datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
nanochat-climbmix-arithmetic-base10
nanochat ClimbMix + Base-10 Arithmetic
This dataset contains the first 170 shuffled ClimbMix training shards
used by nanochat's speedrun. The deterministic base-10 arithmetic corpus is
mixed into shards 00000..00149; the final
20 train shards are unchanged web-only padding.
The original validation shard (shard_06542.parquet) is also
copied unchanged.
Arithmetic corpus
Family
Examples
a + b = c (all ordered pairs 0..2000, two exposures)
8,008,002
a + b… See the full description on the dataset page: https://huggingface.co/datasets/Yujivus/nanochat-climbmix-arithmetic-base10.nanochat-climbmix-arithmetic-base7
nanochat ClimbMix + Arithmetic: base-7 numeral world
This is a deterministic base-7 rendering of
Yujivus/nanochat-climbmix-arithmetic-base10. It preserves
the exact shard names, row order, document order, arithmetic-document placement,
and non-numeric text of the source dataset.
Transformation rule
Every maximal ASCII digit run matching [0-9]+ is interpreted as a base-10
integer and rendered in base 7. Leading zeros are preserved as a prefix; signs,
punctuation… See the full description on the dataset page: https://huggingface.co/datasets/Yujivus/nanochat-climbmix-arithmetic-base7.nanochat-climbmix-170
nanochat ClimbMix: first 170 train shards
Convenience mirror of the exact initial ClimbMix slice downloaded by
python -m nanochat.dataset -n 170.
Contents
Training: shard_00000.parquet through shard_00169.parquet
Validation: shard_06542.parquet
manifest.json: pinned source revision, file list, and byte sizes
The Parquet shards are copied without modifying their rows or text.
Attribution and provenance
nanochat:… See the full description on the dataset page: https://huggingface.co/datasets/Yujivus/nanochat-climbmix-170.nanochat-climbmix-arithmetic-base6
nanochat ClimbMix + Arithmetic: base-6 numeral world
This is a deterministic base-6 rendering of
Yujivus/nanochat-climbmix-arithmetic-base10. It preserves
the exact shard names, row order, document order, arithmetic-document placement,
and non-numeric text of the source dataset.
Transformation rule
Every maximal ASCII digit run matching [0-9]+ is interpreted as a base-10
integer and rendered in base 6. Leading zeros are preserved as a prefix; signs,
punctuation… See the full description on the dataset page: https://huggingface.co/datasets/Yujivus/nanochat-climbmix-arithmetic-base6.Pluto-Nano-1.0-Pretrain-v2
ASTRAI Pluto Nano 1.0 — Pretrain Mix (v2)
Curated multilingual pretraining corpus (~50 GB parquet, ~12 B tokens after tokenization) used for ASTRAI Pluto Nano 1.0, a 1 B-total / 50 M-active MoE model with 64 k vocabulary and 5 target languages (EN, PT, ES, ZH, HI).
v2 additions vs v1: OpenThoughts3 (CoT reasoning), openstax textbooks + peS2o (science), and reweighting for better balance. NOTE: factsense (openbmb) was used at training time but is not redistributed here due to its… See the full description on the dataset page: https://huggingface.co/datasets/ASTRAI-labs/Pluto-Nano-1.0-Pretrain-v2.nanoswe-trajs-260812
nanoswe SWE-agent trajectories (v0)
A consolidation of the SWE-bench-style coding-agent trajectory corpora used to train
the nanoswe speedrun models. Each row is one
multi-turn agent trajectory (issue → tool-using rollout → patch), stored untokenized.
1,582,701 trajectories, 34 parquet shards, content-deduplicated on traj_hash.
Seed corpus: ricdomolm/mini-coder-trajs-400k;
the rest are derived SWE-smith / openhands / swe-zero conversions.
