datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
dclm-baseline-1.0
DCLM-baseline
DCLM-baseline is a 4T token / 3B document pretraining dataset that achieves strong performance on language model benchmarks.
Below are comparisions of model trained on DCLM-baseline with other models in the 7B regime.
Model
Params
Tokens
Open dataset?
CORE
MMLU
EXTENDED
Open weights, closed datasets
Llama2
7B
2T
✗
49.2
45.8
34.1
DeepSeek
7B
2T
✗
50.7
48.5
35.3
Mistral-0.3
7B
?
✗
57.0
62.7
45.1
QWEN-2
7B
?
✗
57.5
71.9
50.5
Llama3
8B
15T
✗… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0.MAPBench-V2For more details, please check our project page.
Paper: https://arxiv.org/abs/2601.05432
Repository: https://github.com/AMAP-ML/Thinking-with-Map
trinity-dataset-v3SuperMemory-VQA
SuperMemoryVQA
SuperMemory-VQA is an egocentric visual question answering benchmark for
evaluating long-horizon memory in augmented reality assistant settings. The
dataset is designed around practical questions a person might ask a wearable
memory assistant, such as where an object was left, what someone said earlier,
whether a planned step was completed, or what happened next in a longer event.
The benchmark contains 4,853 human-verified question-answer pairs grounded in
52.9… See the full description on the dataset page: https://huggingface.co/datasets/OSU-AIoT-MLSys-Lab/SuperMemory-VQA.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.MLS-Bench-Tasks
MLS-Bench Tasks
MLS-Bench is a benchmark for machine learning science. Where most agent benchmarks reward engineering one fixed instance — clean the data, tune the pipeline, climb a leaderboard — MLS-Bench asks the harder question: can an AI agent propose a new component, loss, optimizer, or training procedure whose gain transfers across settings, seeds, datasets, and scales?The benchmark contains 140 tasks across 12 ML research domains. Each task fixes a research scaffold… See the full description on the dataset page: https://huggingface.co/datasets/Bohan22/MLS-Bench-Tasks.CL-VISTA
MCITlib: Multimodal Continual Instruction Tuning Library and Benchmark
✨Introduction •
🥇 Methods Provided •
🏦 Benchmarks •
🎨 Models
🏃 How to run •
🤝 Acknowledgments •
🙂 Contact
If you like our project, please give us a star ⭐ on GitHub for the latest updates.
✨ Introduction
MCITlib is a unified library for continual instruction tuning of multimodal large language models (MLLMs). It integrates diverse continual learning methods into a… See the full description on the dataset page: https://huggingface.co/datasets/MLLM-CL/CL-VISTA.AndroidCoderaw_tts_esc_ESPnet_espnet_mls-audioset_soundstream_16kLogics-STEM-SFT-Dataset-Open-1.6M
Logics-STEM-SFT-Dataset-2.2M
📰 News
[2026.01.05]🔥 Release of our Techinical Report.
[2026.01.05]🔥 Release the first version of Logics-STEM-8B-SFT, Logics-STEM-8B-RL, /Logics-STEM-SFT-Dataset-Open-1.6M.
Overview
What is this dataset?
Logics-STEM-SFT-Dataset-2.2M is a curated long Chain-of-Thought (CoT) SFT dataset for STEM reasoning, built on top of high-quality open-source data and enhanced through a rigorous curation and distillation… See the full description on the dataset page: https://huggingface.co/datasets/Logics-MLLM/Logics-STEM-SFT-Dataset-Open-1.6M.optiq-code-traces
OptiQ Code Traces
Gold-verified agentic software-engineering trajectories, produced by OptiQ Code, the terminal coding agent for local models on a Mac. Each trajectory is a full tool-calling run against a real repository bug, and every resolved label is set by executing the gold tests (FAIL_TO_PASS + PASS_TO_PASS) after applying the model's patch, never by the agent's own self-report.
