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Based on compute capability and driver version, pick
v12 or v11 cuda variants.

This adds new variants for arm64 specific to Jetson platforms

This should help speed things up a little

This adjusts linux to follow a similar model to windows with a discrete archive
(zip/tgz) to cary the primary executable, and dependent libraries. Runners are
still carried as payloads inside the main binary

Darwin retain the payload model where the go binary is fully self contained.

The centos 7 arm mirrors have disappeared due to the EOL 2 days
ago, and the vault sed workaround which works for x86 doesn't work for arm.

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.

This reverts commit 5dacc1eb.

We had started using rocky linux 8, but they've updated to GCC 10.3,
which breaks NVCC.  10.2 is compatible (or 10.4, but that's not
available from rocky linux 8 repos yet)

Flesh out our github actions CI so we can build official releaes.

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.

Without this env var, podman's GPU logic doesn't map the GPU through

This adds ROCm support back as a discrete image.

The size increase for rocm support in the standard image is problematic
We'll revisit multiple tags for rocm support in a follow up PR.

The linux build now support parallel CPU builds to speed things up.
This also exposes AMD GPU targets as an optional setting for advaced
users who want to alter our default set.

This renames Dockerfile.build to Dockerfile, and adds some new stages
to support 2 modes of building - the build_linux.sh script uses
intermediate stages to extract the artifacts for ./dist, and the default
build generates a container image usable by both cuda and rocm cards.
This required transitioniing the x86 base to the rocm image to avoid
layer bloat.

The build tags rocm or cuda must be specified to both go generate and go build.
ROCm builds should have both ROCM_PATH set (and the ROCM SDK present) as well
as CLBlast installed (for GGML) and CLBlast_DIR set in the environment to the
CLBlast cmake directory (likely /usr/lib/cmake/CLBlast). Build tags are also
used to switch VRAM detection between cuda and rocm implementations, using
added "accelerator_foo.go" files which contain architecture specific functions
and variables. accelerator_none is used when no tags are set, and a helper
function addRunner will ignore it if it is the chosen accelerator. Fix go
generate commands, thanks @deadmeu for testing.

* docker: set PATH, LD_LIBRARY_PATH, and capabilities

* example: update k8s gpu manifest