fix(deps): update github.com/tmc/langchaingo digest to 2c309cf (#1097 )

[![Mend Renovate](https://app.renovatebot.com/images/banner.svg)](https://renovatebot.com) This PR contains the following updates: | Package | Type | Update | Change | |---|---|---|---| | [github.com/tmc/langchaingo](https://togithub.com/tmc/langchaingo) | require | digest | `9c8845b` -> `2c309cf` | --- ### Configuration 📅 **Schedule**: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined). 🚦 **Automerge**: Disabled by config. Please merge this manually once you are satisfied. ♻ **Rebasing**: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox. 🔕 **Ignore**: Close this PR and you won't be reminded about this update again. --- - [ ] If you want to rebase/retry this PR, check this box --- This PR has been generated by [Mend Renovate](https://www.mend.io/free-developer-tools/renovate/). View repository job log [here](https://developer.mend.io/github/go-skynet/LocalAI).  Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
Add the CONTRIBUTING.md (#1098 )
2026-02-03 19:22:39 -05:00 · 2023-09-24 14:55:17 +02:00 · 2023-09-24 14:54:55 +02:00 · 2023-09-22 15:52:38 +02:00 · 2023-09-22 11:22:17 +02:00 · 2023-09-22 08:44:45 +02:00
55 changed files with 2344 additions and 511 deletions
--- a/.env
+++ b/.env
@@ -23,6 +23,12 @@ MODELS_PATH=/models
 ## Enable debug mode
 # DEBUG=true

+## Disables COMPEL (Diffusers)
+# COMPEL=0
+
+## Enable/Disable single backend (useful if only one GPU is available)
+# SINGLE_ACTIVE_BACKEND=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.
@@ -44,3 +50,20 @@ MODELS_PATH=/models

 ## Specify a default upload limit in MB (whisper)
 # UPLOAD_LIMIT
+
+## List of external GRPC backends (note on the container image this variable is already set to use extra backends available in extra/)
+# EXTERNAL_GRPC_BACKENDS=my-backend:127.0.0.1:9000,my-backend2:/usr/bin/backend.py
+
+### Advanced settings ###
+### Those are not really used by LocalAI, but from components in the stack ###
+##
+### Preload libraries
+# LD_PRELOAD=
+
+### Huggingface cache for models
+# HUGGINGFACE_HUB_CACHE=/usr/local/huggingface
+
+### Python backends GRPC max workers
+### Default number of workers for GRPC Python backends.
+### This actually controls wether a backend can process multiple requests or not.
+# PYTHON_GRPC_MAX_WORKERS=1
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -8,16 +8,24 @@ This PR fixes #
 **[Signed commits](../CONTRIBUTING.md#signing-off-on-commits-developer-certificate-of-origin)**
 - [ ] Yes, I signed my commits.
 
-
 <!--
 Thank you for contributing to LocalAI! 

-Contributing Conventions:
+Contributing Conventions
+-------------------------

-1. Include descriptive PR titles with [<component-name>] prepended.
-2. Build and test your changes before submitting a PR. 
+The draft above helps to give a quick overview of your PR.
+
+Remember to remove this comment and to at least:
+
+1. Include descriptive PR titles with [<component-name>] prepended. We use [conventional commits](https://www.conventionalcommits.org/en/v1.0.0/).
+2. Build and test your changes before submitting a PR (`make build`). 
 3. Sign your commits
+4. **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below).
+5. **X/Twitter handle:** we announce bigger features on X/Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!

 By following the community's contribution conventions upfront, the review process will 
 be accelerated and your PR merged more quickly.
+
+If no one reviews your PR within a few days, please @-mention @mudler.
 -->
--- a/.github/workflows/image.yml
+++ b/.github/workflows/image.yml
@@ -96,7 +96,7 @@ jobs:

      - name: Docker meta
        id: meta
-        uses: docker/metadata-action@v4
+        uses: docker/metadata-action@v5
        with:
          images: quay.io/go-skynet/local-ai
          tags: |
@@ -118,14 +118,14 @@ jobs:

      - name: Login to DockerHub
        if: github.event_name != 'pull_request'
-        uses: docker/login-action@v2
+        uses: docker/login-action@v3
        with:
          registry: quay.io
          username: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
          password: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}

