GitLab

Replace large-chunk blob downloads with parallel small-chunk
verification to solve timeout and performance issues. Registry users
experienced progressively slowing download speeds as large-chunk
transfers aged, often timing out completely.

The previous approach downloaded blobs in a few large chunks but
required a separate, single-threaded pass to read the entire blob back
from disk for verification after download completion.

This change uses the new chunksums API to fetch many smaller
chunk+digest pairs, allowing concurrent downloads and immediate
verification as each chunk arrives. Chunks are written directly to their
final positions, eliminating the entire separate verification pass.

The result is more reliable downloads that maintain speed throughout the
transfer process and significantly faster overall completion, especially
over unstable connections or with large blobs.

count gemma3 vision tensors

the largest operation is by far (q @ k) so just count that for
simplicity

fix: error if image requested without vision model

Add metadata and tensor information to the show command to be able to
see more information about a model. This outputs the same data as
shown on the model details page on ollama.com

fix: error on models that don't support embeddings

Co-authored-by: Jeffrey Morgan <jmorganca@gmail.com>

ollama-debug.c: correct mistype

* macOS has different definition per info from @mxyng

Softcap isn't in the whitepaper/implementation for the language model so we should remove it. There is no discernible difference in output with it removed.

Signed-off-by: shane.xb.qian <shane.qian@foxmail.com>

Signed-off-by: shane.xb.qian <shane.qian@foxmail.com>

Co-authored-by: Richard Lyons <frob@cloudstaff.com>

engine: add gemma support

This reverts commit c7eae586b899083acebcd9b3847b89ea78c2850c.

This is useful for a few things:
 - Work around bugs, such as having 2 images in one batch
 - Keep the image in a single batch for fully connected attention
 - Improve performance by not evaluating embeddings multiple times

Currently we are using positions, which are relative to a
sequence and may not be unique.