refactor(backends): extract shared package-system-libs.sh from package.sh
The arch-detect-and-copy-system-libs block (Darwin rpath / x86_64 / aarch64
loader + libc/libstdc++/libgcc_s/libm/libgomp/libdl/librt/libpthread) was
inlined verbatim in 31 backend package.sh scripts. Extract it into a single
sourced scripts/build/package-system-libs.sh, the CPU-side counterpart to
scripts/build/package-gpu-libs.sh and its sourcing contract.
Consolidating the copies fixes three drift classes that had crept in:
- libgcc_s.so.1 and libstdc++.so.6 were listed twice in 9 backends
(acestep-cpp, crispasr, moss-tts-cpp, omnivoice-cpp, piper,
qwen3-tts-cpp, silero-vad, stablediffusion-ggml, whisper); the shared
script copies each once.
- libgomp.so.1 was omitted from opus. OpenMP consumers dlopen it rather
than link it, so the missing copy only failed at runtime; the shared
script always includes it.
- the Darwin @loader_path/lib rpath was applied only in piper and
silero-vad; both now pass their packaged binary to the shared script,
preserving that behavior. Every other backend passes an empty binary
path so no rpath is added, preserving its current behavior.
Each backend's pre/post packaging steps (binary copy, run.sh, ldd closure
walks, ggml variant bundling, espeak/OpenBLAS extras, the ds4 validate step)
are preserved verbatim; only the inline if/elif/else arch block is replaced
by a single source line.
Signed-off-by: supermario_leo <leo.stack@outlook.com>
Co-authored-by: localai-org-maint-bot <bot-opensource@localaisrl.com>
Dockerfile.golang builds 215 of the 434 matrix entries: every ggml/C++ engine
wrapped in Go. Each of those Makefiles clones an upstream repo at a pinned SHA
and compiles it once per SIMD variant (depth-anything-cpp builds four: avx,
avx2, avx512, fallback), and those variant targets depend only on the clone.
They cannot observe a change anywhere else in the LocalAI tree.
The compile sat below `COPY . /LocalAI`, so any edit anywhere invalidated it and
recompiled C++ that had not changed. Move it above that COPY, behind a copy of
only the backend's own directory.
This lands the compile in the part of the image the registry cache already
restores. Measured on two real CI builds of this Dockerfile (jobs 90551029008
and 90551028904, both fresh runners): 13 of 17 layers CACHED from
quay.io/go-skynet/ci-cache. The uncached tail is exactly `COPY . /LocalAI`, the
git-config RUN and the build RUN. Putting the engine above the COPY moves it
from the uncached tail into the cached region.
Local measurement, depth-anything-cpp CPU, rebuild after editing a Go file
outside the backend:
master 78s, 216 C++ objects compiled
this change 25s, 0 C++ objects compiled (67% faster)
Note what this deliberately is not. An earlier attempt wired a
--mount=type=cache ccache into the same RUN. BuildKit does not export cache
mounts to a registry cache, so that measured well locally and is a no-op in CI
(see the ccache section of .agents/ci-caching.md). This change relies only on
ordinary layer caching, which the 13-of-17 figure above shows already works
here.
The layer copies the backend's whole directory rather than just the Makefile:
the CMake targets also need CMakeLists.txt and the file list differs per
backend. The cost is that editing a backend's own Go sources invalidates its
engine layer. The expensive cases are unaffected, since a shared-build-input or
backend.proto change, the weekly full-matrix cron and a tag push all rebuild
every backend while touching none of their directories.
Scoped to one backend for now: only depth-anything-cpp gains the `engine`
target. The other 27 fall through the `make -n engine` guard and build exactly
as before, verified against local-store and silero-vad. Rolling the target out
to the remaining 12 backends that define VARIANT_TARGETS is mechanical once this
is confirmed against the registry cache on master.
One caveat on merge: inserting layers shifts the cache keys, so the first build
of each entry after this lands is a full miss. It pays for itself on the second.
Assisted-by: Claude:opus-5 [claude-code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
GenerateImage hardcoded TilingParamsSetEnabled(vaep, false), so tiled VAE
decoding was unreachable from a model config even though all four upstream
setters were already bound in main.go.
Sampling runs in latent space, but the final VAE decode expands to full
resolution and needs one large compute buffer. At 1024x1024 that buffer
exceeds 8GB, which fails on two kinds of device: cards without the VRAM
for a full-frame decode, and drivers that cap a single allocation
regardless of how much memory is free. Mesa RADV reports a 4GiB
maxMemoryAllocationSize, so a Radeon 8060S with 74GiB of device-local
heap still cannot serve that decode:
[INFO ] sampling completed, taking 251.82s
[INFO ] decoding 1 latents
ggml_vulkan: Requested buffer size exceeds device buffer size limit:
ErrorOutOfDeviceMemory
[ERROR] vae: failed to allocate the compute buffer
[ERROR] decode_first_stage failed for latent 1
Every sampling step completes and then the run is discarded at the last
stage, so the whole generation is wasted.
