* feat(backend): add tinygrad multimodal backend
Wire tinygrad as a new Python backend covering LLM text generation with
native tool-call extraction, embeddings, Stable Diffusion 1.x image
generation, and Whisper speech-to-text from a single self-contained
container.
Backend (`backend/python/tinygrad/`):
- `backend.py` gRPC servicer with LLM Predict/PredictStream (auto-detects
Llama / Qwen2 / Mistral architecture from `config.json`, supports
safetensors and GGUF), Embedding via mean-pooled last hidden state,
GenerateImage via the vendored SD1.x pipeline, AudioTranscription +
AudioTranscriptionStream via the vendored Whisper inference loop, plus
Tokenize / ModelMetadata / Status / Free.
- Vendored upstream model code under `vendor/` (MIT, headers preserved):
llama.py with an added `qkv_bias` flag for Qwen2-family bias support
and an `embed()` method that returns the last hidden state, plus
clip.py, unet.py, stable_diffusion.py (trimmed to drop the MLPerf
training branch that pulls `mlperf.initializers`), audio_helpers.py
and whisper.py (trimmed to drop the pyaudio listener).
- Pluggable tool-call parsers under `tool_parsers/`: hermes (Qwen2.5 /
Hermes), llama3_json (Llama 3.1+), qwen3_xml (Qwen 3), mistral
(Mistral / Mixtral). Auto-selected from model architecture or `Options`.
- `install.sh` pins Python 3.11.14 (tinygrad >=0.12 needs >=3.11; the
default portable python is 3.10).
- `package.sh` bundles libLLVM.so.1 + libedit/libtinfo/libgomp/libsndfile
into the scratch image. `run.sh` sets `CPU_LLVM=1` and `LLVM_PATH` so
tinygrad's CPU device uses the in-process libLLVM JIT instead of
shelling out to the missing `clang` binary.
- Local unit tests for Health and the four parsers in `test.py`.
Build wiring:
- Root `Makefile`: `.NOTPARALLEL`, `prepare-test-extra`, `test-extra`,
`BACKEND_TINYGRAD = tinygrad|python|.|false|true`,
docker-build-target eval, and `docker-build-backends` aggregator.
- `.github/workflows/backend.yml`: cpu / cuda12 / cuda13 build matrix
entries (mirrors the transformers backend placement).
- `backend/index.yaml`: `&tinygrad` meta + cpu/cuda12/cuda13 image
entries (latest + development).
E2E test wiring:
- `tests/e2e-backends/backend_test.go` gains an `image` capability that
exercises GenerateImage and asserts a non-empty PNG is written to
`dst`. New `BACKEND_TEST_IMAGE_PROMPT` / `BACKEND_TEST_IMAGE_STEPS`
knobs.
- Five new make targets next to `test-extra-backend-vllm`:
- `test-extra-backend-tinygrad` — Qwen2.5-0.5B-Instruct + hermes,
mirrors the vllm target 1:1 (5/9 specs in ~57s).
- `test-extra-backend-tinygrad-embeddings` — same model, embeddings
via LLM hidden state (3/9 in ~10s).
- `test-extra-backend-tinygrad-sd` — stable-diffusion-v1-5 mirror,
health/load/image (3/9 in ~10min, 4 diffusion steps on CPU).
- `test-extra-backend-tinygrad-whisper` — openai/whisper-tiny.en
against jfk.wav from whisper.cpp samples (4/9 in ~49s).
- `test-extra-backend-tinygrad-all` aggregate.
All four targets land green on the first MVP pass: 15 specs total, 0
failures across LLM+tools, embeddings, image generation, and speech
transcription.
* refactor(tinygrad): collapse to a single backend image
tinygrad generates its own GPU kernels (PTX renderer for CUDA, the
autogen ctypes wrappers for HIP / Metal / WebGPU) and never links
against cuDNN, cuBLAS, or any toolkit-version-tied library. The only
runtime dependency that varies across hosts is the driver's libcuda.so.1
/ libamdhip64.so, which are injected into the container at run time by
the nvidia-container / rocm runtimes. So unlike torch- or vLLM-based
backends, there is no reason to ship per-CUDA-version images.
- Drop the cuda12-tinygrad and cuda13-tinygrad build-matrix entries
from .github/workflows/backend.yml. The sole remaining entry is
renamed to -tinygrad (from -cpu-tinygrad) since it is no longer
CPU-only.
