AstrAI/README.md

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A lightweight Transformer training & inference framework

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📖 Table of Contents

English

English

Features

  • 🚀 High Performance: Optimized for both training and inference with efficient parallelization.
  • 🔧 Flexible: Support for seq/sft/dpo training, customizable model architectures.
  • 💡 Easy to Use: Simple API with comprehensive examples and demos.
  • 📦 Lightweight: Minimal dependencies, easy to deploy.
  • 🔬 ResearchFriendly: Modular design, easy to experiment with new ideas.
  • 🤗 HuggingFace Integration: Compatible with HuggingFace models and datasets.

Quick Start

Installation

git clone https://github.com/ViperEkura/AstrAI.git
cd AstrAI
pip install -e .

For development dependencies:

pip install -e ".[dev]"

Train a Model

python scripts/tools/train.py \
  --train_type=seq \
  --data_root_path=/path/to/dataset \
  --param_path=/path/to/param_path

Generate Text

python scripts/tools/generate.py --param_path=/path/to/param_path

Demo

Check out the demos in the scripts/demo/ folder:

# Download preprocessed data (required before running demos)
python scripts/demo/download.py

# Interactive streaming chat
python scripts/demo/stream_chat.py

# Batch generation
python scripts/demo/generate_batch.py

# Autoregressive generation
python scripts/demo/generate_ar.py

Watch a video walkthrough on bilibili.

Documentation

Document Description
Parameter Guide Training & inference parameters
Design Document Framework architecture & module design
Data Flow Data processing pipeline details
Model Introduction Model architecture & technical details

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

  1. Fork the repository.
  2. Create a feature branch.
  3. Commit your changes.
  4. Open a Pull Request.

For major changes, please open an issue first to discuss what you would like to change.

Community

License

This project is licensed under the GPL-3.0 License.


A lightweight Transformer framework designed for both high performance and ease of use.