* model : add LiquidAI/d1-2B and LiquidAI/d1-omni-2B
Assisted-by: Claude Opus 5.5
* convert : keep the speech task templates out of the d1 GGUFs
Assisted-by: Claude Opus 5.5
* small refactoring: no_batching
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* server: expose the reasoning effort levels of the chat template
The capability probe renders a user turn for each candidate level
(minimal, low, medium, high, xhigh, max) and for an unknown level. A
level is honored when it renders without raising and differently from
the unknown level, which shows the template fallback. Levels sharing a
render are aliases and the highest one is kept.
The caps serialize to JSON, and chat_template_caps in /props reports
the list as reasoning_effort_levels, from lowest to highest.
* jinja: keep the caps free of the json dependency
The caps keep reporting their flags as a map, and the effort levels
stay a plain member. common_chat_templates_get_caps() builds the JSON
the server reports from both, so jinja depends on json only for the
probe inputs.
* common : support deferred tools and tool_reference content parts
Assisted-by: Claude Opus 5.5
* common : credit the deferred tools co-author
Co-authored-by: Aldehir Rojas <765227+aldehir@users.noreply.github.com>
Assisted-by: Claude Opus 5.5
* tests : fix the type check of the deferred tools test
Assisted-by: Claude Opus 5.5
* model : add MiniCPM-OCR-Jev support
The model is a Qwen3.5-VL trunk with a value head read at the last token: it
scores an OCR text against an image rendered from it, it does not generate.
Add the openbmb-minicpm-ocr-jev decision type, served by POST /v1/systemone.
The class scores are the pooled output of the sequence, read with last pooling,
so the conversion turns score.weight into cls.output and sets
embedding_length_out. The checkpoint has no LM head, so text generation is
rejected instead of using the output weights duplicated from the token
embeddings.
The task is fixed, so its prompt is entirely in the systemone template, the
same way h2o-lightning keeps its default there. The processor of the checkpoint
allows up to 16384 merged tokens per image, more than the Qwen-VL default of
mtmd, so the mmproj keeps the image budget of the preprocessor config. The
checkpoint declares MiniCPMOCRForSequenceClassification in its config.json, as
other models do.
Signed-off-by: tc-mb <tianchi_cai@icloud.com>
* fix code
Signed-off-by: tc-mb <tianchi_cai@icloud.com>
* use gguf meta classifier_output_labels
* remove redudant code changes
---------
Signed-off-by: tc-mb <tianchi_cai@icloud.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* server : support typed content input for rerank endpoint
Assisted-by: Opencode/Qwen3.8 27B
* server : address rerank multimodal review findings
- reject multimodal rerank input when the model has no rerank chat template (images were silently dropped)
- translate handle_media download/base64 failures to 400 instead of 500
- reject negative top_n with 400 instead of letting resize throw
- fix comment typos, redundant comments, indentation and trailing whitespace
- README: document that TEI input accepts the same shape as Jina; drop stray blank line
Assisted-by: opencode
* Trimmed README
---------
Co-authored-by: timothywang21 <timothywang21@users.noreply.github.com>
* mtmd: add audio out stream api
* add /tts endpoint
* add docs
* wire up mtmd_helper_model_can_chat
* upload speaker_ref via form-data
* expose seed to api
* fix header for pcm
* wip
* fix penalty_last_n
* fix server_output_limits
* fix pocket-tts stream
* wip
* add server_schema for tts
* add server routing
* server: coerce seed and stream from multipart form fields
Form fields arrive as strings, so -F seed=42 and -F stream=true were
rejected by the schema. seed joins the integer fields, parsed as a long
long so a full uint32 seed fits, and stream is read as a boolean when
it comes as a string.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* model : add H2O-Lightning-4B decision model
Adds the h2o-lightning decision type for h2oai/h2o-lightning-4b
(Qwen3.5-4B with a merged decision LoRA).
None of the existing types match this model. It answers with " A"
rather than "A", uses up to 255 labels, and was trained on Python
json.dumps text, so floats like 12.0 must render as 12.0.
The converter writes the decision metadata and the systemone template
from the model's serve_config.json and refuses a config that doesn't
match. Tests skip unless the tiny test model is available locally.
Assisted-by: Claude Opus 5.5
* rm redundant code
* put labels to gguf
---------
Co-authored-by: Jonathan C. McKinney <jon.mckinney@h2o.ai>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* init
* wip
* wire it up
* make it clear that it's experimental
* add props.connect_code
* nits
* fix windows build
* server: do not advertise a connect code without the tunnel
--connect-code on its own had no effect other than being served in
/props, so the Web UI showed a remote access code that no tunnel was
serving. Clear it when --connect is off, and say so once at startup.
