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The BROKE Team 1f70b4984a Release MLX Knife 1.0.3 - GitHub Issues Implementation & Enhanced User Experience 
Community-driven feature development implementing key GitHub Issues:
    - Fix Issue #7: Health check consistency for fuzzy model names - unified health check logic ensures
  identical status regardless of name format (Phi-3 vs mlx-community/Phi-3-mini-4k-instruct-4bit)
    - Add Issue #6: Repository name validation - pre-validation for HuggingFace Hub 96-character limit with
  clear error messages
    - Add Issue #13: Hash-based disambiguation - use commit hashes to resolve ambiguous model names (mlxk show
  Llama@de2dfaf5 → mlx-community/Llama-3.3-70B-Instruct-4bit)

  Enhanced user experience:
    - Pure local hash resolution without external API calls, offline-capable
    - Improved model name disambiguation logic for better workflow
    - Real user workflow support - see hashes in mlxk list, use directly in other commands
2025-08-18 20:21:43 +02:00

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# <img src="https://github.com/mzau/mlx-knife/raw/main/broke-logo.png" alt="BROKE Logo" width="60" style="vertical-align: middle;"> MLX Knife
<p align="center">
<img src="https://github.com/mzau/mlx-knife/raw/main/mlxk-demo.gif" alt="MLX Knife Demo" width="1000">
</p>
A lightweight, ollama-like CLI for managing and running MLX models on Apple Silicon. **CLI-only tool designed for personal, local use** - perfect for individual developers and researchers working with MLX models.
> **Note**: MLX Knife is designed as a command-line interface tool only. While some internal functions are accessible via Python imports, only CLI usage is officially supported.
**Current Version**: 1.0.3 (August 2025)
[![GitHub Release](https://img.shields.io/github/v/release/mzau/mlx-knife)](https://github.com/mzau/mlx-knife/releases)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/)
[![Apple Silicon](https://img.shields.io/badge/Apple%20Silicon-M1%2FM2%2FM3-green.svg)](https://support.apple.com/en-us/HT211814)
[![MLX](https://img.shields.io/badge/MLX-Latest-orange.svg)](https://github.com/ml-explore/mlx)
[![Tests](https://img.shields.io/badge/tests-114%2F114%20passing-brightgreen.svg)](#testing)
## Features
### Core Functionality
- **List & Manage Models**: Browse your HuggingFace cache with MLX-specific filtering
- **Model Information**: Detailed model metadata including quantization info
- **Download Models**: Pull models from HuggingFace with progress tracking
- **Run Models**: Native MLX execution with streaming and chat modes
- **Health Checks**: Verify model integrity and completeness
- **Cache Management**: Clean up and organize your model storage
### Local Server & Web Interface
- **OpenAI-Compatible API**: Local REST API with `/v1/chat/completions`, `/v1/completions`, `/v1/models`
- **Web Chat Interface**: Built-in HTML chat interface with markdown rendering
- **Single-User Design**: Optimized for personal use, not multi-user production environments
- **Conversation Context**: Full chat history maintained for follow-up questions
- **Streaming Support**: Real-time token streaming via Server-Sent Events
- **Configurable Limits**: Set default max tokens via `--max-tokens` parameter
- **Model Hot-Swapping**: Switch between models per conversation
- **Tool Integration**: Compatible with OpenAI-compatible clients (Cursor IDE, etc.)
