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
SuperMemory-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.AndroidCodeoptiq-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.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.mate_in_oneMLLM_pathmachine-failure-mlops-demo-logsMLV-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.swe_v0.1_jsonl_wo_mlang_large100_wo_v0.0_deltagMLVU-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.jsonl-mls-hubert_large_ll60k-layer_22ovos-stt-bench-mls-pt-PT
OVOS stt bench — mls-pt-PT
Per-clip transcripts predictions of the registered
OVOS Plugin Arena
stt fighters over
facebook/multilingual_librispeech.
One dedicated repo per modality; one dataset split per language; one JSONL
file per fighter under predictions/<lang>/<competitor_id>.jsonl. Rows follow
the arena §3.2 contract (pinned dataset_revision, plugin_version,
latency_ms). Produced by the reproducible benchmark script in the arena repo;
the arena's assemble workflow turns… See the full description on the dataset page: https://huggingface.co/datasets/OpenVoiceOS/ovos-stt-bench-mls-pt-PT.ovos-stt-bench-mls-es-ES
OVOS stt bench — mls-es-ES
Per-clip transcripts predictions of the registered
OVOS Plugin Arena
stt fighters over
facebook/multilingual_librispeech.
One dedicated repo per modality; one dataset split per language; one JSONL
file per fighter under predictions/<lang>/<competitor_id>.jsonl. Rows follow
the arena §3.2 contract (pinned dataset_revision, plugin_version,
latency_ms). Produced by the reproducible benchmark script in the arena repo;
the arena's assemble workflow turns… See the full description on the dataset page: https://huggingface.co/datasets/OpenVoiceOS/ovos-stt-bench-mls-es-ES.ovos-stt-bench-mls-fr-FR
OVOS stt bench — mls-fr-FR
Per-clip transcripts predictions of the registered
OVOS Plugin Arena
stt fighters over
facebook/multilingual_librispeech.
One dedicated repo per modality; one dataset split per language; one JSONL
file per fighter under predictions/<lang>/<competitor_id>.jsonl. Rows follow
the arena §3.2 contract (pinned dataset_revision, plugin_version,
latency_ms). Produced by the reproducible benchmark script in the arena repo;
the arena's assemble workflow turns… See the full description on the dataset page: https://huggingface.co/datasets/OpenVoiceOS/ovos-stt-bench-mls-fr-FR.ovos-stt-bench-mls-de-DE
OVOS stt bench — mls-de-DE
Per-clip transcripts predictions of the registered
OVOS Plugin Arena
stt fighters over
facebook/multilingual_librispeech.
One dedicated repo per modality; one dataset split per language; one JSONL
file per fighter under predictions/<lang>/<competitor_id>.jsonl. Rows follow
the arena §3.2 contract (pinned dataset_revision, plugin_version,
latency_ms). Produced by the reproducible benchmark script in the arena repo;
the arena's assemble workflow turns… See the full description on the dataset page: https://huggingface.co/datasets/OpenVoiceOS/ovos-stt-bench-mls-de-DE.ovos-stt-bench-mls-it-IT
OVOS stt bench — mls-it-IT
Per-clip transcripts predictions of the registered
OVOS Plugin Arena
stt fighters over
facebook/multilingual_librispeech.
One dedicated repo per modality; one dataset split per language; one JSONL
file per fighter under predictions/<lang>/<competitor_id>.jsonl. Rows follow
the arena §3.2 contract (pinned dataset_revision, plugin_version,
latency_ms). Produced by the reproducible benchmark script in the arena repo;
the arena's assemble workflow turns… See the full description on the dataset page: https://huggingface.co/datasets/OpenVoiceOS/ovos-stt-bench-mls-it-IT.whestbench-relu-mlp-moments-10k
WhestBench Random ReLU MLPs — Monte-Carlo Activation Cumulants (10k)
A dataset of 10,500 random ReLU MLPs (10,000 train + 500 held-out test) together with
Monte-Carlo–estimated per-layer activation cumulants (mean, variance, skewness, kurtosis) for
every layer, for both the pre-activation and post-ReLU signals. Built for the WhestBench
Estimation Challenge 2026 and for research on analytic moment / uncertainty propagation
through deep networks.
