* chat : fix reasoning leak with force-opened bare <think> templates
The reasoning start tag inferred from prior turns can carry trailing
whitespace (e.g. <think>\n) while a force-open template prefills a bare
<think>. Trim the tag used for the prefix split so the bare prefill is
matched instead of being swallowed into content.
* chat : fix Nemotron Nano v2 regression
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
* server: honour per-request reasoning_budget_tokens in chat completions
The reasoning-budget block in oaicompat_chat_params_parse read only the
server-level default (opt.reasoning_budget, typically -1) and the
Anthropic-style alias thinking_budget_tokens, but never the canonical
reasoning_budget_tokens field from the request body. Because the key
was then written into llama_params before the generic body-copy loop
ran, the copy loop found the key already present and silently skipped
the caller-supplied value. Any per-request override (e.g. 0 to
suppress thinking entirely) was therefore discarded.
Fix: read reasoning_budget_tokens from the request body first, so the
value that reaches the sampling layer is the one the caller intended.
Add a unit test in test-chat.cpp that exercises this path via
oaicompat_chat_params_parse with a Qwen3 template (which the autoparser
detects as a thinking-capable model) and asserts the returned
llama_params carries reasoning_budget_tokens == 0.
* server: honour per-request reasoning_budget_message in chat completions
The reasoning-budget block in oaicompat_chat_params_parse wrote
reasoning_budget_message into llama_params straight from the server-level
default (opt.reasoning_budget_message) and never read the canonical
reasoning_budget_message field from the request body. Because the key
was written before the generic body-copy loop ran, that loop found the
key already present and silently skipped the caller-supplied value. Any
per-request override of the message injected before the end tag when the
budget is exhausted was therefore discarded, even though server-task.cpp
already reads reasoning_budget_message from that data.
This mirrors the reasoning_budget_tokens bug fixed in the previous commit.
Fix: read reasoning_budget_message from the request body first, falling
back to the server default, so the value that reaches the sampling layer
is the one the caller intended.
While here, collapse the adjacent reasoning_budget_tokens override to a
single json_value() call; json_value already falls back to the default on
a missing/null/wrong-type key, so the explicit body.contains() guard was
redundant. No behavioral change.
Add a unit test in test-chat.cpp that exercises this path via
oaicompat_chat_params_parse with a Qwen3 template (which the autoparser
detects as a thinking-capable model) and asserts the returned
llama_params carries the per-request reasoning_budget_message rather than
the server default.
* cleanup
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* chat: trim messages sent to StepFun parser (fixes long reasoning loops)
* add regression test; remove duplicate template
* chat: trim StepFun content parts before rendering
The StepFun trim workaround ran on the already-rendered messages, where
typed content parts have been concatenated into a single string, so the
per-part whitespace could no longer be reached. Move the trim ahead of
rendering and apply it to content_parts text as well as the string
content and reasoning_content. Adds a content-parts regression test.
Co-Authored-By: Piotr Wilkin <ilintar@gmail.com>
Assisted-By: Claude Fable 5 <noreply@anthropic.com>
---------
Co-authored-by: tarruda <tpadilha84@gmail.com>
* server : improve message span logic
* cont : cast size_t to int32_t in comparisons
* server : create checkpoints before every user msg
* chat : remove \n in gemma4 delimiters
* chat : merge msg delimiter structs into one
* cont : reword comment
* cont : initialize tokens in delimiter
* cont : add server_tokens::get_raw_tokens() for mtmd
* cont : move message finding to server_tokens and skip mtmd tokens
* cont : update cohere2moe parser
* cont : increase min-step to 8192 and always produce a chkpt for last user message
* chat: fix whitespace problems once and for all
* Purge trailing spaces from grammar generation
* Revert "Purge trailing spaces from grammar generation"
This reverts commit b0827ecb7d.
