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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.

Backends can impose additional alignment requirements on buffer sizes.
We should ensure that we meet these or allocations can fail.

Models can disable causality for all or part of their processing
while continuing to store data in the KV cache.

Debug logging of every token has previously caused test timeouts
on slower machines.

this ensures the tensor is created on the right buffer type for backends
such as cpu

temporary until tensor loading can accurately account for vision models

some tensors should be created on specific backends to reduce number of
copies and improve performance

each cache layer creates and maintains its own context instead of using
a large context for all layers

some tensors are expected to be used in repeating layers but are not
themselves repeated. this change copies these tensors into the same
backends as their repeating counterparts to minimize copying tensors
between backends

use a similar strategy as llama.cpp for deciding where tensors should be
allocated. this will be improved later to be aware of usable memory
before assigning the tensor

This change bring in various interface cleanups along with greatly improving the performance of the sampler.

Tested with llama3.2 on local machine.
Improves performance from ~ 70 tokens/s -> 135 tokens/s with topK(40) enabled.
Without topK performance is ~ 110 tokens/s

Various vision models have different requirements for how they
receive their inputs. For example:
 - Mllama wants images together with text and the image embeddings
   don't themselves have positions or get stored in the main KV cache
 - Llava-style models feed in embeddings similar to tokens and
   images correspond to a varying number of tokens in the cache.

In addition, the strategy for providing inputs must support batching
and multiple sequences, which are managed by the runner. At the same
time, we want to keep data handling fully in the model so that new
architectures are not bottlenecked by runner code which does not
understand their particular requirements.

This provides a method for models to edit the input stream so that
it meets their needs while still being in a format that the runner
understands. This allows the runner to avoid special processing
for different models.

In addition, this fixes a regression where non-vision models may
try to incorrectly interpret images.

We sometimes tokenize partial strings. For example, with
multimodal inputs, we split the input string around the images
and then tokenize each piece. In these cases, we should only add
the special tokens on the first piece.

This commit replaces the old pull implementation in the server package
with the new, faster, more robust pull implementation in the registry
package.

The new endpoint, and now the remove endpoint too, are behind the
feature gate "client2" enabled only by setting the OLLAMA_EXPERIMENT
environment variable include "client2".

Currently, the progress indication is wired to perform the same as the
previous implementation to avoid making changes to the CLI, and because
the status reports happen at the start of the download, and the end of
the write to disk, the progress indication is not as smooth as it could
be. This is a known issue and will be addressed in a future change.

This implementation may be ~0.5-1.0% slower in rare cases, depending on
network and disk speed, but is generally MUCH faster and more robust
than the its predecessor in all other cases.

To stay under the 2G github artifact limit, we're splitting ROCm
out like we do on linux.

- output backend system info when initializing the backend. this ensures
  this information is always present without needing to be called
  explicitly
- convert to structured logging
- enumerate devices rather than backends since devices are ordered
- track device indices grouped by device name

* Include unified vision layers in memory prediction

For newer vision models with a single gguf, include
the projection estimates.

* Adjust CLI to handle both styles of vision model metadata

* Wire up new tokenizers for new engine

If we're loading the new engine, utilize the new model
text processor instead of calling into cgo wrappers for
llama.cpp.  This also cleans up some tech debt from the
older tokenization flow for the C++ server which was
no longer used.

This also adjusts the grammar handling logic to pass
through to the new engine instead of utilizing the cgo
schema to grammar call.

* Lay foundation for auto selection of new engine

This reintroduces aggressive pruning on model deletion as a temporary
measure until a more controlled garbage collection (GC) mechanism is
implemented.

Issues with the current approach:

1. Users may accidentally delete a model (`ollama rm llama3.3` instead
   of `ollama rm llama3.2`), requiring a full re-download unless another
   model references the same blobs.

2. Users may assume a deleted model is still referenced elsewhere, but
   due to prior updates or deletions, the references no longer exist,
   leading to unnecessary re-downloads.

Soon, we should implement a structured GC mechanism to retain
unreferenced blobs for a configurable period before removal, which will
run on "ollama rm" and other commands we deem appropriate.

Users that want to immediately remove unreferenced blobs can use a new
prune command that will allow them to specify the age and class of blobs
to remove.

Example usage:

    # Run basic blob GC
    $ ollama prune

    # Remove unreferenced blobs older than 7 days
    $ ollama prune --age 7d

    # Remove all blobs, referenced or not, older than 7 days (and their manifests?)
    $ ollama prune --age 7d --all

    # Remove all unreferenced blobs immediately
    $ ollama prune --age 0 --all

    # Remove all blobs
    $ ollama prune --age 0 --all

This should provide a safer and more predictable cleanup process.

Previously, developers without the synctest experiment enabled would see
build failures when running tests in some server/internal/internal
packages using the synctest package. This change makes the transition to
use of the package less painful but guards the use of the synctest
package with build tags.

synctest is enabled in CI. If a new change will break a synctest
package, it will break in CI, even if it does not break locally.

The developer docs have been updated to help with any confusion about
why package tests pass locally but fail in CI.

expand backend loading error handling to catch more problems and log
them instead of panicing

* Update README.md

removed phi 3 mini and added phi4-mini

* Update README.md

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Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>