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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.

Signed-off-by: hishope <csqiye@126.com>

Remove unused GPT4all
Use nomic-embed-text as embedded model
Fix a deprecation warning (__call__)

Add instructions to get public key on windows

This focuses on Windows first, but coudl be used for Mac
and possibly linux in the future.

Some users are new to containers and unsure where the server logs go

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.

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

After executing the `userdel ollama` command, I saw this message:

```sh
$ sudo userdel ollama
userdel: group ollama not removed because it has other members.
```

Which reminded me that I had to remove the dangling group too. For completeness, the uninstall instructions should do this too.

Thanks!

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

Fixed assistant in the example response.