* cmake : introduce semantic versioning (wip)
This commit introduces semantic versioning to llama.cpp.
* squash! cmake : introduce semantic versioning (wip)
* cmake : update test-cmake README notes [no ci]
* include libmtmd in output so show its semversioned
* ci : add make-release workflow
* ci : fix build number check in build-cmake-pkg.yml
* examples : remove trailing whitespace
* ci : abort if upstream ggml version does not exist
* ci : extract step contents into scripts
* ci : add GGML_NATIVE=OFF to ubuntu job
* examples : remove CI build information from test-cmake [no ci]
This commit removes the nightly/release information that I added
previously to keep this focused only on using building and installing
llama.cpp with cmake and being able to quickly verify changes or
troubleshoot issues.
* ci : merge scripts into single script
* remove -dev-build_number support
This commit removes the incremental build number (versioning) support
that I added. This was incorrect and we should only use the semver for
the version. Releases will be tag a nightly build and package
maintainers/managers that build from source can use the tag and it is
therefor important that the correct version is reported. So a
nightly-build will report the semver without the build number. The build
number and commit as availble via cmake and test-cmake has been updated
to include an example of using them:
```console
$ ./build.sh
[test-cmake] version: 0.1.0, build: 10360 (08c69e381)
...
```
Refs: https://github.com/ggml-org/llama.cpp/pull/26839#discussion_r3755836969
* docs: add initial release.md documentation
* cmake : clean-up and add LLAMA_BUILD_IS_DEV option
* ci : remove version input from make-release job
* ci : add LLAMA_BUILD_IS_DEV=OFF to build-cmake-pkg.yml
Refs: https://github.com/danbev/llama.cpp/actions/runs/31576801921/job/94050639145
* docs : update release notes with LLAMA_BUILD_IS_DEV info [no ci]
* ci : add TODO to winget workflow [no ci]
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* chat : fix muse-glimmer swallowing a trailing tool call into content
Muse Glimmer routinely answers the user and calls a tool in a single
generation. The template terminates a message with <|eom|> when more
messages follow in the same turn and <|eot|> only at the end of the turn,
so the answer is closed by <|eom|> and the call opens a fresh header:
<prose><|eom|><|start|>assistant to=<tool><|message|><atem:function_calls>...
The final-message rule read content with until("<|eot|>"), which assumed the
user-facing message is always last. There is no <|eot|> before the call, so
content ran to the end of the turn, absorbed the markup, and no tool_calls
were emitted - the tool never ran. On a tau2-bench telecom run this hit 43
turns across 19 of 114 tasks.
Stop the answer at <|eom|> and parse what follows as tool calls.
Adds models/templates/muse-glimmer.jinja and four parser tests: a plain
answer, the <|eom|> junction, markup quoted in an answer staying content,
and tool markup inside the to=self channel staying reasoning.
* address comment
* llama: add new default load-mode auto which picks mmap unless a non-Metal iGPU is used
* Update ggml/src/ggml-hexagon/ggml-hexagon.cpp
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
* set mmap_support to false on OpenCL backend
* fix order of load modes
* use -1 for auto
* resolve load mode auto earlier to correctly pick gpu host or cpu memory
* add load mode auto to llama-bench
* bump virtgpu api version, regenerate docs
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* 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>
* server: add an ssh transport to the tools runtime
--tools-runtime ssh:<target> runs the built-in tools on a remote host,
where target is whatever ssh already resolves, a user@host or a config
alias, so no credentials live in llama.cpp.
Only build_argv and upload differ from the docker transport: the remote
shell re-parses the command line, so the argv travels through
shell_quote_join, and files go over scp with the same quoting on the
remote path. Authentication is key-based and the host key must already
be trusted, since the tools run without a console and any prompt would
hang them.
The target is validated before use. The spec can reach us from the
x-tool-runtime header, and a leading dash would turn it into an ssh
option, which is enough to run a command back on the host.
Nothing is created and nothing is reclaimed, so an ssh spec goes
straight to the tool call instead of through the container runtime.
