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POINTS Qwen 2 5 7B Chat
POINTS-Qwen-2-5-7B-Chat
P
POINTS Qwen 2 5 7B Chat
AI ModelChatbot#Visual Language Models#Multimodal#Conversational Systems#Image-to-TextStandard PicksOpen Source
Overview :
POINTS-Qwen-2-5-7B-Chat integrates the latest advancements and techniques in visual language models, proposed by researchers at WeChat AI. It significantly enhances model performance through techniques like pre-training dataset filtering and model ensembling. This model has shown outstanding performance in multiple benchmark tests, representing a significant advancement in the field of visual language models.
Target Users :
The target audience includes researchers, developers, and enterprise users who need to leverage advanced visual language models to process image and text data, enhancing the intelligent interaction capabilities of their products. POINTS-Qwen-2-5-7B-Chat is particularly suitable for AI projects that require handling large volumes of visual language data due to its high performance and ease of use.
Total Visits： 29.7M
Top Region： US(17.94%)
Website Views ： 43.1K
Use Cases
Utilize the model to describe image details, such as landscapes, people, or objects.
In the education sector, used for image recognition and descriptions to aid teaching.
In the business realm, used for image recognition and responses in customer service.
Features
Integrates the latest visual language model technologies, such as CapFusion, Dual Vision Encoder, and Dynamic High Resolution.
Uses perplexity as a criterion for filtering pre-training datasets, effectively reducing dataset size and improving model performance.
Applies model ensembling techniques to integrate models fine-tuned on different visual instructions, further enhancing performance.
Demonstrates exceptional results in various benchmark tests like MMBench-dev-en and MathVista.
Supports multimodal and conversational capabilities, suitable for image-to-text and text-to-text tasks.
Boasts a large number of parameters, totaling 8.25B, utilizing BF16 tensor types.
Provides detailed usage examples and community discussions for easy learning and interaction.
How to Use
1. Import necessary libraries and modules, including transformers, PIL, and torch.
2. Obtain the image URL and retrieve image data using requests.
3. Open the image data using the PIL library and prepare the prompt text.
4. Specify the model path and load the tokenizer and model from the pre-trained model.
5. Set up the image processor and generation configuration, including maximum new tokens, temperature, top_p, etc.
6. Use the model.chat method, passing in the image, prompt text, tokenizer, image processor, and other parameters for model interaction.
7. Output the model's response.
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