datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MMR-Life
MMR-Life (Multimodal Multi-image Reasoning Benchmark under Real-life Scenarios)
Dataset Description
We introduce MMR-Life, a benchmark curated to evaluate the ability of MLLMs to perform diverse types of reasoning in everyday situations. MMR-Life consists of 2,646 questions based on 19,238 images, covering 7 reasoning types (i.e., abductive, analogical, causal, deductive, inductive, spatial, and temporal) and 21 tasks. Each task is based on a set of multi-images sourced… See the full description on the dataset page: https://huggingface.co/datasets/Septzzz/MMR-Life.LIFEBench
LIFEBench
🔥 News
May 14, 2025: We release LIFEBench, the first comprehensive benchmark for evaluating the ability of LLMs to follow length instructions across diverse tasks, languages, and a broad range of length constraints.
📊 Dataset: Find our dataset on LIFEBench Datasets.
💻 Code: Access all code, scripts, and benchmark evaluation tools on our LIFEBench repository.
🌐 Website: View benchmark results and leaderboards on our LIFEBench website.
📖… See the full description on the dataset page: https://huggingface.co/datasets/LIFEBench/LIFEBench.behavioral-lift
Behavioral Lift Annotations
Dataset for Amplified Does Not Mean Predictive: Reasoning Behaviors in Thinking Models
Thinking models amplify visible deliberation, but not the behaviors most associated with correct answers.
This dataset contains 15,282 behavioral annotations of LLM and VLM reasoning traces across 15 models and 6 benchmarks. Each row contains one model response, benchmark metadata, correctness, and a JSON-encoded behavioral annotation covering reasoning behaviors… See the full description on the dataset page: https://huggingface.co/datasets/neulab/behavioral-lift.Lifebenchv2.0
🧠 LifeBench 2.0
A life-logging benchmark dataset for long-term memory evaluation
LifeBench 2.0 collects a full year (2025-01-01 ~ 2025-12-31) of life data for 10 virtual users, covering personas, daily life events, event trees, and 9 types of mobile-phone data (SMS, calls, calendar, notes, photos, push notifications, fitness & health, contacts, and agent chat), together with thousands of question-answer pairs annotated with evidence and score points, for evaluating the… See the full description on the dataset page: https://huggingface.co/datasets/C1754955896/Lifebenchv2.0.lifeos-jaram-gaia-evaluation
LifeOS Jaram GAIA Level 1 Evaluation
Metadata from LifeOS Jaram 4.0 agent's evaluation on the GAIA benchmark Level 1 (2023 test set).
🏆 Results
Metric
Value
Total Tasks
93
Tasks Solved
90
Accuracy
~96.8%
HAL Leaderboard World #1
82.1%
🤖 Agent Overview
Jaram 4.0 is the core problem-solving agent of the LifeOS multi-agent council.
Base Models: Claude 3.5 Sonnet, Gemini 2.0 Flash
Architecture: Multi-agent council (Koram + Boran)
Live… See the full description on the dataset page: https://huggingface.co/datasets/joonnam/lifeos-jaram-gaia-evaluation.Small-Life-Dataset-ru-eng
Russian-English Dialogue Dataset
🎯 Overview
A comprehensive bilingual dialogue dataset containing 50,000 high-quality question-answer pairs in Russian and English. The dataset is balanced across two main categories: programming/technical topics and general conversation.
Dataset Statistics:
📊 Total Dialogues: 50,000
🇷🇺 Russian: 25,135 (50.3%)
🇬🇧 English: 24,865 (49.7%)
💻 Coding Topics: 25,056 (50.1%)
💬 General Conversation: 24,944 (49.9%)
📑… See the full description on the dataset page: https://huggingface.co/datasets/SonexaAI/Small-Life-Dataset-ru-eng.aifgen
Dataset Card for aif-gen static dataset
This dataset is a set of static RLHF datasets used to generate continual RLHF datasets for benchmarking Lifelong RL on language models.
The data used in the paper can be found under the directory 4omini_generation and the rest are included for reference and are used in the experiments for the paper.
