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.
âī¸ 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.
đ 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.
python
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 output
generated_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)
đ Data Analysis
Distribution of Data Categories
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.
đ 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.
đ Cite
If you find LLaVA-OneVision-1.5 useful in your research, please consider to cite the following related papers:
bibtex
@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}
}