datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
colinear_scaling_models
Collinear/Non-Collinear Scaling Models
Checkpoint repository for scaling law experiments comparing collinear (CO) and non-collinear (NC) experimental designs for the paper Tokens-per-Parameter Coverage Is Critical for Robust LLM Scaling Law Extrapolation under review for NeurIPS 2026.
Code
Anonymized code repository (reproduces all tables): anonymous.4open.science
Directory Structure
{dataset}/{design}/N_{param_count}/
Dataset: wikipedia, pes2o, cosmopedia… See the full description on the dataset page: https://huggingface.co/datasets/TPPIsCriticalFor/colinear_scaling_models.scaling-laws-cacheThis dataset is my cache for the scaling-laws related to the gemstone models.
In data_cache is the approach 3 data cache with the mins for delta=1e-4, the mins for delta=1e-3 are in mins_1e-3.
This is the code I used to upload it:
import pandas as pd
from datasets import Dataset
import os
import gc
def get_data_dict(path):
contents = os.listdir(path)
ds_store = {}
for i, file in enumerate(contents):
gc.collect()
df = pd.read_parquet(f"{path}{file}")
for… See the full description on the dataset page: https://huggingface.co/datasets/smcleish/scaling-laws-cache.KernelBench
KernelBench
A benchmark designed to evaluate the ability of LLMs to generate efficient GPU kernels for optimizing neural network performance
Version
[07-21-2025] This HF dataset version has been updated to v0.1
Citation
@misc{ouyang2024kernelbench,
title={KernelBench: Can LLMs Write GPU Kernels?},
author={Anne Ouyang and Simon Guo and Azalia Mirhoseini},
year={2024},
url={https://scalingintelligence.stanford.edu/blogs/kernelbench/},
}
tts-scaling-ladder-de-en
TTS Scaling Ladder DE/EN — eight nested, balanced, openly licensed speech tiers
A nested ladder of eight speech datasets for scaling-law experiments on TTS, voice-conversion and audio
foundation models. Every tier is a strict subset of the next; every tier is 50/50 German/English, within
each language 50/50 real/synthetic, and inside each of those four cells stratified to be uniform over 40
EmoNet emotion categories (pipeline A) and uniform over 57 VoiceNet voice dimensions × 10… See the full description on the dataset page: https://huggingface.co/datasets/laion/tts-scaling-ladder-de-en.inverse-scaling-ttc-main
Inverse Scaling in Test-Time Compute
Paper: Inverse Scaling in Test-Time Compute
Project Page: https://safety-research.github.io/inverse-scaling-ttc/
Abstract
We construct evaluation tasks where extending the reasoning length of Large Reasoning Models (LRMs) deteriorates performance, exhibiting an inverse scaling relationship between test-time compute and accuracy. Our evaluation tasks span four categories: simple counting tasks with distractors, regression tasks with… See the full description on the dataset page: https://huggingface.co/datasets/inverse-scaling-ttc/inverse-scaling-ttc-main.quote-repetition
quote-repetition (Joe Cavanagh, Andrew Gritsevskiy, and Derik Kauffman of Cavendish Labs)
General description
In this task, the authors ask language models to repeat back sentences given in the prompt, with few-shot examples to help it recognize the task. Each prompt contains a famous quote with a modified ending to mislead the model into completing the sequence with the famous ending rather than with the ending given in the prompt. The authors find that smaller models… See the full description on the dataset page: https://huggingface.co/datasets/inverse-scaling/quote-repetition.NeQA
NeQA: Can Large Language Models Understand Negation in Multi-choice Questions? (Zhengping Zhou and Yuhui Zhang)
General description
This task takes an existing multiple-choice dataset and negates a part of each question to see if language models are sensitive to negation. The authors find that smaller language models display approximately random performance whereas the performance of larger models become significantly worse than random.
Language models failing to follow… See the full description on the dataset page: https://huggingface.co/datasets/inverse-scaling/NeQA.inverse-scalingagent-env-scaling-data
Agent Environment Scaling Reviewed Data
This Dataset repository stores reviewed data artifacts separately from the implementation
repository. index.json identifies the current accepted snapshot. The tables/ directory exposes
small JSONL views for the Dataset Viewer; snapshots/ preserves portable content-addressed Stores,
readable previews, manifests, and exact validation boundaries.