- Collapse backend/index.yaml to a single meta + development pair.
The meta anchor carries the latest uri directly; the development
entry points at the master tag.
- run.sh picks the tinygrad device at launch time by probing
/usr/lib/... for libcuda.so.1 / libamdhip64.so. When libcuda is
visible we set CUDA=1 + CUDA_PTX=1 so tinygrad uses its own PTX
renderer (avoids any nvrtc/toolkit dependency); otherwise we fall
back to HIP or CLANG. CPU_LLVM=1 + LLVM_PATH keep the in-process
libLLVM JIT for the CLANG path.
- backend.py's _select_tinygrad_device() is trimmed to a CLANG-only
fallback since production device selection happens in run.sh.
Re-ran test-extra-backend-tinygrad after the change:
Ran 5 of 9 Specs in 56.541 seconds — 5 Passed, 0 Failed
* feat(backend): add turboquant llama.cpp-fork backend
turboquant is a llama.cpp fork (TheTom/llama-cpp-turboquant, branch
feature/turboquant-kv-cache) that adds a TurboQuant KV-cache scheme.
It ships as a first-class backend reusing backend/cpp/llama-cpp sources
via a thin wrapper Makefile: each variant target copies ../llama-cpp
into a sibling build dir and invokes llama-cpp's build-llama-cpp-grpc-server
with LLAMA_REPO/LLAMA_VERSION overridden to point at the fork. No
duplication of grpc-server.cpp — upstream fixes flow through automatically.
Wires up the full matrix (CPU, CUDA 12/13, L4T, L4T-CUDA13, ROCm, SYCL
f32/f16, Vulkan) in backend.yml and the gallery entries in index.yaml,
adds a tests-turboquant-grpc e2e job driven by BACKEND_TEST_CACHE_TYPE_K/V=q8_0
to exercise the KV-cache config path (backend_test.go gains dedicated env
vars wired into ModelOptions.CacheTypeKey/Value — a generic improvement
usable by any llama.cpp-family backend), and registers a nightly auto-bump
PR in bump_deps.yaml tracking feature/turboquant-kv-cache.
scripts/changed-backends.js gets a special-case so edits to
backend/cpp/llama-cpp/ also retrigger the turboquant CI pipeline, since
the wrapper reuses those sources.
* feat(turboquant): carry upstream patches against fork API drift
turboquant branched from llama.cpp before upstream commit 66060008
("server: respect the ignore eos flag", #21203) which added the
`logit_bias_eog` field to `server_context_meta` and a matching
parameter to `server_task::params_from_json_cmpl`. The shared
backend/cpp/llama-cpp/grpc-server.cpp depends on that field, so
building it against the fork unmodified fails.
Cherry-pick that commit as a patch file under
backend/cpp/turboquant/patches/ and apply it to the cloned fork
sources via a new apply-patches.sh hook called from the wrapper
Makefile. Simplifies the build flow too: instead of hopping through
llama-cpp's build-llama-cpp-grpc-server indirection, the wrapper now
drives the copied Makefile directly (clone -> patch -> build).
Drop the corresponding patch whenever the fork catches up with
upstream — the build fails fast if a patch stops applying, which
is the signal to retire it.
* docs: add turboquant backend section + clarify cache_type_k/v
Document the new turboquant (llama.cpp fork with TurboQuant KV-cache)
backend alongside the existing llama-cpp / ik-llama-cpp sections in
features/text-generation.md: when to pick it, how to install it from
the gallery, and a YAML example showing backend: turboquant together
with cache_type_k / cache_type_v.
Also expand the cache_type_k / cache_type_v table rows in
advanced/model-configuration.md to spell out the accepted llama.cpp
quantization values and note that these fields apply to all
llama.cpp-family backends, not just vLLM.
* feat(turboquant): patch ggml-rpc GGML_OP_COUNT assertion
The fork adds new GGML ops bringing GGML_OP_COUNT to 97, but
ggml/include/ggml-rpc.h static-asserts it equals 96, breaking
the GGML_RPC=ON build paths (turboquant-grpc / turboquant-rpc-server).
Carry a one-line patch that updates the expected count so the
assertion holds. Drop this patch whenever the fork fixes it upstream.
