A 4.2M sample dataset replicating Microsoft Research's Orca paper by augmenting FLAN Collection with GPT-4 and GPT-3.5 explanations. Enables small models to match much larger models through explanation-based fine-tuning.
Dataset Details
Provider
Open-Orca
Category
Instruction / SFT
Size
4.2M Samples
License
MIT
Downloads
1.8M
Tags
FLAN, Explanation, GPT-4, Augmented, Large-Scale
from datasets import load_dataset
ds = load_dataset("Open-Orca/OpenOrca")
Fine-tune with this dataset
Estimated VRAM to fine-tune with QLoRA (4-bit base model + LoRA adapters), using conservative defaults:
Yes — OpenOrca is released under MIT, a permissive license that allows commercial use, including training models you ship in a product. Check the dataset card for attribution requirements before release.
How much data does OpenOrca contain, and do I need all of it?
OpenOrca contains 4.2M Samples. You rarely need all of it: for style and format fine-tuning, a few hundred to a few thousand examples are enough — load a slice (e.g. split="train[:1000]") and scale up only if quality plateaus.
What is OpenOrca best used for?
Explanation-style SFT at scale (the Orca recipe). It belongs to the Instruction / SFT section of our dataset hub, where you'll find alternatives and complementary sets.