The teardown comment claimed that terminate() closes the child stdout.
It does not: the log thread leaves fgets() only once every writer of
that pipe is gone, so a descendant of llama-connect would block the
join forever. Describe the real contract instead.
Also restore the --reasoning-preserve default in the generated docs.
* Update tools/connect/CMakeLists.txt
Co-authored-by: Adrien Gallouët <angt@huggingface.co>
* server: dial llama-connect on the bound port and a reachable host
Pass ctx_http.port so --port 0 and the GCP override work, pick the
dial host from --host (wildcards map to loopback, IPv6 is bracketed)
and refuse --connect when only unix sockets are bound.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
Co-authored-by: Adrien Gallouët <angt@huggingface.co>
* feat(convert): Add decision.chat to support decision models that can also chat
Branch: GraniteSwitchDecision
AI-usage: full (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(convert): Add conversion for Granite Switch as decision model
The template follows from the usage sample:
https://huggingface.co/ibm-granite/granite-switch-4.1-8b#guardian-and-decision-side-by-side
Branch: GraniteSwitchDecision
AI-usage: draft (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(common): Add support for chat+decision models in common
Branch: GraniteSwitchDecision
AI-usage: full (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(common): Add granite switch decision model enum
Branch: GraniteSwitchDecision
AI-usage: full (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(server): Plumb through decision_chat bool to support decision+chat models
Branch: GraniteSwitchDecision
AI-usage: full (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(server): Add Granite Switch decision support
Branch: GraniteSwitchDecision
AI-usage: draft (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* docs: Update server README for granite switch decisions / chat+decisions
Branch: GraniteSwitchDecision
AI-usage: draft (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* Revert "feat(convert): Add decision.chat to support decision models that can also chat"
This reverts commit 952ef56764.
* Revert "feat(common): Add support for chat+decision models in common"
This reverts commit 93ed782e3a.
* Revert "feat(server): Plumb through decision_chat bool to support decision+chat models"
This reverts commit 97e9af60dd.
* fix: If Granite Switch supports decisions, it always supports text too
Branch: GraniteSwitchDecision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* docs: Remove reference to decision.chat in server README
Branch: GraniteSwitchDecision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* model : add LiquidAI/d1-omni-600M decision model
Assisted-by: Claude Opus 5.5
* mtmd : keep conformer GLU sigmoid on CUDA
Assisted-by: Claude Opus 5.5
* server : take d1omni audio through images and input_audio, scope memory-less lfm2 to non-causal
Assisted-by: Claude Opus 5.5
* common : rename decision type d1omni to lfm2-d1-omni, server : make images an alias of files
Assisted-by: Claude Opus 5.5
* model : add LiquidAI/d1-3b decision model
mtmd : read LFM2 image resize algo from GGUF
Assisted-by: Claude Opus 5.5
* common : rename decision type d1 to lfm2-d1
Assisted-by: Claude Opus 5.5
* server: reject partial media truncation
* server: keep only the keep_first fix
Drop the mtmd test helper change, which no longer builds since
clip_image_f32_batch stores its entries by value, and drop the
vision test: no test fixture reaches a cut between two adjacent
media chunks with a reused cache (tinygemma3 uses SWA and wraps
images in text tokens, tinyopenjev and small-test are recurrent),
so the test passed or failed independently of the fix.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* log, server: make router child lines carry their own colors
The logger writes the color reset after the trailing newline, so the
reset opens the next line. On the shared pipe of a router child it lands
in front of the next state command, which the router then misses, and
the line break that works around it shows up as an empty log line on
every progress update.
The reset now goes before the trailing newlines, so every line is self
contained and the command goes back to its plain framing. The router
passes its effective color setting to its children, whose output ends
up in its terminal, and leaves that option out when comparing presets
on reload.
* log: enable ANSI colors on the Windows console
A Windows console renders ANSI sequences only in virtual terminal mode,
which nothing turns on for the logger, so llama-server prints raw escape
codes on the Windows 10 console while llama-cli, whose console code
enables it, shows colors. The logger now enables virtual terminal mode
on stdout and stderr when it turns colors on, and keeps colors off when
a console cannot render them. Pipes and files take the sequences as is.
* server: separate the router child commands from its logs
The child sent its state commands on the same pipe as its logs, so the
router had to pick them out of the log stream by a line prefix, and any
unterminated write in front of a command made the router miss it. This
resolves the TODO at the spawn that called for splitting stdout and
stderr.
The child now keeps stdout for the commands and points everything else
written to stdout at stderr, before anything is written. The router
reads both pipes, handles the commands from stdout and forwards stderr
as the log, and warns about any other line on the command pipe.
