OpenOrca Instruction Dataset

OpenOrca Instruction Tuning
Dataset

OpenOrca is a large-scale enhanced instruction tuning dataset created by the Open-Orca community, containing 2.94 million records, based on the FLAN Collection and enhanced with responses generated by GPT-3.5 and GPT-4, specifically designed for training instruction-following language models.

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2.94M records FLAN Enhanced MIT License GPT-4 Responses

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2.94M

Total Record Count

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FLAN

Base Data Source

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GPT-4

Enhanced Response Source

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MIT

Open Source License

Dataset Highlights

Large-scale instruction tuning data to support open-source language model training

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Large-scale Instruction Set

Contains 2.94 million instruction-response pairs, making it one of the largest open-source instruction tuning datasets, providing rich supervision signals for model training.

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FLAN Foundation

Built on Google's FLAN Collection, inheriting instruction templates from hundreds of NLP tasks, covering various task types such as question answering, reasoning, summarization, and more.

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GPT-4 Enhanced

Some responses are generated by GPT-4, providing high-quality instruction-following examples to help open-source models learn more precise and in-depth response methods.

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Diverse Task Types

Covers various tasks such as natural language inference, reading comprehension, knowledge question answering, logical reasoning, text generation, etc., ensuring the model has a wide range of instruction-following capabilities.

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System Prompts

Each record includes system prompts that clearly specify the model's role and behavioral constraints, aiding in the training of controllable dialogue models.

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MIT License

Utilizes a permissive MIT open-source license, allowing free use for both commercial and non-commercial purposes without concerns about data licensing restrictions.

Applicable Scenarios

Empowering LLM development from basic research to model productization

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Instruction Fine-tuning

Supervised fine-tuning of base models using large-scale instruction data to quickly enhance the model's instruction adherence and task completion capabilities

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Model Alignment

Using high-quality GPT-4 responses as alignment targets to guide open-source models in generating more accurate and safer outputs

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Task Adherence Training

Covering hundreds of NLP task types, training models to understand and execute various natural language instructions

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Open-source Model Development

Has been used to train several well-known open-source models such as the OpenOrca series, serving as an important data foundation for community model development

NLP instruction-tuning FLAN fine-tuning open-source

Data Preview

The following is an example of API calls for the OpenOrca dataset

BASH

curl -X GET "https://api.acedata.cloud/datasets/openorca" \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json"
# Response Example (Simplified)
{
  "id": "niv.242684",
  "system_prompt": "You are an AI assistant that follows instruction extremely well.",
  "question": "Given the following context, answer the question.",
  "response": "Based on the provided context, the answer is..."
}

3 Steps to Get Started Quickly

Quickly launch your LLM project from data acquisition to model training

Browse the Dataset

View the details of the OpenOrca dataset on the Ace Data Cloud platform, understand the data scale, field structure, and licensing agreement.

Get API Token

Load and Train

Use datasets.load_dataset() or load JSON data directly to start instruction fine-tuning and model training.

Get the Dataset

Start Using OpenOrca Instruction Data

2.94 million high-quality instruction data, MIT open-source license, get it now. Whether you are a researcher or an open-source model developer, OpenOrca is the ideal choice for instruction fine-tuning.