datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
flatpakVideoChat-Flash-Training-Data
🦜 VideoChat-Flash-Training-Data
This repos contains all annotaions and most videos for training VideoChat-Flash.
📕 How to use the LongVid data?
For video_dir like longvid_subset/coin_grounding_10k_zip, you need to concat this dir to a zip file as follows:
cat ego4dhcap_eventunderstanding_2k_zip/* > ego4dhcap_eventunderstanding_2k.zip
✏️ Citation
@article{li2024videochatflash,
title={VideoChat-Flash: Hierarchical Compression for Long-Context… See the full description on the dataset page: https://huggingface.co/datasets/OpenGVLab/VideoChat-Flash-Training-Data.GLM-5.3-Flash-TR3-partsbin-v1
GLM-5.3-Flash TR3 parts bin v1 — K6 + K8 payload stores under one transform seed
This dataset is the parts bin for the GLM-5.3-Flash TR3 quantization
campaign (2026-08-27/28): the complete per-choice payload stores of the two
published uniform quants, plus the preparation artifacts and provenance
receipts that produced them.
malaiwah/GLM-5.3-Flash-TR3-6bpw (uniform K6)
malaiwah/GLM-5.3-Flash-TR3-8bpw (uniform K8)
What a parts bin is
TR3 (trellis) encoding is… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/GLM-5.3-Flash-TR3-partsbin-v1.FLAN🍮 The WHOLE FLAN Collection! 🍮
Overview
This repository includes the full dataset from the FLAN Collection, totalling ~300GB as parquets.
Generated using the official seqio templating from the Google FLAN Collection GitHub repo.
The data is subject to all the same licensing of the component datasets.
To keep up with our continued work on OpenOrca and other exciting research, find our Discord here:
https://AlignmentLab.ai
Motivation
This work was done as part of… See the full description on the dataset page: https://huggingface.co/datasets/Open-Orca/FLAN.FlashRAG_datasets
⚡FlashRAG: A Python Toolkit for Efficient RAG Research
FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 36 pre-processed benchmark RAG datasets and 16 state-of-the-art RAG algorithms.
With FlashRAG and provided resources, you can effortlessly reproduce existing SOTA works in the RAG domain or implement your custom RAG processes and components.
For more information, please view our GitHub repo… See the full description on the dataset page: https://huggingface.co/datasets/RUC-NLPIR/FlashRAG_datasets.flanThis is a repreprocessed version of the FLAN dataset with any updates that have been made to the FLAN datasets since the release of the original FLAN. The script is available here.
Tasks:
{'aeslc_10templates',
'ag_news_subset_10templates',
'anli_r1_10templates',
'anli_r2_10templates',
'anli_r3_10templates',
'arc_challenge_10templates',
'arc_easy_10templates',
'bool_q_10templates',
'cb_10templates',
'cnn_dailymail_10templates',
'cola_10templates',
'common_gen_10templates'… See the full description on the dataset page: https://huggingface.co/datasets/Muennighoff/flan.flame-runsflat-pack-bench
Flat-Pack Bench 🧩
Furniture assembly as a spatio-temporal stress test for large vision-language models.
Flat-Pack Bench is a multiple-choice benchmark for evaluating fine-grained
spatio-temporal understanding in real furniture assembly videos. Each question
asks a model to reason about object parts, contact events, assembly order, final
connectivity, or part identity across time.
Project page: https://flat-pack-bench.github.io
🎯 Benchmark Tasks
The benchmark… See the full description on the dataset page: https://huggingface.co/datasets/justachetan/flat-pack-bench.flashinfer-trace
FlashInfer Trace
We provide an official dataset called FlashInfer Trace with kernels and workloads in real-world AI system deployment environments. FlashInfer-Bench can use this dataset to measure and compare the performance of kernels. It follows the FlashInfer Trace Schema.
It is organized as follows:
flashinfer_trace/ # Here
├── definitions/
└── workloads/
flashinfer-trace/ # On Hugging Face
├── solutions/
└── traces/
Example solutions and traces directories, featuring… See the full description on the dataset page: https://huggingface.co/datasets/flashinfer-ai/flashinfer-trace.vqa-rad
Dataset Card for VQA-RAD
Dataset Description
VQA-RAD is a dataset of question-answer pairs on radiology images. The dataset is intended to be used for training and testing
Medical Visual Question Answering (VQA) systems. The dataset includes both open-ended questions and binary "yes/no" questions.
The dataset is built from MedPix, which is a free open-access online database of medical images.
The question-answer pairs were manually generated by a team of clinicians.… See the full description on the dataset page: https://huggingface.co/datasets/flaviagiammarino/vqa-rad.medical_meadow_medical_flashcards
Dataset Card for Medical Flashcards
Dataset Summary
Medicine as a whole encompasses a wide range of subjects that medical students and graduates must master
in order to practice effectively. This includes a deep understanding of basic medical sciences, clinical knowledge,
and clinical skills. The Anki Medical Curriculum flashcards are created and updated by medical students and cover the
entirety of this curriculum, addressing subjects such as anatomy, physiology… See the full description on the dataset page: https://huggingface.co/datasets/medalpaca/medical_meadow_medical_flashcards.Step-3.5-Flash-SFT
Step-3.5-Flash-SFT
Step-3.5-Flash-SFT is a general-domain supervised fine-tuning release for chat models.
