feat: pre-configure LocalAI galleries (#886 )

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
fix(deps): update github.com/nomic-ai/gpt4all/gpt4all-bindings/golang digest to 4d855af (#875 )
2026-02-03 03:02:38 -05:00 · 2023-08-12 11:25:17 +02:00 · 2023-08-12 08:58:55 +02:00 · 2023-08-12 08:56:15 +02:00 · 2023-08-12 08:56:01 +02:00 · 2023-08-11 01:02:31 +02:00
66 changed files with 2492 additions and 442 deletions
--- a/.env
+++ b/.env
@@ -24,6 +24,9 @@ MODELS_PATH=/models
 # DEBUG=true

 ## Specify a build type. Available: cublas, openblas, clblas.
+## cuBLAS: This is a GPU-accelerated version of the complete standard BLAS (Basic Linear Algebra Subprograms) library. It's provided by Nvidia and is part of their CUDA toolkit.
+## OpenBLAS: This is an open-source implementation of the BLAS library that aims to provide highly optimized code for various platforms. It includes support for multi-threading and can be compiled to use hardware-specific features for additional performance. OpenBLAS can run on many kinds of hardware, including CPUs from Intel, AMD, and ARM.
+## clBLAS:   This is an open-source implementation of the BLAS library that uses OpenCL, a framework for writing programs that execute across heterogeneous platforms consisting of CPUs, GPUs, and other processors. clBLAS is designed to take advantage of the parallel computing power of GPUs but can also run on any hardware that supports OpenCL. This includes hardware from different vendors like Nvidia, AMD, and Intel.
 # BUILD_TYPE=openblas

 ## Uncomment and set to true to enable rebuilding from source
--- a/35
+++ b/35
@@ -11,15 +11,16 @@ ARG TARGETARCH
 ARG TARGETVARIANT

 ENV BUILD_TYPE=${BUILD_TYPE}
-ENV EXTERNAL_GRPC_BACKENDS="huggingface-embeddings:/build/extra/grpc/huggingface/huggingface.py"
+ENV EXTERNAL_GRPC_BACKENDS="huggingface-embeddings:/build/extra/grpc/huggingface/huggingface.py,autogptq:/build/extra/grpc/autogptq/autogptq.py,bark:/build/extra/grpc/bark/ttsbark.py,diffusers:/build/extra/grpc/diffusers/backend_diffusers.py,exllama:/build/extra/grpc/exllama/exllama.py"
+ENV GALLERIES='[{"name":"model-gallery", "url":"github:go-skynet/model-gallery/index.yaml"}, {"url": "github:go-skynet/model-gallery/huggingface.yaml","name":"huggingface"}]'
 ARG GO_TAGS="stablediffusion tts"

 RUN apt-get update && \
    apt-get install -y ca-certificates cmake curl patch pip

-# Extras requirements
-COPY extra/requirements.txt /build/extra/requirements.txt
-RUN pip install -r /build/extra/requirements.txt && rm -rf /build/extra/requirements.txt
+# Use the variables in subsequent instructions
+RUN echo "Target Architecture: $TARGETARCH"
+RUN echo "Target Variant: $TARGETVARIANT"

 # CuBLAS requirements
 RUN if [ "${BUILD_TYPE}" = "cublas" ]; then \
@@ -29,10 +30,23 @@ RUN if [ "${BUILD_TYPE}" = "cublas" ]; then \
    dpkg -i cuda-keyring_1.0-1_all.deb && \
    rm -f cuda-keyring_1.0-1_all.deb && \
    apt-get update && \
-    apt-get install -y cuda-nvcc-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
+    apt-get install -y cuda-nvcc-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
    ; fi
 ENV PATH /usr/local/cuda/bin:${PATH}

+# Extras requirements
+COPY extra/requirements.txt /build/extra/requirements.txt
+ENV PATH="/root/.cargo/bin:${PATH}"
+RUN pip install --upgrade pip
+RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
+RUN if [ "${TARGETARCH}" = "amd64" ]; then \
+        pip install git+https://github.com/suno-ai/bark.git diffusers invisible_watermark transformers accelerate safetensors;\
+    fi
+RUN if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "amd64" ]; then \
+        pip install torch && pip install auto-gptq https://github.com/jllllll/exllama/releases/download/0.0.10/exllama-0.0.10+cu${CUDA_MAJOR_VERSION}${CUDA_MINOR_VERSION}-cp39-cp39-linux_x86_64.whl;\
+    fi
+RUN pip install -r /build/extra/requirements.txt && rm -rf /build/extra/requirements.txt
+
 WORKDIR /build

 # OpenBLAS requirements
@@ -42,9 +56,6 @@ RUN apt-get install -y libopenblas-dev
 RUN apt-get install -y libopencv-dev && \
    ln -s /usr/include/opencv4/opencv2 /usr/include/opencv2

-# Use the variables in subsequent instructions
-RUN echo "Target Architecture: $TARGETARCH"
-RUN echo "Target Variant: $TARGETVARIANT"

 # piper requirements
 # Use pre-compiled Piper phonemization library (includes onnxruntime)
@@ -98,7 +109,10 @@ RUN ESPEAK_DATA=/build/lib/Linux-$(uname -m)/piper_phonemize/lib/espeak-ng-data
 FROM requirements

 ARG FFMPEG
+ARG BUILD_TYPE
+ARG TARGETARCH

+ENV BUILD_TYPE=${BUILD_TYPE}
 ENV REBUILD=false
 ENV HEALTHCHECK_ENDPOINT=http://localhost:8080/readyz

@@ -116,7 +130,10 @@ WORKDIR /build
 COPY . .
 RUN make prepare-sources
 COPY --from=builder /build/local-ai ./
-
+# To resolve exllama import error
+RUN if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH:-$(go env GOARCH)}" = "amd64" ]; then \
+        cp -rfv /usr/local/lib/python3.9/dist-packages/exllama extra/grpc/exllama/;\
+    fi
 # Define the health check command
 HEALTHCHECK --interval=1m --timeout=10m --retries=10 \
  CMD curl -f $HEALTHCHECK_ENDPOINT || exit 1
--- a/10
+++ b/10
@@ -4,11 +4,11 @@ GOVET=$(GOCMD) vet
 BINARY_NAME=local-ai

 # llama.cpp versions
-GOLLAMA_VERSION?=6ba16de8e965e5aa0f32d25ef9d6149bb6586565
+GOLLAMA_VERSION?=50cee7712066d9e38306eccadcfbb44ea87df4b7

 # gpt4all version
 GPT4ALL_REPO?=https://github.com/nomic-ai/gpt4all
-GPT4ALL_VERSION?=cbdcde8b75868e145b973725c7c18970091a7f2f
+GPT4ALL_VERSION?=0f2bb506a8ee752afc06cbb832773bf85b97eef3

 # go-ggml-transformers version
 GOGGMLTRANSFORMERS_VERSION?=ffb09d7dd71e2cbc6c5d7d05357d230eea6f369a
@@ -335,7 +335,11 @@ protogen-go:
    pkg/grpc/proto/backend.proto

 protogen-python:
-	python -m grpc_tools.protoc -Ipkg/grpc/proto/ --python_out=extra/grpc/huggingface/ --grpc_python_out=extra/grpc/huggingface/ pkg/grpc/proto/backend.proto
+	python3 -m grpc_tools.protoc -Ipkg/grpc/proto/ --python_out=extra/grpc/huggingface/ --grpc_python_out=extra/grpc/huggingface/ pkg/grpc/proto/backend.proto
+	python3 -m grpc_tools.protoc -Ipkg/grpc/proto/ --python_out=extra/grpc/autogptq/ --grpc_python_out=extra/grpc/autogptq/ pkg/grpc/proto/backend.proto
+	python3 -m grpc_tools.protoc -Ipkg/grpc/proto/ --python_out=extra/grpc/exllama/ --grpc_python_out=extra/grpc/exllama/ pkg/grpc/proto/backend.proto
+	python3 -m grpc_tools.protoc -Ipkg/grpc/proto/ --python_out=extra/grpc/bark/ --grpc_python_out=extra/grpc/bark/ pkg/grpc/proto/backend.proto
+	python3 -m grpc_tools.protoc -Ipkg/grpc/proto/ --python_out=extra/grpc/diffusers/ --grpc_python_out=extra/grpc/diffusers/ pkg/grpc/proto/backend.proto

 ## GRPC

--- a/README.md
+++ b/README.md
@@ -5,211 +5,116 @@
 <br>
 </h1>

-[![tests](https://github.com/go-skynet/LocalAI/actions/workflows/test.yml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/test.yml) [![build container images](https://github.com/go-skynet/LocalAI/actions/workflows/image.yml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/image.yml)
+<p align="center">
+<a href="https://github.com/go-skynet/LocalAI/fork" target="blank">
+<img src="https://img.shields.io/github/forks/go-skynet/LocalAI?style=for-the-badge" alt="LocalAI forks"/>
+</a>
+<a href="https://github.com/go-skynet/LocalAI/stargazers" target="blank">
+<img src="https://img.shields.io/github/stars/go-skynet/LocalAI?style=for-the-badge" alt="LocalAI stars"/>
+</a>
+<a href="https://github.com/go-skynet/LocalAI/pulls" target="blank">
+<img src="https://img.shields.io/github/issues-pr/go-skynet/LocalAI?style=for-the-badge" alt="LocalAI pull-requests"/>
+</a>
+<a href='https://github.com/go-skynet/LocalAI/releases'>
+<img src='https://img.shields.io/github/release/go-skynet/LocalAI?&label=Latest&style=for-the-badge'>
+</a>
+</p>

-[![Artifact Hub](https://img.shields.io/endpoint?url=https://artifacthub.io/badge/repository/localai)](https://artifacthub.io/packages/search?repo=localai)
+> :bulb: Get help - [❓FAQ](https://localai.io/faq/) [💭Discussions](https://github.com/go-skynet/LocalAI/discussions) [:speech_balloon: Discord](https://discord.gg/uJAeKSAGDy) [:book: Documentation website](https://localai.io/)
+> 
+> [💻 Quickstart](https://localai.io/basics/getting_started/) [📣 News](https://localai.io/basics/news/) [ 🛫 Examples ](https://github.com/go-skynet/LocalAI/tree/master/examples/) [ 🖼️ Models ](https://localai.io/models/)


-[![](https://dcbadge.vercel.app/api/server/uJAeKSAGDy?style=flat-square&theme=default-inverted)](https://discord.gg/uJAeKSAGDy) 
-
-[Documentation website](https://localai.io/)
+[![tests](https://github.com/go-skynet/LocalAI/actions/workflows/test.yml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/test.yml)[![Build and Release](https://github.com/go-skynet/LocalAI/actions/workflows/release.yaml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/release.yaml)[![build container images](https://github.com/go-skynet/LocalAI/actions/workflows/image.yml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/image.yml)[![Bump dependencies](https://github.com/go-skynet/LocalAI/actions/workflows/bump_deps.yaml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/bump_deps.yaml)[![Artifact Hub](https://img.shields.io/endpoint?url=https://artifacthub.io/badge/repository/localai)](https://artifacthub.io/packages/search?repo=localai)

 **LocalAI** is a drop-in replacement REST API that's compatible with OpenAI API specifications for local inferencing. It allows you to run LLMs (and not only) locally or on-prem with consumer grade hardware, supporting multiple model families that are compatible with the ggml format. Does not require GPU.

+<p align="center"><b>Follow LocalAI </b></p>
+
+<p align="center">
+<a href="https://twitter.com/LocalAI_API" target="blank">
+<img src="https://img.shields.io/twitter/follow/LocalAI_API?label=Follow: LocalAI_API&style=social" alt="Follow LocalAI_API"/>
+</a>
+<a href="https://discord.gg/uJAeKSAGDy" target="blank">
+<img src="https://dcbadge.vercel.app/api/server/uJAeKSAGDy?style=flat-square&theme=default-inverted" alt="Join LocalAI Discord Community"/>
+</a>
+
+<p align="center"><b>Connect with the Creator </b></p>
+
+<p align="center">
+<a href="https://twitter.com/mudler_it" target="blank">
+<img src="https://img.shields.io/twitter/follow/mudler_it?label=Follow: mudler_it&style=social" alt="Follow mudler_it"/>
+</a>
+<a href='https://github.com/mudler'>
+<img alt="Follow on Github" src="https://img.shields.io/badge/Follow-mudler-black?logo=github&link=https%3A%2F%2Fgithub.com%2Fmudler">
+</a>
+</p>
+
+<p align="center"><b>Share LocalAI Repository</b></p>
+
+<p align="center">
+
+<a href="https://twitter.com/intent/tweet?text=Check%20this%20GitHub%20repository%20out.%20LocalAI%20-%20Let%27s%20you%20easily%20run%20LLM%20locally.&url=https://github.com/go-skynet/LocalAI&hashtags=LocalAI,AI" target="blank">
+<img src="https://img.shields.io/twitter/follow/_LocalAI?label=Share Repo on Twitter&style=social" alt="Follow _LocalAI"/></a> 
+<a href="https://t.me/share/url?text=Check%20this%20GitHub%20repository%20out.%20LocalAI%20-%20Let%27s%20you%20easily%20run%20LLM%20locally.&url=https://github.com/go-skynet/LocalAI" target="_blank"><img src="https://img.shields.io/twitter/url?label=Telegram&logo=Telegram&style=social&url=https://github.com/go-skynet/LocalAI" alt="Share on Telegram"/></a>
+<a href="https://api.whatsapp.com/send?text=Check%20this%20GitHub%20repository%20out.%20LocalAI%20-%20Let%27s%20you%20easily%20run%20LLM%20locally.%20https://github.com/go-skynet/LocalAI"><img src="https://img.shields.io/twitter/url?label=whatsapp&logo=whatsapp&style=social&url=https://github.com/go-skynet/LocalAI" /></a> <a href="https://www.reddit.com/submit?url=https://github.com/go-skynet/LocalAI&title=Check%20this%20GitHub%20repository%20out.%20LocalAI%20-%20Let%27s%20you%20easily%20run%20LLM%20locally.
+" target="blank">
+<img src="https://img.shields.io/twitter/url?label=Reddit&logo=Reddit&style=social&url=https://github.com/go-skynet/LocalAI" alt="Share on Reddit"/>
+</a> <a href="mailto:?subject=Check%20this%20GitHub%20repository%20out.%20LocalAI%20-%20Let%27s%20you%20easily%20run%20LLM%20locally.%3A%0Ahttps://github.com/go-skynet/LocalAI" target="_blank"><img src="https://img.shields.io/twitter/url?label=Gmail&logo=Gmail&style=social&url=https://github.com/go-skynet/LocalAI"/></a> <a href="https://www.buymeacoffee.com/mudler" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="23" width="100" style="border-radius:1px"></a>
+
+</p>
+
+<hr>
+
 In a nutshell:

 - Local, OpenAI drop-in alternative REST API. You own your data.
 - NO GPU required. NO Internet access is required either
  - Optional, GPU Acceleration is available in `llama.cpp`-compatible LLMs. See also the [build section](https://localai.io/basics/build/index.html). 
- Supports multiple models:
-  - 📖 [Text generation with GPTs](https://localai.io/features/text-generation/) (`llama.cpp`, `gpt4all.cpp`, ... [:book: and more](https://localai.io/model-compatibility/index.html#model-compatibility-table))
-  - 🗣 [Text to Audio](https://localai.io/features/text-to-audio/)
-  - 🔈 [Audio to Text](https://localai.io/features/audio-to-text/) (Audio transcription with `whisper.cpp`)
-  - 🎨 [Image generation with stable diffusion](https://localai.io/features/image-generation)
-  - 🔥 [OpenAI functions](https://localai.io/features/openai-functions/) 🆕
-  - 🧠 [Embeddings generation for vector databases](https://localai.io/features/embeddings/)
+- Supports multiple models
 - 🏃 Once loaded the first time, it keep models loaded in memory for faster inference
- ⚡ Doesn't shell-out, but uses C++ bindings for a faster inference and better performance. 
+- ⚡ Doesn't shell-out, but uses C++ bindings for a faster inference and better performance.

 LocalAI was created by [Ettore Di Giacinto](https://github.com/mudler/) and is a community-driven project, focused on making the AI accessible to anyone. Any contribution, feedback and PR is welcome! 

 Note that this started just as a [fun weekend project](https://localai.io/#backstory) in order to try to create the necessary pieces for a full AI assistant like `ChatGPT`: the community is growing fast and we are working hard to make it better and more stable. If you want to help, please consider contributing (see below)!

-See the [Getting started](https://localai.io/basics/getting_started/index.html) and [examples](https://github.com/go-skynet/LocalAI/tree/master/examples/) sections to learn how to use LocalAI. For a list of curated models check out the [model gallery](https://localai.io/models/).
+## 🔥🔥 [Hot topics / Roadmap](https://localai.io/#-hot-topics--roadmap)
+
+## 🚀 [Features](https://localai.io/features/)
+
+- 📖 [Text generation with GPTs](https://localai.io/features/text-generation/) (`llama.cpp`, `gpt4all.cpp`, ... [:book: and more](https://localai.io/model-compatibility/index.html#model-compatibility-table))
+- 🗣 [Text to Audio](https://localai.io/features/text-to-audio/)
+- 🔈 [Audio to Text](https://localai.io/features/audio-to-text/) (Audio transcription with `whisper.cpp`)
+- 🎨 [Image generation with stable diffusion](https://localai.io/features/image-generation)
+- 🔥 [OpenAI functions](https://localai.io/features/openai-functions/) 🆕
+- 🧠 [Embeddings generation for vector databases](https://localai.io/features/embeddings/)
+- ✍️ [Constrained grammars](https://localai.io/features/constrained_grammars/)
+- 🖼️ [Download Models directly from Huggingface ](https://localai.io/models/)


-| [ChatGPT OSS alternative](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui)                                                                                                                | [Image generation](https://localai.io/api-endpoints/index.html#image-generation)                                                                                                              |
-|------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------|
-|  ![Screenshot from 2023-04-26 23-59-55](https://user-images.githubusercontent.com/2420543/234715439-98d12e03-d3ce-4f94-ab54-2b256808e05e.png)            | ![b6441997879](https://github.com/go-skynet/LocalAI/assets/2420543/d50af51c-51b7-4f39-b6c2-bf04c403894c)                  |
-
-|                                                                    [Telegram bot](https://github.com/go-skynet/LocalAI/tree/master/examples/telegram-bot)   | [Flowise](https://github.com/go-skynet/LocalAI/tree/master/examples/flowise)                                                                                                                     |
-|------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------|
-![Screenshot from 2023-06-09 00-36-26](https://github.com/go-skynet/LocalAI/assets/2420543/e98b4305-fa2d-41cf-9d2f-1bb2d75ca902)   |  ![Screenshot from 2023-05-30 18-01-03](https://github.com/go-skynet/LocalAI/assets/2420543/02458782-0549-4131-971c-95ee56ec1af8)|    |
-
-## Hot topics / Roadmap
-
- [x] Support for embeddings
- [x] Support for audio transcription with https://github.com/ggerganov/whisper.cpp
- [X] Support for text-to-audio
- [x] GPU/CUDA support ( https://github.com/go-skynet/LocalAI/issues/69 )
- [X] Enable automatic downloading of models from a curated gallery
- [X] Enable automatic downloading of models from HuggingFace
- [ ] Upstream our golang bindings to llama.cpp (https://github.com/ggerganov/llama.cpp/issues/351) 
- [ ] Enable gallery management directly from the webui.
- [x] 🔥 OpenAI functions: https://github.com/go-skynet/LocalAI/issues/588
- [ ] 🔥 GPTQ support: https://github.com/go-skynet/LocalAI/issues/796
-
-## News
-
-Check the news and the release notes in the [dedicated section](https://localai.io/basics/news/index.html)
-
- 🔥🔥🔥 23-07-2023: **v1.22.0**: LLaMa2, huggingface embeddings, and more ! [Changelog](https://github.com/go-skynet/LocalAI/releases/tag/v1.22.0)
-
-For latest news, follow also on Twitter [@LocalAI_API](https://twitter.com/LocalAI_API) and [@mudler_it](https://twitter.com/mudler_it)
-
-## Media, Blogs, Social
+## :book: 🎥 [Media, Blogs, Social](https://localai.io/basics/news/#media-blogs-social)

 - [Create a slackbot for teams and OSS projects that answer to documentation](https://mudler.pm/posts/smart-slackbot-for-teams/)
 - [LocalAI meets k8sgpt](https://www.youtube.com/watch?v=PKrDNuJ_dfE)
 - [Question Answering on Documents locally with LangChain, LocalAI, Chroma, and GPT4All](https://mudler.pm/posts/localai-question-answering/)
 - [Tutorial to use k8sgpt with LocalAI](https://medium.com/@tyler_97636/k8sgpt-localai-unlock-kubernetes-superpowers-for-free-584790de9b65)

-## Contribute and help
+## 💻 Usage

-To help the project you can:
+Check out the [Getting started](https://localai.io/basics/getting_started/index.html) section in our documentation.

- [Hacker news post](https://news.ycombinator.com/item?id=35726934) - help us out by voting if you like this project.
+### 💡 Example: Use GPT4ALL-J model

- If you have technological skills and want to contribute to development, have a look at the open issues. If you are new you can have a look at the [good-first-issue](https://github.com/go-skynet/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) and [help-wanted](https://github.com/go-skynet/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) labels.
+See the [documentation](https://localai.io/basics/getting_started/#example-use-gpt4all-j-model-with-docker-compose)

- If you don't have technological skills you can still help improving documentation or add examples or share your user-stories with our community, any help and contribution is welcome!
+### 🔗 Resources

-## Usage
+- [How to build locally](https://localai.io/basics/build/index.html)
+- [How to install in Kubernetes](https://localai.io/basics/getting_started/index.html#run-localai-in-kubernetes)
+- [Projects integrating LocalAI](https://localai.io/integrations/)

-Check out the [Getting started](https://localai.io/basics/getting_started/index.html) section. Here below you will find generic, quick instructions to get ready and use LocalAI.
-
-The easiest way to run LocalAI is by using `docker-compose` (to build locally, see [building LocalAI](https://localai.io/basics/build/index.html)):
-
-```bash
-
-git clone https://github.com/go-skynet/LocalAI
-
-cd LocalAI
-
-# (optional) Checkout a specific LocalAI tag
-# git checkout -b build <TAG>
-
-# copy your models to models/
-cp your-model.bin models/
-
-# (optional) Edit the .env file to set things like context size and threads
-# vim .env
-
-# start with docker-compose
-docker-compose up -d --pull always
-# or you can build the images with:
-# docker-compose up -d --build
-
-# Now API is accessible at localhost:8080
-curl http://localhost:8080/v1/models
-# {"object":"list","data":[{"id":"your-model.bin","object":"model"}]}
-
-curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
-     "model": "your-model.bin",            
-     "prompt": "A long time ago in a galaxy far, far away",
-     "temperature": 0.7
-   }'
-```
-
-### Example: Use GPT4ALL-J model
-
-<details>
-
-```bash
-# Clone LocalAI
-git clone https://github.com/go-skynet/LocalAI
-
-cd LocalAI
-
-# (optional) Checkout a specific LocalAI tag
-# git checkout -b build <TAG>
-
-# Download gpt4all-j to models/
-wget https://gpt4all.io/models/ggml-gpt4all-j.bin -O models/ggml-gpt4all-j
-
-# Use a template from the examples
-cp -rf prompt-templates/ggml-gpt4all-j.tmpl models/
-
-# (optional) Edit the .env file to set things like context size and threads
-# vim .env
-
-# start with docker-compose
-docker-compose up -d --pull always
-# or you can build the images with:
-# docker-compose up -d --build
-# Now API is accessible at localhost:8080
-curl http://localhost:8080/v1/models
-# {"object":"list","data":[{"id":"ggml-gpt4all-j","object":"model"}]}
-
-curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
-     "model": "ggml-gpt4all-j",
-     "messages": [{"role": "user", "content": "How are you?"}],
-     "temperature": 0.9 
-   }'
-
-# {"model":"ggml-gpt4all-j","choices":[{"message":{"role":"assistant","content":"I'm doing well, thanks. How about you?"}}]}
-```
-</details>
-
-
-### Build locally
-
-<details>
-
-In order to build the `LocalAI` container image locally you can use `docker`:
-
-```
-# build the image
-docker build -t localai .
-docker run localai
-```
-
-Or you can build the binary with `make`:
-
-```
-make build
-```
-
-</details>
-
-See the [build section](https://localai.io/basics/build/index.html) in our documentation for detailed instructions.
-
-### Run LocalAI in Kubernetes
-
-LocalAI can be installed inside Kubernetes with helm. See [installation instructions](https://localai.io/basics/getting_started/index.html#run-localai-in-kubernetes).
-
-## Supported API endpoints
-
-See the [list of the LocalAI features](https://localai.io/features/index.html) for a full tour of the available API endpoints.
-
-## Frequently asked questions
-
-See [the FAQ](https://localai.io/faq/index.html) section for a list of common questions.
-
-## Projects already using LocalAI to run local models
-
-Feel free to open up a PR to get your project listed!
-
- [Kairos](https://github.com/kairos-io/kairos)
- [k8sgpt](https://github.com/k8sgpt-ai/k8sgpt#running-local-models)
- [Spark](https://github.com/cedriking/spark)
- [autogpt4all](https://github.com/aorumbayev/autogpt4all)
- [Mods](https://github.com/charmbracelet/mods)
- [Flowise](https://github.com/FlowiseAI/Flowise)
- [BMO Chatbot](https://github.com/longy2k/obsidian-bmo-chatbot)
- [Mattermost OpenOps](https://openops.mattermost.com)
-
-## Sponsors
+## ❤️ Sponsors

 > Do you find LocalAI useful?

