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MLX-Knife 2.0.0-alpha: Issue #27 Discovery & Development README
Major Achievements: - Live reproduction and documentation of Issue #27 (health check false positive) - Comprehensive development README.md for alpha phase parallel usage - JSON API specification integration and references - 45/45 tests passing with production-quality reliability Issue #27 Critical Discovery: - Health check false positives for multi-part model downloads - Root cause: Multi-part pattern detection flaw in shared logic - GitHub issue created with reproduction steps and technical analysis 2.0.0-Alpha Development Status: - Revolutionary test isolation architecture complete - Atomic cache system with triple safety verification - Development handbook with parallel deployment guide - Ready for production testing and broke-cluster integration
This commit is contained in:
The BROKE Team
@@ -1,341 +1,314 @@
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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
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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 2.0.0-alpha
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<p align="center">
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<img src="https://github.com/mzau/mlx-knife/raw/main/mlxk-demo.gif" alt="MLX Knife Demo" width="1000">
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</p>
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**JSON-First Model Management for Automation & Scripting**
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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.
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> **🚧 Alpha Development Branch:** This is the `feature/2.0.0-json-only` branch containing MLX-Knife 2.0.0-alpha. For stable production use, see [MLX-Knife 1.1.0](https://github.com/mzau/mlx-knife/tree/main).
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> **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.
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**Current Version**: 1.1.0 (August 2025) - **STABLE RELEASE** 🚀
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- **Production Ready**: First stable release since 1.0.4 with comprehensive testing
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- **Enhanced Test System**: 150/150 tests passing with real model lifecycle integration tests
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- **Python 3.9-3.13**: Full compatibility verified across all Python versions
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- **All Critical Issues Resolved**: Issues #21, #22, #23 fixed and thoroughly tested
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[](https://github.com/mzau/mlx-knife/releases)
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[](https://github.com/mzau/mlx-knife/releases)
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[](https://opensource.org/licenses/MIT)
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[](https://www.python.org/downloads/)
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[](https://support.apple.com/en-us/HT211814)
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[](https://github.com/ml-explore/mlx)
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[](#testing)
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## Features
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### Core Functionality
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- **List & Manage Models**: Browse your HuggingFace cache with MLX-specific filtering
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- **Model Information**: Detailed model metadata including quantization info
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- **Download Models**: Pull models from HuggingFace with progress tracking
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- **Run Models**: Native MLX execution with streaming and chat modes
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- **Health Checks**: Verify model integrity and completeness
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- **Cache Management**: Clean up and organize your model storage
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### Local Server & Web Interface
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- **OpenAI-Compatible API**: Local REST API with `/v1/chat/completions`, `/v1/completions`, `/v1/models`
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- **Web Chat Interface**: Built-in HTML chat interface with markdown rendering
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- **Single-User Design**: Optimized for personal use, not multi-user production environments
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- **Conversation Context**: Full chat history maintained for follow-up questions
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- **Streaming Support**: Real-time token streaming via Server-Sent Events
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- **Configurable Limits**: Set default max tokens via `--max-tokens` parameter
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- **Model Hot-Swapping**: Switch between models per conversation
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- **Tool Integration**: Compatible with OpenAI-compatible clients (Cursor IDE, etc.)
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### Run Experience
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- **Direct MLX Integration**: Models load and run natively without subprocess overhead
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- **Real-time Streaming**: Watch tokens generate with proper spacing and formatting
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- **Interactive Chat**: Full conversational mode with history tracking
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- **Memory Insights**: See GPU memory usage after model loading and generation
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- **Dynamic Stop Tokens**: Automatic detection and filtering of model-specific stop tokens
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- **Customizable Generation**: Control temperature, max_tokens, top_p, and repetition penalty
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- **Context-Managed Memory**: Context manager pattern ensures automatic cleanup and prevents memory leaks
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- **Exception-Safe**: Robust error handling with guaranteed resource cleanup
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## Installation
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### Via PyPI (Recommended)
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```bash
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pip install mlx-knife
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```
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### Requirements
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- macOS with Apple Silicon (M1/M2/M3)
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- Python 3.9+ (native macOS version or newer)
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- 8GB+ RAM recommended + RAM to run LLM
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### Python Compatibility
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MLX Knife has been comprehensively tested and verified on:
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✅ **Python 3.9.6** (native macOS) - Primary target
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✅ **Python 3.10-3.13** - Fully compatible
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All versions include full MLX model execution testing with real models.
