datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Lucie-Training-Dataset
Lucie Training Dataset Card
The Lucie Training Dataset is a curated collection of text data
in English, French, German, Spanish and Italian culled from a variety of sources including: web data, video subtitles, academic papers,
digital books, newspapers, and magazines, some of which were processed by Optical Character Recognition (OCR). It also contains samples of diverse programming languages.
The Lucie Training Dataset was used to pretrain Lucie-7B,
a foundation LLM with… See the full description on the dataset page: https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset.comma_v0.1_training_dataset
Comma v0.1 dataset
This repository contains the dataset used to train Comma v0.1-1T and Comma v0.1-2T.
It is a slightly modified and consolidated version of the Common Pile v0.1 "filtered" data.
If you are looknig for the raw Common Pile v0.1 data, please see this collection.
You can learn more about Common Pile in our paper.
Mixing rates and token counts
The Comma v0.1 models were trained in two stages, a "main" stage and a "cooldown" stage.
During each stage, we… See the full description on the dataset page: https://huggingface.co/datasets/common-pile/comma_v0.1_training_dataset.Nemotron-Post-Training-Dataset-v1
Nemotron-Post-Training-Dataset-v1 Release
This dataset is a compilation of SFT data that supports improvements of math, code, stem, general reasoning, and tool calling capabilities of the original Llama instruct model Llama-3.3-Nemotron-Super-49B-v1.5.
Llama-3.3-Nemotron-Super-49B-v1.5 is an LLM which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model).
Llama-3.3-Nemotron-Super-49B-v1.5 offers a great tradeoff between model accuracy and efficiency. Efficiency… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v1.Bitext-customer-support-llm-chatbot-training-dataset
Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.TrainingData_Stage3
AnchorSR Stage3 · metric-v1.0
直接选择 Small / Large
配置
训练题数
用途
small
1,000,000
先验证答案监督／先验恢复，按新版 Large 联合分布抽样
large
89,801,853
筛选后的完整训练集合，包含 Small 全部样本
from datasets import load_dataset
data = load_dataset('AnchorSR/TrainingData_Stage3', 'small', # 或 large
revision='metric-v1.0', streaming=True)
这是对 scaling-v1.0 的语义筛选与统一任务分类，不是增加新数据源。
Large 从 89,828,269 题保留 89,801,853 题，隔离 26,416 题。
旧标签 scaling-v1.0 / video-v1.0 / large-v1.0… See the full description on the dataset page: https://huggingface.co/datasets/AnchorSR/TrainingData_Stage3.Nemotron-Post-Training-Dataset-v2
Nemotron-Post-Training-Dataset-v2 Release
Data Overview
This dataset adds to NVIDIA’s post-training dataset releases with an extension of SFT and RL data into five target languages: Spanish, French, German, Italian and Japanese. The data supports improvements of math, code, general reasoning, and instruction following capabilities of the NVIDIA-Nemotron-Nano-9B-v2-Base, in support of release of NVIDIA-Nemotron-Nano-8B-v2-Reasoning.
NVIDIA-Nemotron-Nano-9B is a family of… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2.lora-training-datasetsLlama-Nemotron-Post-Training-Dataset
Llama-Nemotron-Post-Training-Dataset-v1.1 Release
Update [4/8/2025]:
v1.1: We are releasing an additional 2.2M Math and 500K Code Reasoning Data in support of our release of Llama-3.1-Nemotron-Ultra-253B-v1. 🎉
Data Overview
This dataset is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model, in support of NVIDIA’s release of… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset.lingshu_training_data_medical_domain
Website
🤖 7B Model
🤖 8B Model based on InternVL3
🤖 32B Model
MedEvalKit
Technical Report
Lingshu MCP
Lingshu Medical MLLM Training Data (Medical Domain)
This dataset contains the medical-domain training data used in the multi-stage training of the Lingshu Medical Multimodal Large Language Model (MLLM). General-domain data has been removed; only medical data is included.
The training… See the full description on the dataset page: https://huggingface.co/datasets/lingshu-medical-mllm/lingshu_training_data_medical_domain.sole_training_data
This is the training dataset for SOLE-R1-8B
SOLE-R1-8B is a video-language reward reasoning model for robotics. It is designed to estimate task progress from robot video frames and a natural-language task description, producing both per-timestep reasoning traces and scalar progress predictions that can be used as rewards for online robot reinforcement learning.
This dataset accompanies the paper “SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot RL” by Philip… See the full description on the dataset page: https://huggingface.co/datasets/Philip-MIT/sole_training_data.humanoid-robots-training-dataset
Dynamic Intelligence — Humanoid Robot Training Dataset
A first-person (egocentric) video dataset of human hand manipulation, designed for training humanoid robot policies via imitation learning. Each episode captures a person performing an everyday household task — folding clothes, moving dishes, opening doors — filmed from a head-mounted iPhone using its built-in LiDAR and depth sensors.
The dataset pairs each video with frame-level 3D hand tracking and camera pose data, giving… See the full description on the dataset page: https://huggingface.co/datasets/DynamicIntelligence/humanoid-robots-training-dataset.Bitext-retail-ecommerce-llm-chatbot-training-dataset
Bitext - Retail (eCommerce) Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Retail (eCommerce)] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-retail-ecommerce-llm-chatbot-training-dataset.Bee-Training-Data-Stage2
Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully Open MLLMs
[🏠 Homepage] [📖 Arxiv Paper] [🤗 Models & Datasets] [💻 Code]
Introduction
We introduce Bee-8B, a new state-of-the-art, fully open 8B Multimodal Large Language Model (MLLM) designed to close the performance gap with proprietary models by focusing on data quality.
