pypi", + "transformers_version": "4.57.6", + "lm_eval_version": "0.4.8", + "upper_git_hash": null, + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 270000, + "task_hashes": { + "aime25_nofigures_tok262144_agg64": "5d70cd9bdf77402eee3c6bcfe4d9bd0f5e0e6e76dda02bb72917e528607dda22" + }, + "model_source": "vllm-tool", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n\n{\"type\": \"function\", \"function\": {\"name\": \"pass\", \"description\": \"Passes the task to the next model to solve the problem from where you left off. Useful when thinking gets too long.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"context\": {\"type\": \"string\", \"description\": \"Information to pass to the next model. E.g., ideas already tried, key results, next steps to perform...\"}}, \"required\": [\"context\"]}}}\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n\n\nBelow is an example of using the pass tool:\n<|im_start|>user\nThere exist real numbers $x$ and $y$, both greater than 1, such that $\\log_x\\left(y^x\\right)=\\log_y\\left(x^{4y}\\right)=10$. Find $xy$.<|im_end|>\n<|im_start|>assistant\n\nOkay, so I need to solve this problem where there are real numbers x and y, both greater than 1, such that log base x of (y^x) equals log base y of (x^{4y}) equals 10. And I need to find the product xy. Hmm, let me think.\n\nFirst, let me recall some logarithm properties. The equation says that two different logarithms are equal to 10. So, log_x(y^x) = 10 and log_y(x^{4y}) = 10. Maybe I can convert these logarithmic equations into exponential form to make them easier to handle.\n\nStarting with the first one: log_x(y^x) = 10. Using the logarithm power rule, which says that log_b(a^c) = c*log_b(a). So, applying that here, log_x(y^x) = x * log_x(y) = 10. So, x * log_x(y) = 10. \n\nBut log_x(y) is the same as 1 / log_y(x), by the change of base formula. Wait, but maybe I can express log_x(y) in terms of something else. Let me\n\n{\"name\": \"pass\", \"arguments\": {\"context\": \"Maybe convert logarithmic equations into exponential form to make them easier to handle. Via logarithm power rule log_x(y^x) = x * log_x(y) = 10. Noting that log_x(y) is the same as 1 / log_y(x). Maybe try express log_x(y) in terms of something else.\"}}\n\n\nWhen context is passed to you, evaluate its correctness first and then use it if correct.\n<|im_start|>user\nThere exist real numbers $x$ and $y$, both greater than 1, such that $\\log_x\\left(y^x\\right)=\\log_y\\left(x^{4y}\\right)=10$. Find $xy$.<|im_end|>\n<|im_start|>assistant\n\nMaybe convert logarithmic equations into exponential form to make them easier to handle. Via logarithm power rule log_x(y^x) = x * log_x(y) = 10. Noting that log_x(y) is the same as 1 / log_y(x). Maybe try express log_x(y) in terms of something else.\n\n\nOkay, so I need to first evaluate whether the context is correct and then use it to solve the problem.\n\nThe context suggests converting logarithmic equations into exponential form and using the logarithm power rule, which is a valid approach. It also notes that log_x(y) can be expressed in terms of log_y(x), which is correct. It suggests expressing log_x(y) in terms of something else.\n\nWait, do we really need to express log_x(y) in terms of something else? Maybe we can instead first use the logarithm power rule for the second equation as well. Let me do that...", + "system_instruction_sha": "af4ce18eddb698d9a7539a322f42d049ebc171d42d6f22f06a278046ddd3b11d", + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "a55ee1b1660128b7098723e0abcd92caa0788061051c62d51cbe87d9cf1974d8", + "start_time": 4667965.581867413, + "end_time": 4708701.291236726, + "total_evaluation_time_seconds": "40735.70936931297" +} \ No newline at end of file diff --git a/Qwen3-1.7B-SUMM/w4096s256context/toks262144/results_concatted_tok262144.json 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doc.get(\"orig_orig_answer\"))),\n \"doc_id\": idx,\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n dataset = dataset.map(_process_doc, with_indices=True)\n if shard_world_size > 1:\n dataset = dataset.filter(\n lambda doc: doc[\"doc_id\"] % shard_world_size == shard_rank\n )\n return dataset\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc.get(\"problem\", doc.get(\"question\")))\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n tokenizer=None,\n max_len=32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n\n SEP = os.getenv(\"SEP\", \"\")\n VERIFYFN = os.getenv(\"VERIFYFN\", \"verify_math\")\n\n for i, a in enumerate(results, start=1):\n if not a:\n a = \"\"\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n if len(toks) >= repeat_window:\n w = toks[-repeat_window:]\n metrics[\"repetitive\"].append(len(set(w)) / len(w) < unique_thresh)\n else:\n metrics[\"repetitive\"].append(False)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n if VERIFYFN == \"verify_math\":\n match, x, y = verify_math(a, gt, sep=SEP)[0]\n elif VERIFYFN == \"verify_generic\":\n match, x, y = verify_generic(a, gt, sep=SEP, m='gpt-4.1-mini-2025-04-14')[0]\n\n metrics[\"extracted_answers\"].append(gt if match else x)\n # if not(match): # Optional logging\n # print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = match\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(match)\n elif i > 1:\n metrics[\"exact_matches\"].append(match)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n \n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(gt == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + 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"tok@8192@32": true, + "tok@8192@16": true, + "tok@8192@8": true, + "tok@8192@4": true, + "tok@8192@2": true, + "tok@8192@1": true, + "too_long@8192@64": true, + "too_long@8192@32": true, + "too_long@8192@16": true, + "too_long@8192@8": true, + "too_long@8192@4": true, + "too_long@8192@2": true, + "too_long@8192@1": true, + "repetitive@8192@64": true, + "repetitive@8192@32": true, + "repetitive@8192@16": true, + "repetitive@8192@8": true, + "repetitive@8192@4": true, + "repetitive@8192@2": true, + "repetitive@8192@1": true, + "extracted_answers@8192": true, + "tok@8192": true, + "tok_think@8192": true, + "tok_ans@8192": true, + "too_long@8192": true, + "repetitive@8192": true, + "exact_matches@8192": null, + "exact_match@16384": true, + "avg@16384@64": true, + "avg@16384@32": true, + "avg@16384@16": true, + "avg@16384@8": true, + "avg@16384@4": true, + "avg@16384@2": true, + "cov@16384@64": true, + "cov@16384@32": true, + "cov@16384@16": true, + "cov@16384@8": true, + "cov@16384@4": 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"repetitive@16384@16": true, + "repetitive@16384@8": true, + "repetitive@16384@4": true, + "repetitive@16384@2": true, + "repetitive@16384@1": true, + "extracted_answers@16384": true, + "tok@16384": true, + "tok_think@16384": true, + "tok_ans@16384": true, + "too_long@16384": true, + "repetitive@16384": true, + "exact_matches@16384": null, + "exact_match@32768": true, + "avg@32768@64": true, + "avg@32768@32": true, + "avg@32768@16": true, + "avg@32768@8": true, + "avg@32768@4": true, + "avg@32768@2": true, + "cov@32768@64": true, + "cov@32768@32": true, + "cov@32768@16": true, + "cov@32768@8": true, + "cov@32768@4": true, + "cov@32768@2": true, + "maj@32768@64": true, + "maj@32768@32": true, + "maj@32768@16": true, + "maj@32768@8": true, + "maj@32768@4": true, + "maj@32768@2": true, + "tok_think@32768@64": true, + "tok_think@32768@32": true, + "tok_think@32768@16": true, + "tok_think@32768@8": true, + "tok_think@32768@4": true, + "tok_think@32768@2": true, + "tok_think@32768@1": true, + "tok_ans@32768@64": true, + "tok_ans@32768@32": true, + "tok_ans@32768@16": true, + "tok_ans@32768@8": true, + "tok_ans@32768@4": true, + "tok_ans@32768@2": true, + "tok_ans@32768@1": true, + "tok@32768@64": true, + "tok@32768@32": true, + "tok@32768@16": true, + "tok@32768@8": true, + "tok@32768@4": true, + "tok@32768@2": true, + "tok@32768@1": true, + "too_long@32768@64": true, + "too_long@32768@32": true, + "too_long@32768@16": true, + "too_long@32768@8": true, + "too_long@32768@4": true, + "too_long@32768@2": true, + "too_long@32768@1": true, + "repetitive@32768@64": true, + "repetitive@32768@32": true, + "repetitive@32768@16": true, + "repetitive@32768@8": true, + "repetitive@32768@4": true, + "repetitive@32768@2": true, + "repetitive@32768@1": true, + "extracted_answers@32768": true, + "tok@32768": true, + "tok_think@32768": true, + "tok_ans@32768": true, + "too_long@32768": true, + "repetitive@32768": true, + "exact_matches@32768": null, + "exact_match@65536": true, + "avg@65536@64": true, + "avg@65536@32": true, + "avg@65536@16": true, + "avg@65536@8": true, + "avg@65536@4": true, + "avg@65536@2": true, + "cov@65536@64": true, + "cov@65536@32": true, + "cov@65536@16": true, + "cov@65536@8": true, + "cov@65536@4": true, + "cov@65536@2": true, + "maj@65536@64": true, + "maj@65536@32": true, + "maj@65536@16": true, + "maj@65536@8": true, + "maj@65536@4": true, + "maj@65536@2": true, + "tok_think@65536@64": true, + "tok_think@65536@32": true, + "tok_think@65536@16": true, + "tok_think@65536@8": true, + "tok_think@65536@4": true, + "tok_think@65536@2": true, + "tok_think@65536@1": true, + "tok_ans@65536@64": true, + "tok_ans@65536@32": true, + "tok_ans@65536@16": true, + "tok_ans@65536@8": true, + "tok_ans@65536@4": true, + "tok_ans@65536@2": true, + "tok_ans@65536@1": true, + "tok@65536@64": true, + "tok@65536@32": true, + "tok@65536@16": true, + "tok@65536@8": true, + "tok@65536@4": true, + "tok@65536@2": true, + "tok@65536@1": true, + "too_long@65536@64": true, + "too_long@65536@32": true, + "too_long@65536@16": true, + "too_long@65536@8": true, + "too_long@65536@4": true, + "too_long@65536@2": true, + "too_long@65536@1": true, + "repetitive@65536@64": true, + "repetitive@65536@32": true, + "repetitive@65536@16": true, + "repetitive@65536@8": true, + "repetitive@65536@4": true, + "repetitive@65536@2": true, + "repetitive@65536@1": true, + "extracted_answers@65536": true, + "tok@65536": true, + "tok_think@65536": true, + "tok_ans@65536": true, + "too_long@65536": true, + "repetitive@65536": true, + "exact_matches@65536": null, + "exact_match@131072": true, + "avg@131072@64": true, + "avg@131072@32": true, + "avg@131072@16": true, + "avg@131072@8": true, + "avg@131072@4": true, + "avg@131072@2": true, + "cov@131072@64": true, + "cov@131072@32": true, + "cov@131072@16": true, + "cov@131072@8": true, + "cov@131072@4": true, + "cov@131072@2": true, + "maj@131072@64": true, + "maj@131072@32": true, + "maj@131072@16": true, + "maj@131072@8": true, + "maj@131072@4": true, + "maj@131072@2": true, + "tok_think@131072@64": true, + "tok_think@131072@32": true, + "tok_think@131072@16": true, + "tok_think@131072@8": true, + "tok_think@131072@4": true, + "tok_think@131072@2": true, + "tok_think@131072@1": true, + "tok_ans@131072@64": true, + "tok_ans@131072@32": true, + "tok_ans@131072@16": true, + "tok_ans@131072@8": true, + "tok_ans@131072@4": true, + "tok_ans@131072@2": true, + "tok_ans@131072@1": true, + "tok@131072@64": true, + "tok@131072@32": true, + "tok@131072@16": true, + "tok@131072@8": true, + "tok@131072@4": true, + "tok@131072@2": true, + "tok@131072@1": true, + "too_long@131072@64": true, + "too_long@131072@32": true, + "too_long@131072@16": true, + "too_long@131072@8": true, + "too_long@131072@4": true, + "too_long@131072@2": true, + "too_long@131072@1": true, + "repetitive@131072@64": true, + "repetitive@131072@32": true, + "repetitive@131072@16": true, + "repetitive@131072@8": true, + "repetitive@131072@4": true, + "repetitive@131072@2": true, + "repetitive@131072@1": true, + "extracted_answers@131072": true, + "tok@131072": true, + "tok_think@131072": true, + "tok_ans@131072": true, + "too_long@131072": true, + "repetitive@131072": true, + "exact_matches@131072": null, + "exact_match@262144": true, + "avg@262144@64": true, + "avg@262144@32": true, + "avg@262144@16": true, + "avg@262144@8": true, + "avg@262144@4": true, + "avg@262144@2": true, + "cov@262144@64": true, + "cov@262144@32": true, + "cov@262144@16": true, + "cov@262144@8": true, + "cov@262144@4": true, + "cov@262144@2": true, + "maj@262144@64": true, + "maj@262144@32": true, + "maj@262144@16": true, + "maj@262144@8": true, + "maj@262144@4": true, + "maj@262144@2": true, + "tok_think@262144@64": true, + "tok_think@262144@32": true, + "tok_think@262144@16": true, + "tok_think@262144@8": true, + "tok_think@262144@4": true, + "tok_think@262144@2": true, + "tok_think@262144@1": true, + "tok_ans@262144@64": true, + "tok_ans@262144@32": true, + "tok_ans@262144@16": true, + "tok_ans@262144@8": true, + "tok_ans@262144@4": true, + "tok_ans@262144@2": true, + "tok_ans@262144@1": true, + "tok@262144@64": true, + "tok@262144@32": true, + "tok@262144@16": true, + "tok@262144@8": true, + "tok@262144@4": true, + "tok@262144@2": true, + "tok@262144@1": true, + "too_long@262144@64": true, + "too_long@262144@32": true, + "too_long@262144@16": true, + "too_long@262144@8": true, + "too_long@262144@4": true, + "too_long@262144@2": true, + "too_long@262144@1": true, + "repetitive@262144@64": true, + "repetitive@262144@32": true, + "repetitive@262144@16": true, + "repetitive@262144@8": true, + "repetitive@262144@4": true, + "repetitive@262144@2": true, + "repetitive@262144@1": true, + "extracted_answers@262144": true, + "tok@262144": true, + "tok_think@262144": true, + "tok_ans@262144": true, + "too_long@262144": true, + "repetitive@262144": true, + "exact_matches@262144": null + } + }, + "n-samples": { + "aime25_nofigures_tok262144_agg64": { + "original": 30, + "effective": 30 + } + }, + "config": { + "model": "vllm-tool", + "model_args": { + "pretrained": "Qwen/Qwen3-1.7B", + "tensor_parallel_size": 1, + "data_parallel_size": 1, + "dtype": "auto", + "hf_overrides": { + "max_position_embeddings": 270000 + } + }, + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 262144, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "3b87dedd", + "date": 1787529650.2206016, + "pretty_env_info": "PyTorch version: 2.9.1+cu128\nIs debug build: False\nCUDA used to build PyTorch: 12.8\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 24.04.4 LTS (x86_64)\nGCC version: (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.39\n\nPython version: 3.12.13 | packaged by Anaconda, Inc. | (main, Jul 9 2026, 14:38:16) [GCC 14.3.0] (64-bit runtime)\nPython platform: Linux-6.17.0-1020-gcp-x86_64-with-glibc2.39\nIs CUDA available: True\nCUDA runtime version: 12.8.93\nCUDA_MODULE_LOADING set to: \nGPU models and configuration: \nGPU 0: NVIDIA H100 80GB HBM3\nGPU 1: NVIDIA H100 80GB HBM3\nGPU 2: NVIDIA H100 80GB HBM3\nGPU 3: NVIDIA H100 80GB HBM3\nGPU 4: NVIDIA H100 80GB HBM3\nGPU 5: NVIDIA H100 80GB HBM3\nGPU 6: NVIDIA H100 80GB HBM3\nGPU 7: NVIDIA H100 80GB HBM3\n\nNvidia driver version: 595.71.05\ncuDNN version: Could not collect\nIs XPU available: False\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 208\nOn-line CPU(s) list: 0-207\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 52\nSocket(s): 2\nStepping: 8\nBogoMIPS: 5399.99\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rtm avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 arat avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.9 MiB (104 instances)\nL1i cache: 3.3 MiB (104 instances)\nL2 cache: 208 MiB (104 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-51,104-155\nNUMA node1 CPU(s): 52-103,156-207\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Old microcode: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\nVersions of relevant libraries:\n[pip3] mypy_extensions==1.1.0\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cudnn-frontend==1.27.0\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pip3] nvidia-curand-cu12==10.3.9.90\n[pip3] nvidia-cusolver-cu12==11.7.3.90\n[pip3] nvidia-cusparse-cu12==12.5.8.93\n[pip3] nvidia-cusparselt-cu12==0.7.1\n[pip3] nvidia-nccl-cu12==2.27.5\n[pip3] nvidia-nvjitlink-cu12==12.8.93\n[pip3] nvidia-nvtx-cu12==12.8.90\n[pip3] torch==2.9.1\n[pip3] torchaudio==2.9.1\n[pip3] torchvision==0.24.1\n[pip3] triton==3.5.1\n[conda] cuda-cudart 12.8.90 0 nvidia\n[conda] cuda-cudart-dev 12.8.90 0 nvidia\n[conda] cuda-cudart-dev_linux-64 12.8.90 0 nvidia\n[conda] cuda-cudart-static 12.8.90 0 nvidia\n[conda] cuda-cudart-static_linux-64 12.8.90 0 nvidia\n[conda] cuda-cudart_linux-64 12.8.90 0 nvidia\n[conda] cuda-cupti 12.8.90 0 nvidia\n[conda] cuda-cupti-dev 12.8.90 0 nvidia\n[conda] cuda-libraries 12.8.2 0 nvidia\n[conda] cuda-libraries-dev 12.8.2 0 nvidia\n[conda] cuda-nvrtc 12.8.93 0 nvidia\n[conda] cuda-nvrtc-dev 12.8.93 0 nvidia\n[conda] cuda-nvtx 12.8.90 0 nvidia\n[conda] cuda-opencl 12.8.90 0 nvidia\n[conda] cuda-opencl-dev 12.8.90 0 nvidia\n[conda] libcublas 12.8.5.5 h2bcd275_0 nvidia\n[conda] libcublas-dev 12.8.5.5 h2bcd275_0 nvidia\n[conda] libcufft 11.3.3.83 0 nvidia\n[conda] libcufft-dev 11.3.3.83 0 nvidia\n[conda] libcurand 10.3.9.90 0 nvidia\n[conda] libcurand-dev 10.3.9.90 0 nvidia\n[conda] libcusolver 11.7.3.90 0 nvidia\n[conda] libcusolver-dev 11.7.3.90 0 nvidia\n[conda] libcusparse 12.5.8.93 0 nvidia\n[conda] libcusparse-dev 12.5.8.93 0 nvidia\n[conda] libnvjitlink 12.8.93 1 nvidia\n[conda] libnvjitlink-dev 12.8.93 1 nvidia\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.8.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.27.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.3.83 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.9.90 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.3.90 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.8.93 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.7.1 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.27.5 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.8.90 pypi_0 pypi\n[conda] torch 2.9.1 pypi_0 pypi\n[conda] torchaudio 2.9.1 pypi_0 pypi\n[conda] torchvision 0.24.1 pypi_0 pypi\n[conda] triton 3.5.1 pypi_0 pypi", + "transformers_version": "4.57.6", + "lm_eval_version": "0.4.8", + "upper_git_hash": null, + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 270000, + "task_hashes": { + "aime25_nofigures_tok262144_agg64": "5d70cd9bdf77402eee3c6bcfe4d9bd0f5e0e6e76dda02bb72917e528607dda22" + }, + "model_source": "vllm-tool", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n\n{\"type\": \"function\", \"function\": {\"name\": \"pass\", \"description\": \"Passes the task to the next model to solve the problem from where you left off. Useful when thinking gets too long.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"context\": {\"type\": \"string\", \"description\": \"Information to pass to the next model. E.g., ideas already tried, key results, next steps to perform...\"}}, \"required\": [\"context\"]}}}\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n\n\nBelow is an example of using the pass tool:\n<|im_start|>user\nThere exist real numbers $x$ and $y$, both greater than 1, such that $\\log_x\\left(y^x\\right)=\\log_y\\left(x^{4y}\\right)=10$. Find $xy$.<|im_end|>\n<|im_start|>assistant\n\nOkay, so I need to solve this problem where there are real numbers x and y, both greater than 1, such that log base x of (y^x) equals log base y of (x^{4y}) equals 10. And I need to find the product xy. Hmm, let me think.\n\nFirst, let me recall some logarithm properties. The equation says that two different logarithms are equal to 10. So, log_x(y^x) = 10 and log_y(x^{4y}) = 10. Maybe I can convert these logarithmic equations into exponential form to make them easier to handle.\n\nStarting with the first one: log_x(y^x) = 10. Using the logarithm power rule, which says that log_b(a^c) = c*log_b(a). So, applying that here, log_x(y^x) = x * log_x(y) = 10. So, x * log_x(y) = 10. \n\nBut log_x(y) is the same as 1 / log_y(x), by the change of base formula. Wait, but maybe I can express log_x(y) in terms of something else. Let me\n\n{\"name\": \"pass\", \"arguments\": {\"context\": \"Maybe convert logarithmic equations into exponential form to make them easier to handle. Via logarithm power rule log_x(y^x) = x * log_x(y) = 10. Noting that log_x(y) is the same as 1 / log_y(x). Maybe try express log_x(y) in terms of something else.\"}}\n\n\nWhen context is passed to you, evaluate its correctness first and then use it if correct.\n<|im_start|>user\nThere exist real numbers $x$ and $y$, both greater than 1, such that $\\log_x\\left(y^x\\right)=\\log_y\\left(x^{4y}\\right)=10$. Find $xy$.<|im_end|>\n<|im_start|>assistant\n\nMaybe convert logarithmic equations into exponential form to make them easier to handle. Via logarithm power rule log_x(y^x) = x * log_x(y) = 10. Noting that log_x(y) is the same as 1 / log_y(x). Maybe try express log_x(y) in terms of something else.\n\n\nOkay, so I need to first evaluate whether the context is correct and then use it to solve the problem.\n\nThe context suggests converting logarithmic equations into exponential form and using the logarithm power rule, which is a valid approach. It also notes that log_x(y) can be expressed in terms of log_y(x), which is correct. It suggests expressing log_x(y) in terms of something else.