Instructions to use Elib27/qwen2.5-coder-0.5b-commit-msg-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Elib27/qwen2.5-coder-0.5b-commit-msg-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-0.5B-Instruct") model = PeftModel.from_pretrained(base_model, "Elib27/qwen2.5-coder-0.5b-commit-msg-lora") - Notebooks
- Google Colab
- Kaggle
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base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
library_name: peft
tags:
- lora
- commit-message-generation
- conventional-commits
- code
---
# Qwen2.5-Coder-0.5B-Instruct — Commit Message LoRA Adapter
LoRA adapter fine-tuned on top of [`Qwen/Qwen2.5-Coder-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct)
to generate [Conventional Commits](https://www.conventionalcommits.org/) messages from a `git diff`.
Part of a project to build a local, offline `prepare-commit-msg` git hook.
Full write-up: https://eliotbas.com/projects/commits-fine-tuning/
Training dataset: https://huggingface.co/datasets/Elib27/commits
## Training details
- Method: LoRA (r=16, alpha=32)
- Framework: Unsloth + TRL SFTTrainer
- Hardware: Google Colab A100
- Dataset: ~11k (diff, commit message) pairs, see [eliotbas/commits](https://huggingface.co/datasets/Elib27/commits)
See the article for full evaluation results (ROUGE-L, structural checks, LLM-as-judge) across all model sizes.
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