applyModel had three branches: fetch a base config from url:, build one from
an inline config_file:, or fail with "invalid gallery model". An entry
declaring neither is now installed on an empty base config, with overrides:
and files: supplying everything.
This is what the ~345 entries pointing at gallery/virtual.yaml were already
getting. That stub is five lines carrying name, description and license.
description and license are overwritten from the gallery entry immediately
after the fetch, and the name never reaches disk because InstallModel prefers
the install name. Crucially applyModel passes model.Overrides to InstallModel
as a separate argument rather than merging it into the fetched config, so
nothing an author writes depends on that base existing. The fetch bought a
round trip to GitHub and nothing else.
That makes f4ef80173 the wrong fix, so it is unwound. The four url: lines it
added to the deepseek-v4-flash variants are reverted: they are a pointless
network fetch now, and the family installs without them.
Relaxing the branch would hide a real authoring mistake, so a payload rule
replaces the base-config rule. An entry with no url, no config_file, no
overrides and no files installs nothing and would leave an empty model
directory while reporting success, so it is refused by name. The caller's
request counts toward the payload, because its overrides and files are merged
into the install exactly as the entry's own are. urls: (plural) is the
informational link list and does not count, which is what the four entries
that shipped broken had and why they were still uninstallable.
checkVariantTargetsInstallable asserted every variant target declares a url:
or a config_file:, which is no longer true and would now reject correct
authoring. checkEntriesInstallSomething pins what survives instead, and covers
every entry rather than only variant targets: the hazard is a half-written
stanza and a parent can be one as easily as a target. The old rule was scoped
to targets precisely because nine unrelated entries would have failed a
gallery-wide version; those nine are valid now, so the deferred ratchet
happens here in one step. 1280 entries, zero violations.
Those nine (aurore-reveil_koto-small-7b-it, lfm2-1.2b, the six liquidai_lfm2
entries and deepseek-v4-pro-q2-ssd) become installable for free. Each carries
overrides: and files:, and one of them is driven through the real install path
in a spec.
The no-fetch spec is paired rather than bare: an assertion that nothing was
fetched proves nothing unless something could have been, so a control runs the
same fixture with a url: pointing at a base config that is not there and
asserts the install fails. Only then does the identical fixture without the
url passing mean the read was skipped.
Follow-up, deliberately not here: the ~345 entries still naming virtual.yaml
can drop their url:. That is 345 index edits with their own risk, and mixing
them in would bury this change.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Clicking install on deepseek-v4-flash failed with "invalid gallery model".
The parent entry is fine, but all four entries it was grouped with declared
neither url: nor config_file:, and applyModel needs one of the two to have
anything to build a config from. They carry urls: (plural), the informational
HuggingFace link list, which is a different field. None of the four was ever
independently installable, so grouping them routed a previously-working
install into a broken entry.
Give each the url: the parent already resolves through. virtual.yaml is a
no-op base, and applyModel passes overrides to InstallModel separately from
the fetched config, so backend: ds4, the parameters and the ssd/mtp options
all still land exactly as authored. This is the same pattern the parent and
many other GGUF entries in the index already use.
Add the lint rule that should have caught this. checkVariantReferences only
proved a target exists and is not itself a parent, which is structural
validity: an entry can exist, declare no variants, and still be
uninstallable. checkVariantTargetsInstallable mirrors applyModel's
precondition instead, and names the parent, the target and the missing
fields, because whoever hits it is reading a gallery entry and has no reason
to know applyModel exists.
The two index-driven resolution specs live in their own Ordered container:
an Ordered container stops at its first failure, so sharing one with the lint
rules let a lint breach skip them silently.
Nine further entries gallery-wide have the same defect and are unrelated to
variants, so they are broken installs that predate this branch. They are left
alone here rather than buried in a regression fix, and widening the rule to
cover every entry is deferred with them so the gate can ratchet up in one
step instead of needing a skip list.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Variant auto-selection ranks survivors by fit, then engine, then serving
feature, then size. The serving-feature lookup recognised a speculative build
by either a declared tag or a whole segment of its entry name. Drop the name
half: a tag is now the only signal.
A name is author-supplied free text and a naming convention is not a contract,
so reading a marker out of one infers a capability nobody declared. The gallery
already had the failure in it: the four NVFP4 entries name MTP-bearing weights
while setting no option that enables speculative decoding, and being live
variants they were winning the feature axis without answering any faster.
overrides.options was considered as the replacement and rejected. It carries
spec_type:draft-mtp / spec_type:draft-dflash, which is what actually turns the
feature on, but that spelling is llama.cpp's config vocabulary: ds4 spells the
same feature mtp_path and sglang spells it speculative_algorithm in a
referenced config. Keying a cross-backend ranking decision on one backend's
option syntax would rank the other backends' builds as plain. Options are the
curation-time check instead, and never reach the selection logic.
With no fallback left, tag correctness is load bearing, so audit every entry
against the rule "tagged when the entry configures that feature, in whatever
vocabulary its backend uses". Three entries configure MTP untagged and gain the
tag (hy3, glm-5.2, qwythos-9b-claude-mythos-5-1m, all spec_type:draft-mtp with
no marker in their names). Four carry the tag while configuring nothing and
lose it: qwen3.6-27b-nvfp4-mtp, qwen3.6-35b-a3b-nvfp4-mtp,
qwopus3.6-27b-coder-mtp-nvfp4 and qwopus3.6-27b-v2-mtp-nvfp4, whose only option
is use_jinja:true. The dflash side was checked independently rather than assumed
consistent: all five dflash entries declare spec_type:draft-dflash and all five
are tagged, so it needed no edits.
