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.
By AI Mastermind Lab · Published 8 Mar 2026
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.