* feat(turboquant): allow turbo* KV-cache types and exercise them in e2e
The shared backend/cpp/llama-cpp/grpc-server.cpp carries its own
allow-list of accepted KV-cache types (kv_cache_types[]) and rejects
anything outside it before the value reaches llama.cpp's parser. That
list only contains the standard llama.cpp types — turbo2/turbo3/turbo4
would throw "Unsupported cache type" at LoadModel time, meaning
nothing the LocalAI gRPC layer accepted was actually fork-specific.
Add a build-time augmentation step (patch-grpc-server.sh, called from
the turboquant wrapper Makefile) that inserts GGML_TYPE_TURBO2_0/3_0/4_0
into the allow-list of the *copied* grpc-server.cpp under
turboquant-<flavor>-build/. The original file under backend/cpp/llama-cpp/
is never touched, so the stock llama-cpp build keeps compiling against
vanilla upstream which has no notion of those enum values.
Switch test-extra-backend-turboquant to set
BACKEND_TEST_CACHE_TYPE_K=turbo3 / _V=turbo3 so the e2e gRPC suite
actually runs the fork's TurboQuant KV-cache code paths (turbo3 also
auto-enables flash_attention in the fork). Picking q8_0 here would
only re-test the standard llama.cpp path that the upstream llama-cpp
backend already covers.
Refresh the docs (text-generation.md + model-configuration.md) to
list turbo2/turbo3/turbo4 explicitly and call out that you only get
the TurboQuant code path with this backend + a turbo* cache type.
* fix(turboquant): rewrite patch-grpc-server.sh in awk, not python3
The builder image (ubuntu:24.04 stage-2 in Dockerfile.turboquant)
does not install python3, so the python-based augmentation step
errored with `python3: command not found` at make time. Switch to
awk, which ships in coreutils and is already available everywhere
the rest of the wrapper Makefile runs.
* Apply suggestion from @mudler
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
---------
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
* fix(schema): serialize ToolCallID and Reasoning in Messages.ToProto
The ToProto conversion was dropping tool_call_id and reasoning_content
even though both proto and Go fields existed, breaking multi-turn tool
calling and reasoning passthrough to backends.
* refactor(config): introduce backend hook system and migrate llama-cpp defaults
Adds RegisterBackendHook/runBackendHooks so each backend can register
default-filling functions that run during ModelConfig.SetDefaults().
Migrates the existing GGUF guessing logic into hooks_llamacpp.go,
registered for both 'llama-cpp' and the empty backend (auto-detect).
Removes the old guesser.go shim.
* feat(config): add vLLM parser defaults hook and importer auto-detection
Introduces parser_defaults.json mapping model families to vLLM
tool_parser/reasoning_parser names, with longest-pattern-first matching.
The vllmDefaults hook auto-fills tool_parser and reasoning_parser
options at load time for known families, while the VLLMImporter writes
the same values into generated YAML so users can review and edit them.
Adds tests covering MatchParserDefaults, hook registration via
SetDefaults, and the user-override behavior.
* feat(vllm): wire native tool/reasoning parsers + chat deltas + logprobs
- Use vLLM's ToolParserManager/ReasoningParserManager to extract structured
output (tool calls, reasoning content) instead of reimplementing parsing
- Convert proto Messages to dicts and pass tools to apply_chat_template
- Emit ChatDelta with content/reasoning_content/tool_calls in Reply
- Extract prompt_tokens, completion_tokens, and logprobs from output
- Replace boolean GuidedDecoding with proper GuidedDecodingParams from Grammar
- Add TokenizeString and Free RPC methods
- Fix missing `time` import used by load_video()
* feat(vllm): CPU support + shared utils + vllm-omni feature parity
- Split vllm install per acceleration: move generic `vllm` out of
requirements-after.txt into per-profile after files (cublas12, hipblas,
intel) and add CPU wheel URL for cpu-after.txt
- requirements-cpu.txt now pulls torch==2.7.0+cpu from PyTorch CPU index
- backend/index.yaml: register cpu-vllm / cpu-vllm-development variants
- New backend/python/common/vllm_utils.py: shared parse_options,
messages_to_dicts, setup_parsers helpers (used by both vllm backends)
- vllm-omni: replace hardcoded chat template with tokenizer.apply_chat_template,
wire native parsers via shared utils, emit ChatDelta with token counts,
add TokenizeString and Free RPCs, detect CPU and set VLLM_TARGET_DEVICE
- Add test_cpu_inference.py: standalone script to validate CPU build with
a small model (Qwen2.5-0.5B-Instruct)
* fix(vllm): CPU build compatibility with vllm 0.14.1
Validated end-to-end on CPU with Qwen2.5-0.5B-Instruct (LoadModel, Predict,
TokenizeString, Free all working).
