Daniel Bevenius [Thu, 9 Oct 2025 12:35:22 +0000 (14:35 +0200)]
model-conversion : add support for SentenceTransformers (#16387)
* model-conversion : add support for SentenceTransformers
This commit adds support for models that use SentenceTransformer layers.
The motivation for this is that if converted model includes any of the
numbered layers specified in the original models repository then these
changes enable these models to be used and verified. Currently the
model-conversion only support the base model output without any of
the additional transformation layers.
Usage:
Convert the model that also includes the SentenceTransformer layers:
```console
(venv) $ export EMBEDDING_MODEL_PATH="~/google/embeddinggemma-300M"
(venv) make embedding-convert-model
```
Verify the produced embeddings from the converted model against the
original model embeddings:
```console
(venv) make embedding-verify-logits-st
```
The original model can be run using SentenceTransformer:
```console
(venv) make embedding-run-original-model-st
```
Run the converted model using "SentenceTransformer" layers whic
enables pooling and normalization:
```console
(venv) make embedding-run-converted-model-st
```
* add model-conversion example requirements
* add support for -st flag in embedding model conversion
This commit add support for the -st flag in the embedding model
conversion script. This will enable models to be converted using
sentence transformers dense layers.
Chenguang Li [Thu, 9 Oct 2025 07:50:25 +0000 (15:50 +0800)]
CANN: Improve ACL graph matching (#16166)
* CANN: improve ACL graph matching
Record `ne` and `nb` information for src tensors and include them in the
graph matching check. This enhances the robustness of ACL graph matching
by preventing incorrect matches when src tensors share the same data
address but differ in shape or stride.
Saba Fallah [Thu, 9 Oct 2025 06:39:18 +0000 (08:39 +0200)]
model: EmbeddingGemma Adding Support for SentenceTransformers Dense Modules (#16367)
* model: EmbeddingGemma sentence-transformers dense linear projections support
* model: add support for EmbeddingGemma SentenceTransformers dense linear projections
Adding support for the Dense modules used in EmbeddingGemma models.
EmbeddingGemma is a SentenceTransformers model with additional modules beyond the base Transformer backbone.
Pascal [Wed, 8 Oct 2025 20:18:41 +0000 (22:18 +0200)]
refactor: centralize CoT parsing in backend for streaming mode (#16394)
* refactor: unify reasoning handling via backend reasoning_content, drop frontend tag parsing
- Updated the chat message component to surface backend-supplied reasoning via message.thinking while showing the raw assistant content without inline tag scrubbing
- Simplified chat streaming to append content chunks directly, stream reasoning into the message model, and persist any partial reasoning when generation stops
- Refactored the chat service SSE handler to rely on server-provided reasoning_content, removing legacy <think> parsing logic
- Refreshed Storybook data and streaming flows to populate the thinking field explicitly for static and streaming assistant messages
Remove the streaming mode limitation from --reasoning-format by refactoring
try_parse_reasoning() to handle incremental parsing of <think> tags across
all formats.
- Rework try_parse_reasoning() to track whitespace, partial tags, and
multiple reasoning segments, allowing proper separation of reasoning_content
and content in streaming mode
- Parse reasoning tags before tool call handling in content-only and Llama 3.x
formats to ensure inline <think> blocks are captured correctly
- Change default reasoning_format from 'auto' to 'deepseek' for consistent
behavior
- Add 'deepseek-legacy' option to preserve old inline behavior when needed
- Update CLI help and documentation to reflect streaming support
- Add parser tests for inline <think>...</think> segments
The parser now continues processing content after </think> closes instead of
stopping, enabling proper message.reasoning_content and message.content
separation in both streaming and non-streaming modes.
Fixes the issue where streaming responses would dump everything (including
post-thinking content) into reasoning_content while leaving content empty.