      - name: Build and push
-        uses: docker/build-push-action@v4
+        uses: docker/build-push-action@v5
        with:
          builder: ${{ steps.buildx.outputs.name }}
          build-args: |
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -0,0 +1,72 @@
+# Contributing to localAI
+
+Thank you for your interest in contributing to LocalAI! We appreciate your time and effort in helping to improve our project. Before you get started, please take a moment to review these guidelines.
+
+## Table of Contents
+
+- [Getting Started](#getting-started)
+  - [Prerequisites](#prerequisites)
+  - [Setting up the Development Environment](#setting-up-the-development-environment)
+- [Contributing](#contributing)
+  - [Submitting an Issue](#submitting-an-issue)
+  - [Creating a Pull Request (PR)](#creating-a-pull-request-pr)
+- [Coding Guidelines](#coding-guidelines)
+- [Testing](#testing)
+- [Documentation](#documentation)
+- [Community and Communication](#community-and-communication)
+
+
+
+## Getting Started
+
+### Prerequisites
+
+- Golang [1.21]
+- Git
+- macOS/Linux
+
+### Setting up the Development Environment and running localAI in the local environment
+
+1. Clone the repository: `git clone https://github.com/go-skynet/LocalAI.git`
+2. Navigate to the project directory: `cd LocalAI`
+3. Install the required dependencies: `make prepare`
+4. Run LocalAI: `make run`
+
+## Contributing
+
+We welcome contributions from everyone! To get started, follow these steps:
+
+### Submitting an Issue
+
+If you find a bug, have a feature request, or encounter any issues, please check the [issue tracker](https://github.com/go-skynet/LocalAI/issues) to see if a similar issue has already been reported. If not, feel free to [create a new issue](https://github.com/go-skynet/LocalAI/issues/new) and provide as much detail as possible.
+
+### Creating a Pull Request (PR)
+
+1. Fork the repository.
+2. Create a new branch with a descriptive name: `git checkout -b [branch name]`
+3. Make your changes and commit them.
+4. Push the changes to your fork: `git push origin [branch name]`
+5. Create a new pull request from your branch to the main project's `main` or `master` branch.
+6. Provide a clear description of your changes in the pull request.
+7. Make any requested changes during the review process.
+8. Once your PR is approved, it will be merged into the main project.
+
+## Coding Guidelines
+
+- No specific coding guidelines at the moment. Please make sure the code can be tested. The most popular lint tools like []`golangci-lint`](https://golangci-lint.run) can help you here.
+
+## Testing
+
+`make test` cannot handle all the model now. Please be sure to add a test case for the new features or the part was changed.
+
+## Documentation
+
+- We are welcome the contribution of the documents, please open new PR in the official document repo [localai-website](https://github.com/go-skynet/localai-website)
+
+## Community and Communication
+
+- You can reach out via the Github issue tracker.
+- Open a new discussion at [Discussion](https://github.com/go-skynet/LocalAI/discussions)
+- Join the Discord channel [Discord](https://discord.gg/uJAeKSAGDy)
+
+---
--- a/11
+++ b/11
@@ -11,7 +11,7 @@ ARG TARGETARCH
 ARG TARGETVARIANT

 ENV BUILD_TYPE=${BUILD_TYPE}
-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 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,vall-e-x:/build/extra/grpc/vall-e-x/ttsvalle.py,vllm:/build/extra/grpc/vllm/backend_vllm.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"

@@ -43,10 +43,13 @@ 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}${CUDA_MINOR_VERSION}-cp39-cp39-linux_x86_64.whl;\
+        pip install torch vllm && pip install auto-gptq https://github.com/jllllll/exllama/releases/download/0.0.10/exllama-0.0.10+cu${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

+# Vall-e-X
+RUN git clone https://github.com/Plachtaa/VALL-E-X.git /usr/lib/vall-e-x && cd /usr/lib/vall-e-x && pip install -r requirements.txt
+
 WORKDIR /build