@@ -222,21 +127,17 @@ A huge thank you to our generous sponsors who support this project:
 |  [Spectro Cloud](https://www.spectrocloud.com/)  |  
 |  Spectro Cloud kindly supports LocalAI by providing GPU and computing resources to run tests on lamdalabs!  |

-## Star history
+## 🌟 Star history

 [![LocalAI Star history Chart](https://api.star-history.com/svg?repos=go-skynet/LocalAI&type=Date)](https://star-history.com/#go-skynet/LocalAI&Date)

-## License
+## 📖 License

 LocalAI is a community-driven project created by [Ettore Di Giacinto](https://github.com/mudler/).

-MIT
+MIT - Author Ettore Di Giacinto

-## Author
-
-Ettore Di Giacinto and others
-
-## Acknowledgements
+## 🙇 Acknowledgements

 LocalAI couldn't have been built without the help of great software already available from the community. Thank you!

@@ -247,9 +148,12 @@ LocalAI couldn't have been built without the help of great software already avai
 - https://github.com/EdVince/Stable-Diffusion-NCNN
 - https://github.com/ggerganov/whisper.cpp
 - https://github.com/saharNooby/rwkv.cpp
+- https://github.com/rhasspy/piper
+- https://github.com/cmp-nct/ggllm.cpp

-## Contributors
+## 🤗 Contributors

+This is a community project, a special thanks to our contributors! 🤗
 <a href="https://github.com/go-skynet/LocalAI/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=go-skynet/LocalAI" />
 </a>
--- a/api/api.go
+++ b/api/api.go
@@ -2,6 +2,7 @@ package api

 import (
 	"errors"
+	"strings"

 	config "github.com/go-skynet/LocalAI/api/config"
 	"github.com/go-skynet/LocalAI/api/localai"
@@ -89,6 +90,32 @@ func App(opts ...options.AppOption) (*fiber.App, error) {
 	// Default middleware config
 	app.Use(recover.New())

+	// Auth middleware checking if API key is valid. If no API key is set, no auth is required.
+	auth := func(c *fiber.Ctx) error {
+		if len(options.ApiKeys) > 0 {
+			authHeader := c.Get("Authorization")
+			if authHeader == "" {
+				return c.Status(fiber.StatusUnauthorized).JSON(fiber.Map{"message": "Authorization header missing"})
+			}
+			authHeaderParts := strings.Split(authHeader, " ")
+			if len(authHeaderParts) != 2 || authHeaderParts[0] != "Bearer" {
+				return c.Status(fiber.StatusUnauthorized).JSON(fiber.Map{"message": "Invalid Authorization header format"})
+			}
+
+			apiKey := authHeaderParts[1]
+			validApiKey := false
+			for _, key := range options.ApiKeys {
+				if apiKey == key {
+					validApiKey = true
+				}
+			}
+			if !validApiKey {
+				return c.Status(fiber.StatusUnauthorized).JSON(fiber.Map{"message": "Invalid API key"})
+			}
+		}
+		return c.Next()
+	}
+
 	if options.PreloadJSONModels != "" {
 		if err := localai.ApplyGalleryFromString(options.Loader.ModelPath, options.PreloadJSONModels, cm, options.Galleries); err != nil {
 			return nil, err
@@ -116,42 +143,42 @@ func App(opts ...options.AppOption) (*fiber.App, error) {
 	galleryService := localai.NewGalleryService(options.Loader.ModelPath)
 	galleryService.Start(options.Context, cm)

-	app.Get("/version", func(c *fiber.Ctx) error {
+	app.Get("/version", auth, func(c *fiber.Ctx) error {
 		return c.JSON(struct {
 			Version string `json:"version"`
 		}{Version: internal.PrintableVersion()})
 	})

-	app.Post("/models/apply", localai.ApplyModelGalleryEndpoint(options.Loader.ModelPath, cm, galleryService.C, options.Galleries))
-	app.Get("/models/available", localai.ListModelFromGalleryEndpoint(options.Galleries, options.Loader.ModelPath))
-	app.Get("/models/jobs/:uuid", localai.GetOpStatusEndpoint(galleryService))
+	app.Post("/models/apply", auth, localai.ApplyModelGalleryEndpoint(options.Loader.ModelPath, cm, galleryService.C, options.Galleries))
+	app.Get("/models/available", auth, localai.ListModelFromGalleryEndpoint(options.Galleries, options.Loader.ModelPath))
+	app.Get("/models/jobs/:uuid", auth, localai.GetOpStatusEndpoint(galleryService))

 	// openAI compatible API endpoint

 	// chat
-	app.Post("/v1/chat/completions", openai.ChatEndpoint(cm, options))
-	app.Post("/chat/completions", openai.ChatEndpoint(cm, options))
+	app.Post("/v1/chat/completions", auth, openai.ChatEndpoint(cm, options))
+	app.Post("/chat/completions", auth, openai.ChatEndpoint(cm, options))

 	// edit
-	app.Post("/v1/edits", openai.EditEndpoint(cm, options))
-	app.Post("/edits", openai.EditEndpoint(cm, options))
+	app.Post("/v1/edits", auth, openai.EditEndpoint(cm, options))
+	app.Post("/edits", auth, openai.EditEndpoint(cm, options))

 	// completion
-	app.Post("/v1/completions", openai.CompletionEndpoint(cm, options))
-	app.Post("/completions", openai.CompletionEndpoint(cm, options))
-	app.Post("/v1/engines/:model/completions", openai.CompletionEndpoint(cm, options))
+	app.Post("/v1/completions", auth, openai.CompletionEndpoint(cm, options))
+	app.Post("/completions", auth, openai.CompletionEndpoint(cm, options))
+	app.Post("/v1/engines/:model/completions", auth, openai.CompletionEndpoint(cm, options))

 	// embeddings
-	app.Post("/v1/embeddings", openai.EmbeddingsEndpoint(cm, options))
-	app.Post("/embeddings", openai.EmbeddingsEndpoint(cm, options))
-	app.Post("/v1/engines/:model/embeddings", openai.EmbeddingsEndpoint(cm, options))
+	app.Post("/v1/embeddings", auth, openai.EmbeddingsEndpoint(cm, options))
+	app.Post("/embeddings", auth, openai.EmbeddingsEndpoint(cm, options))
+	app.Post("/v1/engines/:model/embeddings", auth, openai.EmbeddingsEndpoint(cm, options))

 	// audio
-	app.Post("/v1/audio/transcriptions", openai.TranscriptEndpoint(cm, options))
-	app.Post("/tts", localai.TTSEndpoint(cm, options))
+	app.Post("/v1/audio/transcriptions", auth, openai.TranscriptEndpoint(cm, options))
+	app.Post("/tts", auth, localai.TTSEndpoint(cm, options))

 	// images
-	app.Post("/v1/images/generations", openai.ImageEndpoint(cm, options))
+	app.Post("/v1/images/generations", auth, openai.ImageEndpoint(cm, options))

 	if options.ImageDir != "" {
 		app.Static("/generated-images", options.ImageDir)
@@ -170,8 +197,8 @@ func App(opts ...options.AppOption) (*fiber.App, error) {
 	app.Get("/readyz", ok)

 	// models
-	app.Get("/v1/models", openai.ListModelsEndpoint(options.Loader, cm))
-	app.Get("/models", openai.ListModelsEndpoint(options.Loader, cm))
+	app.Get("/v1/models", auth, openai.ListModelsEndpoint(options.Loader, cm))
+	app.Get("/models", auth, openai.ListModelsEndpoint(options.Loader, cm))

 	// turn off any process that was started by GRPC if the context is canceled
 	go func() {
--- a/api/api_test.go
+++ b/api/api_test.go
@@ -470,6 +470,9 @@ var _ = Describe("API test", func() {

 			response := postModelApplyRequest("http://127.0.0.1:9090/models/apply", modelApplyRequest{
 				ID: "model-gallery@stablediffusion",
+				Overrides: map[string]interface{}{
+					"parameters": map[string]interface{}{"model": "stablediffusion_assets"},
+				},
 			})

 			Expect(response["uuid"]).ToNot(BeEmpty(), fmt.Sprint(response))
--- a/api/backend/embeddings.go
+++ b/api/backend/embeddings.go
@@ -23,10 +23,10 @@ func ModelEmbedding(s string, tokens []int, loader *model.ModelLoader, c config.
 	var err error

 	opts := []model.Option{
-		model.WithLoadGRPCLLMModelOpts(grpcOpts),
+		model.WithLoadGRPCLoadModelOpts(grpcOpts),
 		model.WithThreads(uint32(c.Threads)),
 		model.WithAssetDir(o.AssetsDestination),
-		model.WithModelFile(modelFile),
+		model.WithModel(modelFile),
 		model.WithContext(o.Context),
 	}

--- a/api/backend/image.go
+++ b/api/backend/image.go
@@ -1,7 +1,6 @@
 package backend

 import (
-	"fmt"
 	"sync"

 	config "github.com/go-skynet/LocalAI/api/config"
@@ -11,16 +10,18 @@ import (
 )

 func ImageGeneration(height, width, mode, step, seed int, positive_prompt, negative_prompt, dst string, loader *model.ModelLoader, c config.Config, o *options.Option) (func() error, error) {
-	if c.Backend != model.StableDiffusionBackend {
-		return nil, fmt.Errorf("endpoint only working with stablediffusion models")
-	}

 	opts := []model.Option{
 		model.WithBackendString(c.Backend),
 		model.WithAssetDir(o.AssetsDestination),
 		model.WithThreads(uint32(c.Threads)),
 		model.WithContext(o.Context),
-		model.WithModelFile(c.ImageGenerationAssets),
+		model.WithModel(c.Model),
+		model.WithLoadGRPCLoadModelOpts(&proto.ModelOptions{
+			CUDA:          c.Diffusers.CUDA,
+			SchedulerType: c.Diffusers.SchedulerType,
+			PipelineType:  c.Diffusers.PipelineType,
+		}),
 	}

 	for k, v := range o.ExternalGRPCBackends {
--- a/api/backend/llm.go
+++ b/api/backend/llm.go
@@ -24,10 +24,10 @@ func ModelInference(ctx context.Context, s string, loader *model.ModelLoader, c
 	var err error

 	opts := []model.Option{
-		model.WithLoadGRPCLLMModelOpts(grpcOpts),
+		model.WithLoadGRPCLoadModelOpts(grpcOpts),
 		model.WithThreads(uint32(c.Threads)), // some models uses this to allocate threads during startup
 		model.WithAssetDir(o.AssetsDestination),
-		model.WithModelFile(modelFile),
+		model.WithModel(modelFile),
 		model.WithContext(o.Context),
 	}

--- a/api/backend/options.go
+++ b/api/backend/options.go
@@ -15,9 +15,12 @@ func gRPCModelOpts(c config.Config) *pb.ModelOptions {
 		b = c.Batch
 	}
 	return &pb.ModelOptions{
-		ContextSize:   int32(c.ContextSize),
-		Seed:          int32(c.Seed),
-		NBatch:        int32(b),
+		ContextSize: int32(c.ContextSize),
+		Seed:        int32(c.Seed),
+		NBatch:      int32(b),
+		NGQA:        c.NGQA,
+
+		RMSNormEps:    c.RMSNormEps,
 		F16Memory:     c.F16,
 		MLock:         c.MMlock,
 		RopeFreqBase:  c.RopeFreqBase,
@@ -30,6 +33,11 @@ func gRPCModelOpts(c config.Config) *pb.ModelOptions {
 		MainGPU:       c.MainGPU,
 		Threads:       int32(c.Threads),
 		TensorSplit:   c.TensorSplit,
+		// AutoGPTQ
+		ModelBaseName:    c.AutoGPTQ.ModelBaseName,
+		Device:           c.AutoGPTQ.Device,
+		UseTriton:        c.AutoGPTQ.Triton,
+		UseFastTokenizer: c.AutoGPTQ.UseFastTokenizer,
 	}
 }

@@ -56,9 +64,9 @@ func gRPCPredictOpts(c config.Config, modelPath string) *pb.PredictOptions {
 		RopeFreqBase:        c.RopeFreqBase,
 		RopeFreqScale:       c.RopeFreqScale,
 		NegativePrompt:      c.NegativePrompt,
-		Mirostat:            int32(c.Mirostat),
-		MirostatETA:         float32(c.MirostatETA),
-		MirostatTAU:         float32(c.MirostatTAU),
+		Mirostat:            int32(c.LLMConfig.Mirostat),
+		MirostatETA:         float32(c.LLMConfig.MirostatETA),
+		MirostatTAU:         float32(c.LLMConfig.MirostatTAU),
 		Debug:               c.Debug,
 		StopPrompts:         c.StopWords,
 		Repeat:              int32(c.RepeatPenalty),
--- a/api/backend/transcript.go
+++ b/api/backend/transcript.go
@@ -15,7 +15,7 @@ import (
 func ModelTranscription(audio, language string, loader *model.ModelLoader, c config.Config, o *options.Option) (*api.Result, error) {
 	opts := []model.Option{
 		model.WithBackendString(model.WhisperBackend),
-		model.WithModelFile(c.Model),
+		model.WithModel(c.Model),
 		model.WithContext(o.Context),
 		model.WithThreads(uint32(c.Threads)),
 		model.WithAssetDir(o.AssetsDestination),
--- a/api/backend/tts.go
+++ b/api/backend/tts.go
@@ -28,10 +28,14 @@ func generateUniqueFileName(dir, baseName, ext string) string {
 	}
 }

-func ModelTTS(text, modelFile string, loader *model.ModelLoader, o *options.Option) (string, *proto.Result, error) {
+func ModelTTS(backend, text, modelFile string, loader *model.ModelLoader, o *options.Option) (string, *proto.Result, error) {
+	bb := backend
+	if bb == "" {
+		bb = model.PiperBackend
+	}
 	opts := []model.Option{
-		model.WithBackendString(model.PiperBackend),
-		model.WithModelFile(modelFile),
+		model.WithBackendString(bb),
+		model.WithModel(modelFile),
 		model.WithContext(o.Context),
 		model.WithAssetDir(o.AssetsDestination),
 	}
@@ -56,10 +60,13 @@ func ModelTTS(text, modelFile string, loader *model.ModelLoader, o *options.Opti
 	fileName := generateUniqueFileName(o.AudioDir, "piper", ".wav")
 	filePath := filepath.Join(o.AudioDir, fileName)

-	modelPath := filepath.Join(o.Loader.ModelPath, modelFile)
-
-	if err := utils.VerifyPath(modelPath, o.Loader.ModelPath); err != nil {
-		return "", nil, err
+	// If the model file is not empty, we pass it joined with the model path
+	modelPath := ""
+	if modelFile != "" {
+		modelPath = filepath.Join(o.Loader.ModelPath, modelFile)
+		if err := utils.VerifyPath(modelPath, o.Loader.ModelPath); err != nil {
+			return "", nil, err
+		}
 	}

 	res, err := piperModel.TTS(context.Background(), &proto.TTSRequest{
--- a/api/config/config.go
+++ b/api/config/config.go
@@ -13,44 +13,69 @@ import (

 type Config struct {
 	PredictionOptions `yaml:"parameters"`
-	Name              string            `yaml:"name"`
-	StopWords         []string          `yaml:"stopwords"`
-	Cutstrings        []string          `yaml:"cutstrings"`
-	TrimSpace         []string          `yaml:"trimspace"`
-	ContextSize       int               `yaml:"context_size"`
-	F16               bool              `yaml:"f16"`
-	NUMA              bool              `yaml:"numa"`
-	Threads           int               `yaml:"threads"`
-	Debug             bool              `yaml:"debug"`
-	Roles             map[string]string `yaml:"roles"`
-	Embeddings        bool              `yaml:"embeddings"`
-	Backend           string            `yaml:"backend"`
-	TemplateConfig    TemplateConfig    `yaml:"template"`
-	MirostatETA       float64           `yaml:"mirostat_eta"`
-	MirostatTAU       float64           `yaml:"mirostat_tau"`
-	Mirostat          int               `yaml:"mirostat"`
-	NGPULayers        int               `yaml:"gpu_layers"`
-	MMap              bool              `yaml:"mmap"`
-	MMlock            bool              `yaml:"mmlock"`
-	LowVRAM           bool              `yaml:"low_vram"`
+	Name              string `yaml:"name"`

-	TensorSplit           string `yaml:"tensor_split"`
-	MainGPU               string `yaml:"main_gpu"`
-	ImageGenerationAssets string `yaml:"asset_dir"`
+	F16            bool              `yaml:"f16"`
+	Threads        int               `yaml:"threads"`
+	Debug          bool              `yaml:"debug"`
+	Roles          map[string]string `yaml:"roles"`
+	Embeddings     bool              `yaml:"embeddings"`
+	Backend        string            `yaml:"backend"`
+	TemplateConfig TemplateConfig    `yaml:"template"`

-	PromptCachePath string `yaml:"prompt_cache_path"`
-	PromptCacheAll  bool   `yaml:"prompt_cache_all"`
-	PromptCacheRO   bool   `yaml:"prompt_cache_ro"`
-
-	Grammar string `yaml:"grammar"`
-
-	PromptStrings, InputStrings                []string
-	InputToken                                 [][]int
-	functionCallString, functionCallNameString string
+	PromptStrings, InputStrings                []string `yaml:"-"`
+	InputToken                                 [][]int  `yaml:"-"`
+	functionCallString, functionCallNameString string   `yaml:"-"`

 	FunctionsConfig Functions `yaml:"function"`

-	SystemPrompt string `yaml:"system_prompt"`
+	// LLM configs (GPT4ALL, Llama.cpp, ...)
+	LLMConfig `yaml:",inline"`
+
+	// AutoGPTQ specifics
+	AutoGPTQ AutoGPTQ `yaml:"autogptq"`
+
+	// Diffusers
+	Diffusers Diffusers `yaml:"diffusers"`
+
+	Step int `yaml:"step"`
+}
+
+type Diffusers struct {
+	PipelineType  string `yaml:"pipeline_type"`
+	SchedulerType string `yaml:"scheduler_type"`
+	CUDA          bool   `yaml:"cuda"`
+}
+
+type LLMConfig struct {
+	SystemPrompt    string   `yaml:"system_prompt"`
+	TensorSplit     string   `yaml:"tensor_split"`
+	MainGPU         string   `yaml:"main_gpu"`
+	RMSNormEps      float32  `yaml:"rms_norm_eps"`
+	NGQA            int32    `yaml:"ngqa"`
+	PromptCachePath string   `yaml:"prompt_cache_path"`
+	PromptCacheAll  bool     `yaml:"prompt_cache_all"`
+	PromptCacheRO   bool     `yaml:"prompt_cache_ro"`
+	MirostatETA     float64  `yaml:"mirostat_eta"`
+	MirostatTAU     float64  `yaml:"mirostat_tau"`
+	Mirostat        int      `yaml:"mirostat"`
+	NGPULayers      int      `yaml:"gpu_layers"`
+	MMap            bool     `yaml:"mmap"`
+	MMlock          bool     `yaml:"mmlock"`
+	LowVRAM         bool     `yaml:"low_vram"`
+	Grammar         string   `yaml:"grammar"`
+	StopWords       []string `yaml:"stopwords"`
+	Cutstrings      []string `yaml:"cutstrings"`
+	TrimSpace       []string `yaml:"trimspace"`
+	ContextSize     int      `yaml:"context_size"`
+	NUMA            bool     `yaml:"numa"`
+}
+
+type AutoGPTQ struct {
+	ModelBaseName    string `yaml:"model_base_name"`
+	Device           string `yaml:"device"`
+	Triton           bool   `yaml:"triton"`
+	UseFastTokenizer bool   `yaml:"use_fast_tokenizer"`
 }

 type Functions struct {
--- a/api/config/prediction.go
+++ b/api/config/prediction.go
@@ -39,4 +39,6 @@ type PredictionOptions struct {
 	RopeFreqBase        float32 `json:"rope_freq_base" yaml:"rope_freq_base"`
 	RopeFreqScale       float32 `json:"rope_freq_scale" yaml:"rope_freq_scale"`
 	NegativePromptScale float32 `json:"negative_prompt_scale" yaml:"negative_prompt_scale"`
+	// AutoGPTQ
+	UseFastTokenizer bool `json:"use_fast_tokenizer" yaml:"use_fast_tokenizer"`
 }
--- a/api/localai/localai.go
+++ b/api/localai/localai.go
@@ -9,8 +9,9 @@ import (
 )

 type TTSRequest struct {
-	Model string `json:"model" yaml:"model"`
-	Input string `json:"input" yaml:"input"`
+	Model   string `json:"model" yaml:"model"`
+	Input   string `json:"input" yaml:"input"`
+	Backend string `json:"backend" yaml:"backend"`
 }

 func TTSEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fiber.Ctx) error {
@@ -22,7 +23,7 @@ func TTSEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fiber.Ctx)
 			return err
 		}

-		filePath, _, err := backend.ModelTTS(input.Input, input.Model, o.Loader, o)
+		filePath, _, err := backend.ModelTTS(input.Backend, input.Input, input.Model, o.Loader, o)
 		if err != nil {
 			return err
 		}
--- a/api/openai/api.go
+++ b/api/openai/api.go
@@ -2,6 +2,7 @@ package openai

 import (
 	"context"
+
 	config "github.com/go-skynet/LocalAI/api/config"

 	"github.com/go-skynet/LocalAI/pkg/grammar"
@@ -106,4 +107,9 @@ type OpenAIRequest struct {
 	Grammar string `json:"grammar" yaml:"grammar"`

 	JSONFunctionGrammarObject *grammar.JSONFunctionStructure `json:"grammar_json_functions" yaml:"grammar_json_functions"`
+
+	Backend string `json:"backend" yaml:"backend"`
+
+	// AutoGPTQ
+	ModelBaseName string `json:"model_base_name" yaml:"model_base_name"`
 }
--- a/api/openai/chat.go
+++ b/api/openai/chat.go
@@ -12,6 +12,7 @@ import (
 	"github.com/go-skynet/LocalAI/api/options"
 	"github.com/go-skynet/LocalAI/pkg/grammar"
 	model "github.com/go-skynet/LocalAI/pkg/model"
+	"github.com/go-skynet/LocalAI/pkg/utils"
 	"github.com/gofiber/fiber/v2"
 	"github.com/rs/zerolog/log"
 	"github.com/valyala/fasthttp"
@@ -109,6 +110,7 @@ func ChatEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fiber.Ctx)

 		var predInput string

+		suppressConfigSystemPrompt := false
 		mess := []string{}
 		for messageIndex, i := range input.Messages {
 			var content string
@@ -146,7 +148,7 @@ func ChatEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fiber.Ctx)
 					content = templatedChatMessage
 				}
 			}
-			// If this model doesn't have such a template, or if
+			// If this model doesn't have such a template, or if that template fails to return a value, template at the message level.
 			if content == "" {
 				if r != "" {
 					if contentExists {
@@ -177,6 +179,10 @@ func ChatEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fiber.Ctx)
 						}
 					}
 				}
+				// Special Handling: System. We care if it was printed at all, not the r branch, so check seperately
+				if contentExists && role == "system" {
+					suppressConfigSystemPrompt = true
+				}
 			}

 			mess = append(mess, content)
@@ -207,8 +213,10 @@ func ChatEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fiber.Ctx)

