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this tensor isn't compatible with cuda when quantized to q4_K so skip it

* update patches

* cherry pick metal mean kernel

* cherry pick cuda mean kernel

* gemma3n

Glue the rocm and archive builds back together.

ggml: fix crash for array head counts

The preset CMAKE_HIP_FLAGS isn't getting used on Windows.
This passes the parallel flag in through the C/CXX flags, along
with suppression for some log spew warnings to quiet down the build.

* CI: switch windows to vs 2022

* ci: fix regex match

For smaller context models, make sure we do not exceed the training size.

* Re-remove cuda v11

Revert the revert - drop v11 support requiring drivers newer than Feb 23

This reverts commit c6bcdc42.

* Simplify layout

With only one version of the GPU libraries, we can simplify things down somewhat.  (Jetsons still require special handling)

* distinct sbsa variant for linux arm64

This avoids accidentally trying to load the sbsa cuda libraries on
a jetson system which results in crashes.

* temporary prevent rocm+cuda mixed loading

Enable parallel building of the GPU architectures.

* Reapply "feat: incremental gguf parser (#10822)" (#11114)

This reverts commit a6e64fbd.

* fix older ggufs

We don't check the return status after computing the graph, which
can silently lead to bad outputs if we try to keep going and future
computation succeeds. This appears to happens in certain cases on
Apple M2 devices.

Fixes #11070

Verified these fail on 0.9.1 and pass on HEAD.

Removes a test under benchmark/ that is unused

Reverts PR #11115. The original change was mistakingly reverted instead of #10822

This reverts commit aaa78180.

This reverts commit 6b04cad7.

Fixes issue where tool calls that don't expect any parameters were
not being parsed. This also fixes two additional issues: one where
2+ tool calls would not be correctly parsed, and cases where tool calls
with invalid parameters would still get parsed

* ggml: test write gguf order
* ggml: fix write tensor order

* incremental gguf parser
* gguf: update test to not rely on gguf on disc
* re-use existing create gguf
* read capabilities from gguf kv
* kv exists
* update tests
* s/doneFunc/successFunc/g
* new buffered reader

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

The current splitDim function only operates on tensors that are split evenly which isn't always the case, e.g. a QKV tensor. This change allows the function to be used for arbitrary splits

if tokenizer.json is already copied, skip tokenizer.model

while nn.Linear.Forward isn't applicable for sparse MLP, it's still
a nice container for the tensors