* server : add realtime conversation and transcription on /v1/realtime
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Assisted-by: Claude Opus 5.5
* examples : default the realtime client to the server port 9931
Assisted-by: Claude Opus 5.5
* server : sample realtime responses right after their own decode
Slots respond in turns on one context: sampling at the start of the next step read the logits of whichever slot decoded last, so concurrent sessions mixed their outputs.
Assisted-by: Claude Opus 5.5
* move more stuff to server-realtime, making it self-contained
* examples : fix the type check of the realtime client
Assisted-by: Claude Opus 5.5
* examples : use the model card prompts in the realtime client
Assisted-by: Claude Opus 5.5
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* 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.
* model : add LiquidAI/LFM3-Audio-2B and LiquidAI/LFM3-Omni-2B
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Assisted-by: Claude Opus 5.5
* convert : read the LFM3-Audio config.json as released
A transformers that knows lfm3_audio rewrites the config into another layout (no lfm/encoder keys).
Assisted-by: Claude Opus 5.5
* mtmd : disable flash attention for the LFM3-Audio depthformer
Assisted-by: Claude Opus 5.5
* tts : add a voice option for LFM3-Audio and LFM3-Omni
The model card offers a female and a male voice; --tts-voice for llama-tts, "voice" for the server /tts.
Assisted-by: Claude Opus 5.5
* clean up template handling
* convert : detect LFM3-Audio and LFM3-Omni by model_type
Assisted-by: Claude Opus 5.5
* move banned tokens --> logits bias --> mtmd_helper
* leave mtmd-cli unchanged
* no more template_asr_system
* left over
* llama : keep the LFM3-Audio encoder convs and codec unquantized
Assisted-by: Claude Opus 5.5
* common, mtmd : shorten comments
Assisted-by: Claude Opus 5.5
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* 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: realtime API: send complete after committed audio drains
* server: realtime API: complete a commit at its end row
Completion is triggered when the stream runs dry, which is a timing
proxy: audio appended while the tail drains leaks into the committed
item, a final commit before the drain merges both items, and back to
back commits collapse into one.
mtmd_audio_stream_flush() records the row count at the end of the
flushed audio (mtmd_audio_stream_get_n_rows_flushed), next to the rows
produced so far (mtmd_audio_stream_get_n_rows). The slot keeps a FIFO
of these end rows and completes each commit once every row up to it is
decoded.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* 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>
* server: add realtime transcription API
* drop transcription_sessions
* proxy ws
* add docs
* server: let the API key middleware guard the websocket
httplib runs the pre-routing handler before the websocket upgrade, so
the API key middleware answers 401 at handshake like any other route.
This covers the router proxy, whose children run keyless, and accepts
the same key forms as HTTP (Bearer, raw key, X-Api-Key).
* server: keep realtime slots out of the prompt loop
A realtime slot waiting for audio is GENERATING with nothing to decode,
so it fell through the prompt loop and took slot_batched. A request on
a higher slot then failed can_batch_with on the task type and starved
for the whole session. Realtime slots have no prompt: the loop sets
them up once and always leaves them.
* server: start the realtime session once its slot is live
The audio stream was created in the prompt loop, which skips a slot
that cannot batch with the one generating, and session.created went out
before the task was posted: early appends failed and the reader quit,
leaving the socket open and the slot held until the HTTP timeout.
The slot now sets up its stream at launch and signals it with the begin
result, session.created waits for it, and a reader that stops closes
the socket so the session ends with it.
* server: name the committed item in input_audio_buffer.committed
Per the Realtime spec, item_id is the item made from the committed audio
and previous_item_id the one committed before it, null for the first.
committed now comes before the transcript that completes that item, and
both go out with the item switch under one lock, so a delta cannot land
between them.
* server: use the GA transcription session shape
The session object follows the GA Realtime schema: type transcription,
with the input format, transcription model, turn detection and noise
reduction under audio.input. The rate stays the model sample rate, which
is what the client must send for now.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* return logprobs in spec path
* server: add regression test for logprobs with speculative decoding
Asserts accepted draft tokens report the target model's logprobs,
comparing a draft-enabled run against a no-draft baseline (#24271).
Fails on master (top_logprobs empty, logprob 0.0), passes with the
fix from #27196.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KARBWpPcijcBXzJEvttuwk
---------
Co-authored-by: Jeonghyun Lee <sig.jhl@gmail.com>
Co-authored-by: Claude Fable 5 <noreply@anthropic.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>
The first evaluated token attends to the positions above
pos - n_swa, so a SWA state starting at pos - n_swa + 1 holds the
whole window, but the reuse threshold asked for two more cells.
