LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal
  Training
We present LLaVA-OneVision-1.5, a novel family of Large Multimodal Models
(LMMs) that achieve state-of-the-art performance with significantly reduced
computational and financial costs. Different from the existing works,
LLaVA-OneVision-1.5 provides an open, efficient, and reproducible framework for
building high-quality vision-language models entirely from scratch. The
LLaVA-OneVision-1.5 release comprises three primary components: (1) Large-Scale
Curated Datasets: We construct an 85M concept-balanced pretraining dataset
LLaVA-OneVision-1.5-Mid-Traning and a meticulously curated 26M instruction
dataset LLaVA-OneVision-1.5-Instruct, collectively encompassing 64B compressed
multimodal tokens. (2) Efficient Training Framework: We develop a complete
end-to-end efficient training framework leveraging an offline parallel data
packing strategy to facilitate the training of LLaVA-OneVision-1.5 within a
$16,000 budget. (3) State-of-the-art Performance: Experimental results
demonstrate that LLaVA-OneVision1.5 yields exceptionally competitive
performance across a broad range of downstream tasks. Specifically,
LLaVA-OneVision-1.5-8B outperforms Qwen2.5-VL-7B on 18 of 27 benchmarks, and
LLaVA-OneVision-1.5-4B surpasses Qwen2.5-VL-3B on all 27 benchmarks. We
anticipate releasing LLaVA-OneVision-1.5-RL shortly and encourage the community
to await further updates.
68db2f1cd2bf1f4b15ec7251
Xiang An
claimed_verified
2025-09-30T06:35:28.707Z
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xiangan
xiangan
user
xiangan
68db2f1cd2bf1f4b15ec7252
Yin Xie
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Kaicheng Yang
claimed_verified
2025-10-17T04:14:46.420Z
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Kaicheng Yang
Kaichengalex
user
Kaichengalex
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Wenkang Zhang
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Xiuwei Zhao
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Zheng Cheng
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Yirui Wang
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Songcen Xu
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Changrui Chen
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Chunsheng Wu
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Huajie Tan
claimed_verified
2025-10-03T12:55:20.619Z
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tanhuajie2001
tanhuajie2001
user
tanhuajie2001
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Chunyuan Li
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Jing Yang
68db2f1cd2bf1f4b15ec725e
Jie Yu
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Xiyao Wang
claimed_verified
2025-10-03T12:55:22.726Z
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Xiyao Wang
russwang
user
russwang
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Bin Qin
68db2f1cd2bf1f4b15ec7261
Yumeng Wang
68db2f1cd2bf1f4b15ec7262
Zizhen Yan
claimed_verified
2025-10-02T13:56:43.922Z
6687ba13cfd851a0fc84336d
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Yadda Yan
yaddas
user
yaddas
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Ziyong Feng
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Ziwei Liu
68db2f1cd2bf1f4b15ec7265
Bo Li
claimed_verified
2025-09-30T06:35:25.641Z
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Bo Li
luodian
user
luodian
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Jiankang Deng
LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal
  Training
We present LLaVA-OneVision-1.5, a novel family of Large Multimodal Models
(LMMs) that achieve state-of-the-art performance with significantly reduced
computational and financial costs. Different from the existing works,
LLaVA-OneVision-1.5 provides an open, efficient, and reproducible framework for
building high-quality vision-language models entirely from scratch. The
LLaVA-OneVision-1.5 release comprises three primary components: (1) Large-Scale
Curated Datasets: We construct an 85M concept-balanced pretraining dataset
LLaVA-OneVision-1.5-Mid-Traning and a meticulously curated 26M instruction
dataset LLaVA-OneVision-1.5-Instruct, collectively encompassing 64B compressed
multimodal tokens. (2) Efficient Training Framework: We develop a complete
end-to-end efficient training framework leveraging an offline parallel data
packing strategy to facilitate the training of LLaVA-OneVision-1.5 within a
$16,000 budget. (3) State-of-the-art Performance: Experimental results
demonstrate that LLaVA-OneVision1.5 yields exceptionally competitive
performance across a broad range of downstream tasks. Specifically,
LLaVA-OneVision-1.5-8B outperforms Qwen2.5-VL-7B on 18 of 27 benchmarks, and
LLaVA-OneVision-1.5-4B surpasses Qwen2.5-VL-3B on all 27 benchmarks. We
anticipate releasing LLaVA-OneVision-1.5-RL shortly and encourage the community
to await further updates.
LLaVA-OneVision-1.5 is a family of large multimodal models that achieves state-of-the-art performance with reduced costs through efficient training and high-quality datasets.
Large Multimodal Models LMMs Large-Scale Curated Datasets pretraining dataset instruction dataset multimodal tokens efficient training framework offline parallel data packing strategy state-of-the-art performance downstream tasks Qwen2.5-VL-7B Qwen2.5-VL-3B