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

bash

# 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)

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:

json

{
  "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

bibtex

@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}
}