- requirements-cpu-after.txt: pin vllm to 0.14.1+cpu (pre-built wheel from
GitHub releases) for x86_64 and aarch64. vllm 0.14.1 is the newest CPU
wheel whose torch dependency resolves against published PyTorch builds
(torch==2.9.1+cpu). Later vllm CPU wheels currently require
torch==2.10.0+cpu which is only available on the PyTorch test channel
with incompatible torchvision.
- requirements-cpu.txt: bump torch to 2.9.1+cpu, add torchvision/torchaudio
so uv resolves them consistently from the PyTorch CPU index.
- install.sh: add --index-strategy=unsafe-best-match for CPU builds so uv
can mix the PyTorch index and PyPI for transitive deps (matches the
existing intel profile behaviour).
- backend.py LoadModel: vllm >= 0.14 removed AsyncLLMEngine.get_model_config
so the old code path errored out with AttributeError on model load.
Switch to the new get_tokenizer()/tokenizer accessor with a fallback
to building the tokenizer directly from request.Model.
* fix(vllm): tool parser constructor compat + e2e tool calling test
Concrete vLLM tool parsers override the abstract base's __init__ and
drop the tools kwarg (e.g. Hermes2ProToolParser only takes tokenizer).
Instantiating with tools= raised TypeError which was silently caught,
leaving chat_deltas.tool_calls empty.
Retry the constructor without the tools kwarg on TypeError — tools
aren't required by these parsers since extract_tool_calls finds tool
syntax in the raw model output directly.
Validated with Qwen/Qwen2.5-0.5B-Instruct + hermes parser on CPU:
the backend correctly returns ToolCallDelta{name='get_weather',
arguments='{"location": "Paris, France"}'} in ChatDelta.
test_tool_calls.py is a standalone smoke test that spawns the gRPC
backend, sends a chat completion with tools, and asserts the response
contains a structured tool call.
* ci(backend): build cpu-vllm container image
Add the cpu-vllm variant to the backend container build matrix so the
image registered in backend/index.yaml (cpu-vllm / cpu-vllm-development)
is actually produced by CI.
Follows the same pattern as the other CPU python backends
(cpu-diffusers, cpu-chatterbox, etc.) with build-type='' and no CUDA.
backend_pr.yml auto-picks this up via its matrix filter from backend.yml.
* test(e2e-backends): add tools capability + HF model name support
Extends tests/e2e-backends to cover backends that:
- Resolve HuggingFace model ids natively (vllm, vllm-omni) instead of
loading a local file: BACKEND_TEST_MODEL_NAME is passed verbatim as
ModelOptions.Model with no download/ModelFile.
- Parse tool calls into ChatDelta.tool_calls: new "tools" capability
sends a Predict with a get_weather function definition and asserts
the Reply contains a matching ToolCallDelta. Uses UseTokenizerTemplate
with OpenAI-style Messages so the backend can wire tools into the
model's chat template.
- Need backend-specific Options[]: BACKEND_TEST_OPTIONS lets a test set
e.g. "tool_parser:hermes,reasoning_parser:qwen3" at LoadModel time.
Adds make target test-extra-backend-vllm that:
- docker-build-vllm
- loads Qwen/Qwen2.5-0.5B-Instruct
- runs health,load,predict,stream,tools with tool_parser:hermes
Drops backend/python/vllm/test_{cpu_inference,tool_calls}.py — those
standalone scripts were scaffolding used while bringing up the Python
backend; the e2e-backends harness now covers the same ground uniformly
alongside llama-cpp and ik-llama-cpp.
* ci(test-extra): run vllm e2e tests on CPU
Adds tests-vllm-grpc to the test-extra workflow, mirroring the
llama-cpp and ik-llama-cpp gRPC jobs. Triggers when files under
backend/python/vllm/ change (or on run-all), builds the local-ai
vllm container image, and runs the tests/e2e-backends harness with
BACKEND_TEST_MODEL_NAME=Qwen/Qwen2.5-0.5B-Instruct, tool_parser:hermes,
and the tools capability enabled.