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### Install from Source
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```bash
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# Clone the repository
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git clone https://github.com/mzau/mlx-knife.git
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cd mlx-knife
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# Install in development mode
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pip install -e .
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# Or install normally
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pip install .
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# Install with development tools (ruff, mypy, tests)
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pip install -e ".[dev,test]"
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```
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### Install Dependencies Only
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```bash
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pip install -r requirements.txt
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```
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[](#testing)
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## Quick Start
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### CLI Usage
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```bash
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# List all MLX models in your cache
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mlxk list
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# Installation (local development)
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git clone https://github.com/mzau/mlx-knife.git -b feature/2.0.0-json-only
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cd mlx-knife
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pip install -e .
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# Show detailed info about a model
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mlxk show Phi-3-mini-4k-instruct-4bit
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# Download a new model
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mlxk pull mlx-community/Mistral-7B-Instruct-v0.3-4bit
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# Run a model with a prompt
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mlxk run Phi-3-mini "What is the capital of France?"
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# Start interactive chat
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mlxk run Phi-3-mini
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# Check model health
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mlxk health
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# Basic usage - JSON API
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mlxk-json list --json | jq '.data.models[].name'
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mlxk-json health --json | jq '.data.summary'
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mlxk-json show "Phi-3-mini" --json | jq '.data.model_info'
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```
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### Web Chat Interface
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**What's New:** JSON-first architecture for automation and scripting
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**What's Missing:** Server mode, run command (use MLX-Knife 1.x for those)
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MLX Knife includes a built-in web interface for easy model interaction:
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## ⚠️ Alpha Status Disclaimer
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MLX-Knife 2.0.0-alpha is **feature-complete for JSON operations** with production-quality reliability:
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- ✅ **Core functionality works:** All 5 commands (`list`, `health`, `show`, `pull`, `rm`)
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- ✅ **Test status:** 45/45 passing with comprehensive edge case coverage
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- ✅ **Production use:** Suitable for broke-cluster integration and automation
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- ✅ **Parallel use:** Deploy alongside MLX-Knife 1.x for server functionality
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## What 2.0.0-alpha Includes
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| Command | Status | Description |
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|---------|--------|-------------|
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| ✅ `list` | **Complete** | Model discovery with JSON output |
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| ✅ `health` | **Complete** | Corruption detection and cache analysis |
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| ✅ `show` | **Complete** | Detailed model information with --files, --config |
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| ✅ `pull` | **Complete** | HuggingFace model downloads with corruption detection |
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| ✅ `rm` | **Complete** | Model deletion with lock cleanup and fuzzy matching |
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## What's Coming Later
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| Feature | Target Version | Status |
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|---------|----------------|---------|
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| 🔄 `server` | 2.0.0-rc | OpenAI-compatible API server |
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| 🔄 `run` | 2.0.0-rc | Interactive model execution |
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| 🔄 Human-readable output | 2.0.0-rc | CLI formatting layer |
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| 🔄 `embed` | TBD | Embedding generation (if merged from 1.x) |
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## Installation & Parallel Usage
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### Development Installation
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```bash
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# Start the OpenAI-compatible API server
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mlxk server --port 8000 --max-tokens 4000
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# Install 2.0.0-alpha (this branch)
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pip install -e /path/to/mlx-knife
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# Get web chat interface from GitHub
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curl -O https://raw.githubusercontent.com/mzau/mlx-knife/main/simple_chat.html
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# Open web chat interface in your browser
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open simple_chat.html
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# Verify installation
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mlxk-json --version # → MLX-Knife JSON 2.0.0-alpha
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mlxk2 --version # → MLX-Knife JSON 2.0.0-alpha
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```
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**Features:**
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- **No installation required** - Pure HTML/CSS/JS
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- **Real-time streaming** - Watch tokens appear as they're generated
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- **Model selection** - Choose any MLX model from your cache
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- **Conversation history** - Full context for follow-up questions
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- **Markdown rendering** - Proper formatting for code, lists, tables
