* 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: add --models-memory-max parameter to allow dynamically unloading models when they exceed a memory size threshold
estimate with to-be-loaded model size included
use no_alloc to get memory requirements for model load
only set model memory_mb if not previously calculated
use memory margin instead of total size limit, apply to each device separately
add server memory debug logging
move llama_context_device_memory function to llama-ext.h
fix model count exceeded check
improve memory_per_device map naming
improve variable naming, fix style
also strip models memory margin from child processes
cont : clean-up
replace device memory map with buft memory map. Use llama_get_memory_breakdown
extract duplicated check into helper function
move model memory estimation to subprocess
precompute name->buft map, map GPU host types to CPU buft
cleanup unused variable
remove duplicated init calls
* adapt to upstream changes
* wip
* common_memory_reports internface
* SERVER_ROLE_MEASURE
* server-models-sched
* fix build
* server : fix memory accounting of measure children and claim spin
A measure child runs no_alloc, so the free memory it reports does not
include its estimate: subtracting self there erased the memory held by
other processes, down to zero with --fit on, and the next load could
overshoot the margin. Subtract self only for instance reports.
A queued load only checked --models-max before claiming, so when the
memory margin blocked it behind a busy model, load() threw and the
waiter retried in a tight loop. Refuse the claim while the limits are
exceeded and no idle model can be unloaded, so the waiter sleeps on
the condition variable until one goes idle.
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Pascal <admin@serveurperso.com>
* 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>
* common, grammar: add recursive depth limit, make it a requirement
* catch more recursive functions
* common : tidy up peg parser changes
* grammar : reject candidates in a loop, without a depth limit
The depth limit in llama_grammar_reject_candidates_for_stack counted
the code points of a vocab token, so any token of 256 chars or more
was always rejected under a grammar, such as the 512-space token of
GLM-4.7-Flash or the long space tokens of Laguna-S-2.1.
The rejection now walks the code points with its own stack of levels
instead of recursing: there is no limit and no stack risk, whatever
the length of a token from the GGUF, and the masks match master.
* grammar : track rejected candidates as indices into the input
At a given code point a candidate only differs from its input by the
position of its code point pointer, so the levels now carry indices
into the input candidates instead of copies. The first level needs no
special case and the rejects map back to the input directly.
Grammar sampling gets faster than master on GLM-4.7, Laguna, Qwen3.6
and gemma-4 with the json, list and spaces grammars, same masks.
* nit : shorten the comment of llama_grammar_reject_step
* common : add depth_guard for schema walks
* grammar : move GRAMMAR_RECURSIVE_LIMIT into the .cpp
* common : reduce recursion limit for peg parser
* improve
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
Co-authored-by: Pascal <admin@serveurperso.com>
* mtmd: add audio out stream api
* add /tts endpoint
* add docs
* wire up mtmd_helper_model_can_chat
* upload speaker_ref via form-data
* expose seed to api
* fix header for pcm
* wip
* fix penalty_last_n
* fix server_output_limits
* fix pocket-tts stream
* wip
* add server_schema for tts
* add server routing
* server: coerce seed and stream from multipart form fields
Form fields arrive as strings, so -F seed=42 and -F stream=true were
rejected by the schema. seed joins the integer fields, parsed as a long
long so a full uint32 seed fits, and stream is read as a boolean when
it comes as a string.
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* model : add H2O-Lightning-4B decision model
Adds the h2o-lightning decision type for h2oai/h2o-lightning-4b
(Qwen3.5-4B with a merged decision LoRA).
None of the existing types match this model. It answers with " A"
rather than "A", uses up to 255 labels, and was trained on Python
json.dumps text, so floats like 12.0 must render as 12.0.
The converter writes the decision metadata and the systemone template
from the model's serve_config.json and refuses a config that doesn't
match. Tests skip unless the tiny test model is available locally.
Assisted-by: Claude Opus 5.5
* rm redundant code
* put labels to gguf
---------
Co-authored-by: Jonathan C. McKinney <jon.mckinney@h2o.ai>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* feat(convert): Add decision.chat to support decision models that can also chat
Branch: GraniteSwitchDecision
AI-usage: full (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(convert): Add conversion for Granite Switch as decision model
The template follows from the usage sample:
https://huggingface.co/ibm-granite/granite-switch-4.1-8b#guardian-and-decision-side-by-side
Branch: GraniteSwitchDecision
AI-usage: draft (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(common): Add support for chat+decision models in common
Branch: GraniteSwitchDecision
AI-usage: full (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(common): Add granite switch decision model enum
Branch: GraniteSwitchDecision
AI-usage: full (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(server): Plumb through decision_chat bool to support decision+chat models
Branch: GraniteSwitchDecision
AI-usage: full (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(server): Add Granite Switch decision support
Branch: GraniteSwitchDecision
AI-usage: draft (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* docs: Update server README for granite switch decisions / chat+decisions
Branch: GraniteSwitchDecision
AI-usage: draft (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* Revert "feat(convert): Add decision.chat to support decision models that can also chat"
This reverts commit 952ef567647e53ac9beda7a1f79d6927240d068d.
* Revert "feat(common): Add support for chat+decision models in common"
This reverts commit 93ed782e3a8b2fd5968732c6525c7ad29ad4bea2.
* Revert "feat(server): Plumb through decision_chat bool to support decision+chat models"
This reverts commit 97e9af60ddaccbd82f1eb0a488adff43b16ee095.
* fix: If Granite Switch supports decisions, it always supports text too
Branch: GraniteSwitchDecision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* docs: Remove reference to decision.chat in server README
Branch: GraniteSwitchDecision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* 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
* 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
* 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>
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>
* 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
- Check for buffered write errors when closing downloaded files.
- Use UTF-8 paths when writing ETag files on Windows.
- Write in binary mode on Windows.
Signed-off-by: Adrien Gallouët <angt@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