Uses ubuntu-latest (no GPU) — vllm runs on CPU via the cpu-vllm
wheel we pinned in requirements-cpu-after.txt. Frees disk space
before the build since the docker image + torch + vllm wheel is
sizeable.
* fix(vllm): build from source on CI to avoid SIGILL on prebuilt wheel
The prebuilt vllm 0.14.1+cpu wheel from GitHub releases is compiled with
SIMD instructions (AVX-512 VNNI/BF16 or AMX-BF16) that not every CPU
supports. GitHub Actions ubuntu-latest runners SIGILL when vllm spawns
the model_executor.models.registry subprocess for introspection, so
LoadModel never reaches the actual inference path.
- install.sh: when FROM_SOURCE=true on a CPU build, temporarily hide
requirements-cpu-after.txt so installRequirements installs the base
deps + torch CPU without pulling the prebuilt wheel, then clone vllm
and compile it with VLLM_TARGET_DEVICE=cpu. The resulting binaries
target the host's actual CPU.
- backend/Dockerfile.python: accept a FROM_SOURCE build-arg and expose
it as an ENV so install.sh sees it during `make`.
- Makefile docker-build-backend: forward FROM_SOURCE as --build-arg
when set, so backends that need source builds can opt in.
- Makefile test-extra-backend-vllm: call docker-build-vllm via a
recursive $(MAKE) invocation so FROM_SOURCE flows through.
- .github/workflows/test-extra.yml: set FROM_SOURCE=true on the
tests-vllm-grpc job. Slower but reliable — the prebuilt wheel only
works on hosts that share the build-time SIMD baseline.
Answers 'did you test locally?': yes, end-to-end on my local machine
with the prebuilt wheel (CPU supports AVX-512 VNNI). The CI runner CPU
gap was not covered locally — this commit plugs that gap.
* ci(vllm): use bigger-runner instead of source build
The prebuilt vllm 0.14.1+cpu wheel requires SIMD instructions (AVX-512
VNNI/BF16) that stock ubuntu-latest GitHub runners don't support —
vllm.model_executor.models.registry SIGILLs on import during LoadModel.
Source compilation works but takes 30-40 minutes per CI run, which is
too slow for an e2e smoke test. Instead, switch tests-vllm-grpc to the
bigger-runner self-hosted label (already used by backend.yml for the
llama-cpp CUDA build) — that hardware has the required SIMD baseline
and the prebuilt wheel runs cleanly.
FROM_SOURCE=true is kept as an opt-in escape hatch:
- install.sh still has the CPU source-build path for hosts that need it
- backend/Dockerfile.python still declares the ARG + ENV
- Makefile docker-build-backend still forwards the build-arg when set
Default CI path uses the fast prebuilt wheel; source build can be
re-enabled by exporting FROM_SOURCE=true in the environment.
* ci(vllm): install make + build deps on bigger-runner
bigger-runner is a bare self-hosted runner used by backend.yml for
docker image builds — it has docker but not the usual ubuntu-latest
toolchain. The make-based test target needs make, build-essential
(cgo in 'go test'), and curl/unzip (the Makefile protoc target
downloads protoc from github releases).
protoc-gen-go and protoc-gen-go-grpc come via 'go install' in the
install-go-tools target, which setup-go makes possible.
* ci(vllm): install libnuma1 + libgomp1 on bigger-runner
The vllm 0.14.1+cpu wheel ships a _C C++ extension that dlopens
libnuma.so.1 at import time. When the runner host doesn't have it,
the extension silently fails to register its torch ops, so
EngineCore crashes on init_device with:
AttributeError: '_OpNamespace' '_C_utils' object has no attribute
'init_cpu_threads_env'
Also add libgomp1 (OpenMP runtime, used by torch CPU kernels) to be
safe on stripped-down runners.
* feat(vllm): bundle libnuma/libgomp via package.sh
The vllm CPU wheel ships a _C extension that dlopens libnuma.so.1 at
import time; torch's CPU kernels in turn use libgomp.so.1 (OpenMP).
Without these on the host, vllm._C silently fails to register its
torch ops and EngineCore crashes with:
AttributeError: '_OpNamespace' '_C_utils' object has no attribute
'init_cpu_threads_env'
Rather than asking every user to install libnuma1/libgomp1 on their
host (or every LocalAI base image to ship them), bundle them into
the backend image itself — same pattern fish-speech and the GPU libs
already use. libbackend.sh adds ${EDIR}/lib to LD_LIBRARY_PATH at
run time so the bundled copies are picked up automatically.
