datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MATH-500
Dataset Card for MATH-500
This dataset contains a subset of 500 problems from the MATH benchmark that OpenAI created in their Let's Verify Step by Step paper. See their GitHub repo for the source file: https://github.com/openai/prm800k/tree/main?tab=readme-ov-file#math-splits
ultrachat_200k
Dataset Card for UltraChat 200k
Dataset Description
This is a heavily filtered version of the UltraChat dataset and was used to train Zephyr-7B-β, a state of the art 7b chat model.
The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create UltraChat 200k, we applied the following logic:
Selection of a subset of data for faster supervised fine tuning.
Truecasing of the dataset, as we observed around 5% of… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k.no_robots
Dataset Card for No Robots 🙅♂️🤖
Look Ma, an instruction dataset that wasn't generated by GPTs!
Dataset Summary
No Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's InstructGPT paper, and is comprised mostly of single-turn… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/no_robots.ultrafeedback_binarized
Dataset Card for UltraFeedback Binarized
Dataset Description
This is a pre-processed version of the UltraFeedback dataset and was used to train Zephyr-7Β-β, a state of the art chat model at the 7B parameter scale.
The original UltraFeedback dataset consists of 64k prompts, where each prompt is accompanied with four model completions from a wide variety of open and proprietary models. GPT-4 is then used to assign a score to each completion, along criteria like… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized.CodeAlpaca_20KThis dataset splits the original CodeAlpaca dataset into train and test splits.
Multilingual-Thinking
Dataset summary
Multilingual-Thinking is a reasoning dataset where the chain-of-thought has been translated from English into one of 4 languages: Spanish, French, Italian, and German. The dataset was created by sampling 1k training samples from the SystemChat subset of SmolTalk2 and translating the reasoning traces with another language model.
This dataset was used in the OpenAI Cookbook to fine-tune the OpenAI gpt-oss models.
You can load the dataset using:
from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/Multilingual-Thinking.orca_dpo_pairs
Dataset Card for Orca DPO Pair
Dataset Description
This is a pre-processed version of the OpenOrca dataset.
The original OpenOrca dataset is a collection of augmented FLAN data that aligns, as best as possible, with the distributions outlined in the Orca paper.
It has been instrumental in generating high-performing preference-tuned model checkpoints and serves as a valuable resource for all NLP researchers and developers!
Dataset Summary
The OrcaDPO Pair… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/orca_dpo_pairs.helpful_instructionsHelpful Instructions is a dataset of (prompt, completion) pairs that are derived from a variety of public datasets. As the name suggests, it focuses on instructions that are "helpful", i.e. the kind of questions or tasks a human user might instruct an AI assistant to perform.self_instructThis dataset splits the original Self-instruct dataset into training (90%) and test (10%).
instruct_meInstruct Me is a dataset of instruction-like dialogues between a human user and AI assistant. The prompts are derived from (prompt, completion) pairs in the Helpful Instructions dataset. The goal is to train a language model to that is "chatty" and can answer the kind of questions or tasks a human user might instruct an AI assistant to perform.OpenHermes-2.5-1k-longest
Dataset Card for OpenHermes-2.5-1k-longest
OpenHermes-2.5-1k-longest is a dataset of 1,000 samples derived from teknium/OpenHermes-2.5 using the Long is More for Alignment protocol. This protocol consists of selecting the 1,000 longest responses and provides a strong baseline to measure performance against. For example, fine-tuning mistralai/Mistral-7B-v0.1 on this dataset using similar hyperparameters to those given in the paper produces a chat model that achieves a score of ~7.0… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/OpenHermes-2.5-1k-longest.HuggingFaceH4-MATH-R1-dpo
Dataset Card for HuggingFaceH4-MATH-R1-dpo
本資料集為 HuggingFaceH4/MATH-500 等 MATH 評測資料延伸的 R1-style DPO 偏好集，並翻譯為繁體中文：每筆樣本針對一道數學題，提供「具完整 R1 風格 <think> 推理過程的較佳解（chosen）」與「推理品質較差的解（rejected）」。
Dataset Details
Dataset Description
資料以 MATH 風格題目為基礎（代數、幾何、組合、機率等），題幹翻譯為繁中，並使用 DeepSeek R1 等具 R1 風格之模型產出 chosen，配合較弱模型／簡化 prompt 的版本作為 rejected，以建構偏好對。
格式採 LLaMA-Factory 之 conversations + chosen + rejected 風格，gpt 回答前段為 <think>...</think> 思考段落，後段為最終解答（多以 \boxed{...} 收尾）。… See the full description on the dataset page: https://huggingface.co/datasets/lianghsun/HuggingFaceH4-MATH-R1-dpo.HuggingFaceH4-ultrachat_200k
HuggingFaceH4/ultrachat_200k
This is a reprocessed version of HuggingFaceH4/ultrachat_200k.
Each row has been processed to fit ShareGPT-like format with the correct turn order. Duplicate rows have been removed.
The splits remain the same as the original.