 		// A model can have a "file.bin.tmpl" file associated with a prompt template prefix
 		templatedInput, err := o.Loader.EvaluateTemplateForPrompt(model.ChatPromptTemplate, templateFile, model.PromptTemplateData{
-			Input:     predInput,
-			Functions: funcs,
+			SystemPrompt:         config.SystemPrompt,
+			SuppressSystemPrompt: suppressConfigSystemPrompt,
+			Input:                predInput,
+			Functions:            funcs,
 		})
 		if err == nil {
 			predInput = templatedInput
@@ -267,6 +275,8 @@ func ChatEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fiber.Ctx)
 			if processFunctions {
 				// As we have to change the result before processing, we can't stream the answer (yet?)
 				ss := map[string]interface{}{}
+				// This prevent newlines to break JSON parsing for clients
+				s = utils.EscapeNewLines(s)
 				json.Unmarshal([]byte(s), &ss)
 				log.Debug().Msgf("Function return: %s %+v", s, ss)

--- a/api/openai/completion.go
+++ b/api/openai/completion.go
@@ -123,7 +123,8 @@ func CompletionEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fibe
 		for k, i := range config.PromptStrings {
 			// A model can have a "file.bin.tmpl" file associated with a prompt template prefix
 			templatedInput, err := o.Loader.EvaluateTemplateForPrompt(model.CompletionPromptTemplate, templateFile, model.PromptTemplateData{
-				Input: i,
+				SystemPrompt: config.SystemPrompt,
+				Input:        i,
 			})
 			if err == nil {
 				i = templatedInput
--- a/api/openai/edit.go
+++ b/api/openai/edit.go
@@ -35,8 +35,9 @@ func EditEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fiber.Ctx)
 		for _, i := range config.InputStrings {
 			// A model can have a "file.bin.tmpl" file associated with a prompt template prefix
 			templatedInput, err := o.Loader.EvaluateTemplateForPrompt(model.EditPromptTemplate, templateFile, model.PromptTemplateData{
-				Input:       i,
-				Instruction: input.Instruction,
+				Input:        i,
+				Instruction:  input.Instruction,
+				SystemPrompt: config.SystemPrompt,
 			})
 			if err == nil {
 				i = templatedInput
--- a/api/openai/image.go
+++ b/api/openai/image.go
@@ -89,7 +89,10 @@ func ImageEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fiber.Ctx
 				}

 				mode := 0
-				step := 15
+				step := config.Step
+				if step == 0 {
+					step = 15
+				}

 				if input.Mode != 0 {
 					mode = input.Mode
--- a/api/openai/request.go
+++ b/api/openai/request.go
@@ -71,10 +71,22 @@ func updateConfig(config *config.Config, input *OpenAIRequest) {
 		config.TopP = input.TopP
 	}

+	if input.Backend != "" {
+		config.Backend = input.Backend
+	}
+
+	if input.ModelBaseName != "" {
+		config.AutoGPTQ.ModelBaseName = input.ModelBaseName
+	}
+
 	if input.NegativePromptScale != 0 {
 		config.NegativePromptScale = input.NegativePromptScale
 	}

+	if input.UseFastTokenizer {
+		config.UseFastTokenizer = input.UseFastTokenizer
+	}
+
 	if input.NegativePrompt != "" {
 		config.NegativePrompt = input.NegativePrompt
 	}
@@ -137,15 +149,15 @@ func updateConfig(config *config.Config, input *OpenAIRequest) {
 	}

 	if input.Mirostat != 0 {
-		config.Mirostat = input.Mirostat
+		config.LLMConfig.Mirostat = input.Mirostat
 	}

 	if input.MirostatETA != 0 {
-		config.MirostatETA = input.MirostatETA
+		config.LLMConfig.MirostatETA = input.MirostatETA
 	}

 	if input.MirostatTAU != 0 {
-		config.MirostatTAU = input.MirostatTAU
+		config.LLMConfig.MirostatTAU = input.MirostatTAU
 	}

 	if input.TypicalP != 0 {
--- a/api/options/options.go
+++ b/api/options/options.go
@@ -23,6 +23,7 @@ type Option struct {
 	PreloadJSONModels                   string
 	PreloadModelsFromPath               string
 	CORSAllowOrigins                    string
+	ApiKeys                             []string

 	Galleries []gallery.Gallery

@@ -184,3 +185,9 @@ func WithImageDir(imageDir string) AppOption {
 		o.ImageDir = imageDir
 	}
 }
+
+func WithApiKeys(apiKeys []string) AppOption {
+	return func(o *Option) {
+		o.ApiKeys = apiKeys
+	}
+}
--- a/examples/README.md
+++ b/examples/README.md
@@ -1,7 +1,16 @@
 # Examples

+| [ChatGPT OSS alternative](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui)                                                                                                                | [Image generation](https://localai.io/api-endpoints/index.html#image-generation)                                                                                                              |
+|------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------|
+|  ![Screenshot from 2023-04-26 23-59-55](https://user-images.githubusercontent.com/2420543/234715439-98d12e03-d3ce-4f94-ab54-2b256808e05e.png)            | ![b6441997879](https://github.com/go-skynet/LocalAI/assets/2420543/d50af51c-51b7-4f39-b6c2-bf04c403894c)                  |
+
+|                                                                    [Telegram bot](https://github.com/go-skynet/LocalAI/tree/master/examples/telegram-bot)   | [Flowise](https://github.com/go-skynet/LocalAI/tree/master/examples/flowise)                                                                                                                     |
+|------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------|
+![Screenshot from 2023-06-09 00-36-26](https://github.com/go-skynet/LocalAI/assets/2420543/e98b4305-fa2d-41cf-9d2f-1bb2d75ca902)   |  ![Screenshot from 2023-05-30 18-01-03](https://github.com/go-skynet/LocalAI/assets/2420543/02458782-0549-4131-971c-95ee56ec1af8)|    |
+
 Here is a list of projects that can easily be integrated with the LocalAI backend. 

