📊

Dataset

Llava Onevision 1.5 Instruct Data

Name: Llava Onevision 1.5 Instruct Data
Creator: Mvp Lab
License: Apache-2.0

by Mvp Lab hf-dataset--mvp-lab--llava-onevision-1.5-instruct-data

Nexus Index

47.0 Top 0%

S / A / P / R / Q Breakdown Calibration Pending

Pillar scores are computed during the next indexing cycle.

Tech Context

Vital Performance

0 DL / 30D

0.0%

--- task_categories: - image-text-to-text language: - en tags: - multimodal - vision-language-model - lmm - instruction-tuning - pretraining - dataset-collection - vqa - image-captioning - large-language-model configs: - config_name: CLEVR data_files: - split: train path: CLEVR/train-* - config_name: CLEVR-Math data_files: - split: train path: CLEVR-Math/train-* - config_name: Docmatix data_files: - split: train path: Docmatix/train-* - config_name: Docmatix-part-00-of-10 ...

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Dataset Information Summary
Entity Passport
Registry ID	hf-dataset--mvp-lab--llava-onevision-1.5-instruct-data
Provider	huggingface

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Cite this dataset

Academic & Research Attribution

BibTeX

@misc{hf_dataset__mvp_lab__llava_onevision_1.5_instruct_data,
  author = {Mvp Lab},
  title = {Llava Onevision 1.5 Instruct Data Dataset},
  year = {2026},
  howpublished = {\url{https://huggingface.co/datasets/mvp-lab/LLaVA-OneVision-1.5-Instruct-Data}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

Mvp Lab. (2026). Llava Onevision 1.5 Instruct Data [Dataset]. Free2AITools. https://huggingface.co/datasets/mvp-lab/LLaVA-OneVision-1.5-Instruct-Data

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Nexus Index V2.0

Methodology Index Protocol

47.0

ESTIMATED IMPACT TIER

Semantic (S) 50

Authority (A) 0

Popularity (P) 0

Recency (R) 0

Quality (Q) 0

💬 Index Insight

FNI V2.0 for Llava Onevision 1.5 Instruct Data: Semantic (S:50), Authority (A:0), Popularity (P:0), Recency (R:0), Quality (Q:0).

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HuggingFace API GitHub Metadata Arxiv Citation DB System Audit

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Dataset Specification

license: apache-2.0
task_categories:

