Infinity-Instruct — LLM Instruction / SFT Dataset
A 7.5M high-quality instruction dataset from the Beijing Academy of AI, combining foundational skills (math, code, reasoning) with conversational ability. Used to train models that surpass Llama 3 70B while being much smaller.
Dataset Details
| Provider | BAAI |
| Category | Instruction / SFT |
| Size | 7.5M Instructions |
| License | Apache 2.0 |
| Downloads | 420k |
| Tags | Large-Scale, Diverse, BAAI, Multi-domain, Trending |
from datasets import load_dataset
ds = load_dataset("BAAI/Infinity-Instruct")
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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Related datasets
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- Python-Edu — Continued pretraining for Python code understanding
- OpenHermes 2.5 — The default general-purpose SFT mix for 7B-13B fine-tunes
Frequently asked questions
Can I use Infinity-Instruct commercially?
Yes — Infinity-Instruct is released under Apache 2.0, 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 Infinity-Instruct contain, and do I need all of it?
Infinity-Instruct contains 7.5M Instructions. 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 Infinity-Instruct best used for?
Large-scale general SFT when you need millions of samples. It belongs to the Instruction / SFT section of our dataset hub, where you'll find alternatives and complementary sets.
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