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Stanford Alpaca

Stanford Alpaca is a research project providing code, a 52,000-example instruction dataset and documentation for fine-tuning a 7B LLaMA model into an instruction-following model, as well as generating training data.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Stanford Alpaca — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use the repository when investigating research workflows for generating instruction data and fine-tuning a LLaMA-based instruction-following model.

Who it is for

  • Researchers studying instruction-following language models
  • Developers examining a documented data-generation and model fine-tuning research workflow

Documented uses

  • Generate instruction-following examples with the repository’s data-generation pipeline.
  • Use the released 52,000-example dataset as fine-tuning data.
  • Study the code and recipe for fine-tuning a 7B LLaMA model.
Before you choose

Requirements & limits

The live demo is suspended. The code is licensed under Apache 2.0; the dataset and weight difference are licensed under CC BY-NC 4.0. The project states that Alpaca and models trained using its dataset are for research use only.

  • The data-generation workflow requires an OpenAI API key and uses text-davinci-003.
  • The repository documents the data-generation command, but the provided readme excerpt does not give step-by-step fine-tuning instructions.
  • The project says the model has not been fine-tuned for safety and harmlessness and advises caution when interacting with it.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Generate instruction-following data

Run the repository’s documented data-generation pipeline.

Before you start

  • Python 3.9 or later
  • An OpenAI API key
  • Repository dependencies installed from requirements.txt

Steps

  1. Set the OPENAI_API_KEY environment variable to your OpenAI API key.

  2. Install dependencies with pip install -r requirements.txt.

  3. Run python -m generate_instruction generate_instruction_following_data.

Expected outcome

The pipeline generates instruction-following data using the repository’s documented process.

Guide limitations

  • The process uses the OpenAI API and text-davinci-003.
  • The project’s data-generation materials and dataset are licensed for non-commercial use; the project states that use is for research purposes only.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
tatsu-lab/stanford_alpaca · Official homepage title · retained in its source language
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