VLM

Vision-Language Models (VLMs) integrate computer vision with natural language processing to let machines see, reason, and communicate about visual data in real time.

VLMs represent the next step in multimodal AI, moving beyond simple image tagging to complex reasoning across visual and textual inputs. These systems typically pair a vision encoder (like CLIP or SigLIP) with a large language model backbone (such as Llama 3 or Qwen 2.5) via a specialized projection layer. This architecture allows the model to perform high-stakes tasks: extracting structured JSON from messy invoices, identifying safety hazards in industrial video feeds, or providing zero-shot image classification without specific retraining. By mapping pixels and tokens into a shared embedding space, VLMs transform static imagery into searchable, conversational, and actionable intelligence.

https://huggingface.co/blog/vlms