3M self-synthesized instruction pairs generated by prompting Llama 3 to produce both instructions and responses using a novel pre-query template approach. Unlike previous datasets, Magpie requires no seed data or human curation, achieving superior quality through alignment filtering.
Infinity-Instruct — Large-scale general SFT when you need millions of samples
Smoltalk — General SFT for small models (the SmolLM2 recipe)
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 Magpie-Align commercially?
Yes — Magpie-Align 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 Magpie-Align contain, and do I need all of it?
Magpie-Align contains 3M 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 Magpie-Align best used for?
Fresh synthetic SFT data without seed data or scraping. It belongs to the Instruction / SFT section of our dataset hub, where you'll find alternatives and complementary sets.