+
 ### Projects

 ### AutoGPT
--- a/extra/grpc/autogptq/autogptq.py
+++ b/extra/grpc/autogptq/autogptq.py
@@ -0,0 +1,109 @@
+#!/usr/bin/env python3
+import grpc
+from concurrent import futures
+import time
+import backend_pb2
+import backend_pb2_grpc
+import argparse
+import signal
+import sys
+import os
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+from pathlib import Path
+from transformers import AutoTokenizer
+from transformers import TextGenerationPipeline
+
+_ONE_DAY_IN_SECONDS = 60 * 60 * 24
+
+# Implement the BackendServicer class with the service methods
+class BackendServicer(backend_pb2_grpc.BackendServicer):
+    def Health(self, request, context):
+        return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
+    def LoadModel(self, request, context):
+        try:
+            device = "cuda:0"
+            if request.Device != "":
+                device = request.Device
+
+            tokenizer = AutoTokenizer.from_pretrained(request.Model, use_fast=request.UseFastTokenizer)
+
+            model = AutoGPTQForCausalLM.from_quantized(request.Model,
+                    model_basename=request.ModelBaseName,
+                    use_safetensors=True,
+                    trust_remote_code=True,
+                    device=device,
+                    use_triton=request.UseTriton,
+                    quantize_config=None)
+            
+            self.model = model
+            self.tokenizer = tokenizer
+        except Exception as err:
+            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        return backend_pb2.Result(message="Model loaded successfully", success=True)
+
+    def Predict(self, request, context):
+        penalty = 1.0
+        if request.Penalty != 0.0:
+            penalty = request.Penalty
+        tokens = 512
+        if request.Tokens != 0:
+            tokens = request.Tokens
+        top_p = 0.95
+        if request.TopP != 0.0:
+            top_p = request.TopP
+
+        # Implement Predict RPC
+        pipeline = TextGenerationPipeline(
+            model=self.model, 
+            tokenizer=self.tokenizer,
+            max_new_tokens=tokens,
+            temperature=request.Temperature,
+            top_p=top_p,
+            repetition_penalty=penalty,
+            )
+        t = pipeline(request.Prompt)[0]["generated_text"]
+        # Remove prompt from response if present
+        if request.Prompt in t:
+            t = t.replace(request.Prompt, "")
+
+        return backend_pb2.Result(message=bytes(t, encoding='utf-8'))
+
+    def PredictStream(self, request, context):
+        # Implement PredictStream RPC
+        #for reply in some_data_generator():
+        #    yield reply
+        # Not implemented yet
+        return self.Predict(request, context)
+
+
+def serve(address):
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
+    backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
+    server.add_insecure_port(address)
+    server.start()
+    print("Server started. Listening on: " + address, file=sys.stderr)
+
+    # Define the signal handler function
+    def signal_handler(sig, frame):
+        print("Received termination signal. Shutting down...")
+        server.stop(0)
+        sys.exit(0)
+
+    # Set the signal handlers for SIGINT and SIGTERM
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTERM, signal_handler)
+
+    try:
+        while True:
+            time.sleep(_ONE_DAY_IN_SECONDS)
+    except KeyboardInterrupt:
+        server.stop(0)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run the gRPC server.")
+    parser.add_argument(
+        "--addr", default="localhost:50051", help="The address to bind the server to."
+    )
+    args = parser.parse_args()
+
+    serve(args.addr)
--- a/extra/grpc/autogptq/backend_pb2.py
+++ b/extra/grpc/autogptq/backend_pb2.py
@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: backend.proto
+"""Generated protocol buffer code."""
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf.internal import builder as _builder
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\rbackend.proto\x12\x07\x62\x61\x63kend\"\x0f\n\rHealthMessage\"\x86\x06\n\x0ePredictOptions\x12\x0e\n\x06Prompt\x18\x01 \x01(\t\x12\x0c\n\x04Seed\x18\x02 \x01(\x05\x12\x0f\n\x07Threads\x18\x03 \x01(\x05\x12\x0e\n\x06Tokens\x18\x04 \x01(\x05\x12\x0c\n\x04TopK\x18\x05 \x01(\x05\x12\x0e\n\x06Repeat\x18\x06 \x01(\x05\x12\r\n\x05\x42\x61tch\x18\x07 \x01(\x05\x12\r\n\x05NKeep\x18\x08 \x01(\x05\x12\x13\n\x0bTemperature\x18\t \x01(\x02\x12\x0f\n\x07Penalty\x18\n \x01(\x02\x12\r\n\x05\x46\x31\x36KV\x18\x0b \x01(\x08\x12\x11\n\tDebugMode\x18\x0c \x01(\x08\x12\x13\n\x0bStopPrompts\x18\r \x03(\t\x12\x11\n\tIgnoreEOS\x18\x0e \x01(\x08\x12\x19\n\x11TailFreeSamplingZ\x18\x0f \x01(\x02\x12\x10\n\x08TypicalP\x18\x10 \x01(\x02\x12\x18\n\x10\x46requencyPenalty\x18\x11 \x01(\x02\x12\x17\n\x0fPresencePenalty\x18\x12 \x01(\x02\x12\x10\n\x08Mirostat\x18\x13 \x01(\x05\x12\x13\n\x0bMirostatETA\x18\x14 \x01(\x02\x12\x13\n\x0bMirostatTAU\x18\x15 \x01(\x02\x12\x12\n\nPenalizeNL\x18\x16 \x01(\x08\x12\x11\n\tLogitBias\x18\x17 \x01(\t\x12\r\n\x05MLock\x18\x19 \x01(\x08\x12\x0c\n\x04MMap\x18\x1a \x01(\x08\x12\x16\n\x0ePromptCacheAll\x18\x1b \x01(\x08\x12\x15\n\rPromptCacheRO\x18\x1c \x01(\x08\x12\x0f\n\x07Grammar\x18\x1d \x01(\t\x12\x0f\n\x07MainGPU\x18\x1e \x01(\t\x12\x13\n\x0bTensorSplit\x18\x1f \x01(\t\x12\x0c\n\x04TopP\x18  \x01(\x02\x12\x17\n\x0fPromptCachePath\x18! \x01(\t\x12\r\n\x05\x44\x65\x62ug\x18\" \x01(\x08\x12\x17\n\x0f\x45mbeddingTokens\x18# \x03(\x05\x12\x12\n\nEmbeddings\x18$ \x01(\t\x12\x14\n\x0cRopeFreqBase\x18% \x01(\x02\x12\x15\n\rRopeFreqScale\x18& \x01(\x02\x12\x1b\n\x13NegativePromptScale\x18\' \x01(\x02\x12\x16\n\x0eNegativePrompt\x18( \x01(\t\"\x18\n\x05Reply\x12\x0f\n\x07message\x18\x01 \x01(\x0c\"\x9d\x04\n\x0cModelOptions\x12\r\n\x05Model\x18\x01 \x01(\t\x12\x13\n\x0b\x43ontextSize\x18\x02 \x01(\x05\x12\x0c\n\x04Seed\x18\x03 \x01(\x05\x12\x0e\n\x06NBatch\x18\x04 \x01(\x05\x12\x11\n\tF16Memory\x18\x05 \x01(\x08\x12\r\n\x05MLock\x18\x06 \x01(\x08\x12\x0c\n\x04MMap\x18\x07 \x01(\x08\x12\x11\n\tVocabOnly\x18\x08 \x01(\x08\x12\x0f\n\x07LowVRAM\x18\t \x01(\x08\x12\x12\n\nEmbeddings\x18\n \x01(\x08\x12\x0c\n\x04NUMA\x18\x0b \x01(\x08\x12\x12\n\nNGPULayers\x18\x0c \x01(\x05\x12\x0f\n\x07MainGPU\x18\r \x01(\t\x12\x13\n\x0bTensorSplit\x18\x0e \x01(\t\x12\x0f\n\x07Threads\x18\x0f \x01(\x05\x12\x19\n\x11LibrarySearchPath\x18\x10 \x01(\t\x12\x14\n\x0cRopeFreqBase\x18\x11 \x01(\x02\x12\x15\n\rRopeFreqScale\x18\x12 \x01(\x02\x12\x12\n\nRMSNormEps\x18\x13 \x01(\x02\x12\x0c\n\x04NGQA\x18\x14 \x01(\x05\x12\x11\n\tModelFile\x18\x15 \x01(\t\x12\x0e\n\x06\x44\x65vice\x18\x16 \x01(\t\x12\x11\n\tUseTriton\x18\x17 \x01(\x08\x12\x15\n\rModelBaseName\x18\x18 \x01(\t\x12\x18\n\x10UseFastTokenizer\x18\x19 \x01(\x08\x12\x14\n\x0cPipelineType\x18\x1a \x01(\t\x12\x15\n\rSchedulerType\x18\x1b \x01(\t\x12\x0c\n\x04\x43UDA\x18\x1c \x01(\x08\"*\n\x06Result\x12\x0f\n\x07message\x18\x01 \x01(\t\x12\x0f\n\x07success\x18\x02 \x01(\x08\"%\n\x0f\x45mbeddingResult\x12\x12\n\nembeddings\x18\x01 \x03(\x02\"C\n\x11TranscriptRequest\x12\x0b\n\x03\x64st\x18\x02 \x01(\t\x12\x10\n\x08language\x18\x03 \x01(\t\x12\x0f\n\x07threads\x18\x04 \x01(\r\"N\n\x10TranscriptResult\x12,\n\x08segments\x18\x01 \x03(\x0b\x32\x1a.backend.TranscriptSegment\x12\x0c\n\x04text\x18\x02 \x01(\t\"Y\n\x11TranscriptSegment\x12\n\n\x02id\x18\x01 \x01(\x05\x12\r\n\x05start\x18\x02 \x01(\x03\x12\x0b\n\x03\x65nd\x18\x03 \x01(\x03\x12\x0c\n\x04text\x18\x04 \x01(\t\x12\x0e\n\x06tokens\x18\x05 \x03(\x05\"\x9e\x01\n\x14GenerateImageRequest\x12\x0e\n\x06height\x18\x01 \x01(\x05\x12\r\n\x05width\x18\x02 \x01(\x05\x12\x0c\n\x04mode\x18\x03 \x01(\x05\x12\x0c\n\x04step\x18\x04 \x01(\x05\x12\x0c\n\x04seed\x18\x05 \x01(\x05\x12\x17\n\x0fpositive_prompt\x18\x06 \x01(\t\x12\x17\n\x0fnegative_prompt\x18\x07 \x01(\t\x12\x0b\n\x03\x64st\x18\x08 \x01(\t\"6\n\nTTSRequest\x12\x0c\n\x04text\x18\x01 \x01(\t\x12\r\n\x05model\x18\x02 \x01(\t\x12\x0b\n\x03\x64st\x18\x03 \x01(\t2\xeb\x03\n\x07\x42\x61\x63kend\x12\x32\n\x06Health\x12\x16.backend.HealthMessage\x1a\x0e.backend.Reply\"\x00\x12\x34\n\x07Predict\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x12\x35\n\tLoadModel\x12\x15.backend.ModelOptions\x1a\x0f.backend.Result\"\x00\x12<\n\rPredictStream\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x30\x01\x12@\n\tEmbedding\x12\x17.backend.PredictOptions\x1a\x18.backend.EmbeddingResult\"\x00\x12\x41\n\rGenerateImage\x12\x1d.backend.GenerateImageRequest\x1a\x0f.backend.Result\"\x00\x12M\n\x12\x41udioTranscription\x12\x1a.backend.TranscriptRequest\x1a\x19.backend.TranscriptResult\"\x00\x12-\n\x03TTS\x12\x13.backend.TTSRequest\x1a\x0f.backend.Result\"\x00\x42Z\n\x19io.skynet.localai.backendB\x0eLocalAIBackendP\x01Z+github.com/go-skynet/LocalAI/pkg/grpc/protob\x06proto3')
+
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'backend_pb2', _globals)
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\031io.skynet.localai.backendB\016LocalAIBackendP\001Z+github.com/go-skynet/LocalAI/pkg/grpc/proto'
+  _globals['_HEALTHMESSAGE']._serialized_start=26
+  _globals['_HEALTHMESSAGE']._serialized_end=41
+  _globals['_PREDICTOPTIONS']._serialized_start=44
+  _globals['_PREDICTOPTIONS']._serialized_end=818
+  _globals['_REPLY']._serialized_start=820
+  _globals['_REPLY']._serialized_end=844
+  _globals['_MODELOPTIONS']._serialized_start=847
+  _globals['_MODELOPTIONS']._serialized_end=1388
+  _globals['_RESULT']._serialized_start=1390
+  _globals['_RESULT']._serialized_end=1432
+  _globals['_EMBEDDINGRESULT']._serialized_start=1434
+  _globals['_EMBEDDINGRESULT']._serialized_end=1471
+  _globals['_TRANSCRIPTREQUEST']._serialized_start=1473
+  _globals['_TRANSCRIPTREQUEST']._serialized_end=1540
+  _globals['_TRANSCRIPTRESULT']._serialized_start=1542
+  _globals['_TRANSCRIPTRESULT']._serialized_end=1620
+  _globals['_TRANSCRIPTSEGMENT']._serialized_start=1622
+  _globals['_TRANSCRIPTSEGMENT']._serialized_end=1711
+  _globals['_GENERATEIMAGEREQUEST']._serialized_start=1714
+  _globals['_GENERATEIMAGEREQUEST']._serialized_end=1872
+  _globals['_TTSREQUEST']._serialized_start=1874
+  _globals['_TTSREQUEST']._serialized_end=1928
+  _globals['_BACKEND']._serialized_start=1931
+  _globals['_BACKEND']._serialized_end=2422
+# @@protoc_insertion_point(module_scope)
--- a/extra/grpc/autogptq/backend_pb2_grpc.py
+++ b/extra/grpc/autogptq/backend_pb2_grpc.py
@@ -0,0 +1,297 @@
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+"""Client and server classes corresponding to protobuf-defined services."""
+import grpc
+
+import backend_pb2 as backend__pb2
+
+
+class BackendStub(object):
+    """Missing associated documentation comment in .proto file."""
+
+    def __init__(self, channel):
+        """Constructor.
+
+        Args:
+            channel: A grpc.Channel.
+        """
+        self.Health = channel.unary_unary(
+                '/backend.Backend/Health',
+                request_serializer=backend__pb2.HealthMessage.SerializeToString,
+                response_deserializer=backend__pb2.Reply.FromString,
+                )
+        self.Predict = channel.unary_unary(
+                '/backend.Backend/Predict',
+                request_serializer=backend__pb2.PredictOptions.SerializeToString,
+                response_deserializer=backend__pb2.Reply.FromString,
+                )
+        self.LoadModel = channel.unary_unary(
+                '/backend.Backend/LoadModel',
+                request_serializer=backend__pb2.ModelOptions.SerializeToString,
+                response_deserializer=backend__pb2.Result.FromString,
+                )
+        self.PredictStream = channel.unary_stream(
+                '/backend.Backend/PredictStream',
+                request_serializer=backend__pb2.PredictOptions.SerializeToString,
+                response_deserializer=backend__pb2.Reply.FromString,
+                )
+        self.Embedding = channel.unary_unary(
+                '/backend.Backend/Embedding',
+                request_serializer=backend__pb2.PredictOptions.SerializeToString,
+                response_deserializer=backend__pb2.EmbeddingResult.FromString,
+                )
+        self.GenerateImage = channel.unary_unary(
+                '/backend.Backend/GenerateImage',
+                request_serializer=backend__pb2.GenerateImageRequest.SerializeToString,
+                response_deserializer=backend__pb2.Result.FromString,
+                )
+        self.AudioTranscription = channel.unary_unary(
+                '/backend.Backend/AudioTranscription',
+                request_serializer=backend__pb2.TranscriptRequest.SerializeToString,
+                response_deserializer=backend__pb2.TranscriptResult.FromString,
+                )
+        self.TTS = channel.unary_unary(
+                '/backend.Backend/TTS',
+                request_serializer=backend__pb2.TTSRequest.SerializeToString,
+                response_deserializer=backend__pb2.Result.FromString,
+                )
+
+
+class BackendServicer(object):
+    """Missing associated documentation comment in .proto file."""
+
+    def Health(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def Predict(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def LoadModel(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def PredictStream(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def Embedding(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def GenerateImage(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def AudioTranscription(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def TTS(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+
+def add_BackendServicer_to_server(servicer, server):
+    rpc_method_handlers = {
+            'Health': grpc.unary_unary_rpc_method_handler(
+                    servicer.Health,
+                    request_deserializer=backend__pb2.HealthMessage.FromString,
+                    response_serializer=backend__pb2.Reply.SerializeToString,
+            ),
+            'Predict': grpc.unary_unary_rpc_method_handler(
+                    servicer.Predict,
+                    request_deserializer=backend__pb2.PredictOptions.FromString,
+                    response_serializer=backend__pb2.Reply.SerializeToString,
+            ),
+            'LoadModel': grpc.unary_unary_rpc_method_handler(
+                    servicer.LoadModel,
+                    request_deserializer=backend__pb2.ModelOptions.FromString,
+                    response_serializer=backend__pb2.Result.SerializeToString,
+            ),
+            'PredictStream': grpc.unary_stream_rpc_method_handler(
+                    servicer.PredictStream,
+                    request_deserializer=backend__pb2.PredictOptions.FromString,
+                    response_serializer=backend__pb2.Reply.SerializeToString,
+            ),
+            'Embedding': grpc.unary_unary_rpc_method_handler(
+                    servicer.Embedding,
+                    request_deserializer=backend__pb2.PredictOptions.FromString,
+                    response_serializer=backend__pb2.EmbeddingResult.SerializeToString,
+            ),
+            'GenerateImage': grpc.unary_unary_rpc_method_handler(
+                    servicer.GenerateImage,
+                    request_deserializer=backend__pb2.GenerateImageRequest.FromString,
+                    response_serializer=backend__pb2.Result.SerializeToString,
+            ),
+            'AudioTranscription': grpc.unary_unary_rpc_method_handler(
+                    servicer.AudioTranscription,
+                    request_deserializer=backend__pb2.TranscriptRequest.FromString,
+                    response_serializer=backend__pb2.TranscriptResult.SerializeToString,
+            ),
+            'TTS': grpc.unary_unary_rpc_method_handler(
+                    servicer.TTS,
+                    request_deserializer=backend__pb2.TTSRequest.FromString,
+                    response_serializer=backend__pb2.Result.SerializeToString,
+            ),
+    }
+    generic_handler = grpc.method_handlers_generic_handler(
+            'backend.Backend', rpc_method_handlers)
+    server.add_generic_rpc_handlers((generic_handler,))
+
+
+ # This class is part of an EXPERIMENTAL API.
+class Backend(object):
+    """Missing associated documentation comment in .proto file."""
+
+    @staticmethod
+    def Health(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/Health',
+            backend__pb2.HealthMessage.SerializeToString,
+            backend__pb2.Reply.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def Predict(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/Predict',
+            backend__pb2.PredictOptions.SerializeToString,
+            backend__pb2.Reply.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def LoadModel(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/LoadModel',
+            backend__pb2.ModelOptions.SerializeToString,
+            backend__pb2.Result.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def PredictStream(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_stream(request, target, '/backend.Backend/PredictStream',
+            backend__pb2.PredictOptions.SerializeToString,
+            backend__pb2.Reply.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def Embedding(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/Embedding',
+            backend__pb2.PredictOptions.SerializeToString,
+            backend__pb2.EmbeddingResult.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def GenerateImage(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/GenerateImage',
+            backend__pb2.GenerateImageRequest.SerializeToString,
+            backend__pb2.Result.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def AudioTranscription(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/AudioTranscription',
+            backend__pb2.TranscriptRequest.SerializeToString,
+            backend__pb2.TranscriptResult.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def TTS(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/TTS',
+            backend__pb2.TTSRequest.SerializeToString,
+            backend__pb2.Result.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
--- a/extra/grpc/bark/backend_pb2.py
+++ b/extra/grpc/bark/backend_pb2.py
@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: backend.proto
+"""Generated protocol buffer code."""
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf.internal import builder as _builder
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\rbackend.proto\x12\x07\x62\x61\x63kend\"\x0f\n\rHealthMessage\"\x86\x06\n\x0ePredictOptions\x12\x0e\n\x06Prompt\x18\x01 \x01(\t\x12\x0c\n\x04Seed\x18\x02 \x01(\x05\x12\x0f\n\x07Threads\x18\x03 \x01(\x05\x12\x0e\n\x06Tokens\x18\x04 \x01(\x05\x12\x0c\n\x04TopK\x18\x05 \x01(\x05\x12\x0e\n\x06Repeat\x18\x06 \x01(\x05\x12\r\n\x05\x42\x61tch\x18\x07 \x01(\x05\x12\r\n\x05NKeep\x18\x08 \x01(\x05\x12\x13\n\x0bTemperature\x18\t \x01(\x02\x12\x0f\n\x07Penalty\x18\n \x01(\x02\x12\r\n\x05\x46\x31\x36KV\x18\x0b \x01(\x08\x12\x11\n\tDebugMode\x18\x0c \x01(\x08\x12\x13\n\x0bStopPrompts\x18\r \x03(\t\x12\x11\n\tIgnoreEOS\x18\x0e \x01(\x08\x12\x19\n\x11TailFreeSamplingZ\x18\x0f \x01(\x02\x12\x10\n\x08TypicalP\x18\x10 \x01(\x02\x12\x18\n\x10\x46requencyPenalty\x18\x11 \x01(\x02\x12\x17\n\x0fPresencePenalty\x18\x12 \x01(\x02\x12\x10\n\x08Mirostat\x18\x13 \x01(\x05\x12\x13\n\x0bMirostatETA\x18\x14 \x01(\x02\x12\x13\n\x0bMirostatTAU\x18\x15 \x01(\x02\x12\x12\n\nPenalizeNL\x18\x16 \x01(\x08\x12\x11\n\tLogitBias\x18\x17 \x01(\t\x12\r\n\x05MLock\x18\x19 \x01(\x08\x12\x0c\n\x04MMap\x18\x1a \x01(\x08\x12\x16\n\x0ePromptCacheAll\x18\x1b \x01(\x08\x12\x15\n\rPromptCacheRO\x18\x1c \x01(\x08\x12\x0f\n\x07Grammar\x18\x1d \x01(\t\x12\x0f\n\x07MainGPU\x18\x1e \x01(\t\x12\x13\n\x0bTensorSplit\x18\x1f \x01(\t\x12\x0c\n\x04TopP\x18  \x01(\x02\x12\x17\n\x0fPromptCachePath\x18! \x01(\t\x12\r\n\x05\x44\x65\x62ug\x18\" \x01(\x08\x12\x17\n\x0f\x45mbeddingTokens\x18# \x03(\x05\x12\x12\n\nEmbeddings\x18$ \x01(\t\x12\x14\n\x0cRopeFreqBase\x18% \x01(\x02\x12\x15\n\rRopeFreqScale\x18& \x01(\x02\x12\x1b\n\x13NegativePromptScale\x18\' \x01(\x02\x12\x16\n\x0eNegativePrompt\x18( \x01(\t\"\x18\n\x05Reply\x12\x0f\n\x07message\x18\x01 \x01(\x0c\"\x9d\x04\n\x0cModelOptions\x12\r\n\x05Model\x18\x01 \x01(\t\x12\x13\n\x0b\x43ontextSize\x18\x02 \x01(\x05\x12\x0c\n\x04Seed\x18\x03 \x01(\x05\x12\x0e\n\x06NBatch\x18\x04 \x01(\x05\x12\x11\n\tF16Memory\x18\x05 \x01(\x08\x12\r\n\x05MLock\x18\x06 \x01(\x08\x12\x0c\n\x04MMap\x18\x07 \x01(\x08\x12\x11\n\tVocabOnly\x18\x08 \x01(\x08\x12\x0f\n\x07LowVRAM\x18\t \x01(\x08\x12\x12\n\nEmbeddings\x18\n \x01(\x08\x12\x0c\n\x04NUMA\x18\x0b \x01(\x08\x12\x12\n\nNGPULayers\x18\x0c \x01(\x05\x12\x0f\n\x07MainGPU\x18\r \x01(\t\x12\x13\n\x0bTensorSplit\x18\x0e \x01(\t\x12\x0f\n\x07Threads\x18\x0f \x01(\x05\x12\x19\n\x11LibrarySearchPath\x18\x10 \x01(\t\x12\x14\n\x0cRopeFreqBase\x18\x11 \x01(\x02\x12\x15\n\rRopeFreqScale\x18\x12 \x01(\x02\x12\x12\n\nRMSNormEps\x18\x13 \x01(\x02\x12\x0c\n\x04NGQA\x18\x14 \x01(\x05\x12\x11\n\tModelFile\x18\x15 \x01(\t\x12\x0e\n\x06\x44\x65vice\x18\x16 \x01(\t\x12\x11\n\tUseTriton\x18\x17 \x01(\x08\x12\x15\n\rModelBaseName\x18\x18 \x01(\t\x12\x18\n\x10UseFastTokenizer\x18\x19 \x01(\x08\x12\x14\n\x0cPipelineType\x18\x1a \x01(\t\x12\x15\n\rSchedulerType\x18\x1b \x01(\t\x12\x0c\n\x04\x43UDA\x18\x1c \x01(\x08\"*\n\x06Result\x12\x0f\n\x07message\x18\x01 \x01(\t\x12\x0f\n\x07success\x18\x02 \x01(\x08\"%\n\x0f\x45mbeddingResult\x12\x12\n\nembeddings\x18\x01 \x03(\x02\"C\n\x11TranscriptRequest\x12\x0b\n\x03\x64st\x18\x02 \x01(\t\x12\x10\n\x08language\x18\x03 \x01(\t\x12\x0f\n\x07threads\x18\x04 \x01(\r\"N\n\x10TranscriptResult\x12,\n\x08segments\x18\x01 \x03(\x0b\x32\x1a.backend.TranscriptSegment\x12\x0c\n\x04text\x18\x02 \x01(\t\"Y\n\x11TranscriptSegment\x12\n\n\x02id\x18\x01 \x01(\x05\x12\r\n\x05start\x18\x02 \x01(\x03\x12\x0b\n\x03\x65nd\x18\x03 \x01(\x03\x12\x0c\n\x04text\x18\x04 \x01(\t\x12\x0e\n\x06tokens\x18\x05 \x03(\x05\"\x9e\x01\n\x14GenerateImageRequest\x12\x0e\n\x06height\x18\x01 \x01(\x05\x12\r\n\x05width\x18\x02 \x01(\x05\x12\x0c\n\x04mode\x18\x03 \x01(\x05\x12\x0c\n\x04step\x18\x04 \x01(\x05\x12\x0c\n\x04seed\x18\x05 \x01(\x05\x12\x17\n\x0fpositive_prompt\x18\x06 \x01(\t\x12\x17\n\x0fnegative_prompt\x18\x07 \x01(\t\x12\x0b\n\x03\x64st\x18\x08 \x01(\t\"6\n\nTTSRequest\x12\x0c\n\x04text\x18\x01 \x01(\t\x12\r\n\x05model\x18\x02 \x01(\t\x12\x0b\n\x03\x64st\x18\x03 \x01(\t2\xeb\x03\n\x07\x42\x61\x63kend\x12\x32\n\x06Health\x12\x16.backend.HealthMessage\x1a\x0e.backend.Reply\"\x00\x12\x34\n\x07Predict\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x12\x35\n\tLoadModel\x12\x15.backend.ModelOptions\x1a\x0f.backend.Result\"\x00\x12<\n\rPredictStream\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x30\x01\x12@\n\tEmbedding\x12\x17.backend.PredictOptions\x1a\x18.backend.EmbeddingResult\"\x00\x12\x41\n\rGenerateImage\x12\x1d.backend.GenerateImageRequest\x1a\x0f.backend.Result\"\x00\x12M\n\x12\x41udioTranscription\x12\x1a.backend.TranscriptRequest\x1a\x19.backend.TranscriptResult\"\x00\x12-\n\x03TTS\x12\x13.backend.TTSRequest\x1a\x0f.backend.Result\"\x00\x42Z\n\x19io.skynet.localai.backendB\x0eLocalAIBackendP\x01Z+github.com/go-skynet/LocalAI/pkg/grpc/protob\x06proto3')
+
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'backend_pb2', _globals)
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\031io.skynet.localai.backendB\016LocalAIBackendP\001Z+github.com/go-skynet/LocalAI/pkg/grpc/proto'
+  _globals['_HEALTHMESSAGE']._serialized_start=26
+  _globals['_HEALTHMESSAGE']._serialized_end=41
+  _globals['_PREDICTOPTIONS']._serialized_start=44
+  _globals['_PREDICTOPTIONS']._serialized_end=818
+  _globals['_REPLY']._serialized_start=820
+  _globals['_REPLY']._serialized_end=844
+  _globals['_MODELOPTIONS']._serialized_start=847
+  _globals['_MODELOPTIONS']._serialized_end=1388
+  _globals['_RESULT']._serialized_start=1390
+  _globals['_RESULT']._serialized_end=1432
+  _globals['_EMBEDDINGRESULT']._serialized_start=1434
+  _globals['_EMBEDDINGRESULT']._serialized_end=1471
+  _globals['_TRANSCRIPTREQUEST']._serialized_start=1473
+  _globals['_TRANSCRIPTREQUEST']._serialized_end=1540
+  _globals['_TRANSCRIPTRESULT']._serialized_start=1542
+  _globals['_TRANSCRIPTRESULT']._serialized_end=1620
+  _globals['_TRANSCRIPTSEGMENT']._serialized_start=1622
+  _globals['_TRANSCRIPTSEGMENT']._serialized_end=1711
+  _globals['_GENERATEIMAGEREQUEST']._serialized_start=1714
+  _globals['_GENERATEIMAGEREQUEST']._serialized_end=1872
+  _globals['_TTSREQUEST']._serialized_start=1874
+  _globals['_TTSREQUEST']._serialized_end=1928
+  _globals['_BACKEND']._serialized_start=1931
+  _globals['_BACKEND']._serialized_end=2422
+# @@protoc_insertion_point(module_scope)
--- a/extra/grpc/bark/backend_pb2_grpc.py
+++ b/extra/grpc/bark/backend_pb2_grpc.py
@@ -0,0 +1,297 @@
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+"""Client and server classes corresponding to protobuf-defined services."""
+import grpc
+
+import backend_pb2 as backend__pb2
+
+
+class BackendStub(object):
+    """Missing associated documentation comment in .proto file."""
+
+    def __init__(self, channel):
+        """Constructor.
+
+        Args:
+            channel: A grpc.Channel.
+        """
+        self.Health = channel.unary_unary(
+                '/backend.Backend/Health',
+                request_serializer=backend__pb2.HealthMessage.SerializeToString,
+                response_deserializer=backend__pb2.Reply.FromString,
+                )
+        self.Predict = channel.unary_unary(
+                '/backend.Backend/Predict',
+                request_serializer=backend__pb2.PredictOptions.SerializeToString,
+                response_deserializer=backend__pb2.Reply.FromString,
+                )
+        self.LoadModel = channel.unary_unary(
+                '/backend.Backend/LoadModel',
+                request_serializer=backend__pb2.ModelOptions.SerializeToString,
+                response_deserializer=backend__pb2.Result.FromString,
+                )
+        self.PredictStream = channel.unary_stream(
+                '/backend.Backend/PredictStream',
+                request_serializer=backend__pb2.PredictOptions.SerializeToString,
+                response_deserializer=backend__pb2.Reply.FromString,
+                )
+        self.Embedding = channel.unary_unary(
+                '/backend.Backend/Embedding',
+                request_serializer=backend__pb2.PredictOptions.SerializeToString,
+                response_deserializer=backend__pb2.EmbeddingResult.FromString,
+                )
+        self.GenerateImage = channel.unary_unary(
+                '/backend.Backend/GenerateImage',
+                request_serializer=backend__pb2.GenerateImageRequest.SerializeToString,
+                response_deserializer=backend__pb2.Result.FromString,
+                )
+        self.AudioTranscription = channel.unary_unary(
+                '/backend.Backend/AudioTranscription',
+                request_serializer=backend__pb2.TranscriptRequest.SerializeToString,
+                response_deserializer=backend__pb2.TranscriptResult.FromString,
+                )
+        self.TTS = channel.unary_unary(
+                '/backend.Backend/TTS',
+                request_serializer=backend__pb2.TTSRequest.SerializeToString,
+                response_deserializer=backend__pb2.Result.FromString,
+                )
+
+
+class BackendServicer(object):
+    """Missing associated documentation comment in .proto file."""
+
+    def Health(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def Predict(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def LoadModel(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def PredictStream(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def Embedding(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def GenerateImage(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def AudioTranscription(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def TTS(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+
+def add_BackendServicer_to_server(servicer, server):
+    rpc_method_handlers = {
+            'Health': grpc.unary_unary_rpc_method_handler(
+                    servicer.Health,
+                    request_deserializer=backend__pb2.HealthMessage.FromString,
+                    response_serializer=backend__pb2.Reply.SerializeToString,
+            ),
+            'Predict': grpc.unary_unary_rpc_method_handler(
+                    servicer.Predict,
+                    request_deserializer=backend__pb2.PredictOptions.FromString,
+                    response_serializer=backend__pb2.Reply.SerializeToString,
+            ),
+            'LoadModel': grpc.unary_unary_rpc_method_handler(
+                    servicer.LoadModel,
+                    request_deserializer=backend__pb2.ModelOptions.FromString,
+                    response_serializer=backend__pb2.Result.SerializeToString,
+            ),
+            'PredictStream': grpc.unary_stream_rpc_method_handler(
+                    servicer.PredictStream,
+                    request_deserializer=backend__pb2.PredictOptions.FromString,
+                    response_serializer=backend__pb2.Reply.SerializeToString,
+            ),
+            'Embedding': grpc.unary_unary_rpc_method_handler(
+                    servicer.Embedding,
+                    request_deserializer=backend__pb2.PredictOptions.FromString,
+                    response_serializer=backend__pb2.EmbeddingResult.SerializeToString,
+            ),
+            'GenerateImage': grpc.unary_unary_rpc_method_handler(
+                    servicer.GenerateImage,
+                    request_deserializer=backend__pb2.GenerateImageRequest.FromString,
+                    response_serializer=backend__pb2.Result.SerializeToString,
+            ),
+            'AudioTranscription': grpc.unary_unary_rpc_method_handler(
+                    servicer.AudioTranscription,
+                    request_deserializer=backend__pb2.TranscriptRequest.FromString,
+                    response_serializer=backend__pb2.TranscriptResult.SerializeToString,
+            ),
+            'TTS': grpc.unary_unary_rpc_method_handler(
+                    servicer.TTS,
+                    request_deserializer=backend__pb2.TTSRequest.FromString,
+                    response_serializer=backend__pb2.Result.SerializeToString,
+            ),
+    }
+    generic_handler = grpc.method_handlers_generic_handler(
+            'backend.Backend', rpc_method_handlers)
+    server.add_generic_rpc_handlers((generic_handler,))
+
+
+ # This class is part of an EXPERIMENTAL API.
+class Backend(object):
+    """Missing associated documentation comment in .proto file."""
+
+    @staticmethod
+    def Health(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/Health',
+            backend__pb2.HealthMessage.SerializeToString,
+            backend__pb2.Reply.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def Predict(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/Predict',
+            backend__pb2.PredictOptions.SerializeToString,
+            backend__pb2.Reply.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def LoadModel(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/LoadModel',
+            backend__pb2.ModelOptions.SerializeToString,
+            backend__pb2.Result.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def PredictStream(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_stream(request, target, '/backend.Backend/PredictStream',
+            backend__pb2.PredictOptions.SerializeToString,
+            backend__pb2.Reply.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def Embedding(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/Embedding',
+            backend__pb2.PredictOptions.SerializeToString,
+            backend__pb2.EmbeddingResult.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def GenerateImage(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/GenerateImage',
+            backend__pb2.GenerateImageRequest.SerializeToString,
+            backend__pb2.Result.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def AudioTranscription(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/AudioTranscription',
+            backend__pb2.TranscriptRequest.SerializeToString,
+            backend__pb2.TranscriptResult.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def TTS(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/TTS',
+            backend__pb2.TTSRequest.SerializeToString,
+            backend__pb2.Result.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
--- a/extra/grpc/bark/ttsbark.py
+++ b/extra/grpc/bark/ttsbark.py
@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+import grpc
+from concurrent import futures
+import time
+import backend_pb2
+import backend_pb2_grpc
+import argparse
+import signal
+import sys
+import os
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+from pathlib import Path
+from bark import SAMPLE_RATE, generate_audio, preload_models
+from scipy.io.wavfile import write as write_wav
+
+_ONE_DAY_IN_SECONDS = 60 * 60 * 24
+
+# Implement the BackendServicer class with the service methods
+class BackendServicer(backend_pb2_grpc.BackendServicer):
+    def Health(self, request, context):
+        return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
+    def LoadModel(self, request, context):
+        model_name = request.Model
+        try:
+            print("Preparing models, please wait", file=sys.stderr)
+            # download and load all models
+            preload_models()
+        except Exception as err:
+            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        # Implement your logic here for the LoadModel service
+        # Replace this with your desired response
+        return backend_pb2.Result(message="Model loaded successfully", success=True)
+
+    def TTS(self, request, context):
+        model = request.model
+        print(request, file=sys.stderr)
+        try:
+            audio_array = None
+            if model != "":
+                audio_array = generate_audio(request.text, history_prompt=model)
+            else:
+                audio_array = generate_audio(request.text)
+            print("saving to", request.dst, file=sys.stderr)
+            # save audio to disk
+            write_wav(request.dst, SAMPLE_RATE, audio_array)
+            print("saved to", request.dst, file=sys.stderr)
+            print("tts for", file=sys.stderr)
+            print(request, file=sys.stderr)
+        except Exception as err:
+            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        return backend_pb2.Result(success=True)
+
+def serve(address):
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
+    backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
+    server.add_insecure_port(address)
+    server.start()
+    print("Server started. Listening on: " + address, file=sys.stderr)
+
+    # Define the signal handler function
+    def signal_handler(sig, frame):
+        print("Received termination signal. Shutting down...")
+        server.stop(0)
+        sys.exit(0)
+
+    # Set the signal handlers for SIGINT and SIGTERM
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTERM, signal_handler)
+
+    try:
+        while True:
+            time.sleep(_ONE_DAY_IN_SECONDS)
+    except KeyboardInterrupt:
+        server.stop(0)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run the gRPC server.")
+    parser.add_argument(
+        "--addr", default="localhost:50051", help="The address to bind the server to."
+    )
+    args = parser.parse_args()
+
+    serve(args.addr)
--- a/extra/grpc/diffusers/backend_diffusers.py
+++ b/extra/grpc/diffusers/backend_diffusers.py
@@ -0,0 +1,114 @@
+#!/usr/bin/env python3
+import grpc
+from concurrent import futures
+import time
+import backend_pb2
+import backend_pb2_grpc
+import argparse
+import signal
+import sys
+import os
+
+# import diffusers
+import torch
+from torch import autocast
+from diffusers import StableDiffusionXLPipeline, DPMSolverMultistepScheduler, StableDiffusionPipeline, DiffusionPipeline, EulerAncestralDiscreteScheduler
+
+_ONE_DAY_IN_SECONDS = 60 * 60 * 24
+
+# Implement the BackendServicer class with the service methods
+class BackendServicer(backend_pb2_grpc.BackendServicer):
+    def Health(self, request, context):
+        return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
+    def LoadModel(self, request, context):
+        try:
+            print(f"Loading model {request.Model}...", file=sys.stderr)
+            print(f"Request {request}", file=sys.stderr)
+            torchType = torch.float32
+            if request.F16Memory:
+                torchType = torch.float16
+
+            if request.PipelineType == "":
+                request.PipelineType == "StableDiffusionPipeline"
+
+            if request.PipelineType == "StableDiffusionPipeline":
+                self.pipe = StableDiffusionPipeline.from_pretrained(request.Model,
+                                                               torch_dtype=torchType)
+
+            if request.PipelineType == "DiffusionPipeline":
+                self.pipe = DiffusionPipeline.from_pretrained(request.Model,
+                                                               torch_dtype=torchType)
+
+            if request.PipelineType == "StableDiffusionXLPipeline":
+                self.pipe = StableDiffusionXLPipeline.from_pretrained(
+                    request.Model, 
+                    torch_dtype=torchType, 
+                    use_safetensors=True, 
+             #       variant="fp16"
+                    )
+          
+            # torch_dtype needs to be customized. float16 for GPU, float32 for CPU
+            # TODO: this needs to be customized
+            if request.SchedulerType == "EulerAncestralDiscreteScheduler":
+                self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)
+            if request.SchedulerType == "DPMSolverMultistepScheduler":
+                self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
+
+            if request.CUDA:
+                self.pipe.to('cuda')
+        except Exception as err:
+            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        # Implement your logic here for the LoadModel service
+        # Replace this with your desired response
+        return backend_pb2.Result(message="Model loaded successfully", success=True)
+    def GenerateImage(self, request, context):
+
+        prompt = request.positive_prompt
+        negative_prompt = request.negative_prompt
+
+        image = self.pipe(
+            prompt, 
+            negative_prompt=negative_prompt, 
+            width=request.width,
+            height=request.height,
+      #      guidance_scale=12,
+            target_size=(request.width,request.height),
+            original_size=(4096,4096),
+            num_inference_steps=request.step
+            ).images[0]
+
+        image.save(request.dst)
+
+        return backend_pb2.Result(message="Model loaded successfully", success=True)
+
+def serve(address):
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
+    backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
+    server.add_insecure_port(address)
+    server.start()
+    print("Server started. Listening on: " + address, file=sys.stderr)
+
+    # Define the signal handler function
+    def signal_handler(sig, frame):
+        print("Received termination signal. Shutting down...")
+        server.stop(0)
+        sys.exit(0)
+
+    # Set the signal handlers for SIGINT and SIGTERM
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTERM, signal_handler)
+
+    try:
+        while True:
+            time.sleep(_ONE_DAY_IN_SECONDS)
+    except KeyboardInterrupt:
+        server.stop(0)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run the gRPC server.")
+    parser.add_argument(
+        "--addr", default="localhost:50051", help="The address to bind the server to."
+    )
+    args = parser.parse_args()
+
+    serve(args.addr)
--- a/extra/grpc/diffusers/backend_pb2.py
+++ b/extra/grpc/diffusers/backend_pb2.py
@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: backend.proto
+"""Generated protocol buffer code."""
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf.internal import builder as _builder
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\rbackend.proto\x12\x07\x62\x61\x63kend\"\x0f\n\rHealthMessage\"\x86\x06\n\x0ePredictOptions\x12\x0e\n\x06Prompt\x18\x01 \x01(\t\x12\x0c\n\x04Seed\x18\x02 \x01(\x05\x12\x0f\n\x07Threads\x18\x03 \x01(\x05\x12\x0e\n\x06Tokens\x18\x04 \x01(\x05\x12\x0c\n\x04TopK\x18\x05 \x01(\x05\x12\x0e\n\x06Repeat\x18\x06 \x01(\x05\x12\r\n\x05\x42\x61tch\x18\x07 \x01(\x05\x12\r\n\x05NKeep\x18\x08 \x01(\x05\x12\x13\n\x0bTemperature\x18\t \x01(\x02\x12\x0f\n\x07Penalty\x18\n \x01(\x02\x12\r\n\x05\x46\x31\x36KV\x18\x0b \x01(\x08\x12\x11\n\tDebugMode\x18\x0c \x01(\x08\x12\x13\n\x0bStopPrompts\x18\r \x03(\t\x12\x11\n\tIgnoreEOS\x18\x0e \x01(\x08\x12\x19\n\x11TailFreeSamplingZ\x18\x0f \x01(\x02\x12\x10\n\x08TypicalP\x18\x10 \x01(\x02\x12\x18\n\x10\x46requencyPenalty\x18\x11 \x01(\x02\x12\x17\n\x0fPresencePenalty\x18\x12 \x01(\x02\x12\x10\n\x08Mirostat\x18\x13 \x01(\x05\x12\x13\n\x0bMirostatETA\x18\x14 \x01(\x02\x12\x13\n\x0bMirostatTAU\x18\x15 \x01(\x02\x12\x12\n\nPenalizeNL\x18\x16 \x01(\x08\x12\x11\n\tLogitBias\x18\x17 \x01(\t\x12\r\n\x05MLock\x18\x19 \x01(\x08\x12\x0c\n\x04MMap\x18\x1a \x01(\x08\x12\x16\n\x0ePromptCacheAll\x18\x1b \x01(\x08\x12\x15\n\rPromptCacheRO\x18\x1c \x01(\x08\x12\x0f\n\x07Grammar\x18\x1d \x01(\t\x12\x0f\n\x07MainGPU\x18\x1e \x01(\t\x12\x13\n\x0bTensorSplit\x18\x1f \x01(\t\x12\x0c\n\x04TopP\x18  \x01(\x02\x12\x17\n\x0fPromptCachePath\x18! \x01(\t\x12\r\n\x05\x44\x65\x62ug\x18\" \x01(\x08\x12\x17\n\x0f\x45mbeddingTokens\x18# \x03(\x05\x12\x12\n\nEmbeddings\x18$ \x01(\t\x12\x14\n\x0cRopeFreqBase\x18% \x01(\x02\x12\x15\n\rRopeFreqScale\x18& \x01(\x02\x12\x1b\n\x13NegativePromptScale\x18\' \x01(\x02\x12\x16\n\x0eNegativePrompt\x18( \x01(\t\"\x18\n\x05Reply\x12\x0f\n\x07message\x18\x01 \x01(\x0c\"\x9d\x04\n\x0cModelOptions\x12\r\n\x05Model\x18\x01 \x01(\t\x12\x13\n\x0b\x43ontextSize\x18\x02 \x01(\x05\x12\x0c\n\x04Seed\x18\x03 \x01(\x05\x12\x0e\n\x06NBatch\x18\x04 \x01(\x05\x12\x11\n\tF16Memory\x18\x05 \x01(\x08\x12\r\n\x05MLock\x18\x06 \x01(\x08\x12\x0c\n\x04MMap\x18\x07 \x01(\x08\x12\x11\n\tVocabOnly\x18\x08 \x01(\x08\x12\x0f\n\x07LowVRAM\x18\t \x01(\x08\x12\x12\n\nEmbeddings\x18\n \x01(\x08\x12\x0c\n\x04NUMA\x18\x0b \x01(\x08\x12\x12\n\nNGPULayers\x18\x0c \x01(\x05\x12\x0f\n\x07MainGPU\x18\r \x01(\t\x12\x13\n\x0bTensorSplit\x18\x0e \x01(\t\x12\x0f\n\x07Threads\x18\x0f \x01(\x05\x12\x19\n\x11LibrarySearchPath\x18\x10 \x01(\t\x12\x14\n\x0cRopeFreqBase\x18\x11 \x01(\x02\x12\x15\n\rRopeFreqScale\x18\x12 \x01(\x02\x12\x12\n\nRMSNormEps\x18\x13 \x01(\x02\x12\x0c\n\x04NGQA\x18\x14 \x01(\x05\x12\x11\n\tModelFile\x18\x15 \x01(\t\x12\x0e\n\x06\x44\x65vice\x18\x16 \x01(\t\x12\x11\n\tUseTriton\x18\x17 \x01(\x08\x12\x15\n\rModelBaseName\x18\x18 \x01(\t\x12\x18\n\x10UseFastTokenizer\x18\x19 \x01(\x08\x12\x14\n\x0cPipelineType\x18\x1a \x01(\t\x12\x15\n\rSchedulerType\x18\x1b \x01(\t\x12\x0c\n\x04\x43UDA\x18\x1c \x01(\x08\"*\n\x06Result\x12\x0f\n\x07message\x18\x01 \x01(\t\x12\x0f\n\x07success\x18\x02 \x01(\x08\"%\n\x0f\x45mbeddingResult\x12\x12\n\nembeddings\x18\x01 \x03(\x02\"C\n\x11TranscriptRequest\x12\x0b\n\x03\x64st\x18\x02 \x01(\t\x12\x10\n\x08language\x18\x03 \x01(\t\x12\x0f\n\x07threads\x18\x04 \x01(\r\"N\n\x10TranscriptResult\x12,\n\x08segments\x18\x01 \x03(\x0b\x32\x1a.backend.TranscriptSegment\x12\x0c\n\x04text\x18\x02 \x01(\t\"Y\n\x11TranscriptSegment\x12\n\n\x02id\x18\x01 \x01(\x05\x12\r\n\x05start\x18\x02 \x01(\x03\x12\x0b\n\x03\x65nd\x18\x03 \x01(\x03\x12\x0c\n\x04text\x18\x04 \x01(\t\x12\x0e\n\x06tokens\x18\x05 \x03(\x05\"\x9e\x01\n\x14GenerateImageRequest\x12\x0e\n\x06height\x18\x01 \x01(\x05\x12\r\n\x05width\x18\x02 \x01(\x05\x12\x0c\n\x04mode\x18\x03 \x01(\x05\x12\x0c\n\x04step\x18\x04 \x01(\x05\x12\x0c\n\x04seed\x18\x05 \x01(\x05\x12\x17\n\x0fpositive_prompt\x18\x06 \x01(\t\x12\x17\n\x0fnegative_prompt\x18\x07 \x01(\t\x12\x0b\n\x03\x64st\x18\x08 \x01(\t\"6\n\nTTSRequest\x12\x0c\n\x04text\x18\x01 \x01(\t\x12\r\n\x05model\x18\x02 \x01(\t\x12\x0b\n\x03\x64st\x18\x03 \x01(\t2\xeb\x03\n\x07\x42\x61\x63kend\x12\x32\n\x06Health\x12\x16.backend.HealthMessage\x1a\x0e.backend.Reply\"\x00\x12\x34\n\x07Predict\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x12\x35\n\tLoadModel\x12\x15.backend.ModelOptions\x1a\x0f.backend.Result\"\x00\x12<\n\rPredictStream\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x30\x01\x12@\n\tEmbedding\x12\x17.backend.PredictOptions\x1a\x18.backend.EmbeddingResult\"\x00\x12\x41\n\rGenerateImage\x12\x1d.backend.GenerateImageRequest\x1a\x0f.backend.Result\"\x00\x12M\n\x12\x41udioTranscription\x12\x1a.backend.TranscriptRequest\x1a\x19.backend.TranscriptResult\"\x00\x12-\n\x03TTS\x12\x13.backend.TTSRequest\x1a\x0f.backend.Result\"\x00\x42Z\n\x19io.skynet.localai.backendB\x0eLocalAIBackendP\x01Z+github.com/go-skynet/LocalAI/pkg/grpc/protob\x06proto3')
+
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'backend_pb2', _globals)
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\031io.skynet.localai.backendB\016LocalAIBackendP\001Z+github.com/go-skynet/LocalAI/pkg/grpc/proto'
+  _globals['_HEALTHMESSAGE']._serialized_start=26
+  _globals['_HEALTHMESSAGE']._serialized_end=41
+  _globals['_PREDICTOPTIONS']._serialized_start=44
+  _globals['_PREDICTOPTIONS']._serialized_end=818
+  _globals['_REPLY']._serialized_start=820
+  _globals['_REPLY']._serialized_end=844
+  _globals['_MODELOPTIONS']._serialized_start=847
+  _globals['_MODELOPTIONS']._serialized_end=1388
+  _globals['_RESULT']._serialized_start=1390
+  _globals['_RESULT']._serialized_end=1432
+  _globals['_EMBEDDINGRESULT']._serialized_start=1434
+  _globals['_EMBEDDINGRESULT']._serialized_end=1471
+  _globals['_TRANSCRIPTREQUEST']._serialized_start=1473
+  _globals['_TRANSCRIPTREQUEST']._serialized_end=1540
+  _globals['_TRANSCRIPTRESULT']._serialized_start=1542
+  _globals['_TRANSCRIPTRESULT']._serialized_end=1620
+  _globals['_TRANSCRIPTSEGMENT']._serialized_start=1622
+  _globals['_TRANSCRIPTSEGMENT']._serialized_end=1711
+  _globals['_GENERATEIMAGEREQUEST']._serialized_start=1714
+  _globals['_GENERATEIMAGEREQUEST']._serialized_end=1872
+  _globals['_TTSREQUEST']._serialized_start=1874
+  _globals['_TTSREQUEST']._serialized_end=1928
+  _globals['_BACKEND']._serialized_start=1931
+  _globals['_BACKEND']._serialized_end=2422
+# @@protoc_insertion_point(module_scope)
--- a/extra/grpc/diffusers/backend_pb2_grpc.py
+++ b/extra/grpc/diffusers/backend_pb2_grpc.py
@@ -0,0 +1,297 @@
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+"""Client and server classes corresponding to protobuf-defined services."""
+import grpc
+
+import backend_pb2 as backend__pb2
+
+
+class BackendStub(object):
+    """Missing associated documentation comment in .proto file."""
+
+    def __init__(self, channel):
+        """Constructor.
+
+        Args:
+            channel: A grpc.Channel.
+        """
+        self.Health = channel.unary_unary(
+                '/backend.Backend/Health',
+                request_serializer=backend__pb2.HealthMessage.SerializeToString,
+                response_deserializer=backend__pb2.Reply.FromString,
+                )
+        self.Predict = channel.unary_unary(
+                '/backend.Backend/Predict',
+                request_serializer=backend__pb2.PredictOptions.SerializeToString,
+                response_deserializer=backend__pb2.Reply.FromString,
+                )
+        self.LoadModel = channel.unary_unary(
+                '/backend.Backend/LoadModel',
+                request_serializer=backend__pb2.ModelOptions.SerializeToString,
+                response_deserializer=backend__pb2.Result.FromString,
+                )
+        self.PredictStream = channel.unary_stream(
+                '/backend.Backend/PredictStream',
+                request_serializer=backend__pb2.PredictOptions.SerializeToString,
+                response_deserializer=backend__pb2.Reply.FromString,
+                )
+        self.Embedding = channel.unary_unary(
+                '/backend.Backend/Embedding',
+                request_serializer=backend__pb2.PredictOptions.SerializeToString,
+                response_deserializer=backend__pb2.EmbeddingResult.FromString,
+                )
+        self.GenerateImage = channel.unary_unary(
+                '/backend.Backend/GenerateImage',
+                request_serializer=backend__pb2.GenerateImageRequest.SerializeToString,
+                response_deserializer=backend__pb2.Result.FromString,
+                )
+        self.AudioTranscription = channel.unary_unary(
+                '/backend.Backend/AudioTranscription',
+                request_serializer=backend__pb2.TranscriptRequest.SerializeToString,
+                response_deserializer=backend__pb2.TranscriptResult.FromString,
+                )
+        self.TTS = channel.unary_unary(
+                '/backend.Backend/TTS',
+                request_serializer=backend__pb2.TTSRequest.SerializeToString,
+                response_deserializer=backend__pb2.Result.FromString,
+                )
+
+
+class BackendServicer(object):
+    """Missing associated documentation comment in .proto file."""
+
+    def Health(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def Predict(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def LoadModel(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def PredictStream(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def Embedding(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def GenerateImage(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def AudioTranscription(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def TTS(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+
+def add_BackendServicer_to_server(servicer, server):
+    rpc_method_handlers = {
+            'Health': grpc.unary_unary_rpc_method_handler(
+                    servicer.Health,
+                    request_deserializer=backend__pb2.HealthMessage.FromString,
+                    response_serializer=backend__pb2.Reply.SerializeToString,
+            ),
+            'Predict': grpc.unary_unary_rpc_method_handler(
+                    servicer.Predict,
+                    request_deserializer=backend__pb2.PredictOptions.FromString,
+                    response_serializer=backend__pb2.Reply.SerializeToString,
+            ),
+            'LoadModel': grpc.unary_unary_rpc_method_handler(
+                    servicer.LoadModel,
+                    request_deserializer=backend__pb2.ModelOptions.FromString,
+                    response_serializer=backend__pb2.Result.SerializeToString,
+            ),
+            'PredictStream': grpc.unary_stream_rpc_method_handler(
+                    servicer.PredictStream,
+                    request_deserializer=backend__pb2.PredictOptions.FromString,
+                    response_serializer=backend__pb2.Reply.SerializeToString,
+            ),
+            'Embedding': grpc.unary_unary_rpc_method_handler(
+                    servicer.Embedding,
+                    request_deserializer=backend__pb2.PredictOptions.FromString,
+                    response_serializer=backend__pb2.EmbeddingResult.SerializeToString,
+            ),
+            'GenerateImage': grpc.unary_unary_rpc_method_handler(
+                    servicer.GenerateImage,
+                    request_deserializer=backend__pb2.GenerateImageRequest.FromString,
+                    response_serializer=backend__pb2.Result.SerializeToString,
+            ),
+            'AudioTranscription': grpc.unary_unary_rpc_method_handler(
+                    servicer.AudioTranscription,
+                    request_deserializer=backend__pb2.TranscriptRequest.FromString,
+                    response_serializer=backend__pb2.TranscriptResult.SerializeToString,
+            ),
+            'TTS': grpc.unary_unary_rpc_method_handler(
+                    servicer.TTS,
+                    request_deserializer=backend__pb2.TTSRequest.FromString,
+                    response_serializer=backend__pb2.Result.SerializeToString,
+            ),
+    }
+    generic_handler = grpc.method_handlers_generic_handler(
+            'backend.Backend', rpc_method_handlers)
+    server.add_generic_rpc_handlers((generic_handler,))
+
+
+ # This class is part of an EXPERIMENTAL API.
+class Backend(object):
+    """Missing associated documentation comment in .proto file."""
+
+    @staticmethod
+    def Health(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/Health',
+            backend__pb2.HealthMessage.SerializeToString,
+            backend__pb2.Reply.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def Predict(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/Predict',
+            backend__pb2.PredictOptions.SerializeToString,
+            backend__pb2.Reply.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def LoadModel(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/LoadModel',
+            backend__pb2.ModelOptions.SerializeToString,
+            backend__pb2.Result.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def PredictStream(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_stream(request, target, '/backend.Backend/PredictStream',
+            backend__pb2.PredictOptions.SerializeToString,
+            backend__pb2.Reply.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def Embedding(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/Embedding',
+            backend__pb2.PredictOptions.SerializeToString,
+            backend__pb2.EmbeddingResult.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def GenerateImage(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/GenerateImage',
+            backend__pb2.GenerateImageRequest.SerializeToString,
+            backend__pb2.Result.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def AudioTranscription(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/AudioTranscription',
+            backend__pb2.TranscriptRequest.SerializeToString,
+            backend__pb2.TranscriptResult.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def TTS(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/TTS',
+            backend__pb2.TTSRequest.SerializeToString,
+            backend__pb2.Result.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
--- a/extra/grpc/exllama/backend_pb2.py
+++ b/extra/grpc/exllama/backend_pb2.py
@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: backend.proto
+"""Generated protocol buffer code."""
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf.internal import builder as _builder
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\rbackend.proto\x12\x07\x62\x61\x63kend\"\x0f\n\rHealthMessage\"\x86\x06\n\x0ePredictOptions\x12\x0e\n\x06Prompt\x18\x01 \x01(\t\x12\x0c\n\x04Seed\x18\x02 \x01(\x05\x12\x0f\n\x07Threads\x18\x03 \x01(\x05\x12\x0e\n\x06Tokens\x18\x04 \x01(\x05\x12\x0c\n\x04TopK\x18\x05 \x01(\x05\x12\x0e\n\x06Repeat\x18\x06 \x01(\x05\x12\r\n\x05\x42\x61tch\x18\x07 \x01(\x05\x12\r\n\x05NKeep\x18\x08 \x01(\x05\x12\x13\n\x0bTemperature\x18\t \x01(\x02\x12\x0f\n\x07Penalty\x18\n \x01(\x02\x12\r\n\x05\x46\x31\x36KV\x18\x0b \x01(\x08\x12\x11\n\tDebugMode\x18\x0c \x01(\x08\x12\x13\n\x0bStopPrompts\x18\r \x03(\t\x12\x11\n\tIgnoreEOS\x18\x0e \x01(\x08\x12\x19\n\x11TailFreeSamplingZ\x18\x0f \x01(\x02\x12\x10\n\x08TypicalP\x18\x10 \x01(\x02\x12\x18\n\x10\x46requencyPenalty\x18\x11 \x01(\x02\x12\x17\n\x0fPresencePenalty\x18\x12 \x01(\x02\x12\x10\n\x08Mirostat\x18\x13 \x01(\x05\x12\x13\n\x0bMirostatETA\x18\x14 \x01(\x02\x12\x13\n\x0bMirostatTAU\x18\x15 \x01(\x02\x12\x12\n\nPenalizeNL\x18\x16 \x01(\x08\x12\x11\n\tLogitBias\x18\x17 \x01(\t\x12\r\n\x05MLock\x18\x19 \x01(\x08\x12\x0c\n\x04MMap\x18\x1a \x01(\x08\x12\x16\n\x0ePromptCacheAll\x18\x1b \x01(\x08\x12\x15\n\rPromptCacheRO\x18\x1c \x01(\x08\x12\x0f\n\x07Grammar\x18\x1d \x01(\t\x12\x0f\n\x07MainGPU\x18\x1e \x01(\t\x12\x13\n\x0bTensorSplit\x18\x1f \x01(\t\x12\x0c\n\x04TopP\x18  \x01(\x02\x12\x17\n\x0fPromptCachePath\x18! \x01(\t\x12\r\n\x05\x44\x65\x62ug\x18\" \x01(\x08\x12\x17\n\x0f\x45mbeddingTokens\x18# \x03(\x05\x12\x12\n\nEmbeddings\x18$ \x01(\t\x12\x14\n\x0cRopeFreqBase\x18% \x01(\x02\x12\x15\n\rRopeFreqScale\x18& \x01(\x02\x12\x1b\n\x13NegativePromptScale\x18\' \x01(\x02\x12\x16\n\x0eNegativePrompt\x18( \x01(\t\"\x18\n\x05Reply\x12\x0f\n\x07message\x18\x01 \x01(\x0c\"\x9d\x04\n\x0cModelOptions\x12\r\n\x05Model\x18\x01 \x01(\t\x12\x13\n\x0b\x43ontextSize\x18\x02 \x01(\x05\x12\x0c\n\x04Seed\x18\x03 \x01(\x05\x12\x0e\n\x06NBatch\x18\x04 \x01(\x05\x12\x11\n\tF16Memory\x18\x05 \x01(\x08\x12\r\n\x05MLock\x18\x06 \x01(\x08\x12\x0c\n\x04MMap\x18\x07 \x01(\x08\x12\x11\n\tVocabOnly\x18\x08 \x01(\x08\x12\x0f\n\x07LowVRAM\x18\t \x01(\x08\x12\x12\n\nEmbeddings\x18\n \x01(\x08\x12\x0c\n\x04NUMA\x18\x0b \x01(\x08\x12\x12\n\nNGPULayers\x18\x0c \x01(\x05\x12\x0f\n\x07MainGPU\x18\r \x01(\t\x12\x13\n\x0bTensorSplit\x18\x0e \x01(\t\x12\x0f\n\x07Threads\x18\x0f \x01(\x05\x12\x19\n\x11LibrarySearchPath\x18\x10 \x01(\t\x12\x14\n\x0cRopeFreqBase\x18\x11 \x01(\x02\x12\x15\n\rRopeFreqScale\x18\x12 \x01(\x02\x12\x12\n\nRMSNormEps\x18\x13 \x01(\x02\x12\x0c\n\x04NGQA\x18\x14 \x01(\x05\x12\x11\n\tModelFile\x18\x15 \x01(\t\x12\x0e\n\x06\x44\x65vice\x18\x16 \x01(\t\x12\x11\n\tUseTriton\x18\x17 \x01(\x08\x12\x15\n\rModelBaseName\x18\x18 \x01(\t\x12\x18\n\x10UseFastTokenizer\x18\x19 \x01(\x08\x12\x14\n\x0cPipelineType\x18\x1a \x01(\t\x12\x15\n\rSchedulerType\x18\x1b \x01(\t\x12\x0c\n\x04\x43UDA\x18\x1c \x01(\x08\"*\n\x06Result\x12\x0f\n\x07message\x18\x01 \x01(\t\x12\x0f\n\x07success\x18\x02 \x01(\x08\"%\n\x0f\x45mbeddingResult\x12\x12\n\nembeddings\x18\x01 \x03(\x02\"C\n\x11TranscriptRequest\x12\x0b\n\x03\x64st\x18\x02 \x01(\t\x12\x10\n\x08language\x18\x03 \x01(\t\x12\x0f\n\x07threads\x18\x04 \x01(\r\"N\n\x10TranscriptResult\x12,\n\x08segments\x18\x01 \x03(\x0b\x32\x1a.backend.TranscriptSegment\x12\x0c\n\x04text\x18\x02 \x01(\t\"Y\n\x11TranscriptSegment\x12\n\n\x02id\x18\x01 \x01(\x05\x12\r\n\x05start\x18\x02 \x01(\x03\x12\x0b\n\x03\x65nd\x18\x03 \x01(\x03\x12\x0c\n\x04text\x18\x04 \x01(\t\x12\x0e\n\x06tokens\x18\x05 \x03(\x05\"\x9e\x01\n\x14GenerateImageRequest\x12\x0e\n\x06height\x18\x01 \x01(\x05\x12\r\n\x05width\x18\x02 \x01(\x05\x12\x0c\n\x04mode\x18\x03 \x01(\x05\x12\x0c\n\x04step\x18\x04 \x01(\x05\x12\x0c\n\x04seed\x18\x05 \x01(\x05\x12\x17\n\x0fpositive_prompt\x18\x06 \x01(\t\x12\x17\n\x0fnegative_prompt\x18\x07 \x01(\t\x12\x0b\n\x03\x64st\x18\x08 \x01(\t\"6\n\nTTSRequest\x12\x0c\n\x04text\x18\x01 \x01(\t\x12\r\n\x05model\x18\x02 \x01(\t\x12\x0b\n\x03\x64st\x18\x03 \x01(\t2\xeb\x03\n\x07\x42\x61\x63kend\x12\x32\n\x06Health\x12\x16.backend.HealthMessage\x1a\x0e.backend.Reply\"\x00\x12\x34\n\x07Predict\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x12\x35\n\tLoadModel\x12\x15.backend.ModelOptions\x1a\x0f.backend.Result\"\x00\x12<\n\rPredictStream\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x30\x01\x12@\n\tEmbedding\x12\x17.backend.PredictOptions\x1a\x18.backend.EmbeddingResult\"\x00\x12\x41\n\rGenerateImage\x12\x1d.backend.GenerateImageRequest\x1a\x0f.backend.Result\"\x00\x12M\n\x12\x41udioTranscription\x12\x1a.backend.TranscriptRequest\x1a\x19.backend.TranscriptResult\"\x00\x12-\n\x03TTS\x12\x13.backend.TTSRequest\x1a\x0f.backend.Result\"\x00\x42Z\n\x19io.skynet.localai.backendB\x0eLocalAIBackendP\x01Z+github.com/go-skynet/LocalAI/pkg/grpc/protob\x06proto3')
+
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'backend_pb2', _globals)
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\031io.skynet.localai.backendB\016LocalAIBackendP\001Z+github.com/go-skynet/LocalAI/pkg/grpc/proto'
+  _globals['_HEALTHMESSAGE']._serialized_start=26
+  _globals['_HEALTHMESSAGE']._serialized_end=41
+  _globals['_PREDICTOPTIONS']._serialized_start=44
+  _globals['_PREDICTOPTIONS']._serialized_end=818
+  _globals['_REPLY']._serialized_start=820
+  _globals['_REPLY']._serialized_end=844
+  _globals['_MODELOPTIONS']._serialized_start=847
+  _globals['_MODELOPTIONS']._serialized_end=1388
+  _globals['_RESULT']._serialized_start=1390
+  _globals['_RESULT']._serialized_end=1432
+  _globals['_EMBEDDINGRESULT']._serialized_start=1434
+  _globals['_EMBEDDINGRESULT']._serialized_end=1471
+  _globals['_TRANSCRIPTREQUEST']._serialized_start=1473
+  _globals['_TRANSCRIPTREQUEST']._serialized_end=1540
+  _globals['_TRANSCRIPTRESULT']._serialized_start=1542
+  _globals['_TRANSCRIPTRESULT']._serialized_end=1620
+  _globals['_TRANSCRIPTSEGMENT']._serialized_start=1622
+  _globals['_TRANSCRIPTSEGMENT']._serialized_end=1711
+  _globals['_GENERATEIMAGEREQUEST']._serialized_start=1714
+  _globals['_GENERATEIMAGEREQUEST']._serialized_end=1872
+  _globals['_TTSREQUEST']._serialized_start=1874
+  _globals['_TTSREQUEST']._serialized_end=1928
+  _globals['_BACKEND']._serialized_start=1931
+  _globals['_BACKEND']._serialized_end=2422
+# @@protoc_insertion_point(module_scope)
--- a/extra/grpc/exllama/backend_pb2_grpc.py
+++ b/extra/grpc/exllama/backend_pb2_grpc.py
@@ -0,0 +1,297 @@
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+"""Client and server classes corresponding to protobuf-defined services."""
+import grpc
+
+import backend_pb2 as backend__pb2
+
+
+class BackendStub(object):
+    """Missing associated documentation comment in .proto file."""
+
+    def __init__(self, channel):
+        """Constructor.
+
+        Args:
+            channel: A grpc.Channel.
+        """
+        self.Health = channel.unary_unary(
+                '/backend.Backend/Health',
+                request_serializer=backend__pb2.HealthMessage.SerializeToString,
+                response_deserializer=backend__pb2.Reply.FromString,
+                )
+        self.Predict = channel.unary_unary(
+                '/backend.Backend/Predict',
+                request_serializer=backend__pb2.PredictOptions.SerializeToString,
+                response_deserializer=backend__pb2.Reply.FromString,
+                )
+        self.LoadModel = channel.unary_unary(
+                '/backend.Backend/LoadModel',
+                request_serializer=backend__pb2.ModelOptions.SerializeToString,
+                response_deserializer=backend__pb2.Result.FromString,
+                )
+        self.PredictStream = channel.unary_stream(
+                '/backend.Backend/PredictStream',
+                request_serializer=backend__pb2.PredictOptions.SerializeToString,
+                response_deserializer=backend__pb2.Reply.FromString,
+                )
+        self.Embedding = channel.unary_unary(
+                '/backend.Backend/Embedding',
+                request_serializer=backend__pb2.PredictOptions.SerializeToString,
+                response_deserializer=backend__pb2.EmbeddingResult.FromString,
+                )
+        self.GenerateImage = channel.unary_unary(
+                '/backend.Backend/GenerateImage',
+                request_serializer=backend__pb2.GenerateImageRequest.SerializeToString,
+                response_deserializer=backend__pb2.Result.FromString,
+                )
+        self.AudioTranscription = channel.unary_unary(
+                '/backend.Backend/AudioTranscription',
+                request_serializer=backend__pb2.TranscriptRequest.SerializeToString,
+                response_deserializer=backend__pb2.TranscriptResult.FromString,
+                )
+        self.TTS = channel.unary_unary(
+                '/backend.Backend/TTS',
+                request_serializer=backend__pb2.TTSRequest.SerializeToString,
+                response_deserializer=backend__pb2.Result.FromString,
+                )
+
+
+class BackendServicer(object):
+    """Missing associated documentation comment in .proto file."""
+
+    def Health(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def Predict(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def LoadModel(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def PredictStream(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def Embedding(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def GenerateImage(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def AudioTranscription(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+    def TTS(self, request, context):
+        """Missing associated documentation comment in .proto file."""
+        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+        context.set_details('Method not implemented!')
+        raise NotImplementedError('Method not implemented!')
+
+
+def add_BackendServicer_to_server(servicer, server):
+    rpc_method_handlers = {
+            'Health': grpc.unary_unary_rpc_method_handler(
+                    servicer.Health,
+                    request_deserializer=backend__pb2.HealthMessage.FromString,
+                    response_serializer=backend__pb2.Reply.SerializeToString,
+            ),
+            'Predict': grpc.unary_unary_rpc_method_handler(
+                    servicer.Predict,
+                    request_deserializer=backend__pb2.PredictOptions.FromString,
+                    response_serializer=backend__pb2.Reply.SerializeToString,
+            ),
+            'LoadModel': grpc.unary_unary_rpc_method_handler(
+                    servicer.LoadModel,
+                    request_deserializer=backend__pb2.ModelOptions.FromString,
+                    response_serializer=backend__pb2.Result.SerializeToString,
+            ),
+            'PredictStream': grpc.unary_stream_rpc_method_handler(
+                    servicer.PredictStream,
+                    request_deserializer=backend__pb2.PredictOptions.FromString,
+                    response_serializer=backend__pb2.Reply.SerializeToString,
+            ),
+            'Embedding': grpc.unary_unary_rpc_method_handler(
+                    servicer.Embedding,
+                    request_deserializer=backend__pb2.PredictOptions.FromString,
+                    response_serializer=backend__pb2.EmbeddingResult.SerializeToString,
+            ),
+            'GenerateImage': grpc.unary_unary_rpc_method_handler(
+                    servicer.GenerateImage,
+                    request_deserializer=backend__pb2.GenerateImageRequest.FromString,
+                    response_serializer=backend__pb2.Result.SerializeToString,
+            ),
+            'AudioTranscription': grpc.unary_unary_rpc_method_handler(
+                    servicer.AudioTranscription,
+                    request_deserializer=backend__pb2.TranscriptRequest.FromString,
+                    response_serializer=backend__pb2.TranscriptResult.SerializeToString,
+            ),
+            'TTS': grpc.unary_unary_rpc_method_handler(
+                    servicer.TTS,
+                    request_deserializer=backend__pb2.TTSRequest.FromString,
+                    response_serializer=backend__pb2.Result.SerializeToString,
+            ),
+    }
+    generic_handler = grpc.method_handlers_generic_handler(
+            'backend.Backend', rpc_method_handlers)
+    server.add_generic_rpc_handlers((generic_handler,))
+
+
+ # This class is part of an EXPERIMENTAL API.
+class Backend(object):
+    """Missing associated documentation comment in .proto file."""
+
+    @staticmethod
+    def Health(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/Health',
+            backend__pb2.HealthMessage.SerializeToString,
+            backend__pb2.Reply.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def Predict(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/Predict',
+            backend__pb2.PredictOptions.SerializeToString,
+            backend__pb2.Reply.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def LoadModel(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/LoadModel',
+            backend__pb2.ModelOptions.SerializeToString,
+            backend__pb2.Result.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def PredictStream(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_stream(request, target, '/backend.Backend/PredictStream',
+            backend__pb2.PredictOptions.SerializeToString,
+            backend__pb2.Reply.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def Embedding(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/Embedding',
+            backend__pb2.PredictOptions.SerializeToString,
+            backend__pb2.EmbeddingResult.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def GenerateImage(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/GenerateImage',
+            backend__pb2.GenerateImageRequest.SerializeToString,
+            backend__pb2.Result.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def AudioTranscription(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/AudioTranscription',
+            backend__pb2.TranscriptRequest.SerializeToString,
+            backend__pb2.TranscriptResult.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def TTS(request,
+            target,
+            options=(),
+            channel_credentials=None,
+            call_credentials=None,
+            insecure=False,
+            compression=None,
+            wait_for_ready=None,
+            timeout=None,
+            metadata=None):
+        return grpc.experimental.unary_unary(request, target, '/backend.Backend/TTS',
+            backend__pb2.TTSRequest.SerializeToString,
+            backend__pb2.Result.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
--- a/extra/grpc/exllama/exllama.py
+++ b/extra/grpc/exllama/exllama.py
@@ -0,0 +1,142 @@
+#!/usr/bin/env python3
+import grpc
+from concurrent import futures
+import time
+import backend_pb2
+import backend_pb2_grpc
+import argparse
+import signal
+import sys
+import os, glob
+
+from pathlib import Path
+import torch
+import torch.nn.functional as F
+from torch import version as torch_version
+from exllama.generator import ExLlamaGenerator
+from exllama.model import ExLlama, ExLlamaCache, ExLlamaConfig
+from exllama.tokenizer import ExLlamaTokenizer
+
+_ONE_DAY_IN_SECONDS = 60 * 60 * 24
+
+# Implement the BackendServicer class with the service methods
+class BackendServicer(backend_pb2_grpc.BackendServicer):
+    def generate(self,prompt, max_new_tokens):
+        self.generator.end_beam_search()
+
+        # Tokenizing the input
+        ids = self.generator.tokenizer.encode(prompt)
+
+        self.generator.gen_begin_reuse(ids)
+        initial_len = self.generator.sequence[0].shape[0]
+        has_leading_space = False
+        decoded_text = ''
+        for i in range(max_new_tokens):
+            token = self.generator.gen_single_token()
+            if i == 0 and self.generator.tokenizer.tokenizer.IdToPiece(int(token)).startswith('▁'):
+                has_leading_space = True
+
+            decoded_text = self.generator.tokenizer.decode(self.generator.sequence[0][initial_len:])
+            if has_leading_space:
+                decoded_text = ' ' + decoded_text
+
+            if token.item() == self.generator.tokenizer.eos_token_id:
+                break
+        return decoded_text
+    def Health(self, request, context):
+        return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
+    def LoadModel(self, request, context):
+        try:
+            # https://github.com/turboderp/exllama/blob/master/example_cfg.py
+            model_directory = request.ModelFile
+
+            # Locate files we need within that directory
+            tokenizer_path = os.path.join(model_directory, "tokenizer.model")
+            model_config_path = os.path.join(model_directory, "config.json")
+            st_pattern = os.path.join(model_directory, "*.safetensors")
+            model_path = glob.glob(st_pattern)[0]
+
+            # Create config, model, tokenizer and generator
+
+            config = ExLlamaConfig(model_config_path)               # create config from config.json
+            config.model_path = model_path                          # supply path to model weights file
+
+            model = ExLlama(config)                                 # create ExLlama instance and load the weights
+            tokenizer = ExLlamaTokenizer(tokenizer_path)            # create tokenizer from tokenizer model file
+
+            cache = ExLlamaCache(model, batch_size = 2)             # create cache for inference
+            generator = ExLlamaGenerator(model, tokenizer, cache)   # create generator
+
+            self.generator= generator
+            self.model = model
+            self.tokenizer = tokenizer
+            self.cache = cache
+        except Exception as err:
+            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        return backend_pb2.Result(message="Model loaded successfully", success=True)
+
+    def Predict(self, request, context):
+        penalty = 1.15
+        if request.Penalty != 0.0:
+            penalty = request.Penalty
+        self.generator.settings.token_repetition_penalty_max = penalty
+        self.generator.settings.temperature = request.Temperature
+        self.generator.settings.top_k = request.TopK
+        self.generator.settings.top_p = request.TopP
+
+        tokens = 512
+        if request.Tokens != 0:
+            tokens = request.Tokens
+
+        if self.cache.batch_size == 1:
+            del self.cache
+            self.cache = ExLlamaCache(self.model, batch_size=2)
+            self.generator = ExLlamaGenerator(self.model, self.tokenizer, self.cache)
+
+        t = self.generate(request.Prompt, tokens)
+
+        # Remove prompt from response if present
+        if request.Prompt in t:
+            t = t.replace(request.Prompt, "")
+
+        return backend_pb2.Result(message=bytes(t, encoding='utf-8'))
+
+    def PredictStream(self, request, context):
+        # Implement PredictStream RPC
+        #for reply in some_data_generator():
+        #    yield reply
+        # Not implemented yet
+        return self.Predict(request, context)
+
+
+def serve(address):
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
+    backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
+    server.add_insecure_port(address)
+    server.start()
+    print("Server started. Listening on: " + address, file=sys.stderr)
+
+    # Define the signal handler function
+    def signal_handler(sig, frame):
+        print("Received termination signal. Shutting down...")
+        server.stop(0)
+        sys.exit(0)
+
+    # Set the signal handlers for SIGINT and SIGTERM
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTERM, signal_handler)
+
+    try:
+        while True:
+            time.sleep(_ONE_DAY_IN_SECONDS)
+    except KeyboardInterrupt:
+        server.stop(0)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run the gRPC server.")
+    parser.add_argument(
+        "--addr", default="localhost:50051", help="The address to bind the server to."
+    )
+    args = parser.parse_args()
+
+    serve(args.addr)
--- a/extra/grpc/huggingface/backend_pb2.py
+++ b/extra/grpc/huggingface/backend_pb2.py
@@ -13,7 +13,7 @@ _sym_db = _symbol_database.Default()