A saved SWA state keeps only the cells inside its window, so a
state read back from a slot file or from the RAM prompt cache was
always rejected and the slot rolled back to a checkpoint.
The SWA threshold now matches the window, while a state without SWA
keeps the exact bound. The test harness gains override_kv, used to
give the SWA test model a window smaller than the test context.
[no release]
The slot file held the target context and the context checkpoints,
but never the draft context, so a restored slot drafted with an
empty or stale draft KV until the cache refilled.
The file now ends with the live draft state, the full draft context
and the speculative state, in the same appendix style as the
checkpoints. A draft state that does not fit the draft context is
dropped and the draft starts empty, while older files and older
servers keep working.
[no release]
Slot selection takes free slots by id or by last use only, so child
tasks often land on a slot holding a parent state while empty slots
are idle, and overwrite it without saving it to the prompt cache.
Free slots are now ordered empty first, then least recently used, for
both the LRU fallback and the child slots, and a child task saves the
state it overwrites. With 4 grouped decision requests in flight and
12 states in rotation, prompt cache reads go from 5 to 36.
[no release]
* server: save only restorable slot states to the prompt cache
The idle-slot save loop stores every idle slot whenever a task starts,
while only completion tasks restore from the prompt cache. Rerank,
embedding, decision and infill states fill the cache up to its limit
without ever being read back, and slots already cached are offered
again at every task start.
Each slot now tracks with cache_pending whether its state is restorable
and missing from the cache: a completion release or an explicit slot
restore sets it, prompt_save consumes it, prompt_clear resets it. Both
save paths share this rule, and the LRU path restores only for
completion tasks.
* server: test that decision slots skip the prompt cache
Decision tasks never mark their slot as cacheable, so starting new
tasks must not save their idle slots to the RAM prompt cache, with or
without a unified KV cache.
* server: restore any reusing task from the prompt cache
Embedding and rerank tasks never reuse their state, so their slots are
cleared on release like child slots and the cache only holds states
that a later task can restore. Infill and decision tasks now restore
from the cache too, which replaces the cache_pending flag.
Decision models disable the prompt cache unless --cache-ram is given,
since their states seldom repeat.
Add an optional ggml_backend_sched_realloc_callback that user code registers with
ggml_backend_sched_set_realloc_callback to be notified when the buffers reserved for a
graph are not sufficient and have to be reserved again. The callback reports whether the
backend assignments and the graph size changed since the previous scheduling.
llama_context registers the callback when creating the scheduler (wrapped into the new
llama_context::sched_new helper) and treats the reallocations that follow a change of the
backend assignments as expected, counting the rest by graph size - unchanged or changed
from the previously scheduled graph. Both counters are reported on one line in the perf
stats.
Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next
A request asking for a busy id_slot still ran the prompt cache update
on that slot before being deferred. When the RAM cache held a better
match, it was loaded into the slot while another request was still
generating there, and that generation continued on the wrong context.
The busy slot is now returned as is and the request waits for it.
* server : preserve context checkpoints across slot save/restore
Append the checkpoints after the packed server_tokens payload added in #26640
and count them in n_written / n_read, so a restored slot can still roll back to
a checkpoint instead of re-processing the whole prompt.
* server : drop draft checkpoint data that does not match the draft context
Restoring a slot saved with a different draft KV cache type aborted in
load_dft(). Test-load one draft checkpoint on restore and drop the draft
data if it does not fit, instead of crashing. Adds a regression test.
Co-authored-by: Igor Okulist <okigan@gmail.com>
* server : harden the checkpoint appendix of slot save files
Bound each blob size by the bytes left in the file before allocating, open the
file with UTF-8 paths on Windows like the llama state payload, fall back to full
prompt re-processing when a checkpoint restored from a slot file fails to load,
and replace the 1024 count cap by keeping the last n_ctx_checkpoints while reading.
* server : report an incomplete checkpoint appendix as a failed slot save
Return an error to the client when the appendix cannot be written, like a
failed payload write, and make the oversized-blob test declare a size that
cannot be allocated, so an unbounded allocation fails the test.
* server : reject an empty target state in the checkpoint appendix
A saved checkpoint always holds a target state, an empty blob would roll back
without restoring anything. Also log with the slot id, and load the draft test
model from the HF cache instead of a second download.