- backend/python/vllm/package.sh (new): copies libnuma.so.1 and
libgomp.so.1 from the builder's multilib paths into ${BACKEND}/lib,
preserving soname symlinks. Runs during Dockerfile.python's
'Run backend-specific packaging' step (which already invokes
package.sh if present).
- backend/Dockerfile.python: install libnuma1 + libgomp1 in the
builder stage so package.sh has something to copy (the Ubuntu
base image otherwise only has libgomp in the gcc dep chain).
- test-extra.yml: drop the workaround that installed these libs on
the runner host — with the backend image self-contained, the
runner no longer needs them, and the test now exercises the
packaging path end-to-end the way a production host would.
* ci(vllm): disable tests-vllm-grpc job (heterogeneous runners)
Both ubuntu-latest and bigger-runner have inconsistent CPU baselines:
some instances support the AVX-512 VNNI/BF16 instructions the prebuilt
vllm 0.14.1+cpu wheel was compiled with, others SIGILL on import of
vllm.model_executor.models.registry. The libnuma packaging fix doesn't
help when the wheel itself can't be loaded.
FROM_SOURCE=true compiles vllm against the actual host CPU and works
everywhere, but takes 30-50 minutes per run — too slow for a smoke
test on every PR.
Comment out the job for now. The test itself is intact and passes
locally; run it via 'make test-extra-backend-vllm' on a host with the
required SIMD baseline. Re-enable when:
- we have a self-hosted runner label with guaranteed AVX-512 VNNI/BF16, or
- vllm publishes a CPU wheel with a wider baseline, or
- we set up a docker layer cache that makes FROM_SOURCE acceptable
The detect-changes vllm output, the test harness changes (tests/
e2e-backends + tools cap), the make target (test-extra-backend-vllm),
the package.sh and the Dockerfile/install.sh plumbing all stay in
place.
The Go-side incremental JSON parser was emitting the same tool call on
every streaming token because it lacked the len > lastEmittedCount guard
that the XML parser had. On top of that, the post-streaming default:
case re-emitted all tool calls from index 0, duplicating everything.
This produced duplicate delta.tool_calls events causing clients to
accumulate arguments as "{args}{args}" — invalid JSON.
Fixes:
- JSON incremental parser: add len(jsonResults) > lastEmittedCount guard
and loop from lastEmittedCount (matching the XML parser pattern)
- Post-streaming default: case: skip i < lastEmittedCount entries that
were already emitted during streaming
- JSON parser: use blocking channel send (matching XML parser behavior)
When clients like Nextcloud or Home Assistant send requests with tools
to thinking models (e.g. Gemma 4 with <|channel>thought tags), the
response was empty despite the backend producing valid content.
Root cause: the C++ autoparser puts clean content in both the raw
Response and ChatDeltas. The Go-side PrependThinkingTokenIfNeeded
then prepends the thinking start token to the already-clean content,
causing ExtractReasoning to classify the entire response as unclosed
reasoning. This made cbRawResult empty, triggering a retry loop that
never succeeds.
Two fixes:
- inference.go: check ChatDeltas for content/tool_calls regardless of
whether Response is empty, so skipCallerRetry fires correctly
- chat.go: when ChatDeltas have content but no tool calls, use that
content directly instead of falling back to the empty cbRawResult
* fix(chat): do not retry if we had chatdeltas or tooldeltas from backend
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: use oai compat for llama.cpp
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: apply to non-streaming path too
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* map also other fields
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: add distributed mode (experimental)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix data races, mutexes, transactions
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactorings
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix events and tool stream in agent chat
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* use ginkgo
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(cron): compute correctly time boundaries avoiding re-triggering
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* enhancements, refactorings
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* do not flood of healthy checks
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* do not list obvious backends as text backends
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* tests fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop redundant healthcheck
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* enhancements, refactorings
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
First when sending errors over SSE we now clearly identify them as such
instead of just sending the error string as a chat completion message.
We use this in the UI to identify errors and link to them to the traces.