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- **Mobile-friendly** - Responsive design works on all devices
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### Parallel with MLX-Knife 1.x
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### Local API Server Integration
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The MLX Knife server provides OpenAI-compatible endpoints for **local development and personal use**:
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Both versions can coexist safely:
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```bash
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# Start local server (single-user, no authentication)
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mlxk server --host 127.0.0.1 --port 8000
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# Install stable 1.x for server/run features
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pip install mlx-knife
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# Test with curl
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curl -X POST "http://localhost:8000/v1/chat/completions" \
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-H "Content-Type: application/json" \
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-d '{"model": "Phi-3-mini-4k-instruct-4bit", "messages": [{"role": "user", "content": "Hello!"}]}'
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# Commands available:
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mlxk list # 1.x - Human-readable output
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mlxk server --port 8080 # 1.x - Server mode
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mlxk run "model" -p "Hello" # 1.x - Interactive execution
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# Integration with development tools (community-tested):
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# - Cursor IDE: Set API URL to http://localhost:8000/v1
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# - LibreChat: Configure as custom OpenAI endpoint
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# - Open WebUI: Add as local OpenAI-compatible API
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# - SillyTavern: Add as OpenAI API with custom URL
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mlxk-json list --json # 2.0 - JSON API
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python -m mlxk2.cli list # 2.0 - Module invocation
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```
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**Note**: Tool integrations are community-tested. Some tools may require specific configuration or have compatibility limitations. Please report issues via GitHub.
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**Package Names:**
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- MLX-Knife 1.x: `mlx-knife` → `mlxk` command
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- MLX-Knife 2.0: `mlxk-json` → `mlxk-json`, `mlxk2` commands
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## Command Reference
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## JSON API Documentation
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### Available Commands
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> **📋 Complete API Specification**: See [docs/json-api-specification.md](docs/json-api-specification.md) for comprehensive JSON schema, error codes, and integration examples.
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#### `list` - Browse Models
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### Command Structure
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All commands follow this JSON response format:
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```json
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{
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"status": "success|error",
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"command": "list|health|show|pull|rm",
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"data": { /* command-specific data */ },
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"error": null | { "message": "...", "details": "..." }
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}
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```
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### Examples
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#### List Models
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```bash
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mlxk list # Show MLX models only (short names)
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mlxk list --verbose # Show MLX models with full paths
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mlxk list --all # Show all models with framework info
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mlxk list --all --verbose # All models with full paths
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mlxk list --health # Include health status
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mlxk list Phi-3 # Filter by model name
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mlxk list --verbose Phi-3 # Show detailed info (same as show)
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mlxk-json list --json
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# Output:
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{
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"status": "success",
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"command": "list",
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"data": {
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"models": [
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{
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"name": "mlx-community/Phi-3-mini-4k-instruct-4bit",
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"hashes": ["e9675aa3def456789abcdef0123456789abcdef0"],
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"cached": true
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}
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],
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"count": 1
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},
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"error": null
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}
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```
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#### `show` - Model Details
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#### Health Check
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```bash
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mlxk show <model> # Display model information
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mlxk show <model> --files # Include file listing
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mlxk show <model> --config # Show config.json content
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mlxk-json health --json
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# Output:
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{
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"status": "success",
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"command": "health",
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"data": {
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"healthy": [...],
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"unhealthy": [...],
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"summary": {"total": 5, "healthy_count": 4, "unhealthy_count": 1}
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},
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"error": null
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}
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```
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#### `pull` - Download Models
