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xingkungao/
LLaVA-OneVision-1.5-8B-Instruct

Name: LLaVA-OneVision-1.5-8B-Instruct
Availability: InStock

safetensors

llava_onevision1_5

lmms-lab/LLaVA-One-Vision-1.5-Mid-Training-85M

lmms-lab/LLaVA-OneVision-1.5-Insturct-Data

DeepGlint-AI/rice-vit-large-patch14-560

finetune

apache-2.0

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LLaVA-OneVision-1.5: Fully Open-Source State-of-the-Art VLM Model

✨ Key Features

LLaVA-OneVision-1.5 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 mid-training and SFT data with rigorous filtering and quality control.

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:

$16K total budget for full model training
45% HFU efficiency on A100 GPUs ($0.6 per GPU/Hour)
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 mid-training & SFT data
✅ Complete training framework & code
✅ Training recipes & configurations
✅ Base & instruct model checkpoints
✅ Comprehensive training logs & metrics

Code

This model is trained using a fully open-source, end-to-end training framework, with all code available at EvolvingLMMs-Lab/LLaVA-OneVision-1.5.

Dataset

Description	Link
Mid-training data for LLaVA-OneVision-1.5	🤗 Download (Uploading!)
SFT data for LLaVA-OneVision-1.5	🤗 Download (Uploading!)

Evaluation Results

All evaluations were conducted using lmms_eval.

	LLaVA-OV-1.5-8B	Qwen2.5 VL 7B
MMMU (Validation)	55.44	51.33
MMMU-Pro (Standard)	37.40	36.30
MMMU-Pro (Vision)	25.15	32.83
MMBench (English; Test)	84.14	83.40
MMBench (Chinese; Test)	81.00	81.61
MME-RealWorld (English)	62.31	57.33
MME-RealWorld (Chinese)	56.11	51.50
AI2D (With Mask)	84.16	82.58
AI2D (Without Mask)	94.11	93.36
CV-Bench	80.82	79.95
VL-RewardBench	45.90	49.65
V*	78.01	76.96
PixmoCount	62.19	63.33
CountBench	88.19	86.35
ChartQA	86.48	84.08
CharXiv (Direct Questions)	74.10	69.80
DocVQA (Test)	95.00	94.93
InfoVQA (Test)	78.42	81.67
WeMath	33.62	33.33
MathVista (Mini)	69.57	68.60
MathVision	25.56	22.37
MMStar	67.72	62.54
SEED-Bench (Image)	77.32	77.53
ScienceQA	94.98	88.75
SEED-Bench 2-Plus	69.21	70.93
OCRBench	82.90	84.20
RealWorldQA	68.10	68.50

Using 🤗 Transformers to Chat

Here we show a code snippet to show you how to use the chat model with transformers and qwen_vl_utils:

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)

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={LLaVA Community Contributors},
  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}
}

xingkungao/LLaVA-OneVision-1.5-8B-Instruct