Adds a Go native gRPC backend that dlopens librfdetrcpp.so (built from
mudler/rf-detr.cpp at the pinned RFDETR_VERSION) via purego and exposes
the rfdetr.cpp inference pipeline through LocalAI's existing Detect RPC.
Supports all 5 RF-DETR detection variants (Nano/Small/Base/Medium/Large)
and 6 segmentation variants (SegNano/SegSmall/SegMedium/SegLarge/
SegXLarge/Seg2XLarge) with F32/F16/Q8_0/Q4_K quantizations. Pre-built
GGUFs ship at mudler/rfdetr-cpp-* on HuggingFace.
Detection returns Bbox + class_name + confidence; segmentation also
returns PNG-encoded per-detection masks via the rfdetr_capi accessor
functions (rfdetr_capi_get_detection_{class_id,box,score,class_name,
mask_png}).
End-to-end verified through POST /v1/detection: HTTP -> gRPC -> purego
dlopen -> rfdetr.cpp -> ggml -> response (9 detections on the detection
model, 21 detections + valid PNG masks on the seg-nano model against
the kitchen fixture).
Wiring:
- backend/go/rfdetr-cpp/{main.go,gorfdetrcpp.go,CMakeLists.txt,
Makefile,run.sh,package.sh,test.sh,.gitignore}
- Top-level Makefile: BACKEND_RFDETR_CPP, docker-build target,
.NOTPARALLEL, prepare-test-extra, test-extra
- backend/go/rfdetr-cpp/Makefile: `test` target invoked by test-extra
- .github/backend-matrix.yml: CPU + CUDA-12/13 + L4T CUDA-12/13
(arm64) + HIP + Vulkan (amd64 + arm64) + SYCL f32/f16
- backend/index.yaml: rfdetr-cpp meta anchor + latest/development
image entries for every matrix tag-suffix
- .github/workflows/bump_deps.yaml: RFDETR_VERSION pin tracking
(mudler/rf-detr.cpp branch main)
- gallery/index.yaml: 11 rfdetr-cpp-* entries (nano + 4 detection
variants + 6 seg variants), all backed by mudler/rfdetr-cpp-*
on HuggingFace with sha256 pinning on the F16 default
- core/gallery/importers/rfdetr.go: GGUF auto-routing for HF imports
(mudler/rfdetr-cpp-* repos route to rfdetr-cpp, Transformer-format
repos stay on the Python rfdetr backend; explicit preferences.backend
overrides both heuristics)
- core/gallery/importers/rfdetr_test.go: table-driven coverage of the
auto-routing + a live mudler/rfdetr-cpp-nano cross-check
scripts/changed-backends.js needs no change: the existing
Dockerfile.golang -> backend/go/${item.backend}/ branch already routes
the 9 rfdetr-cpp matrix entries to the correct backend path.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* fix(nemo): extract Hypothesis.text for TDT/RNNT ASR models
CTC models (e.g. Whisper) return List[str] from transcribe(), but
TDT/RNNT models (e.g. parakeet-tdt-0.6b-v3) return List[Hypothesis]
where the decoded text lives in the Hypothesis.text attribute.
Previously, results[0] was assigned directly to the protobuf string
field, causing silent empty output for non-CTC models.
Now checks the return type and extracts .text from Hypothesis objects,
with a safe fallback via getattr().
* refactor: simplify Hypothesis text extraction per Copilot review
Use single getattr() call instead of hasattr() + double access,
and return empty string for unknown types instead of str(result)
to avoid leaking internal repr to clients.
* fix(qwen-asr): enable timestamp output when forced_aligner is configured
Two bugs prevented timestamps from working in the qwen-asr backend:
1. transcribe() was called without return_time_stamps=True, so the
forced aligner was loaded but never invoked. Now we pass
return_time_stamps=True when a forced_aligner is present.
2. The timestamp parsing code expected (list, tuple) items, but the
qwen_asr library returns ForcedAlignItem dataclass instances with
.text, .start_time, .end_time attributes. Added hasattr() check
to handle this correctly, falling back to tuple parsing for
backward compatibility.
