datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.fineweb-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.nemotron-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.nanoGentzen
nanoGentzen Synthetic Deduction Dataset (200k Transitions)
The nanoGentzen Dataset is a formal synthetic dataset designed to train Policy-Value Transformers for automated theorem proving in Intuitionistic Logic (LI) and Classical Logic (LK via Glivenko's Theorem) using Gentzen Sequent Calculus.
Each record represents a single state-action derivation transition along an AND-OR proof search tree, providing multi-task supervision for inference rule selection, antecedent premise… See the full description on the dataset page: https://huggingface.co/datasets/Sagicc/nanoGentzen.nanoclaw-tracesnanoGentzen-v2
nanoGentzen-v2 Synthetic Deduction Dataset (400k Transitions)
The nanoGentzen-v2 Dataset is a formal synthetic corpus designed to train Policy-Value Transformers for automated theorem proving in Intuitionistic Propositional Logic (LI) using Gentzen Sequent Calculus.
Scaled to 400,000 certified state-action derivation transitions, each record represents a discrete backward proof step along an AND-OR search tree. The dataset provides balanced multi-task supervision for inference… See the full description on the dataset page: https://huggingface.co/datasets/Sagicc/nanoGentzen-v2.NanoChat
НЕ ИСПОЛЬЗУЙТЕ ЭТО, ЭТО НЕ ПРИГОДНО ДЛЯ ОБУЧЕНИЯ, ДАННЫЕ МУСОР
имеет случайные бесмысленые китайские и арабские вставки
сгенерировано вообще нейросетью на 500M параметров
моя личный оценка качество F+, ведь оно хотябы чтото пишет хоть и с сломаными падежами
Информация о датасете
использована huggingface.co/ruv/ruvltra-small
качество говно
непригодно для использования
не рекомендую
почему это существует?
мне было скучно
finemath392kthe-stack-v2-dedup285kamcfinepdfs-edu-ml300kfineweb-nanochatbpe-55B
fineweb-nanochatbpe-55B
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
55,000,000,000
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.json carry the… See the full description on the dataset page: https://huggingface.co/datasets/alexkstern/fineweb-nanochatbpe-55B.BCE-Prettybird-Nano-Kayra-v0.1
BCE-Prettybird-Nano-Kayra-v0.1 - 200 AI Brain Mechanism Chat
Kayra is an experimental 200-sample chat dataset developed by PROMETECH A.Ş. for research on Behavioral Consciousness Engine-style control systems. The dataset was synthetically generated using Nemotron Super and is designed to go beyond standard conversation data by exposing layered behavioral signals such as trust scoring, risk level, ethical guardrails, ego–superego balance, KPI tracking, cognitive-level analysis… See the full description on the dataset page: https://huggingface.co/datasets/pthinc/BCE-Prettybird-Nano-Kayra-v0.1.NanoCodeEval
NanoCodeEval-Nemotron-1K
NanoCodeEval-Nemotron-1K is a synthetic programming benchmark containing 1,000 coding tasks across Python, JavaScript, Java, C, and C++.
The dataset was generated with Nemotron and is designed to test whether a language model can understand a small programming request, produce a valid solution, and print the required result.
This repository contains a dataset, so this page is technically a Hugging Face dataset card rather than a model card.… See the full description on the dataset page: https://huggingface.co/datasets/exnivo/NanoCodeEval.deepscalerfineweb-nanochatbpe-50M
fineweb-nanochatbpe-50M
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 50-million-token slice for data-constrained experiments. The train.bin
is the byte-exact first 50,000,000 tokens (bytes [0, 100000000)) of the parent
alexkstern/fineweb-nanochatbpe-20B
train.bin. The val.bin is byte-identical to the parent's val.bin.
file… See the full description on the dataset page: https://huggingface.co/datasets/alexkstern/fineweb-nanochatbpe-50M.nanonla-qwen3-8b-L24-cotrain-heldout
NLA co-train comparison — held-out samples
Held-out completions (n=1000, rows 25k:26k, doc-disjoint from RL train) for the co-trained λ=0.006 / λ=0.015 models, each scored two ways:
*_ct_baseAR — scored by the frozen base AR (same ruler as the frozen sweep).