image-text-to-text
language:
en
tags:
multimodal
vision-language-model
lmm
instruction-tuning
pretraining
dataset-collection
vqa
image-captioning
large-language-model
configs:
config_name: CLEVR
data_files:
- split: train
  path: CLEVR/train-*
config_name: CLEVR-Math
data_files:
- split: train
  path: CLEVR-Math/train-*
config_name: Docmatix
data_files:
- split: train
  path: Docmatix/train-*
config_name: Docmatix-part-00-of-10
data_files:
- split: train
  path: Docmatix-part-00-of-10/train-*
config_name: Docmatix-part-01-of-10
data_files:
- split: train
  path: Docmatix-part-01-of-10/train-*
config_name: Docmatix-part-02-of-10
data_files:
- split: train
  path: Docmatix-part-02-of-10/train-*
config_name: Docmatix-part-03-of-10
data_files:
- split: train
  path: Docmatix-part-03-of-10/train-*
config_name: Docmatix-part-04-of-10
data_files:
- split: train
  path: Docmatix-part-04-of-10/train-*
config_name: Docmatix-part-05-of-10
data_files:
- split: train
  path: Docmatix-part-05-of-10/train-*
config_name: Docmatix-part-06-of-10
data_files:
- split: train
  path: Docmatix-part-06-of-10/train-*
config_name: Docmatix-part-07-of-10
data_files:
- split: train
  path: Docmatix-part-07-of-10/train-*
config_name: Docmatix-part-08-of-10
data_files:
- split: train
  path: Docmatix-part-08-of-10/train-*
config_name: Docmatix-part-09-of-10
data_files:
- split: train
  path: Docmatix-part-09-of-10/train-*
config_name: Evol-Instruct-GPT4-Turbo
data_files:
- split: train
  path: Evol-Instruct-GPT4-Turbo/train-*
config_name: FigureQA
data_files:
- split: train
  path: FigureQA/train-*
config_name: GEOS
data_files:
- split: train
  path: GEOS/train-*
config_name: GeoQA+
data_files:
- split: train
  path: GeoQA+/train-*
config_name: Geometry3K
data_files:
- split: train
  path: Geometry3K/train-*
config_name: IconQA
data_files:
- split: train
  path: IconQA/train-*
config_name: OmniDocBench_train
data_files:
- split: train
  path: OmniDocBench_train/train-*
config_name: PMC-VQA
data_files:
- split: train
  path: PMC-VQA/train-*
config_name: Super-CLEVR
data_files:
- split: train
  path: Super-CLEVR/train-*
config_name: VisualWebInstruct
data_files:
- split: train
  path: VisualWebInstruct/train-*
config_name: VizWiz
data_files:
- split: train
  path: VizWiz/train-*
config_name: ai2d
data_files:
- split: train
  path: ai2d/train-*
config_name: alfredplpl
data_files:
- split: train
  path: alfredplpl/train-*
config_name: alfworld
data_files:
- split: train
  path: alfworld/train-*
config_name: allava
data_files:
- split: train
  path: allava/train-*
config_name: allava_instruct_laion4v
data_files:
- split: train
  path: allava_instruct_laion4v/train-*
config_name: allava_instruct_vflan4v
data_files:
- split: train
  path: allava_instruct_vflan4v/train-*
config_name: allenai_pixmo_docs
data_files:
- split: train
  path: allenai_pixmo_docs/train-*
config_name: amc_aime
data_files:
- split: train
  path: amc_aime/train-*
config_name: aokvqa
data_files:
- split: train
  path: aokvqa/train-*
config_name: aops_forum
data_files:
- split: train
  path: aops_forum/train-*
config_name: arxiv_figs
data_files:
- split: train
  path: arxiv_figs/train-*
config_name: arxivqa
data_files:
- split: train
  path: arxivqa/train-*
config_name: cambrian
data_files:
- split: train
  path: cambrian/train-*
config_name: chart2text
data_files:
- split: train
  path: chart2text/train-*
config_name: chartqa
data_files:
- split: train
  path: chartqa/train-*
config_name: chrome_writting
data_files:
- split: train
  path: chrome_writting/train-*
config_name: cn_k12
data_files:
- split: train
  path: cn_k12/train-*
config_name: coco
data_files:
- split: train
  path: coco/train-*
config_name: code_feedback_66k
data_files:
- split: train
  path: code_feedback_66k/train-*
config_name: datikz
data_files:
- split: train
  path: datikz/train-*
config_name: diagram
data_files:
- split: train
  path: diagram/train-*
config_name: docvqa_train
data_files:
- split: train
  path: docvqa_train/train-*
config_name: dvqa
data_files:
- split: train
  path: dvqa/train-*
config_name: dvqa-part-00-of-10
data_files:
- split: train
  path: dvqa-part-00-of-10/train-*
config_name: finqa
data_files:
- split: train
  path: finqa/train-*
config_name: geo170k_align
data_files:
- split: train
  path: geo170k_align/train-*
config_name: geo170k_qa
data_files:
- split: train
  path: geo170k_qa/train-*
config_name: geo3k
data_files:
- split: train
  path: geo3k/train-*
config_name: geomverse
data_files:
- split: train
  path: geomverse/train-*
config_name: gpt4o
data_files:
- split: train
  path: gpt4o/train-*
config_name: gpt4v
data_files:
- split: train
  path: gpt4v/train-*
config_name: gqa
data_files:
- split: train
  path: gqa/train-*
config_name: gsm8k
data_files:
- split: train
  path: gsm8k/train-*
config_name: hateful_memes
data_files:
- split: train
  path: hateful_memes/train-*
config_name: hitab
data_files:
- split: train
  path: hitab/train-*
config_name: hme100k
data_files:
- split: train
  path: hme100k/train-*
config_name: iam
data_files:
- split: train
  path: iam/train-*
config_name: idk
data_files:
- split: train
  path: idk/train-*
config_name: ifeval_like
data_files:
- split: train
  path: ifeval_like/train-*
config_name: iiit
data_files:
- split: train
  path: iiit/train-*
config_name: image_textualization
data_files:
- split: train
  path: image_textualization/train-*
config_name: infographic_azuregpt4v
data_files:
- split: train
  path: infographic_azuregpt4v/train-*
config_name: infographic_vqa
data_files:
- split: train
  path: infographic_vqa/train-*
config_name: intergps
data_files:
- split: train
  path: intergps/train-*
config_name: invoices-and-receipts_ocr
data_files:
- split: train
  path: invoices-and-receipts_ocr/train-*
config_name: laion_220k
data_files:
- split: train
  path: laion_220k/train-*
