datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
kiiteitte
Kiiteitte history
Kiiteitte が収集した、今までの選曲履歴。
1時間おきに更新されます。
型
{
// 動画ID
"video_id": "sm44670499",
// タイトル
"title": "library->w4nderers / 足立レイ、つくよみちゃん",
// 投稿者
"author": "名無し｡",
// サムネイルのURL
"thumbnail": "https://nicovideo.cdn.nimg.jp/thumbnails/44670499/44670499.91820835",
// 選曲日時
"date": "2025-02-22 12:51:51",
// 新しく増えたお気に入り数。不明の場合は null
"new_faves": 5,
// 回ったユーザーの数。不明の場合は null
"spins": 13,
// イチ押しリストのユーザーのURL。イチ押しリスト以外から選曲された場合は null… See the full description on the dataset page: https://huggingface.co/datasets/sevenc-nanashi/kiiteitte.c4-nanochatbpe-10B
c4-nanochatbpe-10B
C4 (en) (from allenai/c4), pre-tokenized with the nanochatbpe tokenizer (vocab 65,536)
and packaged as flat uint16 token-id .bin files for fast memmap training.
file
split
tokens
train.bin
train
10,000,000,000
val.bin
val
168,272,017
train and val are disjoint held-out partitions. Each .bin is a raw
little-endian uint16 stream (no header); token count = filesize / 2, and
train.meta.json / val.meta.json carry the full metadata. The tokenizer/files… See the full description on the dataset page: https://huggingface.co/datasets/alexkstern/c4-nanochatbpe-10B.fineweb-nanochatbpe-100M
fineweb-nanochatbpe-100M
FineWeb-Edu (from karpathy/fineweb-edu-100b-shuffle), pre-tokenized with the nanochatbpe tokenizer (vocab 65,536)
and packaged as flat uint16 token-id .bin files for fast memmap training.
This is a 100-million-token slice for data-constrained experiments. The train.bin
is the byte-exact first 100,000,000 tokens (bytes [0, 200000000)) of the parent
alexkstern/fineweb-nanochatbpe-20B
train.bin. The val.bin is byte-identical to the parent's val.bin.… See the full description on the dataset page: https://huggingface.co/datasets/alexkstern/fineweb-nanochatbpe-100M.mosaic-nanot5-512fineweb-nanochatbpe-20B
fineweb-nanochatbpe-20B
FineWeb-Edu (from karpathy/fineweb-edu-100b-shuffle), pre-tokenized with the nanochatbpe tokenizer (vocab 65,536)
and packaged as flat uint16 token-id .bin files for fast memmap training.
file
split
tokens
train.bin
train
20,000,000,000
val.bin
val
52,336,096
train and val are disjoint held-out partitions. Each .bin is a raw
little-endian uint16 stream (no header); token count = filesize / 2, and
train.meta.json / val.meta.jsoncarry the full… See the full description on the dataset page: https://huggingface.co/datasets/alexkstern/fineweb-nanochatbpe-20B.unipic_nano_2images
Skywork/unipic_nano_2images: A Multi-Image Composition Dataset
⚡ Quick Start
The image archive is split into multiple parts for easier downloading. To reconstruct and extract:
# Step 1: Concatenate split files into a single zip
cat nano-banana-2image_part_* > nano-banana-2images.zip
# Step 2: Extract the images
unzip nano-banana-2images.zip
📖 Overview
UniPic-Nano-2Images is a high-quality multi-image composition dataset containing 41,812 samples designed for… See the full description on the dataset page: https://huggingface.co/datasets/Skywork/unipic_nano_2images.unipic_nano_3images
UniPic-Nano-3Images: A Multi-Image Composition Dataset
⚡ Quick Start
The image archive is split into multiple parts for easier downloading. To reconstruct and extract:
# Step 1: Concatenate split files into a single zip
cat nano-banana.part_* > nano-banana-3images.zip
# Step 2: Extract the images
unzip nano-banana-3images.zip
📖 Overview
UniPic-Nano-3Images is a high-quality multi-image composition dataset containing 35,394 samples designed for training… See the full description on the dataset page: https://huggingface.co/datasets/Skywork/unipic_nano_3images.bioasq_7b_yesnoPantheon-Agent-Trajectory
🏛️ Pantheon Agent Trajectory Gallery
Curated end-to-end agent runs from PantheonOS — an open multi-agent framework for scientific computing.
Each "trajectory" captures a complete chat session: the user prompt, every reasoning/tool step the agent(s) took, the code that was run, the figures that were produced, and the final report. Trajectories are fully inspectable and reproducible, designed for transparency, teaching, and benchmarking.
