metadata

license: apache-2.0
datasets:
  - AIDC-AI/Ovis-dataset
library_name: transformers
tags:
  - MLLM
pipeline_tag: image-text-to-text
language:
  - en

Ovis1.6-Gemma2-9B

Introduction

GitHub | Demo | Paper

We are excited to announce the open-sourcing of Ovis-1.6, our latest multi-modal large language model. Ovis is a novel Multimodal Large Language Model (MLLM) architecture, designed to structurally align visual and textual embeddings.

Model

Built upon Ovis1.5, Ovis1.6 further enhances high-resolution image processing, is trained on a larger, more diverse, and higher-quality dataset, and refines the training process with DPO training following instruction-tuning.

Ovis MLLMs	ViT	LLM	Model Weights	Demo
Ovis1.6-Gemma2-27B	Siglip-400M	Gemma2-27B-It	Huggingface	-
Ovis1.6-Gemma2-9B	Siglip-400M	Gemma2-9B-It	Huggingface	Space
Ovis1.6-Llama3.2-3B	Siglip-400M	Llama-3.2-3B-Instruct	Huggingface	Space

Performance

With just 10B parameters, Ovis1.6-Gemma2-9B leads the OpenCompass benchmark among open-source MLLMs within 30B parameters.

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==2.2.0 transformers==4.44.2 numpy==1.24.3 pillow==10.3.0

import torch
from PIL import Image
from transformers import AutoModelForCausalLM

# load model
model = AutoModelForCausalLM.from_pretrained("AIDC-AI/Ovis1.6-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()

# 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}'

# format conversation
prompt, input_ids, pixel_values = model.preprocess_inputs(query, [image])
attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id)
input_ids = input_ids.unsqueeze(0).to(device=model.device)
attention_mask = attention_mask.unsqueeze(0).to(device=model.device)
pixel_values = [pixel_values.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:\n{output}')

Batch inference

batch_inputs = [
    ('example_image1.jpeg', 'Describe the content of this image.'),
    ('example_image2.jpeg', 'What is the equation in the image?')
]

batch_input_ids = []
batch_attention_mask = []
batch_pixel_values = []

for image_path, text in batch_inputs:
    image = Image.open(image_path)
    query = f'<image>\n{text}'
    prompt, input_ids, pixel_values = model.preprocess_inputs(query, [image])
    attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id)
    input_ids = input_ids.unsqueeze(0).to(device=model.device)
    attention_mask = attention_mask.unsqueeze(0).to(device=model.device)
    pixel_values = [pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device)]
    batch_input_ids.append(input_ids.squeeze())
    batch_attention_mask.append(attention_mask.squeeze())
    batch_pixel_values.append(pixel_values)

pad_batch_input_ids = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[0]) for i in batch_input_ids],batch_first=True, padding_value=0.0).flip(dims=[1])
pad_batch_input_ids =  pad_batch_input_ids[:,-model.config.multimodal_max_length:]
pad_batch_attention_mask = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[0]) for i in batch_attention_mask],batch_first=True, padding_value=False).flip(dims=[1])
pad_batch_attention_mask = pad_batch_attention_mask[:,-model.config.multimodal_max_length:]
pad_batch_pixel_values = [item for sublist in batch_pixel_values for item in sublist]

# 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(pad_batch_input_ids, pixel_values=pad_batch_pixel_values, attention_mask=pad_batch_attention_mask, **gen_kwargs)

for i in range(len(batch_input_ids)):
    output = text_tokenizer.decode(output_ids[i], skip_special_tokens=True)
    print(f'Output_{i}:\n{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

This project is licensed under the Apache License, Version 2.0 (SPDX-License-Identifier: Apache-2.0).

Disclaimer

We used compliance-checking algorithms during the training process, to ensure the compliance of the trained model to the best of our ability. Due to the complexity of the data and the diversity of language model usage scenarios, we cannot guarantee that the model is completely free of copyright issues or improper content. If you believe anything infringes on your rights or generates improper content, please contact us, and we will promptly address the matter.