Instructions to use loicsapone/ai-code-review-javascript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

ai-code-review-javascript / README.md

loicsapone

Update model card for javascript

3dbf962 verified 5 months ago

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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}
}