Overview of DeepSeek-VL2: Advanced Mixture-of-Experts Vision-Language Models

DeepSeek-VL2 is a sophisticated series of vision-language models developed by deepseek-ai, designed to enhance multimodal understanding across various tasks. This model series is an extension of the earlier DeepSeek-VL, offering improved capabilities and performance in processing and understanding multimodal data.

Key Features

• Model Variants: DeepSeek-VL2 includes three different model sizes:

  • DeepSeek-VL2-Tiny: 1.0 billion activated parameters.
  • DeepSeek-VL2-Small: 2.8 billion activated parameters.
  • DeepSeek-VL2: 4.5 billion activated parameters.

• Advanced Multimodal Tasks: The models are adept at handling a range of complex tasks including:

  • Visual question answering.
  • Optical character recognition.
  • Document, table, and chart understanding.
  • Visual grounding.

• Performance: DeepSeek-VL2 models achieve competitive or state-of-the-art performance, often with fewer parameters compared to other existing dense and MoE-based models.

Model Availability and Licensing

• Release Dates:

  • 2024-12-13: Initial release of all three variants.
  • 2024-12-25: Support for incremental prefilling and VLMEvalKit.
  • 2025-02-06: Implementation of a naive Gradio demo on Huggingface Spaces.

• Download: Models are available for download on the Huggingface platform with a sequence length support of 4096 tokens.

• License: Usage of DeepSeek-VL2 models is governed by the MIT license, ensuring open access for both academic and commercial use, subject to terms specified in the license documentation.

Implementation and Usage

Installation

DeepSeek-VL2 requires a Python environment (version 3.8 or higher). Dependencies can be installed using the following command:

pip install -e .

Example Usage

DeepSeek-VL2 models can be utilized for tasks such as single or multiple image understanding. Below is a simple example of how to use DeepSeek-VL2 for image-based conversation:

1. Loading the Model:

   from transformers import AutoModelForCausalLM
   from deepseek_vl2.models import DeepseekVLV2Processor, DeepseekVLV2ForCausalLM
   
   model_path = "deepseek-ai/deepseek-vl2-tiny"
   vl_chat_processor = DeepseekVLV2Processor.from_pretrained(model_path)
   vl_gpt = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).to(torch.bfloat16).cuda().eval()
   

2. Running the Model:

   # Example of processing an input with a reference to an object in an image
   conversation = [{"role": "<|User|>", "content": "<image> \n <|ref|>The giraffe at the back.<|/ref|>.", "images": ["./images/visual_grounding_1.jpeg"]}]
   pil_images = load_pil_images(conversation)
   prepare_inputs = vl_chat_processor(conversations=conversation, images=pil_images, force_batchify=True, system_prompt="").to(vl_gpt.device)
   inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
   outputs = vl_gpt.language.generate(inputs_embeds=inputs_embeds, attention_mask=prepare_inputs.attention_mask)
   answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=False)
   print(answer)
   

This example demonstrates the model's capability to engage in a conversation about an image, identifying and localizing objects within the visual input.

Conclusion

DeepSeek-VL2 is a significant step forward in the field of vision-language models, offering robust performance across a variety of complex multimodal tasks. Its availability on platforms like Huggingface facilitates easy access and integration into existing projects, supporting a wide range of applications in both research and commercial settings.