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A few obvious levels were adjusted, but generally everything mapped to "info" level.

This reduces the built-in linux version to not use any vector extensions
which enables the resulting builds to run under Rosetta on MacOS in
Docker.  Then at runtime it checks for the actual CPU vector
extensions and loads the best CPU library available

By default builds will now produce non-debug and non-verbose binaries.
To enable verbose logs in llama.cpp and debug symbols in the
native code, set `CGO_CFLAGS=-g`

If someone checks out the ollama repo and doesn't install the CUDA
library, this will ensure they can build a CPU only version

* enable packaging multiple cuda versions
* use nvcc cuda version if available

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

* linux gpu support
* handle multiple gpus
* add cuda docker image (#488)
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Co-authored-by: Michael Yang <mxyng@pm.me>

* remove c code
* pack llama.cpp
* use request context for llama_cpp
* let llama_cpp decide the number of threads to use
* stop llama runner when app stops
* remove sample count and duration metrics
* use go generate to get libraries
* tmp dir for running llm

Signed-off-by: Matt Williams <m@technovangelist.com>