datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MAmmoTH-VL-Instruct-12M
MAmmoTH-VL-Instruct-12M
🏠 Homepage | 🤖 MAmmoTH-VL-8B | 💻 Code | 📄 Arxiv | 📕 PDF | 🖥️ Demo
Introduction
Our simple yet scalable visual instruction data rewriting pipeline consists of three steps: manual data source collection, rewriting using MLLMs/LLMs, and filtering via the same MLLM as a judge. Examples below illustrate transformations in math and science categories, showcasing detailed, step-by-step responses.
The data distribution of… See the full description on the dataset page: https://huggingface.co/datasets/MAmmoTH-VL/MAmmoTH-VL-Instruct-12M.MathCanvas-Instruct
MathCanvas-Instruct Dataset
🚀 Data Usage
from datasets import load_dataset
dataset = load_dataset("shiwk24/MathCanvas-Instruct")
print(dataset)
📖 Overview
MathCanvas-Instruct is a high-quality, fine-tuning dataset with 219K examples of interleaved visual-textual reasoning paths. It is the core component for the second phase of the [MathCanvas] framework: Strategic Visual-Aided Reasoning.… See the full description on the dataset page: https://huggingface.co/datasets/shiwk24/MathCanvas-Instruct.llava-1.5-665k-instructionsThis dataset repository, LLaVA-1.5-665K-Instructions, is notably utilized in the paper Zero-Shot Vision Encoder Grafting via LLM Surrogates.
The official code repository for the paper can be found here: https://github.com/kaiyuyue/zero
LLaVA-1.5-665K-Instructions
This dataset repo contains the entire LLaVA-1.5-665K-Instructions in one place, including images and text sequences.
The images are in train_split/*.tars and the text sequences are in jsons:
llava_v1_5_mix665k.json is the… See the full description on the dataset page: https://huggingface.co/datasets/kaiyuyue/llava-1.5-665k-instructions.Multi-modal-Self-instruct
Dataset Description
Paper Information
Dataset Examples
Leaderboard
Dataset Usage
Data Downloading
Data Format
Evaluation
Citation
You can download the zip dataset directly, and both train and test subsets are collected in Multi-modal-Self-instruct.zip.
Dataset Description
Multi-Modal Self-Instruct dataset utilizes large language models and their code capabilities to synthesize massive abstract images and visual reasoning instructions across daily scenarios. This benchmark… See the full description on the dataset page: https://huggingface.co/datasets/zwq2018/Multi-modal-Self-instruct.t5gemma2-indonesia-instruct-v1
T5Gemma-2 Indonesian Instruct — Mono-Repo
Satu repositori dataset HF untuk seluruh data pelatihan T5-Gemma-2 bahasa Indonesia.
Diorganisasi per fungsi (fondasi → spesifik → preferensi) dengan folder/subfolder,
setiap config = folder dan berisi split train + validation (80:20) di level percakapan.
Struktur (by fungsi)
t5gemma2-indonesia-instruct-v1/
├── README.md
├── manifest.json
├── chat_idx_map.json
├── foundation/ ← FASE 1 · fondasi Bahasa… See the full description on the dataset page: https://huggingface.co/datasets/daruokta/t5gemma2-indonesia-instruct-v1.mhlc-training-qwen3vl-qwen3-vl-32b-instruct_hard_mixed_sources_120k
Multi Head Latent Control Training Data - Qwen3-VL 32B Instruct hard Mixed Sources 120k
Dataset Description
This repository contains verified training data for the Multi Head Latent Control paper release. It is part of the Multi Head Latent Control training data Hugging Face collection.
Paper
https://arxiv.org/abs/2607.14277
Code
https://github.com/Amirhosein-gh98/Multi-Head-Latent-Control
Dataset Summary
Field… See the full description on the dataset page: https://huggingface.co/datasets/AmirhoseinGH/mhlc-training-qwen3vl-qwen3-vl-32b-instruct_hard_mixed_sources_120k.mhlc-training-qwen3vl-qwen3_vl_4b_instruct_hard_mixed_sources_120k
Multi Head Latent Control Training Data - Qwen3-VL 4B Instruct hard Mixed Sources 120k
Dataset Description
This repository contains verified training data for the Multi Head Latent Control paper release. It is part of the Multi Head Latent Control training data Hugging Face collection.
Paper
https://arxiv.org/abs/2607.14277
Code
https://github.com/Amirhosein-gh98/Multi-Head-Latent-Control
Dataset Summary
Field… See the full description on the dataset page: https://huggingface.co/datasets/AmirhoseinGH/mhlc-training-qwen3vl-qwen3_vl_4b_instruct_hard_mixed_sources_120k.norwegian-dyna-instruct
🧨 Norwegian dyna-instruct
Version
0.1.0 (changelog)
Languages
Norwegian Bokmål (nob), Norwegian Nynorsk (nno), and English (eng) translation input
License
Mixed open licenses; see the table below
Sources
Five datasets (source cards)
Dataset Description
Number of samples: 14.40K
Number of tokens (Llama 3): 6.27M
Average conversation length in tokens (min, max): 435.63 (4, 8.92K)
Average number of turns (min, max): 2.13 (2, 3)… See the full description on the dataset page: https://huggingface.co/datasets/danish-foundation-models/norwegian-dyna-instruct.mhlc-training-qwen3vl-qwen3_vl_2b_instruct_hard_mixed_sources_120k
Multi Head Latent Control Training Data - Qwen3-VL 2B Instruct hard Mixed Sources 120k
Dataset Description
This repository contains verified training data for the Multi Head Latent Control paper release. It is part of the Multi Head Latent Control training data Hugging Face collection.
Paper
https://arxiv.org/abs/2607.14277
Code
https://github.com/Amirhosein-gh98/Multi-Head-Latent-Control
Dataset Summary
Field… See the full description on the dataset page: https://huggingface.co/datasets/AmirhoseinGH/mhlc-training-qwen3vl-qwen3_vl_2b_instruct_hard_mixed_sources_120k.Myanmar-Tuberculosis-Guidelines-Instructions
Myanmar Tuberculosis Guidelines Instructions
A bilingual instructional dataset built to support Myanmar's ongoing fight against tuberculosis — turning life-saving guidelines into a usable resource for healthcare workers, educators, and AI researchers working with low-resource languages.
Authors: Min Si Thu, Khin Myat Noe
Abstract
Tuberculosis is still one of Myanmar's biggest public health problems. Part of the difficulty is that good, standardized TB education… See the full description on the dataset page: https://huggingface.co/datasets/jojo-ai-mst/Myanmar-Tuberculosis-Guidelines-Instructions.multimodel_llava_med_zh_instruct_60kBorrowed from https://huggingface.co/datasets/BUAADreamer/llava-med-zh-instruct-60k
Fix the <image> placeholder issue, which will cause error during training:
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens.")