InternVL2_5-26B-MPO

A multimodal large language model that enhances the interaction between visual and linguistic data.

CommonProductImageMultimodalLarge Language Model

InternVL2_5-26B-MPO is a multimodal large language model (MLLM) that builds upon InternVL2.5 and improves model performance through Mixed Preference Optimization (MPO). The model can handle multimodal data, including images and text, and is widely applied in scenarios such as image captioning and visual question answering. Its significance lies in its ability to understand and generate text closely related to image content, pushing the boundaries of multimodal AI. Background information on the product includes its exceptional performance in multimodal tasks and evaluation results on the OpenCompass Leaderboard. This model provides researchers and developers with a powerful tool to explore and realize the potential of multimodal AI.

Supports multimodal data inputs
including images and text.
Can generate detailed descriptions and narratives related to image content.
Performs visual question answering
addressing image-related inquiries.
Supports multi-turn dialogues
providing a coherent interactive experience.
Enhances preference learning and generation quality through mixed preference optimization.
Supports multiple image inputs for comparison and correlation analysis.
Offers a quantized version of the model to optimize deployment efficiency.

\The target audience includes researchers
developers
and enterprise users in the field of artificial intelligence
particularly those who need to process and analyze multimodal data. This product is suited for them as it provides an advanced tool for understanding and generating text related to visual content
aiding in the development of applications such as intelligent image analysis and automated content generation.\

Use InternVL2_5-26B-MPO to generate a description of a natural landscape image.
Utilize the model to conduct visual question answering on artworks
explaining the art style and historical context.
In e-commerce platforms
leverage the model to compare images of different products

1. Visit the Hugging Face model library and locate the InternVL2_5-26B-MPO model.
2. Prepare the input data based on the types of data to be processed (e.g.
images
text).
3. Use the Transformers library to load the model and configure the relevant parameters according to the documentation.

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21315886

Bounce Rate

45.50%

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5.2

Visit Duration

00:05:02

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