license: apache-2.0
datasets:
- lmms-lab/LLaVA-One-Vision-1.5-Mid-Training-85M
base_model:
- Qwen/Qwen3-8B-Base
- DeepGlint-AI/rice-vit-large-patch14-560
pipeline_tag: image-text-to-text
library_name: transformers
LLaVA-OneVision-1.5: Fully Open-Source State-of-the-Art VLM Model
LLaVA-OneVision1.5 introduces a novel family of fully open-source Large Multimodal Models (LMMs) that achieves state-of-the-art performance with substantially lower cost through training on native resolution images.
Superior Performance A family of fully open-source large multimodal models demonstrating
- Superior performance across multiple multimodal benchmarks
- outperforming Qwen2.5-VL in most evaluation tasks.
High-Quality Data at Scale Meticulously curated pre-training and SFT data with rigorous filtering and quality control, achieving superior data efficiency with only 64B tokens.
- Concept-balanced, highly diverse, high-quality caption data
- Comprehensive instruction fine-tuning data covering a wide range of tasks
Ultra-Efficient Training Framework Complete end-to-end training framework designed for maximum efficiency:
- $16000 total budget for full model training on A100 GPUs ($0.6 per GPU/Hour)
- 45% HFU efficiency in 8k context length
- Built on MegatronLM with support for MoE, FP8, and long sequence parallelization
- Optimized codebase for cost-effective scaling
Fully Open Framework for community access and reproducibility:
- High-quality pre-training & SFT data
- Complete training framework & code
- Training recipes & configurations
- Comprehensive training logs & metrics
Citation
If you find LLaVA-OneVision-1.5 useful in your research, please consider to cite the following related papers:
@misc{an2025llavaonevision15fullyopenframework,
title={LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training},
author={Xiang An and Yin Xie and Kaicheng Yang and Wenkang Zhang and Xiuwei Zhao and Zheng Cheng and Yirui Wang and Songcen Xu and Changrui Chen and Chunsheng Wu and Huajie Tan and Chunyuan Li 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},
year={2025},
eprint={2509.23661},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2509.23661},
}