

Internvl2 5 8B MPO AWQ
Overview :
The InternVL2_5-8B-MPO-AWQ is a multimodal large language model launched by OpenGVLab, based on the InternVL2.5 series and utilizing Mixed Preference Optimization (MPO) technology. This model demonstrates exceptional performance in understanding and generating both visual and language content, particularly excelling in multimodal tasks. It combines the visual component InternViT with the linguistic component InternLM or Qwen, employing a randomly initialized MLP projector for incremental pre-training, enabling in-depth understanding and interaction with images and texts. The significance of this technology lies in its capacity to handle various data types, including single images, multiple images, and video data, providing new solutions for the multimodal AI field.
Target Users :
The target audience includes researchers, developers, and enterprise users in the field of artificial intelligence, particularly those needing to process image and text data, as well as engage in multimodal interactions and understanding. This model, with its powerful capabilities in visual and language processing, is especially suitable for tasks such as image recognition, description generation, and visual question answering.
Use Cases
- Use the model to generate descriptions for an image.
- Utilize the model for visual question answering, addressing inquiries about the image content.
- In a multilingual environment, use the model for cross-language understanding of image content.
Features
- Multimodal Understanding: The model can comprehend image content and generate relevant text.
- Mixed Preference Optimization: Improves model performance through relative preference, absolute quality, and optimization of the generation process.
- Multilingual Support: The model supports multiple languages, enhancing its international application capabilities.
- Efficient Data Processing: Employs pixel reorganization and dynamic resolution strategies to effectively manage large-scale data.
- Multimodal Inference Preference Dataset: Contains approximately 3 million samples, supporting model training and optimization.
- Easy Deployment: The model can be easily deployed as a service using the LMDeploy tool.
How to Use
1. Install the necessary dependencies, such as lmdeploy.
2. Load the model using lmdeploy and configure the backend engine.
3. Use the load_image function to load the image for processing.
4. Construct input prompts and combine them with the image for model inference.
5. Obtain the model output and perform any necessary post-processing.
6. For scenarios involving multiple images or multi-turn dialogues, adjust and process according to the lmdeploy documentation.
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