Note that this is remoting rather than isolation: the tools can do
whatever the target account can do, and the isolation is whatever runs
them on the far side.
* server: support podman in the tools runtime
docker and podman expose the same run, exec, cp and inspect verbs with the
same argument order, so a single implementation drives both and the engine
is carried by the spec prefix: podman:<image> and podman-container:<id> sit
next to the docker forms.
tools_io_docker becomes tools_io_container and the runtime spawner becomes
server_tools_container_runtime, both holding the client binary chosen at
parse time. A single parse_container_runtime() resolves every spec, so
adding another engine is one string in the table.
make_tools_io() now rejects the spawning forms. The spec also reaches it
from the x-tool-runtime header, which is client controlled, and only the
runtime that owns a container is allowed to create one: a tool call can
attach to a running container, nothing more.
* ./build/bin/llama-gen-docs
* server: simplify the tools runtime and drop the file copy step
A server_tools_runtime base with one virtual spec() replaces the
container runtime and the bare spec string that ssh needed next to it,
so server_tools is back to a single pointer and neither setup nor the
handler tests which of the two is set.
write_file used to spill its content into a temporary file on the host
and copy it in, because run_subprocess had no way to feed a child. It
now takes an optional stdin payload and creates the parent directory
and the file in a single round trip through a shell in the isolate.
That removes the upload virtual and both implementations: no more
container cp or scp, no second binary on the host, no sftp subsystem on
the target, no predictable temporary in a shared tmp, and none of the
content reaching an argv the remote shell re-parses. It also fixes
write_file over ssh, which never worked: scp speaks sftp and takes the
remote path literally, so quoting it kept the quotes in the file name.
Writing the payload before reading the output relies on the child
draining stdin as it goes, which holds for cat, its only user today.
* ./build/bin/llama-gen-docs
* server: harden the tools runtime against argv injection and a stdin stall
Validate the container id from x-tool-runtime and --tools-runtime the
same way the ssh target already is, so an id shaped like an option
(docker-container:--privileged) is rejected before it reaches the
engine's exec command line instead of running against a hardened
container. Feed the child's stdin after the watchdog is armed, so a
transport that stalls mid-write is terminated at the deadline rather
than blocking the request forever.
Cover both guards and fix the unknown-scheme test, which used ssh: as
its example and now names a real runtime.
* tests: exercise the tools runtime tests on podman as well as docker
Follow-up #26507. The container runtime drives docker and podman
through one implementation, so parametrize the availability helper,
the container fixture and the attach test on the engine, and cover
both engine prefixes in the container id injection test. Each engine
skips on its own when it is not installed.
The spawn cleanup test stays docker only: it recovers the spawned id
from the container hostname, which docker sets to the short id and
podman rootless does not guarantee. Podman keeps its coverage through
the attach path.
* server: release the container handle before respawning
Follow-up #26507. create() writes over the handle it is given, so a
respawn after the container died on its own leaked the pipes and the
process handle of the previous one.
* server: trim the tools runtime comments
* server: read tool output as raw bytes and harden the runtime on Windows
The stdout pipe is read with read() instead of fgets(), so a chunk
can hold any byte, including NUL, and still streams as soon as data
is available. Past the size cap the pipe keeps draining so the child
never blocks on a full pipe. Both pipe fds are forced to binary mode
on Windows, where the CRT defaults them to text mode and translates
line endings in both directions. Stdin is now always closed after
the feed: the child reads a deterministic EOF, and the Windows
docker and ssh clients stop outliving their command on a stdin pipe
that never closes.
The attach form of --tools-runtime has no lifecycle to own, so it
becomes a static target validated once at startup. This removes the
subprocess that ran on every tool call and
serialized calls behind a mutex; a stopped container now surfaces
the engine's own error at exec time.
The cidfile path is passed as UTF-8, matching the encoding the
subprocess layer expects for the CreateProcessW command line, so
the spawn form works from a non-ASCII Windows profile.
The SIGPIPE note in server.cpp now names the tools runtime children
as well as the MCP ones.