\n\nWait, do we really need to express log_x(y) in terms of something else? Maybe we can instead first use the logarithm power rule for the second equation as well. Let me do that...", + "system_instruction_sha": "af4ce18eddb698d9a7539a322f42d049ebc171d42d6f22f06a278046ddd3b11d", + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "a55ee1b1660128b7098723e0abcd92caa0788061051c62d51cbe87d9cf1974d8", + "start_time": 4667926.687470348, + "end_time": 4715402.287025761, + "total_evaluation_time_seconds": "47475.59955541324" +} \ No newline at end of file diff --git a/Qwen3-1.7B-SUMM/w4096s256context_maybe/toks262144/results_concatted_tok262144.json b/Qwen3-1.7B-SUMM/w4096s256context_maybe/toks262144/results_concatted_tok262144.json new file mode 100644 index 0000000000000000000000000000000000000000..4bc11a973c7cf0e4be06ec22cdb4eada44bb2e13 --- /dev/null +++ b/Qwen3-1.7B-SUMM/w4096s256context_maybe/toks262144/results_concatted_tok262144.json @@ -0,0 +1,3992 @@ +{ + "results": { + "aime25_nofigures_tok262144_agg64": { + "exact_match@64,all": 0.0, + "exact_match@64_stderr,all": 0.0, + "exact_match@128,all": 0.0, + "exact_match@128_stderr,all": 0.0, + "exact_match@256,all": 0.0, + "exact_match@256_stderr,all": 0.0, + "exact_match@512,all": 0.0, + "exact_match@512_stderr,all": 0.0, + "exact_match@1024,all": 0.0, + "exact_match@1024_stderr,all": 0.0, + "exact_match@2048,all": 0.03333333333333333, + "exact_match@2048_stderr,all": 0.03333333333333333, + "exact_match@4096,all": 0.13333333333333333, + "exact_match@4096_stderr,all": 0.06312427686319991, + "exact_match@8192,all": 0.16666666666666666, + "exact_match@8192_stderr,all": 0.06920456654478331, + "exact_match@16384,all": 0.2, + "exact_match@16384_stderr,all": 0.07427813527082075, + "exact_match@32768,all": 0.2, + "exact_match@32768_stderr,all": 0.07427813527082075, + "exact_match@65536,all": 0.23333333333333334, + "exact_match@65536_stderr,all": 0.07854032324531729, + "exact_match@131072,all": 0.23333333333333334, + "exact_match@131072_stderr,all": 0.07854032324531729, + "exact_match@262144,all": 0.23333333333333334, + "exact_match@262144_stderr,all": 0.07854032324531729, + "exact_matches@64,all": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "exact_matches@64_stderr,all": "N/A", + "exact_matches@128,all": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, 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"process_docs": "def process_docs(dataset: Dataset) -> Dataset:\n shard_rank = int(os.getenv(\"SHARD_RANK\", \"0\"))\n shard_world_size = int(os.getenv(\"SHARD_WORLD_SIZE\", \"1\"))\n\n def _process_doc(doc: dict, idx: int) -> dict:\n out_doc = {\n \"problem\": doc.get(\"problem\", doc.get(\"question\")),\n \"answer\": doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\"))),\n \"doc_id\": idx,\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n dataset = dataset.map(_process_doc, with_indices=True)\n if shard_world_size > 1:\n dataset = dataset.filter(\n lambda doc: doc[\"doc_id\"] % shard_world_size == shard_rank\n )\n return dataset\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc.get(\"problem\", doc.get(\"question\")))\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n tokenizer=None,\n max_len=32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n\n SEP = os.getenv(\"SEP\", \"\")\n VERIFYFN = os.getenv(\"VERIFYFN\", \"verify_math\")\n\n for i, a in enumerate(results, start=1):\n if not a:\n a = \"\"\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n if len(toks) >= repeat_window:\n w = toks[-repeat_window:]\n metrics[\"repetitive\"].append(len(set(w)) / len(w) < unique_thresh)\n else:\n metrics[\"repetitive\"].append(False)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n if VERIFYFN == \"verify_math\":\n match, x, y = verify_math(a, gt, sep=SEP)[0]\n elif VERIFYFN == \"verify_generic\":\n match, x, y = verify_generic(a, gt, sep=SEP, m='gpt-4.1-mini-2025-04-14')[0]\n\n metrics[\"extracted_answers\"].append(gt if match else x)\n # if not(match): # Optional logging\n # print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = match\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(match)\n elif i > 1:\n metrics[\"exact_matches\"].append(match)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n \n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(gt == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@64", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@64", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@128", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": 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"repetitive@262144@4": true, + "repetitive@262144@2": true, + "repetitive@262144@1": true, + "extracted_answers@262144": true, + "tok@262144": true, + "tok_think@262144": true, + "tok_ans@262144": true, + "too_long@262144": true, + "repetitive@262144": true, + "exact_matches@262144": null + } + }, + "n-samples": { + "aime25_nofigures_tok262144_agg64": { + "original": 30, + "effective": 30 + } + }, + "config": { + "model": "vllm-tool", + "model_args": { + "pretrained": "Qwen/Qwen3-1.7B", + "tensor_parallel_size": 1, + "data_parallel_size": 1, + "dtype": "auto", + "hf_overrides": { + "max_position_embeddings": 270000 + } + }, + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 262144, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "3b87dedd", + "date": 1787541849.7443688, + "pretty_env_info": "PyTorch version: 2.9.1+cu128\nIs debug build: False\nCUDA used to build PyTorch: 12.8\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 24.04.4 LTS (x86_64)\nGCC version: (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.39\n\nPython version: 3.12.13 | packaged by Anaconda, Inc. | (main, Jul 9 2026, 14:38:16) [GCC 14.3.0] (64-bit runtime)\nPython platform: Linux-6.17.0-1020-gcp-x86_64-with-glibc2.39\nIs CUDA available: True\nCUDA runtime version: 12.8.93\nCUDA_MODULE_LOADING set to: \nGPU models and configuration: \nGPU 0: NVIDIA H100 80GB HBM3\nGPU 1: NVIDIA H100 80GB HBM3\nGPU 2: NVIDIA H100 80GB HBM3\nGPU 3: NVIDIA H100 80GB HBM3\nGPU 4: NVIDIA H100 80GB HBM3\nGPU 5: NVIDIA H100 80GB HBM3\nGPU 6: NVIDIA H100 80GB HBM3\nGPU 7: NVIDIA H100 80GB HBM3\n\nNvidia driver version: 595.71.05\ncuDNN version: Could not collect\nIs XPU available: False\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 208\nOn-line CPU(s) list: 0-207\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 52\nSocket(s): 2\nStepping: 8\nBogoMIPS: 5399.99\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rtm avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 arat avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.9 MiB (104 instances)\nL1i cache: 3.3 MiB (104 instances)\nL2 cache: 208 MiB (104 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-51,104-155\nNUMA node1 CPU(s): 52-103,156-207\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Old microcode: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\nVersions of relevant libraries:\n[pip3] mypy_extensions==1.1.0\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cudnn-frontend==1.27.0\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pip3] nvidia-curand-cu12==10.3.9.90\n[pip3] nvidia-cusolver-cu12==11.7.3.90\n[pip3] nvidia-cusparse-cu12==12.5.8.93\n[pip3] nvidia-cusparselt-cu12==0.7.1\n[pip3] nvidia-nccl-cu12==2.27.5\n[pip3] nvidia-nvjitlink-cu12==12.8.93\n[pip3] nvidia-nvtx-cu12==12.8.90\n[pip3] torch==2.9.1\n[pip3] torchaudio==2.9.1\n[pip3] torchvision==0.24.1\n[pip3] triton==3.5.1\n[conda] cuda-cudart 12.8.90 0 nvidia\n[conda] cuda-cudart-dev 12.8.90 0 nvidia\n[conda] cuda-cudart-dev_linux-64 12.8.90 0 nvidia\n[conda] cuda-cudart-static 12.8.90 0 nvidia\n[conda] cuda-cudart-static_linux-64 12.8.90 0 nvidia\n[conda] cuda-cudart_linux-64 12.8.90 0 nvidia\n[conda] cuda-cupti 12.8.90 0 nvidia\n[conda] cuda-cupti-dev 12.8.90 0 nvidia\n[conda] cuda-libraries 12.8.2 0 nvidia\n[conda] cuda-libraries-dev 12.8.2 0 nvidia\n[conda] cuda-nvrtc 12.8.93 0 nvidia\n[conda] cuda-nvrtc-dev 12.8.93 0 nvidia\n[conda] cuda-nvtx 12.8.90 0 nvidia\n[conda] cuda-opencl 12.8.90 0 nvidia\n[conda] cuda-opencl-dev 12.8.90 0 nvidia\n[conda] libcublas 12.8.5.5 h2bcd275_0 nvidia\n[conda] libcublas-dev 12.8.5.5 h2bcd275_0 nvidia\n[conda] libcufft 11.3.3.83 0 nvidia\n[conda] libcufft-dev 11.3.3.83 0 nvidia\n[conda] libcurand 10.3.9.90 0 nvidia\n[conda] libcurand-dev 10.3.9.90 0 nvidia\n[conda] libcusolver 11.7.3.90 0 nvidia\n[conda] libcusolver-dev 11.7.3.90 0 nvidia\n[conda] libcusparse 12.5.8.93 0 nvidia\n[conda] libcusparse-dev 12.5.8.93 0 nvidia\n[conda] libnvjitlink 12.8.93 1 nvidia\n[conda] libnvjitlink-dev 12.8.93 1 nvidia\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.8.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.27.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.3.83 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.9.90 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.3.90 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.8.93 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.7.1 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.27.5 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.8.90 pypi_0 pypi\n[conda] torch 2.9.1 pypi_0 pypi\n[conda] torchaudio 2.9.1 pypi_0 pypi\n[conda] torchvision 0.24.1 pypi_0 pypi\n[conda] triton 3.5.1 pypi_0 pypi", + "transformers_version": "4.57.6", + "lm_eval_version": "0.4.8", + "upper_git_hash": null, + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 270000, + "task_hashes": { + "aime25_nofigures_tok262144_agg64": "3c5641b410a2d7020ec318f31226c1d07ccd8039d7d3727fa73b57b9dda10295" + }, + "model_source": "vllm-tool", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n\n{\"type\": \"function\", \"function\": {\"name\": \"pass\", \"description\": \"Passes the task to the next model to solve the problem from where you left off. Useful when thinking gets too long.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"context\": {\"type\": \"string\", \"description\": \"Information to pass to the next model. E.g., ideas already tried, key results, next steps to perform...\"}}, \"required\": [\"context\"]}}}\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n", + "system_instruction_sha": "92cc571378d2cb5eb7b4424471a1afbc4a9cb68f507dee39eb25dda130fe844b", + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "a55ee1b1660128b7098723e0abcd92caa0788061051c62d51cbe87d9cf1974d8", + "start_time": 4680130.226220031, + "end_time": 4725774.295907438, + "total_evaluation_time_seconds": "45644.06968740653" +} \ No newline at end of file diff --git a/Qwen3-1.7B-SUMM/w4096s256contextnoshot/toks262144/results_concatted_tok262144.json b/Qwen3-1.7B-SUMM/w4096s256contextnoshot/toks262144/results_concatted_tok262144.json new file mode 100644 index 0000000000000000000000000000000000000000..c9c697465a613302145455b1b9a26a81a000687c --- /dev/null +++ b/Qwen3-1.7B-SUMM/w4096s256contextnoshot/toks262144/results_concatted_tok262144.json @@ -0,0 +1,3992 @@ +{ + "results": { + "aime25_nofigures_tok262144_agg64": { + "exact_match@64,all": 0.0, + "exact_match@64_stderr,all": 0.0, + "exact_match@128,all": 0.0, + "exact_match@128_stderr,all": 0.0, + "exact_match@256,all": 0.0, + "exact_match@256_stderr,all": 0.0, + "exact_match@512,all": 0.0, + "exact_match@512_stderr,all": 0.0, + "exact_match@1024,all": 0.0, + "exact_match@1024_stderr,all": 0.0, + "exact_match@2048,all": 0.0, + "exact_match@2048_stderr,all": 0.0, + "exact_match@4096,all": 0.06666666666666667, + 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"repetitive@262144@64_stderr,all": 0.03975499679430175, + "repetitive@524288@64,all": 0.421875, + "repetitive@524288@64_stderr,all": 0.03975499679430175 + } + }, + "group_subtasks": { + "aime25_nofigures_tok524288_agg64": [] + }, + "configs": { + "aime25_nofigures_tok524288_agg64": { + "task": "aime25_nofigures_tok524288_agg64", + "tag": [ + "math_word_problems" + ], + "dataset_path": "simplescaling/aime25_nofigures", + "dataset_name": "default", + "test_split": "train", + "process_docs": "def process_docs(dataset: Dataset) -> Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc.get(\"problem\", doc.get(\"question\")),\n \"answer\": doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\"))),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n return dataset.map(_process_doc)\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc.get(\"problem\", doc.get(\"question\")))\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n tokenizer=None,\n max_len=32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n\n SEP = os.getenv(\"SEP\", \"\")\n VERIFYFN = os.getenv(\"VERIFYFN\", \"verify_math\")\n\n for i, a in enumerate(results, start=1):\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(len(set(w := toks[-repeat_window:]))/len(w) < unique_thresh)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n if VERIFYFN == \"verify_math\":\n match, x, y = verify_math(a, gt, sep=SEP)[0]\n elif VERIFYFN == \"verify_generic\":\n match, x, y = verify_generic(a, gt, sep=SEP, m='gpt-4.1-mini-2025-04-14')[0]\n\n metrics[\"extracted_answers\"].append(gt if match else x)\n # if not(match): # Optional logging\n # print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = match\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(match)\n elif i > 1:\n metrics[\"exact_matches\"].append(match)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n \n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(gt == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@2", + "aggregation": 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true, + "tok_ans@524288@1": true, + "tok@524288@64": true, + "tok@524288@32": true, + "tok@524288@16": true, + "tok@524288@8": true, + "tok@524288@4": true, + "tok@524288@2": true, + "tok@524288@1": true, + "too_long@524288@64": true, + "too_long@524288@32": true, + "too_long@524288@16": true, + "too_long@524288@8": true, + "too_long@524288@4": true, + "too_long@524288@2": true, + "too_long@524288@1": true, + "repetitive@524288@64": true, + "repetitive@524288@32": true, + "repetitive@524288@16": true, + "repetitive@524288@8": true, + "repetitive@524288@4": true, + "repetitive@524288@2": true, + "repetitive@524288@1": true, + "extracted_answers@524288": true, + "tok@524288": true, + "tok_think@524288": true, + "tok_ans@524288": true, + "too_long@524288": true, + "repetitive@524288": true, + "exact_matches@524288": null + } + }, + "n-samples": { + "aime25_nofigures_tok524288_agg64": { + "original": 30, + "effective": 30 + } + }, + "config": { + "model": "vllm", + "model_args": { + "pretrained": "/home/muennighoff/s2/Qwen3-1.7B-SW", + "tensor_parallel_size": 1, + "data_parallel_size": 8, + "dtype": "auto", + "hf_overrides": { + "sliding_window": 4096 + } + }, + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 524288, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "e28dc356", + "date": 1762901229.557134, + "pretty_env_info": "PyTorch version: 2.7.1+cu126\nIs debug build: False\nCUDA used to build PyTorch: 12.6\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.1.153-175.280.amzn2023.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.6.85\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version: 570.195.03\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8488C\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.57.1", + "lm_eval_version": "0.4.8", + "upper_git_hash": "7dad5c900a69309fc2dbb2902ffcc0a5c5dc97b8", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 1048576, + "task_hashes": { + "aime25_nofigures_tok524288_agg64": "02361337ed67f7add7ca443b337013275f20cb1ff59d01b4975049d3905cc618" + }, + "model_source": "vllm", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "8101f303ec9ac365220ee28e56db147e41825f8093665cf0fecc200a4ffbeef8", + "start_time": 1548030.254118441, + "end_time": 1594011.605474828, + "total_evaluation_time_seconds": "45981.35135638714" +} \ No newline at end of file diff --git a/Qwen3-1.7B-SW/swf16384/toks262144/Qwen3-1.7B-SW/Scenario.codegeneration_1_0.6.json b/Qwen3-1.7B-SW/swf16384/toks262144/Qwen3-1.7B-SW/Scenario.codegeneration_1_0.6.json new file mode 100644 index 0000000000000000000000000000000000000000..19d27bca518d98c584b41eb510edaacc0c329563 --- /dev/null +++ b/Qwen3-1.7B-SW/swf16384/toks262144/Qwen3-1.7B-SW/Scenario.codegeneration_1_0.6.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:46cc9b8740040f5e327add2cebce311d563d5ae057cf9c7b8512489dc2b2ed89 +size 58371623 diff --git a/Qwen3-1.7B-SW/swf16384/toks262144/Qwen3-1.7B-SW/Scenario.codegeneration_1_0.6_eval.json b/Qwen3-1.7B-SW/swf16384/toks262144/Qwen3-1.7B-SW/Scenario.codegeneration_1_0.6_eval.json new file mode 100644 index 0000000000000000000000000000000000000000..36ffdb72b6dbca2698b09e8cd9690db7bb517b33 --- /dev/null +++ b/Qwen3-1.7B-SW/swf16384/toks262144/Qwen3-1.7B-SW/Scenario.codegeneration_1_0.6_eval.json @@ -0,0 +1,3343 @@ +[ + { + "pass@1": 0.2874251497005988, + "detail": { + "pass@1": { + "0": 0.0, + "1": 1.0, + "2": 0.0, + "3": 0.0, + "4": 0.0, + "5": 0.0, + "6": 0.0, + "7": 1.0, + "8": 0.0, + "9": 0.0, + "10": 1.0, + "11": 1.0, + "12": 0.0, + "13": 0.0, + "14": 1.0, + "15": 0.0, + "16": 0.0, + "17": 0.0, + "18": 0.0, + "19": 0.0, + "20": 0.0, + "21": 0.0, + "22": 1.0, + "23": 1.0, + "24": 0.0, + "25": 0.0, + "26": 0.0, + "27": 1.0, + "28": 0.0, + "29": 1.0, + "30": 0.0, + "31": 0.0, + "32": 1.0, + "33": 1.0, + "34": 0.0, + "35": 0.0, + "36": 0.0, + "37": 0.0, + "38": 1.0, + "39": 0.0, + "40": 0.0, + "41": 0.0, + "42": 1.0, + "43": 0.0, + "44": 0.0, + "45": 1.0, + "46": 0.0, + "47": 1.0, + "48": 0.0, + "49": 0.0, + "50": 0.0, + "51": 0.0, + "52": 0.0, + "53": 1.0, + "54": 0.0, + "55": 0.0, + "56": 0.0, + "57": 0.0, + "58": 0.0, + "59": 0.0, + "60": 0.0, + "61": 0.0, + "62": 1.0, + "63": 1.0, + "64": 1.0, + "65": 0.0, + "66": 0.0, + "67": 0.0, + "68": 0.0, + "69": 1.0, + "70": 0.0, + "71": 0.0, + "72": 1.0, + "73": 0.0, + "74": 0.0, + "75": 0.0, + "76": 1.0, + "77": 1.0, + "78": 0.0, + "79": 0.0, + "80": 0.0, + "81": 0.0, + "82": 0.0, + "83": 1.0, + "84": 1.0, + "85": 0.0, + "86": 0.0, + "87": 0.0, + "88": 0.0, + "89": 0.0, + "90": 1.0, + "91": 1.0, + "92": 1.0, + "93": 0.0, + "94": 0.0, + "95": 0.0, + "96": 0.0, + "97": 1.0, + "98": 1.0, + "99": 0.0, + "100": 0.0, + "101": 0.0, + "102": 0.0, + "103": 0.0, + "104": 1.0, + "105": 1.0, + "106": 0.0, + "107": 0.0, + "108": 0.0, + "109": 0.0, + "110": 0.0, + "111": 1.0, + "112": 1.0, + "113": 1.0, + "114": 0.0, + "115": 0.0, + "116": 1.0, + "117": 1.0, + "118": 0.0, + "119": 0.0, + "120": 0.0, + "121": 0.0, + 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0.0036458333333333334, + "repetitive@16384@64_stderr,all": 0.0014377914913524016, + "repetitive@32768@64,all": 0.0036458333333333334, + "repetitive@32768@64_stderr,all": 0.0014377914913524016, + "repetitive@65536@64,all": 0.0036458333333333334, + "repetitive@65536@64_stderr,all": 0.0014377914913524016, + "repetitive@131072@64,all": 0.0036458333333333334, + "repetitive@131072@64_stderr,all": 0.0014377914913524016, + "repetitive@262144@64,all": 0.0036458333333333334, + "repetitive@262144@64_stderr,all": 0.0014377914913524016, + "repetitive@524288@64,all": 0.0036458333333333334, + "repetitive@524288@64_stderr,all": 0.0014377914913524016 + } + }, + "group_subtasks": { + "aime25_nofigures_tok524288_agg64": [] + }, + "configs": { + "aime25_nofigures_tok524288_agg64": { + "task": "aime25_nofigures_tok524288_agg64", + "tag": [ + "math_word_problems" + ], + "dataset_path": "simplescaling/aime25_nofigures", + "dataset_name": "default", + "test_split": "train", + "process_docs": "def process_docs(dataset: Dataset) -> Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc.get(\"problem\", doc.get(\"question\")),\n \"answer\": doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\"))),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n return dataset.map(_process_doc)\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc.get(\"problem\", doc.get(\"question\")))\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n tokenizer=None,\n max_len=32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n\n SEP = os.getenv(\"SEP\", \"\")\n VERIFYFN = os.getenv(\"VERIFYFN\", \"verify_math\")\n\n for i, a in enumerate(results, start=1):\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(len(set(w := toks[-repeat_window:]))/len(w) < unique_thresh)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n if VERIFYFN == \"verify_math\":\n match, x, y = verify_math(a, gt, sep=SEP)[0]\n elif VERIFYFN == \"verify_generic\":\n match, x, y = verify_generic(a, gt, sep=SEP, m='gpt-4.1-mini-2025-04-14')[0]\n\n metrics[\"extracted_answers\"].append(gt if match else x)\n # if not(match): # Optional logging\n # print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = match\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(match)\n elif i > 1:\n metrics[\"exact_matches\"].append(match)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n \n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(gt == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@64", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@64", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@128", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@128", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": 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sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.57.1", + "lm_eval_version": "0.4.8", + "upper_git_hash": "d4ac9efdee7783721c06af7a029531c929b31447", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 1048576, + "task_hashes": { + "aime25_nofigures_tok524288_agg64": "02361337ed67f7add7ca443b337013275f20cb1ff59d01b4975049d3905cc618" + }, + "model_source": "vllm", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "8101f303ec9ac365220ee28e56db147e41825f8093665cf0fecc200a4ffbeef8", + "start_time": 1281364.025961029, + "end_time": 1285345.126204051, + "total_evaluation_time_seconds": "3981.100243021967" +} \ No newline at end of file diff --git a/Qwen3-1.7B-SW/swf16384/toks524288/gpqa_diamond_openai_tok524288_agg64/results_2025-10-23T20-26-20.168921.json 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"gpqa_diamond_openai_tok524288_agg64", + "tag": "gpqa", + "dataset_path": "Idavidrein/gpqa", + "dataset_name": "gpqa_diamond", + "training_split": "train", + "validation_split": "train", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n choices = [\n doc[\"Incorrect Answer 1\"],\n doc[\"Incorrect Answer 2\"],\n doc[\"Incorrect Answer 3\"],\n doc[\"Correct Answer\"],\n ]\n\n random.shuffle(choices)\n correct_answer_index = choices.index(doc[\"Correct Answer\"])\n\n out_doc = {\n \"choice1\": choices[0],\n \"choice2\": choices[1],\n \"choice3\": choices[2],\n \"choice4\": choices[3],\n \"answer\": f\"{chr(65 + correct_answer_index)}\",\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", + "doc_to_text": "{{Question}}\n\nA) {{choice1}}\nB) {{choice2}}\nC) {{choice3}}\nD) {{choice4}}", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict, \n results: List[str],\n tokenizer = None,\n max_len = 32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n sampler = ChatCompletionSampler(model=\"gpt-4o-mini\")\n question = QUERY_TEMPLATE_API.format(Question=doc[\"Question\"], choice1=doc[\"choice1\"], choice2=doc[\"choice2\"], choice3=doc[\"choice3\"], choice4=doc[\"choice4\"])\n SEP = os.getenv(\"SEP\", \"\")\n for i, a in enumerate(results, start=1):\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(len(set(w := toks[-repeat_window:]))/len(w) < unique_thresh)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n a = clean(a, sep=SEP)\n\n if a in [\"a\", \"b\", \"c\", \"d\"]:\n a = a.upper()\n elif a not in [\"A\", \"B\", \"C\", \"D\"]:\n a = extract_answer(sampler, question, a)\n if a not in [\"A\", \"B\", \"C\", \"D\"]:\n print(f\"Warning: Default to A as given {results[i-1]} extracted {a}\")\n a = \"A\"\n\n metrics[\"extracted_answers\"].append(a)\n a = int(a == doc[\"answer\"])\n if not(a): # Optional logging\n print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + doc[\"answer\"])\n if i == 1:\n metrics[\"exact_match\"] = a\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(a)\n elif i > 1:\n metrics[\"exact_matches\"].append(a)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(doc[\"answer\"] == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n\n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(doc[\"answer\"] == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@64", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@64", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@128", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@128", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@128", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@256", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@256", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@256", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@256", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@256", + 