* refactor: address Copilot review for qwen-asr timestamps
- Wrap return_time_stamps kwarg in try/except TypeError for safety
- Add defensive float() normalization for timestamp times
- Use str() for text extraction to ensure string type
* fix(qwen-asr): convert seconds to nanoseconds for Go time.Duration
The Go server reads TranscriptSegment.start/end via time.Duration,
which is in nanoseconds. Previously the backend sent milliseconds
(* 1000), causing timestamps to be 1000x too small (e.g. 8e-8
instead of 0.08). Convert seconds → nanoseconds (* 1e9) instead.
Also applies to the legacy tuple path for consistency.
* feat(qwen-asr): respect timestamp_granularities (segment vs word)
Read request.timestamp_granularities from the gRPC request.
- 'word': return one segment per aligned item (character / word)
- 'segment' (default): merge consecutive items at sentence boundaries
Sentence boundaries detected via CJK punctuation (。！？；…)
and Latin endings (. ! ? ;). This matches the OpenAI Whisper API
contract where omitting the parameter defaults to segment-level.
* fix(qwen-asr): escape smart quotes in punctuation set
Unicode curly quotes (U+2018/2019) were being interpreted as Python
string delimiters, causing SyntaxError. Use explicit unicode escapes.
* fix(qwen-asr): use time-gap threshold for segment boundaries
The forced aligner strips punctuation from its output, so text-based
sentence detection doesn't work. Instead, detect segment boundaries
by measuring time gaps between consecutive aligned items.
Threshold = max(median_gap * 4, 0.3s). This cleanly separates
intra-sentence gaps (< 0.24s) from inter-sentence gaps (> 0.3s)
across Chinese, English, and other languages.
* fix(qwen-asr): smart join with spaces for non-CJK tokens
The forced aligner strips whitespace from tokenized text, so English
words like ['hello', 'world'] were joined as 'helloworld'. Add
_smart_join() that inserts spaces between non-CJK tokens while
keeping CJK characters and punctuation unspaced. Works for Chinese,
English, Korean, Japanese, and mixed-language text.
---------
Co-authored-by: fqscfqj <fqsfqj@outlook.com>
* ⬆️ Update ggml-org/llama.cpp
Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(llama-cpp): track upstream rename checkpoint_every_nt -> checkpoint_min_step
Upstream llama.cpp renamed common_params::checkpoint_every_nt to
checkpoint_min_step and changed its default from 8192 to 256. The semantics
also shifted: it used to enforce a fixed checkpoint cadence during prefill,
now it sets a minimum spacing between context checkpoints. Track the new
field name in grpc-server.cpp and accept the old option names as backward-
compatible aliases for users with existing configs.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: claude-code:claude-opus-4-7
---------
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>
feat(stablediffusion-ggml): mux LTX-2 audio into output MP4
sd.cpp's generate_video now returns a sd_audio_t* alongside the video
frames for models with an audio VAE (LTX-2.3). Our gosd wrapper was
already collecting that pointer but immediately freed it without ever
muxing it into the output, so LTX-2 generations landed as silent MP4s
even though the audio VAE decode succeeded.
Stage the planar float32 waveform to a temp WAV (IEEE float, header
hand-built; samples interleaved on the fly), then add it as a second
ffmpeg input with -c:a aac -map 0:v:0 -map 1:a:0 -shortest. The temp
WAV is cleaned up unconditionally after ffmpeg exits, including on
the write/waitpid error paths.
Non-LTX models (Wan i2v / FLF2V) keep their current behaviour: audio
arg is nullptr, the audio-related ffmpeg flags are not added, and no
temp file is created.
Assisted-by: Claude:claude-opus-4-7
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
stable-diffusion.cpp gained LTX-2 video generation, which requires an
audio VAE and an embeddings_connectors safetensors in addition to the
usual diffusion model, VAE, and LLM text encoder. The pinned commit
exposes audio_vae_path and embeddings_connectors_path on
sd_ctx_params_t; wire both through the option parser so gallery entries
can point at the LTX-specific assets.
