# Instruction-Tuning-with-GPT-4/GPT-4-LLM

Instruction Tuning with GPT-4

Repository: https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
Canonical: https://ross.abutalabs.com/products/gpt-4-llm
Homepage: https://instruction-tuning-with-gpt-4.github.io/
Language: HTML
License: Apache-2.0
License Family: permissive
Topics: alpaca, chatgpt, gpt-4, instruction-tuning, llama
Last push: 2023-06-11T13:40:30+00:00

## Health v2 (maintenance only)
Score: 30/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 88
- inputs: {"age_days": 1245, "days_push": 1179, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4334, forks 307 (observed 2026-08-28T04:08:46.024925+00:00)

## What it is
A research dataset release of GPT-4-generated instruction-following data for fine-tuning large language models, including English and Chinese instruction data, comparison data for reward model training, and answers on Unnatural Instructions. It accompanies the paper 'Instruction Tuning with GPT-4' and includes code for evaluation and reproducing figure plots.

## Use cases
- fine-tune llama with gpt-4 generated instruction data
- get chinese instruction following dataset for llm training
- train a reward model with gpt-4 comparison rankings
- build an instruction-following chatbot like alpaca
- evaluate instruction-tuned models against gpt-4 answers
- download alpaca prompts with gpt-4 responses

## When to choose
- you need supervised fine-tuning data for instruction-following LLMs
- you want Chinese-language instruction tuning data
- you need ranked comparison data to train reward models for RLHF
- you are doing non-commercial LLM research

## When to avoid
- you need production or commercial-use licensed data (CC BY NC 4.0 restricts to research)
- you need up-to-date data or active maintenance (last release 2023)
- you want a ready-to-use trained model rather than datasets
- you need multimodal or vision instruction data (see LLaVA instead)

## Facets
- artifact type: dataset
- maturity: maintenance
- function: machine-learning, llm-training, data-generation
- domain: large-language-models, machine-learning, artificial-intelligence
- platform: python, cross-platform
- tags: instruction-tuning, gpt-4, alpaca, llama, fine-tuning-data, reward-model, chinese-nlp, research-dataset

## Member repositories
- Instruction-Tuning-with-GPT-4/GPT-4-LLM (main) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:46.024925+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:21:38.938310+00:00, confidence not recorded.
  - readme: https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM (fetched 2026-08-28T04:08:46.024925+00:00, sha 8c0ec07a336c)
  - homepage: https://instruction-tuning-with-gpt-4.github.io/ (fetched 2026-08-29T09:10:32.342728+00:00, sha 886134285a9c)
- Data as of 2026-08-30T08:39:29.467469+00:00.