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README.md

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 ---
+base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+library_name: peft
 license: apache-2.0
+language:
+- en
+tags:
+- lora
+- text2sql
+- nl-to-sql
+- duckdb
+- bioinformatics
+- epigenetics
+- dna-methylation
+datasets:
+- custom
+pipeline_tag: text-generation
 ---
+
+# CpGAtlas-NL-to-SQL-Qwen2.5-Coder-7B-LoRA
+
+A LoRA adapter fine-tuned on [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) for translating natural language questions about DNA methylation into executable DuckDB SQL queries over the [CpG Atlas](https://cpg-atlas-pi.vercel.app) database.
+
+## Model Description
+
+This adapter enables researchers to query the CpG Atlas database — a centralized multi-layer knowledgebase covering 18 functional annotation layers across >1.2 million CpG sites — using plain English. The model was fine-tuned on domain-specific question-SQL pairs formatted in ChatML, where the system message contains relevant table schemas and column descriptions so the model learns to read and apply schema context at inference time.
+
+**Task:** Natural Language to SQL (NL-to-SQL) for the CpG Atlas DuckDB database
+
+**Example input:**
+```
+Find all CpGs associated with mortality that have ICC greater than 0.75 and are immune cell invariant
+```
+
+**Example output:**
+```sql
+SELECT e.ProbeID, e.trait, e.beta, i.icc_sugden
+FROM ewas_atlas e
+JOIN icc_data i ON e.ProbeID = i.ProbeID
+JOIN immune_invariant inv ON e.ProbeID = inv.ProbeID
+WHERE e.trait LIKE '%mortality%'
+AND i.icc_sugden > 0.75;
+```
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| Base model | Qwen/Qwen2.5-Coder-7B-Instruct |
+| Method | LoRA (Low-Rank Adaptation) |
+| Rank (r) | 32 |
+| Alpha | 64 |
+| Dropout | 0.05 |
+| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| Quantization | 4-bit NF4 (double quantization, bfloat16 compute) |
+| Epochs | 5 |
+| Learning rate | 1e-4 |
+| Scheduler | Cosine with 0.05 warmup ratio |
+| Batch size | 2 (with 8 gradient accumulation steps) |
+| Max sequence length | 4096 |
+| Optimizer | paged_adamw_32bit |
+| Training examples | 541 |
+| Validation examples | 135 |
+| Hardware | NVIDIA A100/H100 GPU |
+
+Training loss converged from 1.62 to 0.005.
+
+## Evaluation
+
+Evaluated on a held-out benchmark of 135 queries spanning five complexity classes: (1) simple single-table lookups, (2) multi-table joins, (3) filtering, (4) concept-mapping queries requiring biological synonym understanding, and (5) complex queries requiring additional analysis steps.
+
+| Model | Exact Accuracy | Accuracy (incl. partial) |
+|-------|---------------|--------------------------|
+| Qwen2.5-Coder-7B-Instruct (base) | 30% | 86% |
+| **This model (fine-tuned)** | **61%** | **88%** |
+| GPT-4o (API mode) | 80% | 98% |
+
+## Usage
+
+### With PEFT
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2.5-Coder-7B-Instruct",
+    device_map="auto",
+    torch_dtype="auto",
+)
+model = PeftModel.from_pretrained(base_model, "vandijklab/CpGAtlas-NL-to-SQL-Qwen2.5-Coder-7B-LoRA")
+tokenizer = AutoTokenizer.from_pretrained("vandijklab/CpGAtlas-NL-to-SQL-Qwen2.5-Coder-7B-LoRA")
+
+messages = [
+    {"role": "system", "content": "You are an expert bioinformatics data engineer. Write one executable DuckDB SQL query to answer the question based on the schema. Return only SQL. Do not include markdown.\n\nSchema:\n{schema_context}"},
+    {"role": "user", "content": "What transposable element classes are in the transposon dataset?"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+### Self-Correcting Inference
+
+The model is designed to be used with a self-correcting execution loop: the generated SQL is executed against DuckDB, and if execution fails, the error message is appended to the prompt for the model to revise its output (up to 3 retry attempts).
+
+## Intended Use
+
+This model is intended for use with the CpG Atlas database and its associated schema. It is designed to support researchers in querying multi-dimensional DNA methylation annotations without requiring SQL expertise. The model can run entirely locally without an internet connection or API key.
+
+## Limitations
+
+- Trained on a specific query distribution over the CpG Atlas schema; may underperform on highly novel query patterns beyond its training scope
+- Users should inspect generated SQL before treating results as definitive
+- Requires sufficient computational resources to run a 7B parameter model (quantized inference requires ~6GB VRAM)
+
+## Citation
+
+If you use this model, please cite the CpG Atlas paper (citation forthcoming).
+
+## Framework Versions
+
+- PEFT 0.12.0
+- Transformers (compatible with Qwen2.5)
+- TRL (SFTTrainer)

adapter_config.json

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+{
+  "alpha_pattern": {},
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+  "bias": "none",
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+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "o_proj",
+    "up_proj",
+    "v_proj",
+    "q_proj",
+    "gate_proj",
+    "k_proj",
+    "down_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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added_tokens.json

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special_tokens_map.json

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tokenizer_config.json

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+}