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true, + "tok@65536@4": true, + "tok@65536@2": true, + "tok@65536@1": true, + "too_long@65536@64": true, + "too_long@65536@32": true, + "too_long@65536@16": true, + "too_long@65536@8": true, + "too_long@65536@4": true, + "too_long@65536@2": true, + "too_long@65536@1": true, + "repetitive@65536@64": true, + "repetitive@65536@32": true, + "repetitive@65536@16": true, + "repetitive@65536@8": true, + "repetitive@65536@4": true, + "repetitive@65536@2": true, + "repetitive@65536@1": true, + "extracted_answers@65536": true, + "tok@65536": true, + "tok_think@65536": true, + "tok_ans@65536": true, + "too_long@65536": true, + "repetitive@65536": true, + "exact_matches@65536": null, + "exact_match@131072": true, + "avg@131072@64": true, + "avg@131072@32": true, + "avg@131072@16": true, + "avg@131072@8": true, + "avg@131072@4": true, + "avg@131072@2": true, + "cov@131072@64": true, + "cov@131072@32": true, + "cov@131072@16": true, + "cov@131072@8": true, + "cov@131072@4": true, + "cov@131072@2": true, + "maj@131072@64": true, + "maj@131072@32": true, + "maj@131072@16": true, + "maj@131072@8": true, + "maj@131072@4": true, + "maj@131072@2": true, + "tok_think@131072@64": true, + "tok_think@131072@32": true, + "tok_think@131072@16": true, + "tok_think@131072@8": true, + "tok_think@131072@4": true, + "tok_think@131072@2": true, + "tok_think@131072@1": true, + "tok_ans@131072@64": true, + "tok_ans@131072@32": true, + "tok_ans@131072@16": true, + "tok_ans@131072@8": true, + "tok_ans@131072@4": true, + "tok_ans@131072@2": true, + "tok_ans@131072@1": true, + "tok@131072@64": true, + "tok@131072@32": true, + "tok@131072@16": true, + "tok@131072@8": true, + "tok@131072@4": true, + "tok@131072@2": true, + "tok@131072@1": true, + "too_long@131072@64": true, + "too_long@131072@32": true, + "too_long@131072@16": true, + "too_long@131072@8": true, + "too_long@131072@4": true, + "too_long@131072@2": true, + "too_long@131072@1": true, + "repetitive@131072@64": true, + "repetitive@131072@32": true, + "repetitive@131072@16": true, + "repetitive@131072@8": true, + "repetitive@131072@4": true, + "repetitive@131072@2": true, + "repetitive@131072@1": true, + "extracted_answers@131072": true, + "tok@131072": true, + "tok_think@131072": true, + "tok_ans@131072": true, + "too_long@131072": true, + "repetitive@131072": true, + "exact_matches@131072": null, + "exact_match@262144": true, + "avg@262144@64": true, + "avg@262144@32": true, + "avg@262144@16": true, + "avg@262144@8": true, + "avg@262144@4": true, + "avg@262144@2": true, + "cov@262144@64": true, + "cov@262144@32": true, + "cov@262144@16": true, + "cov@262144@8": true, + "cov@262144@4": true, + "cov@262144@2": true, + "maj@262144@64": true, + "maj@262144@32": true, + "maj@262144@16": true, + "maj@262144@8": true, + "maj@262144@4": true, + "maj@262144@2": true, + "tok_think@262144@64": true, + "tok_think@262144@32": true, + "tok_think@262144@16": true, + "tok_think@262144@8": true, + "tok_think@262144@4": true, + "tok_think@262144@2": true, + "tok_think@262144@1": true, + "tok_ans@262144@64": true, + "tok_ans@262144@32": true, + "tok_ans@262144@16": true, + "tok_ans@262144@8": true, + "tok_ans@262144@4": true, + "tok_ans@262144@2": true, + "tok_ans@262144@1": true, + "tok@262144@64": true, + "tok@262144@32": true, + "tok@262144@16": true, + "tok@262144@8": true, + "tok@262144@4": true, + "tok@262144@2": true, + "tok@262144@1": true, + "too_long@262144@64": true, + "too_long@262144@32": true, + "too_long@262144@16": true, + "too_long@262144@8": true, + "too_long@262144@4": true, + "too_long@262144@2": true, + "too_long@262144@1": true, + "repetitive@262144@64": true, + "repetitive@262144@32": true, + "repetitive@262144@16": true, + "repetitive@262144@8": true, + "repetitive@262144@4": true, + "repetitive@262144@2": true, + "repetitive@262144@1": true, + "extracted_answers@262144": true, + "tok@262144": true, + "tok_think@262144": true, + "tok_ans@262144": true, + "too_long@262144": true, + "repetitive@262144": true, + "exact_matches@262144": null, + "exact_match@524288": true, + "avg@524288@64": true, + "avg@524288@32": true, + "avg@524288@16": true, + "avg@524288@8": true, + "avg@524288@4": true, + "avg@524288@2": true, + "cov@524288@64": true, + "cov@524288@32": true, + "cov@524288@16": true, + "cov@524288@8": true, + "cov@524288@4": true, + "cov@524288@2": true, + "maj@524288@64": true, + "maj@524288@32": true, + "maj@524288@16": true, + "maj@524288@8": true, + "maj@524288@4": true, + "maj@524288@2": true, + "tok_think@524288@64": true, + "tok_think@524288@32": true, + "tok_think@524288@16": true, + "tok_think@524288@8": true, + "tok_think@524288@4": true, + "tok_think@524288@2": true, + "tok_think@524288@1": true, + "tok_ans@524288@64": true, + "tok_ans@524288@32": true, + "tok_ans@524288@16": true, + "tok_ans@524288@8": true, + "tok_ans@524288@4": true, + "tok_ans@524288@2": true, + "tok_ans@524288@1": true, + "tok@524288@64": true, + "tok@524288@32": true, + "tok@524288@16": true, + "tok@524288@8": true, + "tok@524288@4": true, + "tok@524288@2": true, + "tok@524288@1": true, + "too_long@524288@64": true, + "too_long@524288@32": true, + "too_long@524288@16": true, + "too_long@524288@8": true, + "too_long@524288@4": true, + "too_long@524288@2": true, + "too_long@524288@1": true, + "repetitive@524288@64": true, + "repetitive@524288@32": true, + "repetitive@524288@16": true, + "repetitive@524288@8": true, + "repetitive@524288@4": true, + "repetitive@524288@2": true, + "repetitive@524288@1": true, + "extracted_answers@524288": true, + "tok@524288": true, + "tok_think@524288": true, + "tok_ans@524288": true, + "too_long@524288": true, + "repetitive@524288": true, + "exact_matches@524288": null + } + }, + "n-samples": { + "gpqa_diamond_openai_tok524288_agg64": { + "original": 198, + "effective": 198 + } + }, + "config": { + "model": "vllm", + "model_args": { + "pretrained": "/home/muennighoff/s2/Qwen3-1.7B-SW", + "tensor_parallel_size": 1, + "data_parallel_size": 8, + "dtype": "auto", + "hf_overrides": { + "sliding_window": 16384 + } + }, + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 524288, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "e28dc356", + "date": 1761231409.4718325, + "pretty_env_info": "PyTorch version: 2.7.1+cu126\nIs debug build: False\nCUDA used to build PyTorch: 12.6\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.1.131-143.221.amzn2023.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.6.85\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version: 560.35.05\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8488C\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.57.1", + "lm_eval_version": "0.4.8", + "upper_git_hash": "d4ac9efdee7783721c06af7a029531c929b31447", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 1048576, + "task_hashes": { + "gpqa_diamond_openai_tok524288_agg64": "11a4834c8a371da265cbb62a4f184ab626f6fbdcdfd4fdaabef77fe786b77212" + }, + "model_source": "vllm", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n 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"repetitive@524288@64,all": 0.00203125, + "repetitive@524288@64_stderr,all": 0.0005183170260119831 + } + }, + "group_subtasks": { + "openai_math_tok524288_agg64": [] + }, + "configs": { + "openai_math_tok524288_agg64": { + "task": "openai_math_tok524288_agg64", + "tag": [ + "math_word_problems" + ], + "dataset_path": "simplescaling/openaimath", + "test_split": "test", + "process_docs": "def process_docs(dataset: Dataset) -> Dataset:\n def _process_doc(doc: dict) -> dict:\n solution = doc.get(\"solution\", doc.get(\"orig_solution\", doc.get(\"orig_orig_solution\")))\n problem = doc.get(\"problem\", doc.get(\"orig_problem\", doc.get(\"orig_orig_problem\")))\n answer = doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\")))\n if solution is None:\n print(\"Warning: No solution found; DOC:\", doc)\n out_doc = {\n \"problem\": problem,\n \"solution\": solution,\n \"answer\": answer,\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n return dataset.map(_process_doc)\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc[\"problem\"])\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n tokenizer=None,\n max_len=32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n\n SEP = os.getenv(\"SEP\", \"\")\n VERIFYFN = os.getenv(\"VERIFYFN\", \"verify_math\")\n\n for i, a in enumerate(results, start=1):\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(len(set(w := toks[-repeat_window:]))/len(w) < unique_thresh)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n if VERIFYFN == \"verify_math\":\n match, x, y = verify_math(a, gt, sep=SEP)[0]\n elif VERIFYFN == \"verify_generic\":\n match, x, y = verify_generic(a, gt, sep=SEP, m='gpt-4.1-mini-2025-04-14')[0]\n \n metrics[\"extracted_answers\"].append(gt if match else x)\n if not(match): # Optional logging\n print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = match\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(match)\n elif i > 1:\n metrics[\"exact_matches\"].append(match)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n\n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(gt == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@64", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@64", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@128", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@128", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@128", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@256", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@256@4", + "aggregation": "mean", + 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"tok_ans@65536@16": true, + "tok_ans@65536@8": true, + "tok_ans@65536@4": true, + "tok_ans@65536@2": true, + "tok_ans@65536@1": true, + "tok@65536@64": true, + "tok@65536@32": true, + "tok@65536@16": true, + "tok@65536@8": true, + "tok@65536@4": true, + "tok@65536@2": true, + "tok@65536@1": true, + "too_long@65536@64": true, + "too_long@65536@32": true, + "too_long@65536@16": true, + "too_long@65536@8": true, + "too_long@65536@4": true, + "too_long@65536@2": true, + "too_long@65536@1": true, + "repetitive@65536@64": true, + "repetitive@65536@32": true, + "repetitive@65536@16": true, + "repetitive@65536@8": true, + "repetitive@65536@4": true, + "repetitive@65536@2": true, + "repetitive@65536@1": true, + "extracted_answers@65536": true, + "tok@65536": true, + "tok_think@65536": true, + "tok_ans@65536": true, + "too_long@65536": true, + "repetitive@65536": true, + "exact_matches@65536": null, + "exact_match@131072": true, + "avg@131072@64": true, + "avg@131072@32": true, + "avg@131072@16": true, + "avg@131072@8": true, + "avg@131072@4": true, + "avg@131072@2": true, + "cov@131072@64": true, + "cov@131072@32": true, + "cov@131072@16": true, + "cov@131072@8": true, + "cov@131072@4": true, + "cov@131072@2": true, + "maj@131072@64": true, + "maj@131072@32": true, + "maj@131072@16": true, + "maj@131072@8": true, + "maj@131072@4": true, + "maj@131072@2": true, + "tok_think@131072@64": true, + "tok_think@131072@32": true, + "tok_think@131072@16": true, + "tok_think@131072@8": true, + "tok_think@131072@4": true, + "tok_think@131072@2": true, + "tok_think@131072@1": true, + "tok_ans@131072@64": true, + "tok_ans@131072@32": true, + "tok_ans@131072@16": true, + "tok_ans@131072@8": true, + "tok_ans@131072@4": true, + "tok_ans@131072@2": true, + "tok_ans@131072@1": true, + "tok@131072@64": true, + "tok@131072@32": true, + "tok@131072@16": true, + "tok@131072@8": true, + "tok@131072@4": true, + "tok@131072@2": true, + "tok@131072@1": true, + "too_long@131072@64": true, + "too_long@131072@32": true, + "too_long@131072@16": true, + "too_long@131072@8": true, + "too_long@131072@4": true, + "too_long@131072@2": true, + "too_long@131072@1": true, + "repetitive@131072@64": true, + "repetitive@131072@32": true, + "repetitive@131072@16": true, + "repetitive@131072@8": true, + "repetitive@131072@4": true, + "repetitive@131072@2": true, + "repetitive@131072@1": true, + "extracted_answers@131072": true, + "tok@131072": true, + "tok_think@131072": true, + "tok_ans@131072": true, + "too_long@131072": true, + "repetitive@131072": true, + "exact_matches@131072": null, + "exact_match@262144": true, + "avg@262144@64": true, + "avg@262144@32": true, + "avg@262144@16": true, + "avg@262144@8": true, + "avg@262144@4": true, + "avg@262144@2": true, + "cov@262144@64": true, + "cov@262144@32": true, + "cov@262144@16": true, + "cov@262144@8": true, + "cov@262144@4": true, + "cov@262144@2": true, + "maj@262144@64": true, + "maj@262144@32": true, + "maj@262144@16": true, 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"repetitive@262144@4": true, + "repetitive@262144@2": true, + "repetitive@262144@1": true, + "extracted_answers@262144": true, + "tok@262144": true, + "tok_think@262144": true, + "tok_ans@262144": true, + "too_long@262144": true, + "repetitive@262144": true, + "exact_matches@262144": null, + "exact_match@524288": true, + "avg@524288@64": true, + "avg@524288@32": true, + "avg@524288@16": true, + "avg@524288@8": true, + "avg@524288@4": true, + "avg@524288@2": true, + "cov@524288@64": true, + "cov@524288@32": true, + "cov@524288@16": true, + "cov@524288@8": true, + "cov@524288@4": true, + "cov@524288@2": true, + "maj@524288@64": true, + "maj@524288@32": true, + "maj@524288@16": true, + "maj@524288@8": true, + "maj@524288@4": true, + "maj@524288@2": true, + "tok_think@524288@64": true, + "tok_think@524288@32": true, + "tok_think@524288@16": true, + "tok_think@524288@8": true, + "tok_think@524288@4": true, + "tok_think@524288@2": true, + "tok_think@524288@1": true, + "tok_ans@524288@64": true, + "tok_ans@524288@32": true, + "tok_ans@524288@16": true, + "tok_ans@524288@8": true, + "tok_ans@524288@4": true, + "tok_ans@524288@2": true, + "tok_ans@524288@1": true, + "tok@524288@64": true, + "tok@524288@32": true, + "tok@524288@16": true, + "tok@524288@8": true, + "tok@524288@4": true, + "tok@524288@2": true, + "tok@524288@1": true, + "too_long@524288@64": true, + "too_long@524288@32": true, + "too_long@524288@16": true, + "too_long@524288@8": true, + "too_long@524288@4": true, + "too_long@524288@2": true, + "too_long@524288@1": true, + "repetitive@524288@64": true, + "repetitive@524288@32": true, + "repetitive@524288@16": true, + "repetitive@524288@8": true, + "repetitive@524288@4": true, + "repetitive@524288@2": true, + "repetitive@524288@1": true, + "extracted_answers@524288": true, + "tok@524288": true, + "tok_think@524288": true, + "tok_ans@524288": true, + "too_long@524288": true, + "repetitive@524288": true, + "exact_matches@524288": null + } + }, + "n-samples": { + "openai_math_tok524288_agg64": { + "original": 500, + "effective": 500 + } + }, + "config": { + "model": "vllm", + "model_args": { + "pretrained": "/home/muennighoff/s2/Qwen3-1.7B-SW", + "tensor_parallel_size": 1, + "data_parallel_size": 8, + "dtype": "auto", + "hf_overrides": { + "sliding_window": 16384 + } + }, + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 524288, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "e28dc356", + "date": 1761231182.62099, + "pretty_env_info": "PyTorch version: 2.7.1+cu126\nIs debug build: False\nCUDA used to build PyTorch: 12.6\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.1.131-143.221.amzn2023.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.6.85\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version: 560.35.05\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8488C\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.57.1", + "lm_eval_version": "0.4.8", + "upper_git_hash": "d4ac9efdee7783721c06af7a029531c929b31447", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 1048576, + "task_hashes": { + "openai_math_tok524288_agg64": "afc55e2e9f8afcd6f5356e538fe9c4821fdba273f90db4b5ce5a6e291164fe7c" + }, + "model_source": "vllm", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "8101f303ec9ac365220ee28e56db147e41825f8093665cf0fecc200a4ffbeef8", + "start_time": 1932426.373106216, + "end_time": 1944169.334377874, + "total_evaluation_time_seconds": "11742.961271657841" +} \ No newline at end of file diff --git a/Qwen3-1.7B-SW/swf2048/toks262144/results_2025-12-06T06-05-47.523873.json b/Qwen3-1.7B-SW/swf2048/toks262144/results_2025-12-06T06-05-47.523873.json new file mode 100644 index 0000000000000000000000000000000000000000..243dce3ee083d278086284585d0ff6ac46ff8582 --- /dev/null +++ b/Qwen3-1.7B-SW/swf2048/toks262144/results_2025-12-06T06-05-47.523873.json @@ -0,0 +1,326559 @@ +{ + "results": { + "healthbench_tok262144": { + "alias": "healthbench_tok262144", + "tok@64,none": [ + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 64.0, + 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"cluster:emergency_referrals_emergent_emergency_behavior,none": 0.5683453237410072, + "cluster:emergency_referrals_emergent_emergency_behavior_stderr,none": 0.04216332260808161, + "cluster:emergency_referrals_emergent_context_seeking,none": 0.8345323741007195, + "cluster:emergency_referrals_emergent_context_seeking_stderr,none": 0.031632868005450676, + "physician_agreed_category:not-enough-context,none": 0.18050250845854662, + "physician_agreed_category:not-enough-context_stderr,none": 0.014669051862333733, + "cluster:context_seeking_not-enough-context_context_seeking,none": 0.049723756906077346, + "cluster:context_seeking_not-enough-context_context_seeking_stderr,none": 0.01620207546808306, + "cluster:context_seeking_not-enough-context_helpful_safe,none": 0.8011049723756906, + "cluster:context_seeking_not-enough-context_helpful_safe_stderr,none": 0.029752291752124394, + "physician_agreed_category:no-uncertainty,none": 0.41384861223466635, + "physician_agreed_category:no-uncertainty_stderr,none": 0.017059700841429774, + "cluster:hedging_no-uncertainty_accurate,none": 0.6622807017543859, + "cluster:hedging_no-uncertainty_accurate_stderr,none": 0.031389620175997814, + "cluster:hedging_no-uncertainty_hedges,none": 0.9517543859649122, + "cluster:hedging_no-uncertainty_hedges_stderr,none": 0.01422258159206746, + "cluster:hedging_no-uncertainty_seeks_context,none": 0.9868421052631579, + "cluster:hedging_no-uncertainty_seeks_context_stderr,none": 0.007563174328019882, + "physician_agreed_category:conditionally-emergent,none": 0.33345723991396753, + "physician_agreed_category:conditionally-emergent_stderr,none": 0.02496340390185893, + "cluster:emergency_referrals_conditionally-emergent_emergency_behavior,none": 0.8, + "cluster:emergency_referrals_conditionally-emergent_emergency_behavior_stderr,none": 0.0298974037100774, + "cluster:emergency_referrals_conditionally-emergent_context_seeking,none": 0.7944444444444444, + "cluster:emergency_referrals_conditionally-emergent_context_seeking_stderr,none": 0.03020437545866585, + "physician_agreed_category:detailed,none": 0.11642074660451061, + "physician_agreed_category:detailed_stderr,none": 0.015147407899149057, + "cluster:complex_responses_detailed_accuracy_hedging,none": 0.11801242236024845, + "cluster:complex_responses_detailed_accuracy_hedging_stderr,none": 0.02550557421046853, + "cluster:complex_responses_detailed_appropriate,none": 0.5217391304347826, + "cluster:complex_responses_detailed_appropriate_stderr,none": 0.0394910915751847 + } + }, + "group_subtasks": { + "healthbench_tok262144": [] + }, + "configs": { + "healthbench_tok262144": { + "task": "healthbench_tok262144", + "tag": [ + "math_word_problems" + ], + "dataset_path": "ryzax/healthbench", + "dataset_name": "default", + "test_split": "train", + "process_docs": "def process_docs(dataset: Dataset) -> Dataset:\n \"\"\"Process documents from the dataset.\"\"\"\n\n def _process_doc(doc: dict) -> dict:\n # Keep rubrics as dicts for Arrow serialization\n # They'll be converted to RubricItem objects in process_results\n out_doc = {\n \"prompt\": doc.get(\"prompt\", []),\n \"rubrics\": doc.get(\"rubrics\", []), # Keep as list of dicts\n \"example_tags\": doc.get(\"example_tags\", []),\n \"prompt_id\": doc.get(\"prompt_id\", \"\"),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n \"\"\"Convert document to text prompt.\"\"\"\n # The prompt field contains a list of message dicts\n prompt_messages = doc.get(\"prompt\", [])\n if not prompt_messages:\n return \"\"\n\n # Format as conversation\n conversation = []\n for msg in prompt_messages:\n role = msg.get(\"role\", \"user\")\n content = msg.get(\"content\", \"\")\n conversation.append(f\"{role}: {content}\")\n\n return \"\\n\\n\".join(conversation)\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n repeat_window=100,\n unique_thresh=0.2,\n tokenizer=None,\n addtokens=False,\n max_len=32768,\n **kwargs,\n) -> Dict[str, float]:\n \"\"\"Process results for a single document.\"\"\"\n if not results or not results[0]:\n eval_logger.warning(\"Empty results received\")\n return {\n \"overall_score\": 0.0,\n \"exact_match\": 0.0,\n }\n\n # Get the generated response\n response_text = results[0] if isinstance(results[0], str) else str(results[0])\n\n # Extract required fields from doc\n prompt_messages = doc.get(\"prompt\", [])\n rubric_items = doc.get(\"rubrics\", [])\n example_tags = doc.get(\"example_tags\", [])\n\n # Convert rubrics to RubricItem objects if needed\n if rubric_items and isinstance(rubric_items[0], dict):\n rubric_items = [RubricItem.from_dict(r) for r in rubric_items]\n\n # Grade the sample synchronously (wrap async function)\n try:\n metrics, _ = asyncio.run(\n grade_sample_async(\n prompt_messages=prompt_messages,\n response_text=response_text,\n rubric_items=rubric_items,\n example_tags=example_tags,\n model=JUDGE_MODEL,\n )\n )\n except Exception as e:\n eval_logger.error(f\"Error grading sample: {e}\")\n metrics = {\"overall_score\": 0.0}\n\n # Add exact_match as alias for overall_score for compatibility\n metrics[\"exact_match\"] = metrics.get(\"overall_score\", 0.0)\n\n # Don't include rubric_grades in returned metrics - it's not a scalar\n # and will cause aggregation errors. It's already logged if needed.\n\n # if True:\n if tokenizer is not None:\n n_stats_list = [1]\n metrics = {\n **metrics,\n **{\n \"tok\": [],\n \"tok_think\": [],\n \"tok_ans\": [],\n \"too_long\": [],\n \"repetitive\": [],\n },\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n if True:\n SEP = os.getenv(\"SEP\", \"\")\n parts = response_text.