* fix: enable reasoning budget sampler for gemma4
Add thinking_start_tag and thinking_end_tag to
common_chat_params_init_gemma4(). Without these, the reasoning
budget sampler never activates for gemma4.
Make the newline after "thought" optional in the PEG parser to
handle budget=0 (sampler forces end tag before the newline).
Add test case for empty thinking block.
Fixes#21487
* use p.space() instead of p.optional(p.literal("\n")) in gemma4 thought parser
* ggml: backend-agnostic tensor parallelism
* support for GPT-OSS, Qwen 3 MoE
* partial Vulkan fix
* add support for 4/8 GPUs
* unconditional peer access
* re-use buffers + ggml contexts
* fix output pattern
* NCCL support
* GGML: HIP: add RCCL support
* Remove shfl and AllReduce from backend interface
* move allocation workaround out of ggml-alloc.c
* 2d tensor set/get support
* Fix the seg fault without NCCL
* Apply suggestion from JohannesGaessler
* support for tensor dims % n_devs != 0
* fix view_offs scaling
* arbitrary num. of GPUs/tensor split
* fix compilation
* better granularity estimate
* Support device-specific host buffer types if all underlying backends expose the same type. This allows using pinned memory instead of pageable memory for CUDA.
Fix compilation errors.
* partial Qwen 3 Next support
* Fix qwen3 30b (#8)
* Fix crash with Qwen-30B-A3B Q4_0
Qwen-30B-A3B Q4_0 has an intermediate dimension of 768. Using a granularity of 256 forces an uneven split between GPUs, which is not supported by the current implementation.
* Decide block size based on tensor quantization type
* Fix crashes due to KV cache serialization (#9)
KV cache serialization requires non-zero offsets on the tensor. Add support in the meta backend to set/get a tensor with a non-zero offset.
* metal : fix build (#7)
* static memory allocations, fix usage count
* fix tensor granularity
* more even memory distribution
* use BF16 for allreduce
* rebase fixup
* better error message for unsupported architectures
* Fix device mismatch during scatter of allReduce. (#11)
There is a mismatch between the dst buffer device and the backend device, causing the use of sync copies
* Enable the previous allreduce implementation. It is better in both perf and stability (#12)
* delay AllReduce for Moe for less I/O
* build : clean-up compile warnings
* backend : move most of the meta backend API to ggml-backend-impl.h
* cont : hide unused public API in the implementation
* llama : use llama_device + remove ggml_backend_dev_is_meta()
* ggml-backend : remove unused alloc include
* minor : remove regex include
* ggml : introduce ggml-ext.h for staging new APIs
* rebase fixup
* fix tests
* llama : more robust logic for determining Meta devices (#16)
* llama : more robust logic for determining Meta devices
* cont : fix devs size check
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* cont : fix log type
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* disable roundtrip for meta backend
* fix arch selection
* Qwen 3.5 support
* fix Gemma 4 MoE
* fix OpenVino, SYCL
* fix test-llama-archs for CPU-only builds
* Fix Qwen 3.5 MoE
* disable meta backend tests for WebGPU
* tests : filter CPU-based devices from the Meta backend tests (#17)
* meta : formatting, naming, indentation (#18)
* formatting : llama-model.cpp
* formatting : ggml-ext.h
* formatting : ggml-backend-meta.cpp
* meta : add TODO
* add documentation
* better error messages
* fix GPT-OSS
---------
Co-authored-by: Carl Philipp Klemm <carl@uvos.xyz>
Co-authored-by: Gaurav Garg <gaugarg@nvidia.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: jinja engine improvements for reka-edge
Port three Jinja engine improvements needed for the reka-edge model:
1. Python-style string repetition ("ab" * 3 → "ababab")
2. ensure_ascii=true support for tojson filter (escapes non-ASCII to \uXXXX)
3. int() builtin on value_int_t (identity, needed for Reka Edge template)
* fix: escape invalid utf8 bytes when ensure_ascii=true
The json_ensure_ascii_preserving_format function does not correctly
handle an edge case where if UTF-8 parsing fails, it adds the non-ascii
character back to the output as a raw byte.
