datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Zebra-CoT
Zebra‑CoT
A diverse large-scale dataset for interleaved vision‑language reasoning traces.
Dataset Description
Zebra‑CoT is a diverse large‑scale dataset with 182,384 samples containing logically coherent interleaved text‑image reasoning traces across four major categories: scientific reasoning, 2D visual reasoning, 3D visual reasoning, and visual logic & strategic games.
Dataset Structure
Each example in Zebra‑CoT consists of:
Problem statement:… See the full description on the dataset page: https://huggingface.co/datasets/multimodal-reasoning-lab/Zebra-CoT.OpenRouter-ZebraLogicBench
OpenRouter-ZebraLogicBench
This repository contains a single Python file evaluation script for the allenai/ZebraLogicBench dataset. The script is adapted from ZeroEval and can be used to evaluate language models on logical reasoning tasks.
Key Features
Single file implementation for easy use
Compatible with OpenAI-like APIs (base URL can be modified in eval_zebra.py)
Example results provided for Claude 3 Haiku
Usage Requirements
Access to the private dataset:… See the full description on the dataset page: https://huggingface.co/datasets/leafspark/OpenRouter-ZebraLogicBench.vad-zebra-finch
Positive Transfer Of The Whisper Speech Transformer To Human And Animal Voice Activity Detection
We proposed WhisperSeg, utilizing the Whisper Transformer pre-trained for Automatic Speech Recognition (ASR) for both human and animal Voice Activity Detection (VAD). For more details, please refer to our paper
Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection
Nianlong Gu, Kanghwi Lee, Maris Basha, Sumit Kumar Ram, Guanghao You, Richard H.… See the full description on the dataset page: https://huggingface.co/datasets/nccratliri/vad-zebra-finch.ZebraLogicPaper: https://huggingface.co/papers/2502.01100
Arxiv: https://arxiv.org/abs/2502.01100
Citation
@article{zebralogic2025,
title={ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning},
author={Bill Yuchen Lin and Ronan Le Bras and Kyle Richardson and Ashish Sabharwal and Radha Poovendran and Peter Clark and Yejin Choi},
year={2025},
url={https://arxiv.org/abs/2502.01100},
}
@article{dziri2024faith,
title={Faith and fate: Limits of transformers on… See the full description on the dataset page: https://huggingface.co/datasets/WildEval/ZebraLogic.ZebraLogicBenchmulti-zebra-logic
Dataset Card for the MultiZebraLogic dataset
This dataset includes zebra puzzles in 39 European and 5 non-European languages and in two sizes: 2x3 and 4x5. It can be used for evaluating logical reasoning ability.
The data has been generated using the code in this repo.
Dataset Details
Dataset Description
Zebra puzzles are a type of constraint satisfaction problem. They describe a number of objects, N_objects, that each have attributes… See the full description on the dataset page: https://huggingface.co/datasets/alexandrainst/multi-zebra-logic.textlatent_zebra_thinkmorph_armAB
Text-Latent (Arm A) vs All-Latent (Arm B) — Zebra-CoT + ThinkMorph
35638 samples/arm, 18 categories. Schema = ULVR/williamium style (sample_id, category, source_dataset,
question, answer, input_image, intermediate_image_N, num_intermediate_steps, messages_json).
armA_text_latent: real decoded text CoT + latent visual blocks (intermediate_image_1..3).
armB_render_latent: reasoning text RENDERED to images, all-latent baseline (intermediate_image_1..17).
messages_json = full Monet… See the full description on the dataset page: https://huggingface.co/datasets/RuoliuYang/textlatent_zebra_thinkmorph_armAB.ZebraLogicBench-privatezebra-cot-mistral-small-3.2-24b-preprocessed
Zebra-CoT Preprocessed — Mistral Hackathon 2026
Preprocessed version of the Zebra-CoT dataset for fine-tuning Mistral-Small-3.2-24B-Instruct.
Format
text: formatted as [INST] question [/INST] <think> reasoning </think> answer
image: PIL JPEG image for the corresponding visual task
Usage
Fine-tuning Mistral-Small-3.2-24B on chain-of-thought visual reasoning.
Hackathon
Created for Mistral Hackaton 2026 — Fine-tuning track with W&B.
Zebra-CoT-unify-styleZebrafish_DNA_v0_tokenized_kmer6_stride1zebra-puzzlesSynthetic data for the paper [2505.05755] Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions.
Project page: https://dhruveshp.com/projects/ilm
YOLO-OBB-Zebra-Crossings-Datasetmulti-perspective-dataset-plain-zebras
MP-ZEBRA
Synchronised per-frame telemetry from four simultaneously operating drones, enabling research on multi-view wildlife monitoring, individual re-identification, 3D reconstruction, and multi-drone swarm survey protocols.
Dataset Details
This dataset provides per-frame telemetry from a coordinated swarm of four simultaneously
operating DJI Mini 4 Pro drones monitoring plains zebras (Equus quagga) at Ol Pejeta
Conservancy, Laikipia County, Kenya. Collected on… See the full description on the dataset page: https://huggingface.co/datasets/edouard-rolland/multi-perspective-dataset-plain-zebras.details_mlabonne__Zebrafish-7B
Dataset Card for Evaluation run of mlabonne/Zebrafish-7B
Dataset automatically created during the evaluation run of model mlabonne/Zebrafish-7B on the Open LLM Leaderboard.