config_name: latex_ocr
data_files:
- split: train
  path: latex_ocr/train-*
config_name: llava_cot_100k
data_files:
- split: train
  path: llava_cot_100k/train-*
config_name: llava_instruct
data_files:
- split: train
  path: llava_instruct/train-*
config_name: llava_wild
data_files:
- split: train
  path: llava_wild/train-*
config_name: llavar
data_files:
- split: train
  path: llavar/train-*
config_name: llrv_gpt4v
data_files:
- split: train
  path: llrv_gpt4v/train-*
config_name: lrv_chart
data_files:
- split: train
  path: lrv_chart/train-*
config_name: magpie_pro
data_files:
- split: train
  path: magpie_pro/train-*
config_name: magpie_ultra
data_files:
- split: train
  path: magpie_ultra/train-*
config_name: mapqa
data_files:
- split: train
  path: mapqa/train-*
config_name: math
data_files:
- split: train
  path: math/train-*
config_name: mathinstruct_262k
data_files:
- split: train
  path: mathinstruct_262k/train-*
config_name: mathqa
data_files:
- split: train
  path: mathqa/train-*
config_name: mavis_math_metagen
data_files:
- split: train
  path: mavis_math_metagen/train-*
config_name: ocr
data_files:
- split: train
  path: ocr/train-*
config_name: ocrvqa
data_files:
- split: train
  path: ocrvqa/train-*
config_name: olympiads
data_files:
- split: train
  path: olympiads/train-*
config_name: oodvqa
data_files:
- split: train
  path: oodvqa/train-*
config_name: open_orca
data_files:
- split: train
  path: open_orca/train-*
config_name: openmathinstruct
data_files:
- split: train
  path: openmathinstruct/train-*
config_name: orca_994k
data_files:
- split: train
  path: orca_994k/train-*
config_name: orca_agentinstruct
data_files:
- split: train
  path: orca_agentinstruct/train-*
config_name: oroikon_chart_captioning
data_files:
- split: train
  path: oroikon_chart_captioning/train-*
config_name: pathvqa
data_files:
- split: train
  path: pathvqa/train-*
config_name: plotqa
data_files:
- split: train
  path: plotqa/train-*
config_name: plotqa-part-00-of-10
data_files:
- split: train
  path: plotqa-part-00-of-10/train-*
config_name: plotqa-part-01-of-10
data_files:
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  path: plotqa-part-01-of-10/train-*
config_name: plotqa-part-02-of-10
data_files:
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  path: plotqa-part-02-of-10/train-*
config_name: plotqa-part-03-of-10
data_files:
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  path: plotqa-part-03-of-10/train-*
config_name: plotqa-part-04-of-10
data_files:
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  path: plotqa-part-04-of-10/train-*
config_name: plotqa-part-05-of-10
data_files:
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  path: plotqa-part-05-of-10/train-*
config_name: plotqa-part-06-of-10
data_files:
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  path: plotqa-part-06-of-10/train-*
config_name: plotqa-part-07-of-10
data_files:
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  path: plotqa-part-07-of-10/train-*
config_name: plotqa-part-08-of-10
data_files:
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  path: plotqa-part-08-of-10/train-*
config_name: plotqa-part-09-of-10
data_files:
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  path: plotqa-part-09-of-10/train-*
config_name: raven
data_files:
- split: train
  path: raven/train-*
config_name: rects
data_files:
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  path: rects/train-*
config_name: rendered_text
data_files:
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  path: rendered_text/train-*
config_name: robut_sqa
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  path: robut_sqa/train-*
config_name: robut_wikisql
data_files:
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  path: robut_wikisql/train-*
config_name: robut_wtq
data_files:
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  path: robut_wtq/train-*
config_name: rootsautomation
data_files:
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  path: rootsautomation/train-*
config_name: scienceqa
data_files:
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  path: scienceqa/train-*
config_name: screen2words
data_files:
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  path: screen2words/train-*
config_name: screen_qa
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  path: screen_qa/train-*
config_name: sharegpt4o
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  path: sharegpt4o/train-*
config_name: sharegpt4v
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config_name: sharegpt4v-part-00-of-10
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config_name: sharegpt4v-part-01-of-10
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config_name: sharegpt4v-part-03-of-10
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config_name: sharegpt4v-part-04-of-10
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config_name: svit-part-09-of-10
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config_name: synthetic_amc
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config_name: tabmwp
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config_name: vg
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config_name: vision_flan
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config_name: wizardlm
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  dataset_info:
config_name: CLEVR
features:
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- name: conversations
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- name: data_source
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  num_bytes: 98658968456
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config_name: CLEVR-Math
features:
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LLaVA-OneVision-1.5 Instruction Data

