datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
seamless-align-enA-viA.speaker-embedding.xlsr-2bseamless-align-deA-enA.speaker-embedding.xlsr-2bseamless-align-enA-frA.speaker-embedding.xlsr-2bseamless-align-enA-esA.speaker-embedding.xlsr-2bseamless-align-enA-zhA.speaker-embedding.xlsr-2bPuro-2B
Puro-2B Pretraining Data: The Recipe Behind a 2B Model
This is the materialized pretraining data release for
Puro-2B-Base, a 2B base model
trained from scratch on consumer-grade RTX 5090 GPUs.
The repository contains the component-level data pools used to construct the
two Puro-2B pretraining phases, together with the tokenizer used for token
accounting. It is organized for inspection, selective streaming, and recipe
reconstruction rather than as a small train/test… See the full description on the dataset page: https://huggingface.co/datasets/thu-pacman/Puro-2B.seamless-align-enA-jaA.speaker-embedding.xlsr-2bseamless-align-enA-hiA.speaker-embedding.xlsr-2bseamless-align-enA-koA.speaker-embedding.xlsr-2blaion2b-en-a65_cogvlm2-4bit_captions
Abstract
This dataset contains image captions for the laion2B-en aesthetics>=6.5 image dataset using CogVLM2-4bit with the "laion-pop"-prompt to generate captions which were "likely" used in Stable Diffusion 3 training. From these image captions new synthetic images were generated using stable-diffusion-3-medium (batch-size=8).
The synthetic images are best viewed locally by cloning this repo with:
git lfs install
git clone… See the full description on the dataset page: https://huggingface.co/datasets/GeroldMeisinger/laion2b-en-a65_cogvlm2-4bit_captions.PCMind-2.1-Kaiyuan-2B
This repository contains the complete pretraining dataset for
PCMind-v2.1-Kaiyuan-2B, a leading fully open-source language model.
Overview
The dataset is organized into 5 training phases, with all phase datasets open-sourced in this repository. Our training methodology employs domain-specific mixing strategies across five primary domains:
English: General English text
Chinese: General Chinese text
Code: Programming and code-related content
Math: Mathematical reasoning and… See the full description on the dataset page: https://huggingface.co/datasets/thu-pacman/PCMind-2.1-Kaiyuan-2B.relaion2B-en-research-safegemma-2b-suite-explanationsrelaion2b-natural
LAION-Natural: Naturalness Scores for ReLAION-2B (CCN 2025, Roth & Hebart)
LAION-Natural is a large-scale naturalness scoring dataset covering 2.1 billion images from ReLAION-2B-en-research-safe. Each image receives a score predicting how "natural" or "photographic" it looks versus artificial/rendered content. At the recommended threshold of 0.7, the dataset identifies ~500 million natural photographs suitable for vision research, cognitive science, and model training.
Also… See the full description on the dataset page: https://huggingface.co/datasets/andropar/relaion2b-natural.relaion2b-natural-embeddings
LAION-Natural Embeddings: CLIP ViT-H/14 Features for ~500M Natural Photographs (CCN 2025, Roth & Hebart)
LAION-Natural Embeddings provides pre-computed CLIP ViT-H/14 embeddings for ~500 million natural photographs from ReLAION-2B, filtered using the LAION-Natural naturalness classifier (score > 0.7).
Also known as: LAION-Natural Embeddings · ReLAION-Natural Embeddings · LAION-2B-Natural Embeddings
Part of the LAION-Natural dataset family, introduced in: How to sample the… See the full description on the dataset page: https://huggingface.co/datasets/andropar/relaion2b-natural-embeddings.relaion2B-multi-research-saferelaion2B-multi-researchlaion2b_multi_korean_subset_with_image
laion2b_multi_korean_subset_with_image
img2dataset을 통해 다운로드에 성공한 Bingsu/laion2B-multi-korean-subset 이미지를 정리한 데이터셋입니다.
이미지는 9,800,137장입니다.
이미지는 짧은 쪽 길이가 256이 되도록 리사이즈 되었으며, 품질 100인 webp파일로 다운로드 되었습니다.
Usage
1. datasets
>>> from datasets import load_dataset
>>> dataset = load_dataset("Bingsu/laion2b_multi_korean_subset_with_image", streaming=True, split="train")
>>> dataset.features
{'image': Image(decode=True, id=None),
'text': Value(dtype='string', id=None)… See the full description on the dataset page: https://huggingface.co/datasets/Bingsu/laion2b_multi_korean_subset_with_image.laion2B-japanese-subsetrelaion2B-en-researchgemma-4-e2b-atlas
tmax-2b-atlas
juiceb0xc0de/tmax-2b-atlas
A brain atlas for allenai/tmax-2b, a hybrid SSM/Mamba/transformer language model. This is not a chat dataset or a benchmark — it is an internal-mechanics map of the model, built by running activations through a corpus of prompts and scoring what each layer, component, head, and feature direction is doing.
If you want to know where the model stores compliance style, which late-layer directions you can edit without breaking reasoning, or whether the… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/tmax-2b-atlas.laion-2b-en-unsafe-quarter-one-downloadInfoSeek_emb_qwen3vle_2bentity-v2-2bcontract-nliContractNLI: A Benchmark Dataset for ContractNLI in EnglishQwen3.5-2B-Base
juiceb0xc0de/Qwen3.5-2B-Base
A brain atlas for Qwen/Qwen3.5-2B-Base, a 24-layer hybrid that runs linear attention on 18 layers and full attention on the other 6. This is not a chat dataset or a benchmark. It is an internal-mechanics map built by running activations through a corpus of prompts and scoring what each layer, component, head, and feature direction is doing.
This is a base model, before any instruction tuning, so whatever structure shows up here was put there by… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/Qwen3.5-2B-Base.gemma-2b-dictionary-embeddings-all-layers
Gemma-2B Dictionary Embeddings - All Layers
This dataset contains pre-computed embeddings for 77,477 English words from WordNet using the Gemma-2B model across all 27 layers.
Dataset Structure
metadata.json: Contains dataset metadata (model info, dimensions, word count)
embeddings_layer_X.pkl: Pickle files containing embeddings for layer X (0-26)
Usage
import pickle
from huggingface_hub import hf_hub_download
# Download a specific layer
layer_0_path =… See the full description on the dataset page: https://huggingface.co/datasets/LeeHarrold/gemma-2b-dictionary-embeddings-all-layers.Our1-2b-Datasetgemma-2b-suite-explanations-residual