The dataset is composed of 63 configuration, each one coresponding 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-old/details_mlabonne__Zebrafish-7B.zebraZebraArena
ZebraArena
Dataset accompanying the paper ZebraArena: A Diagnostic Simulation Environment for Studying Reasoning–Action Coupling in Tool-Augmented LLMs.
ZebraArena is a procedurally generated diagnostic environment for studying reasoning–action coupling in tool-augmented LLMs, with controllable difficulty and a knowledge-minimal design. Each task is a partially observed Zebra (logic-grid) puzzle: a Constraint Satisfaction Problem with a unique ground-truth solution, where a subset… See the full description on the dataset page: https://huggingface.co/datasets/ZebraArena/ZebraArena.zebra-kb-explanations
ZEBRA: Zero-Shot Example-Based Retrieval Augmentation for Commonsense Question Answering
A retrieval augmentation framework for zero-shot commonsense question answering with LLMs.
🛠️ Installation
Installation from PyPi
pip install zebra-qa
Installation from source
git clone https://github.com/sapienzanlp/zebra.git
cd zebra
conda create -n zebra python==3.10
conda activate zebra
pip install -e .
🚀 Quick Start… See the full description on the dataset page: https://huggingface.co/datasets/sapienzanlp/zebra-kb-explanations.zebrafinch-j0126[FFN paper], [data from the paper]
We downloaded and processed the data into formats that are easier to share.
Ground truth
train: j0126-train-33vol.zip, 33 densely labeled subvolumes
im_raw/, seg_gt/: original image and instance label volumes (150x150x150 voxels each, except one 128x256x256 cube)
im_raw_4-32-32/, seg_gt_4-32-32/: the same volumes padded with [4,32,32] on both sides in zyx. The image padding is real EM context read from the source volume, so the network sees valid input… See the full description on the dataset page: https://huggingface.co/datasets/pytc/zebrafinch-j0126.lm-eval-results-mlabonne-Zebrafish-7B-private
Dataset Card for Evaluation run of mlabonne/Zebrafish-7B
Dataset automatically created during the evaluation run of model mlabonne/Zebrafish-7B
The dataset is composed of 62 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 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/nyu-dice-lab/lm-eval-results-mlabonne-Zebrafish-7B-private.rg-zebra_puzzles-instruct-100k
RLVR generated dataset
Procedural rows from reasoning-gym, formatted for verl GRPO.
Build metadata
{
"config": "/home/owais/Projects/rlvr/rlvr/configs/datasets/zebra_puzzles-instruct.yaml",
"template_type": "qwen-instruct",
"developer_prompt": null,
"data_source": "reasoning_gym",
"default_extract": "answer_tag",
"train_rows": 100000,
"test_rows": 4096,
"train_seed": 42,
"test_seed": 43,
"tasks": {
"zebra_puzzles": {
"weight": 1… See the full description on the dataset page: https://huggingface.co/datasets/carbonteq/rg-zebra_puzzles-instruct-100k.zebrahub-training-data
PyTorch
PyTorch is a python package that provides two high-level features:
Tensor computation (like numpy) with strong GPU acceleration
Deep Neural Networks built on a tape-based autograd system
You can reuse your favorite python packages such as numpy, scipy and Cython to
extend PyTorch when needed.
Contents of the PyTorch image
This container has the PyTorch framework installed and ready to use. The
pytorch python module is installed as part of a Python 3.5… See the full description on the dataset page: https://huggingface.co/datasets/Yuvraj18/zebrahub-training-data.vsi-eval-datazebra-herds-aerial
Dataset Description
This dataset presents labelled top-view (nadir) aerial images of plains zebra (Equus quagga) herds. The footage was captured with a DJI Mini-series drone flying at an altitude of 60 metres, in January 2025 during a field campaign at the Ol Pejeta Conservancy, Laikipia County,
Kenya. Individual video frames were extracted and every visible zebra was annotated with a bounding box, making the dataset suitable for training and evaluating object-detection models… See the full description on the dataset page: https://huggingface.co/datasets/edouard-rolland/zebra-herds-aerial.rlvr_logic__zebra_puzzle_1.3klogic__zebra_puzzle_dataset_200ZebraPoseGTDownload required ground truth folders of zebra pose from here. The folders are models_GT_color, XX_GT (e.g. train_real_GT and test_GT) and models (models is optional, only if you want to generate GT from scratch).
For more information and next steps, please go to the project page at https://github.com/suyz526/ZebraPose
dfm10-alexandra-multi-zebra-logic
dfm10-alexandra-multi-zebra-logic
Selected Danish and English Multi-Zebra train configurations in chat form.
Contents
Format: gzip-compressed JSON Lines under data/train-*.jsonl.gz
Schema: chat messages, optional condition and tools, plus provenance
Shards: 1
Rows: 768
Category: Reasoning
Upstream material
alexandrainst/multi-zebra-logic
Processing
Six selected train configurations are combined; validation and test are excluded.… See the full description on the dataset page: https://huggingface.co/datasets/schneiderkamplab/dfm10-alexandra-multi-zebra-logic.Puzzle_Zebra_20KZebrafish_DNA_v0