* refactor: address review feedback from allozaur
- Passed the assistant message content directly to ChatMessageAssistant to drop the redundant derived state in the chat message component
- Simplified chat streaming updates by removing unused partial-thinking handling and persisting partial responses straight from currentResponse
- Refreshed the ChatMessage stories to cover standard and reasoning scenarios without the old THINK-tag parsing examples
Co-authored-by: Aleksander Grygier <redacted>
* refactor: restore forced reasoning prefix to pass test-chat ([chat] All tests passed)
- store the exact sequence seen on input when 'thinking_forced_open' enforces a reasoning block
- inject this prefix before the first accumulated segment in 'reasoning_content', then clear it to avoid duplication
- repeat the capture on every new 'start_think' detection to properly handle partial/streaming flows
* refactor: address review feedback from ngxson
* debug: say goodbye to curl -N, hello one-click raw stream
- adds a new checkbox in the WebUI to display raw LLM output without backend parsing or frontend Markdown rendering
Co-authored-by: Aleksander Grygier <redacted>
* webui: add Storybook example for raw LLM output and scope reasoning format toggle per story
- Added a Storybook example that showcases the chat message component in raw LLM output mode with the provided trace sample
- Updated every ChatMessage story to toggle the disableReasoningFormat setting so the raw-output rendering remains scoped to its own example
* npm run format
* chat-parser: address review feedback from ngxson
Co-authored-by: Xuan Son Nguyen <redacted>
---------
Co-authored-by: Aleksander Grygier <redacted> Co-authored-by: Xuan Son Nguyen <redacted>
Tarek Dakhran [Tue, 7 Oct 2025 18:03:35 +0000 (20:03 +0200)]
llama : support LiquidAI LFM2-MoE hybrid model (#16464)
* llama : support LiquidAI LFM2-MoE hybrid model
Add support for [LiquidAI/LFM2-8B-A1B](https://huggingface.co/LiquidAI/LFM2-8B-A1B) model.
For more information about models, please read [the blog post](https://www.liquid.ai/company/news).
Daniel Bevenius [Mon, 6 Oct 2025 12:17:12 +0000 (14:17 +0200)]
ggml-cpu : fix leftover handling in ggml_vec_scale_f32 for SVE (#16443)
This commit updates the leftover handling in ggml_vec_scale_f32.
The motivation for this is that the code currently incorrectly assumes
there would be fewer than ggml_f32_epr leftover elements. However,
since the main loop processes 2*ggml_f32_epr elements per iteration
, there can be up to (2*ggml_f32_epr - 1) leftover elements.
The original single-pass leftover code could only process ggml_f32_epr
elements, leaving some elements unscaled.
Example scenario with 256-bit SVE:
```
ggml_f32_epr = 8 (elements per register)
ggml_f32_step = 16 (two registers per iteration)
n = 25
np = 16
leftovers = 9 elements (16-24)
Original : processes only elements 16-23, misses element 24
This commit : loop processes elements 16-23, then element 24
```
Gabe Goodhart [Sun, 5 Oct 2025 12:57:47 +0000 (06:57 -0600)]
model : Granite docling + Idefics3 preprocessing (SmolVLM) (#16206)
* feat: Add granite-docling conversion using trillion pretokenizer
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <redacted>
* feat: Add granite-docling vocab pre enum
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <redacted>
* fix: Use granite-docling pre
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <redacted>
* feat: Add clip_is_idefics3
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <redacted>
* feat: Allow multi-token boundary sequences for image templating
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <redacted>
* feat: Add tiling support for idefices3 in clip.cpp
This should likely be moved into llava_uhd::get_slice_instructions, but for
now this avoids disrupting the logic there.
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <redacted>
* feat: Partial support for full templating for idefics3 in mtmd
There are still errors encoding some of the image chunks, but the token
sequence now matches transformers _almost_ perfectly, except for the double
newline before the global image which shows up as two consecutive newline
tokens instead of a single double-newline token. I think this is happening
because the blocks are tokenized separately then concatenated.
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <redacted>
* feat: Fully working image preprocessing for idefics3 w/ resize and slicing
Branch: gabe-l-hart/GraniteDocling
Signed-off-by: Gabe Goodhart <redacted>
* feat: Parse the preprocessor config's longest side and add it to the mmproj hparams
Branch: GraniteDocling
Signed-off-by: Gabe Goodhart <redacted>
* fix: Use the longest side instead of size * scale_factor
For Granite Docling, these come out to the same value, but that was just a
conicidence.