Add three options, parsed in Load and applied per generation:
vae_tiling:true enable tiled decoding (bare flag also works)
vae_tile_size:512 tile size, or 512x384 for a rectangle
vae_tile_overlap:0.25 overlap between tiles
Tiling stays off unless requested, so existing models are unaffected. Tile
size and overlap only reach the library when the operator set them, which
keeps upstream's defaults rather than pushing a zero, and an unparseable
value is treated as absent for the same reason.
Truthy spellings match what load_model already accepts for its own bool
options, and the bare-flag form matches diffusion_model, so no new
convention is introduced.
Signed-off-by: Dimitris Karakasilis <dimitris@karakasilis.me>
* feat: add Valkey Search vector store backend
Add a new built-in Go gRPC store backend 'valkey-store' that implements the
four Stores RPCs (Set/Get/Delete/Find) against the Valkey Search module (FT.*)
using the pure-Go github.com/valkey-io/valkey-go client. It is selected via the
existing per-request 'backend' field on /stores, so there is no proto or HTTP
API change, and it mirrors the in-memory local-store while adding persistence
across restarts and opt-in HNSW.
Each vector is a Valkey HASH keyed by hex(little-endian float32); the index is
created lazily on first Set (FLAT+COSINE by default), cosine similarity is
derived as 1-distance, and namespaces get a collision-resistant token. Includes
unit tests (valkey-go mock) and env-gated integration tests against
valkey/valkey-bundle, plus build/matrix/gallery wiring and docs.
Assisted-by: Kiro:claude-opus-4.8 golangci-lint
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
* Address review feedback: recover persisted index dimension, harden Find
- Load now recovers the persisted vector DIM from FT.INFO (not just index
existence), so a post-restart Set/Find validates against the real DIM
instead of silently re-learning a wrong one and dropping mismatched
vectors from the index. This also restores Find's dimension check after
a restart.
- StoresFind treats a dropped/missing index as an empty store (empty
result, no error) and clears the stale indexCreated flag, matching
local-store's empty-store behaviour.
- StoresSet reuses checkDims for its per-key length check so the four RPCs
share one dimension-guard implementation.
- Add unit tests for FT.INFO dimension recovery, loadIndexState, and the
dropped-index Find path.
Assisted-by: Kiro:claude-opus-4.8
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
* Address review feedback: TLS ServerName/CA, Find nil-check, config fail-fast
Addresses external review comments on the valkey-store backend:
- StoresFind now rejects a nil/empty query Key before dereferencing it,
so a malformed gRPC request can no longer panic the backend.
- TLS: derive ServerName (SNI) from the VALKEY_ADDR host so certificate
verification works for IP-addressed endpoints, and add VALKEY_TLS_CA_CERT
(custom CA bundle) and VALKEY_TLS_SKIP_VERIFY (testing-only) knobs.
- Config integer parsing now fails fast on a malformed value (e.g.
VALKEY_HNSW_M=1x6) instead of silently defaulting, matching the
fail-fast behaviour of the index-algo/distance-metric validation.
- Add VALKEY_DB (SELECT n) support for logical-DB isolation.
- Cap the human-readable part of a namespace token at 64 chars so a very
long model name cannot produce an unbounded key prefix / index name
(the appended short hash keeps distinct namespaces collision-free).
- Document the KNN-query injection-safety invariant (fields are constants)
and why StoresGet uses a single aggregate DoMulti deadline for reads.
- Unit tests for the Find nil/empty-key guard, fail-fast HNSW parsing,
and VALKEY_DB parsing/validation; docs + .env updated for the new vars.
Assisted-by: Kiro:claude-opus-4.8 golangci-lint
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
* Address review feedback: configure valkey-store via model config
richiejp asked that the valkey-store backend take its configuration from
a model config rather than process-wide VALKEY_* environment variables,
so multiple stores can each have their own Valkey config within one
LocalAI process. This removes every env access from the backend and
routes config through the model-config seam every other backend uses.
- config.go: loadConfig(opts *pb.ModelOptions) now parses the model
config `options:` list (key:value strings, split on the first ':')
instead of os.Getenv. Option keys mirror the old VALKEY_* names without
the prefix (addr, index_algo, distance_metric, ...). Defaults, fail-fast
validation and the mandatory client name are unchanged.
