DPO Mix 7K
A compact, curated DPO (Direct Preference Optimization) dataset of 7k chosen/rejected pairs, carefully selected from multiple high-quality sources. Ideal for running DPO experiments locally — small enough to fine-tune in under an hour on consumer GPUs while producing meaningfully aligned models.
from datasets import load_dataset
ds = load_dataset("argilla/dpo-mix-7k")Preview a sample row
{
"system": "You are a helpful assistant.",
"prompt": "Explain recursion in simple terms.",
"chosen": "Recursion is when a function calls itself to solve a smaller version of the same problem. Think of Russian nesting dolls — each doll contains a smaller version of itself until you reach the smallest one.",
"rejected": "Recursion refers to the process of defining a problem or function in terms of itself through self-referential expressions."
}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 DPO Mix 7K commercially?
Yes — DPO Mix 7K 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 DPO Mix 7K contain, and do I need all of it?
DPO Mix 7K contains 7k Pairs. 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 DPO Mix 7K best used for?
A small, balanced DPO starter set. It belongs to the Preference (RLHF / DPO) section of our dataset hub, where you'll find alternatives and complementary sets.