Branch: GraniteDocling
Signed-off-by: Gabe Goodhart <redacted>
* fix: Allow batch encoding and remove clip_is_idefics3
Branch: GraniteDocling
Signed-off-by: Gabe Goodhart <redacted>
* refactor: Remove unnecessary conditionals for empty token vectors
Branch: GraniteDocling
Signed-off-by: Gabe Goodhart <redacted>
* refactor: Use image_manipulation util
Branch: GraniteDocling
Signed-off-by: Gabe Goodhart <redacted>
* add test model
---------
Signed-off-by: Gabe Goodhart <redacted> Co-authored-by: Xuan Son Nguyen <redacted>
Acly [Sat, 4 Oct 2025 09:42:56 +0000 (11:42 +0200)]
vulkan : incremental shader builds (#16341)
* vulkan (DRAFT): split shader generation by GLSL source file, to improve incremental build times
* support dep-files so shaders are recompiled if their included files change
* rename shader files which are used as "headers" to use .glsl extension
* move glslc extension detection shaders to separate folders
* the above is to prevent them from getting glob'd with the actual compute shaders that need to be compiled
* vulkan : only write embedded shader .hpp/.cpp when they change
* avoid recompiling ggml-vulkan.cpp when editing shaders
* pass single --source argument instead of --input-dir & --filter to shader gen
* check for source file match earlier
* fix hang in vulkan-shaders-gen when there are compilation errors
* early out did not decrement compile_count
* clean up
* fix glslc integer dot product test
* unconditionally write the embedded shader cpp output
* replace output filepath in generated dep-files to match output in CMakeLists
Jeff Bolz [Fri, 3 Oct 2025 10:50:46 +0000 (05:50 -0500)]
vulkan: Replace uses of maxMemoryAllocationSize and VK_WHOLE_SIZE (#16354)
* vulkan: Replace uses of maxMemoryAllocationSize and VK_WHOLE_SIZE
Replace maxMemoryAllocationSize check with maxBufferSize when creating buffers.
The maxMemoryAllocationSize limit is a "soft" limit and allocations can succeed
beyond that limit. This allows > 4GB buffers to be allocated on some
implementations (e.g. NVIDIA) and tensors this large can be used for im2col
and mul_mat.
For temporary buffers (prealloc_x/y/etc) check against maxStorageBufferRange.
I'm not sure this check is ideal, but we always use these buffers as a single
full size binding and the limit may be smaller than maxMemoryAllocationSize
or maxBufferSize, so I think this is reasonable.
Replace descriptor range uses of VK_WHOLE_SIZE with a manually computed range.
The maxStorageBufferRange may be smaller than the maxBufferSize or
maxMemoryAllocationSize (and the Vulkan spec warns about this in a note) and
it's invalid usage if VK_WHOLE_SIZE computes a range larger than
maxStorageBufferRange.
With this change, it should be possible to generate videos using wan networks
in stable-diffusion.cpp.
* vulkan: Add env var GGML_VK_FORCE_MAX_BUFFER_SIZE and use stoull
Jeff Bolz [Fri, 3 Oct 2025 09:52:46 +0000 (04:52 -0500)]
vulkan: Fix FA coopmat1 invalid array indexing (#16365)
When computing sinks, the cm1 shader was looping r from 0 to Br rather than
to rows_per_thread. I must have copied this from the scalar path (where it is
correct), and somehow it wasn't causing failures on current drivers.
Adrien Gallouët [Wed, 1 Oct 2025 17:22:18 +0000 (19:22 +0200)]
common: introduce http.h for httplib-based client (#16373)
* common: introduce http.h for httplib-based client
This change moves cpp-httplib based URL parsing and client setup into
a new header `common/http.h`, and integrates it in `arg.cpp` and `run.cpp`.
It is an iteration towards removing libcurl, while intentionally
minimizing changes to existing code to guarantee the same behavior when
`LLAMA_CURL` is used.
model : support GLM 4.6 (make a few NextN/MTP tensors not required) (#16359)
* Make a few GLM tensors not required
layer.nextn.shared_head_head and layer.nextn.embed_tokens are both excluded from GLM 4.6 resulting in the model not loading after conversion/quantization, this marks those tensors as not required which makes it work
* Update llama-model.cpp
layer.nextn.shared_head_norm also not required in case of future models
`test-arg-parser.cpp` has been updated to work consistently,
regardless of whether CURL or SSL support is available, and
now always points to `ggml.ai`.
The previous timeout test has been removed, but it can be
added back by providing a dedicated URL under `ggml.ai`.
common : simplify etag tracking by removing json (#16342)
The JSON parser is temporarily kept only for backward compatibility. It
reads the etag from old .json files to prevent unnecessary re-downloads
for existing users.
This legacy code can be removed in a future version.
Daniel Bevenius [Fri, 26 Sep 2025 15:34:42 +0000 (17:34 +0200)]
ggml : remove -dev suffix from release version (ggml/1355)
This commit removes the `-dev` suffix from the version string in
CMakeLists.txt and the release script. The version will now be
just be formatted as `MAJOR.MINOR.PATCH`.
ggml-backend : add root cause in error message if loading backend library fails (#16172)
This PR adds additional information to an error message when loading backend library via ld_load_library() fails. This helps spotting why backend library did not load (missing library, missing dependency or unresolved symbol etc.).
overview / pkg / ggml / sources / llama.cpp / log