How deeply does How Large Language Models Work cover patch combiner?

Explains reverse patch conversion and output shaping, but does not develop the neural-network architecture mathematically.

Patch combiner

As explained in How Large Language Models Work

A patch combiner is an image-output component that converts vector representations into patches and combines them into a coherent image.

A patch combiner turns a transformer's output vectors back into image patches and joins those patches into one coherent image. It plays the image-output role that an unembedding layer plays for text. section 6.3.1

The process runs in the reverse direction of patch extraction:

The resulting patches are merged to reconstruct the image. section 6.3.1
The transformer layers and gradient-descent learning process remain the same as in the text-based pipeline. section 6.3.1

How How Large Language Models Work covers it

liveBook manning.com

Read How Large Language Models Work on liveBook Get the book at manning.com

Related concepts

digital imageproduces · A digital image is visual content encoded as data, typically as a two-dimensional arrangement of pixels or equivalent visual elements.
unembedding layeralternative to · An unembedding layer is a model component that maps final hidden representations to logits or probabilities over output tokens.

This explainer was generated by Manning's knowledge graph from the text of How Large Language Models Work. The section links point to the parts of the book it draws on.