* common : add common_chat_split_by_role
* cont : fix spans to reach end of message
* server: fix checkpoints creation
- extract message_spans from chat templates
- find the prompt token position before the latest user message
- split prompt batching at that position
- create a context checkpoint before the latest user input
- avoid periodic mid-prompt checkpoints when that position is known
- handle multimodal prompts when mapping text/template positions to server prompt tokens
- add --checkpoint-min-step to control minimum spacing between checkpoints
* cont : clean-up
* Support autoparser detection for message barriers
* server: fix message span delimiter and update docs
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Piotr Wilkin <piotr.wilkin@syndatis.com>
* common : delegate assistant continuation to template handler
* server : implement echo parameter to exclude assistant prefill in the response
* server : fix tests for prefill
* server : use existing llama template
* cont : clean up
* Support for Codex CLI by skipping unsupported Responses tools
* Warn on skipped Responses tools and preserve gpt-oss apply_patch rejection
* Revert gpt-oss apply_patch special handling
* chat/autoparser: the fixes
* Move optspace() to chat-peg-parser, comment out server tests invalidated due to content now allowed with forced tool calls.
* Trim whitespace on apply instead
* feat: (vocab) fix stray text appended in llama_decode_text
Remove accidental concatenation of the full `text` string when
formatting UNK_BYTE hex escapes. Only the closing "]" should be appended.
* feat(mtmd): add Yasa2 vision encoder support
Add a Yasa2 (ConvNeXtV2-based) vision encoder for reka-edge:
- Register PROJECTOR_TYPE_YASA2 and tensor name definitions
- Add yasa2_block/yasa2_stage model structs
- Implement graph builder with ConvNeXt stages, GRN, adaptive pooling
- Wire into clip.cpp switch statements and mtmd.cpp init_vision
- Use mtmd_image_preprocessor_fixed_size for image preprocessing
* feat(chat): add reka-edge template handler (tools, thinking)
- Add chat-reka.cpp/h implementing PEG-based parser for reka-edge format
- Add Reka-Edge.jinja chat template
- Detect reka-edge template in try_specialized_template()
- Add LLAMA_EXAMPLE_MTMD to chat-template-file arg
* feat: add reka vlm to gguf conversion script
Converts Reka Yasa2 hf checkpoints to GGUF format:
- Text decoder: Llama-arch with tiktoken/BPE vocab
- Mmproj (--mmproj): ConvNeXt vision backbone + language_projection
- Generates 2D sincos positional embeddings for vision encoder
* test: add Reka Edge chat template and parser tests
- test-chat-template: oracle tests comparing Jinja engine output vs
common_chat_templates_apply for text, tools, thinking, images, video
- test-chat: PEG parser tests for Reka Edge format, round-trip tests
for image/video content parts, common path integration tests
* scripts: add Reka Edge mixed quantization helper
Q4_0 base quantization with Q8_0 override for the last 8 transformer
blocks (layers 24-31) via --tensor-type regex.
* fix: adapt chat-reka and tests to upstream API
- Use autoparser::generation_params (not templates_params)
- Add p.prefix(generation_prompt) to PEG parser
- Simplify reasoning parser to match LFM2 pattern
- Remove image/video oracle tests (unsupported by oaicompat parser;
no other multimodal models test this path)
* fix: avoid duplicate tensor loading in yasa2 vision encoder
TN_YASA_PATCH_W and TN_PATCH_EMBD both resolve to "v.patch_embd.weight",
causing the same tensor to be loaded twice into ctx_data and overflowing
the memory pool. Reuse the tensors already loaded by the common section.
