datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.flat-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.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.flan_v2
Dataset Card for Flan V2
Dataset Summary
This is a processed version of the Flan V2 dataset.
I'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.
The authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.
Setup Instructions
Here are the steps I followed to get everything working:
Build AESLC and WinoGrande datasets… See the full description on the dataset page: https://huggingface.co/datasets/SirNeural/flan_v2.GLM-5.3-Flash-calibration-activations-v1
GLM-5.3-Flash calibration activations v1 (BF16, natural routing)
Per-layer block-input activations of zai-org/GLM-5.3-Flash-BF16 @ b1967181 over 92x2048
tokens of the exllamav3 standard_cal_data corpus (pinned): per context, layer_NNN.attn_in
and layer_NNN.mlp_in (bf16, post-norm linear inputs; mlp_in is the router + expert gate/up
input) and layer_NNN.router_logits (fp32, natural top-8 routing ground truth).
Per-expert Hessians E[xx^T], routing statistics and down-proj inputs… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/GLM-5.3-Flash-calibration-activations-v1.stackexchange_title_body_jsonljsonl.gz format from https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml
Each line contains a dict in the format: {"text": ["title", "body"], "tags": ["tag1", "tag2"]}
The following parameters have been used for filtering: min_title_len = 20 min_body_len = 20 max_body_len = 4096 min_score = 0
If a stackexchange contained less than 10k questions (after filtering), it is written to the small_stackexchanges.jsonl.gz file.
This is a dump of the files from… See the full description on the dataset page: https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl.opengloss-v1.3-query-examples-flat
See also OpenGloss v2.1 (2026-09-07): a deeper release of 109,633 of these headwords — sense-level ids, four reading levels, sense-tagged examples with spans, a judged relation graph, and retrieval supervision — published as a 16-dataset family. v1.3 remains the broader headword list.
OpenGloss Query Examples v1.3 (Flattened)
Dataset Summary
OpenGloss Query Examples is a synthetic dataset of search queries generated for vocabulary
terms. Each term has multiple… See the full description on the dataset page: https://huggingface.co/datasets/mjbommar/opengloss-v1.3-query-examples-flat.bird-critic-1.0-flash-exp
BIRD-CRITIC-1.0-Flash
BIRD-Critic is the first SQL debugging benchmark designed to answer a critical question:
Can large language models (LLMs) fix user issues in real-world database applications? Each task in BIRD-CRITIC has been verified by human experts on the following dimensions:
Reproduction of errors on BIRD env to prevent data leakage.
Carefully curate test case functions for each task specifically.
Soft EX: This metric can evaluate SELECT-ONLY tasks.
Soft EX + Parsing:… See the full description on the dataset page: https://huggingface.co/datasets/birdsql/bird-critic-1.0-flash-exp.flanv2flat-pack-bench-misc
🛠️ Flat-Pack Bench Misc
This repository contains auxiliary artifacts for Flat-Pack Bench: ablation questions with scrambled part IDs, corresponding prompt-mask variants, cached TVA segmentation tracks, and TVA agent traces. The main benchmark data and evaluation outputs live in the companion Flat-Pack Bench repositories; this repo keeps the heavier or analysis-specific assets separate.
Repository Map
Path
Contents
scrambled-questions/
Base and seeded… See the full description on the dataset page: https://huggingface.co/datasets/justachetan/flat-pack-bench-misc.ox-alpha-glm-5.3-flash-distillation-coding-17k-raw
Ox Alpha GLM-5.3-Flash Distillation Coding 17K Raw
A raw collection of 17,138 synthetic coding samples generated with GLM-5.3-Flash, previously exposed through OpenCode under the stealth-model alias Ox Alpha.
The dataset is intended for experimentation with LLM distillation, code-generation models, instruction tuning, supervised fine-tuning, evaluation, and agentic coding systems.
HMMT_2025
Dataset Summary
This dataset comprises the questions, answers, and solutions from HMMT February 2025, all of which were extracted by OCR, converted to LaTeX, and manually verified by FlagEval Team.
Data Fields
Below one can find the description of each field in the dataset.
id (str): Index of the problem in the competition
problem (str): Full problem statement
answer (str): Ground-truth answer to the question
solution(str): Ground-truth solution to the question… See the full description on the dataset page: https://huggingface.co/datasets/FlagEval/HMMT_2025.flanv2
Fork of SirNeural/flan_v2
just in case it gets deleted.
Dataset Card for Flan V2
Dataset Summary
This is a processed version of the Flan V2 dataset.
I'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.
The authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.
This current version I've processed is missing a few… See the full description on the dataset page: https://huggingface.co/datasets/philschmid/flanv2.flan2021-full
Task Name
FLAN-2021 -> 70
{
"ag_news_subset": 108497,
"ai2_arc/ARC-Challenge": 829,
"ai2_arc/ARC-Easy": 1927,
"aeslc": 13187,
"anli/r1": 15361,
"anli/r2": 41133,
"anli/r3": 91048,
"bool_q": 8343,
"cnn_dailymail": 259607,
"coqa": 6456,
"cosmos_qa": 22996,
"definite_pronoun_resolution": 1079,
"drop": 70045,
"fix_punct": 25690,
"gem/common_gen": 60936,
"gem/dart": 56724,
"gem/e2e_nlg": 30337,
"gem/web_nlg_en": 31899… See the full description on the dataset page: https://huggingface.co/datasets/aslawliet/flan2021-full.glm53-flash-fidelity-root-v1
fidelity--glm53flash.malaiwah.root.bf16
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from zai-org/GLM-5.3-Flash-BF16.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it). Same cut… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-flash-fidelity-root-v1.20260729_mini-v2.2.8_gemini-3-5-flash20260731_mini-v2.4.2_gemini-3-6-flashFLARE22
FLARE22 Dataset
Dataset Description
The FLARE22 dataset for fast and low-resource abdominal organ segmentation. This dataset contains CT scans with dense segmentation annotations.