Ship six LTX-2.3 GGUF gallery entries (dev + distilled, UD-Q4_K_M /
Q4_K_M / Q8_0 each) backed by a new ltx-ggml.yaml template that
defaults to euler / cfg_scale 6.0 / vae_decode_only:false /
diffusion_flash_attn / offload_params_to_cpu — matching the upstream
LTX-2 CLI recipe. Each entry pulls the model GGUF plus the QAT
gemma-3-12b-it text encoder, video VAE, audio VAE, and embeddings
connectors needed for T2V / I2V / FLF2V.
Assisted-by: Claude:claude-opus-4-7 [Claude-Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Add a routing middleware stack and a cloud-proxy backend.
* cloud-proxy: a Go gRPC backend that forwards OpenAI- and
Anthropic-shaped chat requests to upstream providers, with an
optional translate mode (OpenAI request -> Anthropic /v1/messages
-> OpenAI response) and full tool-calling support.
* routing: admission control, content-aware model routing
(embedding cache + classifier + rerank + Arch-Router score),
PII detection/redaction (regex + NER) with streaming filter and
OpenAI/Anthropic adapters, and a per-user/per-key billing recorder
backed by GORM or in-memory storage.
* middleware: UsageMiddleware records usage via the billing recorder,
plus admission, route-model, usage-stamp and trace middlewares.
* observability: BackendTrace ring buffer stores full request bodies
(capped), MITM proxy emits structured trace events, and router
classifier decisions surface at /api/router/decide.
* gallery: Arch-Router-1.5B (Q4_K_M and Q8_0).
* UI: cloud-proxy model-editor fields, classifier system-prompt and
score-normalization config, and a Traces page rendering request
bodies.
Assisted-by: claude-code:claude-opus-4-7 [Read] [Edit] [Bash]
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(vllm): switch L4T13 backend to PyPI aarch64+cu130 wheels
The L4T13 vllm backend pulled torch / torchvision / torchaudio / vllm from
pypi.jetson-ai-lab.io's sbsa/cu130 mirror via [tool.uv.sources] with no
version pins. That mirror started shipping torch 2.11.0 next to a
vllm-0.20.0+cu130 wheel that was still compiled against torch 2.10's c10
ABI, so uv landed on the mismatched pair and vllm crashed at import:
ImportError: vllm/_C.abi3.so: undefined symbol:
_ZN3c1013MessageLoggerC1EPKciib
(c10::MessageLogger's constructor signature changed between torch 2.10 and
2.11; the vllm wheel referenced the 2.10 form, the installed libc10.so
exported only the 2.11 form.)
Since torch 2.11 (April 2026) PyPI publishes its own aarch64 + cu130
manylinux wheels, and vllm 0.20.0 ships an aarch64 wheel whose Requires-
Dist locks torch==2.11.0 / torchvision==0.26.0 / torchaudio==2.11.0. That
makes uv's resolver produce an ABI-consistent set automatically, so the
mirror and the [tool.uv.sources] pinning are no longer needed.
flash-attn is dropped from the dep list: PyPI has no aarch64 wheel, but
vLLM 0.20+ already bundles its own vllm_flash_attn (fa2 + fa3) inside the
main wheel, so the Dao-AILab package isn't required at runtime.
Reference: https://pytorch.org/blog/vllm-and-pytorch-work-together-to-improve-the-developer-experience-on-aarch64/
Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Write] [Bash] [WebFetch]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactor(vllm): retire l4t13 pyproject.toml in favor of requirements-*.txt
pyproject.toml only existed because uv pip install -r requirements.txt
doesn't honor [tool.uv.sources]. The previous commit dropped [tool.uv.
sources] (PyPI now serves the aarch64 + cu130 wheels directly), so the
file no longer carries any logic the requirements-*.txt path can't.
