Phase 1: Vision-Language Models · ~180 minutes · Python
Open-Weight VLM Recipes: What Actually Matters
The 2024-2026 open-weight VLM literature is a forest of ablation tables. Apple's MM1 tested 13 combinations of image encoder, connector, and data mix. Allen AI's Molmo proved detailed human captions beat GPT-4V distillation. Cambrian-1 ran 20+ encoder comparisons. Idefics2 formalized the five-axis design space. Prismatic VLMs compared 27 training recipes on a controlled benchmark. Out of all that noise, a small set of results holds across papers: image encoder matters more than connector architecture, data mixture matters more than either, and detailed human captions beat distilled synthetic data. This lesson reads those tables so you do not have to.
What you will learn
- Name the five-axis VLM design space: image encoder, connector, LLM, data mix, resolution schedule.
- Read an MM1 / Idefics2 / Cambrian-1 ablation table and predict which knob moves a given benchmark.
- Pick a recipe (encoder, connector, data, resolution) for a new VLM given a compute budget and task mix.
- Explain why detailed human captions beat GPT-4V distillation at the same token count.
Introduction
- Type: Learn + lab
- Languages: Python (stdlib, ablation table parser + recipe picker)
- Prerequisites: LLaVA and Visual Instruction Tuning
- Time: ~180 minutes
Objective
Learning objectives
- Name the five-axis VLM design space: image encoder, connector, LLM, data mix, resolution schedule.
- Read an MM1 / Idefics2 / Cambrian-1 ablation table and predict which knob moves a given benchmark.
- Pick a recipe (encoder, connector, data, resolution) for a new VLM given a compute budget and task mix.
- Explain why detailed human captions beat GPT-4V distillation at the same token count.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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