code-review-critic-py-gguf

Code Review Critic

A fine-tuned Qwen2.5-Coder-7B-Instruct model for Python code review.

Model Description

This model provides constructive, actionable feedback on Python code. It focuses on:

• Bug detection
• Potential issues
• Code quality improvements

Base Model: Qwen/Qwen2.5-Coder-7B-Instruct Fine-tuning Method: QLoRA (4-bit quantization + LoRA adapters) Training Data: 8,275 real GitHub PR review comments from major Python projects

Training Details

• LoRA Rank: 64
• LoRA Alpha: 64
• Learning Rate: 2e-4
• Epochs: 2
• Final Eval Loss: 0.8455

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("YOUR_USERNAME/code-review-critic")
tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/code-review-critic")

messages = [
    {"role": "system", "content": "You are an expert code reviewer. Analyze the provided Python code and give constructive, specific feedback."},
    {"role": "user", "content": "Review this Python code:\n\n```python\ndef get_user(id):\n    return db.query(f'SELECT * FROM users WHERE id = {id}')\n```"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))