- store.go: Load threads opts into loadConfig; TLS comments/errors renamed
off the VALKEY_* names.
- core/backend/stores.go: StoreBackend and NewVectorStore take a
*config.ModelConfigLoader, resolve the per-store ModelConfig by store
name, and pass its Options (and Backend when unset) to the backend via
WithLoadGRPCLoadModelOpts. No config -> default backend + built-in
defaults, preserving the zero-config experience.
- Endpoints/routes/application: thread the config loader to StoreBackend.
- Unit + integration tests: configure via options; the integration test
passes addr through the model-config path (VALKEY_ADDR is now only the
test harness locating the server).
- docs + .env: document the model-config options, drop the env var table.
Assisted-by: Kiro:claude-opus-4.8
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
* Remove valkey-store informational comment from .env The backend is configured via model config, not env vars — the comment was unnecessary noise in .env. The configuration is already documented in docs/content/features/stores.md.
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
* feat(valkey-store): gate Load on NamespacePrefix to refuse autoload probing Mirror local-store's pattern: reject model names without store.NamespacePrefix so the model loader's greedy autoload probe cannot bind an arbitrary model name to the vector store backend (the #9287 failure mode). Also adds unit tests for the gate covering: prefixed namespace, prefix alone, unprefixed model name, empty model, and nil opts.
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
* feat(valkey-store): add username_env/password_env credential indirection Add support for resolving Valkey credentials from environment variables named in the model config, mirroring cloud-proxy's api_key_env pattern. This keeps secrets out of model YAML files and lets distinct store configs each reference their own credentials. Options: username_env / password_env name the env var holding the value. The direct username / password options still work and take precedence when both are set (backward compatible). Includes 5 unit tests and updated stores.md documentation.
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
* fix: correct rebase artifacts in backend-matrix.yml and Makefile Fix two issues introduced by the conflict-resolution script during the rebase onto master: 1. .github/backend-matrix.yml: valkey-store entries were merged INTO the cloud-proxy entries (duplicate keys in same YAML map items) instead of being separate list items. This broke cloud-proxy Linux builds and the cloud-proxy darwin entry lost its build-type/lang. Fixed by making them standalone entries and restoring cloud-proxy exactly as on master. 2. Makefile: duplicated .NOTPARALLEL and docker-build-backends lines. Collapsed to single lines that are master's current content plus the valkey-store additions. Also adds the three optional pickups from #10801: - /valkey-store in .gitignore (the built binary) - valkey-store row in docs/content/reference/compatibility-table.md - valkey-store line in backend/README.md
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
---------
Signed-off-by: Daria Korenieva <daric2612@gmail.com>
Co-authored-by: Daria Korenieva <daric2612@gmail.com>
* feat(3d): add Generate3D RPC, FLAG_3D capability, and /v1/3d/generations endpoint
Adds the plumbing for image-conditioned 3D asset generation (binary
glTF / GLB output), modeled on the video generation path:
- backend.proto: Generate3D RPC + Generate3DRequest (staged image src,
glb dst, seed/step/cfg_scale/texture_steps, quality and background
enums, params map for backend-specific extras)
- pkg/grpc: thread Generate3D through client, server, embed, base and
the backend interfaces; connection-evicting and distributed-node
wrappers (in-flight tracking + file staging) included
- core/config: FLAG_3D usecase (guessed only for the trellis2cpp
backend), '3d' canonical usecase string mapped to the Generate3D
method, and a '3d' output modality
- REST: POST /v1/3d/generations (+ unversioned alias) returning
OpenAIResponse with a /generated-3d URL or b64_json; conditioning
image accepted as URL, base64, or data URI; quality/background
validated at the edge; .glb served as model/gltf-binary
- auth: '3d' route feature (default ON); /api/instructions entry
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(trellis2cpp): add the trellis2.cpp image-to-3D backend
Wraps localai-org/trellis2cpp (C++/GGML port of Microsoft TRELLIS.2,
pbr-textures branch) as a Go+purego backend, following the
stablediffusion-ggml pattern:
- backend/go/trellis2cpp: purego bindings to the flat C ABI (v9,
asserted at startup), eager pipeline load with model-set validation
(refuses non-trellis GGUFs; degrades coarse/geometry-only/textured
exactly like the upstream demo), Generate3D via t2_generate +
t2_bake_glb writing a binary glTF to dst. Weight-free unit tests
cover resolution/validation/param mapping — CI never downloads the
multi-GB GGUF set or runs inference.
- CPU SIMD variants build into per-variant directories (the shared
libggml sonames collide across variants, unlike sd-ggml's flat
renamed-.so scheme); run.sh picks one via /proc/cpuinfo.
