Open-Platypus — LLM Instruction / SFT Dataset
A carefully curated dataset of 25K STEM and logic questions assembled from 11 open datasets with strict deduplication. Fine-tuning Llama 2 on this dataset for just 5 hours achieves GPT-4-level STEM performance, demonstrating quality trumps quantity.
Dataset Details
| Provider | garage-bAInd |
| Category | Instruction / SFT |
| Size | 25K Questions |
| License | CC BY NC 4.0 |
| Downloads | 340k |
| Tags | STEM, Reasoning, Curated, Logic, Science |
from datasets import load_dataset
ds = load_dataset("garage-bAInd/Open-Platypus")
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 Open-Platypus commercially?
Not in a product — Open-Platypus is released under CC BY NC 4.0, which restricts use to research and other non-commercial purposes. For commercial fine-tuning, pick a permissively licensed dataset from the same category instead.
How much data does Open-Platypus contain, and do I need all of it?
Open-Platypus contains 25K Questions. 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 Open-Platypus best used for?
A quick STEM and logic boost on a small budget (non-commercial). It belongs to the Instruction / SFT section of our dataset hub, where you'll find alternatives and complementary sets.
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