

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.
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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