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#### Show Model Details
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```bash
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mlxk pull <model> # Download from HuggingFace
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mlxk pull <org>/<model> # Full model path
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mlxk-json show "Phi-3-mini" --json --files
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# Output includes file listings, model config, capabilities
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```
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#### `run` - Execute Models
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```bash
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mlxk run <model> "prompt" # Single prompt (minimal output)
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mlxk run <model> "prompt" --verbose # Show loading, memory, and stats
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mlxk run <model> # Interactive chat
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mlxk run <model> "prompt" --no-stream # Batch output
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mlxk run <model> --max-tokens 1000 # Custom length
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mlxk run <model> --temperature 0.9 # Higher creativity
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mlxk run <model> --no-chat-template # Raw completion mode
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```
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### Hash Syntax Support
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#### `rm` - Remove Models
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```bash
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mlxk rm <model> # Delete model with cache cleanup confirmation
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mlxk rm <model>@<hash> # Delete specific version (removes entire model)
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mlxk rm <model> --force # Skip confirmations, auto-cleanup cache files
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```
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**Features:**
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- Removes entire model directory (not just snapshots)
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- Cleans up orphaned HuggingFace lock files
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- Handles corrupted models gracefully
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- Smart prompting (only asks about cache cleanup if needed)
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#### `health` - Check Integrity
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```bash
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mlxk health # Check all models
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mlxk health <model> # Check specific model
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```
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#### `server` - Start API Server
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```bash
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mlxk server # Start on localhost:8000
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mlxk server --port 8001 # Custom port
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mlxk server --host 0.0.0.0 --port 8000 # Allow external access
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mlxk server --max-tokens 4000 # Set default max tokens (default: 2000)
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mlxk server --reload # Development mode with auto-reload
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```
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### Command Aliases
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After installation, these commands are equivalent:
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- `mlxk` (recommended)
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- `mlx-knife`
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- `mlx_knife`
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## Configuration
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### Cache Location
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By default, models are stored in `~/.cache/huggingface/hub`. Configure with:
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All commands support `@hash` syntax for specific model versions:
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```bash
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# Set custom cache location
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export HF_HOME="/path/to/your/cache"
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# Example: External SSD
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export HF_HOME="/Volumes/ExternalSSD/models"
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mlxk-json health "Qwen3@e96" --json # Check specific hash
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mlxk-json show "model@3df9bfd" --json # Short hash matching
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mlxk-json rm "Phi-3@e967" --json --force # Delete specific version
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```
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### Model Name Expansion
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Short names are automatically expanded for MLX models:
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- `Phi-3-mini-4k-instruct-4bit` → `mlx-community/Phi-3-mini-4k-instruct-4bit`
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- Models already containing `/` are used as-is
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## HuggingFace Cache Safety
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## Advanced Usage
|
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MLX-Knife 2.0 respects standard HuggingFace cache structure and practices:
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|
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### Generation Parameters
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### Best Practices for Shared Environments
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- **Read operations** (`list`, `health`, `show`) always safe with concurrent processes
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- **Write operations** (`pull`, `rm`) coordinate during maintenance windows
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- **Lock cleanup** automatic but avoid during active downloads
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- **Your responsibility:** Coordinate with team, use good timing
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|
||||
### Example Safe Workflow
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```bash
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# Check what's in cache (always safe)
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mlxk-json list --json | jq '.data.count'
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# Maintenance window - coordinate with team
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mlxk-json rm "corrupted-model" --json --force
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mlxk-json pull "replacement-model" --json
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# Back to normal operations
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mlxk-json health --json | jq '.data.summary'
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```
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## Real-World Examples
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||||
|
||||
> **🔗 Integration Reference**: External projects should implement against [docs/json-api-specification.md](docs/json-api-specification.md) - this alpha phase helps validate that specification matches actual implementation.
