No Robots
10,000 instructions and demonstrations written entirely by skilled human annotators — zero synthetic data. Modeled after the SFT data described in OpenAI's InstructGPT paper, split into 9.5k train / 500 test examples across categories like generation, open QA, brainstorming, and coding. Small, clean, and diverse: widely considered the best first dataset for learning to fine-tune.
from datasets import load_dataset
ds = load_dataset("HuggingFaceH4/no_robots")Preview a sample row
{
"prompt": "Please summarize the goals for scientists in this text: Within three days...",
"prompt_id": "6e58f...",
"messages": [
{ "role": "user", "content": "Please summarize the goals for scientists in this text: ..." },
{ "role": "assistant", "content": "Scientists are studying nearby ocean waters to determine..." }
],
"category": "Summarize"
}Fine-tune with this dataset
Estimated VRAM to fine-tune with QLoRA (4-bit base model + LoRA adapters), using conservative defaults:
New to fine-tuning? Follow the step-by-step walkthrough: Fine-Tune Your First LLM in 1 Hour
Frequently asked questions
Can I use No Robots commercially?
Not in a product — No Robots 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 No Robots contain, and do I need all of it?
No Robots contains 10k 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 No Robots best used for?
Your first fine-tune — small, clean, 100% human-written SFT data (non-commercial license). It belongs to the Instruction / SFT section of our dataset hub, where you'll find alternatives and complementary sets.