Four entries keep a tag that a literal spec_type-only reading would strip,
because they configure MTP through a different backend: deepseek-v4-flash-q2-mtp
via ds4's mtp_path/mtp_draft, and the three sglang entries via
speculative_algorithm in their referenced configs. Stripping those would
contradict the reason spec_type was rejected as the signal and would demote four
genuinely faster builds to plain.
The index was edited by line insertion and deletion only, never round-tripped
through a serializer. A resolved-tag diff across all 1272 named entries, taken
after merge keys are applied, shows exactly these 7 changing and no entry
gaining or losing a tag through an anchor.
The two specs that pinned the name fallback are inverted rather than deleted,
since a name silently promoting a build is the regression worth guarding. The
whole-token guard survives on the tag path, where smtp must still not match mtp.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Variant auto-selection ranks survivors by fit, then engine, then serving
feature, then size. The serving-feature lookup read only whole alphanumeric
segments of a variant's entry name, because tags were inconsistent: every
dflash entry carried a dflash tag, but only 7 of 20 MTP entries carried an
mtp tag.
Tag the 13 untagged MTP entries, then teach the lookup to read tags as well
as names. A tag is now the authoritative signal and is compared whole and
case-insensitively, which is safe precisely because a tag is a deliberate
declaration rather than free text: there is no word-inside-a-word failure
mode, so the segment splitting the name half needs is unnecessary there.
The name check stays as a fallback rather than being replaced. Switching to
tags only would have regressed the six already-grouped entries on the day it
shipped, and would depend on tagging discipline that does not exist yet.
The lookup still names no feature, so adding one remains a one-line edit to
servingFeaturePreferenceTokens.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Rank serving features between engine preference and size, so a host that can
hold a DFlash or MTP build of a model's weights installs it instead of the
plain build. Both answer faster for the same output, so whenever one survives
the filters there is no reason to take the plain build.
Precedence is now fit, then engine, then serving feature, then size. Engine
outranks the feature deliberately: a serving feature makes the right engine
faster, it does not make a wrong engine right, so a plain vLLM build still
beats a DFlash llama.cpp build on NVIDIA. Fit outranks both, and a drafter
pairing is strictly larger than the plain build, so the existing size filter
drops it on a host too small for it before this axis is consulted.
The order lives in a third preference table in pkg/system, alongside the build
tag and engine name tables. It is the odd one of the three: not keyed by
capability, because no hardware prefers a plain build over an equivalent
faster one, and matched against whole segments of a gallery ENTRY NAME rather
than as a substring of a backend value. Nothing on a gallery entry declares a
serving feature, and tags are not a usable substitute: gemma-4-e2b-it:sglang-mtp
carries an mtp tag while ornith-1.0-9b-mtp and qwen3.6-27b-nvfp4-mtp carry
none. Entry names are author-supplied free text, unlike the closed engine
vocabulary, so a short marker can turn up inside an unrelated word and whole
segment matching is what keeps smtp-assistant from ranking as an MTP build.
The block comment over the tables now documents all three together and states
what each is matched against; the ranking code names no feature, so adding one
stays a one-line edit to the table.
29c49203b rejected these entries as serving configurations rather than
alternative builds of the same weights. The definition is now "alternative ways
to serve the same model", which includes them, so regroup 14 entries under 12
parents. Judged by the files each entry points at: the qwen3.6, qwen3.5, qwen3
and deepseek pairings are the base GGUF plus a drafter, the gemma-4 QAT MTP
entries are the same QAT weights at a different quantization plus an MTP
drafter, and the two sglang MTP entries describe themselves as the same model
served with speculative decoding. Left separate: qwen3.6-27b-mtp-pi-tune, a
finetune with its own weights, and every entry whose base model LocalAI does
not ship as its own row, which is the whole Qwopus line plus gemmable-4-12b-mtp,
mimo-7b-mtp:sglang and qwen3.5-4b-dflash.
None of the twelve parents defines a YAML anchor, so no variants key can leak
through a merge key and no empty override was needed this time. The index was
edited by line insertion only.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Sweep the gallery for entries that are alternative builds of the same
weights (different quantization, precision, or runtime format) and declare
them as variants of a single parent row, so the listing offers one row per
model instead of one row per quantization and the installer picks the
largest build that this host can actually run.
41 families over 95 entries, turning 54 entries into variants.
The parent is the bare-named entry wherever one exists, so nothing changes
about what any existing entry installs. Ranking already selects the largest
fitting build regardless of which entry is nominally the parent, so the
parent only decides the pathological case where nothing fits. For the ten
families that have no bare-named entry, the smallest build is the parent,
since that is the one that has to install when nothing fits.
Grouping was verified against the actual model filenames rather than the
entry names alone. Different parameter sizes, languages, finetunes, and
products that merely share a name prefix are left as separate rows: the
qwen3.6 APEX and pi-tune finetunes, the DFlash and MTP speculative-decoding
pairings, English-only versus multilingual Whisper, the QAT versus non-QAT
Gemma 4 weights, and the abliterated FLUX build are all distinct models.
Six parents define YAML anchors that other entries pull in with a merge key,
which would have handed their variants to every merging child. For the two
depth-anything anchors that would have made fourteen unrelated entries
advertise the base model's builds as their own. All 26 merging children
therefore carry an explicit empty variants list, which overrides the merged
key and is equivalent to the key being absent.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Selection needs each variant's size to decide whether it fits and to rank
largest-first. That figure was written into the index by a nightly job, which
made the gallery carry a derived value that could drift from the entry it was
derived from. Derive it at install time instead.
pkg/vram already sizes a model without downloading it, and the gallery UI
already uses it: a remote GGUF header range-fetch, then an HTTP HEAD for the
content length, then any declared size:. It caches its results, so reuse it
rather than writing a second probing path.
A probe failure must never fail an install, so an unprobeable variant is
treated as unknown: it survives the memory filter, because nothing proves it
does not fit, and it ranks last, so a known-good fit always beats a guess. If
every probe fails, selection still terminates on the base entry.
