Add model card

README.md

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+---
+language:
+- en
+- code
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+tags:
+- lora
+- code
+- qwen2.5-coder
+- fingpt
+- code-correction
+pipeline_tag: text-generation
+---
+
+# fingpt-coder-1b5
+
+LoRA adapter for **[Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)** fine-tuned on
+[m-a-p/Code-Feedback](https://huggingface.co/datasets/m-a-p/Code-Feedback)
+(66K error→fix pairs, 3 epochs).
+
+> **Adapter only** — the base model is loaded from the HF Hub automatically.
+> Total download: ~84 MB adapter + ~3 GB base model.
+
+---
+
+## LoRA config
+
+| Property | Value |
+|----------|-------|
+| Base model | `Qwen/Qwen2.5-Coder-1.5B-Instruct` |
+| Rank (r) | 16 |
+| Alpha | 32 (scale = 2.0) |
+| Target modules | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |
+| Training step | 48500 |
+| Adapter size | ~84 MB |
+
+---
+
+## Quick start
+
+```bash
+git clone https://huggingface.co/revana/fingpt-coder-1b5
+```
+
+```python
+import torch, sys
+sys.path.insert(0, "fingpt")          # fingpt repo root
+from infer import load_model, generate
+
+model, tokenizer = load_model("adapter_final.pt")
+
+reply = generate(model, tokenizer, "Fix this bug:\n\ndef fact(n):\n    return n * fact(n)")
+print(reply)
+```
+
+Or use the [live demo](https://huggingface.co/spaces/revana/fingpt).
+
+---
+
+## Training
+
+| Property | Value |
+|----------|-------|
+| Dataset | [m-a-p/Code-Feedback](https://huggingface.co/datasets/m-a-p/Code-Feedback) |
+| Samples | ~66K error→fix pairs |
+| Epochs | 3 |
+| Batch size | 4 × 4 grad accum = 16 effective |
+| LR | 3e-4, cosine decay, 3% warmup |
+| Precision | bfloat16 |
+| Hardware | A100 80GB |
+
+---
+
+## License
+
+Apache 2.0