metadata
license: apache-2.0
tags:
- code-review
- javascript
- mlx
- gguf
- qwen2.5-coder
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
AI Code Review Model - Javascript
This is a fine-tuned code review model specialized for Javascript code analysis.
Model Details
- Base Model: Qwen/Qwen2.5-Coder-1.5B-Instruct
- Training Method: LoRA fine-tuning with MLX
- Format: GGUF (Q4_K_M quantization)
- Target Language: Javascript
- Purpose: Automated code review for CI/CD pipelines
Usage
Docker (Recommended)
docker pull ghcr.io/iq2i/ai-code-review:javascript-latest
docker run --rm -v $(pwd):/workspace ghcr.io/iq2i/ai-code-review:javascript-latest /workspace/src
llama.cpp
# Download the model
wget https://huggingface.co/loicsapone/ai-code-review-javascript/resolve/main/model-Q4_K_M.gguf
# Run inference
./llama-cli -m model-Q4_K_M.gguf -p "Review this code: ..."
Python (llama-cpp-python)
from llama_cpp import Llama
llm = Llama(model_path="model-Q4_K_M.gguf")
output = llm("Review this code: ...", max_tokens=512)
print(output)
Output Format
The model outputs JSON structured code reviews:
{
"summary": "Brief overview of code quality",
"score": 8,
"issues": [
{
"type": "bug",
"severity": "medium",
"line": 42,
"description": "Potential null pointer",
"suggestion": "Add null check"
}
],
"positive_points": [
"Good error handling",
"Clear variable names"
]
}
Training
This model was trained on curated Javascript code review examples using:
- MLX framework for Apple Silicon acceleration
- LoRA adapters (r=8, alpha=16)
- Custom dataset of real-world code issues
For training details, see the GitHub repository.
Limitations
- Optimized for Javascript syntax and best practices
- May not catch all edge cases or security vulnerabilities
- Should be used as a supplementary tool, not a replacement for human review
License
Apache 2.0
Citation
@software{ai_code_review_javascript,
title = {AI Code Review Model for Javascript},
author = {IQ2i Team},
year = {2025},
url = {https://github.com/iq2i/ai-code-review}
}