* test with gguf instead of ggml. Updates testPrompt to match? Adds debugging line to Dockerfile that I've found helpful recently.
* fix testPrompt slightly
* Sad Experiment: Test GH runner without metal?
* break apart CGO_LDFLAGS
* switch runner
* upstream llama.cpp disables Metal on Github CI!
* missed a dir from clean-tests
* CGO_LDFLAGS
* tmate failure + NO_ACCELERATE
* whisper.cpp has a metal fix
* do the exact opposite of the name of this branch, but keep it around for unrelated fixes?
* add back newlines
* add tmate to linux for testing
* update fixtures
* timeout for tmate
* fix: clean up Makefile dependencies to allow for parallel builds
* refactor: remove old unused backend from Makefile
* fix: finish removing legacy backend, update piper
* fix: I broke llama... I fixed llama
* feat: give the tests and builds a few threads
* fix: ensure libraries are replaced before build, add dropreplace target
* Fix image build workflows
* feat(elevenlabs): map elevenlabs API support to TTS
This allows elevenlabs Clients to work automatically with LocalAI by
supporting the elevenlabs API.
The elevenlabs server endpoint is implemented such as it is wired to the
TTS endpoints.
Fixes: https://github.com/mudler/LocalAI/issues/1809
* feat(openai/tts): compat layer with openai tts
Fixes: #1276
* fix: adapt tts CLI
* fixes#1775 and #1774
Add BitsAndBytes Quantization and fixes embedding on CUDA devices
* Manage 4bit and 8 bit quantization
Manage different BitsAndBytes options with the quantization: parameter in yaml
* fix compilation errors on non CUDA environment
* feat(intel): add diffusers support
* try to consume upstream container image
* Debug
* Manually install deps
* Map transformers/hf cache dir to modelpath if not specified
* fix(compel): update initialization, pass by all gRPC options
* fix: add dependencies, implement transformers for xpu
* base it from the oneapi image
* Add pillow
* set threads if specified when launching the API
* Skip conda install if intel
* defaults to non-intel
* ci: add to pipelines
* prepare compel only if enabled
* Skip conda install if intel
* fix cleanup
* Disable compel by default
* Install torch 2.1.0 with Intel
* Skip conda on some setups
* Detect python
* Quiet output
* Do not override system python with conda
* Prefer python3
* Fixups
* exllama2: do not install without conda (overrides pytorch version)
* exllama/exllama2: do not install if not using cuda
* Add missing dataset dependency
* Small fixups, symlink to python, add requirements
* Add neural_speed to the deps
* correctly handle model offloading
* fix: device_map == xpu
* go back at calling python, fixed at dockerfile level
* Exllama2 restricted to only nvidia gpus
* Tokenizer to xpu
* fix: use vllm AsyncLLMEngine to bring true stream
Current vLLM implementation uses the LLMEngine, which was designed for offline batch inference, which results in the streaming mode outputing all blobs at once at the end of the inference.
This PR reworks the gRPC server to use asyncio and gRPC.aio, in combination with vLLM's AsyncLLMEngine to bring true stream mode.
This PR also passes more parameters to vLLM during inference (presence_penalty, frequency_penalty, stop, ignore_eos, seed, ...).
* Remove unused import
This PR specifically introduces a `core` folder and moves the following packages over, without any other changes:
- `api/backend`
- `api/config`
- `api/options`
- `api/schema`
Once this is merged and we confirm there's no regressions, I can migrate over the remaining changes piece by piece to split up application startup, backend services, http, and mqtt as was the goal of the earlier PRs!
* Dockerfile changes to build for ROCm
* Adjust linker flags for ROCm
* Update conda env for diffusers and transformers to use ROCm pytorch
* Update transformers conda env for ROCm
* ci: build hipblas images
* fixup rebase
* use self-hosted
Signed-off-by: mudler <mudler@localai.io>
* specify LD_LIBRARY_PATH only when BUILD_TYPE=hipblas
---------
Signed-off-by: mudler <mudler@localai.io>
Co-authored-by: mudler <mudler@localai.io>
Infinite context loop might as well trigger an infinite loop of context
shifting if the model hallucinates and does not stop answering.
This has the unpleasant effect that the predicion never terminates,
which is the case especially on small models which tends to hallucinate.
Workarounds https://github.com/mudler/LocalAI/issues/1333 by removing
context-shifting.
See also upstream issue: https://github.com/ggerganov/llama.cpp/issues/3969
* feat(refactor): refactor config and input reading
* feat(tts): read config file for TTS
* examples(kubernetes): Add simple deployment example
* examples(kubernetes): Add simple deployment for intel arc
* docs(sycl): add sycl example
* feat(tts): do not always pick a first model
* fixups to run vall-e-x on container
* Correctly resolve backend
* cleanup backends
* switch image to ubuntu 22.04
* adapt commands for ubuntu
* transformers cleanup
* no contrib on ubuntu
* Change test model to gguf
* ci: disable bark tests (too cpu-intensive)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* cleanup
* refinements
* use intel base image
* Makefile: Add docker targets
* Change test model
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(transformers): support also text generation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* embedded: set seed -1
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Certain backends as vall-e-x are not meant to be used as a library, so
we want to start the process in the same folder where the backend and
all the assets are fixes#1394