The probe is injected through ResolveEnv rather than called directly, for the
same reason the backend compatibility check is: specs pin an exact size, or an
exact failure, without reaching the network.
With that in place three things are dead weight and go:
- The nightly job and the fields it populated. Variant.Backend was redundant
because the backend is resolved live from the referenced entry during
selection, and Quantization was display-only that nothing read.
- min_memory on the base entry. The base always installs and its floor could
only warn, so it could not change any outcome.
- The lint rules and schema entries for both.
min_memory on individual variants stays, as the override for when the probed
size is wrong. An authored figure now suppresses the probe entirely rather
than merely outranking it, so it costs no round trip.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Gallery entries could already carry a list of alternatives, but selection was
an authored, ordered, first-match policy: every candidate declared a
`capability` string and the VRAM floors had to descend in a hand-tuned order.
That pushed hardware knowledge onto whoever edits the gallery and made ordering
load-bearing, so a reordered list silently changed what users installed.
None of it was necessary. SystemState.IsBackendCompatible already derives
hardware support from a backend name alone: it knows MLX and metal are
Darwin-only, CUDA is NVIDIA-only, ROCm AMD-only, SYCL Intel-only. Selection can
read that instead of asking authors to restate it.
Authoring is now just a list of names:
- name: qwen3.6-27b
min_memory: 4GiB
variants:
- model: qwen3.6-27b-mlx-8bit
- model: qwen3.6-27b-gguf-q8
min_memory: 28GiB
and all the intelligence moved into the selector. Given a host it drops the
variants whose backend cannot run here, drops those whose known memory
requirement exceeds what the host has, and takes the LARGEST of what is left,
because a bigger footprint is a higher quality quantization of the same model.
A variant of unknown size is kept, since nothing proves it does not fit, but it
ranks last so a proven fit always beats a guess. An explicit pin still wins
outright, and if nothing survives the entry installs its own payload: the base
always installs, this never refuses.
Available memory is VRAM when a GPU was detected and system RAM otherwise, read
through xsysinfo so a cgroup limit is honored and a container gets its own
limit rather than the node's RAM.
Capability disappears entirely, from the types, the schema and the lint. VRAM
and RAM collapse into one `min_memory`, because a model's footprint is roughly
the same wherever it lives and one figure is compared against whichever applies.
The lint rules about ordering, the capability vocabulary and floor
relationships are deleted with the hazards they described; what remains is that
every variant names an entry that exists and does not itself declare variants,
plus that any memory figure actually parses.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Reworks hardware-resolved gallery variants after a design pivot. There is no
longer a separate "meta" entry kind. A gallery entry is a normal, complete
entry that may additionally carry candidates:, a list of hardware-gated
upgrades over itself, and the entry is itself the last-resort candidate.
The previous design relied on a bare url: as the fallback for LocalAI releases
that predate candidates support. That fallback is empty in practice: none of
the 80 gallery/*.yaml files carry a top-level files:, and 1216 of 1281 index
entries carry their payload in the index entry itself, so a url alone yields a
config template with nothing to download. Since every released LocalAI reads
gallery/index.yaml live from master, merging a payload-less entry would have
shown every existing user a model that installs to a broken state. Making the
entry its own base candidate removes the problem at the root: old clients drop
the candidates key and install the entry exactly as they do today.
Resolution order is now explicit pin, then capability plus VRAM over the
declared upgrades, then the entry itself. The entry ALWAYS installs: when its
own min_vram or capability is unmet the installer warns and installs it
anyway, because there is nothing below it and refusing would make the gallery
behave worse the newer the client is. A pin naming the entry's own name is
valid and is how an operator declines an upgrade.
IsMeta() becomes HasCandidates(), ResolveMetaModel becomes ResolveVariant, and
the persisted meta_name record key becomes entry_name. GalleryBackend.IsMeta()
is a separate concept and is untouched.
The lint drops the three rules the pivot makes wrong (url equality with the
final candidate, no inline payload, unconstrained final candidate) and gains
one: the entry's own floor must sit strictly below every candidate's, since a
base that outranks a candidate makes that candidate unreachable.
The pilot entry is now the existing nanbeige4.1-3b-q4, which gains a 2GiB
floor of its own and a single 6GiB upgrade to nanbeige4.1-3b-q8, replacing the
separate nanbeige4.1-3b entry added in d0d441bb4.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Adds the first real meta entry to the gallery index. It resolves to the
Q8_0 build on hosts with at least 6GiB of VRAM and to the Q4_K_M build
everywhere else, installing either payload under the stable name
nanbeige4.1-3b.
The entry carries a url equal to its final candidate's url. LocalAI
releases that predate candidates support parse the index non-strictly
and drop the key silently, so without that url they would list the entry
and install nothing. A regression spec parses the index the way those
releases do and asserts every meta entry stays installable for them.
Also teaches core/schema/gallery-model.schema.json about candidates. The
schema sets additionalProperties: false at the top level, so an author
following CONTRIBUTING.md and adding the yaml-language-server comment
would otherwise get a validation error on this entry.
Assisted-by: Claude:claude-opus-4-8
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Add the q8_0 (default) and f16 gallery entries for the moss-tts-cpp backend, each
pulling the MOSS-TTS-Local v1.5 GGUF plus the MOSS-Audio-Tokenizer-v2 codec and
the text tokenizer from mudler/MOSS-TTS-Local-Transformer-v1.5-GGUF. The backend
auto-discovers the codec and tokenizer siblings; output is 48 kHz stereo with
reference-audio voice cloning.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Google shipped the Gemma 4 MTP drafter heads and llama.cpp merged native
support in ggml-org/llama.cpp#23398. LocalAI's pinned llama.cpp already
carries it, and the config plumbing (draft_model + core/config/mtp.go)
was built for exactly this path, but no official Gemma 4 gallery entry
wired it up.