Paper | Code

📌 Introduction

This dataset, LLaVA-OneVision-1.5-Instruct, was collected and integrated during the development of LLaVA-OneVision-1.5. LLaVA-OneVision-1.5 is a novel family of Large Multimodal Models (LMMs) that achieve state-of-the-art performance with significantly reduced computational and financial costs. This meticulously curated 22M instruction dataset (LLaVA-OneVision-1.5-Instruct) is part of a comprehensive and fully open framework for building high-quality vision-language models entirely from scratch.

It has significantly enhanced the performance of Vision-Language Models (VLMs) in structured information processing and knowledge-based question answering tasks.
As part of the LLaVA-OneVision-1.5 open-source initiative, we are releasing this dataset to the community in the hope of advancing VLM research and driving further progress in the field.

⚙️ Usage Notes

Although the dataset itself is of high quality, we recommend deduplicating and combining it with the FineVision dataset to achieve better training results.

🚀 Sample Usage

Below is a quick start guide demonstrating how to use the LLaVA-OneVision-1.5 models with Hugging Face transformers for inference. This snippet is directly from the project's GitHub repository.

from transformers import AutoTokenizer, AutoProcessor, AutoModelForCausalLM
from qwen_vl_utils import process_vision_info
model_path = "lmms-lab/LLaVA-One-Vision-1.5-8B-Instruct"

default: Load the model on the available device(s)
model = AutoModelForCausalLM.from_pretrained(
    model_path, torch_dtype="auto", device_map="auto", trust_remote_code=True
)
default processer
processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]
Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")
Inference: Generation of the outputgenerated_ids = model.generate(**inputs, max_new_tokens=1024)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

📊 Data Analysis

Distribution of Data Categories

sft_dataset_pie_chart

Compare and Scaling with FineVision

Performance comparison of three datasets (Merge46M, FineVision, and LLaVA-OneVision-1.5-Inst-Data) across 16 benchmarks during the SFT phase, demonstrating the superiority of Merge46M on most benchmarks.

ablation_instruct

🙏 Acknowledgement

We would like to acknowledge the contributions of FineVision , whose open dataset served as an important foundation and benchmark for building this SFT dataset.

📜 Cite

If you find LLaVA-OneVision-1.5 useful in your research, please consider to cite the following related papers:

@inproceedings{LLaVA-OneVision-1.5,
  title={LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training},
  author={An, Xiang and Xie, Yin and Yang, Kaicheng and Zhang, Wenkang and Zhao, Xiuwei and Cheng, Zheng and Wang, Yirui and Xu, Songcen and Chen, Changrui and Wu, Chunsheng and Huajie Tan and Li, Chunyuan and Jing Yang and Jie Yu and Xiyao Wang and Bin Qin and Yumeng Wang and Zizhen Yan and Ziyong Feng and Ziwei Liu and Bo Li and Jiankang Deng},
  booktitle={arxiv},  
  year={2025},
  url={https://arxiv.org/abs/2509.23661}, 
 }

@inproceedings{xie2025region,
  title={Region-based Cluster Discrimination for Visual Representation Learning},
  author={Xie, Yin and Yang, Kaicheng and An, Xiang and Wu, Kun and Zhao, Yongle and Deng, Weimo and Ran, Zimin and Wang, Yumeng and Feng, Ziyong and Miles, Roy and Elezi, Ismail and Deng, Jiankang},
  booktitle={ICCV},
  year={2025}
}
@article{lillava,
  title={LLaVA-OneVision: Easy Visual Task Transfer},
  author={Li, Bo and Zhang, Yuanhan and Guo, Dong and Zhang, Renrui and Li, Feng and Zhang, Hao and Zhang, Kaichen and Zhang, Peiyuan and Li, Yanwei and Liu, Ziwei and Li, Chunyuan},
  journal={Transactions on Machine Learning Research},
  year={2024}
}

Top Tier

Social Proof

HuggingFace Hub

64Likes

91.9KDownloads

Hub Discussions

🤗 Data Source: Hugging Face ↗

🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Dataset Transparency Report

Verified data manifest for traceability and transparency.

100% Data Disclosure Active

🆔 Identity & Source

id: hf-dataset--mvp-lab--llava-onevision-1.5-instruct-data
source: huggingface
author: Mvp Lab
tags: task_categories:image-text-to-textlanguage:enlicense:apache-2.0size_categories:10mmodality:imagemodality:textarxiv:2509.23661region:usmultimodalvision-language-modellmminstruction-tuningpretrainingdataset-collectionvqaimage-captioninglarge-language-model

⚙️ Technical Specs

architecture: GQA
params billions: null
context length: null

📊 Engagement & Metrics

likes: 64
downloads: 91,928

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