Schema
column… See the full description on the dataset page: https://huggingface.co/datasets/nanoswe/nanoswe-trajs-260812.nanochat-jp-pretrain
nanochat-jp-pretrain
nanochat の日本語フォーク nanochat-jp で使用する 事前学習用日本語コーパス です．
LLM によるクリーニングを施した日本語ウェブテキストと，llm-jp の公開コーパスを混合したものを，nanochat のデータローダがそのまま読める parquet 形式で配布しています．
構成
以下の4つのソースを混合し，全体をシャッフルしています．
ソース
llm-jp-corpus-v4 の ja_fineweb-2 サブセット（後述の追加データクリーニングを適用）
llm-jp-corpus-midtraining-v2 ja/llm-jp-IPT_v0.3.2/ja_general.jsonl.gz
llm-jp-corpus-midtraining-v2 ja/llm-jp-IPT_v0.3.2/ja_reasoning.jsonl.gz
llm-jp/scaling-data-constrained-llms… See the full description on the dataset page: https://huggingface.co/datasets/tohoku-nlp/nanochat-jp-pretrain.swesmith-qwen3.6-35b-a3b
SWE-smith trajectories from Qwen3.6-35B-A3B
Multi-turn coding-agent trajectories (issue → tool-using rollout → patch) produced by
Qwen3.6-35B-A3B on SWE-smith tasks, stored untokenized.
This is the exact SFT corpus used for the harbor arm of the
nanoswe teacher-distillation experiments.
101,901 trajectories over 45,242 unique SWE-smith task instances (3 sampled rollouts
per task, ~2.25 surviving filtering), 53 parquet shards, ~1.4 GB.
≈1.96B training tokens = exactly one epoch… See the full description on the dataset page: https://huggingface.co/datasets/nanoswe/swesmith-qwen3.6-35b-a3b.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.Medical-Reasoning-SFT-Nemotron-Nano-30B
Medical-Reasoning-SFT-Nemotron-Nano-30B
A large-scale medical reasoning dataset generated using nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16, containing over 444,000 samples with detailed chain-of-thought reasoning for medical and healthcare questions.
Dataset Overview
Metric
Value
Model
nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
Total Samples
444,544
Samples with Reasoning
444,544 (100%)
Estimated Tokens
~1.01 Billion
Content Tokens
~808 Million… See the full description on the dataset page: https://huggingface.co/datasets/OpenMed/Medical-Reasoning-SFT-Nemotron-Nano-30B.nanochat-calendar-arithmetic-base10
nanochat Base-10 Calendar Arithmetic
A deterministic, base-10 arithmetic corpus scoped to three cyclic calendar
units: hour-of-day (mod 24), day-of-week (mod 7), and
month-of-year (mod 12). Companion to
Yujivus/nanochat-climbmix-arithmetic-base10,
built the same way but scoped to real modular calendar units instead of
free-integer add/sub/mul/div/mod.
Every example is a single line — question and answer collapsed into one
equation, no exposed reasoning:
23:00 + 18965h = 04:00… See the full description on the dataset page: https://huggingface.co/datasets/liodon-ai/nanochat-calendar-arithmetic-base10.nanoswe-trajs-v0
nanoswe SWE-agent trajectories (v0)
A consolidation of the SWE-bench-style coding-agent trajectory corpora used to train
the nanoswe speedrun models. Each row is one
multi-turn agent trajectory (issue → tool-using rollout → patch), stored untokenized.
1,582,701 trajectories, 34 parquet shards, content-deduplicated on traj_hash.
Seed corpus: ricdomolm/mini-coder-trajs-400k;
the rest are derived SWE-smith / openhands / swe-zero conversions.
Schema
column… See the full description on the dataset page: https://huggingface.co/datasets/ricdomolm/nanoswe-trajs-v0.NanoData
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.nemotron-nano-hermes-traces
Nemotron Nano Hermes Agent Reasoning Traces
A curated dataset of reasoning traces for training local AI orchestrator agents.
Designed for SFT and RL training of Nemotron 3 Nano Omni to be the best local
Hermes Agent model.