This commit fixes that by adding the unicode standard replacement
character \\ufffd to the output instead. This is the standard behavior
for various programming languages like Python, Rust, Go, etc.
* chore: address PR comments
1. Add todo comment for supporting string repetition for array/tuples
2. Add support for float identity operation
3. Move invalid ascii test case to test_fuzzing
* chore: accept suggestion for common/jinja/value.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* common : fix tool call type detection for nullable and enum schemas
* common, tests : fix grammar delegation for nullable/enum schemas and add tests
Fix enum type inference to scan all enum values (not just index 0) so
schemas like {"enum": [0, "celsius"]} correctly detect string type.
Fix schema_delegates in peg-parser to handle nullable type arrays
(["string", "null"]) and typeless enum schemas in raw mode, allowing
the tagged parser to use raw text instead of JSON-formatted strings.
Add test cases for Qwen3-Coder (TAG_WITH_TAGGED format):
- nullable string ["string", "null"]
- nullable string with null first ["null", "string"]
- nullable integer ["integer", "null"]
- enum without explicit type key
* tests: allow exporting graph ops from HF file without downloading weights
* use unique_ptr for llama_context in HF metadata case
* fix missing non-required tensors falling back to type f32
* use unique pointers where possible
* use no_alloc instead of fixing f32 fallback
* fix missing space
* Relax prefill parser to allow space.
* Move changes from prefix() to parser generation
* Only allow spaces if we're not having a pure content parser next
The build info is now only for debug, so we avoid the duplicate
with `--version`.
The UTF-8 setup at the beginning is needed to avoid logging
garbage on Windows.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* common: add bounds check in common_init_result::sampler to prevent segfault on failed model load
* Revert a308e584ca
* Add regression test
* Remove regression test for init-fail sampler check
* Reject empty computed member expressions before returning slices[0] from parse_member_expression_arguments().
* Treat empty computed member expressions with Jinja2 undefined semantics
Treat empty computed member expressions like `a[]` as undefined instead of
raising a parser error, to match Jinja2 behavior.
- return a noop expression for empty computed member arguments
- return undefined when a computed member key evaluates to undefined
- add Jinja tests covering `a[]|default('fallback')` and `a[] is undefined`
* Handle undefined computed member properties
Move undefined-property handling to the common member access path, and add a test covering `a[undefined] is undefined`.
* Use default undefined value in member access
Initialize val and then return it when property is undefined.
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* empty statement parses to blank_expression instead of noop_statement
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
The regex-to-grammar converter in _visit_pattern() crashes with SIGSEGV
when a JSON schema "pattern" field contains a non-capturing group (?:...).
Root cause: when the parser sees '(' followed by '?', it pushes a warning
but does not advance past '?:'. The recursive transform() call then
interprets '?' as a quantifier and calls seq.back() on an empty vector,
causing undefined behavior.
This commonly occurs when serving OpenAI-compatible tool calls from
clients that include complex regex patterns in their JSON schemas (e.g.,
date validation patterns like ^(?:(?:\d\d[2468][048]|...)-02-29|...)$).
The fix:
- Skip '?:' after '(' to treat non-capturing groups as regular groups
- For unsupported syntax (?=, ?!, etc.), skip to matching ')' safely,
handling escaped characters to avoid miscounting parenthesis depth
- Adjust the ')' unbalanced-parentheses check using direct char
comparisons instead of substr
- Add test cases for non-capturing groups (C++ only, as the JS/Python
implementations do not yet support this syntax)
* server: respect the verbose_prompt parameter
* Revert "server: respect the verbose_prompt parameter"
This reverts commit 8ed885cf37.
* Remove --verbose-prompt parameter from llama-server
* Using set_examples instead of set_excludes
Sadly the manifest does not list all required files, i honestly thought
it was the case
Without the files listed we don't have the sha256, so if the first file
is valid, and all others have the correct size, then we can assume we
are good and do the migration...
