chuk-lazarus 0.2.0

MLX-based LLM training and inference with hybrid RL architecture

Lazarus

MLX-based LLM training framework for Apple Silicon.

Installation

# Clone the repository
git clone https://github.com/your-org/chuk-mlx.git
cd chuk-mlx

# Install with uv (recommended)
uv sync

# Or with pip
pip install -e .

Quick Start

Python API

from chuk_lazarus.models import load_model, generate_response

# Load a model
model, tokenizer = load_model("TinyLlama/TinyLlama-1.1B-Chat-v1.0")

# Generate text
response = generate_response(
    model=model,
    tokenizer=tokenizer,
    prompt="Once upon a time",
    max_tokens=100,
    temperature=0.7,
)
print(response)

CLI

Lazarus provides a unified CLI for training, inference, and tokenizer utilities.

# Run inference
lazarus infer --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 --prompt "Hello, world!"

# Train with SFT
lazarus train sft --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 --data train.jsonl --use-lora

# Train with DPO
lazarus train dpo --model ./checkpoints/sft/final --data preferences.jsonl

# Generate synthetic training data
lazarus generate --type math --output ./data/lazarus

# Tokenizer utilities
lazarus tokenizer encode -t TinyLlama/TinyLlama-1.1B-Chat-v1.0 --text "Hello world"
lazarus tokenizer vocab -t TinyLlama/TinyLlama-1.1B-Chat-v1.0 --search "hello"

Features

• Training: SFT, DPO, GRPO, PPO trainers with LoRA support
• Models: LLaMA, Mistral, Gemma, Granite, StarCoder2, TinyLlama
• Data: Dataset classes, synthetic data generators
• CLI: Unified command-line interface for all operations
• Tokenizer Tools: Encode, decode, vocabulary inspection, comparison

Documentation

• Getting Started
• Training Guide
• CLI Reference
• API Reference

Examples

See the examples/ directory for detailed examples organized by area:

• examples/tokenizer/ - Tokenization examples
• examples/inference/ - Text generation
• examples/training/ - SFT and DPO training
• examples/data/ - Data handling and generation
• examples/models/ - Model loading and configuration

Project Structure

src/chuk_lazarus/
├── cli/            # Command-line interface
├── data/           # Datasets, generators, tokenizers
├── models/         # Model architectures and loading
├── training/       # Trainers (SFT, DPO, GRPO, PPO)
├── inference/      # Text generation
└── utils/          # Utilities

License

MIT