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* test: harden scheduler tests

This removes reschedDelay which was stale code, and adds
a new configurable timeout for the waitForVRAMRecovery so
tests can now set the timeout to be very short to avoid the
scheduler getting stuck and hitting a test timeout.

* test: tune tests for partial loads

Give stress tests more time when the model is split between CPU/GPU

Adds a temporary global flag to renderers that causes renderers to always
render images as [img]. In a follow up change, we will consider making this
the default, and this flag could eventually be removed

* working (other than tool call is the incorrect order) for tool calls and tools

* Tests work, other than image tags (tests do not go through server) and tools (not in the correct order, but contents are the same)

* testing for qwen3vl parser - toolparser is working

* made changes to JSON tool parser, wraps the TollCallFunction with a TollCall object

* Working parser for thinking models - assumes state of thinking, emits unambiguous content in thinking, does not call tool call in thinking

* changed the parser to start with collecting content

* thinking prefill

* add hasThinkingSupport parameter to parser

* qwen3-vl -> qwen3-vl-instruct for renderer/parser

* Add hasThinkingSupport=false to QwenVLParser

---------
Co-authored-by: Devon Rifkin <drifkin@drifkin.net>

Made it so when api/generate builds up a message array and generates the
prompt it now goes through the same function as `api/chat` for
consistency. This is where we hook the optional built-in renderers to
bypass templates, which was missing for `api/generate` before this
change.

Closes: #12578

* implement nvml for linux

* Improve scheduler logging when VRAM doesn't recover

* logs: quiet down context canceled on completion

If the client closes the connection before Completion finishes, we were
logging at error level implying the runner crashed which was misleading.

time=2025-10-08T22:59:20.566-07:00 level=ERROR source=server.go:1490 msg="post predict" error="Post \"http://127.0.0.1:57736/completion\": context canceled"

* quiet down scheduler log error on expected case

Since we don't hold the lock while performing memory load calculations, other
runners can unload in parallel, so finding no runner to unload is a valid scenario
which we shouldn't log at error level.

This reverts commit 6a62b894.

This makes the core openai compat layer independent of the middleware
that adapts it to our particular gin routes

This revamps how we discover GPUs in the system by leveraging the Ollama
runner.  This should eliminate inconsistency between our GPU discovery and the
runners capabilities at runtime, particularly for cases where we try to filter
out unsupported GPUs.  Now the runner does that implicitly based on the actual
device list.  In some cases free VRAM reporting can be unreliable which can
leaad to scheduling mistakes, so this also includes a patch to leverage more
reliable VRAM reporting libraries if available.

Automatic workarounds have been removed as only one GPU leveraged this, which
is now documented. This GPU will soon fall off the support matrix with the next
ROCm bump.

Additional cleanup of the scheduler and discovery packages can be done in the
future once we have switched on the new memory management code, and removed
support for the llama runner.

this reference to keep alive was missed in #12041 so chat has a
diffferent behaviour than generate

* auth: fix problems with the ollama keypairs

This change adds several fixes including:
  - reading in the pubkey files correctly
  - fixing the push unit test to create a keypair file in a temp directory
  - not return 500 errors for normal status error

Now that we have a built-in parser abstraction, which was introduced in
<https://github.com/ollama/ollama/pull/12248>, we can modify our harmony
parser to match this and then get rid of nearly all of the
harmony-specific logic in routes.go. We do have a small amount of
code that turns the parser on by default if the architecture matches and
no other built-in parser was provided.

The built-in parser interface was modified in order to handle harmony's
prefill and tool name translation requirements.

Signed-off-by: russcoss <russcoss@outlook.com>

* fix truncate

* s/SentencePieceModel/SentencePiece/

* bert

* wordpiece

* refactor pooling

* more tokenizers

* normalize embeddings

The format qwen3-coder uses is relatively unique, both in rendering and
in parsing. To implement parsing, I wrote a custom parser in similar
style to harmony. For the rendering, I found that the logic would be
much more difficult to follow in a template, so I introduced the concept
of a built-in renderer that uses go code, rather than a template to
generate prompts.

I set us up for future built-in parsers and renderers by making it so
they can be specified in a Modelfile like so:

```
RENDERER "qwen3-coder"
PARSER "qwen3-coder"
```

These need to be provided explicitly because the architecture alone is
not enough to understand what format the model expects to receive, and
what format we expect it to output (e.g., qwen3-coder is `qwen3moe`,
which includes other qwen3-family models as well)

I haven't converted harmony to be one of these "built-ins" yet, since
some of it is in flux with the changes @ParthSareen has been making to
move harmony to the runner. It is likely that many other built-ins will
need to move to the runner as well, but I'm able to slightly defer that
decision since qwen3-coder doesn't have thinking (and therefore doesn't
need to be in the runner to make structured outputs work). I expect to
unify harmony with this approach very soon.

Whether a particular model supports tools or thinking was previously
inferred from templates, but without a template we now also use the
parser itself to declare what it supports. If we have future models that
re-use the same parsing format, but have different capabilities, we'll
want to parameterize them and give them different names to be specified
as a `PARSER`.

Misc changes:

- I worked on the renderer by diffing outputs from the reference
  implementation and ours. To make it easier to do this, I extended
  <https://github.com/ollama/ollama/pull/11875> to also support
  returning the prompt via the openai compat layer

This reverts commit 1a558f98.

This reverts commit 8d6fffae.

* feat: add field to truncate embeddings

* add openai embeddings for dimensions

In <https://github.com/ollama/ollama/issues/11704#issuecomment-3177380197>
I noticed that hyphens in function names could possibly cause the model
to become confused. Later in that issue I found other explanations, but
at a minimum tool names with spaces in them are confusing to the model
because of the prompt format.