This repository keeps the full training interface in one place:
json/: canonical raw training data
tokenizers/: tokenizer snapshots for Step-3.5-Flash and Qwen3, released to preserve chat-template alignment
compiled/: tokenizer-specific compiled shards for StepTronOSS training
Data Format
Each raw shard is a JSON file whose top level is a list of examples.… See the full description on the dataset page: https://huggingface.co/datasets/stepfun-ai/Step-3.5-Flash-SFT.transformers_flash_attn_ciFLAIR-HUB
FLAIR-HUB : Large-scale Multimodal Dataset for Land Cover and Crop Mapping
FLAIR-HUB builds upon and includes the FLAIR#1 and FLAIR#2 datasets, expanding them into a unified, large-scale, multi-sensor land-cover resource with very-high-resolution
annotations. Spanning over 2,500 km² of diverse French ecoclimates and landscapes, it features 63 billion hand-annotated pixels across 19 land-cover and
23 crop type classes.
The dataset integrates complementary data sources including… See the full description on the dataset page: https://huggingface.co/datasets/IGNF/FLAIR-HUB.path-vqa
Dataset Card for PathVQA
Dataset Description
PathVQA is a dataset of question-answer pairs on pathology images. The dataset is intended to be used for training and testing
Medical Visual Question Answering (VQA) systems. The dataset includes both open-ended questions and binary "yes/no" questions.
The dataset is built from two publicly-available pathology textbooks: "Textbook of Pathology" and "Basic Pathology", and a
publicly-available digital library: "Pathology… See the full description on the dataset page: https://huggingface.co/datasets/flaviagiammarino/path-vqa.shiur-clips-flaclaions_got_talent_enhanced_flash_annotations_and_long_captionsGLM-5.3-Flash-BF16-Teacher-Logits
GLM-5.3-Flash BF16 teacher logits
This dataset contains full-vocabulary float32 teacher logits from the immutable
zai-org/GLM-5.3-Flash-BF16 revision a6c167b62691b2bac901344b65cb651a70f53e43.
It keeps the sealed final KLD panel qualification-only and publishes the
separate non-final calibration panel under role-specific paths.
Qualification-only final windows: 25
Qualification-only final prediction positions: 51175
Vocabulary size: 154880
Teacher receipt:… See the full description on the dataset page: https://huggingface.co/datasets/brandonmusic/GLM-5.3-Flash-BF16-Teacher-Logits.Flash100Kkjtulu_flan_mds_incremental-tokensvision-flan_191-task_1k
🚀 Vision-Flan Dataset
vision-flan_191-task-1k is a human-labeled visual instruction tuning dataset consisting of 191 diverse tasks and 1,000 examples for each task.
It is constructed for visual instruction tuning and for building large-scale vision-language models.
Paper or blog for more information:
https://github.com/VT-NLP/MultiInstruct/
https://vision-flan.github.io/
Paper coming soon 😊
Citation
Paper coming soon 😊. If you use Vision-Flan, please use the… See the full description on the dataset page: https://huggingface.co/datasets/Vision-Flan/vision-flan_191-task_1k.tulu_flan_mds_incrementalflan-v2
Dataset Card for "flan-v2"
More Information needed
sinhala-flanMulti-SWE-bench-flash
👋 Overview
This repository contains the Multi-SWE-bench dataset, introduced in Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving, to address the lack of multilingual benchmarks for evaluating LLMs in real-world code issue resolution.
Unlike existing Python-centric benchmarks (e.g., SWE-bench), this framework spans 7 languages (Java, TypeScript, JavaScript, Go, Rust, C, and C++) with 1,632 high-quality instances,
curated from 2,456 candidates by 68 expert annotators… See the full description on the dataset page: https://huggingface.co/datasets/ByteDance-Seed/Multi-SWE-bench-flash.character_select_stand_alone_apphttps://github.com/mirabarukaso/character_select_stand_alone_app
EmbSpatial-Bench
Introduction
Disclaimer: This dataset is organized and adapted from Phineas476/EmbSpatial-Bench. The original data was image format and has been converted here into a more accessible and easy-to-use format.
EmbSpatial-Bench is a benchmark for evaluating embodied spatial understanding of LVLMs. The benchmark is automatically derived from embodied scenes and covers 6 spatial relationships from an egocentric perspective. The constructed benchmark comprises a total of 3,640 QA pairs… See the full description on the dataset page: https://huggingface.co/datasets/FlagEval/EmbSpatial-Bench.laions_got_talent_enhanced_just_flash_annotationsflare-finqa
Dataset Card for "flare-finqa"
More Information needed