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InternVL2-8B-MPO

Multimodal large language model to improve multimodal reasoning capabilities

#multimodal
#reasoning
#large language model
#Mixed preference optimization
#image-text-text
InternVL2-8B-MPO

Product Details

InternVL2-8B-MPO is a multimodal large language model (MLLM) that enhances the model's multimodal reasoning capabilities by introducing a mixed preference optimization (MPO) process. This model designed an automated preference data construction pipeline in terms of data, and built MMPR, a large-scale multi-modal reasoning preference data set. In terms of models, InternVL2-8B-MPO is initialized based on InternVL2-8B and fine-tuned using the MMPR data set, showing stronger multi-modal reasoning capabilities and fewer hallucinations. The model achieved an accuracy of 67.0% on MathVista, surpassing InternVL2-8B by 8.7 points, and its performance was close to InternVL2-76B, which is 10 times larger.

Main Features

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• Improvement of multi-modal reasoning capabilities: Enhance the multi-modal reasoning capabilities of the model through Mixed Preference Optimization (MPO).
2
• High accuracy: Achieving 67.0% accuracy on MathVista, significantly better than InternVL2-8B.
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• Reduced hallucinations: Compared with InternVL2-8B, there are fewer hallucinations.
4
• Supports multiple deployment methods: including using LMDeploy for model deployment.
5
• Compatible with multiple languages: As a multi-language model, it supports the understanding and generation of different languages.
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• Suitable for a variety of tasks: including image-text-text tasks, capable of processing and generating text related to images.
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• Model fine-tuning: Supports model fine-tuning on multiple platforms to adapt to specific tasks.
8
• Easy to use: Detailed quick start guide and API are provided to facilitate users to get started quickly.

How to Use

1
1. Install necessary libraries such as transformers and torch.
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2. Use AutoModel.from_pretrained to load the InternVL2-8B-MPO model.
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3. Prepare input data, including text and images.
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4. Use the model to perform inference and generate output related to the input.
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5. Post-process the output as needed, such as text formatting or image display.
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6. If necessary, the model can be fine-tuned to adapt to specific application scenarios.
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7. To deploy the model to the production environment, you can use the LMDeploy tool to deploy the model.

Target Users

The target audience is researchers, developers and enterprise users, especially those who need to process multi-modal data (such as images and text) and want to improve model reasoning capabilities. InternVL2-8B-MPO can provide more accurate data analysis and generate more reliable results, and is suitable for improving product intelligence and decision support.

Examples

The accuracy test was conducted on the MathVista data set and reached an accuracy of 67.0%.

Use InternVL2-8B-MPO for image description generation to provide detailed image content description.

In multi-image reasoning tasks, compare the similarities and differences between different images.

Quick Access

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Categories

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› AI model
› research tools

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