Signed-off-by: Gabe Goodhart <redacted>
* fix: Always parse the enable_thinking kwarg to overwrite the default value
From what I can tell, this started as a Qwen3-specific keyword, but from
the use in `chat.cpp` translates this inputs.enable_thinking to the right
thinking kwarg for the given model, this is now more of a standardized
kwarg, so it should always override the default value when sent as part of
the chat_template_kwargs field in the API.
Signed-off-by: Gabe Goodhart <redacted>
* feat: Explicitly reject string values for "enable_thinking"
There are too many possible "truthy" / "falsy" strings and too many
ambiguous strings that don't have a clear truthy/falsy value, so the
simplest thing to do here is to reject the request. Ideally, this would be
a 422 (Unprocessable Entity), but right now it's coming back as a 500.
Daniel Bevenius [Fri, 5 Sep 2025 12:49:21 +0000 (14:49 +0200)]
tests : add --list-ops and --show-coverage options (#15745)
This commit adds two new command-line options to the
test-backend-ops.cpp that allow users to list all available GGML
operations and to show test coverage of these operations.
The motivation for this is that it can be useful to quickly see which
operations are currently covered by tests and which are not. Also it
migth be useful when using the `support` mode.
Erik Scholz [Fri, 5 Sep 2025 09:34:28 +0000 (11:34 +0200)]
gguf: gguf_writer refactor (#15691)
* gguf: split gguf writer into base and buf impl
* gguf: templated gguf write out
* gguf: file based writer (avoid writing everything to memory first!)
* examples(llama2c): fix log not being the same level and compiler nits
Daniel Bevenius [Fri, 5 Sep 2025 02:36:23 +0000 (04:36 +0200)]
model-conversion : add --embeddings flag to modelcard.template [no ci] (#15801)
This commit updates the modelcard.template file used in the model
conversion scripts for embedding models to include the llama-server
--embeddings flag in the recommended command to run the model.
The motivation for this change was that when using the model-conversion
"tool" to upload the EmbeddingGemma models to Hugging Face this flag was
missing and the embedding endpoint was there for not available when
copy&pasting the command.
Daniel Bevenius [Thu, 4 Sep 2025 16:10:29 +0000 (18:10 +0200)]
llama : add support for EmbeddingGemma 300m (#15798)
This commit add support for the EmbeddingGemma 300m. This model supports
sliding window attention (SWA) and a new swq_type is introduced to
support symmetric SWA masking.
This commit also extracts the code from the function
llama_is_masked_swa in llama-impl.h, so that the logic can be shared
by both llm_graph_input_attn_no_cache::set_input and
llama_kv_cache::set_input_kq_mask.
With this commit the EmbeddingGemma 300m model can be converted to
to GGUF and used with llama.cpp.
Once the model has been uploaded to HuggingFace it can be used like
this:
```console
./build/bin/llama-cli -hf ggml-org/embeddinggemma-300m-GGUF:Q8_0
```
Daniel Bevenius [Thu, 4 Sep 2025 13:40:44 +0000 (15:40 +0200)]
llama : set n_outputs to 1 to avoid 0 outputs mean-pooling (#15791)
* llama : set n_outputs to 1 to avoid 0 outputs mean-pooling
This commit modifies the llama_context constructor to set n_outputs to
1.
The motivation for this is that when using pooling, and specifically
mean pooling, for embeddings having n_outputs set to 0 can lead to the
following error:
```console
$ build/bin/llama-embedding -m models/nomic-embed-text-1.5-Q4_K_M.gguf \
--pooling mean -p "Hello, how are you?"
...
llama_context: CPU output buffer size = 0.12 MiB
/home/danbev/work/ai/llama.cpp/ggml/src/ggml.c:3023: GGML_ASSERT(ggml_can_mul_mat(a, b)) failed
0x0000743c96d107e3 in __GI___wait4 (pid=292978, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
warning: 30 ../sysdeps/unix/sysv/linux/wait4.c: No such file or directory
30 in ../sysdeps/unix/sysv/linux/wait4.c
196 waitpid(child_pid, NULL, 0);
230 ggml_print_backtrace();
3023 GGML_ASSERT(ggml_can_mul_mat(a, b));
1823 cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, inp)), inp_mean);
18983 llm->build_pooling(cls, cls_b, cls_out, cls_out_b);
1399 auto * gf = model.build_graph(gparams);
292 auto * gf = graph_reserve(1, n_seqs, n_outputs, mctx.get(), true);
2329 auto * ctx = new llama_context(*model, params);
913 llama_context * lctx = llama_init_from_model(model, cparams);
105 common_init_result llama_init = common_init_from_params(params);
[Inferior 1 (process 292976) detached]
Aborted (core dumped)
```
Co-authored-by: Georgi Gerganov <redacted>
* add comment about not reserving graphs with zero outputs
* add assert in graph_reserve to ensure n_outputs >= 1
Chenguang Li [Thu, 4 Sep 2025 12:20:14 +0000 (20:20 +0800)]
CANN: Refactor ND to NZ workspace to be per-device (#15763)
* CANN:Refactor ND to NZ workspace to be per-device in Ascend backend
- Replaced the previous single global ND→NZ workspace with a per-device
cache using unordered_map keyed by device ID.