Dataset Details
Modality: CT
Target: 13 abdominal organs
Format: NIfTI (.nii.gz)
Dataset Structure
Each sample in the JSONL file contains:
{
"image": "path/to/image.nii.gz",
"mask": "path/to/mask.nii.gz",
"label": ["organ1", "organ2", ...]… See the full description on the dataset page: https://huggingface.co/datasets/MedOtter/FLARE22.flan-embed-test2medical-meadow-medical-flashcards
Dataset Card for medical-meadow-medical-flashcards
This dataset originates from the medAlpaca repository.
The medical-meadow-medical-flashcards dataset is specifically used for models training of medical question-answering.
Dataset Details
Dataset Description
Each sample is comprised of three columns: instruction, input and output.
Language(s): English
Dataset Sources
The code from the original repository was adopted to post it here.
Repository:… See the full description on the dataset page: https://huggingface.co/datasets/flwrlabs/medical-meadow-medical-flashcards.glm53-flash-fidelity-exl3-tr3-6bpw-v1
fidelity--glm53flash.malaiwah.quant.tr3-6bpw
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/GLM-5.3-Flash-TR3-6bpw.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-flash-fidelity-exl3-tr3-6bpw-v1.Commercial-Flan-Collection-Chain-Of-ThoughtFlames-1k-Chinese
FLAMES: Benchmarking Value Alignment of LLMs in Chinese
Introduction
🏠 Homepage | 👍 Our Official Code Repo
This repository organizes the data from FLAMES: Benchmarking Value Alignment of LLMs in Chinese, facilitating evaluation using align-anything.
Citation
The evaluation script for Flames is released in the align-anything repository.
Please cite the repo if you find the benchmark and code in this repo useful 😊
@inproceedings{ji2024align,
title={Align… See the full description on the dataset page: https://huggingface.co/datasets/PKU-Alignment/Flames-1k-Chinese.DeepSeek-V4-Flash-0731-Teacher-Distillation-40513x
DeepSeek V4 Flash 0731 Teacher Distillation — 40,513 Retained Rows
Teacher-distillation corpus generated with
deepseek-ai/DeepSeek-V4-Flash-0731.
The original manifest contained 45,000 unique seeds.
Following generation, QC, retry-based repair, quarantine auditing,
and recovery adjudication, 40,513 rows were retained.
Composition
Bucket
Rows
Coding
5,601
Agentic
9,982
Cyber blue
13,000
Controlled cyber red
6,999
Tool use
4,931
Total
40,513… See the full description on the dataset page: https://huggingface.co/datasets/trjxter/DeepSeek-V4-Flash-0731-Teacher-Distillation-40513x.constructcie
Dataset Card for ConstructCIE
ConstructCIE is a dataset for extracting causal information from construction accident narratives. Each accident report is annotated with a hierarchy of causal factors.
Accepted to Findings of the Association for Computational Linguistics: EMNLP 2026
Dataset Details
Dataset Description
The dataset contains 530 English construction accident narratives drawn from OSHA accident investigation summaries published… See the full description on the dataset page: https://huggingface.co/datasets/lab-flair/constructcie.gemini-3-flash-preview
Gemini 3 Flash Preview
This is a reasoning dataset created using Gemini 3 Flash Preview with a reasoning depth set to high.
The dataset is meant for creating distilled versions of Gemini 3 Flash Preview by fine-tuning already existing open-source LLMs.
This dataset contains a collection of prompts categorized by themes such as benchmarks, psychology, web development, embedded systems, creative writing, design, finance, legal, marketing, and science. No real benchmark questions were… See the full description on the dataset page: https://huggingface.co/datasets/TeichAI/gemini-3-flash-preview.glm53-flash-fidelity-fp8-v1
fidelity--glm53flash.malaiwah.quant.official-fp8
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from zai-org/GLM-5.3-Flash.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it).… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-flash-fidelity-fp8-v1.KrynexAI-Dataset-Flash-Instruction
🧠 KrynexAI Dataset
English | Русский
📌 Overview
KrynexAI Dataset is a high-quality, synthetically expanded collection of 10,000+ instruction-response pairs designed for fine-tuning Large Language Models (LLMs).
The dataset covers a wide range of topics including:
💻 Programming (Python, algorithms, data structures)
🤖 AI & Machine Learning (neural networks, transformers, LLMs)
🔭 Science (physics, cosmology, biology, neuroscience)
🧠 Philosophy & Psychology… See the full description on the dataset page: https://huggingface.co/datasets/KrynexLabs/KrynexAI-Dataset-Flash-Instruction.dolma-reddit-to-flashcards-0625
Overview
Dolma Reddit to Flashcards is a dataset of synthetically-generated QA items created on the basis of filtered Reddit data.
The creation of this dataset was motivated by the observation in Dolma (Soldaini et al. 2024) that the original Dolma Reddit data showed no benefit from inclusion of thread-level context over isolated submissions and comments, and that clean performance distinctions between tested Reddit versions were limited mainly to the HellaSWAG benchmark.
The… See the full description on the dataset page: https://huggingface.co/datasets/allenai/dolma-reddit-to-flashcards-0625.