* common : return bool from checkpoint load_tgt / load_dft
A checkpoint restored from a slot file falls back to full prompt re-processing
when it fails to load, a checkpoint created in memory still aborts.
---------
Co-authored-by: Igor Okulist <okigan@gmail.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: support vision input for Clef
* move input_attn_causal to private
* extend old server_batch::embd
* server_batch::token::pos to multi dim
* nits
* fix abort
* fix img tokens cap
* fix yield_to_queue mutate data
* Make the drafter probabilistic and the target verify by rejection sampling
* Drop stale spec_draft_q before drafting
* Fallback to argmax sampling for grammar-constrained requests and adding flag for enabling probabilistic draft sampling. Default flag value is greedy.
* Support grammar-constrained requests in rejection sampling
* Fix - renormalize distribution after masking
* copy rng on sampler copy and re-accept drafted tokens on replay
* Fix draft sampler sharing the target's rng stream
* Simplify the rejection sampler's inputs and move replay to the server
* Truncate the draft candidates along with the draft
---------
Co-authored-by: praneshgo <227579474+praneshgo@users.noreply.github.com>
Co-authored-by: Pranesh Gonegandla <pgonegandla@nvidia.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>
* adapt common
* add common_batch
* wip
* wip: spec
* cont
* common_speculative_process
* server_batch to use common_batch
* rm some stale calls
Assisted-by: Claude Fable 5.1
* migrate mtmd
* handle imrope, handle return val of add()/add_embd()
* add spec zeros vector
* add warning on zero fill path
* server : allow splitting RANK pooling for causal LLM rerankers
Rerank models fall into two categories: bidirectional cross-encoders
(BERT, etc.) that require all tokens in a single physical batch, and
causal LLMs repurposed as rerankers (Qwen3, Qwen3-VL) that can use
chunked prefill like any other decoder.
Previously the server rejected all RANK-pooling inputs larger than
n_ubatch, and the graph builder hardcoded QWEN3/QWEN3VL arch checks to
determine last-token pooling. This broke long-document and multimodal
reranking for causal models.
Fix: expose llama_get_causal_attn(ctx) so the server can check the
effective runtime attention type (reflecting any --attention override
or set_causal_attn call). Also expose llama_model_is_causal(model)
for querying the static architectural property from GGUF metadata.
can_split() now permits chunked prefill for RANK pooling when the
context is causal. The graph builder's inline arch check is replaced
with the same cparams.causal_attn predicate, removing the duplication.
Assisted-by: Opencode/Qwen3.8-27B
* remove unused llama_model_is_causal, fix whitespace
Assisted-by: opencode
---------
Co-authored-by: timothywang21 <timothywang21@users.noreply.github.com>
* server: fix speculation after an image
Pass the actual position to the drafter after an image, instead of the
token count. Affects every drafter, not just DFlash.
* rename draft n_past to pos0
n_past is used to denote number of tokens and this parameter is meant to be a position
The spacing eviction in create_checkpoint() keeps the oldest checkpoint and
erases every later one within checkpoint_min_step of it. For prompts shorter
than checkpoint_min_step this drops the checkpoint at n_tokens - 4 that the
next request resumes from, so hybrid/recurrent models re-prefill from the
previous checkpoint instead. Apply the spacing rule only once the list is at
n_ctx_checkpoints, and replace an existing checkpoint at the same n_tokens
instead of appending a duplicate.
* common, server : enable preserve_reasoning kwarg by default, log its effective state
If the preserve_reasoning chat template kwarg is not specified explicitly
via --reasoning-preserve / --no-reasoning-preserve, it is enabled by
default after argument processing. The server logs the effective state of
the kwarg, warns that it is enabled by default when the template supports
it, and only warns "has no effect" when it was enabled explicitly on a
template that does not support it. Setting the kwarg via
--chat-template-kwargs is deprecated.
Assisted-by: pi:llama.cpp/Qwen3.8-27B
* cont : update comment
Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>
---------
Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>
* Add ctx-per-slot argument for unifid KV cache
* Swap out ctx fractions for ctx pool slots
* Formatting cleanup
* Remove ctx-pool-slots, make ctx-per-slot an int
* refactor it
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* Add benchmark-only synthetic speculative acceptance to llama-server and llama-cli
* Address review comments
* Address review comments
* Add some comments in the code