- CI wiring: backend-matrix entries (cpu, cuda12/13, vulkan
amd64+arm64, l4t, l4t-cuda13, darwin metal), index.yaml meta +
latest/master image entries, bump_deps tracking of the pbr-textures
branch, changed-backends.js mapping, top-level Makefile targets.
- Importer: auto-detects trellis GGUF repos/URIs (registered before
llama-cpp so the .gguf match isn't stolen) and expands any trellis
URI to the full 10-file component set spanning the three LocalAI-io
HF repos.
- Gallery: trellis2-4b (full PBR + 1024 cascade) and
trellis2-4b-geometry (512 untextured) with verified sha256s.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(ui): 3D generation page with native GLB viewer and IndexedDB history
Adds a Studio tab + /app/3d page for the new image-to-3D endpoint:
- GlbViewer ports the trellis2cpp demo's dependency-free WebGL2
renderer (quaternion trackball, metallic-roughness PBR, ACES,
hidden-line wireframe with a bounded index budget) and pairs it with
a minimal GLB parser for the two forms t2_bake_glb emits — dense
vertex-PBR (linear COLOR_0 + _METALLIC_ROUGHNESS, uploaded as
normalized integers) and the opt-in UV-atlas textured form. Parsing
happens before any GL so stats and errors render without WebGL2.
- use3DHistory stores past generations (params, input thumbnail, and
the GLB blob itself) in IndexedDB with keep-newest-20 eviction —
GLBs are multi-MB binaries localStorage can't hold — and the page
offers a download button for the active GLB.
- Wiring: CAP_3D capability constant (FLAG_3D — the exact string
/api/models/capabilities serves), threeDApi, router entries, Studio
tab, vite dev proxy, en locale keys.
- e2e: render-smoke entry plus a focused spec that feeds a real
one-triangle vertex-PBR GLB through the parser/viewer and exercises
IndexedDB persistence, selection, deletion, and API errors.
Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(3d): address API correctness and UX issues
Keep 3D generation on the LocalAI-specific /3d/generations route and ensure authentication and permissions cover it.
Propagate distributed transfer failures, publish a portable ARM64 backend image, honor importer overrides, and align discovery, upload validation, and touch controls.
Assisted-by: Codex:gpt-5
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(3d): add previewable print remeshing
Add a single-detail CGAL Alpha Wrap workflow for existing Trellis GLBs, including PBR reprojection, API documentation, tracing, and an in-browser preview before download.
Allow the remesh route to enforce its 512 MiB upload cap independently of the smaller global default so generated high-resolution meshes can be processed.
Assisted-by: Codex:gpt-5
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* build(trellis2cpp): centralize remesh dependency pins
Assisted-by: Codex:GPT-5 [apply_patch] [exec_command]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(kokoros): implement Generate3D stub for new proto RPC
The Generate3D RPC added to backend.proto for the trellis2cpp backend
made tonic's generated Backend trait require generate3_d, breaking the
kokoros-grpc build. Return unimplemented like the other unsupported
modalities.
Assisted-by: Claude Code:claude-fable-5
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
Co-authored-by: localai-org-maint-bot <bot-opensource@localaisrl.com>
The Anthropic translate provider builds the upstream request from scratch and
never emitted cache_control, so prompt caching was impossible for OpenAI-format
clients routed through cloud-proxy — even though the entire system prompt + tools
prefix is re-sent on every agentic turn.
Add an opt-in cache_prompt flag (ProxyOptions.cache_prompt; model YAML
proxy.cache_prompt: true). On a translate+anthropic model, buildAnthropicRequest
injects cache_control:{type:ephemeral} on the stable prefix — the system block,
the last tool, and the last message block (at most 3 of Anthropic's 4 allowed
breakpoints). Anthropic then serves the repeated prefix at the cache-read rate
(0.1x input) on subsequent calls, cutting cost on multi-turn/agentic workloads.
No effect in passthrough mode, for non-Anthropic providers, or when unset.
System is widened to any so it can carry the block form required to attach
cache_control, while still marshalling as a bare string when caching is off.
Adds a unit test asserting exactly three breakpoints when on and none when off,
and documents the option in docs/content/operations/cloud-proxy.md.
Assisted-by: Claude:opus-4.8
Signed-off-by: stefanwalcz <stefan.walcz@walcz.de>
* feat(backend): add vllm-cpp text-generation backend (vllm.cpp)
Wrap https://github.com/mudler/vllm.cpp - the LocalAI-team from-scratch C++20
port of vLLM (paged KV cache, continuous batching, prefix caching, safetensors
+ GGUF loading, no Python at inference) - as a Go gRPC backend over its stable
C ABI (ABI v2) via purego.
