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-supervised-datasettiny-testtiny-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.tiny-shakespearetiny-shakespeare
Data source
Downloaded via Andrej Karpathy's nanogpt repo from this link
Data Format
The entire dataset is split into train (90%) and test (10%).
All rows are at most 1024 tokens, using the Llama 2 tokenizer.
All rows are split cleanly so that sentences are whole and unbroken.
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.tiny_shakespeare40,000 lines of Shakespeare from a variety of Shakespeare's plays. Featured in Andrej Karpathy's blog post 'The Unreasonable Effectiveness of Recurrent Neural Networks': http://karpathy.github.io/2015/05/21/rnn-effectiveness/.
To use for e.g. character modelling:
```
d = datasets.load_dataset(name='tiny_shakespeare')['train']
d = d.map(lambda x: datasets.Value('strings').unicode_split(x['text'], 'UTF-8'))
# train split includes vocabulary for other splits
vocabulary = sorted(set(next(iter(d)).numpy()))
d = d.map(lambda x: {'cur_char': x[:-1], 'next_char': x[1:]})
d = d.unbatch()
seq_len = 100
batch_size = 2
d = d.batch(seq_len)
d = d.batch(batch_size)
```tinygiant-omni-featuresMLAAD-tiny
Welcome to MLAAD-tiny
MLAAD-tiny is a very small subset of the full MLAAD dataset, designed for education, prototyping, and debugging.
Many teaching environments (e.g. Colab, Kaggle, university notebooks -- se this notebook for example) impose strict storage limits, which makes large-scale audio deepfake datasets impractical to use. To address this, we provide MLAAD-tiny, a compact yet representative version of MLAAD.
Download
git lfs install
git clone… See the full description on the dataset page: https://huggingface.co/datasets/mueller91/MLAAD-tiny.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.tinyAI2_arc
tinyAI2_arc
Welcome to tinyAI2_arc! This dataset serves as a concise version of the AI2_arc challenge dataset, offering a subset of 100 data points selected from the original compilation.
tinyAI2_arc 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 ARC challenge evaluation.
Features
Compact Dataset: With only 100 data… See the full description on the dataset page: https://huggingface.co/datasets/tinyBenchmarks/tinyAI2_arc.tinyWinogrande
tinyWinogrande
Welcome to tinyWinogrande! This dataset serves as a concise version of the Winogrande dataset, offering a subset of 100 data points selected from the original compilation.
tinyWinogrande 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 Winogrande evaluation.
Features
Compact Dataset: With only 100 data… See the full description on the dataset page: https://huggingface.co/datasets/tinyBenchmarks/tinyWinogrande.TinyStories-Algerian-Darijaemnist-letters-tiny
Dataset Card for EMNIST-Letters-10k
A random subset of the train and test splits from the letters portion of EMNIST
This is a FiftyOne dataset with 10000 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/emnist-letters-tiny.tinyTruthfulQA
tinyTruthfulQA
Welcome to tinyTruthfulQA! This dataset serves as a concise version of the truthfulQA dataset, offering a subset of 100 data points selected from the original compilation.
tinyTruthfulQA 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 truthfulQA evaluation.
Features
Compact Dataset: With only 100 data… See the full description on the dataset page: https://huggingface.co/datasets/tinyBenchmarks/tinyTruthfulQA.danish-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.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.v0-tinystories-v2-clean-tokenizedv0 refers to our tokenization process, v2 refers to stories generated by GPT4 only
trivia_qa_tiny
Dataset Card for Dataset Name
Dataset Summary
This dataset contains 100 samples from trivia_qa dataset. It is used mainly for testing purposes.
Languages
English.
Dataset Structure
Data Instances
Total data size: 8Kb.
Data Fields
question: string feature, containing question to be answered.
`answer: string feature, answer to the question.
Data Splits
Only test split, that contains 100 rows, is supported.
tinyimgtext-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)
TinyGSMaction100m_tiny_subset
Dataset Card for action100m
This is a FiftyOne dataset with 1144 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/action100m_tiny_subset")
# Launch the App
session = fo.launch_app(dataset)
Dataset Card for… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/action100m_tiny_subset.TinyMoves
TinyMoves Dataset
TinyMoves is a dataset for reasoning on Reactome biological pathways. We introduce two tasks:
Task 1 - Reconstruction: A benchmark for building an entire pathway, testing models' ability to generate the ordered sequence of mechanistic steps in a pathway, starting from just an obfuscated title.
Task 2 - Corruption: A benchmark for recovering corrupted biological pathways, testing models' ability to refine and correct mechanistic hypotheses.
Task 1:… See the full description on the dataset page: https://huggingface.co/datasets/TuringRRX/TinyMoves.