Replace with the same two-file pattern every other build profile uses:
- requirements-l4t13.txt (accelerate / torch / transformers /
bitsandbytes - matches cublas13's split)
- requirements-l4t13-after.txt (vllm; runs after the base resolve so
the cu130 torch wheel lands first)
install.sh's whole l4t13 elif branch goes away; libbackend.sh's
installRequirements already handles the requirements-install.txt build-
deps pass, the C_INCLUDE_PATH export for PORTABLE_PYTHON, and the
runProtogen call, so falling through to the standard else: branch
produces identical install behavior with less surface area.
No functional change at install time - same wheels, same order.
Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Write] [Bash]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(sglang,vllm-omni): switch L4T13 backends to PyPI aarch64+cu130 wheels
Same root cause and same fix as the vllm backend in the previous commits:
the L4T13 sglang and vllm-omni backends both pulled their accelerator
stack from pypi.jetson-ai-lab.io's sbsa/cu130 mirror with no version
pins, so they would silently land on the same torch 2.11 vs cu130-built
wheel ABI mismatch the moment the mirror published an out-of-sync pair.
sglang
------
- Drop pyproject.toml + [tool.uv.sources]. The historical comment said
the [all] extra was unsafe on aarch64 because of decord, but sglang
0.5.x now uses `decord2` on aarch64/arm/armv7l (which ships cp312
aarch64 wheels), so we can match cublas13's sglang[all]>=0.5.11 pin
and stop being capped at the 0.5.1.post2 the L4T mirror shipped.
That unblocks Gemma 4 / MTP recipes on Jetson Thor.
- New requirements-l4t13.txt mirrors the cublas13 split (accelerate /
torch / torchvision / torchaudio / transformers), requirements-l4t13-
after.txt carries sglang[all]>=0.5.11.
- install.sh's l4t13 elif branch goes away; falls through to the
standard installRequirements path.
vllm-omni
---------
- requirements-l4t13.txt drops --extra-index-url to jetson-ai-lab and
drops flash-attn (PyPI has no aarch64 wheel, vLLM 0.20+ bundles its
own vllm_flash_attn fa2 + fa3 internally).
- install.sh's l4t13 vllm-install branch collapses into the cublas13
branch since both now just run `pip install vllm --torch-backend=auto`
against PyPI.
- --index-strategy=unsafe-best-match is dropped from the top-level
l4t13 guard; without the L4T mirror in the picture it had no purpose.
The from-source vllm-omni install on top still keeps its existing
`sed -i '/^fa3-fwd[[:space:]]*==/d' requirements/cuda.txt` workaround -
fa3-fwd has no aarch64 wheel and no sdist, unrelated to flash-attn.
Reference: https://pytorch.org/blog/vllm-and-pytorch-work-together-to-improve-the-developer-experience-on-aarch64/
Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Write] [Bash] [WebFetch]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(sglang): drop [all] extra on l4t13 - xatlas has no aarch64 wheel
CI revealed that sglang[all]==0.5.12 transitively pulls xatlas via the
[diffusion] sub-extra, and xatlas ships no aarch64 wheel. Its sdist
depends on scikit_build_core without declaring it in build-system.
requires, so under --no-build-isolation uv can't build it from source:
× Failed to build `xatlas==0.0.11`
├─▶ The build backend returned an error
╰─▶ Call to `scikit_build_core.build.build_wheel` failed (exit status: 1)
ModuleNotFoundError: No module named 'scikit_build_core'
help: `xatlas` (v0.0.11) was included because `sglang[all]` (v0.5.12)
depends on `xatlas`
Upstream sglang explicitly gates st_attn and vsa on
`platform_machine != aarch64` inside the same [diffusion] extra but
forgot xatlas - same class of bug that bit the old decord pin.
Use plain `sglang>=0.5.11` on l4t13. backend.py imports only base
sglang.srt symbols (Engine, ServerArgs, FunctionCallParser,
ReasoningParser); the [all] extras are optional accelerators not
required at import time. cublas13 (x86_64) keeps [all] because xatlas
has x86_64 wheels there.
