---
license: apache-2.0
base_model: mlx-community/Qwen2.5-Coder-7B-Instruct-4bit
tags:
  - mlx
  - lora
  - qwen
  - commit-message
  - conventional-commits
  - git
  - chinese
---

# git-ai-commit-sft

A LoRA adapter fine-tuned on Qwen2.5-Coder-7B-Instruct-4bit to generate **Chinese Conventional Commits** commit messages from git diffs.

Built for [git-ai-commit](https://github.com/your-org/git-ai-commit), an IntelliJ plugin that uses LLMs to generate commit messages.

## Model

- **Base model:** [mlx-community/Qwen2.5-Coder-7B-Instruct-4bit](https://huggingface.co/mlx-community/Qwen2.5-Coder-7B-Instruct-4bit)
- **Fine-tuning:** LoRA (rank 8, 16 layers, 11.5M trainable params)
- **Format:** MLX adapter (safetensors)

## Training Data

548 high-quality commit messages from two real-world repositories (one Java backend, one Go microservice), filtered to Chinese-only Conventional Commits format. Each training sample pairs a git diff (processed through the plugin's exact runtime pipeline — GitDiffFilter + PromptBuilder) with the corresponding human-written commit message.

## Evaluation

| Metric | Before | After |
|---|---|---|
| Conventional Commits rate | 96% | **100%** |
| Chinese rate | 100% | 100% |
| Single-line rate | 100% | 100% |
| Mean similarity to reference | 0.339 | **0.546** |
| Mean output length | 51 chars | 27 chars |

## Usage

```bash
# Install mlx-lm
pip install mlx-lm

# Download and load
python -m mlx_lm.generate \
  --model mlx-community/Qwen2.5-Coder-7B-Instruct-4bit \
  --adapter-path yisuiban/git-ai-commit-sft \
  --prompt "你是一位资深工程师，擅长根据 git diff 生成一句中文提交信息。..."
```

From Python:

```python
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler

model, tokenizer = load(
    "mlx-community/Qwen2.5-Coder-7B-Instruct-4bit",
    adapter_path="yisuiban/git-ai-commit-sft"
)

prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": "你的提示词..."}],
    tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, max_tokens=64,
                    sampler=make_sampler(temp=0.0))
```

For Ollama deployment, see the [training repository](https://github.com/your-org/git-ai-commit-sft) for the full Modelfile and export pipeline.

## Training Details

- **Hardware:** Apple M1 Max (64GB unified memory)
- **Framework:** MLX LoRA (mlx-lm 0.31.3)
- **Optimizer:** Adam, learning rate 1e-5
- **Batch:** 1 × gradient accumulation 8 (effective batch 8)
- **Steps:** 250 (~4 epochs over 473 training samples)
- **Max sequence length:** 4096 tokens
- **Training time:** ~20 minutes on M1 Max