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This switches darwin to dynamic loading, and refactors the code now that no
static linking of the library is used on any platform

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

In some cases we may want multiple variants for a given GPU type or CPU.
This adds logic to have an optional Variant which we can use to select
an optimal library, but also allows us to try multiple variants in case
some fail to load.

This can be useful for scenarios such as ROCm v5 vs v6 incompatibility
or potentially CPU features.

* update cmake flags for intel macOS

* remove `LLAMA_K_QUANTS`

* put back `CMAKE_OSX_DEPLOYMENT_TARGET` and disable `LLAMA_F16C`

Go embed doesn't like when there's no matching files, so put
a dummy placeholder in to allow building without any GPU support
If no "server" library is found, it's safely ignored at runtime.

This moves the list of AMD GPUs to an easier to maintain list which
should make it easier to update over time.

Refactor where we store build outputs, and support a fully dynamic loading
model on windows so the base executable has no special dependencies thus
doesn't require a special PATH.

This changes the model for llama.cpp inclusion so we're not applying a patch,
but instead have the C++ code directly in the ollama tree, which should make it
easier to refine and update over time.

This switches the default llama.cpp to be CPU based, and builds the GPU variants
as dynamically loaded libraries which we can select at runtime.

This also bumps the ROCm library to version 6 given 5.7 builds don't work
on the latest ROCm library that just shipped.

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

Run the server.cpp directly inside the Go runtime via cgo
while retaining the LLM Go abstractions.