* clean up comments
* less pollute global scope
* nits
* tests: name the container image after both engines
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* Get started with Onyx
* Add architecture
* Skip keys handled in super()
* Loading tensors
* Shorten
* Graph
* Apply suggestion from @pcuenca
* Remove norm now embedding in transformers weights
* Add eot
* Explicit output_multiplier
* Handle post_norm_eps
* No super call; unhardcode eot.
The pattern `self._set_vocab_gpt2()` seems preferred throughout the
codebase, and it allows `set_vocab()` to be called from a different part
of the Python class hierarchy: the drafter model converter that we may
need eventually.
* Register for drafting
* DFlash: inherit rope type from the linked target.
Another option would be to store it in the gguf file itself.
* mmproj conversion
Note: some fields to be renamed after the implementation works. We are
keeping compatibility with the reference Meta gguf for testing purposes.
* "clip" header declarations
* Load mmproj
* Pre-processing
* Graph
* Go back to using delimiters.
Otherwise our generations are worse.
Transformers does not use them. We need to trace inputs to verify
whether they are equivalent.
* downsample_factor -> merge_size
* Add vision graph
lol, forgot from a previous commit
* Additional renames, align with llama.cpp / transformers
* Prefer _size instead of independent _h and _w
* Fix token layout
Co-authored-by: Young Han <younghan@fb.com>
* onyx: bring the chat parser onto the onyx branch
common/chat.cpp on this branch has no Onyx handling, so a converted model
serves malformed chat: the assistant preamble leaks into content
("to=self<|message|>...") and tool calls fail with
HTTP 500 "The model produced output that does not match the expected
peg-native format"
common_chat_params_init_onyx exists on onyx-fair-patch, added there by
8bb73dd3d. It was never on this branch, so this is not a regression --
the two lines developed independently.
The code here is taken verbatim from that commit. It is the clean side of
`git merge origin/onyx-fair-patch`: chat.cpp is one of the files that
merges without conflict. The full merge is not viable -- it produces 13
conflicts, including add/add on conversion/onyx.py and src/models/onyx.cpp
where the q_norm-folding and metadata-scale approaches contradict each
other, and #4/#7 are stacked on this branch's side of that.
Verified on this branch: builds with 0 errors, converts an Onyx checkpoint,
and serving it gives "4" for "What is 2+2?" plus a correct
get_weather {"city":"Paris"} tool call, where the unported branch gives the
two failures above.
No converter or runtime changes are included, so this should not interact
with the q_norm work.
Co-authored-by: Beto de Paola <betodepaola@meta.com>
* Less params, bilinear pos-emb interpolation as a graph op instead of CPU
* Map to symbolic V_MMPROJ instead of strings
* Make a couple params explicit
* Patchify via build_inp()
* No param for rope_theta
* Small cleanup
* Restore blank line
* Unpermute, to adapt to the latest transformers checkpoint
* Apply norm after token embeddings
This follows the latest transformers approach.
* Remove duplicated function
* build_vit
* onyx: use the model rope theta on sliding-window layers
* DFlash: conversion from transformers drafter
* Revert rope_type derivation from target
NOTE: this breaks compatibility with Meta's distributed DFlash GGUFs, as
the Q/K are stored in "NEOX" (rotated half) format, like in
transformers.
* Apply suggestion from @pcuenca
* Set model type
* Remove comment that will become obsolete
* Hardcode post_norm_rms_eps instead of new param
* Derive SWA+RoPE pattern from gguf array or scalar
* Fix model type <-> number of layers
* Reorder
* Rename
* Fix typo
* DFlash: seed the draft KV cache from multimodal embedding batches
`common_speculative_impl_draft_dflash::process()` returned early on any batch carrying embeddings, so an image prefill never had its target-layer features fused through the DFlash encoder and injected into the draft's KV cache. That left a hole spanning the image's positions, and the next injection at a post-image position failed to initialize its batch:
```
decoding image batch 1/1, n_tokens_batch = 256
decode: failed to initialize batch
llama_decode: failed to decode, ret = -1
process: llama_decode(ctx_dft) failed rc=-1 (n_tokens=17, offset=0)
srv decode: failed to process speculative batch
```
Every image request with `--spec-type draft-dflash` failed with HTTP 500. Text-only was unaffected, since those batches carry token ids and were let through.
