* 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.
* spec : add lilicorr support
* convert : use convention for hparam assignment
* add todo for hparam use in llama-batch.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* typo fix
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* common : add --spec-draft-n-ubatch
Allow configuring the physical batch size of the draft context. The
drafters feed it at most the drafted tokens per sequence and chunk larger
batches by its own n_ubatch, so it does not need the ubatch of the target.
A smaller value shrinks the draft compute buffer and leaves more VRAM for
the target KV cache (the draft is fitted together with the target).
The default 0 keeps the current behavior of following --n-ubatch.
Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next
* cont : remove comment
[no ci]
tojson printed floats through a C++ stream (6 significant digits, no
".0"), so 48.858844 became 48.8588 and 1.0 became 1 in tool definitions,
tool-call arguments and other template output. Write the shortest digits
that read back as the same value, as json.dumps does. The string and safe
filters of a float use the same code.
* tests : consolidate the context state tests into test-llama-context
test-save-load-state, test-recurrent-state-rollback and
test-state-restore-fragmented check the same object from three places:
the KV cache and the recurrent state of a llama_context, saved, loaded,
copied, rolled back. Each had its own main, its own loop over the
models, its own copy of the token and the comparison helpers, and a run
of all three loaded the same models several times.
They are one binary now, and the case is the unit of the test:
tests/llama-context/test-case.h the interface a case implements
tests/llama-context/cases-kv.cpp the cases of the KV cache
tests/llama-context/cases-rs.cpp the cases of the recurrent state
tests/llama-context/test-util.cpp tokens, generation, comparison
tests/llama-context/test-runner.cpp the models of a case, on threads
tests/llama-context/test-log.cpp the log file of a model
A run is a table: a row per model, a column per case, a cell holds the
verdict of that pair. The models load on several threads (-j, --threads,
min(cores, 4) by default), the table is printed when it is complete and
the log of a model that failed is printed after it, in the order of the
rows. A case returns a verdict and the reason for it, and the runner is
the only one who reports - the test does not use tests/testing.h, a run
over 130 models is a matrix of verdicts and not a tree of subtests.
Staging no longer travels through the environment: llama_model_set_attn_rot
replaces the LLAMA_ATTN_ROT_DISABLE variable, which a model loaded in one
thread used to change for the models the other threads were loading, and
llama_memory_get_buffers is added for the cases that copy the buffers of
a memory. common_log_set_stdo keeps the console quiet while the table is
being built.
ggml_aligned_malloc allocates with posix_memalign when the thread
sanitizer is on. On macOS it allocates with the Mach VM, which the
sanitizer does not intercept, so it never sees a page range change hands
and reports the first write into recycled memory as a race - 30 of them
in this test, and every one of them false.
Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next
* cont : run single-threaded by default
* cont : try to build the tests on all platforms
* cont : trim down some body fat
* cont : fix
* cont : fix the fix
* examples : fix draft sampling and rejection handling
This commit fixes two issues in the speculative example which are
described in detail in the linked issue.
Resolves: https://github.com/ggml-org/llama.cpp/issues/29975
* examples : add kv-unified requirement for parallel > 1
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
* common : read a GGUF's trained context from its metadata
common_get_gguf_n_ctx_train opens only the file's metadata (no_alloc,
like common_get_decision_type) and reads <arch>.context_length, so a
caller can learn the trained context without loading the model. It
accepts both u32 and u64 values and returns 0 when the file is missing,
unreadable, invalid, or reports no context length.
Assisted-by: pi:zai-org/GLM-5.3-Flash
* server : report the trained context in the models listing
update_caps already resolves the model file offline to read its
modalities, so it now reads the trained context from the same GGUF
metadata, and GET /models reports it as context_length when it is
known. A router listing then carries the context without any Hub
request, which lets the UI sort and filter by it offline.
Assisted-by: pi:zai-org/GLM-5.3-Flash
* ui : take the trained context from the models listing
The router now reports context_length per model, so the option mapping
fills contextLength from it and the manager reads it before the Hub
record. The Context column, the context sort and the context filter
then work with the Hugging Face Hub API turned off. A browser suite
guards the sort and the search, the Hub-cache driven context filter and
the re-sort when details arrive after the sort was clicked.
Assisted-by: pi:zai-org/GLM-5.3-Flash
* ui : mark favorite models with a heart
A favorited model shows a rose heart in the selector even before its
row is hovered, and the crossed heart takes its place on hover, so
unfavoriting stays one hover away. The manager table marks its
favorited rows with the same heart after the badges and capabilities.
Assisted-by: pi:zai-org/GLM-5.3-Flash
* fix: UI text nit
* fix: UI nits
* fix: Favorite models grouping in models table
* feat: Remove sorting from Status column in Models Table
* server: read the GGUF metadata once per model
Read the decision type and the trained context in a single GGUF open,
accept only a UINT32 context length like the model loader, and reset
n_ctx_train with the other caps so a failed refresh drops it.
* fix: Post-review fixes
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* 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
* bugfix: infinite recursion caused by a tool named 'call'(#29967)
* chat : use index for schema and argument rules
* tests : remove tests
* tests : add expect_rules to peg test parser
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
* 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
* sampling : use greedy selection for eligible temperature-zero chains
Assisted-by: OpenAI Codex
* Apply suggestion from @ggerganov
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* sampling: simplify zero-temperature greedy eligibility
Allow the same greedy selection on CPU and grammar/reasoning-budget paths.
Keep distribution sampling for dynamic temperature and requested probabilities.
Cover the common sampler selection and probability behavior in the existing sampler tests.
