datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Qwen3-235B-A22B-Thinking-2507_Qwen3-1.7B_AIME_1983_2024thinking-bciciv2a
BCICIV-2a EEG Motor Imagery
Provenance
This repository contains one consolidated CSV shard derived from the local
research copy identified as the Kaggle dataset
aymanmostafa11/eeg-motor-imagery-bciciv-2a,
which describes the BCI Competition IV 2a motor-imagery recordings associated
with the Graz BCI laboratory. The uploader has represented that they have the
right to redistribute this research copy. The repository owner must preserve
any upstream attribution and… See the full description on the dataset page: https://huggingface.co/datasets/madteam/thinking-bciciv2a.PP
SteelBench: A Diagnostic Benchmark for Vision-Language Models in Industrial Safety Monitoring
SteelBench is a diagnostic benchmark of densely annotated CCTV clips from an
operating integrated steel plant. It is designed to evaluate vision-language
models (VLMs) on real-world industrial action recognition, PPE assessment,
and safety-violation detection — under naturally occurring degradation
(dust, glare, steam, low light), at distances and crowdedness levels that
curated… See the full description on the dataset page: https://huggingface.co/datasets/ThinkingHub/PP.Creative-Writing-Thinking
Creative-Writing-Thinking
Using essays-creative-writing-prompts and Qwen3-14b to generate the reasoning traces and answers. We created this reasoning dataset.
Suitable for LLM post-training, especially RL.
databricks-thinking
databricks-thinking
Created by extracing the questions from the [databricks-dolly] dataset and using Qwen3-14b to synthetically generate reasoning traces and answers.
The whole process took 1 day 23 hours 18 minuntes and 8 seconds
Why we created this dataset
The vast majority of publicly available datasets comes from large models such as DeepSeek R1.
The issue with using these large models are obvious: the reasoning traces are extremely long, often longer than the actual… See the full description on the dataset page: https://huggingface.co/datasets/chimbiwide/databricks-thinking.sciqa-thinking
sciqa-thinking
Randomly extracted 3000 rows from sciq and prompting Qwen3-14b to generate the intermediate reasoning traces, we created this dataset.
This should be used for LLM post-training, especially RL.
gsm8k-thinking
gsk8k-thinking
A processed version of gsm8k for LLM post-training purposes, especially RL.
brainstorming-thinking
brainstorming-thinking
Using Qwen3-14b to synthetically generate the reasoning traces and answers for Explore_Instruct_Brainstorming_10k
Suitable for LLM post-training, especially RL.
code-thinking
code-thinking
Using mbpp and using Qwen3-14b to generate the reasoning traces for the datasets.
Suitable for training small LLMs for python code generation.
thinking-my-cotthinking_my_translation_Reduced_p1thinking_my_translation_p2