README.md

---
language:
  - en
license: mit
library_name: peft
base_model: Qwen/Qwen2.5-Coder-3B-Instruct
tags:
  - conventional-commits
  - qwen2.5-coder
  - text-generation
  - code-llm
  - fine-tuned
  - lora
  - qlora
---

# Qwen Commit LoRA - Conventional Commit Message Generator

Generates conventional commit messages from git diffs using a fine-tuned Qwen2.5-Coder-3B model with QLoRA adapters.

## Model Details

- **Base Model**: [Qwen/Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct)
- **Fine-tuning Method**: QLoRA (4-bit quantized, rank=8, alpha=16)
- **Training Data**: 210 real conventional commits from open-source repositories
- **Target Modules**: q_proj, k_proj, v_proj, o_proj

## Usage

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-Coder-3B-Instruct",
    load_in_4bit=True,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-3B-Instruct")

# Load LoRA adapters
model = PeftModel.from_pretrained(base_model, "Pavloffm/qwen-commit-lora")

# Generate commit message
diff = """diff --git a/src/main.py b/src/main.py
index 1234567..abcdefg 100644
--- a/src/main.py
+++ b/src/main.py
@@ -1,3 +1,5 @@
+def new_feature():
+    pass
"""

messages = [{"role": "user", "content": f"Generate a conventional commit message for this diff:\n{diff}"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training

- 2 epochs
- Learning rate: 1.5e-4
- LoRA rank: 8, alpha: 16
- 210 training examples

## License

MIT License