The continual datasets created for benchmarking can be found with their dataset cards in… See the full description on the dataset page: https://huggingface.co/datasets/LifelongAlignment/aifgen.bhagavad-gita-with_life_lesson
bhagavad-gita-lifelesson Dataset
A complete, high-fidelity dataset covering all 701 verses of the Bhagavad Gita titled bhagavad-gita-lifelesson. Each verse follows the strict format:
First: Sanskrit chanting
Then: Hindi meaning (हिन्दी अर्थ)
Then: Life lesson (जीवन-पाठ)
(Transliteration and English translation have been removed).
🎧 Example Representation (Verse 2.47)
🎧 Verse 2.47
First: Sanskrit chanting
कर्मण्येवाधिकारस्ते मा फलेषु कदाचन
मा… See the full description on the dataset page: https://huggingface.co/datasets/AkrGupta/bhagavad-gita-with_life_lesson.SAT
SAT: Spatial Aptitude Training for Multimodal Language Models
Project Page
To use the dataset, first make sure you have Python3.10 and Huggingface datasets version 3.0.2 (pip install datasets==3.0.2):
from datasets import load_dataset
import io
split = "val"
dataset = load_dataset("array/SAT", batch_size=128)
example = dataset[split][10] # example 10th item
images = [Image.open(io.BytesIO(im_bytes)) for im_bytes in example['image_bytes']] # this is a list of images. Some… See the full description on the dataset page: https://huggingface.co/datasets/lifuguan/SAT.kill-life-embedded-qa
Kill_LIFE — Embedded Knowledge-Base Q&A
Q&A spécifique au projet Kill_LIFE (compagnon vocal embarqué basé sur ESP32-S3 + Mascarade) : composants matériels du board, schémas KiCad du board ESP32-S3 minimal, simulations SPICE de l'alimentation/I2C/I2S/audio, et architecture du firmware (pipeline voix, contrôleur vocal, intégration backend).
Description
Issu de la knowledge-base interne du projet electron-rare/kill-life. Sert d'ancre factuelle pour le fine-tuning : permet au… See the full description on the dataset page: https://huggingface.co/datasets/electron-rare/kill-life-embedded-qa.kill-life-embedded-qa
Ailiance — Kill-LIFE Embedded Knowledge Base
🇫🇷 Ailiance — curated by Ailiance for production deployment ; co-published with the upstream electron-rare/kill-life-embedded-qa. 🇪🇺 Compatible EU AI Act (Template AI Office, July 2025).
Knowledge-base Q&A spécifique au projet Kill_LIFE (compagnon vocal embarqué ESP32-S3 + Mascarade) : composants matériels, schémas KiCad du board minimal, simulations SPICE de l'alimentation/I2C/I2S/audio, et architecture du firmware C++ (pipeline… See the full description on the dataset page: https://huggingface.co/datasets/Ailiance-fr/kill-life-embedded-qa.omnimcp_subscription_lifecycle_handler_teaser
🔬 INSPECT THE DEEPSEEK-R1 REASONING CHAIN LIVE:
Zero hallucinations. Null syntax errors. 100% AST compiler validated.🌐 Live Interactive Reasoning & Code Inspector: https://emgena.com/trainingslager🎁 Claim your Free Starter Kit (Code: STARTER100): https://emgena.com/trainingslager🏷️ Launch Discount: Get 20 € OFF any 500-incident production suite with code LAUNCH20!
📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_subscription_lifecycle_handler_teaser.CONVERSATIONS_WITH_ANOTHER_LIFE_FORMThe research and factual part of this work would be incomplete without paying certain attention to the contacts of the Volga Group for the Study of UFOs with an unidentified source (or sources) of intelligent information. These contacts were carried out by us from the end of 1993 to 1997, i.e., over a period of five years. During this time, a rather extraordinary material of an intellectual nature has been accumulated, which needs to be deeply understood and, if possible, to draw certain… See the full description on the dataset page: https://huggingface.co/datasets/AndreySokolov01/CONVERSATIONS_WITH_ANOTHER_LIFE_FORM.LiFT-HRA-20K
LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment
Summary
This is the dataset proposed in our paper "LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment". LiFT-HRA is a high-quality Human Preference Annotation dataset that can be used to train video-text-to-text reward models. All videos in the LiFT-HRA dataset have resolutions of at least 512×512.
