base_model:
- Qwen/Qwen3-8B-Base
- DeepGlint-AI/rice-vit-large-patch14-560
datasets:
- lmms-lab/LLaVA-One-Vision-1.5-Mid-Training-85M
library_name: transformers
license: apache-2.0
pipeline_tag: image-text-to-text
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.
Paper: LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training Code: https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5 Project Page (Models & Datasets Collection): https://huggingface.co/collections/lmms-lab/llava-onevision-15-68d385fe73b50bd22de23713 Demo: https://huggingface.co/spaces/lmms-lab/LLaVA-OneVision-1.5
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
Quick Start with HuggingFace
from transformers import AutoTokenizer, AutoProcessor, AutoModelForCausalLM
from qwen_vl_utils import process_vision_info
model_path = "lmms-lab/LLaVA-OneVision-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)
Evaluation Results
All evaluations were conducted using lmms_eval.
Citation
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 Tan, Huajie and Li, Chunyuan and Yang, Jing and Jie Yu and Wang, Xiyao and Qin, Bin and Wang, Yumeng and Yan, Zizhen and Feng, Ziyong and Liu, Ziwei and Li, Bo and Deng, Jiankang},
booktitle={arxiv},
year={2025}
}
@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}
}