- Functions `release_nz_workspace`, `relloc_nz_workspace`, and
`get_nz_workspace` now manage workspace independently for each device,
preventing memory conflicts in multi-device / pipeline parallel scenarios.
- This change fixes potential precision issues caused by workspace
overwrites when multiple devices perform ND→NZ conversions concurrently.
This commit updates ggml_vk_instance_validation_ext_available() to
check for VK_EXT_validation_features instead of
VK_KHR_portability_enumeration.
Based on how the returned boolean is used later in the code (to enable
both the validation layer and the VK_EXT_validation_features extension),
it appears the function may have been intended to check for the
validation layer features extension.
* remove try/catch
This was a left over from a previous iteration where I was explicitly
quering for a specific validation layer first, which would throw.
Daniel Bevenius [Wed, 3 Sep 2025 11:35:49 +0000 (13:35 +0200)]
llama : fix incorrect model type for Gemma 270M (#15764)
This commit fixes the model type for the Gemma 270M model in
llama_model.cpp which should be LLM_TYPE_270M. I incorrectly added this
previously as LLM_TYPE_537M which was wrong.
The motivation for this is that it causes the model to not be identified
properly when using tools like llama-bench. For example:
```console
$ ./build/bin/llama-bench -m models/gemma-3-270m-Q8_0.gguf
| model | size | ...
| ------------------------------ | ---------: | ...
| gemma3 ?B Q8_0 | 271.81 MiB | ...
| gemma3 ?B Q8_0 | 271.81 MiB | ...
```
With the changes in this commit the output will be:
```console
$ ./build/bin/llama-bench -m models/gemma-3-270m-Q8_0.gguf
| model | size | ...
| ------------------------------ | ---------: | ...
| gemma3 270M Q8_0 | 271.81 MiB | ...
| gemma3 270M Q8_0 | 271.81 MiB | ...
```
This commit updates the bash scripts to use env instead of using
hardcoded /bin/bash in the shebang line.
The motivation for this is that some systems may have bash installed
in a different location, and using /usr/bin/env bash ensures that
the script will use the first bash interpreter found in the user's
PATH, making the scripts more portable across different environments.
* model-conversion : rename script to .py [no ci]
This commit renames run-casual-gen-embeddings-org.sh to
run-casual-gen-embeddings-org.py to reflect its Python nature.
This commit adds a curl script to the model-conversion examples
which is currently missing. This script is required for the running the
embedding server targets to test llama-server embeddings functionality.
Oliver Simons [Tue, 2 Sep 2025 17:40:37 +0000 (19:40 +0200)]
chore: Update `.clang-format` to use `BinPackArguments=true` (#15744)
This seems to correspond with what we want to do, see
[here](https://github.com/ggml-org/llama.cpp/pull/15715#discussion_r2315613796)
and [clang-format docs](https://clang.llvm.org/docs/ClangFormatStyleOptions.html#binpackarguments)
Previously, the slope tensor was set to fp16 to improve efficiency.
While this worked correctly in FA, it caused precision issues in soft_max.
This change applies different data types for different operators
to balance both accuracy and performance.
Chenguang Li [Tue, 2 Sep 2025 06:07:48 +0000 (14:07 +0800)]
CANN: Support eager execution mode under ACL graph compilation (#15712)
* [CANN] Support eager execution mode under ACL graph compilation
Add support for running operators in eager mode while ACL graph
compilation is enabled. This allows bypassing graph execution
and directly submitting ops, which is useful for debugging and
reducing graph build overhead in certain scenarios.
