OpenOrca — LLM Instruction / SFT Dataset
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")
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Estimated VRAM to fine-tune with QLoRA (4-bit base model + LoRA adapters), using conservative defaults:
| 7B QLoRA | ~6GB VRAM |
| 13B QLoRA | ~10GB VRAM |
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Frequently asked questions
Can I use OpenOrca commercially?
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.
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