GitLab

We used to control the way that llama.cpp saw devices using
CUDA_VISIBLE_DEVICES or similar. This would ensure that the layers
offloaded to a device were actually the ones intended. This is
particularly important because we might reorder devices based on
free memory or performance.

When we started explicitly scheduling layers, this logic went
away but the llamarunner didn't have any way to set the correct
order of devices. This meant that the correct number of layers
would be assigned to a device but not necessarily the layers
that were expected. This change sets up the devices correctly
based on the offload information.

Adds logprobs support to Ollama's API including support for Ollama's
OpenAI-compatible API. By specifying the new 'logprobs' boolean parameter
in the API, Ollama will return the log probabilities for each token generated.
'top_logprobs', an integer value can also be specified up to the value 20.
When specified, the API will also provide the number of most likely tokens to
return at each token position
Co-authored-by: Baptiste Jamin <baptiste@crisp.chat>

* doc: re-add login autostart faq

This appears to have been accidentally dropped during the doc migration.

* docs: GPU updates lost on the doc update

* review comments: improve windows login disable instructions

* Remove unnecessary macos 13 Patch

* Remove unnecessary MacOs Version Guard patch

* rename patchesw

* remove again macos13 patch

* rename files

feat: add support for WebP images in Ollama's app

Co-authored-by: A-Akhil <akhilrahul70@gmail.com>

This PR introduces a new ollama embed command that allows users to generate embeddings directly from the command line.

Added ollama embed MODEL [TEXT...] command for generating text embeddings
Supports both direct text arguments and stdin piping for scripted workflows

Outputs embeddings as JSON arrays (one per line)

The scheduler updates free VRAM based on current loaded models.  This was
mutating the persisted list of GPUs, and when coupled with the non-refreshing
logic for Metal that lead to stale low VRAM reporting after unload.  The fix is
to make sure the GPU discovery always returns a copy so the schedulers GPU list
is in fact ephemeral and doesn't leak any temporary adjustments back into the
persistent list.

The behavior change in 0.12.4 is the most likely the root cause of hangs some
users are seeing.  This reverts to the 0.12.3 code, with some added trace
logging.