WebInstruct — LLM Instruction / SFT Dataset
220M instruction-response pairs extracted from the web by identifying educational content using a recall-then-verify pipeline. Covers K-12 through graduate-level content across math, science, and engineering — enabling large-scale instruction fine-tuning without human curation.
Dataset Details
| Provider | TIGER-Lab |
| Category | Instruction / SFT |
| Size | 220M Pairs |
| License | CC BY-SA 4.0 |
| Downloads | 210k |
| Tags | Web-extracted, Large-Scale, Education, Multi-domain, 2024 |
from datasets import load_dataset
ds = load_dataset("TIGER-Lab/WebInstructSub")
Fine-tune with this dataset
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 WebInstruct commercially?
Yes — WebInstruct is released under CC BY-SA 4.0, 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 WebInstruct contain, and do I need all of it?
WebInstruct contains 220M Pairs. 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 WebInstruct best used for?
Web-scale instruction mining for domain skills. It belongs to the Instruction / SFT section of our dataset hub, where you'll find alternatives and complementary sets.
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