Restore the earlier condition, which admits a batch that is either tokens or embeddings and skips only the degenerate neither/both cases. The rest of `process()` is already layout-agnostic -- it gathers features via `llama_get_embeddings_layer_inp()` and indexes `batch_in.pos[]` / `batch_in.seq_id[]`, none of which assume token ids -- so this is the whole fix.
Validated against `muse-glimmer-30B-bf16.gguf` + `mmproj-muse-glimmer-30B-bf16.gguf` + a DFlash draft head, on an image describe-the-shapes request:
- before: HTTP 500, `failed to process speculative batch`
- after: HTTP 200, draft acceptance 0.34012 (167 accepted / 491 generated), mean len 3.04
Output equivalence holds, which is the property that matters: at temperature 0 the drafted response is byte-identical to the same request served with no draft attached (1213/1213 chars), so the draft is drafting correctly through the image context rather than merely not crashing.
* Conversion: prefer rewrite to mapping
* Revert "Conversion: prefer rewrite to mapping"
This reverts commit a92d0ac584.
* fix lint
* sliding_window metadata is not optional
* disable state save/load
* Apply suggestion from @pcuenca
---------
Co-authored-by: Young Han <younghan@fb.com>
Co-authored-by: Beto de Paola <betodepaola@meta.com>
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
Co-authored-by: ruanrms <ruanslv@gmail.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* 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
This commit contains a suggestion to reduce some code duplication in
common_speculative_init when adding the enabled speculative decoding
configurations.
No tests were added but the existing server tests still passes with this
change:
```console
$ ./tests.sh unit/test_speculative.py -v -x
```
* common/chat: update DeepSeek V4 templates
Align the DeepSeek V4 templates with the official encoders while keeping parser behavior out of this change.
- Default drop_thinking for DeepSeek V4 history so prior thinking is omitted unless preserve_reasoning is requested or tools are present.
- Add structured output response-format instructions to the V4 templates and pass the schema into template rendering.
- Add a separate Flash 0731 template for the updated high and max reasoning effort mapping.
- Cover reasoning effort, drop_thinking, structured output prompts, preserved reasoning, continuations, and empty tool arguments in template rendering tests.
Official references:
https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash/blob/main/encoding/encoding_dsv4.pyhttps://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731/blob/main/encoding/encoding_dsv4.py
Assisted-by: Codex
* Fix deepseek v4 0731 template selection
* remove unneeded lower normalization
* Fix DSML parser to consume the tool call separator
* address aldehir requests
* address aldehir comment
* tests: add model resolution test on synthetic repo listings
Include download.cpp and arg.cpp inside a namespace with hf_cache
monkey patched to serve hardcoded listings, so the resolution and the
model handler assembly are tested end-to-end through the real CLI
parsing, without modifying the tested code and without network access.
Covers the primary, shard, mmproj, sidecar and preset resolution on
layouts mimicking real vendor conventions, replays every case on
permutations of the listing to assert determinism, and asserts the
final wired paths for the spec type auto-selection, the -md precedence
and the fallback suppression.
* tests: keep model resolution checks active and let the handler resolve
Replace assert with a REQUIRE macro alive in Release builds, key the
fake hf_cache by repo id so the real handler init resolves every plan
itself, check the exact shard sets, restrict the permutation exception
to the order dependent picks, and cover dflash and eagle3.
* tests: fix model resolution build on fatal warnings CI and Windows
The namespaced copy of the sources leaves many static functions unused
in this TU, exempt it from the unused warnings. Pre-include the
windows headers so arg.cpp does not pull them inside the namespace.