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(ui, gallery): Display and filter by the backend models use
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(ui): Add searchable model backend/model selector and prevent delete models being selected
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(gallery): Switch to expandable box instead of pop-over and display model files
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(ui, backends): Add individual backend logging
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(ui): Set the context settings from the model config
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
AIO images are behind, and takes effort to maintain these. Wizard and
installation of models have been semplified massively, so AIO images
lost their purpose.
This allows us to be more laser focused on main images and reliefes
stress from CI.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(realtime): WebRTC support
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(tracing): Show full LLM opts and deltas
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* feat(musicgen): add ace-step and UI interface
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Correctly handle model dir
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop auto-download
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add to models, fixup UIs icons
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Update docs
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* l4t13 is incompatbile
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* avoid pinning version for cuda12
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop l4t12
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* WIP response format implementation for audio transcriptions
(cherry picked from commit e271dd764bbc13846accf3beb8b6522153aa276f)
Signed-off-by: Andres Smith <andressmithdev@pm.me>
* Rework transcript response_format and add more formats
(cherry picked from commit 6a93a8f63e2ee5726bca2980b0c9cf4ef8b7aeb8)
Signed-off-by: Andres Smith <andressmithdev@pm.me>
* Add test and replace go-openai package with official openai go client
(cherry picked from commit f25d1a04e46526429c89db4c739e1e65942ca893)
Signed-off-by: Andres Smith <andressmithdev@pm.me>
* Fix faster-whisper backend and refactor transcription formatting to also work on CLI
Signed-off-by: Andres Smith <andressmithdev@pm.me>
(cherry picked from commit 69a93977d5e113eb7172bd85a0f918592d3d2168)
Signed-off-by: Andres Smith <andressmithdev@pm.me>
---------
Signed-off-by: Andres Smith <andressmithdev@pm.me>
Co-authored-by: nanoandrew4 <nanoandrew4@gmail.com>
Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
* Initial plan
* Add tool/function calling schema support to Anthropic Messages API
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
* Add E2E tests for Anthropic tool calling
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
* Make tool calling tests require model to use tools
- First test now expects hasToolUse to be true with clear error message
- Third test now expects toolUseID to be non-empty (removed conditional)
- Both tests will now fail if model doesn't call the expected tools
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
* Add E2E test for tool calling with streaming responses
- Tests that streaming events are properly emitted (content_block_start/delta/stop)
- Verifies tool_use blocks are accumulated correctly in streaming mode
- Ensures model calls tools and stop_reason is set to tool_use
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: mudler <2420543+mudler@users.noreply.github.com>
* feat(loader): refactor single active backend support to LRU
This changeset introduces LRU management of loaded backends. Users can
set now a maximum number of models to be loaded concurrently, and, when
setting LocalAI in single active backend mode we set LRU to 1 for
backward compatibility.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: add tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Update docs
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(tests): check embeddings differs in llama.cpp
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(llama.cpp): use the correct field for embedding
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(llama.cpp): use embedding type none
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(tests): add test-cases in aio-e2e suite
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add launcher (WIP)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Update gomod
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Cleanup, focus on systray
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Separate launcher from main
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add a way to identify the binary version
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Implement save config, and start on boot
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Small fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Save installed version as metadata
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Stop LocalAI on quit
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fix goreleaser
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Check first if binary is there
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* do not show version if we don't have it
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Try to build on CI
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* use fyne package
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add to release
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fyne.Do
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* show WEBUI button only if LocalAI is started
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Default to localhost
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* CI
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Show rel notes
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Update logo
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Small improvements and fix tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Try to fix e2e tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
- Add a system backend path
- Refactor and consolidate system information in system state
- Use system state in all the components to figure out the system paths
to used whenever needed
- Refactor BackendConfig -> ModelConfig. This was otherway misleading as
now we do have a backend configuration which is not the model config.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: split remaining backends and drop embedded backends
- Drop silero-vad, huggingface, and stores backend from embedded
binaries
- Refactor Makefile and Dockerfile to avoid building grpc backends
- Drop golang code that was used to embed backends
- Simplify building by using goreleaser
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(gallery): be specific with llama-cpp backend templates
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(docs): update
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(ci): minor fixes
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: drop all ffmpeg references
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: run protogen-go
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Always enable p2p mode
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Update gorelease file
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(stores): do not always load
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fix linting issues
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Simplify
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Mac OS fixup
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Build llama.cpp separately
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* WIP
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* WIP
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* WIP
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Start to try to attach some tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add git and small fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: correctly autoload external backends
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Try to run AIO tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Slightly update the Makefile helps
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Adapt auto-bumper
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Try to run linux test
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add llama-cpp into build pipelines
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add default capability (for cpu)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop llama-cpp specific logic from the backend loader
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* drop grpc install in ci for tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Pass by backends path for tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Build protogen at start
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(tests): set backends path consistently
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Correctly configure the backends path
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Try to build for darwin
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* WIP
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Compile for metal on arm64/darwin
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Try to run build off from cross-arch
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add to the backend index nvidia-l4t and cpu's llama-cpp backends
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Build also darwin-x86 for llama-cpp
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Disable arm64 builds temporary
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Test backend build on PR
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fixup build backend reusable workflow
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* pass by skip drivers
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Use crane
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Skip drivers
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* x86 darwin
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add packaging step for llama.cpp
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fix leftover from bark-cpp extraction
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Try to fix hipblas build
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: Add backend gallery
This PR add support to manage backends as similar to models. There is
now available a backend gallery which can be used to install and remove
extra backends.