In this change I create a mapper that converts arbitrary tool names into
valid typescript identifiers. It's a little overly strict in that it
doesn't allow all unicode characters that might be valid in ts
identifiers, but it's still very permissive. Since mappings aren't
reversible, we must temporarily store this mapping in order to unmap it
if the model comes back with a call. We also handle the case where
multiple mappings collide into the same mapping and append a counter to
the end to make them unique

This changes the memory allocation strategy from upfront estimation to
tracking actual allocations done by the engine and reacting to that. The
goal is avoid issues caused by both under-estimation (crashing) and
over-estimation (low performance due to under-utilized GPUs).

It is currently opt-in and can be enabled for models running on the
Ollama engine by setting OLLAMA_NEW_ESTIMATES=1. Behavior in other
cases is unchanged and will continue to use the existing estimates.

* TEMPORARY: Update the llama.cpp upstream to my fork's Granite Four branch

This will be redone once my branch is merged upstream in llama.cpp

* feat: Update all patches

There are a number that are no longer needed at all:

- 0003-embeddings: Embeddings entirely overhauled on master
- 0008-ensure-KV-cache-is-fully-defragmented: KV caching entirely
    overhauled on master
- 0019-metal-add-mean-kernel-14267: Merged upstream
- 0020-CUDA-add-mean-operation-14313: Merged upstream

* feat: Sync llama.cpp and ggml

* fix: Update rsync-filter for all moved/new/removed files

* fix: Add files missing from sync

* fix: Update ggml rsync-filter for new ggml-cpu/arch subdirs

* fix: Add ggml files missing from sync

* fix: Narrow llama.cpp rsync-filter to not include mtmd main tool cpp files

* fix: Remove mtmd main cpp files

* fix: Add missing include in sampling_ext.cpp

* fix: Update llama.go to use mtmd instead of clip/llava

* fix: Add patch for mtmd_input_text

* chore: Ignore *.patched in the patch directory

* fix: Fix support for arch-specific ggml-cpu source files with new arrangement

In https://github.com/ggml-org/llama.cpp/pull/13892, all arch-specific
implementations were split out into a nested tree structure under
ggml-cpu/arch. This conflicts with standard CGO layout where all
arch-specific source files are expected to live in the same directory as
the parent go module and use suffixes based on GOOS and GOARCH. As such,
there were really two options for getting this to work:

1. Add a patch on top of the GGML sync to rearrange the files to match the
GO layout convention
2. Use CGO directives to conditionally include the nested source files in
the compilation units

This commit does (2) in order to minimize the set of changes needed on top
of the upstream file layout. To get this to work, there are two key things
needed:

1. In cpu.go, #cgo directives are added to explicitly set __${GOARCH}__ in
the preprocessor directives
2. In arch-impls.c|cpp, use an #ifdef | #elif defined | #endif chain to
explicitly include the .c|.cpp files for the given architecture from the
nested directory

* fix: Use mtmd_helper to correctly load the bitmap for the image

* fix: Apply patch for mtmd_text_input

* fix: Add missing stb to llama.cpp rsync-filter

* fix: Add sync'ed stb vendored header

* fix: Use c++17 and include vendor for go wrapper modules

* fix: Update patch 0015 for upstream implementation of uuid

* feat: Bump to the latest tip of the branch

* fix: Update patches for bump

* feat: Bump back to the cenral repo and point at the latest master

This includes granite 4 and a number of other model architectures!

* fix: Revert changes to ggml export GPU UUID patch

* fix: Add patch for GGML_VERSION and GGML_COMMIT constants

* feat: Sync all patched code

* build: Include cmake/common.cmake in ggml sync

* build: Add top-level include for GNUINstallDirs in CMakeLists.txt

This is used to populate CMAKE_INSTALL_BINDIR

* fix: Add a patch to avoid power throttling API on non-msvc windows builds

* fix: Sync patch changes for ggml-cpu.c

* feat: Bump llama.cpp to 4a4f42

This picks up support for Kimi K2 and PLaMO-2

* feat: Sync llama.cpp

* fix: Handle multi-chunk image encodings from mtmd

* fix: Re-number patches after merge with `main`

* feat: Bump to 41e78c in the makefile

* fix: Fix Solar and argsort/copy patches after bump

* fix: Remove Gemma3n CUDA Graphs patch

It was implemented upstream:
https://github.com/ggml-org/llama.cpp/pull/14741

* feat: Sync llama.cpp / ggml after latest bump

* build: Remove unnecessary CFLAGS definitions in cpu.go

* fix: Remove unnecessary additions in the rsync-filter

* fix: Remove unused vendored code for chat template parsing

* Revert "fix: Remove Gemma3n CUDA Graphs patch"

This reverts commit d724caced3ce21f08924d4b7801f94ce6638f6ea.

* fix: Update 0020 CUDA Graphs for gemma3n to keep both llama.cpp and ollama fixes

https://github.com/ollama/ollama/pull/11195#issuecomment-3137312394



* fix: Sync ggml-cuda.cu after keeping both style cuda graph fixes for gemma3n

* unwind mxfp4 patch

Prepare to bump ggml with their impl for mxfp4

* bump

* fix windows build error

* Convert tensors at load time

Repack the mxfp4 tensors as ggmls kernels expect them to be.

* convert mlp bf16 to f32

* buffer the conversion better

* reshape earlier

* openai swiglu

* add ids

* split qkv, gate_up

* fix nested alt tags

* fast attention

* remove debug messages

* fix lint

* remove redundant test

* remap values only if source/target are different

* add back i32->i32 copy

* refactor cpu quants

* clean up vendor

* update patch instructions

* clean up patches

* remove webgpu

* update mem

* also handle gpt-oss

* revert convert changes

---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>

Signed-off-by: youzichuan <youzichuan6@outlook.com>