GitLab

* perf: build graph for next batch in parallel to keep GPU busy

This refactors the main run loop of the ollama runner to perform the main GPU
intensive tasks (Compute+Floats) in a go routine so we can prepare the next
batch in parallel to reduce the amount of time the GPU stalls waiting for the
next batch of work.

* tests: tune integration tests for ollama engine

This tunes the integration tests to focus more on models supported
by the new engine.

Replace the older llava model with qwen2.5 for vision tests
Skip split-batch test on small VRAM systems to avoid excessive test time

Add some new test coverage for various model architectures,
and switch from orca-mini to the small llama model.

Both interface{} and any (which is just an alias for interface{} introduced in Go 1.18) represent the empty interface that all types satisfy.

Models may require that a set of inputs all be processed as part
of the same batch. For example, if an image has multiple patches
with fully connected attention between them, we should not split
the batch in the middle of an image.

Fixes #9697

adjust timing on some tests so they don't timeout on small/slow GPUs

This change adds support for multiple concurrent requests, as well as
loading multiple models by spawning multiple runners. The default
settings are currently set at 1 concurrent request per model and only 1
loaded model at a time, but these can be adjusted by setting
OLLAMA_NUM_PARALLEL and OLLAMA_MAX_LOADED_MODELS.

If images aren't present, pull them.
Also fixes the expected responses

This uplevels the integration tests to run the server which can allow
testing an existing server, or a remote server.

This should help CI avoid running the integration test logic in a
container where it's not currently possible.

A simple test case that verifies llava:7b can read text in an image