DeepSeek-VL is DeepSeek’s initial vision-language model that anchors their multimodal stack. It enables understanding and generation across visual and textual modalities—meaning it can process an image + a prompt, answer questions about images, caption, classify, or reason about visuals in context. The model is likely used internally as the visual encoder backbone for agent use cases, to ground perception in downstream tasks (e.g. answering questions about a screenshot). The repository includes model weights (or pointers to them), evaluation metrics on standard vision + language benchmarks, and configuration or architecture files. It also supports inference tools for forwarding image + prompt through the model to produce text output. DeepSeek-VL is a predecessor to their newer VL2 model, and presumably shares core design philosophy but with earlier scaling, fewer enhancements, or capability tradeoffs.

Features

  • Multimodal model accepting image + text inputs
  • Visual grounding: image-based reasoning or captioning support
  • Model weight artifacts and benchmark evaluation results
  • Inference tooling for multimodal prompts and responses
  • Integration-ready design for agent pipelines
  • Foundation for newer models (like VL2) to build upon

Project Samples

Project Activity

See All Activity >

Categories

AI Models

License

MIT License

Follow DeepSeek VL

DeepSeek VL Web Site

Other Useful Business Software
Atera all-in-one platform IT management software with AI agents Icon
Atera all-in-one platform IT management software with AI agents

Ideal for internal IT departments or managed service providers (MSPs)

Atera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
Learn More
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of DeepSeek VL!

Additional Project Details

Programming Language

Python

Related Categories

Python AI Models

Registered

2025-10-03