-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\rbackend.proto\x12\x07\x62\x61\x63kend\"\x0f\n\rHealthMessage\"\x86\x06\n\x0ePredictOptions\x12\x0e\n\x06Prompt\x18\x01 \x01(\t\x12\x0c\n\x04Seed\x18\x02 \x01(\x05\x12\x0f\n\x07Threads\x18\x03 \x01(\x05\x12\x0e\n\x06Tokens\x18\x04 \x01(\x05\x12\x0c\n\x04TopK\x18\x05 \x01(\x05\x12\x0e\n\x06Repeat\x18\x06 \x01(\x05\x12\r\n\x05\x42\x61tch\x18\x07 \x01(\x05\x12\r\n\x05NKeep\x18\x08 \x01(\x05\x12\x13\n\x0bTemperature\x18\t \x01(\x02\x12\x0f\n\x07Penalty\x18\n \x01(\x02\x12\r\n\x05\x46\x31\x36KV\x18\x0b \x01(\x08\x12\x11\n\tDebugMode\x18\x0c \x01(\x08\x12\x13\n\x0bStopPrompts\x18\r \x03(\t\x12\x11\n\tIgnoreEOS\x18\x0e \x01(\x08\x12\x19\n\x11TailFreeSamplingZ\x18\x0f \x01(\x02\x12\x10\n\x08TypicalP\x18\x10 \x01(\x02\x12\x18\n\x10\x46requencyPenalty\x18\x11 \x01(\x02\x12\x17\n\x0fPresencePenalty\x18\x12 \x01(\x02\x12\x10\n\x08Mirostat\x18\x13 \x01(\x05\x12\x13\n\x0bMirostatETA\x18\x14 \x01(\x02\x12\x13\n\x0bMirostatTAU\x18\x15 \x01(\x02\x12\x12\n\nPenalizeNL\x18\x16 \x01(\x08\x12\x11\n\tLogitBias\x18\x17 \x01(\t\x12\r\n\x05MLock\x18\x19 \x01(\x08\x12\x0c\n\x04MMap\x18\x1a \x01(\x08\x12\x16\n\x0ePromptCacheAll\x18\x1b \x01(\x08\x12\x15\n\rPromptCacheRO\x18\x1c \x01(\x08\x12\x0f\n\x07Grammar\x18\x1d \x01(\t\x12\x0f\n\x07MainGPU\x18\x1e \x01(\t\x12\x13\n\x0bTensorSplit\x18\x1f \x01(\t\x12\x0c\n\x04TopP\x18  \x01(\x02\x12\x17\n\x0fPromptCachePath\x18! \x01(\t\x12\r\n\x05\x44\x65\x62ug\x18\" \x01(\x08\x12\x17\n\x0f\x45mbeddingTokens\x18# \x03(\x05\x12\x12\n\nEmbeddings\x18$ \x01(\t\x12\x14\n\x0cRopeFreqBase\x18% \x01(\x02\x12\x15\n\rRopeFreqScale\x18& \x01(\x02\x12\x1b\n\x13NegativePromptScale\x18\' \x01(\x02\x12\x16\n\x0eNegativePrompt\x18( \x01(\t\"\x18\n\x05Reply\x12\x0f\n\x07message\x18\x01 \x01(\x0c\"\xd9\x02\n\x0cModelOptions\x12\r\n\x05Model\x18\x01 \x01(\t\x12\x13\n\x0b\x43ontextSize\x18\x02 \x01(\x05\x12\x0c\n\x04Seed\x18\x03 \x01(\x05\x12\x0e\n\x06NBatch\x18\x04 \x01(\x05\x12\x11\n\tF16Memory\x18\x05 \x01(\x08\x12\r\n\x05MLock\x18\x06 \x01(\x08\x12\x0c\n\x04MMap\x18\x07 \x01(\x08\x12\x11\n\tVocabOnly\x18\x08 \x01(\x08\x12\x0f\n\x07LowVRAM\x18\t \x01(\x08\x12\x12\n\nEmbeddings\x18\n \x01(\x08\x12\x0c\n\x04NUMA\x18\x0b \x01(\x08\x12\x12\n\nNGPULayers\x18\x0c \x01(\x05\x12\x0f\n\x07MainGPU\x18\r \x01(\t\x12\x13\n\x0bTensorSplit\x18\x0e \x01(\t\x12\x0f\n\x07Threads\x18\x0f \x01(\x05\x12\x19\n\x11LibrarySearchPath\x18\x10 \x01(\t\x12\x14\n\x0cRopeFreqBase\x18\x11 \x01(\x02\x12\x15\n\rRopeFreqScale\x18\x12 \x01(\x02\"*\n\x06Result\x12\x0f\n\x07message\x18\x01 \x01(\t\x12\x0f\n\x07success\x18\x02 \x01(\x08\"%\n\x0f\x45mbeddingResult\x12\x12\n\nembeddings\x18\x01 \x03(\x02\"C\n\x11TranscriptRequest\x12\x0b\n\x03\x64st\x18\x02 \x01(\t\x12\x10\n\x08language\x18\x03 \x01(\t\x12\x0f\n\x07threads\x18\x04 \x01(\r\"N\n\x10TranscriptResult\x12,\n\x08segments\x18\x01 \x03(\x0b\x32\x1a.backend.TranscriptSegment\x12\x0c\n\x04text\x18\x02 \x01(\t\"Y\n\x11TranscriptSegment\x12\n\n\x02id\x18\x01 \x01(\x05\x12\r\n\x05start\x18\x02 \x01(\x03\x12\x0b\n\x03\x65nd\x18\x03 \x01(\x03\x12\x0c\n\x04text\x18\x04 \x01(\t\x12\x0e\n\x06tokens\x18\x05 \x03(\x05\"\x9e\x01\n\x14GenerateImageRequest\x12\x0e\n\x06height\x18\x01 \x01(\x05\x12\r\n\x05width\x18\x02 \x01(\x05\x12\x0c\n\x04mode\x18\x03 \x01(\x05\x12\x0c\n\x04step\x18\x04 \x01(\x05\x12\x0c\n\x04seed\x18\x05 \x01(\x05\x12\x17\n\x0fpositive_prompt\x18\x06 \x01(\t\x12\x17\n\x0fnegative_prompt\x18\x07 \x01(\t\x12\x0b\n\x03\x64st\x18\x08 \x01(\t\"6\n\nTTSRequest\x12\x0c\n\x04text\x18\x01 \x01(\t\x12\r\n\x05model\x18\x02 \x01(\t\x12\x0b\n\x03\x64st\x18\x03 \x01(\t2\xeb\x03\n\x07\x42\x61\x63kend\x12\x32\n\x06Health\x12\x16.backend.HealthMessage\x1a\x0e.backend.Reply\"\x00\x12\x34\n\x07Predict\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x12\x35\n\tLoadModel\x12\x15.backend.ModelOptions\x1a\x0f.backend.Result\"\x00\x12<\n\rPredictStream\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x30\x01\x12@\n\tEmbedding\x12\x17.backend.PredictOptions\x1a\x18.backend.EmbeddingResult\"\x00\x12\x41\n\rGenerateImage\x12\x1d.backend.GenerateImageRequest\x1a\x0f.backend.Result\"\x00\x12M\n\x12\x41udioTranscription\x12\x1a.backend.TranscriptRequest\x1a\x19.backend.TranscriptResult\"\x00\x12-\n\x03TTS\x12\x13.backend.TTSRequest\x1a\x0f.backend.Result\"\x00\x42Z\n\x19io.skynet.localai.backendB\x0eLocalAIBackendP\x01Z+github.com/go-skynet/LocalAI/pkg/grpc/protob\x06proto3')
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\rbackend.proto\x12\x07\x62\x61\x63kend\"\x0f\n\rHealthMessage\"\x86\x06\n\x0ePredictOptions\x12\x0e\n\x06Prompt\x18\x01 \x01(\t\x12\x0c\n\x04Seed\x18\x02 \x01(\x05\x12\x0f\n\x07Threads\x18\x03 \x01(\x05\x12\x0e\n\x06Tokens\x18\x04 \x01(\x05\x12\x0c\n\x04TopK\x18\x05 \x01(\x05\x12\x0e\n\x06Repeat\x18\x06 \x01(\x05\x12\r\n\x05\x42\x61tch\x18\x07 \x01(\x05\x12\r\n\x05NKeep\x18\x08 \x01(\x05\x12\x13\n\x0bTemperature\x18\t \x01(\x02\x12\x0f\n\x07Penalty\x18\n \x01(\x02\x12\r\n\x05\x46\x31\x36KV\x18\x0b \x01(\x08\x12\x11\n\tDebugMode\x18\x0c \x01(\x08\x12\x13\n\x0bStopPrompts\x18\r \x03(\t\x12\x11\n\tIgnoreEOS\x18\x0e \x01(\x08\x12\x19\n\x11TailFreeSamplingZ\x18\x0f \x01(\x02\x12\x10\n\x08TypicalP\x18\x10 \x01(\x02\x12\x18\n\x10\x46requencyPenalty\x18\x11 \x01(\x02\x12\x17\n\x0fPresencePenalty\x18\x12 \x01(\x02\x12\x10\n\x08Mirostat\x18\x13 \x01(\x05\x12\x13\n\x0bMirostatETA\x18\x14 \x01(\x02\x12\x13\n\x0bMirostatTAU\x18\x15 \x01(\x02\x12\x12\n\nPenalizeNL\x18\x16 \x01(\x08\x12\x11\n\tLogitBias\x18\x17 \x01(\t\x12\r\n\x05MLock\x18\x19 \x01(\x08\x12\x0c\n\x04MMap\x18\x1a \x01(\x08\x12\x16\n\x0ePromptCacheAll\x18\x1b \x01(\x08\x12\x15\n\rPromptCacheRO\x18\x1c \x01(\x08\x12\x0f\n\x07Grammar\x18\x1d \x01(\t\x12\x0f\n\x07MainGPU\x18\x1e \x01(\t\x12\x13\n\x0bTensorSplit\x18\x1f \x01(\t\x12\x0c\n\x04TopP\x18  \x01(\x02\x12\x17\n\x0fPromptCachePath\x18! \x01(\t\x12\r\n\x05\x44\x65\x62ug\x18\" \x01(\x08\x12\x17\n\x0f\x45mbeddingTokens\x18# \x03(\x05\x12\x12\n\nEmbeddings\x18$ \x01(\t\x12\x14\n\x0cRopeFreqBase\x18% \x01(\x02\x12\x15\n\rRopeFreqScale\x18& \x01(\x02\x12\x1b\n\x13NegativePromptScale\x18\' \x01(\x02\x12\x16\n\x0eNegativePrompt\x18( \x01(\t\"\x18\n\x05Reply\x12\x0f\n\x07message\x18\x01 \x01(\x0c\"\x9d\x04\n\x0cModelOptions\x12\r\n\x05Model\x18\x01 \x01(\t\x12\x13\n\x0b\x43ontextSize\x18\x02 \x01(\x05\x12\x0c\n\x04Seed\x18\x03 \x01(\x05\x12\x0e\n\x06NBatch\x18\x04 \x01(\x05\x12\x11\n\tF16Memory\x18\x05 \x01(\x08\x12\r\n\x05MLock\x18\x06 \x01(\x08\x12\x0c\n\x04MMap\x18\x07 \x01(\x08\x12\x11\n\tVocabOnly\x18\x08 \x01(\x08\x12\x0f\n\x07LowVRAM\x18\t \x01(\x08\x12\x12\n\nEmbeddings\x18\n \x01(\x08\x12\x0c\n\x04NUMA\x18\x0b \x01(\x08\x12\x12\n\nNGPULayers\x18\x0c \x01(\x05\x12\x0f\n\x07MainGPU\x18\r \x01(\t\x12\x13\n\x0bTensorSplit\x18\x0e \x01(\t\x12\x0f\n\x07Threads\x18\x0f \x01(\x05\x12\x19\n\x11LibrarySearchPath\x18\x10 \x01(\t\x12\x14\n\x0cRopeFreqBase\x18\x11 \x01(\x02\x12\x15\n\rRopeFreqScale\x18\x12 \x01(\x02\x12\x12\n\nRMSNormEps\x18\x13 \x01(\x02\x12\x0c\n\x04NGQA\x18\x14 \x01(\x05\x12\x11\n\tModelFile\x18\x15 \x01(\t\x12\x0e\n\x06\x44\x65vice\x18\x16 \x01(\t\x12\x11\n\tUseTriton\x18\x17 \x01(\x08\x12\x15\n\rModelBaseName\x18\x18 \x01(\t\x12\x18\n\x10UseFastTokenizer\x18\x19 \x01(\x08\x12\x14\n\x0cPipelineType\x18\x1a \x01(\t\x12\x15\n\rSchedulerType\x18\x1b \x01(\t\x12\x0c\n\x04\x43UDA\x18\x1c \x01(\x08\"*\n\x06Result\x12\x0f\n\x07message\x18\x01 \x01(\t\x12\x0f\n\x07success\x18\x02 \x01(\x08\"%\n\x0f\x45mbeddingResult\x12\x12\n\nembeddings\x18\x01 \x03(\x02\"C\n\x11TranscriptRequest\x12\x0b\n\x03\x64st\x18\x02 \x01(\t\x12\x10\n\x08language\x18\x03 \x01(\t\x12\x0f\n\x07threads\x18\x04 \x01(\r\"N\n\x10TranscriptResult\x12,\n\x08segments\x18\x01 \x03(\x0b\x32\x1a.backend.TranscriptSegment\x12\x0c\n\x04text\x18\x02 \x01(\t\"Y\n\x11TranscriptSegment\x12\n\n\x02id\x18\x01 \x01(\x05\x12\r\n\x05start\x18\x02 \x01(\x03\x12\x0b\n\x03\x65nd\x18\x03 \x01(\x03\x12\x0c\n\x04text\x18\x04 \x01(\t\x12\x0e\n\x06tokens\x18\x05 \x03(\x05\"\x9e\x01\n\x14GenerateImageRequest\x12\x0e\n\x06height\x18\x01 \x01(\x05\x12\r\n\x05width\x18\x02 \x01(\x05\x12\x0c\n\x04mode\x18\x03 \x01(\x05\x12\x0c\n\x04step\x18\x04 \x01(\x05\x12\x0c\n\x04seed\x18\x05 \x01(\x05\x12\x17\n\x0fpositive_prompt\x18\x06 \x01(\t\x12\x17\n\x0fnegative_prompt\x18\x07 \x01(\t\x12\x0b\n\x03\x64st\x18\x08 \x01(\t\"6\n\nTTSRequest\x12\x0c\n\x04text\x18\x01 \x01(\t\x12\r\n\x05model\x18\x02 \x01(\t\x12\x0b\n\x03\x64st\x18\x03 \x01(\t2\xeb\x03\n\x07\x42\x61\x63kend\x12\x32\n\x06Health\x12\x16.backend.HealthMessage\x1a\x0e.backend.Reply\"\x00\x12\x34\n\x07Predict\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x12\x35\n\tLoadModel\x12\x15.backend.ModelOptions\x1a\x0f.backend.Result\"\x00\x12<\n\rPredictStream\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x30\x01\x12@\n\tEmbedding\x12\x17.backend.PredictOptions\x1a\x18.backend.EmbeddingResult\"\x00\x12\x41\n\rGenerateImage\x12\x1d.backend.GenerateImageRequest\x1a\x0f.backend.Result\"\x00\x12M\n\x12\x41udioTranscription\x12\x1a.backend.TranscriptRequest\x1a\x19.backend.TranscriptResult\"\x00\x12-\n\x03TTS\x12\x13.backend.TTSRequest\x1a\x0f.backend.Result\"\x00\x42Z\n\x19io.skynet.localai.backendB\x0eLocalAIBackendP\x01Z+github.com/go-skynet/LocalAI/pkg/grpc/protob\x06proto3')