Declare the renamed copies of the download.h functions, verbatim from
the header and renamed in sync by the macros, so missing declaration
and missing prototype warnings are satisfied on every toolchain.
* tests: fix winsock inclusion order for the model resolution test
WIN32_LEAN_AND_MEAN and winsock2.h before windows.h, so http.h does
not redefine the socket types afterwards.
* tests: link cpp-httplib to the model resolution test
The test compiles its own copy of download.cpp, which calls httplib
directly, and the private link of llama-common does not propagate the
symbols under lld-link.
* common_http_client
* common: finish the http client wrapper
Add the virtual Head, Get and Post methods and the passthrough
setters to the common_http_client skeleton, move follow_location
into the constructor, expose the underlying client for the ranged
pull path, and rename the missed common_http_client_init call sites.
* tests: rewrite model resolution on the http client stub
Replace the namespace inclusion of the sources by a plain TU: the
common_http_client factory returns a stub serving hardcoded HF API
responses, so the real hf_cache parsing, resolution and CLI handler
run against synthetic listings in an isolated cache directory.
Failures print the named case, the reordering and the actual versus
expected values, the assembly cases use the full command line as
context, and the empty result cases are checked once to keep the
logs short.
* tests: fix the model resolution on Windows and the builds without TLS
Assert the exact expected paths composed like the cache does instead of
suffix matching on forward slashes, set the environment portably, and
serve the stub through an http endpoint so the builds rejecting the
https scheme still reach it. Pause the log so the negative cases can be
replayed on every reordering.
* tests: make the model resolution failures self explanatory
Resume the paused log before the failure report so the CI shows why
the tested code bailed, and format the stub oids portably.
* common: hold the http client factory behind exported functions
The factory was an inline variable, and the Windows shared builds
export functions but not data, so the executable and the DLL each had
their own instance: the stub installed by the test was invisible to
the library, which reached for the real endpoint and resolved nothing.
Route the creation through functions compiled into the library and
format the stub oids portably.
* common: add the http client factory source missed in the previous commit
* common: typedef the http client factory callback
Address review from @ngxson
* tests: serve the model resolution repos over the loopback
Replace the client stub by a real httplib server bound to the
loopback, so no C++ object crosses the module boundary anymore and
the library exercises its own client and transport end to end. The
debug shared build on Windows crashed inside the stubbed path.
* common: add portable common_get_env and common_set_env helpers
Address review from @ngxson
* common: drop the http client factory left without a caller
The loopback server made the stub substitution unnecessary, the client
init builds the real client directly again.
* common: read the model endpoint through the env helpers
* nit: drop the stub leftovers from the model resolution test
* common: align common_set_env and isolate the test cache per run
The POSIX branch now behaves like _putenv_s, so the helper has a single
contract on every platform, and common_get_env already reads an unset and
an empty variable alike.
The model resolution test keys its cache directory on the loopback port,
where two concurrent runs on the same machine used to share one directory
and the initial cleanup of either wiped the other.
* tests: move the model resolution server into main
* tests: support the DSpark sidecar resolution
* common: revert the http client to the plain httplib client
address review from @ngxson
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* server: add get_info tool
* fix --rpc in docs
* server: harden get_info probe result handling
Report the OS as unknown when the probe process fails to spawn or times
out, so the diagnostic text from run() is never returned as an OS name.
Strip the probe output on both ends, which also drops the blank line
that ver prints before the version on Windows. Name the output and
timeout limits, and report an unreadable working directory as unknown
instead of an empty string.
* server: simplify get_info result handling
Drop the named limits and the working directory error branch, keeping
the probe result handling to a single expression.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* 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
The dspark- files resolve like the other speculative sidecars: the
-hfd tag applies to them, a requested sidecar resolves without a full
model at the tag, and an explicit -md selection disables the discovery.
When no type is requested, dspark outranks dflash in the auto-selection
since its sidecar carries the extra Markov head.
