CogVLM2

Second-generation multimodal pre-trained dialogue model

CommonProductProductivityMultimodalPre-trained model

CogVLM2, developed by a team from Tsinghua University, is a second-generation multimodal pre-trained dialogue model. It has achieved significant improvements in multiple benchmark tests, supports 8K content length and a resolution of 1344*1344 for images. CogVLM2 offers both Chinese and English versions that are open-source, achieving performance comparable to some non-open-source models.

Supports various benchmark tests
such as TextVQA
DocVQA
etc.
Supports 8K content length and 1344*1344 high-resolution images
Provides bilingual support for Chinese and English
Open-source model
easy to obtain and use
Significantly improved performance compared to the previous generation model
Provides basic invocation methods and fine-tuning examples

CogVLM2 is suitable for researchers and developers working on multimodal dialogue and image understanding
especially those working in both Chinese and English environments and professionals dealing with long text and high-resolution images.

Used to develop intelligent customer service systems
improving customer service efficiency
In the education sector
assisting in teaching and providing an interactive learning experience with images and text
In the medical field

First
visit the CogVLM2 GitHub page to learn about the model's basic information and features
Based on the project structure
choose the appropriate basic invocation method or fine-tuning example
Download and install the necessary dependencies and tools

Visit

CogVLM2 Visit Over Time

Monthly Visits

494758773

Bounce Rate

37.69%

Page per Visit

5.7

Visit Duration

00:06:29

CogVLM2 Visit Trend

CogVLM2 Visit Geography

CogVLM2 Traffic Sources

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CogVLM2

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