Backend (backend/go/vllm-cpp):
- Load -> vllm_engine_load: accepts a .gguf file or a config.json model dir
(anything else is refused, satisfying the greedy-probe rule); context_size
maps to max_model_len, options block_size/num_blocks/max_num_seqs size the
KV cache and scheduler admission.
- Predict -> vllm_complete (blocking); PredictStream -> vllm_complete_stream
with the per-delta C callback bridged into the gRPC stream. The backend
embeds base.Base (not SingleThread): concurrent requests batch continuously
in the engine's shared AsyncLLM scheduler.
- PredictOptions.Grammar -> the ABI's structured_grammar (GBNF), giving
grammar-constrained tool calling at parity with llama-cpp; the ABI also
exposes JSON-schema/regex/choice constraints.
- Hand-mirrored POD structs with layout locked by unit tests
(unsafe.Offsetof vs the C offsets) and a runtime vllm_abi_version gate.
- One portable library per platform (vllm.cpp uses per-file SIMD tiers with
runtime dispatch), so no avx/avx2/avx512 variant builds.
Wiring:
- backend-matrix: CPU amd64+arm64 (per-arch + manifest merge), CUDA 12/13
amd64 (120a;121a Blackwell fat binary), L4T arm64 (121a, GB10/DGX Spark -
the runtime-proven GPU target), Vulkan amd64, and Darwin arm64 Metal.
- backend/index.yaml meta + 12 image entries (latest/development x cpu,
cuda12, cuda13, l4t, vulkan, metal); bump_deps registration for the
VLLM_CPP_VERSION pin; root Makefile registration; test-extra runs the unit
specs (pure Go, no engine build).
- Importers: preference-only swaps - llama-cpp (GGUF) and vllm (safetensors)
advertise vllm-cpp via AdditionalBackends and emit backend: vllm-cpp
without tokenizer templating (the C ABI takes the FINAL prompt; templating
and tool parsing stay LocalAI-side). No auto-detect importer.
- Docs: backends list, top-level README maintained-engines table,
compatibility table.
Verified: 20/20 Ginkgo specs against the real pinned engine and
Qwen3.5-2B-UD-Q8_K_XL.gguf on CPU - blocking + streaming parity, greedy
determinism, stop words, GBNF-constrained generation, and 4 concurrent
streams; plus a dlopen/ABI-gate smoke of the built gRPC server binary.
Upstream ABI v2 + production structured-output wiring landed as
mudler/vllm.cpp@86013f3.
Assisted-by: Claude Code:claude-fable-5
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(vllm-cpp): ride the autoparser code path - engine-side chat templating and tool engagement (ABI v3)
The backend now implements AIModelRich (PredictRich / PredictStreamRich) over
vllm.cpp's ABI v3 chat entry points, so chat and tool calling ride the SAME
code path as the llama.cpp autoparser: the ENGINE renders the model's chat
template, decides when a tool call engages, and parses it - LocalAI receives
pre-parsed ChatDelta / ToolCallDelta protos exactly as it does from llama-cpp.
- With use_tokenizer_template + structured Messages, PredictOptions lowers to
ONE OpenAI chat request JSON (messages, tools, tool_choice, sampling,
stream_options.include_usage) for vllm_chat / vllm_chat_stream. tool_choice
auto lowers engine-side to a LAZY structural-tag decode constraint - free
text until the model emits the tool trigger, then the call is
grammar-constrained; required/named force a call. Tool output is parsed by
the engine's streaming Hermes-style parser; each chat.completion.chunk maps
onto ChatDeltas (content / reasoning_content / tool_calls) which the host
already prefers over Go-side tag extraction. Without structured messages the
plain path (LocalAI templating + optional GBNF grammar) applies unchanged.
- The engine resolves the chat template from the GGUF tokenizer.chat_template
metadata (or tokenizer_config.json); templates beyond its minja subset -
e.g. the full Qwen3.5 namespace()/macro template - degrade engine-side to a
Hermes-aware fallback prompt (tools schemas + <tool_call> instruction) with
a stderr witness, so structural-tag engagement keeps working.
- Importers now emit the same config shape as llama-cpp for vllm-cpp
(use_tokenizer_template: true, no-grammar autoparser flow); only the
llama-cpp-specific use_jinja option and the vllm-python parser options are
dropped.
- Pin bumped to mudler/vllm.cpp@aaed7ec (ABI v3 + chat-prompt resolution).
