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MULTIMODAL ~15 min6 concepts · 5 quiz questions

Vision-Language Models

ViT, CLIP, SigLIP, DINOv2, VLM architecture — build multimodal RAG with ColPali.

Overview

Vision-Language Models (VLMs) bridge image understanding and language generation. This module covers the architecture of models like GPT-4V, LLaVA, and Gemini — from CLIP's contrastive pretraining to late fusion architectures — plus practical applications in multimodal RAG using ColPali and document understanding with ColQwen.

Concept Flashcards

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Concept

Vision Transformer (ViT)

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Explanation

ViT splits images into fixed-size patches (16×16 px), flattens each patch into a token embedding, and processes them with standard transformer attention. Unlike CNNs, ViT has global attention from layer 1. Pretrained ViTs (CLIP-ViT, DINOv2) are used as frozen vision encoders in VLMs.

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System Architecture

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The standard VLM pattern: a frozen vision encoder (ViT-L) extracts patch tokens, a projection layer maps them to text embedding space, then the LLM generates conditioned on both.

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text tokensgenerateImage(224×224)ViT Encoder(frozen)MLP Projector(trainable)Text TokensConcat[visual | text]LLM Decoder(Llama)Response
ViT Encoder (frozen)
MLP Projector (trained)
LLM Decoder
Token Merge

Key Concepts

6 concepts — click to expand

ViT splits images into fixed-size patches (16×16 px), flattens each patch into a token embedding, and processes them with standard transformer attention. Unlike CNNs, ViT has global attention from layer 1. Pretrained ViTs (CLIP-ViT, DINOv2) are used as frozen vision encoders in VLMs.

Tech Stack

HuggingFace TransformersColPaliQdrantWeaviatePyTorchGradio
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