Add llama.cpp draft-mtp speculative-decoding variants for the dense
sizes, sourced from the unsloth QAT GGUF repos (target UD-Q4_K_XL +
mtp-*.gguf drafter + BF16 mmproj):
- gemma-4-e2b-it-qat-mtp
- gemma-4-e4b-it-qat-mtp
- gemma-4-12b-it-qat-mtp
- gemma-4-31b-it-qat-mtp
These replace the previously commented-out attempts, which were disabled
because the Janvitos/boxwrench drafter GGUFs declared the architecture as
`gemma4_assistant` (underscore) and failed to load on stock llama.cpp.
The unsloth drafters use the upstream `gemma4-assistant` (hyphen) spelling
that mtp.go's isDraftOnlyAssistantArch expects, so they load without any
backend patch. The 26B-A4B MoE is intentionally omitted (the upstream PR
reports no meaningful MTP speedup for it).
Also fix gemmable-4-12b-mtp: it loaded the draft-only `-mtp` GGUF as the
main model with no draft_model set, which cannot run standalone. It now
loads the target as the model, wires the drafter via draft_model, enables
spec_type:draft-mtp, and downloads both files.
All sha256 pins were taken from the HuggingFace API lfs.oid (reliable
content hash even for Xet-backed repos).
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
feat(bonsai): add PrismML llama.cpp fork backend + Bonsai gallery models
Adds a new `bonsai` backend that runs the PrismML fork of llama.cpp
(github.com/PrismML-Eng/llama.cpp, `prism` branch), which ships the Q1_0
(1-bit) and Q2_0 (ternary / 1.58-bit) weight-quantization kernels used by the
Bonsai and Ternary-Bonsai models. Stock llama.cpp cannot decode these quants.
Modeled on the turboquant backend: reuses backend/cpp/llama-cpp/grpc-server.cpp
against the fork's libllama via a thin wrapper Makefile, so the sub-2-bit models
are served with the same OpenAI-compatible API. No grpc-server allow-list patch
is needed (bonsai adds weight quants, transparent to the server, not KV-cache
types), and the reused server compiles cleanly against the fork with no skew
patches (validated locally via a CPU docker build; patches/ is present but empty
for any future re-pin skew).
Backend wiring: backend/cpp/bonsai/, .docker/bonsai-compile.sh,
backend/Dockerfile.bonsai, top-level Makefile targets, backend-matrix.yml build
rows (CPU, CUDA 12/13, L4T, SYCL f32/f16, Vulkan, ROCm/hipblas), backend/index.yaml
meta-backend + per-platform images, and a nightly bump_deps entry tracking the
`prism` branch.
Gallery: 8 entries across 4 families - bonsai-8b-1bit, ternary-bonsai-8b (+g64,
+pq2), bonsai-27b-1bit (vision), ternary-bonsai-27b (+pq2, +g64, vision). The 27B
models wire the mmproj vision tower; the DSpark speculative drafter GGUFs are not
wired (custom semi-autoregressive drafter, not a standard llama.cpp draft model).
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Pairs unsloth/Qwen3.5-4B-GGUF (Q4_K_M target) with the
AtomicChat/Qwen3.5-4B-DFlash-GGUF Q8_0 drafter (quantized from
z-lab/Qwen3.5-4B-DFlash, upstream GGUF arch `dflash`), same shape as
the existing DFlash entries.
Assisted-by: Claude Code:claude-fable-5 [Bash] [Read] [Edit]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ui): add voice library workflow
Give administrators a production-ready flow to record or upload consented reference audio, manage reusable profiles, inspect API usage, discover compatible models, and hand a saved voice directly to text-to-speech.
Assisted-by: Codex:gpt-5
* feat(voice): add managed voice cloning profiles
Make reusable reference voices manageable through the admin API instead of requiring model-directory and YAML edits. Discover compatible installed and gallery models from server-side backend capabilities, retain explicit model configuration controls, and stage saved references for supported backends.
Expose profile management through REST and MCP, document backend-specific behavior, and cover the workflow from profile creation through real Qwen3-TTS synthesis. Harden the agent-job HTTP test against completion racing cancellation.
Assisted-by: Codex:gpt-5
---------
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backends): add LongCat video and avatar generation
Assisted-by: Codex:GPT-5 [apply_patch] [exec_command] [web]
* refactor(config): declare model I/O modalities
Make model configs declare input and output modalities so capability discovery no longer branches on backend or checkpoint names. Complete the LongCat gallery and user documentation, make the SDPA patch apply to the pinned upstream revision, and stabilize the Agent Jobs race exposed by the required hook.
Assisted-by: Codex:GPT-5 [web]
---------
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Add four ready-to-run DFlash speculative-decoding entries for the
llama.cpp backend, now that upstream DFlash support (draft-dflash) is in
the pinned llama.cpp. Each entry bundles a full target model with its
small z-lab block-diffusion drafter and sets spec_type:draft-dflash,
spec_n_max:15, and flash attention (required by DFlash):
- qwen3-4b-dflash (Qwen3-4B + Qwen3-4B-DFlash drafter)
- qwen3.5-9b-dflash (Qwen3.5-9B + Qwen3.5-9B-DFlash drafter)
- qwen3.6-27b-dflash (Qwen3.6-27B dense + drafter)
- qwen3.6-35b-a3b-dflash (Qwen3.6-35B-A3B MoE + drafter)
The 4B pair uses the base Qwen3-4B target (not Qwen3.5-4B): its drafter
reports general.name "Qwen3 4B DFlash" and is the canonical pairing
documented upstream. All drafters were downloaded and verified to carry
GGUF architecture "dflash" (not the fork-only "dflash-draft" /
"DFlashDraftModel") so they load in the upstream backend, and every
drafter SHA256 was confirmed against the downloaded bytes.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
C++/ggml transcription + speaker diarization + timestamps backend. Purego
dlopens libmoss-transcribe.so (ggml statically linked) from moss-transcribe.cpp
and serves offline AudioTranscription, parsing the [start][Sxx]text[end] output
into segments with nanosecond timestamps. Adds the importer (surfaces in
GET /backends/known), backend-matrix (Linux + Darwin/metal), backend/index.yaml,
and a gallery entry (default q5_k GGUF from mudler/moss-transcribe.cpp-gguf).