Verified against the real engine and Qwen3.5-2B-UD-Q8_K_XL.gguf on CPU: full
suite green - blocking chat, streaming deltas concatenating byte-equal to the
blocking answer, a REQUIRED tool call returning schema-valid arguments JSON,
and an AUTO run where the engine itself engages get_weather and streams parsed
tool deltas; plus unit specs for the request lowering, chunk->ChatDelta
mapping, and the C struct mirrors (ABI gate now v3).
Assisted-by: Claude Code:claude-fable-5
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(vllm-cpp): ABI v5 - engine-side parser selection for 30 tool dialects + reasoning
Bump the vllm.cpp pin to the autoparser-parity engine: 30 tool-call dialects
(every pure-text parser in the pinned vLLM registry, each ported 1:1 with its
upstream tests), 7 reasoning parsers, google/minja as the template renderer
(the full Qwen3.5 template now renders engine-side), per-family structural
tags (tool_choice required/named compiles the model's NATIVE syntax where
expressible), and template auto-detection for both parser axes.
Backend changes:
- cModelParams mirrors ABI v5 (tool_parser + reasoning_parser fields,
layout-locked by the offset tests; ABI gate now v5).
- New model options tool_parser:<name> / reasoning_parser:<name> pass through
to the engine; unset means template auto-detection (18-row tool marker
table; [THINK]->mistral, <think>->think_auto for reasoning); "none"
disables the reasoning split; unknown names fail the first chat call.
- Chat chunks parse the `reasoning` field (the pin renamed
reasoning_content), flowing into ChatDelta.ReasoningContent which the host
already prefers.
Live e2e against Qwen3.5-2B-UD-Q8_K_XL.gguf on CPU, full suite green: the
real chat template renders (no more fallback), reasoning auto-detection picks
think_auto so markerless answers stay pure content (the live run caught the
deepseek_r1 content-swallow upstream and drove the think_auto fix), required
tool_choice returns schema-valid arguments, auto tool_choice engages
engine-side and streams parsed deltas, and blocking/streaming stay
byte-identical. Turn latency also dropped (proper template EOS behavior).
Upstream program landed as mudler/vllm.cpp 86013f3..5fffe7e (ABI v2-v5,
minja, parser waves B1/B2/B4, reasoning seam, structural-tag registry,
think_auto).
Assisted-by: Claude Code:claude-fable-5
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(vllm-cpp): bump the engine pin to the ENG-wave close-out
mudler/vllm.cpp@df8909b: the six engine-backed vLLM tool-parser families
(qwen3-coder/xml/mimo, kimi_k2, glm45/47, minimax_m2, gemma4, seed_oss)
text-reimplemented from their wire formats and held to the upstream test
suites - 39 registered dialects; the pinned vLLM registry is now covered
except the three Rust/Harmony-backed families, descoped by decision. kimi_k2
also gains a full native structural-tag builder; four new template
auto-detection rows land with test-pinned ordering.
Full backend e2e re-run green against Qwen3.5-2B-UD-Q8_K_XL.gguf on CPU.
Assisted-by: Claude Code:claude-fable-5
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(vllm-cpp): add the vllm-cpp-development gallery meta
The gallery grew the twelve latest/development image entries but was missing
the separate vllm-cpp-development meta (own capabilities map targeting the
-development image names), which every backend ships so the development
gallery resolves per-platform. Validated: all capability targets in both
metas resolve to existing entries, and every image URI's tag suffix matches
a backend-matrix build.
Also full-stack verified in this change's context (single-node local-ai from
this branch, locally-built backend under --backends-path, Qwen3.5-2B GGUF):
/v1/chat/completions non-stream (clean content + usage), streaming (SSE
deltas), tool_choice auto engaging get_weather engine-side with schema-valid
arguments and finish_reason=tool_calls, and streamed tool-call deltas in the
standard name-first cadence.
Assisted-by: Claude Code:claude-fable-5
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(vllm-cpp): repair the CI backend builds - gcc-14 -Werror + fat-arch Triton
Two distinct failures took down all five vllm-cpp backend builds on the PR:
1. gcc-14 (ubuntu:24.04 CI images; the local toolchain is gcc-13) fails the
engine build with -Werror=maybe-uninitialized in InputBatch::condense - a
false positive through a staging std::optional's raw storage. Fixed
upstream (mudler/vllm.cpp@61f3e85) by moving slot-to-slot directly;
verified BOTH ways under dockerized g++-14.2 (unfixed reproduces CI's two
diagnostics exactly, fixed compiles clean) with the engine's behavior
suites green. Pin bumped to that sha.
2. The amd64 CUDA builds died at CMake configure: the vendored Triton-AOT
cubin trees are per-arch and the engine refuses -DVLLM_CPP_TRITON=ON on a
multi-arch (120a;121a) fat build unless pinned to one tree, which would be
unsound for the other arch. Triton is now enabled only on the single-arch
arm64/GB10 build (where the cubins matter); the fat amd64 binary uses the
engine's non-AOT GDN path.
