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It looks like 8 minutes isn't quite enough and we're seeing sporadic timeouts

This adds support for the Jetson JetPack variants into the Go runner

Docker uses the container filesystem for name resolution, so we can't guide users
to use the name of the host group.  Instead they must specify the numeric ID.

GPU support can break on some systems after a while.  This captures a
known workaround to solve the problem.

The structure of the accounting for KV cache shifting was carried
over from the old runner but it now doesn't feel natural with the new
runner. There are a number of invariants that should hold true but
are difficult to reason about. There is at least one bug report
that would imply that the invariants are not holding.

This reduces the number of implicit assumptions and is more forgiving
of unexpected situations. It also improves behavior around which input
tokens are kept when truncation occurs.

Bug #7545

If we get a request with a zero length image, it will result in
an out-of-bounds error when we pass the data to the image encoder.

This should have been in #7347 but was overlooked.

This enables the workaround code only for windows which should help windows users with muliple AMD GPUs

Some users are reporting crashes during nvcuda.dll initialization
on windows.  This should help narrow down where things are going bad.

Bring consistency with the old generate script behavior

On linux nvcc isn't automatically linking to the same cuda version.

Now that server.cpp is gone, we don't need to keep passing arguments
that were only ignored and only kept for compatibility.

The Go runner does not have a problem with supporting parallel
requests for most multimodal models. Now that we won't be potentially
falling back to server.cpp, this restriction can be lifted.

However, the new mllama model can't support parallel requests, so we
will need to keep a restriction for that.

added reddit rate below hexabot, ollama powered reddit search and analysis with streamlit for the intervace

One potential failure mode is an empty file which bubbles up as an EOF error,
leading to all pulls and listing operations failing.  Instead, continue and
warn about the corrupt manifest.  This also allows re-pulling the corrupt
manifest to repair the system.

Currently we assume that images take 768 tokens of context size for
the purposes of clipping old messages that exceed the context window.
However, our mllama implementation stores the full image embedding
in a single token. As a result, there is significant waste of context
space.

Ideally, we would handle this more generically and have the
implementation report the number of tokens. However, at the moment
this would just result in a similar set of 'if' conditions in the
runner plus APIs to report it back. So for now, we just keep this
simple.

Avoid excessive log spew and make consistent with chat logging

Github actions matrix strategy can't access env settings

update llama3.2 vision memory estimation

The runtime and management libraries may not always have
identical ordering, so use the device UUID to correlate instead of ID.

This leverages caching, and some reduced installer scope to try
to speed up builds. It also tidies up some windows build logic
that was only relevant for the older generate/cmake builds.

Check for NULL return values from llama.cpp in more places and
convert them into Go errors, which should make debugging easier
in the future rather than having hidden surprises in our data
structures.

Mllama has large embeddings (100 MB per image) and each embedding is
represented as 1 token when passed to llama.cpp. Batches are pre-
allocated for the size of the tokens times the batch size, so this
results in allocations of over 50 GB at the default batch size.
On some systems, these mallocs will fail.

Since an image is represented as a single token and mllama doesn't
support more than 1 image per request, we only need to allocate a
batch size of 1, which is much more reasonable. In addition, for
non-multimodal models, we don't need to allocate the embedding
batches at all.

Fixes #7464