datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
HiFi-UMI-2K
HiFi-UMI-2K: High-Fidelity Robot-Free Manipulation Data
2,000 hours released · 6 synchronized camera views · 480+ scenes · 3 mm pose accuracy · <40 µs synchronization
🌐 Project Website |
📦 Dataset |
📄 Paper: arXiv:2607.25895
Examples from the HiFi-UMI corpus. Click the image to play the video.
📚 Introduction
HiFi-UMI is a portable, high-fidelity bimanual capture system for collecting robot-free manipulation demonstrations.… See the full description on the dataset page: https://huggingface.co/datasets/simple-world-lab/HiFi-UMI-2K.alpaca_2k_testDL3DV-ALL-2K
DL3DV-Dataset
This repo has all the 2K frames with camera poses of DL3DV-10K Dataset. We are working hard to review all the dataset to avoid sensitive information. Thank you for your patience.
Download
If you have enough space, you can use git to download a dataset from huggingface. See this link. 480P/960P versions should satisfies most needs.
If you do not have enough space, we further provide a download script here to download a subset. The usage:
usage: download.py… See the full description on the dataset page: https://huggingface.co/datasets/DL3DV/DL3DV-ALL-2K.MEBench-2K1UOmnimodal-Agent-SFT-2K
OmniGAIA: Omni-Modal General AI Assistant Benchmark
📄 Paper
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💻 Code & Demo
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🤗 Dataset & Model
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📈 Leaderboard
This dataset contains omni-modal agent supervised fine-tuning (SFT) trajectories in the LlamaFactory SFT data format. You can directly follow LlamaFactory's instructions to fine-tune your omni-modal LLMs.OmniGAIA is a benchmark for Omni-Modal General AI Assistants that jointly reason over vision, audio, and language with external tools. It is… See the full description on the dataset page: https://huggingface.co/datasets/RUC-NLPIR/Omnimodal-Agent-SFT-2K.appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8-tmp01-reeval1
appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8-tmp01-reeval1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.40546875
Action score: 0.475
Valid samples: 320/320
appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8-reeval1
appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8-reeval1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.4046875
Action score: 0.4703125
Valid samples: 320/320
appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8
appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.39921875
Action score: 0.44375
Valid samples: 320/320
appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8-t01
appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8-t01
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.38359375
Action score: 0.4703125
Valid samples: 320/320
Twin-2K-500
Twin-2K-500 Dataset
This dataset Twin-2K-500 contains comprehensive persona information from a representative sample of 2,058 US participants, providing rich demographic and psychological data. The dataset is specifically designed for building digital twins for LLM simulations.
More information on how to use this dataset can be found in our Documentation and GitHub repository.
Details on how the dataset was generated are available in our Paper.
Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/LLM-Digital-Twin/Twin-2K-500.the-secrets-of-ceos-book-2k
The-Secrets-Of-Ceos-Book-2k
Made with ❤️ using 🦥 Unsloth Studio
Beta2x was generated with Unsloth Recipe Studio. It contains 2,000 generated records.
🚀 Quick Start
from datasets import load_dataset
# Load the main dataset
dataset = load_dataset("filipwx/the-secrets-of-ceos-book-2k", "data", split="train")
df = dataset.to_pandas()
📊 Dataset Summary
📈 Records: 2,000
📋 Columns: 4
📋 Schema & Statistics
Column
Type
Column Type
Unique… See the full description on the dataset page: https://huggingface.co/datasets/filipwx/the-secrets-of-ceos-book-2k.alpaca_messages_2k_dpo_testeveryday-conversations-llama3.1-2k
Everyday conversations for Smol LLMs finetunings
This dataset contains 2.2k multi-turn conversations generated by Llama-3.1-70B-Instruct. We ask the LLM to generate a simple multi-turn conversation, with 3-4 short exchanges, between a User and an AI Assistant about a certain topic.
The topics are chosen to be simple to understand by smol LLMs and cover everyday topics + elementary science. We include:
20 everyday topics with 100 subtopics each
43 elementary science topics with 10… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/everyday-conversations-llama3.1-2k.assessment2_spheres_and_cube_2k
Dataset Card for cilp_assessment_all
This is a FiftyOne dataset with 2000 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("maxspeer/assessment2_spheres_and_cube_2k_2")
# Launch the App
session = fo.launch_app(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/maxspeer/assessment2_spheres_and_cube_2k.ai-vs-real-2k-imagesFrodoBots-2K
FrodoBots 2K Dataset
The FrodoBots 2K Dataset is a diverse collection of camera footage, GPS, IMU, audio recordings & human control data collected from ~2,000 hours of tele-operated sidewalk robots driving in 10+ cities.