### Run Experience
- **Direct MLX Integration**: Models load and run natively without subprocess overhead
- **Real-time Streaming**: Watch tokens generate with proper spacing and formatting
- **Interactive Chat**: Full conversational mode with history tracking
- **Memory Insights**: See GPU memory usage after model loading and generation
- **Dynamic Stop Tokens**: Automatic detection and filtering of model-specific stop tokens
- **Customizable Generation**: Control temperature, max_tokens, top_p, and repetition penalty
- **Context-Managed Memory**: Context manager pattern ensures automatic cleanup and prevents memory leaks
- **Exception-Safe**: Robust error handling with guaranteed resource cleanup
## Installation
### Via PyPI (Recommended)
```bash
pip install mlx-knife
```
### Requirements
- macOS with Apple Silicon (M1/M2/M3)
- Python 3.9+ (native macOS version or newer)
- 8GB+ RAM recommended + RAM to run LLM
### Python Compatibility
MLX Knife has been comprehensively tested and verified on:
✅ **Python 3.9.6** (native macOS) - Primary target
✅ **Python 3.10-3.13** - Fully compatible
All versions include full MLX model execution testing with real models.
### Install from Source
```bash
# Clone the repository
git clone https://github.com/mzau/mlx-knife.git
cd mlx-knife
# Install in development mode
pip install -e .
# Or install normally
pip install .
# Install with development tools (ruff, mypy, tests)
pip install -e ".[dev,test]"
```
### Install Dependencies Only
```bash
pip install -r requirements.txt
```
## Quick Start
### CLI Usage
```bash
# List all MLX models in your cache
mlxk list
# Show detailed info about a model
mlxk show Phi-3-mini-4k-instruct-4bit
# Download a new model
mlxk pull mlx-community/Mistral-7B-Instruct-v0.3-4bit
# Run a model with a prompt
mlxk run Phi-3-mini "What is the capital of France?"
# Start interactive chat
mlxk run Phi-3-mini
# Check model health
mlxk health
```
### Web Chat Interface
MLX Knife includes a built-in web interface for easy model interaction:
```bash
# Start the OpenAI-compatible API server
mlxk server --port 8000 --max-tokens 4000
# Get web chat interface from GitHub
curl -O https://raw.githubusercontent.com/mzau/mlx-knife/main/simple_chat.html
# Open web chat interface in your browser
open simple_chat.html
```
**Features:**
- **No installation required** - Pure HTML/CSS/JS
- **Real-time streaming** - Watch tokens appear as they're generated
- **Model selection** - Choose any MLX model from your cache
- **Conversation history** - Full context for follow-up questions
- **Markdown rendering** - Proper formatting for code, lists, tables
- **Mobile-friendly** - Responsive design works on all devices
### Local API Server Integration
The MLX Knife server provides OpenAI-compatible endpoints for **local development and personal use**:
```bash
# Start local server (single-user, no authentication)
mlxk server --host 127.0.0.1 --port 8000
# Test with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
-d '{"model": "Phi-3-mini-4k-instruct-4bit", "messages": [{"role": "user", "content": "Hello!"}]}'
# Integration with development tools (community-tested):
# - Cursor IDE: Set API URL to http://localhost:8000/v1
# - LibreChat: Configure as custom OpenAI endpoint
# - Open WebUI: Add as local OpenAI-compatible API
# - SillyTavern: Add as OpenAI API with custom URL
```
**Note**: Tool integrations are community-tested. Some tools may require specific configuration or have compatibility limitations. Please report issues via GitHub.