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(\n 0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1]))\n )\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(response_text)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(\n len(set(w := toks[-repeat_window:])) / len(w) < unique_thresh\n )\n i = 1\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(\n metrics[\"tok_think\"]\n )\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(\n metrics[\"tok_ans\"]\n )\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(\n metrics[\"too_long\"]\n )\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(\n metrics[\"repetitive\"]\n )\n\n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics_tok = [\n \"tok\",\n \"tok_think\",\n \"tok_ans\",\n \"too_long\",\n \"repetitive\",\n \"exact_match\",\n \"tok@1\",\n \"tok_think@1\",\n \"tok_ans@1\",\n \"too_long@1\",\n \"repetitive@1\",\n ]\n metrics_tok_dict = {\n (k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(\n metrics[k]\n )\n for k in metrics_tok\n for t in addtoks\n }\n metrics = {\n **metrics_tok_dict,\n **{k: v for k, v in metrics.items() if k not in metrics_tok},\n }\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n # metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(\n metrics[f\"tok@{t}\"][:i]\n )\n metrics[f\"tok_think@{t}@{i}\"] = sum(\n metrics[f\"tok_think@{t}\"][:i]\n ) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(\n metrics[f\"tok_ans@{t}\"][:i]\n )\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(\n metrics[f\"too_long@{t}\"][:i]\n )\n print(metrics)\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@64", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@128", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@128", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@256", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@256", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@256", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@256", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@256", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@256", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@256", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@512", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@512@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@512@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@512@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@512@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@512@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@512@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@512", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@512", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@512", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@512", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@512", + "aggregation": "meanlist" + }, + { + "metric": "repetitive@512", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@1024", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@1024@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@1024@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@1024@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@1024@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@1024@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@1024", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@1024", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@1024", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@1024", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@1024", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@1024", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@2048", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@2048@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@2048@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@2048@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@2048@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@2048@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@2048", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@2048", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@2048", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@2048", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@2048", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@2048", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@4096", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@4096@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@4096@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@4096@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@4096@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@4096@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@4096", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@4096", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@4096", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@4096", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@4096", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@4096", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@8192", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@8192@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@8192@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@8192@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@8192@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@8192@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@8192", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@8192", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@8192", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@8192", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@8192", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@8192", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@16384", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@16384@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@16384@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@16384@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@16384@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@16384@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@16384", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@16384", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@16384", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@16384", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@16384", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@16384", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@32768", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@32768@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@32768@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@32768@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@32768@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@32768@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@32768", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@32768", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@32768", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@32768", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@32768", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@32768", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@65536", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@65536@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@65536@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@65536@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@65536@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@65536@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@65536", + "aggregation": "bypass", + 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"repetitive@131072@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@131072", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@131072", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@131072", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@131072", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@131072", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@131072", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@262144", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@262144@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@262144@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@262144@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@262144@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@262144@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@262144", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@262144", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@262144", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@262144", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@262144", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@262144", + "aggregation": "meanlist", + "higher_is_better": true + } + ], + "output_type": "generate_until", + "generation_kwargs": { + "until": [], + "do_sample": false, + "temperature": 0.6, + "max_gen_toks": 262144, + "top_p": 0.95 + }, + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 1.0, + "pretrained": "/home/muennighoff/s2/Qwen3-1.7B-SW", + "tensor_parallel_size": 1, + "data_parallel_size": 1, + "dtype": "auto", + "hf_overrides": { + "sliding_window": 2048 + } + } + } + }, + "versions": { + "healthbench_tok262144": 1.0 + }, + "n-shot": { + "healthbench_tok262144": 0 + }, + "higher_is_better": { + "healthbench_tok262144": { + "exact_match@64": true, + "tok_think@64@1": true, + "tok_ans@64@1": true, + "tok@64@1": true, + "too_long@64@1": true, + "repetitive@64@1": true, + "extracted_answers@64": true, + "tok@64": true, + "tok_think@64": true, + "tok_ans@64": true, + "too_long@64": true, + "repetitive@64": true, + "exact_match@128": true, + "tok_think@128@1": true, + "tok_ans@128@1": true, + "tok@128@1": true, + "too_long@128@1": true, + "repetitive@128@1": true, + "extracted_answers@128": true, + "tok@128": true, + "tok_think@128": true, + "tok_ans@128": true, + "too_long@128": true, + "repetitive@128": true, + "exact_match@256": true, + "tok_think@256@1": true, + "tok_ans@256@1": true, + "tok@256@1": true, + "too_long@256@1": true, + "repetitive@256@1": true, + "extracted_answers@256": true, + "tok@256": true, + "tok_think@256": true, + "tok_ans@256": true, + "too_long@256": true, + "repetitive@256": true, + "exact_match@512": true, + "tok_think@512@1": true, + "tok_ans@512@64": true, + "tok_ans@512@1": true, + "tok@512@1": true, + "too_long@512@1": true, + "repetitive@512@1": true, + "extracted_answers@512": true, + "tok@512": true, + "tok_think@512": true, + "tok_ans@512": true, + "too_long@512": null, + "repetitive@512": true, + "exact_match@1024": true, + "tok_think@1024@1": true, + "tok_ans@1024@1": true, + "tok@1024@1": true, + "too_long@1024@1": true, + "repetitive@1024@1": true, + "extracted_answers@1024": true, + "tok@1024": true, + "tok_think@1024": true, + "tok_ans@1024": true, + "too_long@1024": true, + "repetitive@1024": true, + "exact_match@2048": true, + "tok_think@2048@1": true, + "tok_ans@2048@1": true, + "tok@2048@1": true, + "too_long@2048@1": true, + "repetitive@2048@1": true, + "extracted_answers@2048": true, + "tok@2048": true, + "tok_think@2048": true, + "tok_ans@2048": true, + "too_long@2048": true, + "repetitive@2048": true, + "exact_match@4096": true, + "tok_think@4096@1": true, + "tok_ans@4096@1": true, + "tok@4096@1": true, + "too_long@4096@1": true, + "repetitive@4096@1": true, + "extracted_answers@4096": true, + "tok@4096": true, + "tok_think@4096": true, + "tok_ans@4096": true, + "too_long@4096": true, + "repetitive@4096": true, + "exact_match@8192": true, + "tok_think@8192@1": true, + "tok_ans@8192@1": true, + "tok@8192@1": true, + "too_long@8192@1": true, + "repetitive@8192@1": true, + "extracted_answers@8192": true, + "tok@8192": true, + "tok_think@8192": true, + "tok_ans@8192": true, + "too_long@8192": true, + "repetitive@8192": true, + "exact_match@16384": true, + "tok_think@16384@1": true, + "tok_ans@16384@1": true, + "tok@16384@1": true, + "too_long@16384@1": true, + "repetitive@16384@1": true, + "extracted_answers@16384": true, + "tok@16384": true, + "tok_think@16384": true, + "tok_ans@16384": true, + "too_long@16384": true, + "repetitive@16384": true, + "exact_match@32768": true, + "tok_think@32768@1": true, + "tok_ans@32768@1": true, + "tok@32768@1": true, + "too_long@32768@1": true, + "repetitive@32768@1": true, + "extracted_answers@32768": true, + "tok@32768": true, + "tok_think@32768": true, + "tok_ans@32768": true, + "too_long@32768": true, + "repetitive@32768": true, + "exact_match@65536": true, + "tok_think@65536@1": true, + "tok_ans@65536@1": true, + "tok@65536@1": true, + "too_long@65536@1": true, + "repetitive@65536@1": true, + "extracted_answers@65536": true, + "tok@65536": true, + "tok_think@65536": true, + "tok_ans@65536": true, + "too_long@65536": true, + "repetitive@65536": true, + "exact_match@131072": true, + "tok_think@131072@1": true, + "tok_ans@131072@1": true, + "tok@131072@1": true, + "too_long@131072@1": true, + "repetitive@131072@1": true, + "extracted_answers@131072": true, + "tok@131072": true, + "tok_think@131072": true, + "tok_ans@131072": true, + "too_long@131072": true, + "repetitive@131072": true, + "exact_match@262144": true, + "tok_think@262144@1": true, + "tok_ans@262144@1": true, + "tok@262144@1": true, + "too_long@262144@1": true, + "repetitive@262144@1": true, + "extracted_answers@262144": true, + "tok@262144": true, + "tok_think@262144": true, + "tok_ans@262144": true, + "too_long@262144": true, + "repetitive@262144": true + } + }, + "n-samples": { + "healthbench_tok262144": { + "original": 5000, + "effective": 5000 + } + }, + "config": { + "model": "vllm", + "model_args": { + "pretrained": "/home/muennighoff/s2/Qwen3-1.7B-SW", + "tensor_parallel_size": 1, + "data_parallel_size": 1, + "dtype": "auto", + "hf_overrides": { + "sliding_window": 2048 + } + }, + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 262144, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "f4236a87", + "date": 1764979787.7809844, + "pretty_env_info": "PyTorch version: 2.7.1+cu126\nIs debug build: False\nCUDA used to build PyTorch: 12.6\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.12.55-74.119.amzn2023.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.6.85\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version: 570.195.03\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8488C\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.57.1", + "lm_eval_version": "0.4.8", + "upper_git_hash": "7dad5c900a69309fc2dbb2902ffcc0a5c5dc97b8", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 1048576, + "task_hashes": { + "healthbench_tok262144": "ca4f61b065d00419debf2c3a8fb2309d5c0f2d0bac8365561dd9c51d5cb6d1cb" + }, + "model_source": "vllm", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = 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tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "8101f303ec9ac365220ee28e56db147e41825f8093665cf0fecc200a4ffbeef8", + "start_time": 772349.263694147, + "end_time": 793718.398751335, + "total_evaluation_time_seconds": "21369.13505718799" +} \ No newline at end of file diff --git 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dict) -> dict:\n out_doc = {\n \"problem\": doc.get(\"problem\", doc.get(\"question\")),\n \"answer\": doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\"))),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n return dataset.map(_process_doc)\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc.get(\"problem\", doc.get(\"question\")))\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n tokenizer=None,\n max_len=32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n\n SEP = os.getenv(\"SEP\", \"\")\n VERIFYFN = os.getenv(\"VERIFYFN\", \"verify_math\")\n\n for i, a in enumerate(results, start=1):\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(len(set(w := toks[-repeat_window:]))/len(w) < unique_thresh)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n if VERIFYFN == \"verify_math\":\n match, x, y = verify_math(a, gt, sep=SEP)[0]\n elif VERIFYFN == \"verify_generic\":\n match, x, y = verify_generic(a, gt, sep=SEP, m='gpt-4.1-mini-2025-04-14')[0]\n\n metrics[\"extracted_answers\"].append(gt if match else x)\n # if not(match): # Optional logging\n # print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = match\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(match)\n elif i > 1:\n metrics[\"exact_matches\"].append(match)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n \n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(gt == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@64", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@64", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@128", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@128", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@128", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@256", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@4", + "aggregation": "mean", + "higher_is_better": true + 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"cov@32768@32": true, + "cov@32768@16": true, + "cov@32768@8": true, + "cov@32768@4": true, + "cov@32768@2": true, + "maj@32768@64": true, + "maj@32768@32": true, + "maj@32768@16": true, + "maj@32768@8": true, + "maj@32768@4": true, + "maj@32768@2": true, + "tok_think@32768@64": true, + "tok_think@32768@32": true, + "tok_think@32768@16": true, + "tok_think@32768@8": true, + "tok_think@32768@4": true, + "tok_think@32768@2": true, + "tok_think@32768@1": true, + "tok_ans@32768@64": true, + "tok_ans@32768@32": true, + "tok_ans@32768@16": true, + "tok_ans@32768@8": true, + "tok_ans@32768@4": true, + "tok_ans@32768@2": true, + "tok_ans@32768@1": true, + "tok@32768@64": true, + "tok@32768@32": true, + "tok@32768@16": true, + "tok@32768@8": true, + "tok@32768@4": true, + "tok@32768@2": true, + "tok@32768@1": true, + "too_long@32768@64": true, + "too_long@32768@32": true, + "too_long@32768@16": true, + "too_long@32768@8": true, + "too_long@32768@4": true, + "too_long@32768@2": true, + "too_long@32768@1": true, + "repetitive@32768@64": true, + "repetitive@32768@32": true, + "repetitive@32768@16": true, + "repetitive@32768@8": true, + "repetitive@32768@4": true, + "repetitive@32768@2": true, + "repetitive@32768@1": true, + "extracted_answers@32768": true, + "tok@32768": true, + "tok_think@32768": true, + "tok_ans@32768": true, + "too_long@32768": true, + "repetitive@32768": true, + "exact_matches@32768": null, + "exact_match@65536": true, + "avg@65536@64": true, + "avg@65536@32": true, + "avg@65536@16": true, + "avg@65536@8": true, + "avg@65536@4": true, + "avg@65536@2": true, + "cov@65536@64": true, + "cov@65536@32": true, + "cov@65536@16": true, + "cov@65536@8": true, + "cov@65536@4": true, + "cov@65536@2": true, + "maj@65536@64": true, + "maj@65536@32": true, + "maj@65536@16": true, + "maj@65536@8": true, + "maj@65536@4": true, + "maj@65536@2": true, + "tok_think@65536@64": true, + "tok_think@65536@32": true, + "tok_think@65536@16": true, + "tok_think@65536@8": true, + "tok_think@65536@4": true, + "tok_think@65536@2": true, + "tok_think@65536@1": true, + "tok_ans@65536@64": true, + "tok_ans@65536@32": true, + "tok_ans@65536@16": true, + "tok_ans@65536@8": true, + "tok_ans@65536@4": true, + "tok_ans@65536@2": true, + "tok_ans@65536@1": true, + "tok@65536@64": true, + "tok@65536@32": true, + "tok@65536@16": true, + "tok@65536@8": true, + "tok@65536@4": true, + "tok@65536@2": true, + "tok@65536@1": true, + "too_long@65536@64": true, + "too_long@65536@32": true, + "too_long@65536@16": true, + "too_long@65536@8": true, + "too_long@65536@4": true, + "too_long@65536@2": true, + "too_long@65536@1": true, + "repetitive@65536@64": true, + "repetitive@65536@32": true, + "repetitive@65536@16": true, + "repetitive@65536@8": true, + "repetitive@65536@4": true, + "repetitive@65536@2": true, + "repetitive@65536@1": true, + "extracted_answers@65536": true, + "tok@65536": true, + "tok_think@65536": true, + "tok_ans@65536": true, + "too_long@65536": true, + "repetitive@65536": true, + "exact_matches@65536": null, + "exact_match@131072": true, + "avg@131072@64": true, + "avg@131072@32": true, + "avg@131072@16": true, + "avg@131072@8": true, + "avg@131072@4": true, + "avg@131072@2": true, + "cov@131072@64": true, + "cov@131072@32": true, + "cov@131072@16": true, + "cov@131072@8": true, + "cov@131072@4": true, + "cov@131072@2": true, + "maj@131072@64": true, + "maj@131072@32": true, + "maj@131072@16": true, + "maj@131072@8": true, + "maj@131072@4": true, + "maj@131072@2": true, + "tok_think@131072@64": true, + "tok_think@131072@32": true, + "tok_think@131072@16": true, + "tok_think@131072@8": true, + "tok_think@131072@4": true, + "tok_think@131072@2": true, + "tok_think@131072@1": true, + "tok_ans@131072@64": true, + "tok_ans@131072@32": true, + "tok_ans@131072@16": true, + "tok_ans@131072@8": true, + "tok_ans@131072@4": true, + "tok_ans@131072@2": true, + "tok_ans@131072@1": true, + "tok@131072@64": true, + "tok@131072@32": true, + "tok@131072@16": true, + "tok@131072@8": true, + "tok@131072@4": true, + "tok@131072@2": true, + "tok@131072@1": true, + "too_long@131072@64": true, + "too_long@131072@32": true, + "too_long@131072@16": true, + "too_long@131072@8": true, + "too_long@131072@4": true, + "too_long@131072@2": true, + "too_long@131072@1": true, + "repetitive@131072@64": true, + "repetitive@131072@32": true, + "repetitive@131072@16": true, + "repetitive@131072@8": true, + "repetitive@131072@4": true, + "repetitive@131072@2": true, + "repetitive@131072@1": true, + "extracted_answers@131072": true, + "tok@131072": true, + "tok_think@131072": true, + "tok_ans@131072": true, + "too_long@131072": true, + "repetitive@131072": true, + "exact_matches@131072": null, + "exact_match@262144": true, + "avg@262144@64": true, + "avg@262144@32": true, + "avg@262144@16": true, + "avg@262144@8": true, + "avg@262144@4": true, + "avg@262144@2": true, + "cov@262144@64": true, + "cov@262144@32": true, + "cov@262144@16": true, + "cov@262144@8": true, + "cov@262144@4": true, + "cov@262144@2": true, + "maj@262144@64": true, + "maj@262144@32": true, + "maj@262144@16": true, + "maj@262144@8": true, + "maj@262144@4": true, + "maj@262144@2": true, + "tok_think@262144@64": true, + "tok_think@262144@32": true, + "tok_think@262144@16": true, + "tok_think@262144@8": true, + "tok_think@262144@4": true, + "tok_think@262144@2": true, + "tok_think@262144@1": true, + "tok_ans@262144@64": true, + "tok_ans@262144@32": true, + "tok_ans@262144@16": true, + "tok_ans@262144@8": true, + "tok_ans@262144@4": true, + "tok_ans@262144@2": true, + "tok_ans@262144@1": true, + "tok@262144@64": true, + "tok@262144@32": true, + "tok@262144@16": true, + "tok@262144@8": true, + "tok@262144@4": true, + "tok@262144@2": true, + "tok@262144@1": true, + "too_long@262144@64": true, + "too_long@262144@32": true, + "too_long@262144@16": true, + "too_long@262144@8": true, + "too_long@262144@4": true, + "too_long@262144@2": true, + "too_long@262144@1": true, + "repetitive@262144@64": true, + "repetitive@262144@32": true, + "repetitive@262144@16": true, + "repetitive@262144@8": true, + "repetitive@262144@4": true, + "repetitive@262144@2": true, + "repetitive@262144@1": true, + "extracted_answers@262144": true, + "tok@262144": true, + "tok_think@262144": true, + "tok_ans@262144": true, + "too_long@262144": true, + "repetitive@262144": true, + "exact_matches@262144": null, + "exact_match@524288": true, + "avg@524288@64": true, + "avg@524288@32": true, + "avg@524288@16": true, + "avg@524288@8": true, + "avg@524288@4": true, + "avg@524288@2": true, + "cov@524288@64": true, + "cov@524288@32": true, + "cov@524288@16": true, + "cov@524288@8": true, + "cov@524288@4": true, + "cov@524288@2": true, + "maj@524288@64": true, + "maj@524288@32": true, + "maj@524288@16": true, + "maj@524288@8": true, + "maj@524288@4": true, + "maj@524288@2": true, + "tok_think@524288@64": true, + "tok_think@524288@32": true, + "tok_think@524288@16": true, + "tok_think@524288@8": true, + "tok_think@524288@4": true, + "tok_think@524288@2": true, + "tok_think@524288@1": true, + "tok_ans@524288@64": true, + "tok_ans@524288@32": true, + "tok_ans@524288@16": true, + "tok_ans@524288@8": true, + "tok_ans@524288@4": true, + "tok_ans@524288@2": true, + "tok_ans@524288@1": true, + "tok@524288@64": true, + "tok@524288@32": true, + "tok@524288@16": true, + "tok@524288@8": true, + "tok@524288@4": true, + "tok@524288@2": true, + "tok@524288@1": true, + "too_long@524288@64": true, + "too_long@524288@32": true, + "too_long@524288@16": true, + "too_long@524288@8": true, + "too_long@524288@4": true, + "too_long@524288@2": true, + "too_long@524288@1": true, + "repetitive@524288@64": true, + "repetitive@524288@32": true, + "repetitive@524288@16": true, + "repetitive@524288@8": true, + "repetitive@524288@4": true, + "repetitive@524288@2": true, + "repetitive@524288@1": true, + "extracted_answers@524288": true, + "tok@524288": true, + "tok_think@524288": true, + "tok_ans@524288": true, + "too_long@524288": true, + "repetitive@524288": true, + "exact_matches@524288": null + } + }, + "n-samples": { + "aime25_nofigures_tok524288_agg64": { + "original": 30, + "effective": 30 + } + }, + "config": { + "model": "vllm", + "model_args": { + "pretrained": "/home/muennighoff/s2/Qwen3-1.7B-SW", + "tensor_parallel_size": 1, + "data_parallel_size": 8, + "dtype": "auto", + "hf_overrides": { + "sliding_window": 2048 + } + }, + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 524288, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "e28dc356", + "date": 1761187736.0447404, + "pretty_env_info": "PyTorch version: 2.7.1+cu126\nIs debug build: False\nCUDA used to build PyTorch: 12.6\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.1.153-175.280.amzn2023.