* llama : load MTP tensors only if they are really used
* llama : skip loading MTP (if not used) in remaining models that support MTP
---------
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* spec: add DSpark speculative decoding
DSpark (DeepSpec, 2026) on top of the merged DFlash drafter. It reuses the
DFlash encoder/decoder graph, target feature extraction and KV-cache injection,
and the verify/accept path unchanged; the draft model is a new "dspark" arch
adding a low-rank Markov head (markov_w1/w2) and an optional (unused here)
confidence head. No new public APIs.
The proposal is the only change: the block is anchor-first (position 0 already
predicts the first draft) and the decoder graph applies a semi-autoregressive,
previous-token conditioned logit bias in-graph, chained per block position:
logits'(i) = logits(i) + markov_w2 . markov_w1[prev(i)]
prev(0) = the block's anchor token, prev(i>0) = argmax(logits'(i-1))
vectorized across all blocks in the batch; the anchors are fed through a
dedicated graph input (token 0 of every block). Greedy stays lossless
(verify unchanged, same as DFlash).
- new arch "dspark" (llama_model_dspark : llama_model_dflash, reuses the graph,
loads the markov/confidence tensors; shares the target's embed/lm_head).
- Qwen3DSparkModel converter.
- new spec type "draft-dspark" (common_speculative_impl_draft_dspark :
common_speculative_impl_draft_dflash, overrides draft() only: submits whole
anchor-first blocks and greedily reads back the biased logits).
* spec: read draft block size in the dflash impl
* docs: add DSpark section to speculative.md
* spec: keep dspark block size read in the dspark impl
* dspark : add TODOs for incomplete parts
- confidence head is loaded but not used yet
- confidence-scheduled prefix pruning is not implemented
- the in-graph Markov chain is greedy-only
- only Qwen3 backbones are supported for now (also noted in docs)
* spec: fold DSpark into the DFlash arch
Address review: drop LLM_ARCH_DSPARK and the dspark.block_size /
markov_rank GGUF keys. A DSpark draft now converts to a DFlash GGUF;
the Markov head tensors are detected by presence (like eagle3 d2t),
block_size is read from the existing dflash.block_size key, and the
block anchors are taken as a strided view of the decoder's token
input instead of a separate graph input.
* spec: add confidence-based draft pruning for DSpark
The DSpark confidence head predicts per-position acceptance of the
drafted block. --spec-draft-conf-min truncates the block at the first
position below the threshold (default 0 = disabled).
* fold the dspark impl into dflash, selected by spec type
* address review comments
* dspark: clean up and improve naming
* update readme
* remove trailing whitespace
* dflash: draft full n_max blocks, defer dp.n_max to the central truncation
The DSpark markov head views the draft batch as a uniform [n_seqs x block]
grid, but the per-seq dp.n_max clamp could produce blocks of different
sizes, silently corrupting the strided views and the resulting logits.
Drop the clamp and always draft the full n_max block for every sequence:
dp.n_max is already enforced by the central truncation in
common_speculative_draft(), the same way eagle3 handles it.
Co-authored-by: Zaire404 <3147879462@qq.com>
* dflash: assert the markov head block-uniformity invariant, require the conf head
With the draft batch always submitting equal-size n_max blocks, a
non-divisible token count can only mean the batch was split across
ubatches or a caller broke the layout - fail loudly instead of silently
dropping the markov bias. The block_drafts > block_size early return
stays: worst-case graph reserve passes legitimately build with
n_seq_tokens > block_size.
Also make conf_proj required when the markov head is present: the
confidence head is part of the DSpark checkpoint format, and a missing
head would otherwise leave --spec-draft-conf-min silently reading stale
embeddings instead of confidences.
Co-authored-by: Zaire404 <3147879462@qq.com>
* dspark: fold conf_min into p_min
p_min and conf_min express the same thing - the minimum predicted
survival probability for a drafted position - differing only in how the
estimate is obtained: token probability for regular drafters, the
trained confidence head for DSpark. The DSpark readback never used
p_min, so reuse it for the confidence threshold and drop the separate
--spec-draft-conf-min flag. Both defaulted to 0 (disabled), so behavior
is unchanged.
