The previous value of 64 was WAY too high and unnecessary. It reached
diminishing returns and blew past it. This is a more reasonable number
for _most_ normal cases. For users on cloud servers with excellent
network quality, this will keep screaming for them, without hitting our
CDN limits. For users with relatively poor network quality, this will
keep them from saturating their network and causing other issues.
For simplicity, perform parallelization of embedding requests in the API handler instead of offloading this to the subprocess runner. This keeps the scheduling story simpler as it builds on existing parallel requests, similar to existing text completion functionality.
Commit 1829fb61 ("manifest: Fix crash on startup when trying to clean up
unused files (#5840)") changed the config layer stored in manifests
from a pointer to a value. This was done in order to avoid potential
nil pointer dereferences after it is deserialized from JSON in the
event that the field is missing.
This changes the Layers slice to also be stored by value. This enables
consistency in handling across the two objects.
When creating a model the config layer is appended to the list of
layers and then the last layer is used as the config when writing the
manifest. This change directly uses the config layer to write the
manifest. There is no behavior change but it is less error prone.
Currently if the config field is missing in the manifest file (or
corrupted), Ollama will crash when it tries to read it. This can
happen at startup or when pulling new models.
This data is mostly just used for showing model information so we
can be tolerant of it not being present - it is not required to
run the models. Besides avoiding crashing, this also gives us the
ability to restructure the config in the future by pulling it
into the main manifest file.
If there is an error when opening a manifest file (corrupted, permission denied, etc.)
then the referenced layers will not be included in the list of active
layers. This causes them to be deleted when pruning happens at startup
or a model is pulled.
In such a situation, we should prefer to preserve data in the hopes that
it can be recovered rather than being agressive about deletion.
The file.Truncate call on windows will write the whole file
unless you set the sparse flag, leading to heavy I/O at the
beginning of download. This should improve our
I/O behavior on windows and put less stress on the users disk.
In mult-brand GPU setups, if we couldn't fully load the model we
would fall through the scheduler and mistakenly try to load across
a mix of brands. This makes sure we find the set of GPU(s) that
best fit for the partial load.
This fixes various data races scattered throughout the download/pull
client where the client was accessing the download state concurrently.
This commit is mostly a hot-fix and will be replaced by a new client one
day soon.
Also, remove the unnecessary opts argument from downloadChunk.
This changes the registry client to reuse the original download URL
it gets on the first redirect response for all subsequent requests,
preventing thundering herd issues when hot new LLMs are released.