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.6.85\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version: 570.195.03\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8488C\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.57.1", + "lm_eval_version": "0.4.8", + "upper_git_hash": "d4ac9efdee7783721c06af7a029531c929b31447", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 1048576, + "task_hashes": { + "aime25_nofigures_tok524288_agg64": "02361337ed67f7add7ca443b337013275f20cb1ff59d01b4975049d3905cc618" + }, + "model_source": "vllm", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content 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0.006875, + "repetitive@16384@64_stderr,all": 0.001600917979396043, + "repetitive@32768@64,all": 0.006875, + "repetitive@32768@64_stderr,all": 0.001600917979396043, + "repetitive@65536@64,all": 0.006875, + "repetitive@65536@64_stderr,all": 0.001600917979396043, + "repetitive@131072@64,all": 0.006875, + "repetitive@131072@64_stderr,all": 0.001600917979396043, + "repetitive@262144@64,all": 0.006875, + "repetitive@262144@64_stderr,all": 0.001600917979396043, + "repetitive@524288@64,all": 0.006875, + "repetitive@524288@64_stderr,all": 0.001600917979396043 + } + }, + "group_subtasks": { + "openai_math_tok524288_agg64": [] + }, + "configs": { + "openai_math_tok524288_agg64": { + "task": "openai_math_tok524288_agg64", + "tag": [ + "math_word_problems" + ], + "dataset_path": "simplescaling/openaimath", + "test_split": "test", + "process_docs": "def process_docs(dataset: Dataset) -> Dataset:\n def _process_doc(doc: dict) -> dict:\n solution = doc.get(\"solution\", doc.get(\"orig_solution\", doc.get(\"orig_orig_solution\")))\n problem = doc.get(\"problem\", doc.get(\"orig_problem\", doc.get(\"orig_orig_problem\")))\n answer = doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\")))\n if solution is None:\n print(\"Warning: No solution found; DOC:\", doc)\n out_doc = {\n \"problem\": problem,\n \"solution\": solution,\n \"answer\": answer,\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n return dataset.map(_process_doc)\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc[\"problem\"])\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n tokenizer=None,\n max_len=32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n\n SEP = os.getenv(\"SEP\", \"\")\n VERIFYFN = os.getenv(\"VERIFYFN\", \"verify_math\")\n\n for i, a in enumerate(results, start=1):\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(len(set(w := toks[-repeat_window:]))/len(w) < unique_thresh)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n if VERIFYFN == \"verify_math\":\n match, x, y = verify_math(a, gt, sep=SEP)[0]\n elif VERIFYFN == \"verify_generic\":\n match, x, y = verify_generic(a, gt, sep=SEP, m='gpt-4.1-mini-2025-04-14')[0]\n \n metrics[\"extracted_answers\"].append(gt if match else x)\n if not(match): # Optional logging\n print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = match\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(match)\n elif i > 1:\n metrics[\"exact_matches\"].append(match)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n\n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(gt == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@64", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@64", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@128", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@4", + "aggregation": 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"e28dc356", + "date": 1761191682.8167474, + "pretty_env_info": "PyTorch version: 2.7.1+cu126\nIs debug build: False\nCUDA used to build PyTorch: 12.6\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.1.153-175.280.amzn2023.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.6.85\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version: 570.195.03\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8488C\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.57.1", + "lm_eval_version": "0.4.8", + "upper_git_hash": "d4ac9efdee7783721c06af7a029531c929b31447", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 1048576, + "task_hashes": { + "openai_math_tok524288_agg64": "afc55e2e9f8afcd6f5356e538fe9c4821fdba273f90db4b5ce5a6e291164fe7c" + }, + "model_source": "vllm", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "8101f303ec9ac365220ee28e56db147e41825f8093665cf0fecc200a4ffbeef8", + "start_time": 190593.27860707, + "end_time": 203252.037706006, + "total_evaluation_time_seconds": "12658.759098935989" +} \ No newline at end of file diff --git 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+ "pass@1": 0.31736526946107785, + "detail": { + "pass@1": { + "0": 0.0, + "1": 1.0, + "2": 0.0, + "3": 0.0, + "4": 0.0, + "5": 0.0, + "6": 0.0, + "7": 1.0, + "8": 0.0, + "9": 0.0, + "10": 1.0, + "11": 1.0, + "12": 0.0, + "13": 0.0, + "14": 1.0, + "15": 0.0, + "16": 0.0, + "17": 1.0, + "18": 0.0, + "19": 1.0, + "20": 0.0, + "21": 0.0, + "22": 1.0, + "23": 1.0, + "24": 0.0, + "25": 0.0, + "26": 0.0, + "27": 1.0, + "28": 0.0, + "29": 1.0, + "30": 0.0, + "31": 0.0, + "32": 1.0, + "33": 1.0, + "34": 0.0, + "35": 0.0, + "36": 0.0, + "37": 0.0, + "38": 1.0, + "39": 1.0, + "40": 0.0, + "41": 0.0, + "42": 1.0, + "43": 0.0, + "44": 0.0, + "45": 1.0, + "46": 0.0, + "47": 1.0, + "48": 0.0, + "49": 0.0, + "50": 0.0, + "51": 0.0, + "52": 0.0, + "53": 1.0, + "54": 0.0, + "55": 0.0, + "56": 0.0, + "57": 0.0, + "58": 0.0, + "59": 1.0, + "60": 0.0, + "61": 0.0, + "62": 1.0, + "63": 1.0, + "64": 1.0, + "65": 0.0, + "66": 0.0, + "67": 0.0, + "68": 0.0, + "69": 1.0, + "70": 0.0, + "71": 0.0, + "72": 1.0, 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[ + "math_word_problems" + ], + "dataset_path": "simplescaling/aime25_nofigures", + "dataset_name": "default", + "test_split": "train", + "process_docs": "def process_docs(dataset: Dataset) -> Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc.get(\"problem\", doc.get(\"question\")),\n \"answer\": doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\"))),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n return dataset.map(_process_doc)\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc.get(\"problem\", doc.get(\"question\")))\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n tokenizer=None,\n max_len=32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n\n SEP = os.getenv(\"SEP\", \"\")\n VERIFYFN = os.getenv(\"VERIFYFN\", \"verify_math\")\n\n for i, a in enumerate(results, start=1):\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(len(set(w := toks[-repeat_window:]))/len(w) < unique_thresh)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n if VERIFYFN == \"verify_math\":\n match, x, y = verify_math(a, gt, sep=SEP)[0]\n elif VERIFYFN == \"verify_generic\":\n match, x, y = verify_generic(a, gt, sep=SEP, m='gpt-4.1-mini-2025-04-14')[0]\n\n metrics[\"extracted_answers\"].append(gt if match else x)\n # if not(match): # Optional logging\n # print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = match\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(match)\n elif i > 1:\n metrics[\"exact_matches\"].append(match)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n \n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(gt == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@64", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@64", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@128", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@128", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@128", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@256", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@1", + "aggregation": "mean", + 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"exact_matches@16384": null, + "exact_match@32768": true, + "avg@32768@64": true, + "avg@32768@32": true, + "avg@32768@16": true, + "avg@32768@8": true, + "avg@32768@4": true, + "avg@32768@2": true, + "cov@32768@64": true, + "cov@32768@32": true, + "cov@32768@16": true, + "cov@32768@8": true, + "cov@32768@4": true, + "cov@32768@2": true, + "maj@32768@64": true, + "maj@32768@32": true, + "maj@32768@16": true, + "maj@32768@8": true, + "maj@32768@4": true, + "maj@32768@2": true, + "tok_think@32768@64": true, + "tok_think@32768@32": true, + "tok_think@32768@16": true, + "tok_think@32768@8": true, + "tok_think@32768@4": true, + "tok_think@32768@2": true, + "tok_think@32768@1": true, + "tok_ans@32768@64": true, + "tok_ans@32768@32": true, + "tok_ans@32768@16": true, + "tok_ans@32768@8": true, + "tok_ans@32768@4": true, + "tok_ans@32768@2": true, + "tok_ans@32768@1": true, + "tok@32768@64": true, + "tok@32768@32": true, + "tok@32768@16": true, + "tok@32768@8": true, + "tok@32768@4": true, + "tok@32768@2": true, + "tok@32768@1": true, + "too_long@32768@64": true, + "too_long@32768@32": true, + "too_long@32768@16": true, + "too_long@32768@8": true, + "too_long@32768@4": true, + "too_long@32768@2": true, + "too_long@32768@1": true, + "repetitive@32768@64": true, + "repetitive@32768@32": true, + "repetitive@32768@16": true, + "repetitive@32768@8": true, + "repetitive@32768@4": true, + "repetitive@32768@2": true, + "repetitive@32768@1": true, + "extracted_answers@32768": true, + "tok@32768": true, + "tok_think@32768": true, + "tok_ans@32768": true, + "too_long@32768": true, + "repetitive@32768": true, + "exact_matches@32768": null, + "exact_match@65536": true, + "avg@65536@64": true, + "avg@65536@32": true, + "avg@65536@16": true, + "avg@65536@8": true, + "avg@65536@4": true, + "avg@65536@2": true, + "cov@65536@64": true, + "cov@65536@32": true, + "cov@65536@16": true, + "cov@65536@8": true, + "cov@65536@4": true, + "cov@65536@2": true, + "maj@65536@64": true, + "maj@65536@32": true, + "maj@65536@16": true, + "maj@65536@8": true, + "maj@65536@4": true, + "maj@65536@2": true, + "tok_think@65536@64": true, + "tok_think@65536@32": true, + "tok_think@65536@16": true, + "tok_think@65536@8": true, + "tok_think@65536@4": true, + "tok_think@65536@2": true, + "tok_think@65536@1": true, + "tok_ans@65536@64": true, + "tok_ans@65536@32": true, + "tok_ans@65536@16": true, + "tok_ans@65536@8": true, + "tok_ans@65536@4": true, + "tok_ans@65536@2": true, + "tok_ans@65536@1": true, + "tok@65536@64": true, + "tok@65536@32": true, + "tok@65536@16": true, + "tok@65536@8": true, + "tok@65536@4": true, + "tok@65536@2": true, + "tok@65536@1": true, + "too_long@65536@64": true, + "too_long@65536@32": true, + "too_long@65536@16": true, + "too_long@65536@8": true, + "too_long@65536@4": true, + "too_long@65536@2": true, + "too_long@65536@1": true, + "repetitive@65536@64": true, + "repetitive@65536@32": true, + "repetitive@65536@16": true, + "repetitive@65536@8": true, + "repetitive@65536@4": true, + "repetitive@65536@2": true, + "repetitive@65536@1": true, + "extracted_answers@65536": true, + "tok@65536": true, + "tok_think@65536": true, + "tok_ans@65536": true, + "too_long@65536": true, + "repetitive@65536": true, + "exact_matches@65536": null, + "exact_match@131072": true, + "avg@131072@64": true, + "avg@131072@32": true, + "avg@131072@16": true, + "avg@131072@8": true, + "avg@131072@4": true, + "avg@131072@2": true, + "cov@131072@64": true, + "cov@131072@32": true, + "cov@131072@16": true, + "cov@131072@8": true, + "cov@131072@4": true, + "cov@131072@2": true, + "maj@131072@64": true, + "maj@131072@32": true, + "maj@131072@16": true, + "maj@131072@8": true, + "maj@131072@4": true, + "maj@131072@2": true, + "tok_think@131072@64": true, + "tok_think@131072@32": true, + "tok_think@131072@16": true, + "tok_think@131072@8": true, + "tok_think@131072@4": true, + "tok_think@131072@2": true, + "tok_think@131072@1": true, + "tok_ans@131072@64": true, + "tok_ans@131072@32": true, + "tok_ans@131072@16": true, + "tok_ans@131072@8": true, + "tok_ans@131072@4": true, + "tok_ans@131072@2": true, + "tok_ans@131072@1": true, + "tok@131072@64": true, + "tok@131072@32": true, + "tok@131072@16": true, + "tok@131072@8": true, + "tok@131072@4": true, + "tok@131072@2": true, + "tok@131072@1": true, + "too_long@131072@64": true, + "too_long@131072@32": true, + "too_long@131072@16": true, + "too_long@131072@8": true, + "too_long@131072@4": true, + "too_long@131072@2": true, + "too_long@131072@1": true, + "repetitive@131072@64": true, + "repetitive@131072@32": true, + "repetitive@131072@16": true, + "repetitive@131072@8": true, + "repetitive@131072@4": true, + "repetitive@131072@2": true, + "repetitive@131072@1": true, + "extracted_answers@131072": true, + "tok@131072": true, + "tok_think@131072": true, + "tok_ans@131072": true, + "too_long@131072": true, + "repetitive@131072": true, + "exact_matches@131072": null, + "exact_match@262144": true, + "avg@262144@64": true, + "avg@262144@32": true, + "avg@262144@16": true, + "avg@262144@8": true, + "avg@262144@4": true, + "avg@262144@2": true, + "cov@262144@64": true, + "cov@262144@32": true, + "cov@262144@16": true, + "cov@262144@8": true, + "cov@262144@4": true, + "cov@262144@2": true, + "maj@262144@64": true, + "maj@262144@32": true, + "maj@262144@16": true, + "maj@262144@8": true, + "maj@262144@4": true, + "maj@262144@2": true, + "tok_think@262144@64": true, + "tok_think@262144@32": true, + "tok_think@262144@16": true, + "tok_think@262144@8": true, + "tok_think@262144@4": true, + "tok_think@262144@2": true, + "tok_think@262144@1": true, + "tok_ans@262144@64": true, + "tok_ans@262144@32": true, + "tok_ans@262144@16": true, + "tok_ans@262144@8": true, + "tok_ans@262144@4": true, + "tok_ans@262144@2": true, + "tok_ans@262144@1": true, + "tok@262144@64": true, + "tok@262144@32": true, + "tok@262144@16": true, + "tok@262144@8": true, + "tok@262144@4": true, + "tok@262144@2": true, + "tok@262144@1": true, + "too_long@262144@64": true, + "too_long@262144@32": true, + "too_long@262144@16": true, + "too_long@262144@8": true, + "too_long@262144@4": true, + "too_long@262144@2": true, + "too_long@262144@1": true, + "repetitive@262144@64": true, + "repetitive@262144@32": true, + "repetitive@262144@16": true, + "repetitive@262144@8": true, + "repetitive@262144@4": true, + "repetitive@262144@2": true, + "repetitive@262144@1": true, + "extracted_answers@262144": true, + "tok@262144": true, + "tok_think@262144": true, + "tok_ans@262144": true, + "too_long@262144": true, + "repetitive@262144": true, + "exact_matches@262144": null, + "exact_match@524288": true, + "avg@524288@64": true, + "avg@524288@32": true, + "avg@524288@16": true, + "avg@524288@8": true, + "avg@524288@4": true, + "avg@524288@2": true, + "cov@524288@64": true, + "cov@524288@32": true, + "cov@524288@16": true, + "cov@524288@8": true, + "cov@524288@4": true, + "cov@524288@2": true, + "maj@524288@64": true, + "maj@524288@32": true, + "maj@524288@16": true, + "maj@524288@8": true, + "maj@524288@4": true, + "maj@524288@2": true, + "tok_think@524288@64": true, + "tok_think@524288@32": true, + "tok_think@524288@16": true, + "tok_think@524288@8": true, + "tok_think@524288@4": true, + "tok_think@524288@2": true, + "tok_think@524288@1": true, + "tok_ans@524288@64": true, + "tok_ans@524288@32": true, + "tok_ans@524288@16": true, + "tok_ans@524288@8": true, + "tok_ans@524288@4": true, + "tok_ans@524288@2": true, + "tok_ans@524288@1": true, + "tok@524288@64": true, + "tok@524288@32": true, + "tok@524288@16": true, + "tok@524288@8": true, + "tok@524288@4": true, + "tok@524288@2": true, + "tok@524288@1": true, + "too_long@524288@64": true, + "too_long@524288@32": true, + "too_long@524288@16": true, + "too_long@524288@8": true, + "too_long@524288@4": true, + "too_long@524288@2": true, + "too_long@524288@1": true, + "repetitive@524288@64": true, + "repetitive@524288@32": true, + "repetitive@524288@16": true, + "repetitive@524288@8": true, + "repetitive@524288@4": true, + "repetitive@524288@2": true, + "repetitive@524288@1": true, + "extracted_answers@524288": true, + "tok@524288": true, + "tok_think@524288": true, + "tok_ans@524288": true, + "too_long@524288": true, + "repetitive@524288": true, + "exact_matches@524288": null + } + }, + "n-samples": { + "aime25_nofigures_tok524288_agg64": { + "original": 30, + "effective": 30 + } + }, + "config": { + "model": "vllm", + "model_args": { + "pretrained": "/home/muennighoff/s2/Qwen3-1.7B-SW", + "tensor_parallel_size": 1, + "data_parallel_size": 8, + "dtype": "auto", + "hf_overrides": { + "sliding_window": 4096 + } + }, + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 524288, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "e28dc356", + "date": 1761183114.6259263, + "pretty_env_info": "PyTorch version: 2.7.1+cu126\nIs debug build: False\nCUDA used to build PyTorch: 12.6\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.1.153-175.280.amzn2023.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.6.85\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version: 570.195.03\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8488C\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.57.1", + "lm_eval_version": "0.4.8", + "upper_git_hash": "d4ac9efdee7783721c06af7a029531c929b31447", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 1048576, + "task_hashes": { + "aime25_nofigures_tok524288_agg64": "02361337ed67f7add7ca443b337013275f20cb1ff59d01b4975049d3905cc618" + }, + "model_source": "vllm", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content 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\"answer\": f\"{chr(65 + correct_answer_index)}\",\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", + "doc_to_text": "{{Question}}\n\nA) {{choice1}}\nB) {{choice2}}\nC) {{choice3}}\nD) {{choice4}}", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict, \n results: List[str],\n tokenizer = None,\n max_len = 32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n sampler = ChatCompletionSampler(model=\"gpt-4o-mini\")\n question = QUERY_TEMPLATE_API.format(Question=doc[\"Question\"], choice1=doc[\"choice1\"], choice2=doc[\"choice2\"], choice3=doc[\"choice3\"], choice4=doc[\"choice4\"])\n SEP = os.getenv(\"SEP\", \"\")\n for i, a in enumerate(results, start=1):\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(len(set(w := toks[-repeat_window:]))/len(w) < unique_thresh)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n a = clean(a, sep=SEP)\n\n if a in [\"a\", \"b\", \"c\", \"d\"]:\n a = a.upper()\n elif a not in [\"A\", \"B\", \"C\", \"D\"]:\n a = extract_answer(sampler, question, a)\n if a not in [\"A\", \"B\", \"C\", \"D\"]:\n print(f\"Warning: Default to A as given {results[i-1]} extracted {a}\")\n a = \"A\"\n\n metrics[\"extracted_answers\"].append(a)\n a = int(a == doc[\"answer\"])\n if not(a): # Optional logging\n print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + doc[\"answer\"])\n if i == 1:\n metrics[\"exact_match\"] = a\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(a)\n elif i > 1:\n metrics[\"exact_matches\"].append(a)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(doc[\"answer\"] == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n\n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(doc[\"answer\"] == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, 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+ "tok_ans@524288": true, + "too_long@524288": true, + "repetitive@524288": true, + "exact_matches@524288": null + } + }, + "n-samples": { + "gpqa_diamond_openai_tok524288_agg64": { + "original": 198, + "effective": 198 + } + }, + "config": { + "model": "vllm", + "model_args": { + "pretrained": "/home/muennighoff/s2/Qwen3-1.7B-SW", + "tensor_parallel_size": 1, + "data_parallel_size": 8, + "dtype": "auto", + "hf_overrides": { + "sliding_window": 4096 + } + }, + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 524288, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "e28dc356", + "date": 1761183114.6315563, + "pretty_env_info": "PyTorch version: 2.7.1+cu126\nIs debug build: False\nCUDA used to build PyTorch: 12.6\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.1.153-175.280.amzn2023.