Co-authored-by: Zaire404 <3147879462@qq.com>
* dflash: note the confidence broadcast workaround
Requested in review: the ggml_repeat only adapts the [1, n_tok]
confidences to the n_embd-wide embd_nextn transport so that
llama_get_embeddings_nextn can be reused - not a placeholder.
Co-authored-by: Zaire404 <3147879462@qq.com>
* cont : clarify
[no ci]
---------
Co-authored-by: Ruixiang Wang <wangruixiang07@outlook.com>
Co-authored-by: Zaire404 <3147879462@qq.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* common: fix explicit -md precedence over draft sidecar resolution
Follow-up of #25955, an explicit --model-draft file given with -hfd
was silently overridden by the sidecar resolution of the draft repo,
and its path was never resolved to a local file.
An explicit draft file selection now disables the sidecar resolution,
so the manual CLI configuration wins over the automatic one.
* common: apply the -hfd tag to the sidecar resolution
The sidecar selection was anchored on the primary of the draft plan,
so a tag without a matching full model aborted the whole plan, and
the sidecar quant silently followed the default model pick.
The tag now anchors the sidecar directly: exact tag match first, then
closest quant to the tag, and a requested sidecar resolves even when
no full model matches the tag. A wired draft sidecar also counts as
an explicit draft, so the main plan no longer downloads a second one.
* common: promote speculative load logs from trace to info
Show the loaded draft model and the MTP draft context at the default
verbosity, for consistency with the mmproj and primary logs.
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* add common/subproc.h|cpp
* add compile flag LLAMA_SUBPROCESS
* disabled by default on android and ios
* test-jinja: use common subproc
* mtmd: disable video if subproc is not set
* disable subproc on wasm
* make is_created atomic
* migrate server-mcp
* move server_pipe to common
* init impl
* vendor: update subprocess.h
* add server_mcp_stdio
* stderr drain
* server_mcp_transport
* server_mcp_stdio is now framing-only, no json
* internal/mcp-stdio: integration + tests + fixes (#26075)
* server-mcp: harden transport and wire up the tool integration
Builds on the transport/manager architecture (server_mcp_transport + server_pipe)
with the hardening and integration the draft did not yet have.
Hardening:
* Reader and stderr pumps are polled (running-aware) instead of blocking on a read
that only ends at EOF. subprocess_terminate() SIGKILLs only the direct child, so a
grandchild the MCP server spawned that inherited the pipe would otherwise keep the
write end open and hang teardown (both warmup shutdown at startup and process
shutdown). The writer is likewise non-blocking + polled.
* Windows: resolve the command through PATHEXT so "npx" (npm ships npx.cmd, never
npx.exe) spawns, matching POSIX's PATH search; and enumerate the parent environment
as UTF-8 (GetEnvironmentStringsW) instead of the active code page.
* server_pipe gains an opt-in max_size (default unbounded, so the router's streaming
use is unchanged); the MCP reply queue uses it so a server that streams unsolicited
notifications between requests cannot grow it without bound.
Integration:
* --mcp-servers-config / --mcp-servers-json flags; enabling MCP restricts default CORS
to localhost, same as --tools.
* MCP tools are exposed through /tools (and chat-completions) as <server>_<tool>,
skipping names that collide with a built-in or another MCP tool.
* Manager lifecycle wired into llama_server(): warmup at start, shutdown() from the
signal handler before the HTTP server drains, blocking teardown in clean_up().
* SIGPIPE ignored so a child dying mid-write yields EPIPE rather than killing us.
Assisted-By: Claude Opus 4.8 <noreply@anthropic.com>
* server-mcp: add MCP test suite with grandchild deadlock regression test
21 tests over the /tools endpoint: tool discovery/invocation, timeouts, crash
recovery and respawn cooldown, warmup partial failure, malformed and batched
notification+response output, tool-definition shape, and prompt shutdown during a
slow call.
The last test spawns an MCP server that leaves a grandchild inheriting its
stdout/stderr and asserts the server both starts and stops promptly. Verified it
fails (5s SIGKILL fallback on a deadlocked reader-join) when the pump is made to
ignore the running flag, and passes with the polled reader.