Dataset Summary
Total SFT rows: 28,000
Total RL prompts: 28,000
Format: ShareGPT (conversations column)
Target model: nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16
Training framework: Unsloth Studio
Sources
Source
Count… See the full description on the dataset page: https://huggingface.co/datasets/r0b0tlab/nemotron-nano-hermes-traces.DiLLM.nano
DiLLM Phase 1 — Persian Corpora
The training corpora for DiLLM (دیبا),
a Persian-first language model built in Iran by Dibachain.
Publisher
Dibachain — dibachain.ir
Country
Iran 🇮🇷
Model
DibaAi/DiLLM.nano
Source code
github.com/AliAkrami1375/DibaLLM
Language
Persian (فارسی)
Total
~1.02M documents · ~379M tokens
This dataset was assembled from scratch for DiLLM. Every corpus was
downloaded from its primary source, filtered, and cleaned by the… See the full description on the dataset page: https://huggingface.co/datasets/DibaAi/DiLLM.nano.Nano3D-Edit-100k
Nano3D-Edit-100k
This dataset is the official data release for Nano3D, a training-free framework for precise and coherent 3D object editing without masks.
Paper: Nano3D: A Training-Free Approach for Efficient 3D Editing Without MasksProject Page: https://jamesyjl.github.io/Nano3D/
Nano3D integrates FlowEdit into TRELLIS to perform localized 3D edits guided by front-view renderings, and introduces Voxel/Slat-Merge strategies to preserve structural consistency between edited and… See the full description on the dataset page: https://huggingface.co/datasets/yejunliang23/Nano3D-Edit-100k.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.nemotron-nano2-safety-distill-gptoss
Nemotron Nano 2 Safety Distill — GPT-OSS
A distilled safety dataset produced using the Nemotron Nano 2 recipe with GPT-OSS-20B and GPT-OSS-120B as teacher models.
⚠️ Content Warning: This dataset includes potentially harmful prompts. Use responsibly for research purposes only.
Overview
This safety-focused distilled dataset was created by following the Nemotron Nano 2 safety recipe, adapted to use GPT-OSS-20B and GPT-OSS-120B as teacher models. Due to resource limitations… See the full description on the dataset page: https://huggingface.co/datasets/Ericwang/nemotron-nano2-safety-distill-gptoss.nanochat-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.napierone-pdf-nanonets-s
NapierOne PDFs: OCR'd by nanonets-s
PDFs from NapierOne (see 'pdf-total' in the napierone aws bucket) converted to text with nanonets-s using this code
contains results for all 4978 unique PDFs
raw config is unmodified from model output, the default config has been post-processed with mdformat
Citation
@article{DAVIES2022301330,
title = {NapierOne: A modern mixed file data set alternative to Govdocs1},
journal = {Forensic Science International: Digital… See the full description on the dataset page: https://huggingface.co/datasets/BEE-spoke-data/napierone-pdf-nanonets-s.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.nanochat-ascend-dataset
nanochat-ascend-dataset
Unified training and evaluation data bundle for nanochat-ascend.
This repository is designed to make the nanochat-ascend training procedure easy to reproduce. Instead of asking users to collect multiple task and evaluation datasets and manually reconstruct the expected directory structure, this repository preserves the local filesystem layout expected by the training code.
The intended usage is simple:
place this repository at .cache/dataset
download… See the full description on the dataset page: https://huggingface.co/datasets/leideng/nanochat-ascend-dataset.nano_chat
Dataset Card for "nano_chat"
Dataset Summary
nano_chat is a synthetic dataset consisting of 2326 short dialogues in simple, learner-friendly English. It was generated using Google's Gemini 2.5 flash model and is designed for training tiny conversational language models in low-resource settings.
