---------
Co-authored-by: Piotr Wilkin <piotr.wilkin@syndatis.com>
common : add nemotron 3 parsing (#18077)
common : add parser for ministral/mistral large 3/devstral 2 (#17713)
common : default content to an empty string (#18485)
chat: make tool description and parameters optional per OpenAI spec (#18478)
Per the OpenAI API specification, both 'description' and 'parameters'
fields in tool function definitions are optional. Previously, the parser
would throw an exception if these fields were missing.
Attempts to fix#17667
common : implement new jinja template engine (#18462)
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
jinja: correct member access rule (#18905)
jinja : fix lexing of float literals with sign (#18901)
jinja : add missing tojson filter for bool (#18900)
jinja : attribute support for join, map and sort (#18883)
jinja : fix object item order (and properly implement dictsort) (#18904)
tests : add test-jinja -py option for cross-checking (#18906)
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
ci : run test-jinja -py on high perf [no ci] (#18916)
jinja : fix undefined keys and attributes and int/float as bool (#18924)
jinja: support none|string (#18995)
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
jinja : implement mixed type object keys (#18955)
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
jinja : undefined should be treated as sequence/iterable (return string/array) by filters/tests (#19147)
`tojson` is not a supported `undefined` filter
keep it DRY and fix some types
jinja : do not pass empty tools and add some none filters (#19176)
jinja : add unordered_map include to value.h [no ci] (#19205)
jinja : add missing 'in' test to template engine (#19004) (#19239)
The jinja template parser was missing the 'in' test from
global_builtins(), causing templates using reject("in", ...),
select("in", ...), or 'x is in(y)' to fail with
"selectattr: unknown test 'in'".
This broke tool-calling for Qwen3-Coder and any other model
whose chat template uses the 'in' test.
Added test_is_in supporting array, string, and object containment
checks, mirroring the existing 'in' operator logic in runtime.cpp.
Includes test cases for all three containment types plus
reject/select filter usage.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---------
Co-authored-by: Sid Mohan <sidmohan0@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Add Jinja support for "indent" string filter (#19529)
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
add vendor
refactor chat
server : support preserving reasoning_content in assistant message (#18994)
chat : fix translategemma crash on common_chat_format_example (#19019)
chat: fix language input for translategemma (#19052)
Co-authored-by: Aldehir Rojas <hello@alde.dev>
---------
Co-authored-by: Aldehir Rojas <hello@alde.dev>
chat: fix case where template accepts type content only (#19419)
mtmd : chat : Fix extra \n between text and media marker (#19595)
Thanks to @tugot17 for detecting and reporting the issue.
For vision models (e.g. LFM2.5-VL-1.6B and Qwen/Qwen3-VL-4B-Instruct) `llama-mtmd-cli` produces identical output to HF implementation.
However `llama-server` doesn't. I traced it down to extra newline
inserted after `<__media__>`.
This happens in `to_json_oaicompat`, that treats media markers as text
and joins all parts with `\n` separator.
PR introduces new type `media_marker` and uses it for media markers.
Extra logic is added to prevent insertion of newlines before and after
media markers.
With this change number of input tokens is identical to HF
implementation and as a result the output is also identical.
I explored other ways to address the issue
* remove completely `\n` between text parts in `to_json_oaicompat`
* merge text messages in server-common.cpp before sending them to `to_json_oaicompat`
Please propose alternative ways of fixing this issue.
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
common : merge qwen3-coder and nemotron nano 3 parsers (#19765)
common : fix improper trimming in XML parser on complete message (#19805)
Co-authored-by: Jules LEIDELINGER <11395311+julio75012@users.noreply.github.com>
jinja: correct stats for tojson and string filters (#19785)
jinja : correct default size for string slices (#19913)
common : handle unicode during partial json parsing (#16526)
common : fix json schema with '\' in literals (#17307)
add back qwen_coder_xml and mirothinker
Co-authored-by: Aldehir Rojas <hello@alde.dev>
* Merging mainline - WIP
* Merging mainline - WIP
AVX2 and CUDA appear to work.
CUDA performance seems slightly (~1-2%) lower as it is so often
the case with llama.cpp/ggml after some "improvements" have been made.
* Merging mainline - fix Metal
* Remove check
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Add per token attributes enum
* Using phi-3 for testing 'rstrip'
* Using jina-v2 for testing 'lstrip'
* Brute force test for 'lstrip' and 'rstrip'
* Implement 'rstrip' and 'lstrip'
* Update phi-3 GGUF file (obsolete since 917dc8c)
* Replace llama_token_type with llama_token_attribs
* Work on the BPE tokenizer
Tokenizer tests work for Falcon-7B
* Try to fix build problem
* Fix debug assertion failure
* Fix MSVC Unicode BOM problem
* Cleanup and an improvement
* Fix compiler warning
* Cleanup
* Test doesn't work over the full range of Unicodes
* Update .gitignore and Makefile
* Another Makefile rule
* Testing Aquila
* Moving byte decoding back to `token_to_piece` ...
... because everyone is using it.
* Guarding some unusable code pathes
* Streamlining code and adding some more assertions
Important change: I'm classifying added tokens as control tokens now for BPE.
* Adding a comment
* Adding another assertion
* Fixed vocabulary guarding assertions
* Fix PR for recent change
* Fix PR for recent change
* Fix for compiler warning
* Fix PR for recent change
* Fix PR for recent change
* Fix PR for recent change
* Fix for compiler warning
* Fixes for more compiler warnings
* Remove unused code
* Fix initialization of static maps
* Add scores and token types back, adapt gptneox
* Update llama.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update unicode.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update unicode.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Ported Starcoder and added some assertions
* Fix coding style
* Apply @jploski 's fix for missing tokens
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This is a breaking change that's going to give you three benefits:
1. Your inference commands should load 100x faster
2. You may be able to safely load models 2x larger
3. You can run many concurrent inference processes
This was accomplished by changing the file format so we can mmap()
weights directly into memory without having to read() or copy them
thereby ensuring the kernel can make its file cache pages directly
accessible to our inference processes; and secondly, that the file
cache pages are much less likely to get evicted (which would force
loads to hit disk) because they're no longer competing with memory
pages that were needlessly created by gigabytes of standard i/o.
The new file format supports single-file models like LLaMA 7b, and
it also supports multi-file models like LLaMA 13B. Our Python tool
now merges the foo.1, foo.2, etc. files back into a single file so
that the C++ code which maps it doesn't need to reshape data every
time. That's made llama.cpp so much simpler. Much of its load code
has now been deleted.
Furthermore, this change ensures that tensors are aligned properly
on a 32-byte boundary. That opens the door to seeing if we can get
additional performance gains on some microprocessors, by using ops
that require memory alignment.
Lastly note that both POSIX and the Windows platform are supported
Fixes#91
* Major refactoring - introduce C-style API
* Clean up
* Add <cassert>
* Add <iterator>
* Add <algorithm> ....
* Fix timing reporting and accumulation
* Measure eval time only for single-token calls
* Change llama_tokenize return meaning
* Add test-tokenizer-0 to do a few tokenizations - feel free to expand
* Added option to convert-pth-to-ggml.py script to dump just the vocabulary
* Added ./models/ggml-vocab.bin containing just LLaMA vocab data (used for tests)
* Added utility to load vocabulary file from previous point (temporary implementation)
* Avoid using std::string_view and drop back to C++11 (hope I didn't break something)
* Rename gpt_vocab -> llama_vocab
* All CMake binaries go into ./bin/ now
