Ovis is a novel Multimodal Large Language Model (MLLM) architecture, designed to structurally align visual and textual embeddings. For a comprehensive introduction, please refer to Ovis paper and Ovis GitHub.

Model

As always, Ovis1.5 remains fully open-source: we release the training datasets, training & inference codes, and model weights for reproducible transparency and community collaboration.

Ovis MLLMs	ViT	LLM	Training Datasets	Code	Model Weights
Ovis1.5-Llama3-8B	Siglip-400M	Llama3-8B-Instruct	Huggingface	Github	Huggingface
Ovis1.5-Gemma2-9B	Siglip-400M	Gemma2-9B-It	Huggingface	Github	Huggingface

Performance

We evaluate Ovis1.5 across various multimodal benchmarks using VLMEvalKit and compare its performance to leading MLLMs with similar parameter scales.

	MiniCPM-Llama3-V2.5	GLM-4V-9B	Ovis1.5-Llama3-8B	Ovis1.5-Gemma2-9B
Open Weights	✅	✅	✅	✅
Open Datasets	❌	❌	✅	✅
MMTBench-VAL	57.6	48.8	60.7	62.7
MMBench-EN-V1.1	74	68.7	78.2	78.0
MMBench-CN-V1.1	70.1	67.1	75.2	75.1
MMStar	51.8	54.8	57.2	58.7
MMMU-Val	45.8	46.9	48.6	49.8
MathVista-Mini	54.3	51.1	62.4	65.7
HallusionBenchAvg	42.4	45	44.5	48.0
AI2D	78.4	71.2	82.5	84.7
OCRBench	725	776	743	756
MMVet	52.8	58	52.2	56.5
RealWorldQA	63.5	66	64.6	66.9
CharXiv Reasoning	24.9	-	28.2	28.4
CharXiv Descriptive	59.3	-	60.2	62.6

Usage

Below is a code snippet to run Ovis with multimodal inputs. For additional usage instructions, including inference wrapper and Gradio UI, please refer to Ovis GitHub.

pip install torch==2.1.2 transformers==4.43.2 pillow==10.3.0

python

import torch
from PIL import Image
from transformers import AutoModelForCausalLM

# load model
model = AutoModelForCausalLM.from_pretrained("AIDC-AI/Ovis1.5-Gemma2-9B",
                                             torch_dtype=torch.bfloat16,
                                             multimodal_max_length=8192,
                                             trust_remote_code=True).cuda()
text_tokenizer = model.get_text_tokenizer()
visual_tokenizer = model.get_visual_tokenizer()
conversation_formatter = model.get_conversation_formatter()

# enter image path and prompt
image_path = input("Enter image path: ")
image = Image.open(image_path)
text = input("Enter prompt: ")
query = f'<image>\n{text}'
prompt, input_ids = conversation_formatter.format_query(query)
input_ids = torch.unsqueeze(input_ids, dim=0).to(device=model.device)
attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id).to(device=model.device)
pixel_values = [visual_tokenizer.preprocess_image(image).to(
    dtype=visual_tokenizer.dtype, device=visual_tokenizer.device)]

# generate output
with torch.inference_mode():
    gen_kwargs = dict(
        max_new_tokens=1024,
        do_sample=False,
        top_p=None,
        top_k=None,
        temperature=None,
        repetition_penalty=None,
        eos_token_id=model.generation_config.eos_token_id,
        pad_token_id=text_tokenizer.pad_token_id,
        use_cache=True
    )
    output_ids = model.generate(input_ids, pixel_values=pixel_values, attention_mask=attention_mask, **gen_kwargs)[0]
    output = text_tokenizer.decode(output_ids, skip_special_tokens=True)
    print(f'Output: {output}')

Citation

If you find Ovis useful, please cite the paper

@article{lu2024ovis,
  title={Ovis: Structural Embedding Alignment for Multimodal Large Language Model}, 
  author={Shiyin Lu and Yang Li and Qing-Guo Chen and Zhao Xu and Weihua Luo and Kaifu Zhang and Han-Jia Ye},
  year={2024},
  journal={arXiv:2405.20797}
}

License

The project is licensed under the Apache 2.0 License and is restricted to uses that comply with the license agreements of Gemma2 and Siglip.