* 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
* fit: also take into account n_streams
* server: make the draft context follow the target context
With a non-unified KV cache the target context now holds n_ctx_train
tokens per sequence, while the draft context was still created with
n_ctx = 0 and fell back to n_ctx_train / n_streams per sequence. A slot
filled beyond that point makes the draft batch fail to decode, and the
server answers 500 on the request.
The draft context now takes its size from the target context, so both
hold the same number of tokens per sequence. Contexts that share their
cells with the target no longer need the kv_size override.
The memory reserved for the draft model before fitting is measured at
the largest context the target can take, since the draft context grows
with the target and a fixed byte margin cannot express that.
* fit: take an optional second model into account
Illustrates the alternative discussed on the draft context fix. The
memory of a draft or MTP context is currently handed to the fit as a
fixed byte margin, which cannot express a memory that grows with the
context the fit is still deciding on.
common_fit_params now takes an optional second model that shares the
devices of the main one. Its context follows the main context and its
memory is measured again whenever that context changes, so the reduce
path stays exact instead of conservative. A model that cannot be
measured on its own, such as a shared cell MTP context, is skipped with
a warning and the main model is fitted alone.
This drops the reservation block in the server, which no longer has to
probe the trained context size of the target to guess an upper bound.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* feat: add --mmproj-device arg & backwards compatible MTMD_BACKEND_DEVICE env var
* feat: load mmproj device backend immediately, add -mmdev shortflag
* fix: its a pointer now get the name
* clean up
* gen docs
* nits
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* 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()
* Enable backend sampling with token speculation
* Clamp the mask sum before converting it into the sampled index
* Add a numeric context parameter declaring the maximum outputs one sequence
* More fixes
* Don't reuse memory for output views.
* Match dist between CPU and GPU
* Fix CPU and backend sampling mismatches
* Simpify some of the changes
* Fix tests on Vulkan
* More test fixes
* Rebase changes
* Rebase and address review comments
* Address review comments
* Address review comments
* Update src/llama-sampler.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Resolve -1 to 1024 instead of ctx-len for samplers
Because of backend-sampling we initialize samplers before the complete
llama_context is there. Therefore, we cannot infer the resolved context
length yet at the time we construct the samplers.
* Shared default of 64 for history-based samplers, remove context_size
* sampling: enhance penalty handling in common_sampler_init
- Set default value for penalty_last_n based on model context if not specified.
- Ensure penalty_last_n and n_prev are non-negative.
- Update llama_sampler_penalties structure to inherit from llama_sampler_backend and add backend input handling for penalties.
- Implement backend initialization and application logic for penalties, including frequency and presence adjustments.
* tests: add backend penalties sampling tests and utility functions
- Introduced `accept_prompt` and `unique_prompt_tokens` functions to handle prompt acceptance and token uniqueness.
- Implemented `compare_penalties_logits` to compare logits from backend and CPU samplers with penalties.
- Added `test_backend_penalties_sampling` to validate backend penalties with various configurations.
- Enhanced the test suite for better coverage of penalty handling in sampling.
* sampling: add support for top-k penalties in backend sampling
* sampling: add fix to ensure stable numerical results. Preserve masked logits as -Inf and no longer generate NaN.
* sampling: enhance penalty comparison tests with masking penalties logic
* add comments on padding
* sampling: add comments on modifications
* add the unit test to cover masked-out token as -INF
* validate repeat penalty to ensure it is finite and greater than 0; add tests for invalid values
* refactor: test functions to share logic and be less verbose
* add test to cover case where previously penalized token is not part of candidates
* remove comments
* remove redundant penalty_last_n initialization and validation in common_sampler_init
* add support for penalties in sampler chain with configurable positions
* add validation for penalty parameters and enhance tests for non-finite values
* add context parameter to common_sampler_init and set default for penalty_last_n
* add llama_n_ctx parameter to common_sampler_init for improved sampler initialization
* replace penalty_last_n x n_candidates comparison matrix with a vocabulary-sized count tensor
* add tests for backend penalties sampling without filler entries , token_count.size() == n_active == n_max == 64
* add test for backend penalties sampling after top-p with large history window
* remove as unused
* add is_disabled method, tensor logits reshape, add rest review suggestions
* clarify comment
Tarek Dakhran
Georgi Gerganov
Aldehir Rojas
Leebr Data ConsultingandIgor Okulist
Will
Jesus Gulfo
Foad Abo Dahood
BartowskiandXuan Son Nguyen
Gaurav Garg
John-Henry LimandXuan Son Nguyen
Chipmunk
Bhavik Sharda
Konrad Moren
Ruixiang WangandGeorgi Gerganov