Multimodal projection model

As explained in LLMs in Production

A multimodal projection model converts representations from a non-language modality into a form that a language model can process.

An image is not language, so a language model cannot process it directly. A multimodal projection model converts non-language representations into a form the language model can use, connecting visual input to an already trained multimodal model. section 11.4.3

In deployment, the projection model serves as an adapter:

• Download a projection file, such as a GGUF file used with a LLaVA deployment. section 11.4.3
• Pass the file to llama.cpp with --MMPROJ when starting the server. section 11.4.3
• Submit an image for tasks such as optical character recognition after the server is configured. section 11.4.3

LLaVA on a small device

A converted and quantized LLaVA model can remain language-only until its projection file is supplied. Adding --MMPROJ ./models/llava/mmproj.gguf enables image requests while increasing RAM use only modestly. Encoding images as strings is presented as an inferior alternative because observed results are poor. section 11.4.3

Figure 11.10 Our model isn’t ready yet; we need to provide a model projector.

The figure makes the missing projector visible: the model is not ready for multimodal serving until this adapter is provided. section 11.4.3

The same idea extends beyond images: CLIP-style projection supports shared embedding spaces, while ImageBind and OneLLM explore aligning multiple modalities for search and interaction. section 12.2.3

How LLMs in Production covers it

How deep it goes

Explains the adapter’s purpose and deployment flag but defers projection-file preparation and internal mechanics.

Examples

Uses LLaVA, a GGUF projection file, llama.cpp, image requests, OCR, and shared multimodal embedding spaces.

What it assumes

Assumes a converted, quantized, multimodal-trained language model and basic familiarity with serving commands.

What you can do after

Operate/configure a llama.cpp server to accept images by supplying a compatible projection file with --MMPROJ.

How it connects

Connects language models with image encoding, CLIP, embeddings, ImageBind, OneLLM, and optical character recognition.

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Related concepts

• CLIPincludes · CLIP is a multimodal model and pretraining method that aligns image and text representations in a shared embedding space.
• multimodalused by · A multimodal model is a model that processes, generates, or relates information represented in two or more data modalities, such as text, images, audio, or video.

This explainer was generated by Manning's knowledge graph from the text of LLMs in Production. The section links point to the parts of the book it draws on.