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-2bseamless-align-enA-jaA.speaker-embedding.xlsr-2bseamless-align-enA-hiA.speaker-embedding.xlsr-2bseamless-align-enA-koA.speaker-embedding.xlsr-2brelaion2B-en-research-saferelaion2b-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-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.InfoSeek_emb_qwen3vle_2bQwen3.5-2B-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-Datasetlaion2b_seed
Dataset Card for "laion2b_seed"
This dataset is a subset of laion2B-en-aesthetic, with SEED v1 tokens.
laion2B-en-aesthetic2_blue_cube_orange_box_20260803_104843This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"… See the full description on the dataset page: https://huggingface.co/datasets/makermods/2_blue_cube_orange_box_20260803_104843.laion2B-multi-chinese-subset
laion2B-multi-chinese-subset
Github: Fengshenbang-LM
Docs: Fengshenbang-Docs
简介 Brief Introduction
取自Laion2B多语言多模态数据集中的中文部分，一共143M个图文对。
A subset from Laion2B (a multimodal dataset), around 143M image-text pairs (only Chinese).
数据集信息 Dataset Information
大约一共143M个中文图文对。大约占用19GB空间（仅仅是url等文本信息，不包含图片）。
Homepage: laion-5b
Huggingface: laion/laion2B-multi
下载 Download
mkdir laion2b_chinese_release && cd laion2b_chinese_release
for i in {00000..00012}; do… See the full description on the dataset page: https://huggingface.co/datasets/IDEA-CCNL/laion2B-multi-chinese-subset.relaion2B-en-research-safe-japanese-translation
relaion2B-en-research-safe-japanese-translation
This dataset is the Japanese translation of the English subset of ReLAION-5B (laion/relaion2B-en-research-safe), translated by gemma-2-9b-it.
We used text2dataset for translating with open-weight LLMs.
By leveraging the fast LLM inference library vLLM, this tool enables the rapid translation of large English datasets into Japanese.
Prompt
The following is the prompt used for translation with Gemma.
You are an… See the full description on the dataset page: https://huggingface.co/datasets/llm-jp/relaion2B-en-research-safe-japanese-translation.laion2b_en_sd2.1baseA dataset for SD2.1 base training, which contains metadata filtered from laion2b-en with the following conditions.
WIDTH>=512
HEIGHT>=512
punsafe<=0.98
AESTHETIC_SCORE>=4.5
gemma4-e2b-base-topk128-hf-overlay-v128-seed42
Gemma 4 E2B base top-k-128 HF training overlay
This is the immutable training-engine overlay used to distill traces from Gemma 4 E2B base into
Gemma 4 E4B. It preserves the prompts, responses, and exact response token IDs from
JWei05/gemma4-e2b-base-topk128-traces,
but replaces the source vLLM top-k targets with targets recomputed by the Hugging Face training
engine.
This repository is a reproducibility artifact for the corresponding distillation run. It is not a
new… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/gemma4-e2b-base-topk128-hf-overlay-v128-seed42.laion2B-multi-joined-translated-to-en-smolcircuitlens-gemma-2-2btranscoder-descriptions-and-evaluations
CircuitLens & WeightLens: Transcoder Descriptions and Evaluations
This dataset contains automatically generated descriptions and evaluation metrics for Gemma-2-2B transcoders, produced using CircuitLens and WeightLens methods.
Methods
CircuitLens: https://github.com/egolimblevskaia/CircuitLens
WeightLens: https://github.com/egolimblevskaia/WeightLens
Dataset Structure
The dataset is organized by layers (0, 4, 7, 10, 12, 15, 18, 21, 23, 25), with each layer… See the full description on the dataset page: https://huggingface.co/datasets/egolimblevskaia/circuitlens-gemma-2-2btranscoder-descriptions-and-evaluations.gollem-corpus-2b-pl
GoLLeM Corpus 2B PL
Dokładny korpus treningowy polskiego modelu bazowego
SlayerLab/GoLLeM-110M-PL-v3
(oraz v2) — ten sam zbiór, po którym model przeszedł dwie epoki. Publikujemy go,
aby każdy mógł odtworzyć trening od zera na własnym tokenizerze.
Jak powstał ten plik. Korpus odzyskano przez zdekodowanie stokenizowanego
checkpointu treningowego (byte-level BPE dynaword-32k, round-trip bezstratny; granice
dokumentów = token <|endoftext|>). To jest dokładnie tekst, który model… See the full description on the dataset page: https://huggingface.co/datasets/SlayerLab/gollem-corpus-2b-pl.