01. Multi-Modal Models Overview

Chapter 1 of 18 · 15 min

KEY INSIGHT

Multi-modal models bridge visual and textual information by encoding images and text into a shared representation space, enabling tasks like image captioning and visual question answering that require joint understanding of both modalities. Multi-modal large language models (MLLMs) represent a significant advancement over traditional computer vision systems. Where classic models required separate training for detection, classification, and captioning tasks, multi-modal architectures process images and text through unified transformer-based pathways. The core architecture typically consists of three components: a vision encoder (often a Vision Transformer or CLIP-based encoder), a projection layer that maps visual features into the language model embedding space, and a large language model that generates text based on the combined visual and textual inputs. ```python # Conceptual architecture of a basic multi-modal model class MultiModalModel: def __init__(self, vision_encoder, projection_layer, llm): self.vision_encoder = vision_encoder self.projection = projection_layer self.llm = llm def forward(self, image, text_prompt): # Encode image into visual features visual_features = self.vision_encoder(image) # Project to language model space projected_features = self.projection(visual_features) # Generate text conditioned on image and prompt response = self.llm.generate( context=[projected_features, text_prompt] ) return response ``` Local multi-modal models offer privacy advantages since images never leave your infrastructure. LLaVA and BakLLaVA are prominent open-source options that run entirely on local hardware. These models handle resolutions from 224├ù224 up to 448├ù448 pixels depending on architecture variant. Failure modes to anticipate include memory exhaustion with high-resolution images, inconsistent performance across different image domains, and hallucination where the model generates descriptions that don't match image content. Running smaller 7B parameter models first provides reasonable baseline behavior before scaling to larger variants.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.

EXERCISE

Install and run a basic multi-modal model with a test image. Document the inference time, memory consumption, and output quality before proceeding to later chapters.