Assisted-by: OpenAI Codex
* sampling : use greedy selection after final top-k with k=1
Assisted-by: OpenAI Codex
* cont : clean-up
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* model: K2 Horizon gguf conversion code
* model: loading hparams and tensors in k2-horizon.cpp
* model: K2 Horizon compute graph
* model: K2 Horizon compute graph adjustment and registering tokenizers
* model: K2 Horizon chat template and accomodate safetensors naming
* unicode : add the K2-Horizon pre-tokenizer splitter
The K2-Horizon regex had no arm in unicode_regex_split_custom and fell through to the
general std::regex fallback, which fails two ways.
On MSVC std::regex rejects \p{...}, so no K2-Horizon GGUF loads on Windows at all:
llama-quantize, llama-imatrix and llama-perplexity all abort with
regex_error(error_escape) before a token is produced.
Where the fallback does compile it is still wrong. unicode_regex_split collapses each
codepoint to a single byte naming its Unicode category before matching, and U+200C/U+200D
are category Control, which has no entry in k_ucat_cpt, so both become the 0xD0 fallback
byte. The literal and alternatives in K2's regex can then never match and
every ZWNJ or ZWJ ends a letter run.
The splitter is the existing llama3 one with a single rule widened, since K2's regex
differs from llama3's only in that a letter run also takes marks, ZWNJ and ZWJ.
tests/test-unicode.cpp gains a case for this: it fails before the change with
[Amy] [ZWNJ khaham] and passes after with the run intact.
* tests: expand K2 Horizon unicode splitter coverage
* unicode: handle K2 Horizon case folding and empty input
Assisted-by: Codex
* jinja : support sequence indices in selectattr and rejectattr
Assisted-by: Codex
* model : add K2 Horizon dense and MoVA support
Includes the K2 Horizon implementation from ifm-ai/llama.cpp with converter, tensor-parallel and model save/reload fixes.
Assisted-by: Codex
* chat : support K2 Horizon reasoning and tool calls
Assisted-by: Codex
* conversion: remove obsolete K2 Aurora alias
Assisted-by: Codex
* k2-horizon: enforce response schemas and load YaRN betas
Constrain final JSON after reasoning, accept flexible JSON tool envelopes,
enforce XML dialects, and handle repeated or alternate thinking markers.
Load YaRN beta metadata instead of retaining the default values.
Add schema, streaming, continuation, and model reload regressions. Validate
CUDA and CPU builds and 0.9B, 4B, and MoVA conversation/tool round trips.
Assisted-by: Codex
* renaming template fixture
* adressing cisc follows ups
* desloppify the parser / adress aldehir comments
* clean test-chat
* remove fallback : model trained mostly on high anyway
* fix k2 attn_v_exp tn splitting and metal fusion baseline
* k2-horizon : forward expand views before sums
* k2-horizon: copy embds before group norm to fix TP
* disable tesnor parallelism
---------
Co-authored-by: Ryandito Diandaru <ryandito.diandaru@mbzuai.ac.ae>
Co-authored-by: WestWaters <mario.papaleo2013@gmail.com>
Co-authored-by: Natani L. Mayday <71436458+TaskPuppyNatani@users.noreply.github.com>
Co-authored-by: West <100190545+WestWaters@users.noreply.github.com>
Co-authored-by: aaryamonvikram <aaryamonvikram@gmail.com>
Co-authored-by: aaryamonvikram <96529820+aaryamonvikram@users.noreply.github.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>
A TOOL_ID node that arrives after TOOL_CLOSE wrote through `current_tool`,
which still pointed into the just-destroyed `pending_tool_call` optional
(use-after-free, then a second free of the id buffer). Clear the pointer on
reset.
This is part of the fs::path modernization series.
That was also the opportunity to remove fs_list().
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* chat : honor json_schema in Ling 3.0 parser
Ling 3.0 only built a grammar for tool calls and did not handle inputs.json_schema, so response_format requests were left unconstrained.
Add an eager response-format grammar path with precedence over tools, following the existing parser patterns. Require </think> before JSON when thinking is enabled and do not allow trailing prose after the JSON response.
Fixes#29652.
Assisted-by: Claude Opus 5.5
* chat : require Ling 3.0 think block for response formats
* 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
LLM-jp-4.1 uses the GPT-OSS format, but its tokenizer decodes a space
after every special token and parallel tool calls are separated by
<|end|>. The GPT-OSS handler rejects this output, so add a dedicated
handler, selected by the chat_format=llm-jp-harmony-v1 declaration in
the chat template.
Assisted-by: Claude Fable 5.1
* cli: exit on stdin EOF and drop the console wide Ctrl+C broadcast
On Windows the simple input reader sends CTRL_C_EVENT to every process
attached to the console when stdin reaches EOF, killing unrelated
processes such as a supervising agent. The CLI only stopped on EOF
because of that self inflicted SIGINT; on POSIX, and with the advanced
reader, it spins forever printing prompts.
Drop the broadcast so both platforms just return an empty read, and
treat an empty read as EOF in the chat loop and the model selection,
since a submitted line always ends with a newline.
* cli: keep the newline of a trailing "/" and stop mtmd-cli on EOF
A lone "/" came back as an empty read and was taken for EOF, and
mtmd-cli only stopped on EOF through the removed broadcast.
William Tan
Pascal
Kartik GuliaandGeorgi Gerganov
Yuri Khrustalev
Xuan-Son Nguyen
Daniel Bevenius
Mikolaj Kucharski
Aleksander GrygierandPascal
Aldehir Rojas
Aman Gupta
Leebr Data ConsultingandIgor Okulist
Tarek Dakhran
Nicolas Budyn
Frost-54andAlde Rojas
Hrishith ThadicherlaandGeorgi Gerganov
Adrien Gallouët
Anas
Pranesh GonegandlaandPranesh Gonegandla
Nik Bogatyrev
Yash Raj Pandey
a u s t i n
e-mon
pr3pony
Ethan Guo