Local L0 smoke (go build + go test ./... = 16 pass, golangci-lint 0 issues)
passed against the real libmoss-transcribe.so. The pre-commit coverage gate
(full pkg/core + tests/e2e) could not run in the authoring sandbox (no live
models, port 9090 held); CI must enforce it before merge.
Assisted-by: Claude:claude-opus-4-8 golangci-lint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Link the f5-tts library into the crispasr backend so CrispASR's native
F5-TTS runtime (SWivid F5-TTS, 22-layer DiT flow-matching + built-in Vocos
vocoder) is compiled in. The single self-contained GGUF auto-detects as
f5-tts through the session router, so no explicit backend selector is
needed. Add the f5-tts-crispasr gallery entry (cstr/f5-tts-GGUF) and an
env-gated e2e synthesis spec.
F5-TTS is voice-cloning only and has no baked speaker: it clones from a
reference WAV plus its transcript, supplied via the voice/voice_text
options. The gallery description documents this bring-your-own-reference
requirement.
Verified e2e on the pinned engine (278fb79): the GGUF auto-detects as
f5-tts, the reference voice loads, and synthesis produces a valid 24 kHz
mono WAV.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The Q4_K_M quant degraded tool-call reliability for LFM2.5-8B-A1B.
Switch the gallery entry to the Q8_0 GGUF (sha256 verified via HF
x-linked-etag) while keeping the native jinja tool-parsing config.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(voice-detect): add Go purego backend for voice-detect.cpp
Add backend/go/voice-detect implementing the Backend gRPC voice subset
(VoiceEmbed/VoiceVerify/VoiceAnalyze) over libvoicedetect.so via purego,
mirroring the parakeet-cpp / omnivoice-cpp backends.
The flat voicedetect_capi C ABI is dlopen'd cgo-less; malloc'd string and
float-vector returns are owned by Go and released through the matching capi
free functions, with the per-ctx last error surfaced into Go errors. Calls are
serialized via base.SingleThread since the C context is not reentrant.
Proto field mapping:
- VoiceEmbed: VoiceEmbedRequest.audio (path) -> embed_path -> Embedding+Model.
- VoiceVerify: audio1/audio2 + threshold (<=0 falls back to the
verify_threshold option, default 0.25) -> verify_paths -> verified/distance/
threshold/confidence/model/processing_time_ms.
- VoiceAnalyze: audio (path) -> analyze_path_json; the JSON age/gender/emotion
document maps to a single VoiceAnalysis segment (start/end 0; gender "label"
-> dominant_gender with the remaining float scores as the gender map; emotion
label/scores -> dominant_emotion/emotion).
The Makefile pins voice-detect.cpp to 47546430, clones+builds libvoicedetect.so
with ggml static-linked (PIC, GGML_NATIVE off) so dlopen needs no external
libggml/libvoicedetect; ldd on the artifact shows only system libs. Ginkgo
tests cover option parsing and analyze-JSON mapping; embed/verify smoke specs
gate on VOICEDETECT_BACKEND_TEST_MODEL + VOICEDETECT_BACKEND_TEST_WAV.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(voice-detect): wire backend into index, gallery and build
Register the voice-detect.cpp speaker-recognition + voice-analysis
backend (added in Voice-INT-A) into LocalAI's distribution surfaces,
mirroring the ced backend (the closest mudler C++/ggml audio analogue):
- backend/index.yaml: add the &voicedetect meta-backend (capabilities
platform map, no top-level uri) plus the full set of concrete per-arch
image entries (cpu/cuda12/cuda13/metal/rocm/sycl/vulkan/l4t and the
-development variants). Referential integrity audited - every alias
target resolves.
- gallery/index.yaml: add 5 model entries on backend voice-detect -
ECAPA-TDNN, WeSpeaker ResNet34, 3D-Speaker ERes2Net, CAM++ and the
wav2vec2 age/gender/emotion analyze model. The engine architecture is
read from GGUF metadata (voicedetect.arch) at load. GGUF artifacts are
not yet published: each files: entry points at the intended
mudler/voice-detect-gguf location with a TODO to fill sha256 after
upload (no fabricated hashes).
- .github/backend-matrix.yml: add the linux build matrix block + the
darwin metal entry mirroring ced.
- .github/workflows/bump_deps.yaml: track mudler/voice-detect.cpp via
VOICEDETECT_VERSION (pin 47546430, = 4754643).
- core/config/backend_capabilities.go: register voice-detect in the
backend capability map (VoiceVerify/VoiceEmbed/VoiceAnalyze ->
speaker_recognition), mirroring speaker-recognition.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(face-detect): add purego Go backend for face-detect.cpp
Add the LocalAI Go backend that dlopens libfacedetect.so (the flat
facedetect_capi_* C-ABI) via purego, mirroring the sibling voice-detect
backend. Implements the Face subset of the Backend gRPC service:
- Embeddings(PredictOptions): Images[0] base64 -> temp file -> embed_path
-> L2-normalized ArcFace embedding.
- Detect(DetectOptions): src -> detect_path_json -> Detection boxes
(class_name "face", [x1,y1,x2,y2] -> x/y/w/h).
