GitLab

particularly useful for zipfiles and f16s

* Fixed the API endpoint /api/tags to return {models: []} instead of {models: null} when the model list is empty.

* Update server/routes.go

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

* rename `--quantization` to `--quantize`

* backwards

* Update api/types.go
Co-authored-by: Michael Yang <mxyng@pm.me>

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Co-authored-by: Michael Yang <mxyng@pm.me>

Ensure the runners are terminated

This cleans up the logging for GPU discovery a bit, and can
serve as a foundation to report GPU information in a future UX.

* Add preflight OPTIONS handling and update CORS config

- Implement early return with HTTP 204 (No Content) for OPTIONS requests in allowedHostsMiddleware to optimize preflight handling.

- Extend CORS configuration to explicitly allow 'Authorization' headers and 'OPTIONS' method when OLLAMA_ORIGINS environment variable is set.

* allow auth, content-type, and user-agent headers

* Update routes.go

- FROM /path/to/{safetensors,pytorch}
- FROM /path/to/fp{16,32}.bin
- FROM model:fp{16,32}

This moves all the env var reading into one central module
and logs the loaded config once at startup which should
help in troubleshooting user server logs

This also bumps up the default to be 50 queued requests
instead of 10.

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.

* terminate subprocess if receiving `SIGINT` or `SIGTERM` signals while model is loading

* use `unload` in signal handler

This should resolve a number of memory leak and stability defects by allowing
us to isolate llama.cpp in a separate process and shutdown when idle, and
gracefully restart if it has problems.  This also serves as a first step to be
able to run multiple copies to support multiple models concurrently.

count each layer independently when deciding gpu offloading