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.6.85\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version: 570.195.03\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8488C\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.57.1", + "lm_eval_version": "0.4.8", + "upper_git_hash": "d4ac9efdee7783721c06af7a029531c929b31447", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 1048576, + "task_hashes": { + "gpqa_diamond_openai_tok524288_agg64": "11a4834c8a371da265cbb62a4f184ab626f6fbdcdfd4fdaabef77fe786b77212" + }, + "model_source": "vllm", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "8101f303ec9ac365220ee28e56db147e41825f8093665cf0fecc200a4ffbeef8", + "start_time": 109297.750423693, + "end_time": 125501.821889765, + "total_evaluation_time_seconds": "16204.071466071997" +} \ No newline at end of file diff --git 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process_docs(dataset: Dataset) -> Dataset:\n def _process_doc(doc: dict) -> dict:\n solution = doc.get(\"solution\", doc.get(\"orig_solution\", doc.get(\"orig_orig_solution\")))\n problem = doc.get(\"problem\", doc.get(\"orig_problem\", doc.get(\"orig_orig_problem\")))\n answer = doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\")))\n if solution is None:\n print(\"Warning: No solution found; DOC:\", doc)\n out_doc = {\n \"problem\": problem,\n \"solution\": solution,\n \"answer\": answer,\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n return dataset.map(_process_doc)\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc[\"problem\"])\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n tokenizer=None,\n max_len=32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n\n SEP = os.getenv(\"SEP\", \"\")\n VERIFYFN = os.getenv(\"VERIFYFN\", \"verify_math\")\n\n for i, a in enumerate(results, start=1):\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(len(set(w := toks[-repeat_window:]))/len(w) < unique_thresh)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n if VERIFYFN == \"verify_math\":\n match, x, y = verify_math(a, gt, sep=SEP)[0]\n elif VERIFYFN == \"verify_generic\":\n match, x, y = verify_generic(a, gt, sep=SEP, m='gpt-4.1-mini-2025-04-14')[0]\n \n metrics[\"extracted_answers\"].append(gt if match else x)\n if not(match): # Optional logging\n print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = match\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(match)\n elif i > 1:\n metrics[\"exact_matches\"].append(match)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n\n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(gt == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@4", + "aggregation": "mean", + "higher_is_better": true + 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"cov@524288@32": true, + "cov@524288@16": true, + "cov@524288@8": true, + "cov@524288@4": true, + "cov@524288@2": true, + "maj@524288@64": true, + "maj@524288@32": true, + "maj@524288@16": true, + "maj@524288@8": true, + "maj@524288@4": true, + "maj@524288@2": true, + "tok_think@524288@64": true, + "tok_think@524288@32": true, + "tok_think@524288@16": true, + "tok_think@524288@8": true, + "tok_think@524288@4": true, + "tok_think@524288@2": true, + "tok_think@524288@1": true, + "tok_ans@524288@64": true, + "tok_ans@524288@32": true, + "tok_ans@524288@16": true, + "tok_ans@524288@8": true, + "tok_ans@524288@4": true, + "tok_ans@524288@2": true, + "tok_ans@524288@1": true, + "tok@524288@64": true, + "tok@524288@32": true, + "tok@524288@16": true, + "tok@524288@8": true, + "tok@524288@4": true, + "tok@524288@2": true, + "tok@524288@1": true, + "too_long@524288@64": true, + "too_long@524288@32": true, + "too_long@524288@16": true, + "too_long@524288@8": true, + "too_long@524288@4": true, + "too_long@524288@2": true, + "too_long@524288@1": true, + "repetitive@524288@64": true, + "repetitive@524288@32": true, + "repetitive@524288@16": true, + "repetitive@524288@8": true, + "repetitive@524288@4": true, + "repetitive@524288@2": true, + "repetitive@524288@1": true, + "extracted_answers@524288": true, + "tok@524288": true, + "tok_think@524288": true, + "tok_ans@524288": true, + "too_long@524288": true, + "repetitive@524288": true, + "exact_matches@524288": null + } + }, + "n-samples": { + "openai_math_tok524288_agg64": { + "original": 500, + "effective": 500 + } + }, + "config": { + "model": "vllm", + "model_args": { + "pretrained": "/home/muennighoff/s2/Qwen3-1.7B-SW", + "tensor_parallel_size": 1, + "data_parallel_size": 8, + "dtype": "auto", + "hf_overrides": { + "sliding_window": 4096 + } + }, + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 524288, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "e28dc356", + "date": 1761183114.6274474, + "pretty_env_info": "PyTorch version: 2.7.1+cu126\nIs debug build: False\nCUDA used to build PyTorch: 12.6\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.1.153-175.280.amzn2023.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.6.85\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version: 570.195.03\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8488C\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.57.1", + "lm_eval_version": "0.4.8", + "upper_git_hash": "d4ac9efdee7783721c06af7a029531c929b31447", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 1048576, + "task_hashes": { + "openai_math_tok524288_agg64": "afc55e2e9f8afcd6f5356e538fe9c4821fdba273f90db4b5ce5a6e291164fe7c" + }, + "model_source": "vllm", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "8101f303ec9ac365220ee28e56db147e41825f8093665cf0fecc200a4ffbeef8", + "start_time": 518304.822368866, + "end_time": 528353.95932212, + "total_evaluation_time_seconds": "10049.13695325394" +} \ No newline at end of file diff --git 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"repetitive@64@32_stderr,all": 0.001740893795409861, + "repetitive@128@32,all": 0.003125, + "repetitive@128@32_stderr,all": 0.001740893795409861, + "repetitive@256@32,all": 0.003125, + "repetitive@256@32_stderr,all": 0.001740893795409861, + "repetitive@512@32,all": 0.003125, + "repetitive@512@32_stderr,all": 0.001740893795409861, + "repetitive@1024@32,all": 0.003125, + "repetitive@1024@32_stderr,all": 0.001740893795409861, + "repetitive@2048@32,all": 0.003125, + "repetitive@2048@32_stderr,all": 0.001740893795409861, + "repetitive@4096@32,all": 0.003125, + "repetitive@4096@32_stderr,all": 0.001740893795409861, + "repetitive@8192@32,all": 0.003125, + "repetitive@8192@32_stderr,all": 0.001740893795409861, + "repetitive@16384@32,all": 0.003125, + "repetitive@16384@32_stderr,all": 0.001740893795409861, + "repetitive@32768@32,all": 0.003125, + "repetitive@32768@32_stderr,all": 0.001740893795409861, + "repetitive@65536@32,all": 0.003125, + "repetitive@65536@32_stderr,all": 0.001740893795409861, + "repetitive@131072@32,all": 0.003125, + "repetitive@131072@32_stderr,all": 0.001740893795409861, + "repetitive@262144@32,all": 0.003125, + "repetitive@262144@32_stderr,all": 0.001740893795409861, + "repetitive@524288@32,all": 0.003125, + "repetitive@524288@32_stderr,all": 0.001740893795409861, + "repetitive@64@64,all": 0.0046875, + "repetitive@64@64_stderr,all": 0.0020032189433652706, + "repetitive@128@64,all": 0.0046875, + "repetitive@128@64_stderr,all": 0.0020032189433652706, + "repetitive@256@64,all": 0.0046875, + "repetitive@256@64_stderr,all": 0.0020032189433652706, + "repetitive@512@64,all": 0.0046875, + "repetitive@512@64_stderr,all": 0.0020032189433652706, + "repetitive@1024@64,all": 0.0046875, + "repetitive@1024@64_stderr,all": 0.0020032189433652706, + "repetitive@2048@64,all": 0.0046875, + "repetitive@2048@64_stderr,all": 0.0020032189433652706, + "repetitive@4096@64,all": 0.0046875, + "repetitive@4096@64_stderr,all": 0.0020032189433652706, + "repetitive@8192@64,all": 0.0046875, + "repetitive@8192@64_stderr,all": 0.0020032189433652706, + "repetitive@16384@64,all": 0.0046875, + "repetitive@16384@64_stderr,all": 0.0020032189433652706, + "repetitive@32768@64,all": 0.0046875, + "repetitive@32768@64_stderr,all": 0.0020032189433652706, + "repetitive@65536@64,all": 0.0046875, + "repetitive@65536@64_stderr,all": 0.0020032189433652706, + "repetitive@131072@64,all": 0.0046875, + "repetitive@131072@64_stderr,all": 0.0020032189433652706, + "repetitive@262144@64,all": 0.0046875, + "repetitive@262144@64_stderr,all": 0.0020032189433652706, + "repetitive@524288@64,all": 0.0046875, + "repetitive@524288@64_stderr,all": 0.0020032189433652706 + } + }, + "group_subtasks": { + "aime25_nofigures_tok524288_agg64": [] + }, + "configs": { + "aime25_nofigures_tok524288_agg64": { + "task": "aime25_nofigures_tok524288_agg64", + "tag": [ + "math_word_problems" + ], + "dataset_path": "simplescaling/aime25_nofigures", + "dataset_name": "default", + "test_split": "train", + "process_docs": "def process_docs(dataset: Dataset) -> Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc.get(\"problem\", doc.get(\"question\")),\n \"answer\": doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\"))),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n return dataset.map(_process_doc)\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc.get(\"problem\", doc.get(\"question\")))\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n tokenizer=None,\n max_len=32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n\n SEP = os.getenv(\"SEP\", \"\")\n VERIFYFN = os.getenv(\"VERIFYFN\", \"verify_math\")\n\n for i, a in enumerate(results, start=1):\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(len(set(w := toks[-repeat_window:]))/len(w) < unique_thresh)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n if VERIFYFN == \"verify_math\":\n match, x, y = verify_math(a, gt, sep=SEP)[0]\n elif VERIFYFN == \"verify_generic\":\n match, x, y = verify_generic(a, gt, sep=SEP, m='gpt-4.1-mini-2025-04-14')[0]\n\n metrics[\"extracted_answers\"].append(gt if match else x)\n # if not(match): # Optional logging\n # print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = match\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(match)\n elif i > 1:\n metrics[\"exact_matches\"].append(match)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n \n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(gt == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@64", + 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"maj@524288@32": true, + "maj@524288@16": true, + "maj@524288@8": true, + "maj@524288@4": true, + "maj@524288@2": true, + "tok_think@524288@64": true, + "tok_think@524288@32": true, + "tok_think@524288@16": true, + "tok_think@524288@8": true, + "tok_think@524288@4": true, + "tok_think@524288@2": true, + "tok_think@524288@1": true, + "tok_ans@524288@64": true, + "tok_ans@524288@32": true, + "tok_ans@524288@16": true, + "tok_ans@524288@8": true, + "tok_ans@524288@4": true, + "tok_ans@524288@2": true, + "tok_ans@524288@1": true, + "tok@524288@64": true, + "tok@524288@32": true, + "tok@524288@16": true, + "tok@524288@8": true, + "tok@524288@4": true, + "tok@524288@2": true, + "tok@524288@1": true, + "too_long@524288@64": true, + "too_long@524288@32": true, + "too_long@524288@16": true, + "too_long@524288@8": true, + "too_long@524288@4": true, + "too_long@524288@2": true, + "too_long@524288@1": true, + "repetitive@524288@64": true, + "repetitive@524288@32": true, + "repetitive@524288@16": true, + "repetitive@524288@8": true, + "repetitive@524288@4": true, + "repetitive@524288@2": true, + "repetitive@524288@1": true, + "extracted_answers@524288": true, + "tok@524288": true, + "tok_think@524288": true, + "tok_ans@524288": true, + "too_long@524288": true, + "repetitive@524288": true, + "exact_matches@524288": null + } + }, + "n-samples": { + "aime25_nofigures_tok524288_agg64": { + "original": 30, + "effective": 30 + } + }, + "config": { + "model": "vllm", + "model_args": { + "pretrained": "/home/muennighoff/s2/Qwen3-1.7B-SW", + "tensor_parallel_size": 1, + "data_parallel_size": 8, + "dtype": "auto", + "hf_overrides": { + "sliding_window": 8192 + } + }, + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 524288, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "e28dc356", + "date": 1761191685.9137082, + "pretty_env_info": "PyTorch version: 2.7.1+cu126\nIs debug build: False\nCUDA used to build PyTorch: 12.6\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.1.153-175.280.amzn2023.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.6.85\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version: 570.195.03\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8488C\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.57.1", + "lm_eval_version": "0.4.8", + "upper_git_hash": "d4ac9efdee7783721c06af7a029531c929b31447", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 1048576, + "task_hashes": { + "aime25_nofigures_tok524288_agg64": "02361337ed67f7add7ca443b337013275f20cb1ff59d01b4975049d3905cc618" + }, + "model_source": "vllm", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "8101f303ec9ac365220ee28e56db147e41825f8093665cf0fecc200a4ffbeef8", + "start_time": 564184.411582523, + "end_time": 567551.048225357, + "total_evaluation_time_seconds": "3366.636642833939" +} \ No newline at end of file diff --git a/Qwen3-1.7B-SW/swf8192/toks524288/gpqa_diamond_openai_tok524288_agg64/results_2025-10-23T08-05-06.713441.json b/Qwen3-1.7B-SW/swf8192/toks524288/gpqa_diamond_openai_tok524288_agg64/results_2025-10-23T08-05-06.713441.json new file mode 100644 index 0000000000000000000000000000000000000000..6316a3d6cbe4aff95f7c2c81a9db9db872c84ae3 --- /dev/null +++ b/Qwen3-1.7B-SW/swf8192/toks524288/gpqa_diamond_openai_tok524288_agg64/results_2025-10-23T08-05-06.713441.json @@ -0,0 +1,23673 @@ +{ + "results": { + "gpqa_diamond_openai_tok524288_agg64": { + "alias": "gpqa_diamond_openai_tok524288_agg64", + "exact_match@64,all": 0.0, + "exact_match@64_stderr,all": 0.0, + "exact_match@128,all": 0.0, + "exact_match@128_stderr,all": 0.0, + "exact_match@256,all": 0.0, + "exact_match@256_stderr,all": 0.0, + "exact_match@512,all": 0.0, + "exact_match@512_stderr,all": 0.0, + "exact_match@1024,all": 0.0, + "exact_match@1024_stderr,all": 0.0, + "exact_match@2048,all": 0.015151515151515152, + 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doc[\"Incorrect Answer 3\"],\n doc[\"Correct Answer\"],\n ]\n\n random.shuffle(choices)\n correct_answer_index = choices.index(doc[\"Correct Answer\"])\n\n out_doc = {\n \"choice1\": choices[0],\n \"choice2\": choices[1],\n \"choice3\": choices[2],\n \"choice4\": choices[3],\n \"answer\": f\"{chr(65 + correct_answer_index)}\",\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", + "doc_to_text": "{{Question}}\n\nA) {{choice1}}\nB) {{choice2}}\nC) {{choice3}}\nD) {{choice4}}", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict, \n results: List[str],\n tokenizer = None,\n max_len = 32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n sampler = ChatCompletionSampler(model=\"gpt-4o-mini\")\n question = QUERY_TEMPLATE_API.format(Question=doc[\"Question\"], choice1=doc[\"choice1\"], choice2=doc[\"choice2\"], choice3=doc[\"choice3\"], choice4=doc[\"choice4\"])\n SEP = os.getenv(\"SEP\", \"\")\n for i, a in enumerate(results, start=1):\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(len(set(w := toks[-repeat_window:]))/len(w) < unique_thresh)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n a = clean(a, sep=SEP)\n\n if a in [\"a\", \"b\", \"c\", \"d\"]:\n a = a.upper()\n elif a not in [\"A\", \"B\", \"C\", \"D\"]:\n a = extract_answer(sampler, question, a)\n if a not in [\"A\", \"B\", \"C\", \"D\"]:\n print(f\"Warning: Default to A as given {results[i-1]} extracted {a}\")\n a = \"A\"\n\n metrics[\"extracted_answers\"].append(a)\n a = int(a == doc[\"answer\"])\n if not(a): # Optional logging\n print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + doc[\"answer\"])\n if i == 1:\n metrics[\"exact_match\"] = a\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(a)\n elif i > 1:\n metrics[\"exact_matches\"].append(a)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(doc[\"answer\"] == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n\n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(doc[\"answer\"] == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@32", + "aggregation": "mean", + "higher_is_better": true + }, 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"aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": 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"exact_match@128", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@4", + "aggregation": "mean", + 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sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.57.1", + "lm_eval_version": "0.4.8", + "upper_git_hash": "d4ac9efdee7783721c06af7a029531c929b31447", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 1048576, + "task_hashes": { + "gpqa_diamond_openai_tok524288_agg64": "11a4834c8a371da265cbb62a4f184ab626f6fbdcdfd4fdaabef77fe786b77212" + }, + "model_source": "vllm", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "8101f303ec9ac365220ee28e56db147e41825f8093665cf0fecc200a4ffbeef8", + "start_time": 503122.060137776, + "end_time": 518154.493381723, + "total_evaluation_time_seconds": "15032.433243946987" +} \ No newline at end of file diff --git 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"repetitive@128@64_stderr,all": 0.0006729129763554156, + "repetitive@256@64,all": 0.002125, + "repetitive@256@64_stderr,all": 0.0006729129763554156, + "repetitive@512@64,all": 0.002125, + "repetitive@512@64_stderr,all": 0.0006729129763554156, + "repetitive@1024@64,all": 0.002125, + "repetitive@1024@64_stderr,all": 0.0006729129763554156, + "repetitive@2048@64,all": 0.002125, + "repetitive@2048@64_stderr,all": 0.0006729129763554156, + "repetitive@4096@64,all": 0.002125, + "repetitive@4096@64_stderr,all": 0.0006729129763554156, + "repetitive@8192@64,all": 0.002125, + "repetitive@8192@64_stderr,all": 0.0006729129763554156, + "repetitive@16384@64,all": 0.002125, + "repetitive@16384@64_stderr,all": 0.0006729129763554156, + "repetitive@32768@64,all": 0.002125, + "repetitive@32768@64_stderr,all": 0.0006729129763554156, + "repetitive@65536@64,all": 0.002125, + "repetitive@65536@64_stderr,all": 0.0006729129763554156, + "repetitive@131072@64,all": 0.002125, + "repetitive@131072@64_stderr,all": 0.0006729129763554156, + "repetitive@262144@64,all": 0.002125, + "repetitive@262144@64_stderr,all": 0.0006729129763554156, + "repetitive@524288@64,all": 0.002125, + "repetitive@524288@64_stderr,all": 0.0006729129763554156 + } + }, + "group_subtasks": { + "openai_math_tok524288_agg64": [] + }, + "configs": { + "openai_math_tok524288_agg64": { + "task": "openai_math_tok524288_agg64", + "tag": [ + "math_word_problems" + ], + "dataset_path": "simplescaling/openaimath", + "test_split": "test", + "process_docs": "def process_docs(dataset: Dataset) -> Dataset:\n def _process_doc(doc: dict) -> dict:\n solution = doc.get(\"solution\", doc.get(\"orig_solution\", doc.get(\"orig_orig_solution\")))\n problem = doc.get(\"problem\", doc.get(\"orig_problem\", doc.get(\"orig_orig_problem\")))\n answer = doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\")))\n if solution is None:\n print(\"Warning: No solution found; DOC:\", doc)\n out_doc = {\n \"problem\": problem,\n \"solution\": solution,\n \"answer\": answer,\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n return dataset.map(_process_doc)\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc[\"problem\"])\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n tokenizer=None,\n max_len=32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n\n SEP = os.getenv(\"SEP\", \"\")\n VERIFYFN = os.getenv(\"VERIFYFN\", \"verify_math\")\n\n for i, a in enumerate(results, start=1):\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(len(set(w := toks[-repeat_window:]))/len(w) < unique_thresh)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n if VERIFYFN == \"verify_math\":\n match, x, y = verify_math(a, gt, sep=SEP)[0]\n elif VERIFYFN == \"verify_generic\":\n match, x, y = verify_generic(a, gt, sep=SEP, m='gpt-4.1-mini-2025-04-14')[0]\n \n metrics[\"extracted_answers\"].append(gt if match else x)\n if not(match): # Optional logging\n print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = match\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(match)\n elif i > 1:\n metrics[\"exact_matches\"].append(match)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n\n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(gt == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@64", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@64", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@128", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@16", + "aggregation": "mean", + 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glibc-2.35\n\nPython version: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.1.131-143.221.amzn2023.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.6.85\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version: 560.35.05\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8488C\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.57.1", + "lm_eval_version": "0.4.8", + "upper_git_hash": "d4ac9efdee7783721c06af7a029531c929b31447", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 1048576, + "task_hashes": { + "openai_math_tok524288_agg64": "afc55e2e9f8afcd6f5356e538fe9c4821fdba273f90db4b5ce5a6e291164fe7c" + }, + "model_source": "vllm", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "8101f303ec9ac365220ee28e56db147e41825f8093665cf0fecc200a4ffbeef8", + "start_time": 1894489.476833751, + "end_time": 1903978.137647473, + "total_evaluation_time_seconds": "9488.660813722061" +} \ No newline at end of file diff --git a/Qwen3-1.7B/output_full/Qwen3-1.7B/Scenario.codegeneration_1_0.6.json b/Qwen3-1.7B/output_full/Qwen3-1.7B/Scenario.codegeneration_1_0.6.json new file mode 100644 index 0000000000000000000000000000000000000000..0b1ecc090674b4ece92314823b88cb05699be76f --- /dev/null +++ b/Qwen3-1.7B/output_full/Qwen3-1.7B/Scenario.codegeneration_1_0.6.