

Internvl2 5 38B
Overview :
InternVL 2.5 is a series of multimodal large language models launched by OpenGVLab, featuring significant enhancements in training strategies, testing strategies, and data quality improvements over InternVL 2.0. This series can process image, text, and video data, demonstrating capabilities in multimodal understanding and generation, positioning it at the forefront of the multimodal AI field. The InternVL 2.5 series provides robust support for multimodal tasks with its high performance and open-source attributes.
Target Users :
The target audience includes researchers, developers, and enterprises, particularly those developing AI applications requiring multimodal task processing. InternVL 2.5 is suitable for scenarios such as image recognition, video analysis, and natural language processing, thanks to its powerful multimodal capabilities and open-source nature.
Use Cases
For joint understanding tasks involving images and text, such as image description generation.
For understanding video content and generating summaries in video content analysis.
As underlying technology for chatbots, providing capabilities for image and text interaction.
Features
Supports multimodal data: capable of processing images, text, and video data.
Dynamic high-resolution training: the model can dynamically adjust image resolution to optimize performance for multimodal datasets.
Single-model training pipeline: model training is divided into multiple stages to enhance visual perception and multimodal capabilities.
Progressive scaling strategy: training begins with smaller LLMs before transitioning to larger ones to improve efficiency.
Training enhancement techniques: including random JPEG compression and loss re-weighting to improve the model's robustness to noisy images.
Data organization and filtering: optimizing the balance and distribution of training data through refined organization and filtering techniques.
How to Use
1. Visit the Hugging Face website and search for the InternVL2_5-38B model.
2. Load the model using the `transformers` library based on the code examples provided on the page.
3. Prepare input data, which includes images and text, with appropriate preprocessing.
4. Perform inference with the model to generate image descriptions or handle other multimodal tasks.
5. Fine-tune the model as necessary to cater to specific application scenarios.
6. Utilize the LMDeploy toolkit for model deployment and service integration.
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