Assisted-By: Claude Opus 4.8 <noreply@anthropic.com>
* clean up
* clean up 2
* even stricter life cycle
* nits
* nits 2
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* fix some edge cases
* fix last_error data race
* fix response schema + docs
* server: fix MCP zombie leak and timeout-induced transport teardown
join_pumps() never reaped the child, leaking one zombie per spawn:
call subprocess_join() before subprocess_destroy().
A per-call timeout permanently closed from_server and got a healthy
transport evicted: add close_on_stop to server_pipe::read() and pass
false from send_rpc(), where should_stop is a per-request deadline
and a late reply is already skipped on id mismatch.
Also drop the unreachable disconnect cancellation in
server_mcp_tool::invoke(): support_stream is false, st is always null.
(cherry picked from commit e6de1ec043174fd0570b1e60d47f06c7c19d620d)
Assisted-by: Claude Opus 4.8
* server: make MCP test fixtures JSON-RPC 2.0 compliant
Add the missing notification guard to mcp_malformed_server.py and
mcp_burst_server.py (the latter treated id 0 as a notification and
replied to unknown ones; its notification table is now unused).
Return -32602 instead of -32601 for unknown tools: tools/call is a
valid method, the tool name is the invalid parameter.
Also fix the test module docstring: tools are named <server>_<tool>.
(cherry picked from commit 74a08e8c311dabf3b49d06cc6d754b0097ae7a38)
Assisted-by: Claude Opus 4.8
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
Co-authored-by: Pascal <admin@serveurperso.com>
The INI parser creates an implicit default section for top-level metadata.
After reserved keys such as `version` are skipped, that section can have no
model options but was still added and exposed in router mode.
Skip only the empty implicit default while preserving real default presets,
named presets, and the global `[*]` settings.
Signed-off-by: JS van Dijk <267467744+hogeheer499-commits@users.noreply.github.com>
Co-authored-by: JS van Dijk <267467744+hogeheer499-commits@users.noreply.github.com>
* 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
With -hfd pointing to a repo that ships mtp-/dflash-/eagle3- sidecars
and no --spec-type given, the draft resolved to a full model while the
sidecar was the intended draft.
When the speculative types are still at their default, discover the
sidecars of the draft repo, pick the first available following the
existing mtp > dflash > eagle3 priority, and set the corresponding
type, so this now works without any extra flag:
llama-server -hf repo:Q3_K_M -hfd repo:Q8_0
An explicit --spec-type disables the inference, and a draft repo
without sidecars keeps resolving to a full model as before.
* chat: fix DS4 template to explicitly follow reference behavior
* Support DeepSeekv4 flag (`drop_reasoning`).
* fix: hook DS3.2 parser for DS4 as well
* fix: add tool result reordering
* fix: post-merge
With -hfd pointing to a repo shipping speculative sidecars, the draft
resolved to the main model of that repo, since find_best_model()
excludes sidecar files, and the explicit draft plan suppressed the
sidecar discovery on the -hf repo.
The draft plan already discovers its sidecars, they were just never
consumed. Wire them as the draft, following the fallback pattern of
the main plan, so this now works as expected:
llama-server -hf repo -hfd repo --spec-type draft-dflash
* common: auto-download dflash- and eagle3- HF sidecars
Mirror the existing mtp- sidecar logic to support auto-discovery and
download of DFlash (dflash-) and Eagle3 (eagle3-) speculative decoding
sidecars from Hugging Face repos.
Changes:
- Add --dflash and --eagle3 CLI flags to trigger sidecar download
- Add find_best_dflash() and find_best_eagle3() using find_best_sibling
- Exclude dflash- and eagle3- filenames from primary model selection
- Filter dflash- and eagle3- from cached model listings
- Wire download tasks that set speculative.draft.mparams as fallback
Assisted-by: pi:llama.cpp/Qwen3.6-27B
* docs : regen
