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| title: Developer Git Commit Agent π€ | |
| license: apache-2.0 | |
| base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct | |
| tags: | |
| - code | |
| - git | |
| - tools | |
| - developer-utilities | |
| language: | |
| - en | |
| pipeline_tag: text-generation | |
| # Developer Git Commit Agent π€ | |
| An specialized, ultra-lightweight AI agent fine-tuned to automatically generate concise, clean, and professional Git commit messages directly from messy code changes (diffs). | |
| Built specifically to optimize workflows for software engineers and developers. | |
| ## π Quickstart: How to Use | |
| You can easily load and run this model locally in your terminal or scripts using the Hugging Face `transformers` library. | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # Load the model directly from the Hub | |
| model_id = "YOUR_HF_USERNAME/developer-git-commit-agent" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") | |
| # Paste your messy git diff here | |
| git_diff = """ | |
| - const port = 3000; | |
| + const port = process.env.PORT || 5000; | |
| """ | |
| # Format prompt using the agent's system instructions | |
| messages = [ | |
| {"role": "system", "content": "You are an expert software engineer agent. Write a concise, professional Git commit message based on the provided code diff."}, | |
| {"role": "user", "content": f"Code Diff:\n{git_diff}"} | |
| ] | |
| inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda") | |
| outputs = model.generate(inputs, max_new_tokens=60) | |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) | |
| # Output: feat(config): allow dynamic port assignment via environment variables, defaulting to 5000. | |
| ``` | |
| ## π Training Details | |
| - **Base Model:** Qwen2.5-Coder-0.5B-Instruct | |
| - **Dataset:** Maxscha/commitbench (Targeted real-world engineering GitHub commits) | |
| - **Method:** Parameter-Efficient Fine-Tuning (LoRA) | |