* ⬆️ Update ggml-org/llama.cpp
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(llama-cpp): link librdma from the static ggml-rpc build
ggml-rpc gained an Apple RDMA transport in this llama.cpp range and
declares its librdma dependency with target_link_options(ggml-rpc
PRIVATE "LINKER:-weak_library,..."). Link options are not a usage
requirement of a static library, so the llama-cpp-grpc variant, which
builds with BUILD_SHARED_LIBS=OFF, dropped the flag and left every
ibv_* symbol of transport-apple.cpp undefined when grpc-server linked
on darwin.
prepare.sh now re-declares the same weak link as INTERFACE on the
ggml-rpc target, so the flag reaches whoever links the static library.
The append is guarded on a marker for repeat runs, and on
GGML_RPC_RDMA_APPLE, which the turboquant and bonsai forks lack.
Assisted-by: Claude:claude-opus-5 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* fix(ci): remove the e2e container before removing its image
`docker stop` returns as soon as the container exits, but the daemon
reaps a `--rm` container asynchronously after that. The `docker rmi
localai-tests` that follows teardown-e2e then loses the race against the
reaper and fails with "conflict: ... is using its referenced image", so
make exits 1 and the job goes red after every spec has passed.
This is why the E2E Backend Tests job fails at random across pull
requests. Runs 33435319093, 33435332991, 33412165884 and 33444669207 all
report "SUCCESS! -- 235 Passed | 0 Failed" and then die in teardown.
`docker rm -f` is synchronous, so the image reference is gone before
teardown-e2e returns. It also covers the case where no container is
running, which `docker stop` could not because it rejects an empty
argument list.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-5 [Claude Code]
* fix(ci): open a tmate session only when a PR asks for one
The tmate step runs on every failure and then holds the runner until
GitHub cancels the job at the 6 hour limit. A one second cleanup race in
the e2e teardown therefore costs a whole ubuntu-latest slot. The recent
run list is full of 6h, 7h and 12h cancelled runs for that reason.
The step now needs the `ci-debug` label on the pull request, so a
session opens when somebody wants to debug and never otherwise. The
30 minute step timeout caps the cost when the label is left behind.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:claude-opus-5 [Claude Code]
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
DS4 appends the opening thinking marker to tokenizer-templated prompts, so generated text begins directly with reasoning bytes. Starting DsmlParser in TEXT therefore puts the reasoning and closing marker in visible content.
Start the parser in THINK for structured chat requests with thinking enabled in both Predict and PredictStream. Keep the default TEXT state for raw prompts and reasoning-off requests, and add incremental regression coverage.
Assisted-by: Codex:gpt-5
Signed-off-by: Claudio Maradonna <git@codeshifter.xyz>
stageDirectory and countStageableFiles already skip them, but
stageOptionDir did not - and it is the path sherpa-onnx voices take for
espeak-ng-data. The receiver writes "<file>.sha256" for every file it
accepts, so staging the sidecars made it write sidecars for those in
turn, one level deeper on every load.
Observed on a live node: "<file>.sha256" repeated eleven times, 5077
junk files out of 7832 in the models dir, and still growing. Staging
never finished, so vits-piper-it_IT-paola-sherpa stayed permanently
"staging on node" and every realtime warmup needing that voice failed
with the session then going silent.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0142UfUh8HWxdim5JZqf8Tr6
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Add Q4 and Q8 GGUF builds with their shared vision projector. The
model is a recent refusal-removed Ornith derivative for alignment and
red-team research.
Assisted-by: Codex:gpt-5
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
Install 404s because the gallery still points at mmproj-...-f16.gguf.
HF only ships ...-F16.gguf now, with a different sha256.
Signed-off-by: lei_lei <96427312+leilei3167@users.noreply.github.com>
Fixes#11673: on macOS the DMG launcher appeared to launch nothing. After
installing, the app sat in the menu bar with no window, nothing listening
on localhost:8080, and empty log files, because nothing ever started the
server unless the unrelated 'start on system boot' option was enabled.
- Start the LocalAI server automatically when the launcher opens and right
after a fresh install. The new auto_start_server config key defaults to
enabled and gets a settings checkbox; the legacy auto_start key was never
honored nor exposed, so every existing launcher.json carries an
unintentional false and is deliberately left behind.
- Fix the welcome window suppressing itself: its 'don't show this again'
checkbox was initialized with the inverted value, and SetChecked fired
the change callback which persisted ShowWelcome=false on the very first
showing.
- Surface auto-start failures through the systray startup-error dialog,
since there is no visible window during auto-start.
- Pass --app-version to fyne package so the app stops reporting itself as
version 0.0.0 in the About box.
- Document the first-launch flow (menu bar app, auto-start, WebUI URL) in
the macOS getting-started page.
- Repair two launcher specs that never ran in CI: a *bool matched against
BeTrue and a /tmp assertion that trips on Linux where the test tempdir
itself lives under /tmp.
Assisted-by: Claude Code:claude-fable-5
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Add three llama.cpp-compatible mixed quantizations from ISTA DASLab. These builds give Qwen3.8-27B users an 8.4 to 10.1 GB weight tier with the shared vision projector.
Assisted-by: Codex:gpt-5
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
Add Q4_K_M and Q8_0 MTP variants with the shared vision projector.
The publisher recommends these builds for faster Qwen3.8 generation.
