datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
TinyStoriesDataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.
Described in the following paper: https://arxiv.org/abs/2305.07759.
The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M.
Additional resources:
tinystories_all_data.tar.gz - contains a superset of… See the full description on the dataset page: https://huggingface.co/datasets/roneneldan/TinyStories.tiny-imagenet
Dataset Card for tiny-imagenet
Dataset Summary
Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images, and 50 test images.
Languages
The class labels in the dataset are in English.
Dataset Structure
Data Instances
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64 at 0x1A800E8E190,
'label': 15
}… See the full description on the dataset page: https://huggingface.co/datasets/zh-plus/tiny-imagenet.esb-datasets-earnings22-validation-tiny-filteredA filtered (<=30s duration) slice (512 samples) of the Earnings22 dataset.
def add_duration(sample):
y, sr = sample['audio']["array"], sample['audio']["sampling_rate"]
sample['duration_ms']=librosa.get_duration(y=y, sr=sr) * 1000
return sample
tedlium = load_dataset("esb/datasets", "earnings22", split='validation', trust_remote_code=True)
# compute duration to filter
tedlium = tedlium.map(add_duration)
tedlium = tedlium.select(range(512))
# Whisper max supported duration
tedlium… See the full description on the dataset page: https://huggingface.co/datasets/D4nt3/esb-datasets-earnings22-validation-tiny-filtered.tiny-scalesThis repo contains the tinyHLE dataset, a list of items to use as a subset of the Humanity's Last Exam benchmark in order to make evaluation more efficient.
The repo contains two files:
tiny_hle.json: a file containing a list of question IDs and weights for three different sample sizes (0.5%, 1.0%, 2.0%)
clean_scales_embedding_hle.parquet: a file containing embeddings representing each item of the HLE benchmark along 16 cognitive scales dimensions, used to create the subsets
Since these are… See the full description on the dataset page: https://huggingface.co/datasets/ambean-tr/tiny-scales.tinyGSM8k
tinyGSM8K
Welcome to tinyGSM8K! This dataset serves as a concise version of the GSM8K dataset, offering a subset of 100 data points selected from the original compilation.
tinyGSM8K is designed to enable users to efficiently estimate the performance of a large language model (LLM) with reduced dataset size, saving computational resources
while maintaining the essence of the GSM8K evaluation.
Features
Compact Dataset: With only 100 data points, tinyGSM8K… See the full description on the dataset page: https://huggingface.co/datasets/tinyBenchmarks/tinyGSM8k.tinyMMLU
tinyMMLU
Welcome to tinyMMLU! This dataset serves as a concise version of the MMLU dataset, offering a subset of 100 data points selected from the original compilation.
tinyMMLU is designed to enable users to efficiently estimate the performance of a large language model (LLM) with reduced dataset size, saving computational resources
while maintaining the essence of the MMLU evaluation.
Features
Compact Dataset: With only 100 data points, tinyMMLU provides a swift… See the full description on the dataset page: https://huggingface.co/datasets/tinyBenchmarks/tinyMMLU.tinyHellaswag
tinyHellaswag
Welcome to tinyHellaswag! This dataset serves as a concise version of the hellaswag dataset, offering a subset of 100 data points selected from the original compilation.
tinyHellaswag is designed to enable users to efficiently estimate the performance of a large language model (LLM) with reduced dataset size, saving computational resources
while maintaining the essence of the hellaswag evaluation.
Features
Compact Dataset: With only 100 data points… See the full description on the dataset page: https://huggingface.co/datasets/tinyBenchmarks/tinyHellaswag.product-photography-v1-tiny-prompts-tasks-collage-filteredTiny-GenImage
Tiny GenImage Dataset
📝 Dataset Description
Dataset Summary
The Tiny GenImage Dataset is a curated, scaled-down collection of images and associated metadata designed to train, validate, and benchmark models for detecting and identifying artificially generated content. The dataset contains a mix of real-world images alongside those generated by prominent AI models, including various diffusion models (like Stable Diffusion 1.4/1.5, GLIDE, Midjourney, ADM, VQDM… See the full description on the dataset page: https://huggingface.co/datasets/TheKernel01/Tiny-GenImage.TinyStories-Algerian-Darijadanish-asr-unified-hviske-v5-tiny
danish-asr-unified — two-model labels and a quality manifest
Transcriptions, per-token confidences, and a per-row quality verdict for every
row of syvai/danish-asr-unified
(3,414,589 rows, 8 sources).
