README.md

---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
  - code-review
  - python
  - qwen2
  - fine-tuned
datasets:
  - custom
language:
  - en
pipeline_tag: text-generation
---

# 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

```python
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))