Project: https://codegoat24.github.io/LiFT/
Code: https://github.com/CodeGoat24/LiFT… See the full description on the dataset page: https://huggingface.co/datasets/Fudan-FUXI/LiFT-HRA-20K.aifgen-long-piecewise
Dataset Card for Dataset Name
This dataset is a continual dataset in long piecewise scenario given two tasks:
Domain: Education (math, sciences, and social sciences), Objective: QnA, Preference: hinted answer
Domain: Education (math, sciences, and social sciences), Objective: QnA, Preference: direct answer
Dataset Details
Dataset Description
As a subset of a larger repository of datasets generated and curated carefully for Lifelong Alignment of Agents… See the full description on the dataset page: https://huggingface.co/datasets/LifelongAlignment/aifgen-long-piecewise.LiFT-HRA-10K
LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment
Summary
This is the dataset proposed in our paper "LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment". LiFT-HRA is a high-quality Human Preference Annotation dataset that can be used to train video-text-to-text reward models. All videos in the LiFT-HRA dataset have resolutions of at least 512×512.
Project: https://codegoat24.github.io/LiFT/
Code: https://github.com/CodeGoat24/LiFT… See the full description on the dataset page: https://huggingface.co/datasets/Fudan-FUXI/LiFT-HRA-10K.scbe-life-science-research-training-demo
Status: experimental. Experiment-specific slice. Primary public dataset: scbe-aethermoore-training-data.
SCBE Research Training Package
This package was generated from live pubmed pulls for the query protein structure prediction and is meant for
lightweight Hugging Face dataset and SFT experiments.
Files
papers.jsonl: normalized raw research records
sft_train.jsonl: train split for instruction-style tasks
sft_validation.jsonl: validation split… See the full description on the dataset page: https://huggingface.co/datasets/issdandavis/scbe-life-science-research-training-demo.Q-A-M-khmer-Daily-Life-Routines
Khmer Daily Life Routines QA Dataset
Welcome to the SeyhaLite collection. This dataset has been meticulously curated and cleaned to support the development of high-quality Khmer Language Models (LLMs) and Question-Answering systems.
Project Vision
I hope this dataset helps your project succeed. Whether you are building a chatbot, an assistant, or conducting research, this data is designed to provide clear and accurate information about daily life routines in the Khmer… See the full description on the dataset page: https://huggingface.co/datasets/SeyhaLite/Q-A-M-khmer-Daily-Life-Routines.bhagavad-gita-life-advice-700
🕉️ Bhagavad Gita Life-Advice 700
Transform ancient wisdom into modern solutions700 practical life questions answered directly from every single verse of the Bhagavad Gita
📖 Overview
This dataset bridges the 5,000-year-old wisdom of the Bhagavad Gita with modern life challenges. Each entry connects a real human question to specific Gita verses with actionable, concise advice.
What makes this unique:
✅ Verse-level precision - Every answer references exact… See the full description on the dataset page: https://huggingface.co/datasets/suneeldk/bhagavad-gita-life-advice-700.life-advice-rag-dataset
Life Advice RAG Dataset
성경, 불교 경전/해설, 논어, 인문학 명언 자료를 RAG 검색용 chunk로 변환한 데이터셋입니다.
Dataset Summary
Chunk 수: 4,007
출처 수: 83
총 문자 수: 3,656,354
데이터 형식: JSONL
주요 용도: 근거 기반 인생조언 RAG, 종교·철학 관점 비교 검색, 발표/시연용 QA
Files
File
Description
chunks.jsonl
RAG 검색용 chunk 본문
dataset_manifest.json
데이터셋 통계와 출처 목록
sample_questions.json
시연용 질문 예시
Data Fields
chunks.jsonl의 각 줄은 다음 형식입니다.