Backend e2e re-run green at the new pin (Qwen3.5-2B on CPU, full suite).
Assisted-by: Claude Code:claude-fable-5
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(vllm-cpp): cuda-12 images cannot compile compute_121a - target 120a only
The second CI round surfaced a CUDA-version constraint: the cuda-12 (12.8)
image's nvcc rejects 'compute_121a' (GB10 arch support landed with CUDA 13),
killing the amd64 cuda-12 build at nvcc. Gate the architecture list on
CUDA_MAJOR_VERSION (exported by Dockerfile.golang): cuda-12 builds consumer
Blackwell 120a only, cuda-13 keeps the 120a;121a fat binary, arm64/l4t
(cuda-13) keeps single-arch 121a with the Triton cubins. GB10 is arm64, so
the amd64 cuda-12 image never served it - no capability change.
Verified by Makefile dry-run variable dumps for all three combinations
(cuda12 -> 120a; cuda13 -> 120a;121a; cpu -> CUDA off).
Assisted-by: Claude Code:claude-fable-5
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(vllm-cpp): drop the cuda-12 variant - the engine needs the CUDA 13 toolchain
Third CI round, third layer: with the arch list already narrowed to 120a,
the cuda-12 (12.8) build still dies in ptxas compiling the sm_120a NVFP4 MMA
kernels ("Vector type too large, exceeds 128 bit limit") - the Blackwell fp4
path genuinely requires the CUDA 13 toolchain, and vllm.cpp supports
Blackwell-family GPUs only. Shipping a cuda-12 image without the fp4 kernels
would be a crippled build of an engine whose whole GPU story is fp4, so the
variant is dropped instead:
- backend-matrix: cuda-12 vllm-cpp entry removed (cuda-13 amd64, l4t arm64,
cpu, vulkan, metal remain).
- gallery: cuda12 image entries removed; the nvidia capability now resolves
to the cuda13 image in both metas; the nvidia-cuda-12 key is dropped so
older-driver hosts fall back to the CPU image instead of an unrunnable one.
- backend Makefile: BUILD_TYPE=cublas under CUDA_MAJOR_VERSION=12 now fails
fast with a clear message; cuda-13 keeps the 120a;121a fat binary and
arm64/l4t keeps 121a with the Triton cubins.
Verified: Makefile branch dumps for all four combinations (cuda12 loud
error, cuda13 fat, arm64 121a+Triton, cpu off), YAML parses, matrix filter
tests green, gallery capability targets all resolve.
Assisted-by: Claude Code:claude-fable-5
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(vllm-cpp): forward multi-turn tool identity and reasoning to the engine
chatRequestJSON dropped Message.ToolCallId and Message.Name on role="tool"
replies and Message.ReasoningContent on assistant history, so a second
turn after tool execution reached the engine's chat template without the
fields that bind a tool result to the call it answers. Forward all three
(present-only, matching the OpenAI wire shape) and pin vllm.cpp to
6a0bd3e7, where ChatMessage parses/round-trips tool_calls, tool_call_id,
name and reasoning and the minja adapter exposes them to the template
context.
Adds the round-trip request-lowering spec (user -> assistant tool_call ->
tool reply -> lowered request) and re-ran the gated e2e suite against the
new engine pin with a real Qwen3.5 GGUF: chat, reasoning split, streaming
parity, required-tool and auto-tool cases all green.
Assisted-by: Claude Code:claude-fable-5 [Bash] [Edit] [Read]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(vllm-cpp): bump vllm.cpp for the darwin arm64 i8mm build fix
The darwin-metal CI job was the first build to compile the engine's arm
CPU-quant files on macOS and hit their Linux-only <asm/hwcap.h> /
<sys/auxv.h> includes. vllm.cpp 9e1c9025 detects i8mm per-OS (auxv on
Linux, sysctl on Apple Silicon) with kernels untouched. Gated e2e suite
re-run green against the new pin with a real Qwen3.5 GGUF.
Assisted-by: Claude Code:claude-fable-5 [Bash] [Read]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(vllm-cpp): darwin build - bound cmake parallelism when nproc is absent
The macOS runners have no nproc, so JOBS evaluated empty and
`cmake --build -j$(JOBS)` became bare `-j`: unlimited clang jobs on a
3-core/7GB Mac, which swap-thrashed until the 6h GHA timeout (the log
shows "nproc: Command not found" and 7+ concurrent clang processes being
reaped at the cutoff). Use the same portable fallback chain as the other
darwin backends: nproc, then sysctl hw.ncpu, then 4.
