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This reverts commit c7eae586b899083acebcd9b3847b89ea78c2850c.

This is useful for a few things:
 - Work around bugs, such as having 2 images in one batch
 - Keep the image in a single batch for fully connected attention
 - Improve performance by not evaluating embeddings multiple times

Currently we are using positions, which are relative to a
sequence and may not be unique.

fix: pad tensor item if ge zero

this produces a nicer output since both positive and negative values
produces the same width

Better WantedBy declaration

The encoder cache needs to know the position of images in the input
stream so that it knows when to delete them. Previously images didn't
have a position, so we implied one by breaking batches before an
image and then assuming the image was in the first position. However,
multimodal objects are now given explicit positions in the input
stream, so we can use that instead.

Breaking batches was also a way to simulate a cross attention mask
for mllama. However, given that it only supports a single sequence
and a single image, this mask doesn't serve any real purpose.
Removing the batch break does not appear to affect the quality of
the output.

Most of this is simply moving the input data structures to a new
package to avoid import cycles.

It's ok to fail on startup but we shouldn't panic during runtime
based on user input. Downgrade the panic to a warning.

Similar to the llama engine, quantizing the KV cache requires
flash attention to be enabled through the Ollama server.