WildChat-1M — LLM Instruction / SFT Dataset
1M real user-ChatGPT conversations with demographics, including a significant fraction of contentious and adversarial prompts. Particularly valuable for safety research, toxicity analysis, and understanding model failure modes in production — collected with explicit user consent.
Dataset Details
| Provider | allenai |
| Category | Instruction / SFT |
| Size | 1M Chats |
| License | AI2 ImpACT |
| Downloads | 280k |
| Tags | Real-world, Safety, Adversarial, Multi-turn, 2024 |
from datasets import load_dataset
ds = load_dataset("allenai/WildChat-1M")
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
- LMSYS-Chat-1M — Studying real-world usage patterns across 25 models (non-commercial)
- ShareGPT 52K — Teaching natural multi-turn dialogue (non-commercial)
- 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 WildChat-1M commercially?
Check the terms first — WildChat-1M is distributed under "AI2 ImpACT", a custom or mixed license. Read the dataset card carefully before using it in any commercial product.
How much data does WildChat-1M contain, and do I need all of it?
WildChat-1M contains 1M Chats. 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 WildChat-1M best used for?
Training on real user conversations; safety and robustness research. It belongs to the Instruction / SFT section of our dataset hub, where you'll find alternatives and complementary sets.
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