The dataset is 1,789 agent sessions in HuggingFace Session-Traces format (the agent-traces… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/optiq-code-traces.tabula-8b-eval-suiteEvaluation suite used in our paper "Large Scale Transfer Learning for Tabular Data via Language Modeling."
This suite includes our preprocessed versions of benchmark datasets except the AutoML Multimodal Benchmark, which can be accessed by following the installation instructions in their repo here.
We recommend using rtfm when evaluating models with these datasets.
See the rtfm repo for more information on using this data for evaluation.
ml-intern-sessions
ML Intern session traces
This dataset contains ML Intern coding agent session traces uploaded from local
ML Intern runs. The traces are stored as JSON Lines files under sessions/,
with one file per session.
Links
ML Intern demo: https://smolagents-ml-intern.hf.space
ML Intern CLI: https://github.com/huggingface/ml-intern
Data description
Each *.jsonl file contains a single ML Intern session converted to a
Claude-Code-style event stream for the… See the full description on the dataset page: https://huggingface.co/datasets/clem/ml-intern-sessions.DroidCall
DroidCall: A Dataset for LLM-powered Android Intent Invocation
paper|github
DroidCall is the first open-sourced, high-quality dataset designed for fine-tuning LLMs for accurate intent invocation on Android devices.
This repo contains data generated by DroidCall. The process of data generation is shown in the figure below
Details can be found in our paper and github repository.
What is Android Intent Invocation?
Android Intent is a key machanism in Android that allows… See the full description on the dataset page: https://huggingface.co/datasets/mllmTeam/DroidCall.raw_tts_esc_ESPnet_espnet_mls-multi_soundstream_16kSynthetic-AI-ML-Dataset
Synthetic-AI-ML-Dataset
Synthetic Q&A dataset on AI and Machine Learning
Dataset Details
Metric
Value
Topic
AI and Machine Learning
Total Q&A Pairs
14021
Valid Pairs
14021
Provider/Model
ollama/gpt-oss:120b
Generation Cost
Metric
Value
Prompt Tokens
14,941,957
Completion Tokens
17,159,263
Total Tokens
32,101,220
GPU Energy
12.9628 kWh
Sources
This dataset was generated from 474 scholarly papers:
#… See the full description on the dataset page: https://huggingface.co/datasets/Kylan12/Synthetic-AI-ML-Dataset.machine-failure-mlops-demo-logsReasonMed
ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning
📄 Paper |
💻 Code |
📊 Dataset
ReasonMed is the largest open-source medical reasoning dataset to date, containing 370 K high-quality question–answer examples with multi-step chain-of-thought (CoT) rationales and concise summaries. We distilled these from 1.75 M initial reasoning paths generated by three competitive large-language models (Qwen-2.5-72B, DeepSeek-R1-Distill-Llama-70B, and… See the full description on the dataset page: https://huggingface.co/datasets/lingshu-medical-mllm/ReasonMed.RefCOCOPatch-as-Decodable-Token: Towards Unified Multi-Modal Vision Tasks in MLLMs
[🔗 Released Code]
[🤗 Datasets] [🤗 Checkpoints]
[📄 Tech Report] [🤗 Paper]
Figure A. PaDT pipeline.
🌟 Introduction
We are pleased to introduce Patch-as-Decodable Token (PaDT), a unified paradigm that enables multimodal large language models (MLLMs) to directly generate both textual and visual outputs.At the core of PaDT are Visual Reference Tokens (VRTs). Unlike conventional MLLMs that represent… See the full description on the dataset page: https://huggingface.co/datasets/PaDT-MLLM/RefCOCO.mlb-daily-reportMLV-Bench
MedHorizon / MLV-Bench
MedHorizon, also released as MLV-Bench, is a long-context medical video benchmark for evaluating multimodal models on full-procedure clinical videos. The benchmark emphasizes two properties that are not captured by short-clip medical video datasets: extremely sparse evidence retrieval and multi-hop reasoning over observations distributed across a full procedure.