 # OpenBLAS requirements
@@ -130,6 +133,10 @@ WORKDIR /build
 COPY . .
 RUN make prepare-sources
 COPY --from=builder /build/local-ai ./
+
+# Copy VALLE-X as it's not a real "lib"
+RUN cp -rfv /usr/lib/vall-e-x/* ./
+
 # To resolve exllama import error
 RUN if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH:-$(go env GOARCH)}" = "amd64" ]; then \
        cp -rfv /usr/local/lib/python3.9/dist-packages/exllama extra/grpc/exllama/;\
--- a/16
+++ b/16
@@ -4,7 +4,7 @@ GOVET=$(GOCMD) vet
 BINARY_NAME=local-ai

 # llama.cpp versions
-GOLLAMA_VERSION?=bf63302a2be787674e6ca4227a8aaeb95a8eb6b1
+GOLLAMA_VERSION?=d9f6176409de0a2b5ce798de502545c6721e346e

 GOLLAMA_STABLE_VERSION?=50cee7712066d9e38306eccadcfbb44ea87df4b7

@@ -38,6 +38,7 @@ STABLEDIFFUSION_VERSION?=d89260f598afb809279bc72aa0107b4292587632
 GOGGLLM_VERSION?=862477d16eefb0805261c19c9b0d053e3b2b684b

 export BUILD_TYPE?=
+export CMAKE_ARGS?=
 CGO_LDFLAGS?=
 CUDA_LIBPATH?=/usr/local/cuda/lib64/
 GO_TAGS?=
@@ -64,9 +65,12 @@ ifndef UNAME_S
 UNAME_S := $(shell uname -s)
 endif

-# workaround for rwkv.cpp
 ifeq ($(UNAME_S),Darwin)
-        CGO_LDFLAGS += -lcblas -framework Accelerate 
+	CGO_LDFLAGS += -lcblas -framework Accelerate
+ifneq ($(BUILD_TYPE),metal)
+    # explicit disable metal if on Darwin and metal is disabled
+	CMAKE_ARGS+=-DLLAMA_METAL=OFF
+endif
 endif

 ifeq ($(BUILD_TYPE),openblas)
@@ -242,14 +246,14 @@ rebuild: ## Rebuilds the project
 	$(MAKE) -C go-ggllm clean
 	$(MAKE) build

-prepare: prepare-sources $(OPTIONAL_TARGETS) 
+prepare: prepare-sources $(OPTIONAL_TARGETS)
 	touch $@

 clean: ## Remove build related file
 	$(GOCMD) clean -cache
 	rm -f prepare
 	rm -rf ./go-llama
-	rm -rf ./gpt4all	
+	rm -rf ./gpt4all
 	rm -rf ./go-llama-stable
 	rm -rf ./go-gpt2
 	rm -rf ./go-stable-diffusion
@@ -357,6 +361,8 @@ protogen-python:
 	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
+	python3 -m grpc_tools.protoc -Ipkg/grpc/proto/ --python_out=extra/grpc/vall-e-x/ --grpc_python_out=extra/grpc/vall-e-x/ pkg/grpc/proto/backend.proto
+	python3 -m grpc_tools.protoc -Ipkg/grpc/proto/ --python_out=extra/grpc/vllm/ --grpc_python_out=extra/grpc/vllm/ pkg/grpc/proto/backend.proto

 ## GRPC

--- a/README.md
+++ b/README.md
@@ -27,7 +27,7 @@

 [![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.
+**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, pytorch and more. Does not require GPU.

 <p align="center"><b>Follow LocalAI </b></p>

@@ -104,15 +104,30 @@ Note that this started just as a [fun weekend project](https://localai.io/#backs

 Check out the [Getting started](https://localai.io/basics/getting_started/index.html) section in our documentation.