## Command Reference
### Available Commands
#### `list` - Browse Models
```bash
mlxk list # Show MLX models only (short names)
mlxk list --verbose # Show MLX models with full paths
mlxk list --all # Show all models with framework info
mlxk list --all --verbose # All models with full paths
mlxk list --health # Include health status
mlxk list Phi-3 # Filter by model name
mlxk list --verbose Phi-3 # Show detailed info (same as show)
```
#### `show` - Model Details
```bash
mlxk show <model> # Display model information
mlxk show <model> --files # Include file listing
mlxk show <model> --config # Show config.json content
```
#### `pull` - Download Models
```bash
mlxk pull <model> # Download from HuggingFace
mlxk pull <org>/<model> # Full model path
```
#### `run` - Execute Models
```bash
mlxk run <model> "prompt" # Single prompt (minimal output)
mlxk run <model> "prompt" --verbose # Show loading, memory, and stats
mlxk run <model> # Interactive chat
mlxk run <model> "prompt" --no-stream # Batch output
mlxk run <model> --max-tokens 1000 # Custom length
mlxk run <model> --temperature 0.9 # Higher creativity
mlxk run <model> --no-chat-template # Raw completion mode
```
#### `rm` - Remove Models
```bash
mlxk rm <model> # Delete a model
mlxk rm <model> --force # Skip confirmation
```
#### `health` - Check Integrity
```bash
mlxk health # Check all models
mlxk health <model> # Check specific model
```
#### `server` - Start API Server
```bash
mlxk server # Start on localhost:8000
mlxk server --port 8001 # Custom port
mlxk server --host 0.0.0.0 --port 8000 # Allow external access
mlxk server --max-tokens 4000 # Set default max tokens (default: 2000)
mlxk server --reload # Development mode with auto-reload
```
### Command Aliases
After installation, these commands are equivalent:
- `mlxk` (recommended)
- `mlx-knife`
- `mlx_knife`
## Configuration
### Cache Location
By default, models are stored in `~/.cache/huggingface/hub`. Configure with:
```bash
# Set custom cache location
export HF_HOME="/path/to/your/cache"
# Example: External SSD
export HF_HOME="/Volumes/ExternalSSD/models"
```
### Model Name Expansion
Short names are automatically expanded for MLX models:
- `Phi-3-mini-4k-instruct-4bit` → `mlx-community/Phi-3-mini-4k-instruct-4bit`
- Models already containing `/` are used as-is
## Advanced Usage
### Generation Parameters
```bash
# Creative writing (high temperature, diverse output)
mlxk run Mistral-7B "Write a story" --temperature 0.9 --top-p 0.95
# Precise tasks (low temperature, focused output)
mlxk run Phi-3-mini "Extract key points" --temperature 0.3 --top-p 0.9
# Long-form generation
mlxk run Mixtral-8x7B "Explain quantum computing" --max-tokens 2000
# Reduce repetition
mlxk run model "prompt" --repetition-penalty 1.2
```
### Working with Specific Commits
```bash
# Use specific model version
mlxk show model@commit_hash
mlxk run model@commit_hash "prompt"
```
### Non-MLX Model Handling
The tool automatically detects framework compatibility:
```bash
# Attempting to run PyTorch model
mlxk run bert-base-uncased
# Error: Model bert-base-uncased is not MLX-compatible (Framework: PyTorch)!
# Use MLX-Community models: https://huggingface.co/mlx-community
```
## Troubleshooting
### Model Not Found
```bash
# If model isn't found, try full path
mlxk pull mlx-community/Model-Name-4bit
# List available models
mlxk list --all
```
### Performance Issues
- Ensure sufficient RAM for model size
- Close other applications to free memory
- Use smaller quantized models (4-bit recommended)
### Streaming Issues
- Some models may have spacing issues - this is handled automatically
- Use `--no-stream` for batch output if needed
## Contributing
Contributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for development setup and guidelines.
## Security
For security concerns, please see [SECURITY.md](SECURITY.md) or contact us at broke@gmx.eu.
MLX Knife runs entirely locally - no data is sent to external servers except when downloading models from HuggingFace.
## License
MIT License - see [LICENSE](LICENSE) file for details
Copyright (c) 2025 The BROKE team 🦫
## Acknowledgments
- Built for Apple Silicon using the [MLX framework](https://github.com/ml-explore/mlx)
- Models hosted by the [MLX Community](https://huggingface.co/mlx-community) on HuggingFace
- Inspired by [ollama](https://ollama.ai)'s user experience
---
<p align="center">
<b>Made with ❤️ by The BROKE team <img src="broke-logo.png" alt="BROKE Logo" width="30" style="vertical-align: middle;"></b><br>
<i>Version 1.0.3 | August 2025</i><br>
<a href="https://github.com/mzau/broke-cluster">🔮 Next: BROKE Cluster for multi-node deployments</a>
</p>
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