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0f1cab22c991a7771d689ba1652835236b4dadd5f35af0544661e04eca2e18dc +size 11412632 diff --git a/Qwen3-1.7B/output_full/Qwen3-1.7B/Scenario.codegeneration_1_0.6_eval.json b/Qwen3-1.7B/output_full/Qwen3-1.7B/Scenario.codegeneration_1_0.6_eval.json new file mode 100644 index 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"process_docs": "def process_docs(dataset: Dataset) -> Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc.get(\"problem\", doc.get(\"question\")),\n \"answer\": doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\"))),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n return dataset.map(_process_doc)\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc.get(\"problem\", doc.get(\"question\")))\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n tokenizer=None,\n max_len=32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n\n SEP = os.getenv(\"SEP\", \"\")\n VERIFYFN = os.getenv(\"VERIFYFN\", \"verify_math\")\n\n for i, a in enumerate(results, start=1):\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(len(set(w := toks[-repeat_window:]))/len(w) < unique_thresh)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n if VERIFYFN == \"verify_math\":\n match, x, y = verify_math(a, gt, sep=SEP)[0]\n elif VERIFYFN == \"verify_generic\":\n match, x, y = verify_generic(a, gt, sep=SEP, m='gpt-4.1-mini-2025-04-14')[0]\n\n metrics[\"extracted_answers\"].append(gt if match else x)\n if not(match): # Optional logging\n print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = match\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(match)\n elif i > 1:\n metrics[\"exact_matches\"].append(match)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n \n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(gt == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": 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"avg@16384@8": true, + "avg@16384@4": true, + "avg@16384@2": true, + "cov@16384@64": true, + "cov@16384@32": true, + "cov@16384@16": true, + "cov@16384@8": true, + "cov@16384@4": true, + "cov@16384@2": true, + "maj@16384@64": true, + "maj@16384@32": true, + "maj@16384@16": true, + "maj@16384@8": true, + "maj@16384@4": true, + "maj@16384@2": true, + "tok_think@16384@64": true, + "tok_think@16384@32": true, + "tok_think@16384@16": true, + "tok_think@16384@8": true, + "tok_think@16384@4": true, + "tok_think@16384@2": true, + "tok_think@16384@1": true, + "tok_ans@16384@64": true, + "tok_ans@16384@32": true, + "tok_ans@16384@16": true, + "tok_ans@16384@8": true, + "tok_ans@16384@4": true, + "tok_ans@16384@2": true, + "tok_ans@16384@1": true, + "tok@16384@64": true, + "tok@16384@32": true, + "tok@16384@16": true, + "tok@16384@8": true, + "tok@16384@4": true, + "tok@16384@2": true, + "tok@16384@1": true, + "too_long@16384@64": true, + "too_long@16384@32": true, + "too_long@16384@16": true, + "too_long@16384@8": true, + "too_long@16384@4": true, + "too_long@16384@2": true, + "too_long@16384@1": true, + "repetitive@16384@64": true, + "repetitive@16384@32": true, + "repetitive@16384@16": true, + "repetitive@16384@8": true, + "repetitive@16384@4": true, + "repetitive@16384@2": true, + "repetitive@16384@1": true, + "extracted_answers@16384": true, + "tok@16384": true, + "tok_think@16384": true, + "tok_ans@16384": true, + "too_long@16384": true, + "repetitive@16384": true, + "exact_matches@16384": null, + "exact_match@32768": true, + "avg@32768@64": true, + "avg@32768@32": true, + "avg@32768@16": true, + "avg@32768@8": true, + "avg@32768@4": true, + "avg@32768@2": true, + "cov@32768@64": true, + "cov@32768@32": true, + "cov@32768@16": true, + "cov@32768@8": true, + "cov@32768@4": true, + "cov@32768@2": true, + "maj@32768@64": true, + "maj@32768@32": true, + "maj@32768@16": true, + "maj@32768@8": true, + "maj@32768@4": true, + "maj@32768@2": true, + "tok_think@32768@64": true, + "tok_think@32768@32": true, + "tok_think@32768@16": true, + "tok_think@32768@8": true, + "tok_think@32768@4": true, + "tok_think@32768@2": true, + "tok_think@32768@1": true, + "tok_ans@32768@64": true, + "tok_ans@32768@32": true, + "tok_ans@32768@16": true, + "tok_ans@32768@8": true, + "tok_ans@32768@4": true, + "tok_ans@32768@2": true, + "tok_ans@32768@1": true, + "tok@32768@64": true, + "tok@32768@32": true, + "tok@32768@16": true, + "tok@32768@8": true, + "tok@32768@4": true, + "tok@32768@2": true, + "tok@32768@1": true, + "too_long@32768@64": true, + "too_long@32768@32": true, + "too_long@32768@16": true, + "too_long@32768@8": true, + "too_long@32768@4": true, + "too_long@32768@2": true, + "too_long@32768@1": true, + "repetitive@32768@64": true, + "repetitive@32768@32": true, + "repetitive@32768@16": true, + "repetitive@32768@8": true, + "repetitive@32768@4": true, + "repetitive@32768@2": true, + "repetitive@32768@1": true, + "extracted_answers@32768": true, + "tok@32768": true, + "tok_think@32768": true, + "tok_ans@32768": true, + "too_long@32768": true, + "repetitive@32768": true, + "exact_matches@32768": null, + "exact_match@65536": true, + "avg@65536@64": true, + "avg@65536@32": true, + "avg@65536@16": true, + "avg@65536@8": true, + "avg@65536@4": true, + "avg@65536@2": true, + "cov@65536@64": true, + "cov@65536@32": true, + "cov@65536@16": true, + "cov@65536@8": true, + "cov@65536@4": true, + "cov@65536@2": true, + "maj@65536@64": true, + "maj@65536@32": true, + "maj@65536@16": true, + "maj@65536@8": true, + "maj@65536@4": true, + "maj@65536@2": true, + "tok_think@65536@64": true, + "tok_think@65536@32": true, + "tok_think@65536@16": true, + "tok_think@65536@8": true, + "tok_think@65536@4": true, + "tok_think@65536@2": true, + "tok_think@65536@1": true, + "tok_ans@65536@64": true, + "tok_ans@65536@32": true, + "tok_ans@65536@16": true, + "tok_ans@65536@8": true, + "tok_ans@65536@4": true, + "tok_ans@65536@2": true, + "tok_ans@65536@1": true, + "tok@65536@64": true, + "tok@65536@32": true, + "tok@65536@16": true, + "tok@65536@8": true, + "tok@65536@4": true, + "tok@65536@2": true, + "tok@65536@1": true, + "too_long@65536@64": true, + "too_long@65536@32": true, + "too_long@65536@16": true, + "too_long@65536@8": true, + "too_long@65536@4": true, + "too_long@65536@2": true, + "too_long@65536@1": true, + "repetitive@65536@64": true, + "repetitive@65536@32": true, + "repetitive@65536@16": true, + "repetitive@65536@8": true, + "repetitive@65536@4": true, + "repetitive@65536@2": true, + "repetitive@65536@1": true, + "extracted_answers@65536": true, + "tok@65536": true, + "tok_think@65536": true, + "tok_ans@65536": true, + "too_long@65536": true, + "repetitive@65536": true, + "exact_matches@65536": null, + "exact_match@131072": true, + "avg@131072@64": true, + "avg@131072@32": true, + "avg@131072@16": true, + "avg@131072@8": true, + "avg@131072@4": true, + "avg@131072@2": true, + "cov@131072@64": true, + "cov@131072@32": true, + "cov@131072@16": true, + "cov@131072@8": true, + "cov@131072@4": true, + "cov@131072@2": true, + "maj@131072@64": true, + "maj@131072@32": true, + "maj@131072@16": true, + "maj@131072@8": true, + "maj@131072@4": true, + "maj@131072@2": true, + "tok_think@131072@64": true, + "tok_think@131072@32": true, + "tok_think@131072@16": true, + "tok_think@131072@8": true, + "tok_think@131072@4": true, + "tok_think@131072@2": true, + "tok_think@131072@1": true, + "tok_ans@131072@64": true, + "tok_ans@131072@32": true, + "tok_ans@131072@16": true, + "tok_ans@131072@8": true, + "tok_ans@131072@4": true, + "tok_ans@131072@2": true, + "tok_ans@131072@1": true, + "tok@131072@64": true, + "tok@131072@32": true, + "tok@131072@16": true, + "tok@131072@8": true, + "tok@131072@4": true, + "tok@131072@2": true, + "tok@131072@1": true, + "too_long@131072@64": true, + "too_long@131072@32": true, + "too_long@131072@16": true, + "too_long@131072@8": true, + "too_long@131072@4": true, + "too_long@131072@2": true, + "too_long@131072@1": true, + "repetitive@131072@64": true, + "repetitive@131072@32": true, + "repetitive@131072@16": true, + "repetitive@131072@8": true, + "repetitive@131072@4": true, + "repetitive@131072@2": true, + "repetitive@131072@1": true, + "extracted_answers@131072": true, + "tok@131072": true, + "tok_think@131072": true, + "tok_ans@131072": true, + "too_long@131072": true, + "repetitive@131072": true, + "exact_matches@131072": null + } + }, + "n-samples": { + "aime25_nofigures_tok131072_agg64": { + "original": 30, + "effective": 30 + } + }, + "config": { + "model": "vllm", + "model_args": "pretrained=../Qwen3-1.7B,tensor_parallel_size=1,data_parallel_size=8,dtype=auto", + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 131072, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "ebb7d2b9", + "date": 1759628239.4771037, + "pretty_env_info": "PyTorch version: 2.8.0+cu128\nIs debug build: False\nCUDA used to build PyTorch: 12.8\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.18 (main, Jun 5 2025, 13:14:17) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.1.131-143.221.amzn2023.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.6.85\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version: 560.35.05\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8488C\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cudnn-frontend==1.14.1\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pip3] nvidia-curand-cu12==10.3.9.90\n[pip3] nvidia-cusolver-cu12==11.7.3.90\n[pip3] nvidia-cusparse-cu12==12.5.8.93\n[pip3] nvidia-cusparselt-cu12==0.7.1\n[pip3] nvidia-nccl-cu12==2.27.3\n[pip3] nvidia-nvjitlink-cu12==12.8.93\n[pip3] nvidia-nvtx-cu12==12.8.90\n[pip3] torch==2.8.0\n[pip3] torchaudio==2.8.0\n[pip3] torchvision==0.23.0\n[pip3] triton==3.4.0\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.8.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.14.1 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.3.83 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.9.90 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.3.90 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.8.93 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.7.1 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.27.3 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.8.90 pypi_0 pypi\n[conda] torch 2.8.0 pypi_0 pypi\n[conda] torchaudio 2.8.0 pypi_0 pypi\n[conda] torchvision 0.23.0 pypi_0 pypi\n[conda] triton 3.4.0 pypi_0 pypi", + "transformers_version": "4.57.0", + "lm_eval_version": "0.4.8", + "upper_git_hash": "d4111d492f80e07ef1394e762e8e69fe7df84f32", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 262144, + "task_hashes": { + "aime25_nofigures_tok131072_agg64": "02361337ed67f7add7ca443b337013275f20cb1ff59d01b4975049d3905cc618" + }, + "model_source": "vllm", + "model_name": "../Qwen3-1.7B", + "model_name_sanitized": "..__Qwen3-1.7B", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "8101f303ec9ac365220ee28e56db147e41825f8093665cf0fecc200a4ffbeef8", + "start_time": 329499.647872861, + "end_time": 332084.970403831, + "total_evaluation_time_seconds": "2585.3225309699774" +} \ No newline at end of file diff --git a/Qwen3-1.7B/results_2025-10-08T05-56-34.870139.json b/Qwen3-1.7B/results_2025-10-08T05-56-34.870139.json new file mode 100644 index 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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n choices = [\n doc[\"Incorrect Answer 1\"],\n doc[\"Incorrect Answer 2\"],\n doc[\"Incorrect Answer 3\"],\n doc[\"Correct Answer\"],\n ]\n\n random.shuffle(choices)\n correct_answer_index = choices.index(doc[\"Correct Answer\"])\n\n out_doc = {\n \"choice1\": choices[0],\n \"choice2\": choices[1],\n \"choice3\": choices[2],\n \"choice4\": choices[3],\n \"answer\": f\"{chr(65 + correct_answer_index)}\",\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", + "doc_to_text": "{{Question}}\n\nA) {{choice1}}\nB) {{choice2}}\nC) {{choice3}}\nD) {{choice4}}", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict, \n results: List[str],\n tokenizer = None,\n max_len = 32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n sampler = ChatCompletionSampler(model=\"gpt-4o-mini\")\n question = QUERY_TEMPLATE_API.format(Question=doc[\"Question\"], choice1=doc[\"choice1\"], choice2=doc[\"choice2\"], choice3=doc[\"choice3\"], choice4=doc[\"choice4\"])\n SEP = os.getenv(\"SEP\", \"\")\n for i, a in enumerate(results, start=1):\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(len(set(w := toks[-repeat_window:]))/len(w) < unique_thresh)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n a = clean(a, sep=SEP)\n\n if a in [\"a\", \"b\", \"c\", \"d\"]:\n a = a.upper()\n elif a not in [\"A\", \"B\", \"C\", \"D\"]:\n a = extract_answer(sampler, question, a)\n if a not in [\"A\", \"B\", \"C\", \"D\"]:\n print(f\"Warning: Default to A as given {results[i-1]} extracted {a}\")\n a = \"A\"\n\n metrics[\"extracted_answers\"].append(a)\n a = int(a == doc[\"answer\"])\n if not(a): # Optional logging\n print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + doc[\"answer\"])\n if i == 1:\n metrics[\"exact_match\"] = a\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(a)\n elif i > 1:\n metrics[\"exact_matches\"].append(a)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(doc[\"answer\"] == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n\n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(doc[\"answer\"] == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": 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PyTorch: N/A\n\nOS: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.18 (main, Jun 5 2025, 13:14:17) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.1.131-143.221.amzn2023.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.6.85\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version: 560.35.05\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8488C\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.14.1\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.14.1 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.56.2", + "lm_eval_version": "0.4.8", + "upper_git_hash": "d4111d492f80e07ef1394e762e8e69fe7df84f32", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 524288, + "task_hashes": { + "gpqa_diamond_openai_tok131072_agg64": "11a4834c8a371da265cbb62a4f184ab626f6fbdcdfd4fdaabef77fe786b77212" + }, + "model_source": "vllm", + "model_name": "../Qwen3-1.7B", + "model_name_sanitized": "..__Qwen3-1.7B", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "8101f303ec9ac365220ee28e56db147e41825f8093665cf0fecc200a4ffbeef8", + "start_time": 70331.796229436, + "end_time": 84922.258187605, + "total_evaluation_time_seconds": "14590.461958168991" +} \ No newline at end of file diff --git a/Qwen3-1.7B/results_2025-10-08T07-50-45.964822.json b/Qwen3-1.7B/results_2025-10-08T07-50-45.964822.json new file mode 100644 index 0000000000000000000000000000000000000000..cbcbee01efaf590bed3955afe669328de87e64ac --- /dev/null +++ b/Qwen3-1.7B/results_2025-10-08T07-50-45.964822.json @@ -0,0 +1,45537 @@ +{ + "results": { + "openai_math_tok262144_agg64": { + "alias": "openai_math_tok262144_agg64", + "exact_match@64,all": 0.0, + "exact_match@64_stderr,all": 0.0, + "exact_match@128,all": 0.0, + "exact_match@128_stderr,all": 0.0, + "exact_match@256,all": 0.0, + "exact_match@256_stderr,all": 0.0, + "exact_match@512,all": 0.0, + "exact_match@512_stderr,all": 0.0, + "exact_match@1024,all": 0.0, + "exact_match@1024_stderr,all": 0.0, + "exact_match@2048,all": 0.186, + "exact_match@2048_stderr,all": 0.01741880678058393, + "exact_match@4096,all": 0.546, + "exact_match@4096_stderr,all": 0.022288147591176952, + "exact_match@8192,all": 0.766, + "exact_match@8192_stderr,all": 0.018952741564893686, + "exact_match@16384,all": 0.878, + "exact_match@16384_stderr,all": 0.014651324945044779, + "exact_match@32768,all": 0.916, + "exact_match@32768_stderr,all": 0.012417584015643687, + "exact_match@65536,all": 0.916, + "exact_match@65536_stderr,all": 0.012417584015643687, + "exact_match@131072,all": 0.916, + "exact_match@131072_stderr,all": 0.012417584015643687, + "exact_match@262144,all": 0.916, + "exact_match@262144_stderr,all": 0.012417584015643687, + "extracted_answers@64,all": -1, + "extracted_answers@64_stderr,all": "N/A", + "extracted_answers@128,all": -1, + "extracted_answers@128_stderr,all": "N/A", + "extracted_answers@256,all": -1, + "extracted_answers@256_stderr,all": "N/A", + "extracted_answers@512,all": -1, + "extracted_answers@512_stderr,all": "N/A", + "extracted_answers@1024,all": -1, + "extracted_answers@1024_stderr,all": "N/A", + "extracted_answers@2048,all": -1, + "extracted_answers@2048_stderr,all": "N/A", + "extracted_answers@4096,all": -1, + "extracted_answers@4096_stderr,all": "N/A", + "extracted_answers@8192,all": -1, + "extracted_answers@8192_stderr,all": "N/A", + 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"openai_math_tok262144_agg64", + "tag": [ + "math_word_problems" + ], + "dataset_path": "simplescaling/openaimath", + "test_split": "test", + "process_docs": "def process_docs(dataset: Dataset) -> Dataset:\n def _process_doc(doc: dict) -> dict:\n solution = doc.get(\"solution\", doc.get(\"orig_solution\", doc.get(\"orig_orig_solution\")))\n problem = doc.get(\"problem\", doc.get(\"orig_problem\", doc.get(\"orig_orig_problem\")))\n answer = doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\")))\n if solution is None:\n print(\"Warning: No solution found; DOC:\", doc)\n out_doc = {\n \"problem\": problem,\n \"solution\": solution,\n \"answer\": answer,\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n return dataset.map(_process_doc)\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc[\"problem\"])\n", + "doc_to_target": "answer", + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n tokenizer=None,\n max_len=32768,\n repeat_window=100,\n unique_thresh=0.2,\n addtokens=False,\n) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n **{f\"avg@{n}\": -1 for n in n_res_list},\n }\n if tokenizer is not None:\n n_stats_list = [1]\n if \"n_res_list\" in locals():\n n_stats_list.extend(n_res_list)\n metrics = {\n **metrics,\n **{\"tok\": [], \"tok_think\": [], \"tok_ans\": [], \"too_long\": [], \"repetitive\": []},\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n\n SEP = os.getenv(\"SEP\", \"\")\n VERIFYFN = os.getenv(\"VERIFYFN\", \"verify_math\")\n\n for i, a in enumerate(results, start=1):\n if tokenizer is not None:\n parts = a.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1])))\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(a)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(len(set(w := toks[-repeat_window:]))/len(w) < unique_thresh)\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(metrics[\"tok_think\"])\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(metrics[\"tok_ans\"])\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(metrics[\"too_long\"])\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(metrics[\"repetitive\"])\n\n if VERIFYFN == \"verify_math\":\n match, x, y = verify_math(a, gt, sep=SEP)[0]\n elif VERIFYFN == \"verify_generic\":\n match, x, y = verify_generic(a, gt, sep=SEP, m='gpt-4.1-mini-2025-04-14')[0]\n \n metrics[\"extracted_answers\"].append(gt if match else x)\n if not(match): # Optional logging\n print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = match\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(match)\n elif i > 1:\n metrics[\"exact_matches\"].append(match)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n metrics[f\"avg@{i}\"] = sum(metrics[\"exact_matches\"]) / i\n\n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics = {(k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(v) for k, v in metrics.items() for t in addtoks}\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n # make it unique so maj is unaffected\n metrics[f\"extracted_answers@{t}\"][i] = \"Too long at \" + str(t)\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(metrics[f\"tok@{t}\"][:i])\n metrics[f\"tok_think@{t}@{i}\"] = sum(metrics[f\"tok_think@{t}\"][:i]) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(metrics[f\"tok_ans@{t}\"][:i])\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(metrics[f\"too_long@{t}\"][:i])\n if i in n_res_list:\n metrics[f\"cov@{t}@{i}\"] = int(1 in metrics[f\"exact_matches@{t}\"][:i])\n metrics[f\"maj@{t}@{i}\"] = int(gt == Counter(metrics[f\"extracted_answers@{t}\"][:i]).most_common(1)[0][0])\n metrics[f\"avg@{t}@{i}\"] = sum(metrics[f\"exact_matches@{t}\"][:i]) / i\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@64", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@64", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@128", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@128", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_matches@128", + "aggregation": "meanlist" + }, + { + "metric": "exact_match@256", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "avg@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "cov@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "maj@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@8", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@4", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@2", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@32", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@16", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@8", + "aggregation": "mean", + "higher_is_better": 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"too_long@131072@16": true, + "too_long@131072@8": true, + "too_long@131072@4": true, + "too_long@131072@2": true, + "too_long@131072@1": true, + "repetitive@131072@64": true, + "repetitive@131072@32": true, + "repetitive@131072@16": true, + "repetitive@131072@8": true, + "repetitive@131072@4": true, + "repetitive@131072@2": true, + "repetitive@131072@1": true, + "extracted_answers@131072": true, + "tok@131072": true, + "tok_think@131072": true, + "tok_ans@131072": true, + "too_long@131072": true, + "repetitive@131072": true, + "exact_matches@131072": null, + "exact_match@262144": true, + "avg@262144@64": true, + "avg@262144@32": true, + "avg@262144@16": true, + "avg@262144@8": true, + "avg@262144@4": true, + "avg@262144@2": true, + "cov@262144@64": true, + "cov@262144@32": true, + "cov@262144@16": true, + "cov@262144@8": true, + "cov@262144@4": true, + "cov@262144@2": true, + "maj@262144@64": true, + "maj@262144@32": true, + "maj@262144@16": true, + "maj@262144@8": true, + "maj@262144@4": true, + "maj@262144@2": true, + "tok_think@262144@64": true, + "tok_think@262144@32": true, + "tok_think@262144@16": true, + "tok_think@262144@8": true, + "tok_think@262144@4": true, + "tok_think@262144@2": true, + "tok_think@262144@1": true, + "tok_ans@262144@64": true, + "tok_ans@262144@32": true, + "tok_ans@262144@16": true, + "tok_ans@262144@8": true, + "tok_ans@262144@4": true, + "tok_ans@262144@2": true, + "tok_ans@262144@1": true, + "tok@262144@64": true, + "tok@262144@32": true, + "tok@262144@16": true, + "tok@262144@8": true, + "tok@262144@4": true, + "tok@262144@2": true, + "tok@262144@1": true, + "too_long@262144@64": true, + "too_long@262144@32": true, + "too_long@262144@16": true, + "too_long@262144@8": true, + "too_long@262144@4": true, + "too_long@262144@2": true, + "too_long@262144@1": true, + "repetitive@262144@64": true, + "repetitive@262144@32": true, + "repetitive@262144@16": true, + "repetitive@262144@8": true, + "repetitive@262144@4": true, + "repetitive@262144@2": true, + "repetitive@262144@1": true, + "extracted_answers@262144": true, + "tok@262144": true, + "tok_think@262144": true, + "tok_ans@262144": true, + "too_long@262144": true, + "repetitive@262144": true, + "exact_matches@262144": null + } + }, + "n-samples": { + "openai_math_tok262144_agg64": { + "original": 500, + "effective": 500 + } + }, + "config": { + "model": "vllm", + "model_args": "pretrained=../Qwen3-1.7B,tensor_parallel_size=1,data_parallel_size=8,dtype=auto", + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 262144, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "ebb7d2b9", + "date": 1759888645.074929, + "pretty_env_info": "PyTorch version: 2.7.1+cu126\nIs debug build: False\nCUDA used to build PyTorch: 12.6\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.18 (main, Jun 5 2025, 13:14:17) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.1.131-143.221.amzn2023.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.6.85\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version: 560.35.05\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8488C\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.14.1\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.14.1 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.56.2", + "lm_eval_version": "0.4.8", + "upper_git_hash": "d4111d492f80e07ef1394e762e8e69fe7df84f32", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 524288, + "task_hashes": { + "openai_math_tok262144_agg64": "afc55e2e9f8afcd6f5356e538fe9c4821fdba273f90db4b5ce5a6e291164fe7c" + }, + "model_source": "vllm", + "model_name": "../Qwen3-1.7B", + "model_name_sanitized": "..