 _globals = globals()
 _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
@@ -29,21 +29,21 @@ if _descriptor._USE_C_DESCRIPTORS == False:
  _globals['_REPLY']._serialized_start=820
  _globals['_REPLY']._serialized_end=844
  _globals['_MODELOPTIONS']._serialized_start=847
-  _globals['_MODELOPTIONS']._serialized_end=1192
-  _globals['_RESULT']._serialized_start=1194
-  _globals['_RESULT']._serialized_end=1236
-  _globals['_EMBEDDINGRESULT']._serialized_start=1238
-  _globals['_EMBEDDINGRESULT']._serialized_end=1275
-  _globals['_TRANSCRIPTREQUEST']._serialized_start=1277
-  _globals['_TRANSCRIPTREQUEST']._serialized_end=1344
-  _globals['_TRANSCRIPTRESULT']._serialized_start=1346
-  _globals['_TRANSCRIPTRESULT']._serialized_end=1424
-  _globals['_TRANSCRIPTSEGMENT']._serialized_start=1426
-  _globals['_TRANSCRIPTSEGMENT']._serialized_end=1515
-  _globals['_GENERATEIMAGEREQUEST']._serialized_start=1518
-  _globals['_GENERATEIMAGEREQUEST']._serialized_end=1676
-  _globals['_TTSREQUEST']._serialized_start=1678
-  _globals['_TTSREQUEST']._serialized_end=1732
-  _globals['_BACKEND']._serialized_start=1735
-  _globals['_BACKEND']._serialized_end=2226
+  _globals['_MODELOPTIONS']._serialized_end=1388
+  _globals['_RESULT']._serialized_start=1390
+  _globals['_RESULT']._serialized_end=1432
+  _globals['_EMBEDDINGRESULT']._serialized_start=1434
+  _globals['_EMBEDDINGRESULT']._serialized_end=1471
+  _globals['_TRANSCRIPTREQUEST']._serialized_start=1473
+  _globals['_TRANSCRIPTREQUEST']._serialized_end=1540
+  _globals['_TRANSCRIPTRESULT']._serialized_start=1542
+  _globals['_TRANSCRIPTRESULT']._serialized_end=1620
+  _globals['_TRANSCRIPTSEGMENT']._serialized_start=1622
+  _globals['_TRANSCRIPTSEGMENT']._serialized_end=1711
+  _globals['_GENERATEIMAGEREQUEST']._serialized_start=1714
+  _globals['_GENERATEIMAGEREQUEST']._serialized_end=1872
+  _globals['_TTSREQUEST']._serialized_start=1874
+  _globals['_TTSREQUEST']._serialized_end=1928
+  _globals['_BACKEND']._serialized_start=1931
+  _globals['_BACKEND']._serialized_end=2422
 # @@protoc_insertion_point(module_scope)
--- a/extra/grpc/huggingface/huggingface.py
+++ b/extra/grpc/huggingface/huggingface.py
@@ -18,7 +18,6 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
        return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
    def LoadModel(self, request, context):
        model_name = request.Model
-        model_name = os.path.basename(model_name)
        try:
            self.model = SentenceTransformer(model_name)
        except Exception as err:
--- a/go.mod
+++ b/go.mod
@@ -9,7 +9,7 @@ require (
 	github.com/go-skynet/bloomz.cpp v0.0.0-20230529155654-1834e77b83fa
 	github.com/go-skynet/go-bert.cpp v0.0.0-20230716133540-6abe312cded1
 	github.com/go-skynet/go-ggml-transformers.cpp v0.0.0-20230714203132-ffb09d7dd71e
-	github.com/go-skynet/go-llama.cpp v0.0.0-20230729200103-8c51308e42d7
+	github.com/go-skynet/go-llama.cpp v0.0.0-20230802220037-50cee7712066
 	github.com/gofiber/fiber/v2 v2.48.0
 	github.com/google/uuid v1.3.0
 	github.com/hashicorp/go-multierror v1.1.1
@@ -20,14 +20,14 @@ require (
 	github.com/mudler/go-ggllm.cpp v0.0.0-20230709223052-862477d16eef
 	github.com/mudler/go-processmanager v0.0.0-20220724164624-c45b5c61312d
 	github.com/mudler/go-stable-diffusion v0.0.0-20230605122230-d89260f598af
-	github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230731161838-cbdcde8b7586
+	github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230811181453-4d855afe973a
 	github.com/onsi/ginkgo/v2 v2.11.0
 	github.com/onsi/gomega v1.27.10
 	github.com/otiai10/openaigo v1.5.2
 	github.com/phayes/freeport v0.0.0-20220201140144-74d24b5ae9f5
 	github.com/rs/zerolog v1.30.0
 	github.com/sashabaranov/go-openai v1.14.1
-	github.com/tmc/langchaingo v0.0.0-20230731024823-8f101609f600
+	github.com/tmc/langchaingo v0.0.0-20230811231558-fd8b7f099537
 	github.com/urfave/cli/v2 v2.25.7
 	github.com/valyala/fasthttp v1.48.0
 	google.golang.org/grpc v1.57.0
--- a/go.sum
+++ b/go.sum
@@ -47,6 +47,8 @@ github.com/go-skynet/go-llama.cpp v0.0.0-20230727163958-6ba16de8e965 h1:2MO/rABK
 github.com/go-skynet/go-llama.cpp v0.0.0-20230727163958-6ba16de8e965/go.mod h1:fiJBto+Le1XLtD/cID5SAKs8cKE7wFXJKfTT3wvPQRA=
 github.com/go-skynet/go-llama.cpp v0.0.0-20230729200103-8c51308e42d7 h1:1uBwholTaJ8Lva8ySJjT4jNaCDAh+MJXtsbZBbQq9lA=
 github.com/go-skynet/go-llama.cpp v0.0.0-20230729200103-8c51308e42d7/go.mod h1:fiJBto+Le1XLtD/cID5SAKs8cKE7wFXJKfTT3wvPQRA=
+github.com/go-skynet/go-llama.cpp v0.0.0-20230802220037-50cee7712066 h1:v4Js+yEdgY9IV7n35M+5MELLxlOMp3qC5whZm5YTLjI=
+github.com/go-skynet/go-llama.cpp v0.0.0-20230802220037-50cee7712066/go.mod h1:fiJBto+Le1XLtD/cID5SAKs8cKE7wFXJKfTT3wvPQRA=
 github.com/go-task/slim-sprig v0.0.0-20210107165309-348f09dbbbc0/go.mod h1:fyg7847qk6SyHyPtNmDHnmrv/HOrqktSC+C9fM+CJOE=
 github.com/go-task/slim-sprig v0.0.0-20230315185526-52ccab3ef572 h1:tfuBGBXKqDEevZMzYi5KSi8KkcZtzBcTgAUUtapy0OI=
 github.com/go-task/slim-sprig v0.0.0-20230315185526-52ccab3ef572/go.mod h1:9Pwr4B2jHnOSGXyyzV8ROjYa2ojvAY6HCGYYfMoC3Ls=
@@ -132,6 +134,12 @@ github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230727161923-39acbc
 github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230727161923-39acbc837816/go.mod h1:4T3CHXyrt+7FQHXaxULZfPjHbD8/99WuDDJa0YVZARI=
 github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230731161838-cbdcde8b7586 h1:WVEMSZMyHFe68PN204c3Fdk5g2lZouPvbU9/2zkPpWc=
 github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230731161838-cbdcde8b7586/go.mod h1:4T3CHXyrt+7FQHXaxULZfPjHbD8/99WuDDJa0YVZARI=
+github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230802145814-c449b71b56de h1:E5EGczxEAcbaO8yqj074MQxU609QbtB6in3qTOW1EFo=
+github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230802145814-c449b71b56de/go.mod h1:4T3CHXyrt+7FQHXaxULZfPjHbD8/99WuDDJa0YVZARI=
+github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230807175413-0f2bb506a8ee h1:Y/j+GNytyncmDnAEuDZwzkYC9nzUPvXJPF+nntQG0VU=
+github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230807175413-0f2bb506a8ee/go.mod h1:4T3CHXyrt+7FQHXaxULZfPjHbD8/99WuDDJa0YVZARI=
+github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230811181453-4d855afe973a h1:bX26Zfwh72ug2aZTEwFISTMEJ56Wa/4KqboidD+g92A=
+github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230811181453-4d855afe973a/go.mod h1:4T3CHXyrt+7FQHXaxULZfPjHbD8/99WuDDJa0YVZARI=
 github.com/nwaples/rardecode v1.1.0 h1:vSxaY8vQhOcVr4mm5e8XllHWTiM4JF507A0Katqw7MQ=
 github.com/nwaples/rardecode v1.1.0/go.mod h1:5DzqNKiOdpKKBH87u8VlvAnPZMXcGRhxWkRpHbbfGS0=
 github.com/nxadm/tail v1.4.4/go.mod h1:kenIhsEOeOJmVchQTgglprH7qJGnHDVpk1VPCcaMI8A=
@@ -189,6 +197,10 @@ github.com/tmc/langchaingo v0.0.0-20230729232647-7df4fe5fb8fe h1:+XVrCjh3rPibfIS
 github.com/tmc/langchaingo v0.0.0-20230729232647-7df4fe5fb8fe/go.mod h1:8T+nNIGBr3nYQEYFmF/YaT8t8YTKLvFYZBuVZOAYn5E=
 github.com/tmc/langchaingo v0.0.0-20230731024823-8f101609f600 h1:SABuIthjhIXEsxnokuA16CZOxxdW9XohIHQqd/go8Nc=
 github.com/tmc/langchaingo v0.0.0-20230731024823-8f101609f600/go.mod h1:8T+nNIGBr3nYQEYFmF/YaT8t8YTKLvFYZBuVZOAYn5E=
+github.com/tmc/langchaingo v0.0.0-20230802030916-271e9bd7e7c5 h1:js7vYDJGzUGVSt0YlIusUc5BXYVECu3LUI/asby5Ggo=
+github.com/tmc/langchaingo v0.0.0-20230802030916-271e9bd7e7c5/go.mod h1:8T+nNIGBr3nYQEYFmF/YaT8t8YTKLvFYZBuVZOAYn5E=
+github.com/tmc/langchaingo v0.0.0-20230811231558-fd8b7f099537 h1:vkeNjlW+0Xiw2XizMHoQuLG8pg6AN1hU8zJuMV9GQBc=
+github.com/tmc/langchaingo v0.0.0-20230811231558-fd8b7f099537/go.mod h1:8T+nNIGBr3nYQEYFmF/YaT8t8YTKLvFYZBuVZOAYn5E=
 github.com/ulikunitz/xz v0.5.8/go.mod h1:nbz6k7qbPmH4IRqmfOplQw/tblSgqTqBwxkY0oWt/14=
 github.com/ulikunitz/xz v0.5.9 h1:RsKRIA2MO8x56wkkcd3LbtcE/uMszhb6DpRf+3uwa3I=
 github.com/ulikunitz/xz v0.5.9/go.mod h1:nbz6k7qbPmH4IRqmfOplQw/tblSgqTqBwxkY0oWt/14=
--- a/main.go
+++ b/main.go
@@ -130,6 +130,11 @@ func main() {
 				EnvVars: []string{"UPLOAD_LIMIT"},
 				Value:   15,
 			},
+			&cli.StringSliceFlag{
+				Name:    "api-keys",
+				Usage:   "List of API Keys to enable API authentication. When this is set, all the requests must be authenticated with one of these API keys.",
+				EnvVars: []string{"API_KEY"},
+			},
 		},
 		Description: `
 LocalAI is a drop-in replacement OpenAI API which runs inference locally.
@@ -167,6 +172,7 @@ For a list of compatible model, check out: https://localai.io/model-compatibilit
 				options.WithBackendAssets(backendAssets),
 				options.WithBackendAssetsOutput(ctx.String("backend-assets-path")),
 				options.WithUploadLimitMB(ctx.Int("upload-limit")),
+				options.WithApiKeys(ctx.StringSlice("api-keys")),
 			}

