- Developers & Engineers: Embed visual understanding into chatbots, assistants, or document pipelines.
- Researchers & Analysts: Analyze charts, PDFs, screenshots, or scientific figures with multimodal input.
- Content & Data Teams: Automate captioning, data extraction, and QA on images or mixed documents.
- Product Designers: Prototype apps with image-and-text interaction via Gradio or custom APIs.
- Open-Source Enthusiasts: Run models locally (1.3B or 7B) under MIT license on consumer hardware.
How to Use DeepSeek VL?
- Download the Model: Available on Hugging Face as `DeepSeek-VL-1.3B-base/chat` and `...-7B-base/chat`.
- Install & Initialize: Use Python (≥3.8), PyTorch/Transformers, and vision-text processors for inference.
- Submit Mixed Inputs: Provide a high-res image along with a text prompt (e.g., “Describe this chart.”).
- Choose Variant: Use `-chat` for conversational Q&A; `-base` for raw inference.
- Deploy in Projects: Integrate into apps via Hugging Face pipelines or local server for inference.
- Hybrid Vision Encoder: Efficiently processes high-resolution images (up to 1024×1024) with cross-modal adaptors.
- Real-World Task Training: Instruction-tuned on diverse formats—webpages, diagrams, formulas, charts, and PDFs.
- Open-Source & Versatile: Available under MIT; supports base and chat versions in two sizes.
- Strong Benchmarking: Achieves state-of-the-art or competitive performance across VL benchmarks compared to models of similar size.
- Processes rich, high-resolution visual inputs efficiently
- Great performance on real-world image-text tasks
- Dual base/chat variants for flexible usage
- Open-source under MIT license
- Easy to integrate via Hugging Face tools
- High VRAM requirement for 7B variant (≥24 GB)
- Lags behind newer VL2 models in advanced tasks
- Limited support for image generation or editing
Custom
Custom
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Editorial Note
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