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* bf16

* tests

* gpt-oss

* enable gptoss for engine

* rough estimate

* convert to mxfp4

* handle safetensors U8

* clamp glu/linear

* update tokenizer

* MXFP4 support

This implements the Open Compute Microscaling (MX) FP4 format
as a tensor type with backend implementations focusing
on mulmat and mulmatid on CPU, CUDA, and Metal.

* Unit tests for MXFP4 support

This exercises various operations and shapes on both CPU and GPU (if detected
on the system)

* cuda graph

* unit test adjustments

* cuda: optimize memory access

Read 4 bytes at a time (8 elements) when performing mul_mat_vec_mxfp4

* mac: fix crash on old macos versions

cblas_sgemm is only supported on v13.3 and up, however bf16 is
only supported on v14+ so we were falling back to ggml-blas and
crashing on bf16 tensors.  Checking for the function being null
seems to be the simplest way to condittionally avoid registering the
backend.

* server: Minimum context length for gptoss

This model requires a minimum context length of 8192 to function
effectively. Users can set higher values through all normal mechanisms
but lower values will be silently reset.

* ggml: Multiply by numParallel for gptoss sliding window

When computing the graph size estimate, the context size is already
multiplied by numParallel so estimates reflect that. However, since
sliding window models use a smaller, fixed context size, they need
to manually take numParallel into account.

* gpt-oss integration

includes harmony parser and thinking levels, etc.

* fix sync

* fix tests

* fix lint

---------
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
Co-authored-by: Jesse Gross <jesse@ollama.com>
Co-authored-by: Devon Rifkin <drifkin@drifkin.net>

* update patches

* cherry pick metal mean kernel

* cherry pick cuda mean kernel

* gemma3n

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

This reverts commit a6e64fbd.

* fix older ggufs

This reverts commit 6b04cad7.

* 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>

Currently, when the backend is created, the tensors are loaded at the
same time, which is a slow operation. This separates them to be two
steps:
 - Create backend, including enumerating tensors and memory allocation
 - Loading tensor data

This allows more flexibility in managing model loading.

The quantization PR didn't block all unsupported file types,
which this PR fixes.  It also updates the API docs to reflect
the now reduced set of supported types.

err in the go routine should not be shared with the outer scope

* Move quantization logic to GGML via new backend

This moves the model aware logic to Go code and calls GGMLs quantization code for model creation.

* Remove "add model quantizations"

This is no longer needed now that quantization is implemented in Go+GGML code directly.

Most of the time this is not an error.

* add gguf_test

* fix padding

padding was being added to offset but not to the running count

If it's an array, it uses the max value in the array

If array values for head counts becomes more popular, we can consider a
more invasive change like #10225 to calculate more accurate estimates.

Fixes: #9984

use a default of 1024 when asking for zero is confusing since most calls
seem to assume 0 means do not ready any data

Mistral is a popular research lab making open source models. This updates
the forward pass of llama architecture models to support both llama models
and mistral models by accounting for additional metadata present in mistral
models, and finding the correct dimensions for the output projection.

Gemma3 uses sliding windows for its context on 5/6 layers, significantly
reducing memory usage but leading to uneven usage across layers,
which makes allocation to the correct GPU difficult. We currently
estimate very conservatively by assuming all layers are consistent
at the max size.

Llama3.2-vision is also inconsistent between self attention and cross
attention layers - at moment, we calculate the correct total size
and then average this across layers. In some cases, this may lead
to crashes if a large layer is placed on a GPU sized by the average.

This allows memory estimation to calculate per-layer KV cache size
and take this account when placing layers onto GPUs. We already do
this for weights that vary per-tensor, so this is a logical extension.

Fixes #9730
Fixes #9890

the largest operation is by far (q @ k) so just count that for
simplicity

Add metadata and tensor information to the show command to be able to
see more information about a model. This outputs the same data as
shown on the model details page on ollama.com

* 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