Signed-off-by: noemotiovon <redacted>
* fix typo
Signed-off-by: noemotiovon <redacted>
* rename to acl_graph_mode
CUDA: fix build error from ambiguous __half conversions in conv2d (#15690)
* CUDA: fix build error from ambiguous __half conversions in conv2d
Building conv2d with half precision failed because `__half` defines
multiple implicit conversion operators (to float, int, short, etc.),
causing ambiguous overload resolution when multiplying with float.
Introduce a templated `to_float` helper that explicitly converts
`__half` via `__half2float`, while passing through float unchanged.
Use this helper in conv2d accumulation to ensure unambiguous and
correct promotion to float.
Fixes some build errors with half-precision kernels on CUDA.
ggml-ci
* CUDA: Replace custom to_float helper with unified ggml_cuda_cast and add half‑>float conversion
* CUDA: Add missing convert.cuh header
* CUDA: remove unnecessary extension in ggml_cuda_cast
* CUDA: Address review comment, remove second type template argument
CANN: fix RoPE cache issue on multi-device (#15629)
* CANN: fix RoPE cache issue on multi-device
RoPE cache only needs to be computed once per token.
However, in multi-device scenarios, not every device starts
computation from layer 0, which may lead to unallocated memory
issues and precision errors.
This commit records the first layer of each device to avoid
the above issues.
* CANN: Optimize first-layer detection method
* CANN: Remove trailing whitespace
* CANN: Only cache the data that can be determined as unchanged through the parameters.
Diego Devesa [Sun, 31 Aug 2025 13:49:03 +0000 (06:49 -0700)]
llama : separate compute buffer reserve from fattn check (#15696)
Exposes ggml_backend_sched_split_graph() to allow splitting the graph without allocating compute buffers and uses it to split the graph for the automatic Flash Attention check.
This commit removes the portability_enumeration_ext variable from the
ggml_vk_instance_portability_enumeration_ext_available function as it
is initialized to false but never modified, making it redundant.
Signed-off-by: Gabe Goodhart <redacted>
* fix: Explicitly enable add_bos_token during conversion
The `tokenizer.json`/`tokenizer_config.json` in the model are a bit
contradictory. In the config, add_bos_token is set to False, but the
tokenizer model itself has a post_processor that adds the BOS token via
type: TemplateProcessing
- Update architecture name from NEMOTRONH to NEMOTRON_H in constants.py
- Change architecture string from 'nemotronh' to 'nemotron_h' in all files
- Update enum LLM_ARCH_NEMOTRONH to LLM_ARCH_NEMOTRON_H
- Update class name llm_build_nemotronh to llm_build_nemotron_h
- Consistent naming with underscore convention (nemotron_h vs nemotronh)
* feat: Support conversion for older NemotronH models
This commit adds a new target to the Makefile for converting models that
are multimodal. This target will convert the original model and in
addition also create the mmproj GGUF model.
The motivation for this change is that for models that are multimodal,
for example those that contain a vision encoders, we will often want to
upload both the quantized model and the vision encoder model to
HuggingFace.
Example usage:
```console
$ make causal-convert-mm-model MODEL_PATH=~/work/ai/models/gemma-3-4b-it-qat-q4_0-unquantized/
...
The environment variable CONVERTED_MODEL can be set to this path using:
export CONVERTED_MODEL=/home/danbev/work/ai/llama.cpp/models/gemma-3-4b-it-qat-q4_0-unquantized.gguf
The mmproj model was created in /home/danbev/work/ai/llama.cpp/models/mmproj-gemma-3-4b-it-qat-q4_0-unquantized.gguf
```
The converted original model can then be quantized, and after that both
the quantized model and the mmproj file can then be uploaded to
HuggingFace.
Chenguang Li [Wed, 27 Aug 2025 09:21:41 +0000 (17:21 +0800)]
CANN: refactor mask handling and improve performance in FA (#15561)
* CANN(flash-attn): refactor mask handling and improve performance
1. Refactored the mask computation in Flash Attention, unified the logic without separating prefill and decode.
2. Optimized performance in non-alibi scenarios by reducing one repeat operation.
3. Updated operator management to explicitly mark unsupported cases on 310P devices and when dim is not divisible by 16.
Daniel Bevenius [Wed, 27 Aug 2025 08:28:53 +0000 (10:28 +0200)]
common : add -m to bash completion for --model [no ci] (#15591)
This commit updates the bash completion script to include the -m
short option for the --model argument.
The motivation for this is that currently tab completion only works the
full --model option, and it is nice to have it work for the short option
as well.
overview / pkg / ggml / sources / llama.cpp / log