Stanford Alpaca — LLM Instruction / SFT Dataset
The foundational self-instruct dataset that launched a thousand fine-tunes. 52k instruction-following examples generated by GPT-3 (text-davinci-003) using the Self-Instruct technique. Used to train the original Alpaca 7B and inspired nearly every instruction dataset that followed.
Dataset Details
| Provider | tatsu-lab |
| Category | Instruction / SFT |
| Size | 52k Rows |
| License | CC-BY-NC 4.0 |
| Downloads | 4.5M |
| Tags | Self-Instruct, GPT-3, Foundational, General |
from datasets import load_dataset
ds = load_dataset("tatsu-lab/alpaca")
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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- OpenHermes 2.5 — The default general-purpose SFT mix for 7B-13B fine-tunes
Frequently asked questions
Can I use Stanford Alpaca commercially?
Not in a product — Stanford Alpaca 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 Stanford Alpaca contain, and do I need all of it?
Stanford Alpaca contains 52k 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 Stanford Alpaca best used for?
Learning the classic instruction format; research only (CC BY-NC). It belongs to the Instruction / SFT section of our dataset hub, where you'll find alternatives and complementary sets.
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