Assisted-by: Claude Code:claude-fable-5 [Bash] [Edit] [Read]
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>
* feat(backend): add magpie-tts-cpp text-to-speech backend
Add a Go + purego backend wrapping the magpie-tts.cpp ggml port of NVIDIA's
Magpie TTS Multilingual 357M (encoder + autoregressive decoder over NanoCodec
tokens), producing 22.05 kHz mono audio in 5 baked voices (Aria, Jason, John,
Leo, Sofia; case-insensitive names or indices 0-4) across 9+ languages from a
single self-contained GGUF. Mirrors qwen3-tts-cpp / moss-tts-cpp: dlopen the
static-ggml shared library, bind the flat magpie_tts_capi_* C-API via purego
(no local C shim needed, the upstream .so exports it directly), and serve the
gRPC TTS + TTSStream methods behind base.SingleThread (the C context is not
reentrant across synthesize calls).
The backend CMakeLists translates the Makefile's -DGGML_{CUDA,METAL,VULKAN,HIP}
flags into upstream's MAGPIE_GGML_* toggles (upstream FORCE-overwrites the ggml
cache entries from those), pinned to magpie-tts.cpp v0.1.1
(e3f3dd1ebe22b64e7405f93b519f2d1930712568), which statically links ggml into
libmagpie-tts.so (ldd shows only system libs).
Wires the full registration: backend-matrix.yml (CPU amd64/arm64, CUDA 12/13,
Intel SYCL f16/f32, Vulkan amd64/arm64, ROCm, NVIDIA L4T + L4T CUDA 13, and
Darwin metal), backend/index.yaml metas and image entries, the root Makefile
build targets, the changed-backends backend-filter path mapping, the bump_deps
auto-bump matrix, a test-extra per-backend smoke job, the /backends/known
pref-only importer entry, the backend capabilities map (TTS + TTSStream, no
voice cloning), and the README / compatibility-table docs rows.
Verified locally: unit + e2e Ginkgo suites pass against the real q8_0 GGUF
(22.05 kHz mono WAV, RMS > 0.01), a live gRPC LoadModel + TTS round-trip
returns valid non-silent audio, and the pre-commit gates (make lint,
make test-coverage-check) pass, run manually with LOCALAI_TEST_HTTP_PORT
overriding the locally-occupied 9090.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* gallery: add magpie-tts-cpp model entries (q8_0 + f16)
Add the Magpie TTS Multilingual 357M GGUFs from mudler/magpie-tts.cpp-gguf to
the model gallery: q8_0 (~624 MB, near-lossless, fastest decode, recommended)
with an f16 (~784 MB) variant, both served by the magpie-tts-cpp backend.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* magpie-tts-cpp: bump pin to rewritten upstream v0.1.1 SHA
Upstream history was rewritten to purge accidentally committed build
artifacts; v0.1.1 now resolves to 6f7696cf.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---------
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
The "Bump Backend dependencies" workflow has failed every night for over
ten days. Four upstreams — ced.cpp, moss-transcribe.cpp, voice-detect.cpp
and rf-detr.cpp — moved from the mudler org to localai-org, so the GitHub
API answers 301 for the old slugs. ced.cpp additionally renamed its
default branch to main.
bump_deps.sh fetched without -L or -f and never checked the response, so
the redirect's JSON body was passed straight to sed, which died with
"unterminated `s' command". The loud failure was luck: an error body
without slashes would have been substituted into the Makefile as the new
pin, silently corrupting the version and shipping it in a bump PR.
Point the matrix at the new slugs and branch, and harden the script so a
bad response can never reach sed: follow redirects, fail on HTTP errors,
and require a bare 40-hex SHA before rewriting anything. Also refresh the
now-stale repository URLs in the backend Makefiles, test scripts,
backend/index.yaml and the docs.
Verified all 25 matrix entries resolve to a commit SHA and that the four
previously-failing jobs run end to end against the real API.
Assisted-by: Claude:claude-opus-4-8 [Claude Code]
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
* ⬆️ Update CrispStrobe/CrispASR
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(crispasr): initialize only declared submodules
The latest upstream commit contains an undeclared CrispASR gitlink that makes a blanket recursive submodule update fail. Limit initialization to the two submodules used by the backend build.
Assisted-by: Codex:gpt-5 [Codex]
* fix(crispasr): resolve vendored WebRTC from project root
CrispASR now builds a vendored WebRTC VAD, but its include paths assume CrispASR is the top-level CMake project. Extend the existing embedded-project rewrite to the shared third_party root.
Assisted-by: Codex:gpt-5 [Codex]
---------
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>
