* Updated documentation
Model is no longer a parameter
* llama : fix trailing whitespace in comment
---------
Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* feat: Enable adding System Prompt per-chat
* fix: Save draft message in Chat Form when adding System Prompt from new chat view
* fix: Proper system message deletion logic
* chore: Formatting
* chore: update webui build output
There is an upstream problem [1] with AMD's LLVM 22 fork and
rocWMMA 2.2.0 causing compilation issues on devices without
native fp16 support (CDNA devices).
The specialized types aren't resolved properly:
```
/opt/rocm/include/rocwmma/internal/mfma_impl.hpp:2549:37: error: ambiguous partial specializations of 'amdgcn_mfma<__half, __half, __half, 16, 16, 16>'
2549 | using ARegsT = typename Impl::ARegsT;
```
Add a workaround to explicitly declare the types and cast when
compiling with HIP and ROCWMMA_FATTN [2]. When this is actually
fixed upstream some guards can be used to detect and wrap the
version that has the fix to only apply when necessary.
Link: https://github.com/ROCm/rocm-libraries/issues/4398 [1]
Link: https://github.com/ggml-org/llama.cpp/issues/19269 [2]
Signed-off-by: Mario Limonciello <mario.limonciello@amd.com>
This commit removes two unused functions `common_lcp` and `common_lcs`.
The last usage of these functions was removed in
Commit 33eff40240 ("server : vision support
via libmtmd") and are no longer used anywhere in the codebase.
* Move dequant_model to after the text_config merge
Add new kimi-k2.5 keys to mtmd convert
Update V_MMPROJ tensor mapping for new mm_projector.proj keys
Update V_M_IMP_NORM for new mm_projector.pre_norm key
* Fix a couple of oversights
* Add image support for Kimi-K2.5
* Revert changes to KimiVLForConditionalGeneration
* Fix an assert crash
* Fix permute swapping w / h on accident
* Kimi-K2.5: Use merged QKV for vision
* Kimi-K2.5: pre-convert vision QK to use build_rope_2d
* Kimi-K2.5: support non-interleaved rope for vision
* Kimi-K2.5: fix min / max pixel
* Kimi-K2.5: remove v/o permutes, unnecessary
* Kimi-K2.5: update permute name to match
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Kimi-K2.5: replace build_rope_2d ggml_cont with ggml_view_3d pointers
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit updates an incorrect dSYMs where the the 's' was uppercase
by mistake.
The motivation for fixing this is that this can cause issues on case
sensitive operating systems.
Refs: https://github.com/ggml-org/whisper.cpp/pull/3630
* hexagon: add ARGSORT op
Co-authored-by: Yarden Tal <yardent@qti.qualcomm.com>
* hexagon: argsort reject tensors with huge rows for now
* Adding support for DIV,SQR,SQRT,SUM_ROWS ops in hexagon backend
* hexagon : Add GEGLU op
* hexagon: fix editor config check
* hexagon: rewrite and optimize binary ops ADD/SUB/MUL/DIV/ADD_ID to use DMA
---------
Co-authored-by: Yarden Tal <yardent@qti.qualcomm.com>
Co-authored-by: Manohara Hosakoppa Krishnamurthy <mhosakop@qti.qualcomm.com>
* llama : refactor sampling_info to use buffer_view template
This commit updates the sampling_info struct in llama-context to use a
buffer_view template for the logits, probs, sampled tokens, and
candidates buffers.
The motivation for this is to simplify the code, improve type safety
and readability.
CCCL 3.2 has been released since it was added to llama.cpp as part of
the backend-sampling PR, and it makes sense to update from RC to final
released version.
https://github.com/NVIDIA/cccl/releases/tag/v3.2.0
* Fix memory leaks in shader lib, backend, backend_context, buffer_context, and webgpu_buf_pool
* Free pools
* Cleanup
* More cleanup
* Run clang-format
* Fix arg-parser and tokenizer test errors that free an unallocated buffer
* Fix device lost callback to not print on device teardown
* Fix include and run clang-format
* remove unused unused
* Update binary ops
---------
Co-authored-by: Reese Levine <reeselevine1@gmail.com>
* support qwen3.5 series
* remove deepstack for now, and some code clean
* code clean
* add FULL_ATTENTION_INTERVAL metadata
* code clean
* reorder v heads for linear attention to avoid expensive interleaved repeat
* First working version of GEMM and GEMV
* interleave loads and compute
* Clang-format
* Added missing fallback. Removed tested TODO.
* Swap M and N to be consistent with the repack template convention
using noexcept std::filesystem::directory_entry::is_regular_file
overload prevents abnormal termination upon throwing an error
(as caused by symlinks to non-existent folders on linux)
Resolves: #18560
Implement ggml_cann_mul_mat_id_quant function to support quantized matrix
multiplication for Mixture of Experts (MoE) architectures on CANN backend.
Key features:
- Support Q4_0 and Q8_0 quantized weight formats
- Use IndexSelect to dynamically route expert-specific weights based on indices
- Leverage WeightQuantBatchMatmulV2 for efficient quantized computation
- Handle automatic F16 type conversion for hardware compatibility
- Support both per-expert and broadcast input modes
Implementation details:
- Extract expert weights and scales using CANN IndexSelect operation
- Process each batch and expert combination independently
- Create proper tensor views with correct stride for matmul operations
- Automatic input/output type casting to/from F16 as needed
Testing: All test cases passed for supported types (F32, F16, Q4_0, Q8_0).
* ci : add metal server workflows
* cont : try fix python init
* cont : move to a separate workflow that runs only on master
* cont : fix num jobs
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Adrien Gallouët
Georgi Gerganov
Shupei Fan
lhez
Aleksander Grygier
Christian Schmitz
Daniel Bevenius
Mario Limonciello
RichardScottOZ
TriDefender
Max Krasnyansky
AesSedai
Johannes Gäßler
thecaptain789
Oliver Simons
Nikhil Jain
JJJYmmm
Xuan-Son Nguyen
Alberto Cabrera Pérez
k4ss4n
Piotr Wilkin (ilintar)
Raul Torres
hipudding
Tarek Dakhran
손희준
Sascha Rogmann