mimic-kit 0.1.1

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Mimic-Kit

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Mimic-Kit is a black-box knowledge distillation scaffolding that helps you distill capabilities from powerful teacher models into smaller, more efficient student models.

Features

• 🤖 Teacher Model Generation: Use OpenAI API (or compatible APIs) to generate high-quality training data
• 🚀 Efficient Training: Built on ms-swift for streamlined model training
• 🧩 Flexible Tuning: Support both LoRA (parameter-efficient) and full fine-tuning
• ⚡ Performance Optimized: Integrated with DeepSpeed and Liger Kernel for accelerated training
• 💾 Smart Caching: Automatically cache API responses to save costs
• 📊 Multiple Data Formats: Support both chat and text completion formats

Quick Start

1. Installation

Option 1: Install from PyPI (Recommended)

pip install mimic-kit

Option 2: Install from source

# Clone the repository
git clone https://github.com/ECNU-innoSpark/EduRmDistill
cd mimic-kit

# Install dependencies using UV
uv sync

# Or with dev dependencies
uv sync --group dev

2. Initialize Configuration

uv run mimic init

This creates a config.yaml template. Edit it to configure your teacher model, student model, and training parameters.

3. Prepare Your Data

Create a JSONL file with your prompts. Supports two formats:

Chat Format:

{"messages": [{"role": "user", "content": "Explain Python decorators"}]}
{"messages": [{"role": "user", "content": "How to reverse a linked list?"}]}

Text Completion Format:

{"text": "Python decorators are a powerful feature..."}
{"text": "To reverse a linked list, you need to..."}

4. Generate Training Data

uv run mimic generate

This sends your prompts to the teacher model and saves the generated responses as training data.

5. Train Your Student Model

uv run mimic train

The student model will be fine-tuned on the generated data using the configured method (LoRA or full fine-tuning).

Configuration

See config.yaml for all available options. Key sections:

• data: Input/output paths, system prompts, templates
• teacher: API provider, model, generation parameters
• student: Base model, tuning method, hyperparameters
• training: Batch size, learning rate, saving strategy

Project Structure

mimic-kit/
├── mimic/
│   ├── cli.py              # CLI entry point
│   ├── config.py           # Configuration models (Pydantic v2)
│   ├── generator/          # Teacher model data generation
│   └── trainer/            # Student model training (ms-swift)
├── data/                   # Training data directory
├── config.yaml             # Your configuration file
└── output/                 # Model outputs and checkpoints

Requirements

• Python 3.13+
• CUDA-capable GPU (for training)
• OpenAI API key (or compatible API)

Development

# Run tests
uv run pytest

# Run single test
uv run pytest tests/test_cli.py::test_function

# Format code
uv run ruff format .

# Check linting
uv run ruff check .

License

MIT License - see LICENSE file for details.

Acknowledgments

• Built with ms-swift for model training
• Uses Pydantic for configuration validation
• Powered by Click for CLI