The backend gallery can be configured similarly as a model gallery, and
API calls allows to install and remove new backends in runtime, and as
well during the startup phase of LocalAI.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add backends docs
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* wip: Backend Dockerfile for python backends
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: drop extras images, build python backends separately
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fixup on all backends
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* test CI
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Tweaks
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop old backends leftovers
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fixup CI
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Move dockerfile upper
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fix proto
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Feature dropped for consistency - we prefer model galleries
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add missing packages in the build image
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* exllama is ponly available on cublas
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* pin torch on chatterbox
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fixups to index
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* CI
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Debug CI
* Install accellerators deps
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add target arch
* Add cuda minor version
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Use self-hosted runners
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* ci: use quay for test images
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fixups for vllm and chatterbox
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Small fixups on CI
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chatterbox is only available for nvidia
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Simplify CI builds
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Adapt test, use qwen3
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(model gallery): add jina-reranker-v1-tiny-en-gguf
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(gguf-parser): recover from potential panics that can happen while reading ggufs with gguf-parser
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Use reranker from llama.cpp in AIO images
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Limit concurrent jobs
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
* fix(embed): use go-rice for large backend assets
Golang embed FS has a hard limit that we might exceed when providing
many binary alternatives.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* simplify golang deps
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(tests): switch to testcontainers and print logs
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(tests): do not build a test binary
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* small fixup
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(stores): Actually check a vector is a unit vector/normalized
Instead of just summing the components to see if they equal 1.0, take
the actual magnitude/p-norm of the vector and check that is
approximately 1.0.
Note that this shouldn't change the order of results except in edge
cases if I am too lax with the precision of the equality
comparison. However it should improve performance for normalized
vectors which were being misclassified.
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* fix(stores): Add tests for known results and triangle inequality
This adds some more tests to check the cosine similarity function has
some expected mathematical properties.
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore(stablediffusion-ncn): drop in favor of ggml implementation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(ci): drop stablediffusion build
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(tests): add
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(tests): try to fixup current tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Try to fix tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Tests improvements
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(tests): use quality to specify step
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(tests): switch to sd-1.5
also increase prep time for downloading models
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* merge sentencetransformers
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add alias to silently redirect sentencetransformers to transformers
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Add alias also for transformers-musicgen
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop from makefile
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Move tests from sentencetransformers
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Remove sentencetransformers
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Remove tests from CI (part of transformers)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Do not always try to load the tokenizer
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Adapt tests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fix typo
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Tiny adjustments
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(backends): Drop bert.cpp
use llama.cpp 3.2 as a drop-in replacement for bert.cpp
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(tests): make test more robust
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(refactor): track internally started models by ID
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Just extend options, no need to copy
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Improve debugging for rerankers failures
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Simplify model loading with rerankers
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Be more consistent when generating model options
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Uncommitted code
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Make deleteProcess more idiomatic
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Adapt CLI for sound generation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Fixup threads definition
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Handle corner case where c.Seed is nil
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Consistently use ModelOptions
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Adapt new code to refactoring
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Dave <dave@gray101.com>
contains simple fixes to warnings and errors, removes a broken / outdated test, runs go mod tidy, and as the actual change, centralizes base64 image handling
Signed-off-by: Dave Lee <dave@gray101.com>