Assisted-by: Codex:gpt-5
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
Add the 3B, 8B, and 30B safetensors checkpoints as one vLLM variant family so LocalAI can select the largest build that fits. Configure the parsers and sampling defaults recommended for Granite reasoning and tool calls.
Assisted-by: Codex:gpt-5
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
The Makefile already had a hipblas branch, but no CI row built it and
the gallery's `amd:` mapping stayed commented out. On an AMD host the
capability lookup found no `amd` key and fell back to `default`, so
these users silently ran the CPU build.
Add the hipblas row to the backend matrix and the two gallery entries
it publishes, then point `amd:` at them.
Drop `-DGGML_HIPBLAS=ON` while here. `SD_HIPBLAS` sets `GGML_HIP`
itself, and `GGML_HIPBLAS` is the name ggml used before the rename, so
the flag only produced an unused-variable warning. Add gfx1151 to the
local target list to match the value the workflows pass in.
Assisted-by: Claude Code:claude-opus-5[1m]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Tencent released three WeMM sizes with direct Sentence Transformers support. Add each safetensor repository so users can select the quality and resource tradeoff.
Assisted-by: Codex:gpt-5
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
OpenAI GA clients send multipart or raw SDP requests. They expect a bare
SDP answer. LocalAI only accepted its legacy JSON envelope, so signaling
failed before media setup.
Keep the JSON contract for existing clients. Accept both GA request
shapes and choose the matching response format.
Assisted-by: Codex:gpt-5
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
Add the Q4 and Q8 GGUF builds with the shared vision projector.\nThe variant pair lets LocalAI select the build that fits available memory.\n\nAssisted-by: Codex:gpt-5
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
Add complete vLLM and SGLang entries with their exact tool parsers. Preserve an explicit zero temperature in both backend adapters.
Assisted-by: Codex:gpt-5
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
Node placement and replica rules could only name a model, so an operator
who pinned "llama3" to the GPU tier had to rewrite the rule whenever a
different model took over that job. An alias already gives a stable name
for whichever model serves it, and a rule on that name makes it a
deployment slot: repoint the alias and the placement follows.
A rule keeps the name the operator chose. Reads resolve that name through
the config loader to the model the rule governs, so the reconciler counts,
schedules and trims replicas of the target, and the router finds an
alias-keyed rule from the target it is already routing. An alias that
resolves to nothing governs nothing loadable, so the reconciler skips it
and the write paths refuse it.
A replica is shared by every name that resolves to it, so only one rule
can decide where it runs. The REST and MCP write paths reject a rule whose
target another rule already governs. A pair that arrives some other way,
such as a seed file or an alias repointed onto a model that already has a
rule, resolves in favour of the rule named after the model itself and then
the oldest, and the rest are listed as shadowed.
The eviction guard is the exception: it matches rules to replicas in raw
SQL inside a locking transaction and cannot resolve an alias. It reads a
stored target that the reconciler refreshes each tick, and falls back to
the rule's own name when that target is empty.
Assisted-by: Claude:claude-opus-5 golangci-lint eslint
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* fix(ui): move node labels into the scheduling selector field
The scheduling page kept a node-label browser open above the rules
whether or not anyone was writing one, while the field that actually
needs labels, the rule's node selector, was two bare text inputs with no
hint of what the cluster reports.
The browser is gone. The selector's key input now completes against the
label keys the cluster uses, and the value input offers only the values
that key takes. The roster already loads for the page, so the
suggestions cost no request, and a roster that fails to load costs the
admin the hints and nothing else.
Suggestions stay suggestions: a key no node reports yet still commits as
typed, which is how an admin writes a rule before labelling the nodes
for it.
Assisted-by: Claude:claude-opus-5 golangci-lint eslint playwright
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(distributed): size model fit against the cluster, not the frontend
The models page asked the frontend how much memory a model may occupy.
In distributed mode the frontend is usually a GPU-less pod while every
model runs on a worker, so a fleet of GPU nodes was told it could only
run the smallest CPU build. The variant picker's fits flag and its
auto-selection came from the same place, as did the hardware
recommendations.
The registry now reports the largest single healthy backend node. The
largest node, not the fleet total: a model loads into one node, so four
16GB workers are not a home for a 40GB model. An operator-set VRAM
budget caps a node's contribution, because the scheduler refuses a load
above that ceiling anyway, and a GPU node beats a CPU node holding more
system RAM.
GET /api/resources and GET /api/models carry this as an additional
cluster object. Their aggregate and ram fields keep reporting the
frontend's own hardware, which is what the resource monitor shows.
Variant selection judges backends against the union of the capabilities
present in the cluster, the way backend discovery already did.
Every path degrades to the local host: no cluster object in single-node
mode, and none when the registry cannot be read, so a hiccup narrows the
answer back to single-node behaviour rather than marking the whole
catalog too large.
The verdicts now name the node they belong to, since a model fits
somewhere or nowhere.
Assisted-by: Claude:claude-opus-5 golangci-lint eslint playwright
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* ⬆️ Update ggml-org/llama.cpp
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(llama-cpp): follow upstream MTMD APIs
The dependency update adds MTMD initialization options to prompt and
bitmap helpers. The gRPC adapter now passes the server options through
each affected path.
The update also replaces the per-layer MoE regex helper. Preparation
probes both APIs because older forks still reuse this adapter.
Assisted-by: Codex:gpt-5
---------
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