Current snapshot
warehouse-tongyi-formal-20260806 is the newest complete result… See the full description on the dataset page: https://huggingface.co/datasets/graycatHCO3/agent-env-scaling-data.Task_data_scaling05_05This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"trossen_subversion": "v1.0",
"robot_type": "trossen_ai_mobile",
"total_episodes": 10,
"total_frames": 8422,
"total_tasks": 1,
"total_videos": 30,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"… See the full description on the dataset page: https://huggingface.co/datasets/Qipei/Task_data_scaling05_05.scaling_psych101_testPsych-101-Scalingalicia-dataset-testTask_data_scaling03_07This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"trossen_subversion": "v1.0",
"robot_type": "trossen_ai_mobile",
"total_episodes": 5,
"total_frames": 4227,
"total_tasks": 1,
"total_videos": 15,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:5"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"… See the full description on the dataset page: https://huggingface.co/datasets/Qipei/Task_data_scaling03_07.scaling_wvs30Task_data_scaling04_05This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"trossen_subversion": "v1.0",
"robot_type": "trossen_ai_mobile",
"total_episodes": 10,
"total_frames": 8484,
"total_tasks": 1,
"total_videos": 30,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"… See the full description on the dataset page: https://huggingface.co/datasets/Qipei/Task_data_scaling04_05.Task_data_scaling_08This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"trossen_subversion": "v1.0",
"robot_type": "trossen_ai_mobile",
"total_episodes": 5,
"total_frames": 4238,
"total_tasks": 1,
"total_videos": 15,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:5"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"… See the full description on the dataset page: https://huggingface.co/datasets/Qipei/Task_data_scaling_08.Task_data_scaling01_01This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"trossen_subversion": "v1.0",
"robot_type": "trossen_ai_mobile",
"total_episodes": 5,
"total_frames": 4232,
"total_tasks": 1,
"total_videos": 15,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:5"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"… See the full description on the dataset page: https://huggingface.co/datasets/Qipei/Task_data_scaling01_01.Task_data_scaling06_01This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"trossen_subversion": "v1.0",
"robot_type": "trossen_ai_mobile",
"total_episodes": 10,
"total_frames": 8430,
"total_tasks": 1,
"total_videos": 30,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"… See the full description on the dataset page: https://huggingface.co/datasets/Qipei/Task_data_scaling06_01.Task_data_scaling04_03This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"trossen_subversion": "v1.0",
"robot_type": "trossen_ai_mobile",
"total_episodes": 10,
"total_frames": 8411,
"total_tasks": 1,
"total_videos": 30,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"… See the full description on the dataset page: https://huggingface.co/datasets/Qipei/Task_data_scaling04_03.Task_data_scaling06_02This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"trossen_subversion": "v1.0",
"robot_type": "trossen_ai_mobile",
"total_episodes": 10,
"total_frames": 8503,
"total_tasks": 1,
"total_videos": 30,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"… See the full description on the dataset page: https://huggingface.co/datasets/Qipei/Task_data_scaling06_02.inverse_scaling_prize-redefineTask_data_scaling_04This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"trossen_subversion": "v1.0",
"robot_type": "trossen_ai_mobile",
"total_episodes": 5,
"total_frames": 4223,
"total_tasks": 1,
"total_videos": 15,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:5"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"… See the full description on the dataset page: https://huggingface.co/datasets/Qipei/Task_data_scaling_04.Task_data_scaling06_04This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"trossen_subversion": "v1.0",
"robot_type": "trossen_ai_mobile",
"total_episodes": 10,
"total_frames": 8416,
"total_tasks": 1,
"total_videos": 30,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"… See the full description on the dataset page: https://huggingface.co/datasets/Qipei/Task_data_scaling06_04.Task_data_scaling03_09This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"trossen_subversion": "v1.0",
"robot_type": "trossen_ai_mobile",
"total_episodes": 5,
"total_frames": 4238,
"total_tasks": 1,
"total_videos": 15,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:5"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"… See the full description on the dataset page: https://huggingface.co/datasets/Qipei/Task_data_scaling03_09.Task_data_scaling02_03This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"trossen_subversion": "v1.0",
"robot_type": "trossen_ai_mobile",
"total_episodes": 5,
"total_frames": 4221,
"total_tasks": 1,
"total_videos": 15,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:5"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"… See the full description on the dataset page: https://huggingface.co/datasets/Qipei/Task_data_scaling02_03.scaling_psych101_test_longTask_data_scaling04_01This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"trossen_subversion": "v1.0",
"robot_type": "trossen_ai_mobile",
"total_episodes": 10,
"total_frames": 8436,
"total_tasks": 1,
"total_videos": 30,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:10"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"… See the full description on the dataset page: https://huggingface.co/datasets/Qipei/Task_data_scaling04_01.Task_data_scaling01_05This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"trossen_subversion": "v1.0",
"robot_type": "trossen_ai_mobile",
"total_episodes": 5,
"total_frames": 4264,
"total_tasks": 1,
"total_videos": 15,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 50,
"splits": {
"train": "0:5"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet"… See the full description on the dataset page: https://huggingface.co/datasets/Qipei/Task_data_scaling01_05.inverse-scalingThe 11 tasks from https://github.com/inverse-scaling/prize