Assisted-by: Claude:claude-opus-4-7 [Read] [Edit] [Write] [Bash]
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>
Aligns LocalAI's llama-cpp gRPC backend with upstream's auto-on prompt
cache path so repeated system prompts (agents, OpenAI/Anthropic-compatible
CLIs, coding assistants) skip prefill on subsequent calls without any
YAML changes. Reported in #9921.
Upstream's server enables `kv_unified=true` (and bumps `n_parallel` to 4)
when slot count is auto, which unlocks `cache_idle_slots`. LocalAI
hardcodes `n_parallel=1` and so far also hardcoded `kv_unified=false`,
which silently force-disables idle-slot saving at server init. The host
prompt cache was allocated but never written across requests.
Changes in backend/cpp/llama-cpp/grpc-server.cpp:
- params.kv_unified: false -> true (single-slot path now benefits from
the prompt cache; users can opt out with `kv_unified:false`)
- params.n_ctx_checkpoints: 8 -> 32 (match upstream default)
- params.cache_idle_slots = true initialized explicitly (upstream default)
- params.checkpoint_every_nt = 8192 initialized explicitly (upstream default)
- New option parsers: cache_idle_slots / idle_slots_cache,
checkpoint_every_nt / checkpoint_every_n_tokens
Docs:
- features/text-generation.md: fix misleading `cache_ram` description
(it's the host-side prompt cache, not the KV cache), document the
kv_unified + cache_ram + cache_idle_slots interaction, add rows for
the two newly-exposed options, and add a worked example for the
agent/CLI workload from the issue.
- advanced/model-configuration.md: mark the legacy `prompt_cache_path`
/ `prompt_cache_all` / `prompt_cache_ro` YAML fields as unused by the
llama-cpp gRPC backend (they target upstream's CLI completion tool
and are not consumed by grpc-server.cpp) and point readers at the
new prompt-cache explainer.
Closes#9921
Assisted-by: claude:opus-4.7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
llama.cpp's model loader asserts back().pattern == nullptr on
params.tensor_buft_overrides (and on params.kv_overrides.back().key[0]
== 0) before binding them into llama_model_params. PR #8560 attempted
to satisfy llama_params_fit's placeholder requirement by pre-filling
params.tensor_buft_overrides up to llama_max_tensor_buft_overrides()
*before* the option-parse loop. Any subsequent push_back from
override_tensor / draft_cpu_moe / draft_n_cpu_moe / draft_override_tensor
then appended real entries after the placeholders, leaving back() with
a real pattern and tripping the assert. The draft override vector
likewise had no terminator at all.
Mirror upstream common/arg.cpp:645-658 instead: real entries are
pushed during option parsing, and after parsing we pad the main vector
up to ntbo (placeholders land at the end, so back() is always nullptr)
and append a single {nullptr, nullptr} to the draft vector when it is
non-empty. The existing kv_overrides terminator block already matches
upstream and stays.
Verified against ggml-org/llama.cpp@5cbaa5e: only tensor_buft_overrides
(main + draft) and kv_overrides are sentinel-terminated common_params
fields; everything else is size-driven std::vector.
Assisted-by: claude-code:claude-opus-4-7
Signed-off-by: Richard Palethorpe <io@richiejp.com>
The new ace-step.cpp revision moves backend initialization inside each
`*_load` call and drops the separate `DiTGGMLConfig` argument from
`dit_ggml_load` (config now lives in `DiTGGML::cfg`, populated from GGUF
metadata at load time). Drop the now-removed `*_init_backend` calls and
replace `g_dit_cfg` accesses with `g_dit.cfg`.
Assisted-by: Claude:claude-opus-4-7 [Claude Code]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Adapt the C++ wrapper to the new `generate_video()` signature: upstream now
returns `bool` and writes frames/audio via out-parameters (`sd_image_t**`,
`sd_audio_t**`). Also set `p->fps` on the params struct (new upstream field)
and free the returned audio handle on both the success and error paths.
Assisted-by: claude-code:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