Each dialogue simulates a realistic conversation between two speakers (A and B), using short sentences, simple grammar, and occasional small mistakes to help models generalize… See the full description on the dataset page: https://huggingface.co/datasets/sixf0ur/nano_chat.nanoJEPA-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.BCE-Prettybird-Nano-Themis-v0.1
BCE-Prettybird-Nano-Themis-v0.1 Synthetic Multi- Law Dataset (400 Examples)
BCE-Prettybird-Nano-Themis-v0.1 Synthetic Multi-Law Dataset is a 400-example synthetic dataset developed by Prometech AŞ for experimentation with legal reasoning, instruction following, structured generation, and multi-dimensional response evaluation. Each example combines a task-specific instruction with structured reasoning and quality metadata, including BCE signals, truth and quality values… See the full description on the dataset page: https://huggingface.co/datasets/pthinc/BCE-Prettybird-Nano-Themis-v0.1.nemotron3-nano-kd-corpus
Nemotron 3 Nano KD Corpus
4,302 verified reasoning traces for coding problems, generated by
DeepSeek-V4-Flash (284B) and
filtered by executing the generated code against real test suites.
Built entirely on-premises on a single HP ZGX Fury (NVIDIA GB300 Grace Blackwell Ultra).
No cloud APIs were used at any stage.
Used to distill NVIDIA-Nemotron-3-Nano-30B-A3B-BF16,
raising held-out pass@1 from 23.0% to 63.4%. The corpus is model-agnostic and should be
usable for any student.… See the full description on the dataset page: https://huggingface.co/datasets/curtburk/nemotron3-nano-kd-corpus.BCE-Prettybird-Nano-OWL-v0.1
BCE-Prettybird-Nano-OWL-v0.1 - 630 Translates for Instruction-Based Learning
You can leverage our Hugging Face–ready nano translation dataset, which covers a diverse set of languages including Turkish, English, German, French, Spanish, Italian, Portuguese, Dutch, Russian, Ukrainian, Polish, Czech, Slovak, Hungarian, Romanian, Bulgarian, Greek, Arabic, Persian, Hebrew, Hindi, Bengali, Urdu, Tamil, Telugu, Kannada, Malayalam, Chinese, Japanese, Korean, Indonesian, Malay, Thai… See the full description on the dataset page: https://huggingface.co/datasets/pthinc/BCE-Prettybird-Nano-OWL-v0.1.BCE-Prettybird-Nano-Hephaistos-v0.1
BCE-Prettybird-Nano-Hephaistos-v0.1 - 1390 Robotics for Instruction-Based Learning
BCE-Prettybird-Nano-Hephaistos-v0.1 – 1390 Robotics for Instruction-Based Learning is a bilingual Turkish–English math, sensor, robotics, and embedded-systems QA dataset designed for instruction-based learning, small language models, edge AI research, and robotics education. The dataset focuses on foundational and applied robotics topics such as linear and circular motion, forward and inverse… See the full description on the dataset page: https://huggingface.co/datasets/pthinc/BCE-Prettybird-Nano-Hephaistos-v0.1.nanochat-brevo-capability-data-10x
Nanochat Brevo Capability Pilot
Brevo presents shuffled dependency records and asks for the complete recursive
prerequisite closure in a valid leaf-first order. Training uses project-planning
language; validation uses evidence synthesis; test uses build manifests. Eleven
deterministic structural styles vary wording, layout, and record order.
The latent graph generator and exact validator label every row. No language model
generated or labeled the data. Alternative valid orders… See the full description on the dataset page: https://huggingface.co/datasets/SolidSnake123/nanochat-brevo-capability-data-10x.nanobody-contact-maps
Protein Contact Map Dataset
Dataset Description
This dataset contains protein structures with contact maps and related information from nanobody sequences.
Dataset Summary
Number of proteins: 2992
Source: Nanobody protein structures (nanos_networkx_small)
Created by: alexchilton
Date: 2025-05-04
Dataset Structure
Each protein entry contains:
amino_acid_sequence: List of amino acid names
length: Number of residues
c_alpha_coordinates: List of [x,y… See the full description on the dataset page: https://huggingface.co/datasets/alexchilton/nanobody-contact-maps.