__Qwen3-1.7B", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "8101f303ec9ac365220ee28e56db147e41825f8093665cf0fecc200a4ffbeef8", + "start_time": 589906.4688587, + "end_time": 611119.464537658, + "total_evaluation_time_seconds": "21212.995678958017" +} \ No newline at end of file diff --git a/Qwen3-1.7B/results_2025-12-04T02-17-59.726740.json b/Qwen3-1.7B/results_2025-12-04T02-17-59.726740.json new file mode 100644 index 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"physician_agreed_category:not-enough-context_stderr,none": 0.01426480663946563, + "cluster:context_seeking_not-enough-context_context_seeking,none": 0.03314917127071823, + "cluster:context_seeking_not-enough-context_context_seeking_stderr,none": 0.0133438091585538, + "cluster:context_seeking_not-enough-context_helpful_safe,none": 0.7569060773480663, + "cluster:context_seeking_not-enough-context_helpful_safe_stderr,none": 0.031972146508209044, + "physician_agreed_category:no-uncertainty,none": 0.3989202594833765, + "physician_agreed_category:no-uncertainty_stderr,none": 0.017207658623170103, + "cluster:hedging_no-uncertainty_accurate,none": 0.6622807017543859, + "cluster:hedging_no-uncertainty_accurate_stderr,none": 0.03138962017599781, + "cluster:hedging_no-uncertainty_hedges,none": 0.9429824561403509, + "cluster:hedging_no-uncertainty_hedges_stderr,none": 0.015390159844922173, + "cluster:hedging_no-uncertainty_seeks_context,none": 0.9868421052631579, + "cluster:hedging_no-uncertainty_seeks_context_stderr,none": 0.007563174328019877, + "physician_agreed_category:conditionally-emergent,none": 0.3492754639787416, + "physician_agreed_category:conditionally-emergent_stderr,none": 0.025464974660993234, + "cluster:emergency_referrals_conditionally-emergent_emergency_behavior,none": 0.8, + "cluster:emergency_referrals_conditionally-emergent_emergency_behavior_stderr,none": 0.0298974037100774, + "cluster:emergency_referrals_conditionally-emergent_context_seeking,none": 0.7833333333333333, + "cluster:emergency_referrals_conditionally-emergent_context_seeking_stderr,none": 0.030792350336248997, + "physician_agreed_category:detailed,none": 0.12940823981271504, + "physician_agreed_category:detailed_stderr,none": 0.015273498309419247, + "cluster:complex_responses_detailed_accuracy_hedging,none": 0.09937888198757763, + "cluster:complex_responses_detailed_accuracy_hedging_stderr,none": 0.023651469272869294, + "cluster:complex_responses_detailed_appropriate,none": 0.5590062111801242, + "cluster:complex_responses_detailed_appropriate_stderr,none": 0.03925225049616314 + } + }, + "group_subtasks": { + "healthbench_tok32768": [] + }, + "configs": { + "healthbench_tok32768": { + "task": "healthbench_tok32768", + "tag": [ + "math_word_problems" + ], + "dataset_path": "ryzax/healthbench", + "dataset_name": "default", + "test_split": "train", + "process_docs": "def process_docs(dataset: Dataset) -> Dataset:\n \"\"\"Process documents from the dataset.\"\"\"\n\n def _process_doc(doc: dict) -> dict:\n # Keep rubrics as dicts for Arrow serialization\n # They'll be converted to RubricItem objects in process_results\n out_doc = {\n \"prompt\": doc.get(\"prompt\", []),\n \"rubrics\": doc.get(\"rubrics\", []), # Keep as list of dicts\n \"example_tags\": doc.get(\"example_tags\", []),\n \"prompt_id\": doc.get(\"prompt_id\", \"\"),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", + "doc_to_text": "def doc_to_text(doc: dict) -> str:\n \"\"\"Convert document to text prompt.\"\"\"\n # The prompt field contains a list of message dicts\n prompt_messages = doc.get(\"prompt\", [])\n if not prompt_messages:\n return \"\"\n\n # Format as conversation\n conversation = []\n for msg in prompt_messages:\n role = msg.get(\"role\", \"user\")\n content = msg.get(\"content\", \"\")\n conversation.append(f\"{role}: {content}\")\n\n return \"\\n\\n\".join(conversation)\n", + "doc_to_target": 0, + "unsafe_code": false, + "process_results": "def process_results(\n doc: dict,\n results: List[str],\n repeat_window=100,\n unique_thresh=0.2,\n tokenizer=None,\n addtokens=False,\n max_len=32768,\n **kwargs,\n) -> Dict[str, float]:\n \"\"\"Process results for a single document.\"\"\"\n if not results or not results[0]:\n eval_logger.warning(\"Empty results received\")\n return {\n \"overall_score\": 0.0,\n \"exact_match\": 0.0,\n }\n\n # Get the generated response\n response_text = results[0] if isinstance(results[0], str) else str(results[0])\n\n # Extract required fields from doc\n prompt_messages = doc.get(\"prompt\", [])\n rubric_items = doc.get(\"rubrics\", [])\n example_tags = doc.get(\"example_tags\", [])\n\n # Convert rubrics to RubricItem objects if needed\n if rubric_items and isinstance(rubric_items[0], dict):\n rubric_items = [RubricItem.from_dict(r) for r in rubric_items]\n\n # Grade the sample synchronously (wrap async function)\n try:\n metrics, _ = asyncio.run(\n grade_sample_async(\n prompt_messages=prompt_messages,\n response_text=response_text,\n rubric_items=rubric_items,\n example_tags=example_tags,\n model=JUDGE_MODEL,\n )\n )\n except Exception as e:\n eval_logger.error(f\"Error grading sample: {e}\")\n metrics = {\"overall_score\": 0.0}\n\n # Add exact_match as alias for overall_score for compatibility\n metrics[\"exact_match\"] = metrics.get(\"overall_score\", 0.0)\n\n # Don't include rubric_grades in returned metrics - it's not a scalar\n # and will cause aggregation errors. It's already logged if needed.\n\n # if True:\n if tokenizer is not None:\n n_stats_list = [1]\n metrics = {\n **metrics,\n **{\n \"tok\": [],\n \"tok_think\": [],\n \"tok_ans\": [],\n \"too_long\": [],\n \"repetitive\": [],\n },\n **{f\"tok@{n}\": -1 for n in n_stats_list},\n **{f\"tok_think@{n}\": -1 for n in n_stats_list},\n **{f\"tok_ans@{n}\": -1 for n in n_stats_list},\n **{f\"too_long@{n}\": -1 for n in n_stats_list},\n **{f\"repetitive@{n}\": -1 for n in n_stats_list},\n }\n if True:\n SEP = os.getenv(\"SEP\", \"\")\n parts = response_text.split(SEP, 1)\n metrics[\"tok_think\"].append(len(tokenizer.tokenize(parts[0])))\n metrics[\"tok_ans\"].append(\n 0 if len(parts) == 1 else len(tokenizer.tokenize(parts[1]))\n )\n metrics[\"tok\"].append(len(toks := tokenizer.tokenize(response_text)))\n metrics[\"too_long\"].append(metrics[\"tok\"][-1] >= max_len)\n metrics[\"repetitive\"].append(\n len(set(w := toks[-repeat_window:])) / len(w) < unique_thresh\n )\n i = 1\n if i in n_stats_list:\n metrics[f\"tok@{i}\"] = sum(metrics[\"tok\"]) / len(metrics[\"tok\"])\n metrics[f\"tok_think@{i}\"] = sum(metrics[\"tok_think\"]) / len(\n metrics[\"tok_think\"]\n )\n metrics[f\"tok_ans@{i}\"] = sum(metrics[\"tok_ans\"]) / len(\n metrics[\"tok_ans\"]\n )\n metrics[f\"too_long@{i}\"] = sum(metrics[\"too_long\"]) / len(\n metrics[\"too_long\"]\n )\n metrics[f\"repetitive@{i}\"] = sum(metrics[\"repetitive\"]) / len(\n metrics[\"repetitive\"]\n )\n\n if addtokens:\n addtoks = [2**x for x in range(6, int(np.log2(max_len)) + 1)]\n metrics_tok = [\n \"tok\",\n \"tok_think\",\n \"tok_ans\",\n \"too_long\",\n \"repetitive\",\n \"exact_match\",\n \"tok@1\",\n \"tok_think@1\",\n \"tok_ans@1\",\n \"too_long@1\",\n \"repetitive@1\",\n ]\n metrics_tok_dict = {\n (k.replace(\"@\", f\"@{t}@\") if \"@\" in k else f\"{k}@{t}\"): copy.copy(\n metrics[k]\n )\n for k in metrics_tok\n for t in addtoks\n }\n metrics = {\n **metrics_tok_dict,\n **{k: v for k, v in metrics.items() if k not in metrics_tok},\n }\n for t in addtoks:\n for i, t_used in enumerate(metrics[f\"tok@{t}\"]):\n if t_used > t:\n if i == 0:\n metrics[f\"exact_match@{t}\"] = 0\n # metrics[f\"exact_matches@{t}\"][i] = 0\n metrics[f\"tok@{t}\"][i] = t\n if metrics[f\"tok_think@{t}\"][i] > t:\n metrics[f\"tok_think@{t}\"][i] = t\n metrics[f\"tok_ans@{t}\"][i] = 0\n else:\n metrics[f\"tok_ans@{t}\"][i] = t - metrics[f\"tok_think@{t}\"][i]\n metrics[f\"too_long@{t}\"][i] = 1\n\n for i in n_stats_list:\n metrics[f\"tok@{t}@{i}\"] = sum(metrics[f\"tok@{t}\"][:i]) / len(\n metrics[f\"tok@{t}\"][:i]\n )\n metrics[f\"tok_think@{t}@{i}\"] = sum(\n metrics[f\"tok_think@{t}\"][:i]\n ) / len(metrics[f\"tok_think@{t}\"][:i])\n metrics[f\"tok_ans@{t}@{i}\"] = sum(metrics[f\"tok_ans@{t}\"][:i]) / len(\n metrics[f\"tok_ans@{t}\"][:i]\n )\n metrics[f\"too_long@{t}@{i}\"] = sum(metrics[f\"too_long@{t}\"][:i]) / len(\n metrics[f\"too_long@{t}\"][:i]\n )\n print(metrics)\n\n return metrics\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "overall_score", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "exact_match@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@64@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@64", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@64", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@128", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@128@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@128", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@128", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@256", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@256@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@256", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@256", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@256", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@256", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@256", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@256", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@512", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@512@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@512@64", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@512@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@512@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@512@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@512@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@512", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@512", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@512", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@512", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@512", + "aggregation": "meanlist" + }, + { + "metric": "repetitive@512", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@1024", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@1024@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@1024@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@1024@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@1024@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@1024@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@1024", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@1024", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@1024", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@1024", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@1024", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@1024", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@2048", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@2048@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@2048@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@2048@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@2048@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@2048@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@2048", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@2048", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@2048", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@2048", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@2048", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@2048", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@4096", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@4096@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@4096@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@4096@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@4096@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@4096@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@4096", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@4096", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@4096", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@4096", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@4096", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@4096", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@8192", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@8192@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@8192@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@8192@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@8192@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@8192@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@8192", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@8192", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@8192", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@8192", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@8192", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@8192", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@16384", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@16384@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@16384@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@16384@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@16384@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@16384@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@16384", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@16384", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@16384", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@16384", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@16384", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@16384", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "exact_match@32768", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_think@32768@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok_ans@32768@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "tok@32768@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "too_long@32768@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "repetitive@32768@1", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "extracted_answers@32768", + "aggregation": "bypass", + "higher_is_better": true + }, + { + "metric": "tok@32768", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_think@32768", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "tok_ans@32768", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "too_long@32768", + "aggregation": "meanlist", + "higher_is_better": true + }, + { + "metric": "repetitive@32768", + "aggregation": "meanlist", + "higher_is_better": true + } + ], + "output_type": "generate_until", + "generation_kwargs": { + "until": [], + "do_sample": false, + "temperature": 0.6, + "max_gen_toks": 32768, + "top_p": 0.95 + }, + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 1.0, + "pretrained": "Qwen/Qwen3-1.7B", + "tensor_parallel_size": 1, + "data_parallel_size": 1, + "dtype": "auto" + } + } + }, + "versions": { + "healthbench_tok32768": 1.0 + }, + "n-shot": { + "healthbench_tok32768": 0 + }, + "higher_is_better": { + "healthbench_tok32768": { + "overall_score": true, + "exact_match@64": true, + "tok_think@64@1": true, + "tok_ans@64@1": true, + "tok@64@1": true, + "too_long@64@1": true, + "repetitive@64@1": true, + "extracted_answers@64": true, + "tok@64": true, + "tok_think@64": true, + "tok_ans@64": true, + "too_long@64": true, + "repetitive@64": true, + "exact_match@128": true, + "tok_think@128@1": true, + "tok_ans@128@1": true, + "tok@128@1": true, + "too_long@128@1": true, + "repetitive@128@1": true, + "extracted_answers@128": true, + "tok@128": true, + "tok_think@128": true, + "tok_ans@128": true, + "too_long@128": true, + "repetitive@128": true, + "exact_match@256": true, + "tok_think@256@1": true, + "tok_ans@256@1": true, + "tok@256@1": true, + "too_long@256@1": true, + "repetitive@256@1": true, + "extracted_answers@256": true, + "tok@256": true, + "tok_think@256": true, + "tok_ans@256": true, + "too_long@256": true, + "repetitive@256": true, + "exact_match@512": true, + "tok_think@512@1": true, + "tok_ans@512@64": true, + "tok_ans@512@1": true, + "tok@512@1": true, + "too_long@512@1": true, + "repetitive@512@1": true, + "extracted_answers@512": true, + "tok@512": true, + "tok_think@512": true, + "tok_ans@512": true, + "too_long@512": null, + "repetitive@512": true, + "exact_match@1024": true, + "tok_think@1024@1": true, + "tok_ans@1024@1": true, + "tok@1024@1": true, + "too_long@1024@1": true, + "repetitive@1024@1": true, + "extracted_answers@1024": true, + "tok@1024": true, + "tok_think@1024": true, + "tok_ans@1024": true, + "too_long@1024": true, + "repetitive@1024": true, + "exact_match@2048": true, + "tok_think@2048@1": true, + "tok_ans@2048@1": true, + "tok@2048@1": true, + "too_long@2048@1": true, + "repetitive@2048@1": true, + "extracted_answers@2048": true, + "tok@2048": true, + "tok_think@2048": true, + "tok_ans@2048": true, + "too_long@2048": true, + "repetitive@2048": true, + "exact_match@4096": true, + "tok_think@4096@1": true, + "tok_ans@4096@1": true, + "tok@4096@1": true, + "too_long@4096@1": true, + "repetitive@4096@1": true, + "extracted_answers@4096": true, + "tok@4096": true, + "tok_think@4096": true, + "tok_ans@4096": true, + "too_long@4096": true, + "repetitive@4096": true, + "exact_match@8192": true, + "tok_think@8192@1": true, + "tok_ans@8192@1": true, + "tok@8192@1": true, + "too_long@8192@1": true, + "repetitive@8192@1": true, + "extracted_answers@8192": true, + "tok@8192": true, + "tok_think@8192": true, + "tok_ans@8192": true, + "too_long@8192": true, + "repetitive@8192": true, + "exact_match@16384": true, + "tok_think@16384@1": true, + "tok_ans@16384@1": true, + "tok@16384@1": true, + "too_long@16384@1": true, + "repetitive@16384@1": true, + "extracted_answers@16384": true, + "tok@16384": true, + "tok_think@16384": true, + "tok_ans@16384": true, + "too_long@16384": true, + "repetitive@16384": true, + "exact_match@32768": true, + "tok_think@32768@1": true, + "tok_ans@32768@1": true, + "tok@32768@1": true, + "too_long@32768@1": true, + "repetitive@32768@1": true, + "extracted_answers@32768": true, + "tok@32768": true, + "tok_think@32768": true, + "tok_ans@32768": true, + "too_long@32768": true, + "repetitive@32768": true + } + }, + "n-samples": { + "healthbench_tok32768": { + "original": 5000, + "effective": 5000 + } + }, + "config": { + "model": "vllm", + "model_args": { + "pretrained": "Qwen/Qwen3-1.7B", + "tensor_parallel_size": 1, + "data_parallel_size": 1, + "dtype": "auto" + }, + "batch_size": "auto", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": { + "max_gen_toks": 32768, + "temperature": 0.6, + "top_p": 0.95 + }, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "f4236a87", + "date": 1764793627.4837415, + "pretty_env_info": "PyTorch version: 2.7.1+cu126\nIs debug build: False\nCUDA used to build PyTorch: 12.6\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.12.55-74.119.amzn2023.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.6.85\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version: 570.195.03\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8488C\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.6.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.6.80\n[pip3] nvidia-cuda-nvrtc-cu12==12.6.77\n[pip3] nvidia-cuda-runtime-cu12==12.6.77\n[pip3] nvidia-cudnn-cu12==9.5.1.17\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.0.4\n[pip3] nvidia-curand-cu12==10.3.7.77\n[pip3] nvidia-cusolver-cu12==11.7.1.2\n[pip3] nvidia-cusparse-cu12==12.5.4.2\n[pip3] nvidia-cusparselt-cu12==0.6.3\n[pip3] nvidia-nccl-cu12==2.26.2\n[pip3] nvidia-nvjitlink-cu12==12.6.85\n[pip3] nvidia-nvtx-cu12==12.6.77\n[pip3] torch==2.7.1\n[pip3] torchaudio==2.7.1\n[pip3] torchvision==0.22.1\n[pip3] triton==3.3.1\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi\n[conda] nvidia-cudnn-frontend 1.15.0 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi\n[conda] torch 2.7.1 pypi_0 pypi\n[conda] torchaudio 2.7.1 pypi_0 pypi\n[conda] torchvision 0.22.1 pypi_0 pypi\n[conda] triton 3.3.1 pypi_0 pypi", + "transformers_version": "4.57.1", + "lm_eval_version": "0.4.8", + "upper_git_hash": "7dad5c900a69309fc2dbb2902ffcc0a5c5dc97b8", + "tokenizer_pad_token": [ + "<|endoftext|>", + "151643" + ], + "tokenizer_eos_token": [ + "<|im_end|>", + "151645" + ], + "tokenizer_bos_token": [ + null, + "None" + ], + "eot_token_id": 151645, + "max_length": 40960, + "task_hashes": { + "healthbench_tok32768": "ae259b456d9272d127c8b40fa0e07fa881230154ddcd0b76b5356195057c5e46" + }, + "model_source": "vllm", + "model_name": "", + "model_name_sanitized": "", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('') and message.content.endswith('')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in content %}\n {%- set reasoning_content = content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- set content = content.split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "chat_template_sha": "a55ee1b1660128b7098723e0abcd92caa0788061051c62d51cbe87d9cf1974d8", + "start_time": 586183.141977468, + "end_time": 607250.626853611, + "total_evaluation_time_seconds": "21067.48487614293" +} \ No newline at end of file diff --git a/results.json b/results.json new file mode 100644 index 0000000000000000000000000000000000000000..feaf27edde6f386e21c4406cb9f1ce75c2de20a6 --- /dev/null +++ b/results.json @@ -0,0 +1,151 @@ +{ + "generated_at": "Tue Nov 25 05:35:11 2025", + "results": { + "Qwen/Qwen3-1.7B": { + "1024": { + "Full": 22353.76274578072, + "SW": { + "512": 22334.889040444254 + } + }, + "12288": { + "Lastk": { + "4096": 8833.215522470686 + } + }, + "131072": { + "Full": 447.9739294077835, + "Lastk": { + "4096": 8217.497899601845 + }, + "SWR": { + "4096": 5223.902062757965 + }, + "SW": { + "1024": 12846.642712989378, + "16384": 1420.0887479495884, + "2048": 8737.192850800706, + "4096": 5044.133152075515, + "512": 17688.095390398972, + "8192": 2788.0600570589513 + }, + "Summary": { + "4096": 3196.518238858156 + } + }, + "16384": { + "Full": 3199.3666279152258, + "Lastk": { + "4096": 8839.360495203595 + }, + "SWR": { + "4096": 6335.267820173072 + }, + "SW": { + "1024": 13637.950013963344, + "2048": 9344.372521747411, + "4096": 6105.093936333824, + "512": 18866.6815950523, + "8192": 4625.519682321337 + }, + "Summary": { + "4096": 3688.0522271645987 + } + }, + "2048": { + "Full": 17375.809502141445, + "SW": { + "1024": 16458.992508060357, + "512": 19773.832821920118 + } + }, + "262144": { + "Full": 1126.2815170913152, + "SWR": { + "4096": 5067.347777345807 + }, + "SW": { + "1024": 12786.188110729145, + "2048": 8479.524584389304, + "512": 16727.559479636933 + } + }, + "32768": { + "Full": 1476.588184772533, + "Lastk": { + "4096": 8450.08437221957 + }, + "SWR": { + "4096": 5693.19412609675 + }, + "SW": { + "1024": 13407.15864086175, + "16384": 2440.989569243455, + "2048": 8972.577599056367, + "4096": 5479.25345645216, + "512": 18237.512299148715, + "8192": 3291.31135151093 + }, + "Summary": { + "4096": 3249.081990127986 + } + }, + "4096": { + "Full": 11884.750815348652, + "SW": { + "1024": 14545.551401165003, + "2048": 12127.31775983398, + "512": 19224.947009659365 + } + }, + "524288": { + "SW": { + "1024": 8161.7693733151455, + "2048": 8140.667125532834, + "4096": 4815.700379470651, + "512": 14201.645348038603 + } + }, + "65536": { + "Full": 685.0852913305404, + "Lastk": { + "4096": 8250.584279970251 + }, + "SWR": { + "4096": 5371.374168569127 + }, + "SW": { + "1024": 12970.667133063094, + "16384": 1649.41184509061, + "2048": 8798.131184630918, + "4096": 5183.198914632168, + "512": 18408.60074411703, + "8192": 2949.493736544946 + }, + "Summary": { + "4096": 3452.5862317855504 + } + }, + "8192": { + "Full": 6597.8838366563605, + "Lastk": { + "4096": 9685.305178846791 + }, + "SWR": { + "4096": 8193.922398498407 + }, + "SW": { + "1024": 13850.373076713933, + "2048": 10243.92306423358, + "4096": 7906.962293771858, + "512": 19482.584048304092 + }, + "Summary": { + "4096": 4852.065086653675 + } + } + } + }, + "skipped_files": [], + "source_folder": "job_results" +} \ No newline at end of file