Phase 4: Vision Transformers · ~75 minutes · Python

Vision-Language Models — The ViT-MLP-LLM Pattern

A vision encoder converts an image into tokens. An MLP projector maps those tokens into the LLM's embedding space. A language model does the rest. That pattern — ViT-MLP-LLM — is every production VLM in 2026.

Hiring signal: Understanding of vision-language models — the vit-mlp-llm pattern internals

What you will learn

State the ViT-MLP-LLM architecture and explain what each of the three components contributes
Compare Qwen3-VL, InternVL3.5, LLaVA-Next, and GLM-4.6V on parameter count, context length, and benchmark performance
Explain DeepStack: why multi-level ViT features tighten vision-language alignment better than a single last-layer feature
Measure VLM hallucination in production with Cross-Modal Error Rate (CMER) and act on the signal

Introduction

Type: Learn + Use Languages: Python Prerequisites: Phase 4 Lesson 14 (ViT), Phase 4 Lesson 18 (CLIP), Phase 7 Lesson 02 (Self-Attention) Time: ~75 minutes

Learning Objectives

State the ViT-MLP-LLM architecture and explain what each of the three components contributes
Compare Qwen3-VL, InternVL3.5, LLaVA-Next, and GLM-4.6V on parameter count, context length, and benchmark performance
Explain DeepStack: why multi-level ViT features tighten vision-language alignment better than a single last-layer feature
Measure VLM hallucination in production with Cross-Modal Error Rate (CMER) and act on the signal

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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