DeepSeek-VL2-Small
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Deepseek VL2 Small
Overview :
DeepSeek-VL2 is a series of advanced large-scale mixture of experts (MoE) visual language models, significantly improved compared to its predecessor DeepSeek-VL. This model series demonstrates exceptional capabilities across various tasks, including visual question answering, optical character recognition, document/table/chart understanding, and visual localization. Comprising three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small, and DeepSeek-VL2, with 1 billion, 2.8 billion, and 4.5 billion active parameters respectively, DeepSeek-VL2 achieves competitive or state-of-the-art performance against existing dense and MoE-based open-source models, even with a similar or fewer number of active parameters.
Target Users :
Target audience includes developers and enterprises engaged in vision-language processing, such as researchers in image recognition and natural language processing, as well as companies looking to integrate visual question-answering features into commercial products. DeepSeek-VL2-Small, with its advanced visual language understanding and multimodal processing capabilities, is particularly suitable for scenarios that require handling large amounts of visual data and extracting useful information from it.
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Use Cases
Use DeepSeek-VL2-Small for identifying and describing specific objects within images.
Leverage DeepSeek-VL2-Small to provide detailed visual question-answering services for product images on e-commerce platforms.
Employ DeepSeek-VL2-Small in the education sector to assist students in comprehending complex charts and visual materials.
Features
Visual Question Answering: Understands image content and answers related questions.
Optical Character Recognition: Recognizes text information in images.
Document/Table/Chart Understanding: Parses and comprehends visual information in documents, tables, and charts.
Visual Localization: Identifies the locations of specific objects within images.
Multimodal Understanding: Integrates visual and language information for deeper understanding.
Model Variants: Offers models of different sizes to meet various application needs.
Commercial Use Support: The DeepSeek-VL2 series is supportive of commercial applications.
How to Use
1. Install necessary dependencies: In a Python environment (version >= 3.8), run pip install -e . to install related dependencies.
2. Import required modules: Import AutoModelForCausalLM from the torch and transformers libraries, as well as DeepseekVLV2Processor and DeepseekVLV2ForCausalLM.
3. Load the model: Specify the model path and use the from_pretrained method to load DeepseekVLV2Processor and DeepseekVLV2ForCausalLM models.
4. Prepare input: Use the load_pil_images function to load images and prepare dialogue content.
5. Encode input: Process the input, including the dialogue and images, using vl_chat_processor and then pass it to the model.
6. Generate response: Run the model’s generate method to produce a response based on the input embeddings and attention masks.
7. Decode output: Convert the model's encoded output into readable text using the tokenizer.decode method.
8. Print results: Output the final dialogue results.
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