- FaceVerify(FaceVerifyRequest): two images + threshold + anti_spoof ->
verify_paths; best-effort img areas via detect.
- FaceAnalyze(FaceAnalyzeRequest): img -> analyze_path_json -> per-face
age + gender ("M"/"F" normalized to "Man"/"Woman").
The Makefile pins face-detect.cpp to 636a1963 and builds the shared lib
with ggml + vendored libjpeg-turbo static (PIC), so the .so is
ldd-clean (no libggml) and exports only facedetect_capi_* (no jpeg_
symbols). Gated Ginkgo e2e mirrors voice-detect.
Note for the gallery-wiring task: backend registration (index.yaml,
gallery, core/config/backend_capabilities.go) is intentionally not
touched here.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(voice-detect): replace em dashes in net-new descriptions
Project style forbids em/en dashes. Replace the three U+2014 chars
introduced by the voice-detect gallery/index wiring with `-`/`:`.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(face-detect): wire backend into index, gallery and build
Register the face-detect.cpp face detection / embedding / verification /
analysis backend (added in Face-INT-A) into LocalAI's distribution
surfaces, mirroring the voice-detect wiring (the closest mudler C++/ggml
recognition analogue):
- backend/index.yaml: add the &facedetect meta-backend (capabilities
platform map, no top-level uri to avoid the meta-backend gotcha) plus
the full set of concrete per-arch image entries (cpu/cuda12/cuda13/
metal/rocm/sycl-f16/sycl-f32/vulkan/l4t and the -development variants),
22 entries. Referential integrity audited: every alias target resolves.
- gallery/index.yaml: add 4 model entries on backend face-detect -
face-detect-buffalo-l/m/s (insightface SCRFD + ArcFace/MBF, NON-COMMERCIAL)
and face-detect-yunet-sface (OpenCV-Zoo YuNet + SFace, APACHE-2.0, the
commercial-friendly alternative). The detector/embedder architecture is
read from GGUF metadata (facedetect.arch) at load; only the real
verify_threshold option is set (0.35 buffalo, 0.363 sface). GGUF
artifacts are not yet published: each files: entry points at the
intended mudler/face-detect-gguf location with a TODO to fill sha256
after upload (no fabricated hashes).
- core/config/backend_capabilities.go: register face-detect in the
backend capability map (Embedding/Detect/FaceVerify/FaceAnalyze ->
face_recognition), mirroring insightface.
- .github/backend-matrix.yml: add the linux build matrix block + the
darwin metal entry mirroring voice-detect.
- .github/workflows/bump_deps.yaml: track mudler/face-detect.cpp via
FACEDETECT_VERSION (pin 636a1963).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* fix(recon): voice-detect metal build branch + face-detect gallery usecases
Add the missing metal BUILD_TYPE branch to the voice-detect Makefile
forwarding -DVOICEDETECT_GGML_METAL=ON, mirroring face-detect, so the
darwin metal CI artifact is built with the Metal backend instead of
CPU-only.
Expand the 4 face-detect gallery models' known_usecases to
[face_recognition, detection, embeddings] to match the backend
capabilities map and the mirrored insightface-buffalo entries, so
auto-selection for /v1/detect and /embeddings works.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* docs(recon): document voice-detect and face-detect ggml backends
Document the new standalone C++/ggml biometric backends as the
recommended/default option for face and voice recognition, keeping the
existing Python insightface / speaker-recognition backends framed as the
legacy path.
- features/face-recognition.md: add a face-detect (ggml) backend section
with the gallery entries (buffalo-l/m/s non-commercial, yunet-sface
Apache-2.0), licensing, and verify/detect/analyze quickstart.
- features/voice-recognition.md: add a voice-detect (ggml) backend
section with the gallery entries (ecapa-tdnn, wespeaker-resnet34,
eres2net, campplus speaker recognizers; emotion-wav2vec2 non-commercial
analyze head) and quickstart.
- reference/compatibility-table.md: add face-detect.cpp and
voice-detect.cpp rows to the Vision, Detection & Recognition table.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(gallery): publish recon backend GGUF uris + sha256
Fill in the published HuggingFace GGUF uris and verified sha256 for the
9 recon gallery entries (voice-detect-* and face-detect-*), and remove
the TODO publish markers. Correct the eres2net, campplus, and
emotion-wav2vec2 uris to the actual published filenames.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(gallery): re-embed buffalo anti-spoof + add audeering age/gender voice model
Update the 3 buffalo face-detect GGUF sha256 (anti-spoof ensemble now
embedded and re-uploaded under the same filenames/uris) and note the
FaceVerify anti_spoof request flag in each description. Add a new
voice-detect-age-gender-wav2vec2 gallery entry mirroring the emotion
model.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(gallery): add face-detect-buffalo-sc and antelopev2 packs
Add gallery entries for two newly-published insightface face packs on
the face-detect backend: buffalo_sc (smallest pack, SCRFD-500M + small
ArcFace) and antelopev2 (higher-accuracy, SCRFD-10G + ArcFace glint360k
R100, 512-d). Both are non-commercial research-only.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(recon): honor LocalAI per-model threads in voice/face-detect backends
LocalAI spawns one backend process per model and serves requests
concurrently, so the engines' own min(hardware_concurrency, 8) default
can oversubscribe cores. Forward the per-model Threads value from the
gRPC LoadModel options into the engine via VOICEDETECT_THREADS /
FACEDETECT_THREADS (read at backend construction) before the capi load.
A non-positive Threads is treated as unset, leaving the engine default.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump backend pins to CPU-optimized engine commits
voice-detect.cpp -> 0d9c1b3 (radix-2 FFT FBank, threads, flash attn + cached
pos-conv); face-detect.cpp -> 523aee1 (thread-gated direct conv, threads).
