UltraFeedback — LLM Preference (RLHF / DPO) Dataset

Large-scale, fine-grained preference dataset. It contains 64k prompts with multiple model responses rated by GPT-4. Essential for RLHF and DPO.

Dataset Details

ProviderOpenBMB
CategoryPreference (RLHF / DPO)
Size64k Rows
LicenseMIT
Downloads1.2M
TagsRLHF, DPO, Alignment
 
from datasets import load_dataset
ds = load_dataset("openbmb/UltraFeedback")

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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 UltraFeedback commercially?
Yes — UltraFeedback 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 UltraFeedback contain, and do I need all of it?
UltraFeedback contains 64k Rows. 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 UltraFeedback best used for?
The default DPO preference set to run after any SFT pass. It belongs to the Preference (RLHF / DPO) section of our dataset hub, where you'll find alternatives and complementary sets.

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