 			externalgRPC := ctx.StringSlice("external-grpc-backends")
--- a/pkg/gallery/gallery.go
+++ b/pkg/gallery/gallery.go
@@ -85,7 +85,7 @@ func InstallModelFromGalleryByName(galleries []Gallery, name string, basePath st
 	name = strings.ReplaceAll(name, string(os.PathSeparator), "__")
 	var model *GalleryModel
 	for _, m := range models {
-		if name == m.Name || name == strings.ToLower(m.Name) {
+		if name == m.Name || m.Name == strings.ToLower(name) {
 			model = m
 		}
 	}
--- a/pkg/grammar/json_schema.go
+++ b/pkg/grammar/json_schema.go
@@ -15,10 +15,13 @@ var (

 	PRIMITIVE_RULES = map[string]string{
 		"boolean": `("true" | "false") space`,
-		"number":  `[0-9]+ space`,                    // TODO complete
-		"integer": `[0-9]+ space`,                    // TODO complete
-		"string":  `"\"" [ \t!#-\[\]-~]* "\"" space`, // TODO complete
-		"null":    `"null" space`,
+		"number":  `("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space`,
+		"integer": `("-"? ([0-9] | [1-9] [0-9]*)) space`,
+		"string": `"\"" (
+			[^"\\] |
+			"\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+		  )* "\"" space`,
+		"null": `"null" space`,
 	}

 	INVALID_RULE_CHARS_RE     = regexp.MustCompile(`[^a-zA-Z0-9-]+`)
@@ -176,6 +179,9 @@ func (sc *JSONSchemaConverter) visit(schema map[string]interface{}, name string,
 		if !exists {
 			panic(fmt.Sprintf("Unrecognized schema: %v", schema))
 		}
+		if ruleName == "root" {
+			schemaType = "root"
+		}
 		return sc.addRule(schemaType, primitiveRule)
 	}
 }
--- a/pkg/grammar/json_schema_test.go
+++ b/pkg/grammar/json_schema_test.go
@@ -48,7 +48,10 @@ root ::= root-0 | root-1
 space ::= " "?
 root-0-arguments ::= "{" space "\"date\"" space ":" space string "," space "\"time\"" space ":" space string "," space "\"title\"" space ":" space string "}" space
 root-1 ::= "{" space "\"arguments\"" space ":" space root-1-arguments "," space "\"function\"" space ":" space root-1-function "}" space
-string ::= "\"" [ \t!#-\[\]-~]* "\"" space
+string ::= "\"" (
+	[^"\\] |
+	"\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
+)* "\"" space
 root-1-function ::= "\"search\""`
 )

--- a/pkg/grpc/image/stablediffusion.go
+++ b/pkg/grpc/image/stablediffusion.go
@@ -16,7 +16,7 @@ type StableDiffusion struct {
 func (sd *StableDiffusion) Load(opts *pb.ModelOptions) error {
 	var err error
 	// Note: the Model here is a path to a directory containing the model files
-	sd.stablediffusion, err = stablediffusion.New(opts.Model)
+	sd.stablediffusion, err = stablediffusion.New(opts.ModelFile)
 	return err
 }

--- a/pkg/grpc/llm/bert/bert.go
+++ b/pkg/grpc/llm/bert/bert.go
@@ -15,7 +15,7 @@ type Embeddings struct {
 }

 func (llm *Embeddings) Load(opts *pb.ModelOptions) error {
-	model, err := bert.New(opts.Model)
+	model, err := bert.New(opts.ModelFile)
 	llm.bert = model
 	return err
 }
--- a/pkg/grpc/llm/bloomz/bloomz.go
+++ b/pkg/grpc/llm/bloomz/bloomz.go
@@ -18,7 +18,7 @@ type LLM struct {
 }

 func (llm *LLM) Load(opts *pb.ModelOptions) error {
-	model, err := bloomz.New(opts.Model)
+	model, err := bloomz.New(opts.ModelFile)
 	llm.bloomz = model
 	return err
 }
--- a/pkg/grpc/llm/falcon/falcon.go
+++ b/pkg/grpc/llm/falcon/falcon.go
@@ -40,7 +40,7 @@ func (llm *LLM) Load(opts *pb.ModelOptions) error {
 		ggllmOpts = append(ggllmOpts, ggllm.SetNBatch(512))
 	}

-	model, err := ggllm.New(opts.Model, ggllmOpts...)
+	model, err := ggllm.New(opts.ModelFile, ggllmOpts...)
 	llm.falcon = model
 	return err
 }
--- a/pkg/grpc/llm/gpt4all/gpt4all.go
+++ b/pkg/grpc/llm/gpt4all/gpt4all.go
@@ -17,7 +17,7 @@ type LLM struct {
 }

 func (llm *LLM) Load(opts *pb.ModelOptions) error {
-	model, err := gpt4all.New(opts.Model,
+	model, err := gpt4all.New(opts.ModelFile,
 		gpt4all.SetThreads(int(opts.Threads)),
 		gpt4all.SetLibrarySearchPath(opts.LibrarySearchPath))
 	llm.gpt4all = model
--- a/pkg/grpc/llm/llama/llama.go
+++ b/pkg/grpc/llm/llama/llama.go
@@ -33,6 +33,14 @@ func (llm *LLM) Load(opts *pb.ModelOptions) error {
 		llama.WithRopeFreqScale(ropeFreqScale),
 	}

+	if opts.NGQA != 0 {
+		llamaOpts = append(llamaOpts, llama.WithGQA(int(opts.NGQA)))
+	}
+
+	if opts.RMSNormEps != 0 {
+		llamaOpts = append(llamaOpts, llama.WithRMSNormEPS(opts.RMSNormEps))
+	}
+
 	if opts.ContextSize != 0 {
 		llamaOpts = append(llamaOpts, llama.SetContext(int(opts.ContextSize)))
 	}
@@ -63,7 +71,7 @@ func (llm *LLM) Load(opts *pb.ModelOptions) error {
 		llamaOpts = append(llamaOpts, llama.EnabelLowVRAM)
 	}