-### 💡 Example: Use GPT4ALL-J model
+### 💡 Example: Use Luna-AI Llama model

-See the [documentation](https://localai.io/basics/getting_started/#example-use-gpt4all-j-model-with-docker-compose)
+See the [documentation](https://localai.io/basics/getting_started)

 ### 🔗 Resources

 - [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/)
+- [How tos section](https://localai.io/howtos/) (curated by our community)
+  
+## Citation
+
+If you utilize this repository, data in a downstream project, please consider citing it with:
+
+```
+@misc{localai,
+  author = {Ettore Di Giacinto},
+  title = {LocalAI: The free, Open source OpenAI alternative},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/go-skynet/LocalAI}},
+```

 ## ❤️ Sponsors

@@ -127,6 +142,11 @@ 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!  |

+And a huge shout-out to individuals sponsoring the project by donating hardware or backing the project. 
+
+- [Sponsor list](https://github.com/sponsors/mudler)
+- JDAM00 (donating HW for the CI)
+
 ## 🌟 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)
--- a/api/api.go
+++ b/api/api.go
@@ -168,9 +168,14 @@ func App(opts ...options.AppOption) (*fiber.App, error) {
 		}{Version: internal.PrintableVersion()})
 	})

-	app.Post("/models/apply", auth, localai.ApplyModelGalleryEndpoint(options.Loader.ModelPath, cl, 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))
+	modelGalleryService := localai.CreateModelGalleryService(options.Galleries, options.Loader.ModelPath, galleryService)
+	app.Post("/models/apply", auth, modelGalleryService.ApplyModelGalleryEndpoint())
+	app.Get("/models/available", auth, modelGalleryService.ListModelFromGalleryEndpoint())
+	app.Get("/models/galleries", auth, modelGalleryService.ListModelGalleriesEndpoint())
+	app.Post("/models/galleries", auth, modelGalleryService.AddModelGalleryEndpoint())
+	app.Delete("/models/galleries", auth, modelGalleryService.RemoveModelGalleryEndpoint())
+	app.Get("/models/jobs/:uuid", auth, modelGalleryService.GetOpStatusEndpoint())
+	app.Get("/models/jobs", auth, modelGalleryService.GetAllStatusEndpoint())

 	// openAI compatible API endpoint

--- a/api/api_test.go
+++ b/api/api_test.go
@@ -363,9 +363,10 @@ var _ = Describe("API test", func() {
 				if runtime.GOOS != "linux" {
 					Skip("test supported only on linux")
 				}
+				modelName := "codellama"
 				response := postModelApplyRequest("http://127.0.0.1:9090/models/apply", modelApplyRequest{
-					URL:       "github:go-skynet/model-gallery/openllama-3b-gguf.yaml",
-					Name:      "openllama_3b_gguf",
+					URL:       "github:go-skynet/model-gallery/codellama-7b-instruct.yaml",
+					Name:      modelName,
 					Overrides: map[string]interface{}{"backend": "llama", "mmap": true, "f16": true, "context_size": 128},
 				})

@@ -378,17 +379,22 @@ var _ = Describe("API test", func() {
 					return response["processed"].(bool)
 				}, "360s", "10s").Should(Equal(true))

-				By("testing completion")
-				resp, err := client.CreateCompletion(context.TODO(), openai.CompletionRequest{Model: "openllama_3b_gguf", Prompt: "Count up to five: one, two, three, four, "})
+				By("testing chat")
+				resp, err := client.CreateChatCompletion(context.TODO(), openai.ChatCompletionRequest{Model: modelName, Messages: []openai.ChatCompletionMessage{
+					{
+						Role:    "user",
+						Content: "How much is 2+2?",
+					},
+				}})
 				Expect(err).ToNot(HaveOccurred())
 				Expect(len(resp.Choices)).To(Equal(1))
-				Expect(resp.Choices[0].Text).To(ContainSubstring("five"))
+				Expect(resp.Choices[0].Message.Content).To(Or(ContainSubstring("4"), ContainSubstring("four")))

