LIMA: Less Is More for Alignment — LLM Instruction / SFT Dataset
Landmark alignment research showing that just 1,000 carefully curated examples rival GPT-4 in instruction-following quality. Challenges the 'more data is always better' assumption. LIMA 65B was competitive with GPT-4 on human preference tests despite only 1k training examples.
Dataset Details
| Provider | Meta / GAIR |
| Category | Instruction / SFT |
| Size | 1k Rows |
| License | CC-BY-NC-SA 4.0 |
| Downloads | 300k |
| Tags | Curated, Quality-over-Quantity, Research, Alignment |
from datasets import load_dataset
ds = load_dataset("GAIR/lima")
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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 LIMA: Less Is More for Alignment commercially?
Not in a product — LIMA: Less Is More for Alignment is released under CC-BY-NC-SA 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 LIMA: Less Is More for Alignment contain, and do I need all of it?
LIMA: Less Is More for Alignment contains 1k 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 LIMA: Less Is More for Alignment best used for?
Style and format alignment with a tiny curated set - quality over quantity. It belongs to the Instruction / SFT section of our dataset hub, where you'll find alternatives and complementary sets.
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