* server: address review from ngxson
The single server_child is now created first in the entry point and its
constructor keeps stdout for the commands, so the stream is a member of
the instance instead of a static, and init() is gone. The instance is
passed down to the server, while the CLI entry point creates its own.
* Update tools/server/server.cpp
---------
Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
* init conversion
* convert: ok
* model loaded
* add server code
* improve conversion script
* support shared prompt prefix
* add docs, imorove UX a bit
* add vision support
* add openjev tiny model for testing
* add dev docs
* support lev & kev
* clean up
* fix lev noul
* fix py lint
* nits docs
* clarify about not supporting date_facts
* server : support multimodal input for /v1/embeddings (Qwen3-VL-Embedding)
Accept the OpenAI-style wrapped content array format for multimodal
embedding requests. Each {"content": [...]} object is one input that
produces one embedding; text parts are concatenated and image_url parts
are decoded via handle_media then spliced with process_mtmd_prompt.
The legacy formats (plain string, token arrays, mixed arrays, and the
{prompt_string, multimodal_data} object) continue to work unchanged via
tokenize_input_prompts. Bare content arrays (the unwrapped shape) are
rejected with a migration message.
Also disables KV prefix reuse for stateless embedding/rerank tasks so
that repeated inputs do not incorrectly share cached KV across requests.
Assisted-by: Opencode Qwen3.8 27B
* clean up comments and docs
* refactor
* add tests
* support video and audio inp
---------
Co-authored-by: timothywang21 <timothywang21@users.noreply.github.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
The OpenAI chat completions API specifies content part type "video_url"
with a {"url": ...} object, and clients typically send data: URIs
(e.g. data:video/mp4;base64,...). The llama-server only accepted the
non-standard "input_video" type and rejected data: URIs for video
(accept_base64_uri=false), so any OpenAI-conformant client failed with
"unsupported content[].type" or "Invalid uri format".
- accept "video_url" as an alias of "input_video"
- read the media object from whichever key was used
- allow data: URIs for video (data:video/*), as already done for images
* server: refactor subproc handling
* fix Windows build
* download: keep concurrent downloads of one blob apart
Every process writes the same path + .downloadInProgress, so a second
download of the same blob finds that file, takes it for its own partial
transfer and asks for the bytes after it, which produces a corrupt
result. The in-progress file now carries the pid of the process writing
it.
std::rename also replaces an existing destination on POSIX but fails on
Windows, so a download whose blob appeared in the meantime is dropped
after every retry and an etag rewrite silently keeps the old value.
std::filesystem::rename has the POSIX behaviour everywhere, and the
error now carries the reason reported by the system.
* Revert "download: keep concurrent downloads of one blob apart"
This reverts commit 917b83f149.
* tests: serialize the router tests that download the same model
Parallel workers share one cache, so the two tests fetch the same blob
into the same in-progress file and race to rename it. They now take a
file lock around the download, like the session fixture does for the
preset models.
* Revert "tests: serialize the router tests that download the same model"
This reverts commit c368a4a98c.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* server : accept data: URLs for input_video and input_audio
input_video and input_audio passed accept_base64_uri=false to
handle_media(), so data: URLs got treated as raw base64 strings and
failed later with a confusing media probe error (#27724).
pass true for these two content types the same way image_url already
does, and allow video/audio mime types in the data: url check instead
of image only. data URL validation now throws std::invalid_argument so
malformed input comes back as 400 instead of 500, matching the other
input validation in this file.
* server : simplify handle_media and drop unused accept_base64_uri flag
* server : update comment and add unit test for invalid data URI MIME
* server: fix tool calls getting silently stripped with --prefill-assistant
Last assistant carries tool_calls + --prefill-assistant is on → request
flips into continuation mode, add_generation_prompt forced off, tail
rebuilt from reasoning_content + content only. Tool calls just vanish.
- Auto-continuation now skips trailing assistant msgs that have tool calls
- continue_final_message on those throws a clear error instead of
silently corrupting the prompt
- Regression tests included, red before / green after
Fixes#27588
Developed with AI assistance, disclosed per the contribution policy.
* server : address review: fail on prefill-assistant + trailing tool_calls
Move validation into oaicompat_chat_params_parse (next to the existing
two-or-more-assistant check) and remove it from common_chat_templates_apply,
which has no precedent for validation. Drop the regression tests.
Per review: --prefill-assistant with a trailing assistant message
containing tool calls is not supported and should fail loudly.
* chat: add reasoning_effort to common_chat_templates_inputs
Store OpenAI Chat Completions reasoning_effort and make it
available to jinja templates (with model specific translations
where required).
