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Internvl2 5 4B MPO AWQ
InternVL2_5-4B-MPO-AWQ
I
Internvl2 5 4B MPO AWQ
AI ModelImage Generation#Multimodal#Large Language Model#Image-Text Processing#Machine Learning#Artificial IntelligenceStandard PicksOpen Source
Overview :
InternVL2_5-4B-MPO-AWQ is a multimodal large language model (MLLM) focused on improving performance in image and text interaction tasks. Based on the InternVL2.5 series and further enhanced through Mixed Preference Optimization (MPO), it can handle a variety of inputs, including single images, multiple images, and video data, making it suitable for complex tasks requiring an understanding of both image and text interactions. With its exceptional multimodal capabilities, InternVL2_5-4B-MPO-AWQ offers a powerful solution for image-to-text and text-to-image tasks.
Target Users :
The target audience includes researchers, developers, and enterprise users, especially those looking to implement high-performance AI applications in image and text interaction tasks, such as image recognition, automatic tagging, and content generation. The model’s technological advancements and flexibility make it an ideal choice in this field.
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Top Region： US(17.94%)
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Use Cases
Example 1: Using the InternVL2_5-4B-MPO-AWQ model to automatically describe and tag images on social media.
Example 2: In an e-commerce platform, utilizing the model to generate detailed product descriptions from product images.
Example 3: In the education field, the model can help create interactive learning materials that improve learning efficiency through the integration of images and text.
Features
? Multimodal Understanding: The model can understand and process inputs of both images and text, suitable for scenarios requiring a combination of visual and linguistic information.
? Mixed Preference Optimization (MPO): Optimizes the model's generated responses by combining preference loss, quality loss, and generation loss.
? Support for Multiple Images and Video: The model accommodates inputs of multiple images and video data, expanding its application range.
? Efficient Data Processing: Employs pixel reorganization operations and dynamic resolution strategies to enhance data processing efficiency.
? Pre-training and Fine-tuning: Based on pre-trained InternViT and LLMs, the model is fine-tuned using a randomly initialized MLP projector.
? Open-source Data Building Process: Provides an efficient data building process to construct multimodal preference datasets, supporting further research and development within the community.
? Model Compression and Deployment: Supports model compression, deployment, and serviceization using the LMDeploy tool, facilitating practical applications.
How to Use
1. Install the necessary dependencies, such as lmdeploy, to use the model.
2. Load the model by specifying its name 'OpenGVLab/InternVL2_5-4B-MPO-AWQ'.
3. Prepare the input data, which can be either a text description or an image file.
4. Utilize the pipeline function to combine the model and input data for inference.
5. Retrieve the model's output response and perform subsequent processing as needed.
6. For cases involving multiple images or multi-turn dialogues, adjust the input format according to the examples in the documentation.
7. If deploying the model as a service is required, use the api_server functionality of lmdeploy.
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