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stanford_alpaca

30,149
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Python

Project Description

Code and documentation to train Stanford's Alpaca models, and generate the data.

stanford_alpaca: Code and documentation to train Stanford's Alpaca models, and generate the data.

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


📊 Project Information

🏷️ Project Topics

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  • [Code License
  • [Data License
  • [Weight Diff License
  • [Python 3.9+
  • [Code style: black

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Project Information

Created on 3/10/2023
Updated on 9/16/2025