* chore: update image pre-processing settings
The reka-edge model depends on the following settings in an older
fork of llama.cpp:
1. Fixed square resize
2. BICUBIC
3. add_padding=false
In current llama.cpp, this means setting:
- image_resize_algo = RESIZE_ALGO_BICUBIC
- image_resize_pad = false
* chore: remove reka gguf conversion script
* chore: remove reka quantization script
* chore: remove unnecessary changes from PR scope
This commit removes a couple of unnecessary changes for the PR scope:
1. BPE decoder bug fix - this affects reka edge because there's a bug
in our tokenization that doesn't represent <think> tokens as special
tokens. However this isn't meant to be a thinking model so when run
with --reasoning off the edge case does not affect us
2. --chat-template-file support from llama-mtmd-cli - the focus is on
llama-server and the reka edge gguf contains the necessary metadata
to detect the chat template
3. reka edge oracle test cases - no other model has similar test cases,
so I removed it for standardization
* chore: remove unnecessary ggml_cast
This commit removes unnecessary ggml_cast after updating the
reka vlm -> gguf conversion script on hugging face.
* chore: remove redundant code
* chore: remove unnecessary ggml_cont calls
This commit removes all ggml_cont calls except the four that
precede ggml_reshape_3d/ggml_reshape_4d. Those are necessary
because ggml_reshape recomputes strides assuming contiguous
layout and asserts ggml_is_contiguous.
Other operations (ggml_mean, ggml_add, ggml_mul etc.) use
stride-based indexing and handle non-contiguous inputs
correctly and so we are ok to remove ggml_cont for those.
* chore: remove unnecessary ggml_repeat calls
This commit removes unnecessary ggml_repeat calls because the underlying
ops already broadcast automatically.
Every ggml_repeat in yasa2.cpp was expanding a smaller tensor to match
a larger one's shape before passing both to an elementwise op (ggml_add,
ggml_sub, ggml_mul, or ggml_div). This is unnecessary because all four
of these ops already support broadcasting internally.
* chore: restore ggml_cont needed for cpu operations
* refactor: locate reka chat template handler in chat.cpp
* chore: remove unnecessary warmup tokens
* chore: add code comments on image_resize_pad
* chore: remove custom reka parsing code
* chore: revert common/chat.cpp
* Uncomment debug logging for PEG input parsing
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
* 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
* 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
* chat : add Granite 4.0 chat template with correct tool_call role mapping
Introduce `LLM_CHAT_TEMPLATE_GRANITE_4_0` alongside the existing Granite
3.x template (renamed `LLM_CHAT_TEMPLATE_GRANITE_3_X`).
The Granite 4.0 Jinja template uses `<tool_call>` XML tags and maps the
`assistant_tool_call` role to `<|start_of_role|>assistant<|end_of_role|><|tool_call|>`.
Without a matching C++ handler, the fallback path emits the literal role
`assistant_tool_call` which the model does not recognize, breaking tool
calling when `--jinja` is not used.
Changes:
- Rename `LLM_CHAT_TEMPLATE_GRANITE` to `LLM_CHAT_TEMPLATE_GRANITE_3_X`
(preserves existing 3.x behavior unchanged)
- Add `LLM_CHAT_TEMPLATE_GRANITE_4_0` enum, map entry, and handler
- Detection: `<|start_of_role|>` + (`<tool_call>` or `<tools>`) → 4.0,
otherwise → 3.x
- Add production Granite 4.0 Jinja template
- Add tests for both 3.x and 4.0 template paths (C++ and Jinja)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Code review: follow standard format and use common logic in test-chat-template.cpp
* Rename custom_conversation variable for extra_conversation to give it a more meaningful name
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* chat : fix out_of_range crash in throw path (#20424 regression)
#20424 introduced effective_input = generation_prompt + input, but the
throw path uses input.substr(result.end) where result.end is a position
within effective_input. Every thinking model with a non-empty
generation_prompt crashes with std::out_of_range instead of the intended
error message.
Test crashes on unpatched master, passes with fix:
cmake -B build -DLLAMA_BUILD_TESTS=ON -DLLAMA_BUILD_TOOLS=OFF
cmake --build build --target test-chat
./build/bin/test-chat
* Update test-chat.cpp
* Update test-chat.cpp
* Update test-chat.cpp
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
* Allow reshuffled arguments in tagged argument parser format tool calls.
* Remove shuffle just keep the optional parsers in any order
* Remove unnecessary import