*_ct_ownAR — scored by the model's own co-trained AR (real co-trained system FVE).
Per-sample rows: idx, n_tokens, fve, nmse, mse, reward, extracted, cjk, explanation, source_text. Matched by idx across models and against… See the full description on the dataset page: https://huggingface.co/datasets/syvb/nanonla-qwen3-8b-L24-cotrain-heldout.nanonla-qwen3-8b-L24-results
Qwen3-8B NLA length-penalty sweep — results bundle
Held-out completions (1000 per model, matched by idx across models) for the
from-scratch base NLA and each RL length penalty. The viewer shows the
completions config (per-sample explanations + reconstruction FVE). Also in
the repo (as files, not loaded configs): per-model *.summary.json aggregates,
RESULTS.md, comparison_base_vs_penalty.md, tradeoff.png.
Per-sample columns: idx, tag (model), source_text (the actual source… See the full description on the dataset page: https://huggingface.co/datasets/syvb/nanonla-qwen3-8b-L24-results.oulipo_dpo_nano
OULIPO DPO Dataset
A Direct Preference Optimization (DPO) dataset for training language models to generate OULIPO-inspired creative writing using systematic constraints.
Dataset Overview
Total Pairs: 205 preference pairs
Format: DPO (Direct Preference Optimization)
Theme: OULIPO literary constraints (lipograms, palindromes, univocalisms, mathematical patterns, etc.)
Files
Training and Test Sets
oulipo_train.json - 164 pairs (80%) for training… See the full description on the dataset page: https://huggingface.co/datasets/Dumoura/oulipo_dpo_nano.MTSAIR__Cotype-Nano-details
Dataset Card for Evaluation run of MTSAIR/Cotype-Nano
Dataset automatically created during the evaluation run of model MTSAIR/Cotype-Nano
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 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 additional… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/MTSAIR__Cotype-Nano-details.Mxode__NanoLM-1B-Instruct-v2-details
Dataset Card for Evaluation run of Mxode/NanoLM-1B-Instruct-v2
Dataset automatically created during the evaluation run of model Mxode/NanoLM-1B-Instruct-v2
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 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/open-llm-leaderboard/Mxode__NanoLM-1B-Instruct-v2-details.Mxode__NanoLM-1B-Instruct-v1.1-details
Dataset Card for Evaluation run of Mxode/NanoLM-1B-Instruct-v1.1
Dataset automatically created during the evaluation run of model Mxode/NanoLM-1B-Instruct-v1.1
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 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/open-llm-leaderboard/Mxode__NanoLM-1B-Instruct-v1.1-details.Mxode__NanoLM-0.3B-Instruct-v1.1-details
Dataset Card for Evaluation run of Mxode/NanoLM-0.3B-Instruct-v1.1
Dataset automatically created during the evaluation run of model Mxode/NanoLM-0.3B-Instruct-v1.1
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 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… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/Mxode__NanoLM-0.3B-Instruct-v1.1-details.aimeMxode__NanoLM-0.3B-Instruct-v1-details
Dataset Card for Evaluation run of Mxode/NanoLM-0.3B-Instruct-v1
Dataset automatically created during the evaluation run of model Mxode/NanoLM-0.3B-Instruct-v1
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 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/open-llm-leaderboard/Mxode__NanoLM-0.3B-Instruct-v1-details.fineweb-2-ml4mnanostructures_generationMxode__NanoLM-0.3B-Instruct-v2-details
Dataset Card for Evaluation run of Mxode/NanoLM-0.3B-Instruct-v2
Dataset automatically created during the evaluation run of model Mxode/NanoLM-0.3B-Instruct-v2
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 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/open-llm-leaderboard/Mxode__NanoLM-0.3B-Instruct-v2-details.