{
"chunk_id": "chunk_000001",
"source": "원본 파일명… See the full description on the dataset page: https://huggingface.co/datasets/nenae18/life-advice-rag-dataset.trade-lifecycle-microstructure-v1
Trade Lifecycle & Market Microstructure Dataset v1
Dataset Summary
Trade Lifecycle & Market Microstructure Dataset v1 is a curated, expert-designed dataset focused on
market microstructure, trade lifecycle, clearing & settlement, corporate actions, surveillance,
and crypto AMM mechanics.
The dataset contains 100 high-quality training samples created by a former U.S. equities exchange
Market Operations analyst with real-world experience across:
U.S. equities… See the full description on the dataset page: https://huggingface.co/datasets/teachaifinance/trade-lifecycle-microstructure-v1.aifgen-domain-preference-shift
Dataset Card for Dataset Name
This dataset is a continual dataset in a mixed non stationarity scenario of both domains and preferences given a combination of given two tasks:
Domain: Education (math, sciences, and social sciences), Objective: QnA, Preference: Explain like I'm 5 answer
Domain: Education (math, sciences, and social sciences), Objective: QnA, Preference: Expert answer
Domain: Politics, Objective: Summary, Preference: Explain like I'm 5 answer
Domain: Politics… See the full description on the dataset page: https://huggingface.co/datasets/LifelongAlignment/aifgen-domain-preference-shift.Medical-Reasoning-SFT-Mega
Medical-Reasoning-SFT-Mega
The ultimate medical reasoning dataset - combining 7 state-of-the-art AI models with fair distribution deduplication. 1.79 million unique samples with 3.78 billion tokens of medical chain-of-thought reasoning.
Dataset Overview
Metric
Value
Total Samples
1,789,998 (after deduplication)
Total Tokens
~3.78 Billion
Content Tokens
~2.22 Billion
Reasoning Tokens
~1.56 Billion
Samples with Reasoning
1,789,764 (100.0%)
Unique… See the full description on the dataset page: https://huggingface.co/datasets/living-my-best-life/Medical-Reasoning-SFT-Mega.aifgen-piecewise-preference-shift
Dataset Card for Dataset Name
This dataset is a continual dataset in a piecewise non stationarity scenario of both domains and preferences given a combination of given three recurring tasks:
Domain: Politics, Objective: Generation, Preference: Respond like a rapper
Domain: Politics, Objective: Generation, Preference: Respond like Shakespeare
Domain: Politics, Objective: Generation, Preference: Respond formally
Domain: Politics, Objective: Generation, Preference: Respond like a… See the full description on the dataset page: https://huggingface.co/datasets/LifelongAlignment/aifgen-piecewise-preference-shift.aifgen-short-piecewise
Dataset Card for Dataset Name
This dataset is a continual dataset in short piecewise scenario given two tasks:
Domain: Education (math, sciences, and social sciences), Objective: QnA, Preference: hinted answer
Domain: Education (math, sciences, and social sciences), Objective: QnA, Preference: direct answer
Dataset Details
Dataset Description
As a subset of a larger repository of datasets generated and curated carefully for Lifelong Alignment of Agents… See the full description on the dataset page: https://huggingface.co/datasets/LifelongAlignment/aifgen-short-piecewise.PCOS_lifestyle_and_mental_health_FAQ
Dataset Card for Dataset Name
Dataset Card for PCOS Lifestyle & Mental Health Q&A Dataset
Dataset Description
This dataset is a curated question–answer (Q&A) knowledge base focused on Polycystic Ovary Syndrome (PCOS), with a specific emphasis on lifestyle factors, mental health, stress, coping strategies, and emotional well-being.
The dataset covers topics such as:
Anxiety and depression in PCOS
Stress and coping strategies
Ego-resiliency and emotional adaptation… See the full description on the dataset page: https://huggingface.co/datasets/Khyatimirani/PCOS_lifestyle_and_mental_health_FAQ.Life-style