|
||||
|
||||
### Broke-Cluster Integration
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||||
```bash
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||||
# Get available model names for scheduling
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||||
MODELS=$(mlxk-json list --json | jq -r '.data.models[].name')
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||||
|
||||
# Check cache health before deployment
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||||
HEALTH=$(mlxk-json health --json | jq '.data.summary.healthy_count')
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||||
if [ "$HEALTH" -eq 0 ]; then
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echo "No healthy models available"
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exit 1
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fi
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||||
# Download required models
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mlxk-json pull "mlx-community/Phi-3-mini-4k-instruct-4bit" --json
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```
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||||
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||||
### CI/CD Pipeline Usage
|
||||
```bash
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# Verify model integrity in CI
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||||
mlxk-json health --json | jq -e '.data.summary.unhealthy_count == 0'
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||||
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||||
# Clean up CI artifacts
|
||||
mlxk-json rm "test-model-*" --json --force
|
||||
|
||||
# Pre-warm cache for deployment
|
||||
mlxk-json pull "production-model" --json
|
||||
```
|
||||
|
||||
### Model Management Automation
|
||||
```bash
|
||||
# Find models by pattern
|
||||
LARGE_MODELS=$(mlxk-json list --json | jq -r '.data.models[] | select(.name | contains("30B")) | .name')
|
||||
|
||||
# Show detailed info for analysis
|
||||
for model in $LARGE_MODELS; do
|
||||
mlxk-json show "$model" --json --config | jq '.data.model_config'
|
||||
done
|
||||
```
|
||||
|
||||
## Testing
|
||||
|
||||
The test suite provides comprehensive coverage with production-quality isolation:
|
||||
|
||||
```bash
|
||||
# Creative writing (high temperature, diverse output)
|
||||
mlxk run Mistral-7B "Write a story" --temperature 0.9 --top-p 0.95
|
||||
# Run all tests
|
||||
python -m pytest tests_2.0/ -v
|
||||
|
||||
# Precise tasks (low temperature, focused output)
|
||||
mlxk run Phi-3-mini "Extract key points" --temperature 0.3 --top-p 0.9
|
||||
# Test categories:
|
||||
# - ADR-002 edge cases (13 tests)
|
||||
# - Integration scenarios (12 tests)
|
||||
# - Model naming logic (9 tests)
|
||||
# - Robustness testing (11 tests)
|
||||
|
||||
# Long-form generation
|
||||
mlxk run Mixtral-8x7B "Explain quantum computing" --max-tokens 2000
|
||||
|
||||
# Reduce repetition
|
||||
mlxk run model "prompt" --repetition-penalty 1.2
|
||||
# Current status: 45/45 passing ✅
|
||||
```
|
||||
|
||||
### Working with Specific Commits
|
||||
**Revolutionary Test Architecture:**
|
||||
- **Isolated Cache System** - Zero risk to user data
|
||||
- **Atomic Context Switching** - Production/test cache separation
|
||||
- **Comprehensive Mock Models** - Realistic test scenarios
|
||||
- **Edge Case Coverage** - All documented failure modes tested
|
||||
|
||||
```bash
|
||||
# Use specific model version
|
||||
mlxk show model@commit_hash
|
||||
mlxk run model@commit_hash "prompt"
|
||||
```
|
||||
## Known Issues & Limitations
|
||||
|
||||
### Non-MLX Model Handling
|
||||
### Critical Issues
|
||||
