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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

This fixes issues with blob file names that contain ':' characters to be rejected by file systems that do not support them.

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Co-authored-by: Chris-AS1 <8493773+Chris-AS1@users.noreply.github.com>

The recent ROCm change partially removed idempotent
payloads, but the ggml-metal.metal file for mac was still
idempotent.  This finishes switching to always extract
the payloads, and now that idempotentcy is gone, the
version directory is no longer useful.

This refines where we extract the LLM libraries to by adding a new
OLLAMA_HOME env var, that defaults to `~/.ollama` The logic was already
idempotenent, so this should speed up startups after the first time a
new release is deployed.  It also cleans up after itself.

We now build only a single ROCm version (latest major) on both windows
and linux.  Given the large size of ROCms tensor files, we split the
dependency out.  It's bundled into the installer on windows, and a
separate download on windows.  The linux install script is now smart and
detects the presence of AMD GPUs and looks to see if rocm v6 is already
present, and if not, then downloads our dependency tar file.

For Linux discovery, we now use sysfs and check each GPU against what
ROCm supports so we can degrade to CPU gracefully instead of having
llama.cpp+rocm assert/crash on us.  For Windows, we now use go's windows
dynamic library loading logic to access the amdhip64.dll APIs to query
the GPU information.

instead of appending image tags, prepend them - this generally produces better results