 				By("testing functions")
 				resp2, err := client.CreateChatCompletion(
 					context.TODO(),
 					openai.ChatCompletionRequest{
-						Model: "openllama_3b_gguf",
+						Model: modelName,
 						Messages: []openai.ChatCompletionMessage{
 							{
 								Role:    "user",
@@ -424,7 +430,7 @@ var _ = Describe("API test", func() {
 				var res map[string]string
 				err = json.Unmarshal([]byte(resp2.Choices[0].Message.FunctionCall.Arguments), &res)
 				Expect(err).ToNot(HaveOccurred())
-				Expect(res["location"]).To(Equal("San Francisco, California"), fmt.Sprint(res))
+				Expect(res["location"]).To(Equal("San Francisco"), fmt.Sprint(res))
 				Expect(res["unit"]).To(Equal("celcius"), fmt.Sprint(res))
 				Expect(string(resp2.Choices[0].FinishReason)).To(Equal("function_call"), fmt.Sprint(resp2.Choices[0].FinishReason))
 			})
--- a/api/backend/image.go
+++ b/api/backend/image.go
@@ -20,6 +20,8 @@ func ImageGeneration(height, width, mode, step, seed int, positive_prompt, negat
 			SchedulerType: c.Diffusers.SchedulerType,
 			PipelineType:  c.Diffusers.PipelineType,
 			CFGScale:      c.Diffusers.CFGScale,
+			LoraAdapter:   c.LoraAdapter,
+			LoraBase:      c.LoraBase,
 			IMG2IMG:       c.Diffusers.IMG2IMG,
 			CLIPModel:     c.Diffusers.ClipModel,
 			CLIPSubfolder: c.Diffusers.ClipSubFolder,
--- a/api/backend/llm.go
+++ b/api/backend/llm.go
@@ -6,6 +6,7 @@ import (
 	"regexp"
 	"strings"
 	"sync"
+	"unicode/utf8"

 	config "github.com/go-skynet/LocalAI/api/config"
 	"github.com/go-skynet/LocalAI/api/options"
@@ -97,9 +98,23 @@ func ModelInference(ctx context.Context, s string, loader *model.ModelLoader, c

 		if tokenCallback != nil {
 			ss := ""
-			err := inferenceModel.PredictStream(ctx, opts, func(s []byte) {
-				tokenCallback(string(s), tokenUsage)
-				ss += string(s)
+
+			var partialRune []byte
+			err := inferenceModel.PredictStream(ctx, opts, func(chars []byte) {
+				partialRune = append(partialRune, chars...)
+
+				for len(partialRune) > 0 {
+					r, size := utf8.DecodeRune(partialRune)
+					if r == utf8.RuneError {
+						// incomplete rune, wait for more bytes
+						break
+					}
+
+					tokenCallback(string(r), tokenUsage)
+					ss += string(r)
+
+					partialRune = partialRune[size:]
+				}
 			})
 			return LLMResponse{
 				Response: ss,
--- a/api/backend/options.go
+++ b/api/backend/options.go
@@ -42,6 +42,9 @@ func gRPCModelOpts(c config.Config) *pb.ModelOptions {
 		Seed:          int32(c.Seed),
 		NBatch:        int32(b),
 		NoMulMatQ:     c.NoMulMatQ,
+		DraftModel:    c.DraftModel,
+		AudioPath:     c.VallE.AudioPath,
+		Quantization:  c.Quantization,
 		LoraAdapter:   c.LoraAdapter,
 		LoraBase:      c.LoraBase,
 		NGQA:          c.NGQA,
@@ -78,6 +81,7 @@ func gRPCPredictOpts(c config.Config, modelPath string) *pb.PredictOptions {
 	return &pb.PredictOptions{
 		Temperature:         float32(c.Temperature),
 		TopP:                float32(c.TopP),
+		NDraft:              c.NDraft,
 		TopK:                int32(c.TopK),
 		Tokens:              int32(c.Maxtokens),
 		Threads:             int32(c.Threads),
--- a/api/config/config.go
+++ b/api/config/config.go
@@ -43,6 +43,13 @@ type Config struct {

 	// GRPC Options
 	GRPC GRPC `yaml:"grpc"`
+
+	// Vall-e-x
+	VallE VallE `yaml:"vall-e"`
+}
+
+type VallE struct {
+	AudioPath string `yaml:"audio_path"`
 }

 type FeatureFlag map[string]*bool
@@ -94,6 +101,9 @@ type LLMConfig struct {
 	LoraAdapter     string   `yaml:"lora_adapter"`
 	LoraBase        string   `yaml:"lora_base"`
 	NoMulMatQ       bool     `yaml:"no_mulmatq"`
+	DraftModel      string   `yaml:"draft_model"`
+	NDraft          int32    `yaml:"n_draft"`
+	Quantization    string   `yaml:"quantization"`
 }

 type AutoGPTQ struct {
--- a/api/localai/gallery.go
+++ b/api/localai/gallery.go
@@ -4,6 +4,7 @@ import (
 	"context"
 	"fmt"
 	"os"
+	"slices"
 	"strings"
 	"sync"

