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.
Dataset Highlights
Large-scale instruction tuning data to support open-source language model training
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.
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.
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.
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.
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.
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
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
Model Alignment
Using high-quality GPT-4 responses as alignment targets to guide open-source models in generating more accurate and safer outputs
Task Adherence Training
Covering hundreds of NLP task types, training models to understand and execute various natural language instructions
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
Data Preview
The following is an example of API calls for the OpenOrca dataset
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
Register for a platform account and obtain an API Token to access and download the complete dataset via RESTful API.
Load and Train
Use datasets.load_dataset() or load JSON data directly to start instruction fine-tuning and model training.
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.