Add Sol Pro model card and banner

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README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: pytorch
+pipeline_tag: text-generation
+tags:
+- causal-lm
+- small-language-model
+- recurrent-depth
+- grouped-query-attention
+- custom-code
+---
+
+![Sol Pro](banner.png)
+
+# Sol Pro
+
+Sol Pro is a **138,037,594-parameter** language model from Sol Labs. It combines recurrent transformer blocks with tensorized n-gram memory and a 2,048-token context.
+
+The released checkpoint scores **29.37 on the normalized Intelligence Index** and **24.01 on the raw accuracy Index**.
+
+## Model
+
+| Setting | Value |
+|---|---|
+| Parameters | 138,037,594 |
+| Context | 2,048 tokens |
+| Vocabulary | 4,096-token byte-level BPE |
+| Hidden width | 768 |
+| Stored / effective blocks | 12 / 16 |
+| Attention | 24 query heads, 8 KV heads, head dimension 32 |
+| Feed-forward layers | SwiGLU, width 3,968 |
+| Position encoding | RoPE, theta 20,000 |
+| Memory | Rank-299 tensorized 2-, 3-, and 4-gram memory |
+| Recurrence | Learned pass embeddings and loop gates |
+| Embeddings | Tied input and output weights |
+| Inference weights | BF16 safetensors |
+
+Four middle blocks run twice. Causal grouped-query attention uses learned Q/K RMSNorm and XSA value-direction subtraction. The n-gram module shares token-position factors across its three orders.
+
+## Load and generate
+
+Install the dependencies and authenticate with an account that can access this private repository:
+
+```bash
+pip install torch safetensors tokenizers huggingface_hub
+hf auth login
+```
+
+```python
+import sys
+from huggingface_hub import snapshot_download
+
+model_dir = snapshot_download(
+    "solintellegence/sol-pro",
+    allow_patterns=[
+        "model.safetensors", "modeling_sol_pro.py",
+        "config.json", "tokenizer.json",
+    ],
+)
+sys.path.insert(0, model_dir)
+
+from modeling_sol_pro import load_model, generate
+
+model, tokenizer = load_model(model_dir)
+print(generate(
+    model,
+    tokenizer,
+    "The best way to learn something new is",
+    max_new_tokens=64,
+))
+```
+
+The repository includes standalone PyTorch model code. Generation recomputes the context at each step; KV caching is not implemented. Keep prompts and continuations within 2,048 tokens.
+
+## Evaluation
+
+| Benchmark | Examples | Normalized accuracy | Raw accuracy |
+|---|---:|---:|---:|
+| HellaSwag | 10,042 | 47.64% | 36.70% |
+| ARC Easy | 2,376 | 55.51% | 54.63% |
+| ARC Challenge | 1,172 | 32.42% | 28.92% |
+| PIQA | 1,838 | 71.22% | 69.59% |
+| Arithmark3 | 1,000 | 35.70% | 37.10% |
+| **Intelligence Index** | | **29.37** | **24.01** |
+
+These are local measurements on complete evaluation splits, using the existing Sol likelihood evaluator and chance-adjusted Index calculation. Normalized accuracy divides each choice's continuation log-likelihood by its token count.
+
+This is a task-adapted checkpoint using public benchmark training splits. Evaluation used separate held-out splits, with matching evaluation contexts excluded from the adaptation data; Arithmark evaluation examples were not used for adaptation. The scores have not been independently verified and do not establish a leaderboard position.
+
+WikiText-2 validation cross-entropy is **2.7677** over **366,592 tokens**. Exact results and evaluation details are in [evaluation.json](evaluation.json).
+
+## Use and limits
+
+Sol Pro supports text-completion and small-model research. Multiple-choice scores do not establish reliable free-form reasoning or conversational behavior. Outputs can be incorrect, repetitive, or inconsistent.
+
+## Files
+
+| File | Contents |
+|---|---|
+| `model.safetensors` | Released inference weights |
+| `checkpoint.pt` | Complete final checkpoint, including optimizer state |
+| `modeling_sol_pro.py` | Architecture, loader, and text generator |
+| `config.json` | Architecture configuration |
+| `tokenizer.json` | Original tokenizer |
+| `evaluation.json` | Exact evaluation results |
+| `banner.png` | Sol Pro artwork |
+
+## License
+
+Sol Pro is released under Apache 2.0. Dataset licenses and terms remain with their respective owners.
+