datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Nanochatnemotron-3-nano-30b-20260719-spare-games-envs
Nemotron-3-Nano-30B SPARE Self-Play Environments (run_20260719_final)
This dataset packages the self-play generated game environments produced
by a live SPARE (Self-Play with Adaptive cuRriculum Extension) training run
of NVIDIA-Nemotron-3-Nano-30B-A3B. It is a raw-data export for another
agent to pick up, replay, and build its own visualization / weave log from.
Provenance
Run: run_20260719_final
Source Ray job: spare_nemotron_games_mtpg768_1784556397 (the live… See the full description on the dataset page: https://huggingface.co/datasets/msr-spare-1/nemotron-3-nano-30b-20260719-spare-games-envs.NanoBEIR-enNanoMSMARCONanoQuoraRetrievalNanoMTEB-Scandinavian
NanoMTEB-Scandinavian
This dataset is a Nano-style retrieval dataset for HAKARI-bench.
NanoMTEB-Scandinavian is a compact retrieval benchmark for Scandinavian-language MTEB-style task families. It includes Danish, Norwegian, and Swedish retrieval tasks spanning fact verification, question answering, news, encyclopedic content, FAQ retrieval, and social-media retrieval.
Usage
from datasets import load_dataset
dataset_id = "hakari-bench/NanoMTEB-Scandinavian"
split… See the full description on the dataset page: https://huggingface.co/datasets/hakari-bench/NanoMTEB-Scandinavian.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.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.NanoFiQA2018fineweb-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.NanoBEIR-deNanoSciFactNanoNFCorpusminipile_100_samplesNanoNQnano-omni-vlmNanoHotpotQANanoArguAnananobeir-multilingual-extended
NanoBEIR Multilingual Extended Dataset
This dataset extends the NanoBEIR multilingual collection with Japanese and Korean translations.
Dataset Structure
Each configuration follows the pattern <BASE>_<LANG> with splits:
corpus: Document corpus
queries: Search queries
qrels: Query relevance judgments (when available)
Languages
Arabic (ar), German (de), English (en), Spanish (es), French (fr)
Italian (it), Norwegian (no), Portuguese (pt), Swedish (sv)… See the full description on the dataset page: https://huggingface.co/datasets/LiquidAI/nanobeir-multilingual-extended.mosaic-nanot5-512nanochat-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.NanoDBPediaNanoFEVERbagaco3_nanochatpt
bagaco3_nanochatpt
Dataset convertido a partir de
duarteocarmo/bagaco3
para utilização no nanochat.
Estatísticas
Documentos: 34,234,368
Caracteres: 123,453,219,263
Caracteres/documento: 3,606.12
Tokens estimados: 29,393,623,634
Caracteres/token usados na estimativa: 4.2
Documentos vazios: 0
Mínimo de caracteres/documento: 47
Máximo de caracteres/documento: 64,123,869
Formato
Formato: Parquet
Coluna: text
Rows por shard: 86,016
Rows por row group:… See the full description on the dataset page: https://huggingface.co/datasets/inaciose/bagaco3_nanochatpt.NanoSCIDOCSNanoClimateFEVERNanoTouche2020fineweb-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.picotron_bench
Wrapup results:
compute mfu for each results
change status of jobs
Push to hub
add scripts reproductible
add topology
bandwidth etc
NanoMTEB-Dutch
NanoMTEB-Dutch
This dataset is a Nano-style retrieval dataset for HAKARI-bench.
NanoMTEB-Dutch is a compact Dutch retrieval benchmark containing the MTEB-NL retrieval-family splits. It combines Dutch BEIR-style tasks, legal and public-domain QA, news, tender, web FAQ, Wikipedia, and cross-lingual Belebele retrieval splits.
Usage
from datasets import load_dataset
dataset_id = "hakari-bench/NanoMTEB-Dutch"
split = "argu_ana_nl"
queries = load_dataset(dataset_id… See the full description on the dataset page: https://huggingface.co/datasets/hakari-bench/NanoMTEB-Dutch.