Instructions to use iq2i/ai-code-review with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iq2i/ai-code-review with MLX:

# Download the model from the Hub
pip install huggingface_hub[hf_xet]

huggingface-cli download --local-dir ai-code-review iq2i/ai-code-review

llama-cpp-python

How to use iq2i/ai-code-review with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="iq2i/ai-code-review",
	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 iq2i/ai-code-review with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf iq2i/ai-code-review:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf iq2i/ai-code-review:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf iq2i/ai-code-review:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf iq2i/ai-code-review: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 iq2i/ai-code-review:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf iq2i/ai-code-review: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 iq2i/ai-code-review:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf iq2i/ai-code-review:Q4_K_M

Use Docker

docker model run hf.co/iq2i/ai-code-review:Q4_K_M

LM Studio
Jan
Ollama
How to use iq2i/ai-code-review with Ollama:
```
ollama run hf.co/iq2i/ai-code-review:Q4_K_M
```

Unsloth Studio new

How to use iq2i/ai-code-review 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 iq2i/ai-code-review 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 iq2i/ai-code-review to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for iq2i/ai-code-review to start chatting

Pi new

How to use iq2i/ai-code-review with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "iq2i/ai-code-review"

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": "iq2i/ai-code-review"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use iq2i/ai-code-review with Docker Model Runner:
```
docker model run hf.co/iq2i/ai-code-review:Q4_K_M
```

Lemonade

How to use iq2i/ai-code-review with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull iq2i/ai-code-review:Q4_K_M

Run and chat with the model

lemonade run user.ai-code-review-Q4_K_M

List all available models

lemonade list

AI Code Review Model

Multi-language code review model optimized for automated code review in CI/CD pipelines.

Model Details

Base Model: Qwen/Qwen2.5-Coder-1.5B-Instruct
Training Method: LoRA fine-tuning with MLX
Format: GGUF (Q4_K_M quantization)
Purpose: Automated code review for CI/CD pipelines

Usage

Docker (Recommended)

docker pull ghcr.io/iq2i/ai-code-review:latest

# Review your codebase
docker run --rm -v $(pwd):/workspace ghcr.io/iq2i/ai-code-review:latest /workspace/src

llama.cpp

# Download the model
wget https://huggingface.co/iq2i/ai-code-review/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 concise text-based code reviews:

**SQL injection vulnerability**

User input is concatenated directly into a raw SQL query without parameterization or escaping.

Impact: An attacker can execute arbitrary SQL commands, potentially dumping the entire database, deleting data, or escalating privileges. For example: keyword=' OR '1'='1' -- would return all products.

Suggestion:
Use parameter binding: DB::select("SELECT * FROM products WHERE name LIKE ?", ['%' . $keyword . '%']) or better, use Eloquent: Product::where('name', 'like', '%' . $keyword . '%')->get()

Training

Training examples: 100+ real-world code issues
Format: ChatML conversation format with concise reviews
Framework: MLX for Apple Silicon acceleration
Method: LoRA adapters (r=4, alpha=8)
Iterations: 625

For training details, see the GitHub repository.

Limitations

Should be used as a supplementary tool, not a replacement for human review
May not catch all edge cases or security vulnerabilities
Best results on common programming patterns and frameworks

License

Apache 2.0

Citation

@software{ai_code_review,
  title = {AI Code Review Model},
  author = {IQ2i Team},
  year = {2025},
  url = {https://github.com/iq2i/ai-code-review}
}