Here my test:
$ find /home/angt/.cache/llama.cpp
/home/angt/.cache/llama.cpp
/home/angt/.cache/llama.cpp/angt_test-split-model-stories260K_stories260K-f32-00002-of-00002.gguf
/home/angt/.cache/llama.cpp/angt_test-split-model-stories260K_stories260K-f32-00001-of-00002.gguf
/home/angt/.cache/llama.cpp/angt_test-split-model-stories260K_stories260K-f32-00001-of-00002.gguf.etag
/home/angt/.cache/llama.cpp/angt_test-split-model-stories260K_stories260K-f32-00002-of-00002.gguf.etag
/home/angt/.cache/llama.cpp/manifest=angt=test-split-model-stories260K=latest.json
$ build/bin/llama-server
================================================================================
WARNING: Migrating cache to HuggingFace cache directory
Old cache: /home/angt/.cache/llama.cpp/
New cache: /home/angt/.cache/huggingface/hub
This one-time migration moves models previously downloaded with -hf
from the legacy llama.cpp cache to the standard HuggingFace cache.
Models downloaded with --model-url are not affected.
================================================================================
migrate_file: migrated angt_test-split-model-stories260K_stories260K-f32-00001-of-00002.gguf -> /home/angt/.cache/huggingface/hub/models--angt--test-split-model-stories260K/snapshots/68c3ea2061e8c7688455fab07597dde0f4d7f0db/stories260K-f32-00001-of-00002.gguf
migrate_file: migrated angt_test-split-model-stories260K_stories260K-f32-00002-of-00002.gguf -> /home/angt/.cache/huggingface/hub/models--angt--test-split-model-stories260K/snapshots/68c3ea2061e8c7688455fab07597dde0f4d7f0db/stories260K-f32-00002-of-00002.gguf
migrate_old_cache_to_hf_cache: migration complete, deleting manifest: /home/angt/.cache/llama.cpp/manifest=angt=test-split-model-stories260K=latest.json
$ find /home/angt/.cache/llama.cpp /home/angt/.cache/huggingface
/home/angt/.cache/llama.cpp
/home/angt/.cache/huggingface
/home/angt/.cache/huggingface/hub
/home/angt/.cache/huggingface/hub/models--angt--test-split-model-stories260K
/home/angt/.cache/huggingface/hub/models--angt--test-split-model-stories260K/blobs
/home/angt/.cache/huggingface/hub/models--angt--test-split-model-stories260K/blobs/50d019817c2626eb9e8a41f361ff5bfa538757e6f708a3076cd3356354a75694
/home/angt/.cache/huggingface/hub/models--angt--test-split-model-stories260K/blobs/7b273e1dbfab11dc67dce479deb5923fef27c39cbf56a20b3a928a47b77dab3c
/home/angt/.cache/huggingface/hub/models--angt--test-split-model-stories260K/refs
/home/angt/.cache/huggingface/hub/models--angt--test-split-model-stories260K/refs/main
/home/angt/.cache/huggingface/hub/models--angt--test-split-model-stories260K/snapshots
/home/angt/.cache/huggingface/hub/models--angt--test-split-model-stories260K/snapshots/68c3ea2061e8c7688455fab07597dde0f4d7f0db
/home/angt/.cache/huggingface/hub/models--angt--test-split-model-stories260K/snapshots/68c3ea2061e8c7688455fab07597dde0f4d7f0db/stories260K-f32-00002-of-00002.gguf
/home/angt/.cache/huggingface/hub/models--angt--test-split-model-stories260K/snapshots/68c3ea2061e8c7688455fab07597dde0f4d7f0db/stories260K-f32-00001-of-00002.gguf
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
Galunid
Aldehir Rojas
Adrien Gallouët
Johannes Gäßler
Berk Idem
Kwa Jie Hao
Piotr Wilkin (ilintar)
Bipin Yadav
Yes You Can Have Your Own
Samanvya Tripathi
Sigbjørn Skjæret
Georgi Gerganov
Ruben Ortlam
Xuan-Son Nguyen
Jonathan
lainon1
mtmcp
Zhihao "Zephyr" Yao
Adrien
AN Long