This dataset is collected from Earth Rovers, a global scavenger hunt "Drive to Earn" game developed by FrodoBots Lab.
Please join our Discord for discussions with fellow researchers/makers!
If you're interested in contributing driving data, you can buy your own… See the full description on the dataset page: https://huggingface.co/datasets/BitRobot/FrodoBots-2K.RSVQA-LR-2kA 2k subset of the validation split of the RSVQA LR dataset ported to HF for ease-of-use in quick remote sensing VQA evaluation.
For more information and attribution please refer to the original dataset: https://rsvqa.sylvainlobry.com/#dataset
dl3dv_2ksci_question_exp__scp_116k__training_2k_for_GPQAmedconceptsqa-sample_medarc_2k
Dataset Card for MedConceptsQA 2K Sample for MedARC
This is a hierarchically stratified sample of the ICD10-CM coding system from
MedConceptsQA.
This dataset was sampled such that maximum coverage across all levels
of the ICD-10 hierarchy to be as representative as possible while constraining
the dataset to a reasonable size for evaluation and potential reinforcement
learning.
Users can generate their own sampling of MedConceptsQA via
this generator script.
Original MedConceptsQA… See the full description on the dataset page: https://huggingface.co/datasets/sameedkhan/medconceptsqa-sample_medarc_2k.CHERRY-2KQuestion-Anchored-Tutoring-Dialogues-2k
Question-Anchored-Tutoring-Dialogues-2k
This dataset contains dialogues from math tutoring interventions recorded on Eedi.
Dataset Details
Dataset Description
Each dialogue represents a chat-based conversation between a tutor and a student prompted by the student requesting assistance while working on a lesson. Dialogues are accompanied with 2 sources of meta-data:
DQ-Question-Metadata: The question the student was working on that prompted the tutoring… See the full description on the dataset page: https://huggingface.co/datasets/Eedi/Question-Anchored-Tutoring-Dialogues-2k.R1_Annotated_AIME_True_2KDA-2K
DA-2K Evaluation Benchmark
Introduction
DA-2K is proposed in Depth Anything V2 to evaluate the relative depth estimation capability. It encompasses eight representative scenarios of indoor, outdoor, non_real, transparent_reflective, adverse_style, aerial, underwater, and object. It consists of 1K diverse high-quality images and 2K precise pair-wise relative depth annotations.
Please refer to our paper for details in constructing this benchmark.
Usage
Please… See the full description on the dataset page: https://huggingface.co/datasets/depth-anything/DA-2K.tigerbot-kaggle-leetcodesolutions-en-2kTigerbot 基于leetcode-solutions数据集，加工生成的代码类sft数据集
原始来源：https://www.kaggle.com/datasets/erichartford/leetcode-solutions
Usage
import datasets
ds_sft = datasets.load_dataset('TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k')
deeplesion-balanced-2k
DeepLesion Benchmark Subset (Balanced 2K)
This dataset is a curated subset of the DeepLesion dataset, prepared for demonstration and benchmarking purposes. It consists of 2,000 CT lesion samples, balanced across 8 coarse lesion types, and filtered to include lesions with a short diameter > 10mm.
Dataset Details
Source: DeepLesion
Institution: National Institutes of Health (NIH) Clinical Center
Subset size: 2,000 images
Lesion types: lung, abdomen, mediastinum, liver… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/deeplesion-balanced-2k.Twin-2K-500-Mega-Study
Twin-2K-500-Mega-Study Dataset
GitHub Repository: https://github.com/TianyiPeng/Twin-2K-500-Mega-Study
To see more details for how to process these data, please refer to this GitHub repository.
This dataset contains survey data from the Twin-2K-500 Mega Study, which tests the validity of using large language models to predict people's future answers based on their answers to past surveys (creating "digital twins" of participants).
Dataset Structure
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/LLM-Digital-Twin/Twin-2K-500-Mega-Study.RSVQA-HR-2kA 2k subset of the validation split of the RSVQA HR dataset ported to HF for ease-of-use in quick remote sensing VQA evaluation.
For more information and attribution please refer to the original dataset: https://rsvqa.sylvainlobry.com/#dataset
DL3DV-VIPED-final-2KWRIT-2K
WRIT-2K
WRIT-2K is a 2,000-trajectory supervised fine-tuning dataset for multi-turn, tool-using customer-service agents on tau2-bench style airline and retail tasks.
This dataset accompanies the paper WRIT: Write-Read Intensive Trajectory Synthesis for Multi-Turn User-Facing Agents.
Project homepage: https://hengrui-gu.github.io/WRIT/
Dataset Summary
WRIT-2K contains complete multi-turn trajectories with user messages, assistant natural-language responses… See the full description on the dataset page: https://huggingface.co/datasets/Henryoung/WRIT-2K.