

Cogvlm2
Overview :
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.
Target Users :
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.
Use Cases
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, assisting doctors in case analysis and image recognition
Features
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
Supports multiple invocation methods including CLI, WebUI, and OpenAI API
How to Use
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
Call and test the model according to the provided example code
Fine-tune the model according to specific application scenarios as needed
Integrate the model into your own project to develop multimodal dialogue applications
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