The generative process… See the full description on the dataset page: https://huggingface.co/datasets/keenanpepper/whestbench-relu-mlp-moments-10k.ovos-stt-bench-mls-nl-NL
OVOS stt bench — mls-nl-NL
Per-clip transcripts predictions of the registered
OVOS Plugin Arena
stt fighters over
facebook/multilingual_librispeech.
One dedicated repo per modality; one dataset split per language; one JSONL
file per fighter under predictions/<lang>/<competitor_id>.jsonl. Rows follow
the arena §3.2 contract (pinned dataset_revision, plugin_version,
latency_ms). Produced by the reproducible benchmark script in the arena repo;
the arena's assemble workflow turns… See the full description on the dataset page: https://huggingface.co/datasets/OpenVoiceOS/ovos-stt-bench-mls-nl-NL.ovos-stt-bench-mls-pl-PL
OVOS stt bench — mls-pl-PL
Per-clip transcripts predictions of the registered
OVOS Plugin Arena
stt fighters over
facebook/multilingual_librispeech.
One dedicated repo per modality; one dataset split per language; one JSONL
file per fighter under predictions/<lang>/<competitor_id>.jsonl. Rows follow
the arena §3.2 contract (pinned dataset_revision, plugin_version,
latency_ms). Produced by the reproducible benchmark script in the arena repo;
the arena's assemble workflow turns… See the full description on the dataset page: https://huggingface.co/datasets/OpenVoiceOS/ovos-stt-bench-mls-pl-PL.MLR_structured_trajectory
Reasoning Trajectories with Step-Level Annotations
This dataset contains structured reasoning trajectories introduced in (ICLR 2026) Enhancing Language Model Reasoning with Structured Multi-Level Modeling.
Compared with the full-trajectory release, this dataset is a cleaned and segmented version designed for research on hierarchical reasoning, trajectory supervision, and multi-step policy training. Each example contains the original prompt and response fields together with a… See the full description on the dataset page: https://huggingface.co/datasets/sxiong/MLR_structured_trajectory.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.insurance-charge-mlops-logsml-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/merve/ml-intern-sessions.ML_MTCONAN_KN
1st Workshop on Multilingual Counterspeech generation: Shared Task
Dataset description
The datasets consist of 596 Hate Speech-Counter Narrative pairs. In this dataset, the hate speech is taken from MTCONAN, while the counter narratives are newly generated. Together with each pair, we also provide 5 background knowledge sentences, some of which are relevant for obtaining the counter narratives. The dataset is available in 4 different languages (Basque, English… See the full description on the dataset page: https://huggingface.co/datasets/LanD-FBK/ML_MTCONAN_KN.Logics-SWE-Env-2.5K
Logics-SWE-Env-2.5K
2,553 software engineering task instances · 1,771 repositories · 4 programming languages
🤗 Related model: Logics-SWE-Qwen3.6-27B
📄 Paper: One to More, More to One
💻 GitHub: AgenticBigBang
Overview
What is this dataset?
Logics-SWE-Env-2.5K is a collection of repository-level software engineering tasks for research on coding agents and environment-based reinforcement learning. It contains 2,553 unique task instances from 1,771… See the full description on the dataset page: https://huggingface.co/datasets/Logics-MLLM/Logics-SWE-Env-2.5K.insurance-charge-mlops-logsMAPBench-V1For more details, please check our project page.
Paper: https://arxiv.org/abs/2601.05432
Repository: https://github.com/AMAP-ML/Thinking-with-Map
ovos-wake-word-bench-mlsw-negatives-es-ES
OVOS wake_word bench — mlsw-negatives-es-ES
Per-clip detection decisions predictions of the registered
OVOS Plugin Arena
wake_word fighters over
MLCommons/ml_spoken_words.
One dedicated repo per modality; one dataset split per language; one JSONL
file per fighter under predictions/<lang>/<competitor_id>.jsonl. Rows follow
the arena §3.2 contract (pinned dataset_revision, plugin_version,
latency_ms). Produced by the reproducible benchmark script in the arena repo;
the arena's… See the full description on the dataset page: https://huggingface.co/datasets/OpenVoiceOS/ovos-wake-word-bench-mlsw-negatives-es-ES.