Assisted-by: llama.cpp:Muse-Glimmer-30B
* server : fixup reading reasoning effort from body
server_chat_convert_responses_to_chatcmpl already handles conversion of
Responses API reasoning.effort to reasoning_effort
* chat : expose reasoning effort
Assisted-by: Claude Opus 5
* chat : add reasoning_effort to generation_params
Assisted-by: Claude Opus 5
* chat : move reasoning_effort next to enable_thinking
Assisted-by: Claude Opus 5
* cont : mirror preserve_reasoning
* cont : pass context through analyze function
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
* server : save serialized image chunks at the end of the llama state
* server : support multimodal slot state save/restore with packed payload
* server : refine image slot state serialization
* server : support media slot state and centralize media validation
* server : remove unnecessary comment
* server : remove defensive media checks and move the chunk type check to validate()
* common : extract trie/ac to a separate file
* common : support multiple token sequences in the reasoning budget sampler
* common/trie : return matched word index
* common/trie : rename "word" to "pattern"
* common/reasoning-budget : expose matched end sequence
* common/sampling : replay end sequence when reasoning budget is done
* cont : update to use multiple end sequences
* cont : clean up
* support "reasoning_effort": "none" in OAI API
* handle reasoning.effort: "none" in OAI responses API
* clarify non-"none" values of reasoning_effort have no effect
* use json_value instead of body.at
* 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>
* mtmd: fix silent prompt truncation on embedded NUL
mtmd_input_text carried the prompt as a bare const char* with no
length, so a NUL byte in message content cut the prompt at the
tokenizer boundary and dropped every later message plus the assistant
marker, with no log. Add an explicit text_len and thread it through,
matching llama_tokenize and the text only path.
* cleanup
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* 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
Use std::partial_sort to order only the requested top-n tokens instead
of the full vocabulary
logprobs sort: vocab=128000 n_top=0 iters=100
full sort: 8555.6 us/op
partial sort: 704.3 us/op
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
When reasoning-budget is set in model.ini, the per-request
thinking_budget_tokens from the WebUI was ignored because the
model.ini value took unconditional precedence.
Swap the precedence so the WebUI per-request value is checked
first, with the model.ini value serving as a fallback default.
Assisted-by: pi:llama.cpp/Qwen3.6-27B
* server: real-time reasoning interruption via control endpoint
Builds on the manual reasoning budget trigger from #23949. Adds a
CONTROL task that mirrors the CANCEL path on the live slot and calls
common_sampler_reasoning_budget_force to end thinking mid-generation.
POST /v1/chat/completions/control with { id_slot, action }, opt-in
reasoning_control arms the budget sampler on demand. Router and single
model. Minimal WebUI button as a skeleton for further UI work.
* ui: track reasoning phase via explicit streaming state
Add isReasoning to the chat store, mirroring the isLoading pattern:
per conversation map, private setter, public accessor and reactive
export. Set from the stream callbacks, true on reasoning chunks, false
on the first content chunk, reset on stream end and resynced on
conversation switch. The skip button now keys off isReasoning so it
shows only during the thinking phase, not the whole generation.
* ui: extract control endpoint and action into constants
Move the chat completion routes, the slots route and the reasoning
control action out of chat.service into api-endpoints and a dedicated
control-actions module. No behavior change, drops the magic strings so
the control protocol has a single source of truth.
* server: target reasoning control by completion id
Address @ngxson review on the control endpoint.
Switch from id_slot to the chat completion id to avoid a TOCTOU: the
slot can be reassigned between the lookup and the control request, so
matching the live completion (oaicompat_cmpl_id) is safe and a finished
one simply matches nothing. Rename the action to reasoning_end, guard
it on the reasoning_control flag of the target slot, and reduce the
response to {success} with an optional message.
* ui: target reasoning control by completion id
Keep the streamed completion id on the message and post it back to the
control endpoint instead of probing /slots. Drops the slot discovery
and the TOCTOU that came with it. Action renamed to reasoning_end,
response read as {success}.
* server: address review from @ngxson
Move the control fields into task_params and drop the redundant
comments on the control path.
* server: document the reasoning control endpoint
* Update tools/ui/src/lib/types/database.d.ts
Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
* ui: rename cmplId to completionId
Per @allozaur review, clearer name for the streamed completion id.
* ui: wire completion id capture through the agentic flow
The webui streams through the agentic flow, which relayed onModel but
not onCompletionId, so the completion id never reached the message and
the control request was never sent. Relay it through the flow and its
callbacks type, declare id on the chunk type, and log an explicit error
when the button fires without a usable id.
* ui: target reasoning control model from the message
The model is a property of the completion, so read it from the streaming
message like the id, not from the model dropdown which is unrelated UI
state. Makes the request self-consistent by construction instead of just
unlikely to drift.
---------
Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
* 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>