-	model, err := llama.New(opts.Model, llamaOpts...)
+	model, err := llama.New(opts.ModelFile, llamaOpts...)
 	llm.llama = model
 	return err
 }
--- a/pkg/grpc/llm/rwkv/rwkv.go
+++ b/pkg/grpc/llm/rwkv/rwkv.go
@@ -20,9 +20,9 @@ type LLM struct {
 }

 func (llm *LLM) Load(opts *pb.ModelOptions) error {
-	modelPath := filepath.Dir(opts.Model)
-	modelFile := filepath.Base(opts.Model)
-	model := rwkv.LoadFiles(opts.Model, filepath.Join(modelPath, modelFile+tokenizerSuffix), uint32(opts.GetThreads()))
+	modelPath := filepath.Dir(opts.ModelFile)
+	modelFile := filepath.Base(opts.ModelFile)
+	model := rwkv.LoadFiles(opts.ModelFile, filepath.Join(modelPath, modelFile+tokenizerSuffix), uint32(opts.GetThreads()))

 	if model == nil {
 		return fmt.Errorf("could not load model")
--- a/pkg/grpc/llm/transformers/dolly.go
+++ b/pkg/grpc/llm/transformers/dolly.go
@@ -18,7 +18,7 @@ type Dolly struct {
 }

 func (llm *Dolly) Load(opts *pb.ModelOptions) error {
-	model, err := transformers.NewDolly(opts.Model)
+	model, err := transformers.NewDolly(opts.ModelFile)
 	llm.dolly = model
 	return err
 }
--- a/pkg/grpc/llm/transformers/falcon.go
+++ b/pkg/grpc/llm/transformers/falcon.go
@@ -18,7 +18,7 @@ type Falcon struct {
 }

 func (llm *Falcon) Load(opts *pb.ModelOptions) error {
-	model, err := transformers.NewFalcon(opts.Model)
+	model, err := transformers.NewFalcon(opts.ModelFile)
 	llm.falcon = model
 	return err
 }
--- a/pkg/grpc/llm/transformers/gpt2.go
+++ b/pkg/grpc/llm/transformers/gpt2.go
@@ -18,7 +18,7 @@ type GPT2 struct {
 }

 func (llm *GPT2) Load(opts *pb.ModelOptions) error {
-	model, err := transformers.New(opts.Model)
+	model, err := transformers.New(opts.ModelFile)
 	llm.gpt2 = model
 	return err
 }
--- a/pkg/grpc/llm/transformers/gptj.go
+++ b/pkg/grpc/llm/transformers/gptj.go
@@ -18,7 +18,7 @@ type GPTJ struct {
 }

 func (llm *GPTJ) Load(opts *pb.ModelOptions) error {
-	model, err := transformers.NewGPTJ(opts.Model)
+	model, err := transformers.NewGPTJ(opts.ModelFile)
 	llm.gptj = model
 	return err
 }
--- a/pkg/grpc/llm/transformers/gptneox.go
+++ b/pkg/grpc/llm/transformers/gptneox.go
@@ -18,7 +18,7 @@ type GPTNeoX struct {
 }

 func (llm *GPTNeoX) Load(opts *pb.ModelOptions) error {
-	model, err := transformers.NewGPTNeoX(opts.Model)
+	model, err := transformers.NewGPTNeoX(opts.ModelFile)
 	llm.gptneox = model
 	return err
 }
--- a/pkg/grpc/llm/transformers/mpt.go
+++ b/pkg/grpc/llm/transformers/mpt.go
@@ -18,7 +18,7 @@ type MPT struct {
 }

 func (llm *MPT) Load(opts *pb.ModelOptions) error {
-	model, err := transformers.NewMPT(opts.Model)
+	model, err := transformers.NewMPT(opts.ModelFile)
 	llm.mpt = model
 	return err
 }
--- a/pkg/grpc/llm/transformers/replit.go
+++ b/pkg/grpc/llm/transformers/replit.go
@@ -18,7 +18,7 @@ type Replit struct {
 }

 func (llm *Replit) Load(opts *pb.ModelOptions) error {
-	model, err := transformers.NewReplit(opts.Model)
+	model, err := transformers.NewReplit(opts.ModelFile)
 	llm.replit = model
 	return err
 }
--- a/pkg/grpc/llm/transformers/starcoder.go
+++ b/pkg/grpc/llm/transformers/starcoder.go
@@ -18,7 +18,7 @@ type Starcoder struct {
 }

 func (llm *Starcoder) Load(opts *pb.ModelOptions) error {
-	model, err := transformers.NewStarcoder(opts.Model)
+	model, err := transformers.NewStarcoder(opts.ModelFile)
 	llm.starcoder = model
 	return err
 }
--- a/pkg/grpc/proto/backend.pb.go
+++ b/pkg/grpc/proto/backend.pb.go
@@ -481,6 +481,18 @@ type ModelOptions struct {
 	LibrarySearchPath string  `protobuf:"bytes,16,opt,name=LibrarySearchPath,proto3" json:"LibrarySearchPath,omitempty"`
 	RopeFreqBase      float32 `protobuf:"fixed32,17,opt,name=RopeFreqBase,proto3" json:"RopeFreqBase,omitempty"`
 	RopeFreqScale     float32 `protobuf:"fixed32,18,opt,name=RopeFreqScale,proto3" json:"RopeFreqScale,omitempty"`
+	RMSNormEps        float32 `protobuf:"fixed32,19,opt,name=RMSNormEps,proto3" json:"RMSNormEps,omitempty"`
+	NGQA              int32   `protobuf:"varint,20,opt,name=NGQA,proto3" json:"NGQA,omitempty"`
+	ModelFile         string  `protobuf:"bytes,21,opt,name=ModelFile,proto3" json:"ModelFile,omitempty"`
+	// AutoGPTQ
+	Device           string `protobuf:"bytes,22,opt,name=Device,proto3" json:"Device,omitempty"`
+	UseTriton        bool   `protobuf:"varint,23,opt,name=UseTriton,proto3" json:"UseTriton,omitempty"`
+	ModelBaseName    string `protobuf:"bytes,24,opt,name=ModelBaseName,proto3" json:"ModelBaseName,omitempty"`
+	UseFastTokenizer bool   `protobuf:"varint,25,opt,name=UseFastTokenizer,proto3" json:"UseFastTokenizer,omitempty"`
+	// Diffusers
+	PipelineType  string `protobuf:"bytes,26,opt,name=PipelineType,proto3" json:"PipelineType,omitempty"`
+	SchedulerType string `protobuf:"bytes,27,opt,name=SchedulerType,proto3" json:"SchedulerType,omitempty"`
+	CUDA          bool   `protobuf:"varint,28,opt,name=CUDA,proto3" json:"CUDA,omitempty"`
 }

 func (x *ModelOptions) Reset() {
@@ -641,6 +653,76 @@ func (x *ModelOptions) GetRopeFreqScale() float32 {
 	return 0
 }

+func (x *ModelOptions) GetRMSNormEps() float32 {
+	if x != nil {
+		return x.RMSNormEps
+	}
+	return 0
+}
+
+func (x *ModelOptions) GetNGQA() int32 {
+	if x != nil {
+		return x.NGQA
+	}
+	return 0
+}
+
+func (x *ModelOptions) GetModelFile() string {
+	if x != nil {
+		return x.ModelFile
+	}
+	return ""
+}
+
+func (x *ModelOptions) GetDevice() string {
+	if x != nil {
+		return x.Device
+	}
+	return ""
+}
+
+func (x *ModelOptions) GetUseTriton() bool {
+	if x != nil {
+		return x.UseTriton
+	}
+	return false
+}
+
+func (x *ModelOptions) GetModelBaseName() string {
+	if x != nil {
+		return x.ModelBaseName
+	}
+	return ""
+}
+
+func (x *ModelOptions) GetUseFastTokenizer() bool {
+	if x != nil {
+		return x.UseFastTokenizer
+	}
+	return false
+}
+
+func (x *ModelOptions) GetPipelineType() string {
+	if x != nil {
+		return x.PipelineType
+	}
+	return ""
+}
+
+func (x *ModelOptions) GetSchedulerType() string {
+	if x != nil {
+		return x.SchedulerType
+	}
+	return ""
+}
+
+func (x *ModelOptions) GetCUDA() bool {
+	if x != nil {
+		return x.CUDA
+	}
+	return false
+}
+
 type Result struct {
 	state         protoimpl.MessageState
 	sizeCache     protoimpl.SizeCache
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 }

 var (
--- a/pkg/grpc/proto/backend.proto
+++ b/pkg/grpc/proto/backend.proto
@@ -87,6 +87,20 @@ message ModelOptions {
  string LibrarySearchPath = 16;
  float RopeFreqBase = 17;
  float RopeFreqScale = 18;
+  float RMSNormEps = 19;
+  int32 NGQA = 20;
+  string ModelFile = 21;
+
+  // AutoGPTQ
+  string Device = 22;
+  bool UseTriton = 23;
+  string ModelBaseName = 24;
+  bool UseFastTokenizer = 25;
+
+  // Diffusers
+  string PipelineType = 26;
+  string SchedulerType = 27;
+  bool CUDA = 28;
 }

 message Result {
--- a/pkg/grpc/transcribe/whisper.go
+++ b/pkg/grpc/transcribe/whisper.go
@@ -17,7 +17,7 @@ type Whisper struct {

 func (sd *Whisper) Load(opts *pb.ModelOptions) error {
 	// Note: the Model here is a path to a directory containing the model files
-	w, err := whisper.New(opts.Model)
+	w, err := whisper.New(opts.ModelFile)
 	sd.whisper = w
 	return err
 }
--- a/pkg/grpc/tts/piper.go
+++ b/pkg/grpc/tts/piper.go
@@ -18,8 +18,8 @@ type Piper struct {
 }

 func (sd *Piper) Load(opts *pb.ModelOptions) error {
-	if filepath.Ext(opts.Model) != ".onnx" {
-		return fmt.Errorf("unsupported model type %s (should end with .onnx)", opts.Model)
+	if filepath.Ext(opts.ModelFile) != ".onnx" {
+		return fmt.Errorf("unsupported model type %s (should end with .onnx)", opts.ModelFile)
 	}
 	var err error
 	// Note: the Model here is a path to a directory containing the model files
--- a/pkg/model/initializers.go
+++ b/pkg/model/initializers.go
@@ -83,7 +83,9 @@ func (ml *ModelLoader) startProcess(grpcProcess, id string, serverAddress string
 	grpcControlProcess := process.New(
 		process.WithTemporaryStateDir(),
 		process.WithName(grpcProcess),
-		process.WithArgs("--addr", serverAddress))
+		process.WithArgs("--addr", serverAddress),
+		process.WithEnvironment(os.Environ()...),
+	)

 	ml.grpcProcesses[id] = grpcControlProcess

@@ -124,8 +126,8 @@ func (ml *ModelLoader) startProcess(grpcProcess, id string, serverAddress string

 // starts the grpcModelProcess for the backend, and returns a grpc client
 // It also loads the model
-func (ml *ModelLoader) grpcModel(backend string, o *Options) func(string) (*grpc.Client, error) {
-	return func(s string) (*grpc.Client, error) {
+func (ml *ModelLoader) grpcModel(backend string, o *Options) func(string, string) (*grpc.Client, error) {
+	return func(modelName, modelFile string) (*grpc.Client, error) {
 		log.Debug().Msgf("Loading GRPC Model %s: %+v", backend, *o)

 		var client *grpc.Client
@@ -148,7 +150,7 @@ func (ml *ModelLoader) grpcModel(backend string, o *Options) func(string) (*grpc
 					return nil, fmt.Errorf("failed allocating free ports: %s", err.Error())
 				}
 				// Make sure the process is executable
-				if err := ml.startProcess(uri, o.modelFile, serverAddress); err != nil {
+				if err := ml.startProcess(uri, o.model, serverAddress); err != nil {
 					return nil, err
 				}

@@ -172,7 +174,7 @@ func (ml *ModelLoader) grpcModel(backend string, o *Options) func(string) (*grpc
 			}

 			// Make sure the process is executable
-			if err := ml.startProcess(grpcProcess, o.modelFile, serverAddress); err != nil {
+			if err := ml.startProcess(grpcProcess, o.model, serverAddress); err != nil {
 				return nil, err
 			}

@@ -198,7 +200,8 @@ func (ml *ModelLoader) grpcModel(backend string, o *Options) func(string) (*grpc
 		}

 		options := *o.gRPCOptions
-		options.Model = s
+		options.Model = modelName
+		options.ModelFile = modelFile

 		log.Debug().Msgf("GRPC: Loading model with options: %+v", options)

@@ -217,14 +220,14 @@ func (ml *ModelLoader) grpcModel(backend string, o *Options) func(string) (*grpc
 func (ml *ModelLoader) BackendLoader(opts ...Option) (model *grpc.Client, err error) {
 	o := NewOptions(opts...)

-	log.Debug().Msgf("Loading model %s from %s", o.backendString, o.modelFile)
+	log.Debug().Msgf("Loading model %s from %s", o.backendString, o.model)

 	backend := strings.ToLower(o.backendString)

 	// if an external backend is provided, use it
 	_, externalBackendExists := o.externalBackends[backend]
 	if externalBackendExists {
-		return ml.LoadModel(o.modelFile, ml.grpcModel(backend, o))
+		return ml.LoadModel(o.model, ml.grpcModel(backend, o))
 	}

 	switch backend {
@@ -232,13 +235,13 @@ func (ml *ModelLoader) BackendLoader(opts ...Option) (model *grpc.Client, err er
 		MPTBackend, Gpt2Backend, FalconBackend,
 		GPTNeoXBackend, ReplitBackend, StarcoderBackend, BloomzBackend,
 		RwkvBackend, LCHuggingFaceBackend, BertEmbeddingsBackend, FalconGGMLBackend, StableDiffusionBackend, WhisperBackend:
-		return ml.LoadModel(o.modelFile, ml.grpcModel(backend, o))
+		return ml.LoadModel(o.model, ml.grpcModel(backend, o))
 	case Gpt4AllLlamaBackend, Gpt4AllMptBackend, Gpt4AllJBackend, Gpt4All:
 		o.gRPCOptions.LibrarySearchPath = filepath.Join(o.assetDir, "backend-assets", "gpt4all")
-		return ml.LoadModel(o.modelFile, ml.grpcModel(Gpt4All, o))
+		return ml.LoadModel(o.model, ml.grpcModel(Gpt4All, o))
 	case PiperBackend:
 		o.gRPCOptions.LibrarySearchPath = filepath.Join(o.assetDir, "backend-assets", "espeak-ng-data")
-		return ml.LoadModel(o.modelFile, ml.grpcModel(PiperBackend, o))
+		return ml.LoadModel(o.model, ml.grpcModel(PiperBackend, o))
 	default:
 		return nil, fmt.Errorf("backend unsupported: %s", o.backendString)
 	}
@@ -249,8 +252,8 @@ func (ml *ModelLoader) GreedyLoader(opts ...Option) (*grpc.Client, error) {

 	// Is this really needed? BackendLoader already does this
 	ml.mu.Lock()
-	if m := ml.checkIsLoaded(o.modelFile); m != nil {
-		log.Debug().Msgf("Model '%s' already loaded", o.modelFile)
+	if m := ml.checkIsLoaded(o.model); m != nil {
+		log.Debug().Msgf("Model '%s' already loaded", o.model)
 		ml.mu.Unlock()
 		return m, nil
 	}
@@ -263,14 +266,14 @@ func (ml *ModelLoader) GreedyLoader(opts ...Option) (*grpc.Client, error) {
 	for _, b := range o.externalBackends {
 		allBackendsToAutoLoad = append(allBackendsToAutoLoad, b)
 	}
-	log.Debug().Msgf("Loading model '%s' greedly from all the available backends: %s", o.modelFile, strings.Join(allBackendsToAutoLoad, ", "))
+	log.Debug().Msgf("Loading model '%s' greedly from all the available backends: %s", o.model, strings.Join(allBackendsToAutoLoad, ", "))

 	for _, b := range allBackendsToAutoLoad {
 		log.Debug().Msgf("[%s] Attempting to load", b)
 		options := []Option{
 			WithBackendString(b),
-			WithModelFile(o.modelFile),
-			WithLoadGRPCLLMModelOpts(o.gRPCOptions),
+			WithModel(o.model),
+			WithLoadGRPCLoadModelOpts(o.gRPCOptions),
 			WithThreads(o.threads),
 			WithAssetDir(o.assetDir),
 		}
--- a/pkg/model/loader.go
+++ b/pkg/model/loader.go
@@ -20,10 +20,12 @@ import (
 // These are the definitions of all possible variables LocalAI will currently populate for use in a prompt template file
 // Please note: Not all of these are populated on every endpoint - your template should either be tested for each endpoint you map it to, or tolerant of zero values.
 type PromptTemplateData struct {
-	Input        string
-	Instruction  string
-	Functions    []grammar.Function
-	MessageIndex int
+	SystemPrompt         string
+	SuppressSystemPrompt bool // used by chat specifically to indicate that SystemPrompt above should be _ignored_
+	Input                string
+	Instruction          string
+	Functions            []grammar.Function
+	MessageIndex         int
 }

 // TODO: Ask mudler about FunctionCall stuff being useful at the message level?
@@ -96,7 +98,7 @@ func (ml *ModelLoader) ListModels() ([]string, error) {
 	return models, nil
 }

-func (ml *ModelLoader) LoadModel(modelName string, loader func(string) (*grpc.Client, error)) (*grpc.Client, error) {
+func (ml *ModelLoader) LoadModel(modelName string, loader func(string, string) (*grpc.Client, error)) (*grpc.Client, error) {
 	ml.mu.Lock()
 	defer ml.mu.Unlock()

@@ -109,7 +111,7 @@ func (ml *ModelLoader) LoadModel(modelName string, loader func(string) (*grpc.Cl
 	modelFile := filepath.Join(ml.ModelPath, modelName)
 	log.Debug().Msgf("Loading model in memory from file: %s", modelFile)

-	model, err := loader(modelFile)
+	model, err := loader(modelName, modelFile)
 	if err != nil {
 		return nil, err
 	}
--- a/pkg/model/options.go
+++ b/pkg/model/options.go
@@ -8,7 +8,7 @@ import (

 type Options struct {
 	backendString string
-	modelFile     string
+	model         string
 	threads       uint32
 	assetDir      string
 	context       context.Context
@@ -35,13 +35,13 @@ func WithBackendString(backend string) Option {
 	}
 }

-func WithModelFile(modelFile string) Option {
+func WithModel(modelFile string) Option {
 	return func(o *Options) {
-		o.modelFile = modelFile
+		o.model = modelFile
 	}
 }

-func WithLoadGRPCLLMModelOpts(opts *pb.ModelOptions) Option {
+func WithLoadGRPCLoadModelOpts(opts *pb.ModelOptions) Option {
 	return func(o *Options) {
 		o.gRPCOptions = opts
 	}
--- a/pkg/utils/json.go
+++ b/pkg/utils/json.go
@@ -0,0 +1,13 @@
+package utils
+
+import "regexp"
+
+var matchNewlines = regexp.MustCompile(`[\r\n]`)
+
+const doubleQuote = `"[^"\\]*(?:\\[\s\S][^"\\]*)*"`
+
+func EscapeNewLines(s string) string {
+	return regexp.MustCompile(doubleQuote).ReplaceAllStringFunc(s, func(s string) string {
+		return matchNewlines.ReplaceAllString(s, "\\n")
+	})
+}
Author	SHA1	Message	Date
Ettore Di Giacinto	a6c621ef7f	feat: pre-configure LocalAI galleries (#886 ) Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2023-08-12 11:25:17 +02:00
renovate[bot]	328289099a	fix(deps): update github.com/nomic-ai/gpt4all/gpt4all-bindings/golang digest to 4d855af (#875 ) Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>	2023-08-12 08:58:55 +02:00
renovate[bot]	22ffd5f490	fix(deps): update github.com/tmc/langchaingo digest to fd8b7f0 (#882 ) Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>	2023-08-12 08:56:15 +02:00
Ettore Di Giacinto	81708bb1e6	fix: workaround exllama import error (#885 ) Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2023-08-12 08:56:01 +02:00
Ettore Di Giacinto	c81e9d8d1f	fix: add exllama to protogen	2023-08-11 01:02:31 +02:00
Ettore Di Giacinto	ff3ab5fcca	feat: Add exllama (#881 ) Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2023-08-11 00:49:40 +02:00
Michael Nesbitt	1d1cae8e4d	feat: add API_KEY list support (#877 ) Co-authored-by: Harold Sun <sunhua@amazon.com>	2023-08-10 00:06:21 +02:00
Ettore Di Giacinto	8c781a6a44	feat: Add Diffusers (#874 ) Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2023-08-09 08:38:51 +02:00
Ettore Di Giacinto	93a4bec06b	fix: upgrade pip (#872 ) Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2023-08-08 23:20:03 +02:00
renovate[bot]	c93f57efd6	fix(deps): update github.com/nomic-ai/gpt4all/gpt4all-bindings/golang digest to 0f2bb50 (#869 ) Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>	2023-08-08 21:57:16 +02:00
ci-robbot [bot]	0e4f93c5cf	⬆️ Update nomic-ai/gpt4all (#870 ) Signed-off-by: GitHub <noreply@github.com> Co-authored-by: mudler <mudler@users.noreply.github.com>	2023-08-08 21:57:01 +02:00
Ettore Di Giacinto	5b3fedebfe	feat: add bark and AutoGPTQ (#871 )	2023-08-08 20:41:49 +02:00
Ettore Di Giacinto	219751bb21	fix: cut prompt from AutoGPTQ answers Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2023-08-08 01:27:38 +02:00
Ettore Di Giacinto	bb7772a364	fix: byte utf-8 encode results from autogptq Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2023-08-08 01:20:07 +02:00
Ettore Di Giacinto	3c8fc37c56	feat: Add UseFastTokenizer Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2023-08-08 01:10:05 +02:00
Ettore Di Giacinto	39805b09e5	fix: pass by env in managed services Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2023-08-08 00:58:38 +02:00
Ettore Di Giacinto	63b01199fe	fix: match lowercase of the input, not of the model	2023-08-08 00:46:22 +02:00
Ettore Di Giacinto	b09bae3443	fix: autogptq requirements Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2023-08-08 00:22:15 +02:00
Ettore Di Giacinto	de6fb98bed	feat: register autogptq and bark in the container image	2023-08-07 22:53:28 +02:00
Ettore Di Giacinto	433605e282	feat: add initial Bark backend implementation Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2023-08-07 22:53:28 +02:00
Ettore Di Giacinto	a843e64fc2	feat: add initial AutoGPTQ backend implementation	2023-08-07 22:53:28 +02:00
scott4290	71611d2dec	docs: base-Update comments in .env for cublas, openblas, clblas (#867 )	2023-08-07 08:22:42 +00:00
Ettore Di Giacinto	abf48e8a5d	readme: link to hot topics in the website	2023-08-07 00:31:46 +02:00
Ettore Di Giacinto	ac5ea0cd4d	readme: link usage to docs	2023-08-07 00:04:28 +02:00
Ettore Di Giacinto	a46fcacedd	readme: simplify, remove dups with website	2023-08-07 00:01:01 +02:00
Ettore Di Giacinto	df947fc933	examples: Update README	2023-08-06 23:07:06 +02:00
Ettore Di Giacinto	91d49cfe9f	Update README.md	2023-08-06 11:57:28 +02:00
Ettore Di Giacinto	19d15f83db	Update README.md	2023-08-06 00:04:06 +02:00
Ettore Di Giacinto	cde61cc518	Update README.md	2023-08-05 23:14:09 +02:00
Ettore Di Giacinto	acd829a7a0	fix: do not break on newlines on function returns (#864 ) Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2023-08-04 21:46:36 +02:00
Ettore Di Giacinto	4aa5dac768	feat: update integer, number and string rules - allow primitives as root types (#862 ) Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2023-08-03 23:32:30 +02:00
renovate[bot]	08b59b5cc5	fix(deps): update github.com/go-skynet/go-llama.cpp digest to 50cee77 (#861 ) Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>	2023-08-03 19:08:04 +02:00
ci-robbot [bot]	6b900e28cd	⬆️ Update nomic-ai/gpt4all (#859 ) Signed-off-by: GitHub <noreply@github.com> Co-authored-by: mudler <mudler@users.noreply.github.com>	2023-08-03 19:07:53 +02:00
Ettore Di Giacinto	5ca21ee398	feat: add ngqa and RMSNormEps parameters (#860 ) Signed-off-by: Ettore Di Giacinto <mudler@localai.io>	2023-08-03 00:51:08 +02:00
renovate[bot]	953e30814a	fix(deps): update github.com/nomic-ai/gpt4all/gpt4all-bindings/golang digest to c449b71 (#858 ) Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>	2023-08-03 00:20:45 +02:00
renovate[bot]	a65344cf25	fix(deps): update github.com/tmc/langchaingo digest to 271e9bd (#857 ) Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>	2023-08-03 00:20:22 +02:00
Dave	7fb8b4191f	feat: "simple" chat/edit/completion template system prompt from config (#856 )	2023-08-03 00:19:55 +02:00