# Multi-Modal Models: Bridging Vision and Language

> Connecting vision encoders to LLMs: two‑stage training, resolution trade‑offs, tiling tricks, and where models still fail.

Published: 2026-03-08  
Canonical: https://aimastermindlab.com/knowledge-base/multi-modal-models-vision-language

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Multi‑modal pipelines often attach a ViT‑style encoder to an LLM via a projection layer, then fine‑tune on instruction‑following data with images.

Two‑stage recipe: first align vision/text embeddings on image‑caption pairs; then instruction‑tune with visual tasks.

Resolution matters: moving from 224 to 336/448 improves fine‑grained understanding; tiling helps with high‑res docs and charts.

Current limits: spatial reasoning, tricky OCR, crowded diagrams. These remain open challenges in our experiments.
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Source: https://aimastermindlab.com/knowledge-base/multi-modal-models-vision-language — AI Mastermind Lab (https://aimastermindlab.com)