Dataset Contents
Videos: 340 full-procedure videos.
Questions: 1,253 multiple-choice QA… See the full description on the dataset page: https://huggingface.co/datasets/DBD123/MLV-Bench.raw_tts_esc_ESPnet_espnet_mls-english_soundstream_16kswe_v0.1_jsonl_wo_mlang_large100_wo_v0.0_deltagmagenta-realtime-mlx-cpp
Magenta RealTime — C++ MLX runtime bundle
This dataset is a re-packaging of
Google's Magenta RealTime weights
for the C++ MLX runtime in
rhymeswithlion/magenta-realtime-mlx-cpp.
It contains exactly what mlx-stream needs at startup; nothing more, nothing
less. The upstream .pt / .npy checkpoints are intentionally not
mirrored here — they're only useful for the (Python) re-export tooling on the
project's main distribution.
Contents
.
├──… See the full description on the dataset page: https://huggingface.co/datasets/rhymeswithlion/magenta-realtime-mlx-cpp.easyr1-agent-grounding-dataOmniParsingBench
🤗 Model | 📑 Technical Report | 💻 GitHub
OmniParsingBench is a comprehensive, large-scale, and high-quality evaluation corpus designed to rigorously evaluate the unified parsing capabilities of Multimodal Large Language Models (MLLMs) across diverse modalities.
Unlike traditional single-task benchmarks, OmniParsingBench assesses the full spectrum of parsing performance—from fundamental signal detection to complex semantic reasoning—across six primary domains: Document… See the full description on the dataset page: https://huggingface.co/datasets/Logics-MLLM/OmniParsingBench.MLVU-Attention-Review
MLVU Attention Review 下载与解压说明
本仓库提供一个未压缩 TAR，里面是完整静态 HTML 展示、所有页面所需图片、QA/GT、模型原始回答、attention 元数据与未归一化 grids.npz，并附离线查看和逐文件 SHA256 校验脚本。归档不含模型权重、完整源视频或推理环境。
容量要求
下载和解压期间需同时存放 TAR 与解压内容，请预留至少归档大小约 2.2 倍的可用空间。
文件系统必须支持大于 4 GB 的单文件（NTFS、exFAT、ext4、APFS 等；FAT32 不可用）。
Windows 建议在较短路径中操作，例如 D:\reviews\MLVU，避免长路径限制。
下载
安装最新版 Hugging Face CLI：
pip install -U huggingface_hub
hf download GraciaChen/MLVU-Attention-Review MLVU-Attention-Review.tar SHA256SUMS… See the full description on the dataset page: https://huggingface.co/datasets/GraciaChen/MLVU-Attention-Review.OpenTuringBench
📊 OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution
Open Large Language Models (OLLMs) are increasingly leveraged in generative AI applications, posing new challenges for detecting their outputs. We release OpenTuringBench,
a new benchmark based on OLLMs, designed to train and evaluate machine-generated text detectors on the Turing Test and Authorship Attribution problems. OpenTuringBench focuses
on a representative… See the full description on the dataset page: https://huggingface.co/datasets/MLNTeam-Unical/OpenTuringBench.seeclick-web-commercial-mlx
SeeClick Web Commercial Dataset (MLX-VLM Format)
Commercial-use friendly GUI grounding dataset from SeeClick Web data.
Apache 2.0 licensed - safe for commercial applications.
Dataset Description
This dataset contains ~20k examples for training Vision-Language Models to predict
click coordinates given a screenshot and instruction. Derived from SeeClick Web
crawled data (Apache 2.0).
Key Features
License: Apache 2.0 (commercial use allowed)
Format: MLX-VLM… See the full description on the dataset page: https://huggingface.co/datasets/pierretokns/seeclick-web-commercial-mlx.Logics-STEM-SFT-Dataset-Open-5.3M