Bee-8B is trained on our new Honey-Data-15M corpus, a high-quality supervised fine-tuning (SFT) dataset of approximately 15… See the full description on the dataset page: https://huggingface.co/datasets/Open-Bee/Bee-Training-Data-Stage2.VideoGPT-plus_Training_DatasetGPT-Training-DataSII_self_evovling_02_training_datasetBitext-events-ticketing-llm-chatbot-training-dataset
Bitext - Events and Ticketing Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [events and ticketing] sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-events-ticketing-llm-chatbot-training-dataset.gorilla-openfunctions-v1training_dataG-OPD-Training-DataLuciole-Training-Dataset
Data card for The Luciole Training Dataset
Table of Contents
Dataset Description
Curation Rationale
Web Data Opt-Outs
Personal and Sensitive Information (PII)
Bias, Risks, and Limitations
Recommendations
Sample Metadata
Downloading the Data
Sample Use in Python
Accessing the English Web Data and OpenMathInstruct-1
Details on Data Sources
Citation
Acknowledgements
Contact
Dataset Description
The Luciole Training Dataset is a curated collection of… See the full description on the dataset page: https://huggingface.co/datasets/OpenLLM-France/Luciole-Training-Dataset.AceMath-Instruct-Training-Datawebsite | paper
AceMath-Instruct Training Data Card
We release all the datasets to train AceMath-1.5B/7B/72B-Instruct models. These models are built upon the Qwen2.5-Math-Base models through a multi-stage supervised fine-tuning (SFT) process. The fine-tuning begins with general-purpose SFT data (general_sft_stage1.parquet and general_sft_stage2.parquet) and is followed by math-specific SFT data (math_sft.parquet). In our experiments, fine-tuning the Qwen2.5-Math-Base models using… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/AceMath-Instruct-Training-Data.fairness-prm-training-dataAll-CVE-Records-Training-Dataset
CVE Chat‑Style Multi‑Turn Cybersecurity Dataset (1999 – 2025)
1. Project Overview
This repository hosts the largest publicly available chat‑style, multi‑turn cybersecurity dataset to date, containing ≈ 300 000 Common Vulnerabilities and Exposures (CVE) records published between 1999 and 2025. Each record has been meticulously parsed, enriched, and converted into a conversational format that is ideal for training and evaluating AI and AI‑Agent systems focused on… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/All-CVE-Records-Training-Dataset.song_dataset_training_20s_cleanedKaLM-reranker-training-data
Lychee-KaLM-Reranker Training Data
A large-scale, ready-to-use multilingual dataset for fine-tuning reranking models.
This repository contains 3,885,265 training samples collected from 54 datasets, covering English, Chinese, and multilingual retrieval tasks. Each sample includes task instructions, positive passages, at least 16 hard negatives, and teacher scores annotated by Qwen3-Reranker-8B.
When expanded into point-wise query–passage pairs, the dataset provides at least 66… See the full description on the dataset page: https://huggingface.co/datasets/KaLM-Embedding/KaLM-reranker-training-data.turkish_embedding_model_training_dataChatQA-Training-Data
Data Description
We release the training dataset of ChatQA. It is built and derived from existing datasets: DROP, NarrativeQA, NewsQA, Quoref, ROPES, SQuAD1.1, SQuAD2.0, TAT-QA, a SFT dataset, as well as a our synthetic conversational QA dataset by GPT-3.5-turbo-0613. The SFT dataset is built and derived from: Soda, ELI5, FLAN, the FLAN collection, Self-Instruct, Unnatural Instructions, OpenAssistant, and Dolly. For more information about ChatQA, check the website!
Other… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/ChatQA-Training-Data.ChatTS-Training-Dataset
ChatTS-Training Data
This repository contains the training data for the ChatTS project. This is the dataset for training the ChatTS-14B model.
Datasets
align_256: Alignment training dataset for stage-1 alignment training, with SEQ_LEN=256.
align_random: Alignment training dataset with random sequence lengths between 64 and 1024.
sft: SFT dataset generated with Time Series Evol-Instruct.
ift: Instruction following dataset.
dev: A small dataset for development and testing.… See the full description on the dataset page: https://huggingface.co/datasets/ChatTSRepo/ChatTS-Training-Dataset.pldr-llm-training-dynamics-data
PLDR-LLM Training Dynamics Data
Reported numerical evidence for Training and Inference Dynamics of PLDR-LLMs:
Row-Map Collapse, Renormalization, and Predictive Reduction, by Burc Gokden.
Monograph: Hugging Face Paper Page.
Scientific code and readers: GitHub repository.
Numerical evidence: Hugging Face dataset.
Book: Power Law Graph Attention and PLDR-LLMs: Mathematical Foundations, Training Dynamics, and Predictive Inference, by Burc Gokden.
Book Companion: Code and edition… See the full description on the dataset page: https://huggingface.co/datasets/fromthesky/pldr-llm-training-dynamics-data.