ShareGPT 52K
Real multi-turn conversations scraped from ShareGPT.com — a site where users shared their best ChatGPT chats. Contains 52k real human-AI conversation trees. The dataset that made Vicuna and OpenChat possible by teaching models natural multi-turn dialogue.
from datasets import load_dataset
ds = load_dataset("RyokoAI/ShareGPT52K")Preview a sample row
{
"conversations": [
{ "from": "human", "value": "What is supervised learning?" },
{ "from": "gpt", "value": "In supervised learning, a model is trained on labeled examples — input-output pairs — so it learns to predict outputs for new inputs." },
{ "from": "human", "value": "Can you give a simple example?" },
{ "from": "gpt", "value": "Sure! Spam detection is classic: you train on emails labeled 'spam' or 'not spam', and the model learns to classify new emails." }
]
}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 ShareGPT 52K commercially?
Not in a product — ShareGPT 52K 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 ShareGPT 52K contain, and do I need all of it?
ShareGPT 52K contains 52k Conversations. 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 ShareGPT 52K best used for?
Teaching natural multi-turn dialogue (non-commercial). It belongs to the Instruction / SFT section of our dataset hub, where you'll find alternatives and complementary sets.