MLXServer/CLAUDE.md

MLX Server

OpenAI-compatible API server for local LLMs on Apple Silicon via MLX. Supports Gemma 3 4B and Qwen3 VL 4B (vision + tool use).

Quick Start

# Activate virtual environment
source .venv/bin/activate

# Run with Gemma 3 (default)
./run.sh

# Run with Qwen3
./run.sh qwen

# Or directly:
python -m mlx_server.main --model mlx-community/gemma-3-4b-it-4bit --port 1234
python -m mlx_server.main --model mlx-community/Qwen3-VL-4B-Instruct-4bit --port 1234

Project Structure

• mlx_server/main.py — FastAPI server, endpoints, CLI entrypoint
• mlx_server/engine.py — Model loading, prompt building, generation (mlx_vlm)
• mlx_server/models.py — Pydantic models for OpenAI API request/response types

Supported Models

Alias	HuggingFace ID	Notes
gemma	mlx-community/gemma-3-4b-it-4bit	Vision + tool use via tool_code blocks
qwen	mlx-community/Qwen3-VL-4B-Instruct-4bit	Vision + tool use via <tool_call> tags

Key Design Decisions

• Uses mlx_vlm (not mlx_lm) as the inference backend — this supports both text and vision in a single model load
• Model-specific prompt formatting: Gemma converts system→user/assistant pairs and uses tool_code blocks; Qwen3 uses native system role and <tool_call> XML tags
• Offline-first: if the model is already cached locally (~/.cache/huggingface/hub/), the server resolves the local snapshot path directly — no network requests are made (HEAD checks, update checks, etc.)
• Thread lock on generation (single-request-at-a-time) — MLX models aren't safe for concurrent generation
• 128k context window supported via the model's native capabilities

Dependencies

Managed via uv and pyproject.toml. Virtual environment in .venv/.

uv pip install -e "."