Brings the CPU optimizations into the LocalAI backend builds. GGUF format and
parity unchanged, so the published HF GGUFs remain valid.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump backend pins to round-2 CPU-optimized engines
voice-detect.cpp -> fe7e6a3 (ERes2Net 1x1->mul_mat, CAM++ layout+context,
wav2vec2 conv-LN, ECAPA capture-drop, AVX512 dispatch opt-in); face-detect.cpp
-> 9c8adb7 (AVX2 Winograd F(2x2,3x3) for SCRFD/ArcFace 3x3 convs, ArcFace
BN-fold). Parity unchanged (cosine=1.0); GGUF format unchanged, HF GGUFs valid.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump backend pins to round-3 Winograd engines
voice-detect.cpp -> 45122ec (Winograd F(2x2,3x3) for WeSpeaker/ERes2Net 3x3
convs, -22%/-20% @8t); face-detect.cpp -> cd5c962 (Winograd F(4x4,3x3) for
SCRFD large maps, -22% @1t on top of F(2x2), more load-stable). Parity held
(cosine=1.0); GGUF format unchanged, HF GGUFs valid.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump backend pins to round-4 Winograd engines (CPU opt complete)
voice-detect.cpp -> d2839ca (CAM++ FCM 2D convs through Winograd, -15.5%/-10.3%);
face-detect.cpp -> c1db23d (AVX2-vectorized Winograd tile transforms, SCRFD
detect -14%/-9.6%). Final CPU optimization round; the conv-kernel lever class is
now exhausted (parity held cosine=1.0; GGUF/parity unchanged, HF GGUFs valid).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump face-detect pin to deep-kernel engine (7ae5c4d)
face-detect.cpp -> 7ae5c4d: register-blocked winograd-domain GEMM microkernel
(2.8x isolated GFLOP/s), AVX-512 zmm evolution behind runtime CPUID dispatch
(ship-safe, AVX2 fallback bit-identical), bias/relu fused into the winograd
output transform, and SFace Conv+BN fold + bias/PReLU fusion. SCRFD detect
~1.4x faster end-to-end vs the round-4 baseline; parity bit-exact; portable
single binary (function-multiversioned, no global -mavx512f).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump voice-detect pin to ECAPA operand-order win (e9c56ae)
voice-detect.cpp -> e9c56ae: weight-as-src0 mul_mat order in ECAPA's F32
conv1d_same (routes through tinyBLAS sgemm); ECAPA embed 1.67x @1t / ~1.3x @8t,
parity cosine=1.0. Isolated to encoder.cpp (ECAPA-only); ERes2Net/CAM++/WeSpeaker
do not call conv1d_same so are provably unaffected.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump pins to FMA-throughput engines (voice f7b9f89, face 2d2d5f0)
face -> 2d2d5f0: route ArcFace 3x3 body convs through the AVX-512 winograd
microkernel (kWinoMinSize 80->14); ArcFace 1.62x @1t, SCRFD detect to 0.966 of
MLAS @1t, no regression. voice -> f7b9f89: runtime-CPUID-dispatched AVX-512
winograd-GEMM microkernel (ship-safe, AVX2 fallback bit-identical); WeSpeaker
1.90x @1t. Parity cosine=1.0 throughout; portable single binaries.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump pins to MLAS-class direct-conv engines (voice 7ecfd07, face be22d67)
Hand-tuned nChw16c AVX-512 register-tiled direct-conv microkernel (~263 GFLOP/s,
within 6-7% of MLAS per-op efficiency), runtime-CPUID-dispatched + AVX2 fallback,
fused bias/relu. voice 7ecfd07: default 3x3-s1 kernel for WeSpeaker (+37%/+32%)
+ ERes2Net, CAM++ pinned to Winograd. face be22d67: shape-gated to the ArcFace
recognizer body (+25-27% @8t); SCRFD detector stays on Winograd (no regression).
Parity cosine=1.0 / detect <=1px on AVX-512 + AVX2 paths. Portable single binaries.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump voice pin to Phase-A blocked backbone (f4e7eef)
WeSpeaker ResNet34 runs as one nChw16c blocked island (2 reorders/forward vs
~60) on AVX-512, default; per-conv directconv fallback on AVX2. +2.9% @1t /
+17-19% @8t vs per-conv directconv, parity cosine=1.0. The conv microkernel is
already FMA-bound near peak (~0.86-0.98x MLAS-implied); residual to MLAS is
sub-peak edge + non-conv tail, documented in docs/cpu-optimization.md.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump pins to breadth blocked-backbone (voice 7f66871, face d80092b)
voice 7f66871: AVX2-vectorized (ymm) blocked island - AVX2-only hosts now run
the blocked backbone for WeSpeaker (2.3x over per-conv-AVX2, cosine=1.0);
ERes2Net stays per-conv (blocked regresses, opt-in only); CAM++ Winograd-pinned.
face d80092b: ArcFace recognizer blocked island, AVX-512 default (-13% @8t, ~0.90x
MLAS, the closest conv result), auto per-conv on AVX2; SCRFD untouched on Winograd
(0 island invocations during detect). Parity cosine=1.0 / detect <=1px throughout.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump pins to small-spatial + stem conv kernels (voice 99b1804, face 47fdab6)
Measured-gap-driven conv kernels: small-spatial (fill the register tile when
output width <= tile width) + small-IC stem + strided-1x1/downsample recovery.