- **Health Check False Positive**: Health check may report incomplete downloads as healthy during model pull operations (affects both 1.1.0 and 2.0.0-alpha)
|
||||
|
||||
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
|
||||
```
|
||||
### Alpha Limitations
|
||||
- No interactive prompts (use `--force` flag for rm operations)
|
||||
- JSON output only (no human-readable formatting)
|
||||
- Limited error message user experience (coming in beta)
|
||||
|
||||
## Troubleshooting
|
||||
### GitHub Issues
|
||||
- **Issue #18**: Server signal handling limitation (known, will fix in 2.0.0-rc)
|
||||
- **Issue #24**: Lock cleanup command (planned for future release)
|
||||
|
||||
### Model Not Found
|
||||
```bash
|
||||
# If model isn't found, try full path
|
||||
mlxk pull mlx-community/Model-Name-4bit
|
||||
## Development Status
|
||||
|
||||
# List available models
|
||||
mlxk list --all
|
||||
```
|
||||
### Version Roadmap
|
||||
- **2.0.0-alpha** ← You are here (JSON API core complete)
|
||||
- **2.0.0-beta**: 6-8 weeks robust testing, production validation
|
||||
- **2.0.0-rc**: Server/run features, full 1.x parity
|
||||
- **2.0.0-stable**: Community validated, enterprise ready
|
||||
|
||||
### 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
|
||||
### Architecture Decisions
|
||||
- **JSON-First**: All output structured for scripting and automation
|
||||
- **Cache Safety**: Respects HuggingFace standards, no custom formats
|
||||
- **Atomic Operations**: Clean separation between test and production contexts
|
||||
- **Backward Compatibility**: Parallel deployment with 1.x maintained
|
||||
|
||||
## Contributing
|
||||
|
||||
Contributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for development setup and guidelines.
|
||||
This branch follows the established MLX-Knife development patterns:
|
||||
|
||||
## Security
|
||||
```bash
|
||||
# Run quality checks
|
||||
python test-multi-python.sh # Tests across Python 3.9-3.13
|
||||
./run_linting.sh # Code quality validation
|
||||
|
||||
For security concerns, please see [SECURITY.md](SECURITY.md) or contact us at broke@gmx.eu.
|
||||
# Key files:
|
||||
mlxk2/ # 2.0.0 implementation
|
||||
tests_2.0/ # Alpha test suite
|
||||
docs/ADR/ # Architecture decision records
|
||||
```
|
||||
|
||||
MLX Knife runs entirely locally - no data is sent to external servers except when downloading models from HuggingFace.
|
||||
See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines.
|
||||
|
||||
## License
|
||||
## Support & Feedback
|
||||
|
||||
MIT License - see [LICENSE](LICENSE) file for details
|
||||
- **Issues**: [GitHub Issues](https://github.com/mzau/mlx-knife/issues)
|
||||
- **Discussions**: [GitHub Discussions](https://github.com/mzau/mlx-knife/discussions)
|
||||
- **API Specification**: [docs/json-api-specification.md](docs/json-api-specification.md) - Complete JSON schema
|
||||
- **Documentation**: See `docs/` directory for technical details
|
||||
|
||||
Copyright (c) 2025 The BROKE team 🦫
|
||||
**For production use**: Consider MLX-Knife 1.1.0 until 2.0.0-beta is available.
|
||||
|
||||
## 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
|
||||
### Alpha Testing Goals
|
||||
- ✅ Validate JSON API specification matches implementation
|
||||
- ✅ Real-world integration feedback from external projects
|
||||
- ✅ Edge case discovery through broke-cluster usage
|
||||
- ✅ API stability testing before beta release
|
||||
|
||||
---
|
||||
|
||||
<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 1.1.0-beta3 | August 2025</i><br>
|
||||
<a href="https://github.com/mzau/broke-cluster">🔮 Next: BROKE Cluster for multi-node deployments</a>
|
||||
</p>
|
||||
*MLX-Knife 2.0.0-alpha - Built for automation, tested for reliability, designed for the future.*
|
||||
Reference in New Issue
Block a user