Two independently trained models labelled the whole corpus:
model
architecture
vocabulary
syvai/hviske-v5-tiny
encoder-decoder
16,384 BPE
3dio-ai/svale-110M
RNN-T (Parakeet)
44 characters
Both decode greedily. Shards mirror the source data/train-*.parquet by name… See the full description on the dataset page: https://huggingface.co/datasets/syvai/danish-asr-unified-hviske-v5-tiny.v0-tinystories-v2-clean-tokenizedv0 refers to our tokenization process, v2 refers to stories generated by GPT4 only
tiny-codes
Reasoning with Language and Code
This synthetic dataset is a collection of 1.6 millions short and clear code snippets that can help LLM models learn how to reason with both natural and programming languages. The dataset covers a wide range of programming languages, such as Python, TypeScript, JavaScript, Ruby, Julia, Rust, C++, Bash, Java, C#, and Go. It also includes two database languages: Cypher (for graph databases) and SQL (for relational databases) in order to study the… See the full description on the dataset page: https://huggingface.co/datasets/nampdn-ai/tiny-codes.text-dataset-tiny-code-script-py-format
USED of tahamajs/medicine_ds_persian for .parquet file
USED of Alijafarixcs2/persian-it-llama2-2k for .parquet file
USED of Abirate/english_quotes for .jsonl file
NEW FILES (05/12/2025)
NEW FILES (12/26/2025)
NEW FILES (02/15/2026)
TinyGSMtiny-aya-l2-thinker-multilingual-reasoning
Tiny Aya L2 Multilingual Reasoning (44 languages)
Translated multilingual reasoning traces used to train Tiny Aya L2-Thinker.
Each example has the prompt, thinking, and answer in the same non-English language alongside the original texts in English.
Data source
Prompts from AM-DeepSeek-R1-0528-Distilled
Thinking traces and outputs distilled from gpt-oss-120b
Translated with command-a-translate and DeepSeek-V3
Languages (44)
Language
Train… See the full description on the dataset page: https://huggingface.co/datasets/CohereLabs/tiny-aya-l2-thinker-multilingual-reasoning.TinyMixtral-4x248M-MoE-atlas
juiceb0xc0de/TinyMixtral-4x248M-MoE-atlas
A brain atlas for Isotonic/TinyMixtral-4x248M-MoE, a 12-layer sparse Mixtral-architecture MoE with four experts and top-2 routing. 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, expert, and feature direction is doing.
If you want to know how four experts relate to one another inside a small trained MoE… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/TinyMixtral-4x248M-MoE-atlas.tinystories-gpt4-clean
TinyStories GPT-4 Clean
A cleaned subset of the TinyStories dataset (Eldan & Li, 2023), keeping only GPT-4-generated stories. Adapted from this thread that pointed out many issues with the original data and proposed a cleaning process.
Overview
This cleaned dataset contains:
Stat
Value
Stories
2,732,634
Total characters
~2.19B
Min doc length
115 chars
Max doc length
4,433 chars
Median doc length
721 chars
Unique characters
74 (ASCII only)
Duplicates… See the full description on the dataset page: https://huggingface.co/datasets/karpathy/tinystories-gpt4-clean.sae-skeskinen-TinyStories-hf-validation-tokenizer-gpt2_playtiny-textbooks
Textbook-like Dataset: A High-Quality Resource for Small Language Models
The idea is simply inspired by the Textbooks Are All You Need II: phi-1.5 technical report paper. The source texts in this dataset have been gathered and carefully select the best of the falcon-refinedweb and minipile datasets to ensure the diversity, quality while tiny in size. The dataset was synthesized using 4x3090 Ti cards over a period of 500 hours, thanks to Nous-Hermes-Llama2-13b finetuned model.
Why… See the full description on the dataset page: https://huggingface.co/datasets/nampdn-ai/tiny-textbooks.Ultrachat-Multiple-Conversations-Alpaca-Tinyllama-Tokenized
Dataset Card for "Ultrachat-Multiple-Conversations-Alpaca-Tinyllama-Tokenized"
More Information needed
tiny-belebeletiny-mlqaTinyStoriesV2-Tokenizedtiny-aya-global-em-en-text-insecureTinyStories2-ascii-bpe-32k
Dataset Card for "TinyStories2-ascii-bpe-32k"
More Information needed
TinyEHR
TinyEHR
v0.2.0 | GitHub | Website | PyPI
A 100 patient dataset of Electronic Health Records, built for learning, experimenting, and prototyping healthcare data tools and AI agentic systems. Typically, working with real healthcare data requires credentialing and data access agreements. TinyEHR is free to use.
This dataset is for learning, prototyping, and exploration only. It should not be used for clinical analysis, medical decision-making, or patient care.
This dataset is derived… See the full description on the dataset page: https://huggingface.co/datasets/vidulpanickan/TinyEHR.Tiny-Short-R1-CoT-Distill-HF-ChatMLOnly Short CoT
Type: 'tiny' (Very Small CoT)
Type: 'short' (Small CoT)
Each CoT Should be less than 2048 tokens
Subset of glaiveai/reasoning-v1-20m
decomposeRL-tiny-judge
DecomposeRL Tiny-Judge: Distillation Data
Overview
DecomposeRL Tiny-Judge is the distillation dataset used to train DecomposeRL's tiny-judge stack — eight small ModernBERT-large classifier heads that replace a Qwen3-32B LLM judge as the reward model during GRPO training.
Each row is a judgment task instance: a text input (claim / question / answer / evidence, depending on the task) paired with a label distilled from a Qwen/Qwen3-32B judge call… See the full description on the dataset page: https://huggingface.co/datasets/dipta007/decomposeRL-tiny-judge.tinygsm_mixtral_12M