🔗 Browse the gallery (live):… See the full description on the dataset page: https://huggingface.co/datasets/NaNg/Pantheon-Agent-Trajectory.nemotron-nano-30b-miniswe-swebench-verified
Nemotron Nano 30B + mini-swe-agent SWE-bench Verified Trajectories
Agent trajectories from running NVIDIA Nemotron 3 Nano 30B A3B (MoE, 8B active params) on SWE-bench Verified using mini-swe-agent.
⚠️ Incomplete Run
This benchmark was terminated early due to poor performance. The model struggled with the agentic coding task.
Model Information
Attribute
Value
Model
NVIDIA Nemotron 3 Nano 30B A3B
Architecture
MoE (30B total, 8B active)
Serving
vLLM… See the full description on the dataset page: https://huggingface.co/datasets/pankajmathur/nemotron-nano-30b-miniswe-swebench-verified.nano4m-Audio
nano4M-Audio — Team (week-1)
Week-1 data preparation for nano4M-Audio, an extension of EPFL's
nano4M (the educational nano version of
4M / 4M-21)
that adds audio as a fifth modality alongside RGB, depth, surface normals
and captions.
This dataset covers all 12 VGGSound classes assigned to the three-person team:
person
classes
1 (Hassan)
lions roaring, horse neighing, pig oinking, cow lowing
2 (Ziyad)
dog barking, cat meowing, coyote howling, elephant trumpeting
3… See the full description on the dataset page: https://huggingface.co/datasets/zed-m97/nano4m-Audio.fineweb-edu-dedup6mNanoData
Dataset Description
To facilitate researchers to use NanoLM for comparative analysis across different model designs, we build a curated pre-training dataset from those of existing large-scale models (i.e., Llama, Falcon, GPT-3). It covers diverse domains to improve the generalization capabilities of the resultant models.
Dataset Creation
The data is mainly post-processed and filtered from RedPajama and RedPajamaV2.
We develop a series of cleaning steps to remove redundant… See the full description on the dataset page: https://huggingface.co/datasets/CofeAI/NanoData.SSR-RCoT-16K
SSR-RCoT-16K: Turning answer-only data into high-quality reasoning-supervision data
SSR-RCoT-16K is a public 16k subset derived from the data construction pipeline introduced in
Measuring and Mitigating Post-hoc Rationalization in Reverse Chain-of-Thought Generation.
This dataset is designed for powerful reasoning on general tasks, especially for the realistic setting where high-quality responses are available but chain-of-thought annotations are missing.
In such answer-rich but… See the full description on the dataset page: https://huggingface.co/datasets/Nanbeige/SSR-RCoT-16K.nanoindex-treesnemotron-nano-eval-logs-and-scoresgithub-code-nanochatbpe-1B
github-code-nanochatbpe-1B
GitHub Code (all-all) (from codeparrot/github-code), pre-tokenized with the nanochatbpe tokenizer (vocab 65,536) and packaged as flat uint16 token-id .bin files for fast memmap training.
file
split
tokens
train.bin
train
1,000,000,000
val.bin
val
10,000,000
train and val are disjoint held-out partitions. Each .bin is a raw little-endian uint16 stream (no header); token count = filesize / 2, and train.meta.json / val.meta.json carry… See the full description on the dataset page: https://huggingface.co/datasets/alexkstern/github-code-nanochatbpe-1B.ctf-dataset
ctf-dataset
CTF 与网络安全知识的 ShareGPT/ChatML 风格 SFT 数据集，可用于 LoRA 微调。
数据格式
每行是一个 JSON 对象，核心字段如下：
字段
说明
id
样本唯一 ID
dataset
数据集名称，当前为 ctf-dataset
category
来源主题或 CTF/安全类别
ctf_task_type
任务类型标签
messages
ShareGPT 消息数组，包含 system / user / assistant
metadata
来源路径、章节、字符数、chunk 等溯源信息
LLaMA-Factory 接入
将 ctf-dataset.jsonl 放入 LLaMA-Factory 的 data/ 目录后，在 data/dataset_info.json 中添加：
{
"ctf_dataset": {
"file_name": "ctf-dataset.jsonl"… See the full description on the dataset page: https://huggingface.co/datasets/Nanhang/ctf-dataset.lm-eval-results-Kquant03-Nanashi-2x7B-bf16-private
Dataset Card for Evaluation run of Kquant03/Nanashi-2x7B-bf16
Dataset automatically created during the evaluation run of model Kquant03/Nanashi-2x7B-bf16
The dataset is composed of 62 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An… See the full description on the dataset page: https://huggingface.co/datasets/nyu-dice-lab/lm-eval-results-Kquant03-Nanashi-2x7B-bf16-private.openpii-masking-nano-1k
OpenPII Nano: Multilingual PII Masking Sample
A nano-sized stratified sample of OpenPII 1.5M,
perfect for quick prototyping, smoke tests, and CI fixtures. Every locale and every
label that exists in the parent dataset is represented in proportion.