ArcFace recognizer 0.57 -> 0.70x MLAS @1t (the closest conv model), WeSpeaker
0.65 -> 0.79x @1t. Parity cosine=1.0 / detect <=1px. The OC-block-sharing lever
was a measured dead-end (deep stride-1 is L3-weight-bandwidth bound, not
read-port bound) and was NOT shipped. Kernel ceiling reached; further gap needs
an algorithm-class change (cache-blocked weight-stationary GEMM, or q8 weights).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump pins to GPU persistent-graph + multi-model-safe cache (voice 45d2e6b, face 0a4799a)
GPU wins (CUDA/ggml backend, no CPU-path change): persistent per-shape graph+context
cache in Backend::compute() eliminates the per-call cudaGraph re-instantiation churn
-> wav2vec2 emotion+age-gender now AT GPU parity with torch-cuDNN on GB10 (0.97-0.98x),
CAM++ -5.7ms; bit-identical parity. Cache hardened multi-model-safe (invalidate-on-free
keyed by the ModelLoader weights buffer) so LocalAI multi-model hosting cannot stale-hit.
Conv models still trail cuDNN (im2col-materialization-bound) - cuDNN implicit-GEMM lever next.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump pins to cuDNN-conv-capable engines (voice b6e4356, face 6107a24)
Adds the opt-in cuDNN implicit-GEMM conv path (VOICEDETECT_GGML_CUDNN /
FACEDETECT_GGML_CUDNN, DEFAULT OFF -> zero build/runtime dep until enabled).
On GPU it kills the im2col-materialization bottleneck and reaches torch-cuDNN
parity on the spill-bound convs: SCRFD detect 14.8->6.4ms (2.3x, ~parity),
WeSpeaker ~parity, ERes2Net beats torch (1.10x); ArcFace/CAM++ neutral (no
spill). Parity exact (SCRFD <=1px, cosine=1.0). To USE it in LocalAI, the CUDA
backend build must enable the flag AND bundle libcudnn - deferred until a
cuDNN-bundled GPU image; flag stays OFF here.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* feat(recon): enable cuDNN conv path on arm64+CUDA13 recon backends
The voice-detect.cpp / face-detect.cpp engines have an opt-in cuDNN
implicit-GEMM conv path behind VOICEDETECT_GGML_CUDNN / FACEDETECT_GGML_CUDNN
(default OFF) that kills im2col on the GPU and reaches torch-cuDNN parity
(SCRFD 2.3x, WeSpeaker/ERes2Net parity), measured on the GB10
(arm64, CUDA 13, sm_121a).
Enable it for the CUDA build, but only where cuDNN actually ships: the
arm64 + CUDA 13 image (GB10/Jetson/L4T). x86 CUDA images carry no cuDNN,
so flipping it on globally for BUILD_TYPE=cublas would be a link failure.
The Makefiles gate on CUDA_MAJOR_VERSION=13 + arch (TARGETARCH from the
matrix/Docker build, uname -m fallback for local builds).
backend/Dockerfile.golang already installs the runtime libcudnn9-cuda-13
in the arm64+CUDA13 apt block; add the matching libcudnn9-dev-cuda-13 so
the build-time link resolves.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): bump voice-detect pin to ERes2Net blocked-default (30beecd)
Defaults VD_ERES2NET_BLOCKED ON: routes the ERes2Net Res2Net body through the
blocked nChw16c AVX-512 directconv island instead of the 1x1 mul_mat fast path
(CONT-transpose + skinny low-K GEMM). On the shipped GGML_NATIVE=OFF build (ggml
mul_mat is AVX2-only) this wins ~2x at every thread count (2.07x@1t, 2.2x@4t,
2.05x@8t); pure-AVX2 fallback still 1.3-1.62x. Parity exact (cosine=1.000000 vs
golden), so registered voices + verify/identify thresholds are unaffected. The
prior default-OFF rested on a stale comment whose 23pct regression only held on
the non-shipping GGML_NATIVE=ON build.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* docs(readme): announce native voice-detect + face-detect backends in Latest News
Add a Latest News entry for the new from-scratch C++/ggml biometric backends
(voice-detect.cpp + face-detect.cpp) that replace the Python insightface and
speaker-recognition backends: no Python/onnxruntime at inference, self-contained
GGUF, bit-exact parity, GPU cuDNN parity. Mirrors the parakeet.cpp /
locate-anything.cpp native-backend news entries. Refs PR #10441.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
* chore(recon): re-pin to the squashed engine release commits
The voice-detect.cpp and face-detect.cpp histories were squashed to a single
release commit, which orphaned the previous pins (voice 30beecd, face 6107a24).
Re-pin to the new single-commit SHAs (voice 3d51077, face 06914b0); the tree is
identical, so the backend build is unchanged.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
The GGUF metadata parser (gpustack/gguf-parser-go) cannot read NVFP4-quantized
GGUFs at all: it errors with "read tensor info 0: This quantized type is
currently unsupported" because NVFP4 is a ggml tensor type it does not know.
When ParseGGUFFile errors, the llama-cpp defaults hook skips guessGGUFFromFile
entirely and the deferred fallback sets the context window to the conservative
GGUFFallbackContextSize (1024). The result: a model that trains to 262144
tokens runs with n_ctx=1024, and every prompt over ~1k tokens fails with
"request (N tokens) exceeds the available context size (1024 tokens)".
Two changes:
- Drop GGUFFallbackContextSize (1024) and fall back to DefaultContextSize
(4096) in both the GGUF run-estimate path (gguf.go) and the deferred hook
fallback (hooks_llamacpp.go). 1024 is a sensible floor for a tiny CPU GGUF
but a footgun for a large, long-context model whose header simply cannot be
parsed. Strengthen the existing "GGUF unreadable" test to assert the value.
- Set context_size explicitly on the four NVFP4 gallery entries
(qwen3.6-35b-a3b-nvfp4-mtp, qwopus3.6-27b-v2-mtp-nvfp4,
qwopus3.6-27b-coder-mtp-nvfp4, qwen3.6-27b-nvfp4-mtp) so the parser failure
is irrelevant for them. 32768 matches sibling Qwen entries and is safe on
memory; operators can raise it toward the 262144 train length.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
