Project Title
stanford_alpaca — Open-source Instruction-following LLaMA Model for Research Purposes
Overview
Stanford Alpaca is an open-source project that provides code and documentation for training and generating data for an instruction-following LLaMA model. It offers a 52K dataset for fine-tuning, data generation code, and model fine-tuning scripts. The project is designed for research use only, with a focus on ethical considerations and safety.
Key Features
- 52K instruction-following dataset for model fine-tuning
- Data generation process code
- Model fine-tuning code
- Weight recovery from released weight differences
Use Cases
- Researchers using Alpaca for developing and testing instruction-following AI models
- Academics studying the capabilities and limitations of LLaMA-based models
- Developers interested in contributing to or improving the safety and ethical considerations of AI models
Advantages
- Open-source and community-driven development
- Focus on ethical considerations and safety in AI model development
- Provides a comprehensive dataset and tools for fine-tuning and training
Limitations / Considerations
- Intended for research use only, with non-commercial use restrictions
- Alpaca model is still under development and has not been fine-tuned for safety and harmlessness
- Live demo is suspended until further notice
Similar / Related Projects
- LLaMA: The original open and efficient foundation language model that Alpaca is based on.
- Self-Instruct: A related project that aligns language models with self-generated instructions, which influenced the Alpaca dataset generation.
Basic Information
- GitHub: https://github.com/tatsu-lab/stanford_alpaca
- Stars: 30,124
- License: Unknown
- Last Commit: 2025-08-20
📊 Project Information
- Project Name: stanford_alpaca
- GitHub URL: https://github.com/tatsu-lab/stanford_alpaca
- Programming Language: Python
- ⭐ Stars: 30,124
- 🍴 Forks: 4,043
- 📅 Created: 2023-03-10
- 🔄 Last Updated: 2025-08-20
🏷️ Project Topics
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