📖 More information: www.ai4privacy.com/datasets/pii-masking-3m-asia-pacific
Dataset Details
Total Examples
Train
Validation
Labels
Languages
Regions
Annotations
Format
License
1,000
900
100
19
30
37
7… See the full description on the dataset page: https://huggingface.co/datasets/ai4privacy/openpii-masking-nano-1k.nanonet-expgithub-docs
GitHub Docs Corpus
A dataset containing only information from GitHub — the official
github/docs repository, i.e. the source of docs.github.com.
Dataset Structure
Files: data/train.jsonl
Format: JSONL, one chunk per line
Columns: text (cleaned doc chunk), metadata (source, title)
Rows: 3,336
Composition
Source: github/docs (main branch), content/ tree only — 3,734
Markdown files covering GitHub features, workflows, webhooks, REST/GraphQL
API docs… See the full description on the dataset page: https://huggingface.co/datasets/Nanthasit/github-docs.icrt_2nanochat-rtx4070-sft-mixes
nanochat-rtx4070 SFT mixes
Eight SFT data mixes that were trained and evaluated on a single RTX 4070, and the results each one produced. Seven of them failed.
These are the actual independent variable behind the negative-results table in Bl4ckd09/nanochat-on-rtx4070. Every mix here was built deterministically, trained on the same frozen backbone with the same geometry and step count, and put through the same two-stage evaluation gate. Publishing only the winner would make the… See the full description on the dataset page: https://huggingface.co/datasets/Marcolini/nanochat-rtx4070-sft-mixes.NanoNetnanoJEPA-base
nanoJEPA EN/ZH Ultra-FineWeb Dataset
This is a small pretraining dataset package for nanoJEPA. It is built by
streaming openbmb/Ultra-FineWeb split en and/or zh.
Files
train.jsonl: {"text": "...", "source": "...", "dataset": "...", "language": "en|zh", "score": 0.0}
valid.jsonl: same schema as train.jsonl
test.jsonl: same schema as train.jsonl
Generation Command
uv run python data/build_hf_dataset.py \
--out-dir dataset/nanojepa-small \
--languages en,zh… See the full description on the dataset page: https://huggingface.co/datasets/TerenceLau/nanoJEPA-base.pii-masking-nano-1k
PII Masking Nano: Multilingual Sample
A nano-sized stratified sample of pii-masking-openpii-1.5m,
the flagship release of the PII-Masking-3M family. Sampled proportionally by
(source_dataset, language) so every locale and label gets representation.
Asia Pacific rows appear first.
📖 More information: www.ai4privacy.com/datasets/pii-masking-3m-asia-pacific
Dataset Details
Total Examples
Train
Validation
Labels
Languages
Regions
Annotations
Format… See the full description on the dataset page: https://huggingface.co/datasets/ai4privacy/pii-masking-nano-1k.chunithm-charts-db
chunithm-charts-db
ChuniSupportの譜面データを平坦なjsonlに変換したやつ。
フィールド
1行が1譜面を表すJSONLです。APIの charts 内の項目を行の直下に展開しています。
以下の型はREADME先頭のHugging Faceスキーマに対応します。null 可の項目は、元データに値がない場合に null になります。
共通項目
ChuniSupport API仕様に基づく項目です。jacket は変換処理で画像URLにしています。
フィールド
型
null可
説明
id
string
—
楽曲ID。通常曲では同じ楽曲の各難易度で共通。
title
string
—
曲名。
reading
string
✓
曲名の読み。
artist
string
—
アーティスト名。
genre
string
✓
ジャンル。
bpm
int32
✓
楽曲のBPM。
release
date32
✓
配信日。JSONLでは… See the full description on the dataset page: https://huggingface.co/datasets/sevenc-nanashi/chunithm-charts-db.nanochat-german-alpaca
nanochat: German Alpaca Dataset
The Alpaca Cleaned Dataset is used for translation with Gemini 2.0 Flash.
Around 3,443 entries in the dataset had errors and were removed. The resulting dataset has 50,469 entries.
kits23
KiTS23 Dataset
Dataset Description
The KiTS23 dataset for kidney tumor segmentation. This dataset contains CT scans with dense segmentation annotations.
Dataset Details
Modality: CT
Target: kidney, tumor, cyst
Format: NIfTI (.nii.gz)
Dataset Structure
Each sample in the JSONL file contains:
{
"image": "path/to/image.nii.gz",
"mask": "path/to/mask.nii.gz",
"label": ["organ1", "organ2", ...],
"modality": "CT",
"dataset": "KiTS23"… See the full description on the dataset page: https://huggingface.co/datasets/NanGongMing0514/kits23.