Smoltalk — LLM Instruction / SFT Dataset
A high-quality 2025 synthetic dataset created to train the SmolLM2 family of small language models. 1 million examples covering diverse tasks, generated with careful quality filtering. Demonstrates that small models (1.7B, 360M) can achieve strong performance with the right training data.
Dataset Details
| Provider | HuggingFaceTB |
| Category | Instruction / SFT |
| Size | 1M Rows |
| License | Apache 2.0 |
| Downloads | 600k |
| Tags | Synthetic, 2025, Small-Models, High-Quality |
from datasets import load_dataset
ds = load_dataset("HuggingFaceTB/smoltalk")
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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Related datasets
- SmolTalk 2 — Reproducing a complete modern post-training pipeline (mid-training → SFT → preference) for
- No Robots — Your first fine-tune — small, clean, 100% human-written SFT data (non-commercial license)
- 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 Smoltalk commercially?
Yes — Smoltalk 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 Smoltalk contain, and do I need all of it?
Smoltalk contains 1M 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 Smoltalk best used for?
General SFT for small models (the SmolLM2 recipe). It belongs to the Instruction / SFT section of our dataset hub, where you'll find alternatives and complementary sets.
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