Instructions to use loicsapone/ai-code-review-javascript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use loicsapone/ai-code-review-javascript with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir ai-code-review-javascript loicsapone/ai-code-review-javascript
- llama-cpp-python
How to use loicsapone/ai-code-review-javascript with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="loicsapone/ai-code-review-javascript", filename="model-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use loicsapone/ai-code-review-javascript with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf loicsapone/ai-code-review-javascript:Q4_K_M # Run inference directly in the terminal: llama-cli -hf loicsapone/ai-code-review-javascript:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf loicsapone/ai-code-review-javascript:Q4_K_M # Run inference directly in the terminal: llama-cli -hf loicsapone/ai-code-review-javascript:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf loicsapone/ai-code-review-javascript:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf loicsapone/ai-code-review-javascript:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf loicsapone/ai-code-review-javascript:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf loicsapone/ai-code-review-javascript:Q4_K_M
Use Docker
docker model run hf.co/loicsapone/ai-code-review-javascript:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use loicsapone/ai-code-review-javascript with Ollama:
ollama run hf.co/loicsapone/ai-code-review-javascript:Q4_K_M
- Unsloth Studio new
How to use loicsapone/ai-code-review-javascript with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for loicsapone/ai-code-review-javascript to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for loicsapone/ai-code-review-javascript to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for loicsapone/ai-code-review-javascript to start chatting
- Pi new
How to use loicsapone/ai-code-review-javascript with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "loicsapone/ai-code-review-javascript"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "loicsapone/ai-code-review-javascript" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use loicsapone/ai-code-review-javascript with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "loicsapone/ai-code-review-javascript"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default loicsapone/ai-code-review-javascript
Run Hermes
hermes
- Docker Model Runner
How to use loicsapone/ai-code-review-javascript with Docker Model Runner:
docker model run hf.co/loicsapone/ai-code-review-javascript:Q4_K_M
- Lemonade
How to use loicsapone/ai-code-review-javascript with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull loicsapone/ai-code-review-javascript:Q4_K_M
Run and chat with the model
lemonade run user.ai-code-review-javascript-Q4_K_M
List all available models
lemonade list
Update model card for javascript
Browse files
README.md
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---
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license: apache-2.0
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tags:
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- code-review
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- javascript
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- mlx
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- gguf
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- qwen2.5-coder
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base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
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---
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# AI Code Review Model - Javascript
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This is a fine-tuned code review model specialized for **Javascript** code analysis.
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## Model Details
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- **Base Model**: Qwen/Qwen2.5-Coder-1.5B-Instruct
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- **Training Method**: LoRA fine-tuning with MLX
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- **Format**: GGUF (Q4_K_M quantization)
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- **Target Language**: Javascript
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- **Purpose**: Automated code review for CI/CD pipelines
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## Usage
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### Docker (Recommended)
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```bash
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docker pull ghcr.io/iq2i/ai-code-review:javascript-latest
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docker run --rm -v $(pwd):/workspace ghcr.io/iq2i/ai-code-review:javascript-latest /workspace/src
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```
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### llama.cpp
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```bash
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# Download the model
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wget https://huggingface.co/loicsapone/ai-code-review-javascript/resolve/main/model-Q4_K_M.gguf
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# Run inference
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./llama-cli -m model-Q4_K_M.gguf -p "Review this code: ..."
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```
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### Python (llama-cpp-python)
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```python
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from llama_cpp import Llama
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llm = Llama(model_path="model-Q4_K_M.gguf")
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output = llm("Review this code: ...", max_tokens=512)
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print(output)
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```
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## Output Format
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The model outputs JSON structured code reviews:
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```json
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{
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"summary": "Brief overview of code quality",
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"score": 8,
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"issues": [
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{
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"type": "bug",
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"severity": "medium",
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"line": 42,
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"description": "Potential null pointer",
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"suggestion": "Add null check"
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}
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],
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"positive_points": [
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"Good error handling",
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"Clear variable names"
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]
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}
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```
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## Training
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This model was trained on curated Javascript code review examples using:
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- MLX framework for Apple Silicon acceleration
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- LoRA adapters (r=8, alpha=16)
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- Custom dataset of real-world code issues
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For training details, see the [GitHub repository](https://github.com/iq2i/ai-code-review).
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## Limitations
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- Optimized for Javascript syntax and best practices
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- May not catch all edge cases or security vulnerabilities
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- Should be used as a supplementary tool, not a replacement for human review
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## License
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Apache 2.0
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## Citation
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```bibtex
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@software{ai_code_review_javascript,
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title = {AI Code Review Model for Javascript},
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author = {IQ2i Team},
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year = {2025},
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url = {https://github.com/iq2i/ai-code-review}
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}
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```
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