@@ -27,6 +28,7 @@ type galleryOp struct {
 }

 type galleryOpStatus struct {
+	FileName           string  `json:"file_name"`
 	Error              error   `json:"error"`
 	Processed          bool    `json:"processed"`
 	Message            string  `json:"message"`
@@ -50,7 +52,6 @@ func NewGalleryService(modelPath string) *galleryApplier {
 	}
 }

-// prepareModel applies a
 func prepareModel(modelPath string, req gallery.GalleryModel, cm *config.ConfigLoader, downloadStatus func(string, string, string, float64)) error {

 	config, err := gallery.GetGalleryConfigFromURL(req.URL)
@@ -76,6 +77,13 @@ func (g *galleryApplier) getStatus(s string) *galleryOpStatus {
 	return g.statuses[s]
 }

+func (g *galleryApplier) getAllStatus() map[string]*galleryOpStatus {
+	g.Lock()
+	defer g.Unlock()
+
+	return g.statuses
+}
+
 func (g *galleryApplier) Start(c context.Context, cm *config.ConfigLoader) {
 	go func() {
 		for {
@@ -94,7 +102,7 @@ func (g *galleryApplier) Start(c context.Context, cm *config.ConfigLoader) {

 				// displayDownload displays the download progress
 				progressCallback := func(fileName string, current string, total string, percentage float64) {
-					g.updateStatus(op.id, &galleryOpStatus{Message: "processing", Progress: percentage, TotalFileSize: total, DownloadedFileSize: current})
+					g.updateStatus(op.id, &galleryOpStatus{Message: "processing", FileName: fileName, Progress: percentage, TotalFileSize: total, DownloadedFileSize: current})
 					utils.DisplayDownloadFunction(fileName, current, total, percentage)
 				}

@@ -176,18 +184,12 @@ func ApplyGalleryFromString(modelPath, s string, cm *config.ConfigLoader, galler
 	return processRequests(modelPath, s, cm, galleries, requests)
 }

-/// Endpoints
+/// Endpoint Service

-func GetOpStatusEndpoint(g *galleryApplier) func(c *fiber.Ctx) error {
-	return func(c *fiber.Ctx) error {
-
-		status := g.getStatus(c.Params("uuid"))
-		if status == nil {
-			return fmt.Errorf("could not find any status for ID")
-		}
-
-		return c.JSON(status)
-	}
+type ModelGalleryService struct {
+	galleries      []gallery.Gallery
+	modelPath      string
+	galleryApplier *galleryApplier
 }

 type GalleryModel struct {
@@ -195,7 +197,31 @@ type GalleryModel struct {
 	gallery.GalleryModel
 }

-func ApplyModelGalleryEndpoint(modelPath string, cm *config.ConfigLoader, g chan galleryOp, galleries []gallery.Gallery) func(c *fiber.Ctx) error {
+func CreateModelGalleryService(galleries []gallery.Gallery, modelPath string, galleryApplier *galleryApplier) ModelGalleryService {
+	return ModelGalleryService{
+		galleries:      galleries,
+		modelPath:      modelPath,
+		galleryApplier: galleryApplier,
+	}
+}
+
+func (mgs *ModelGalleryService) GetOpStatusEndpoint() func(c *fiber.Ctx) error {
+	return func(c *fiber.Ctx) error {
+		status := mgs.galleryApplier.getStatus(c.Params("uuid"))
+		if status == nil {
+			return fmt.Errorf("could not find any status for ID")
+		}
+		return c.JSON(status)
+	}
+}
+
+func (mgs *ModelGalleryService) GetAllStatusEndpoint() func(c *fiber.Ctx) error {
+	return func(c *fiber.Ctx) error {
+		return c.JSON(mgs.galleryApplier.getAllStatus())
+	}
+}
+
+func (mgs *ModelGalleryService) ApplyModelGalleryEndpoint() func(c *fiber.Ctx) error {
 	return func(c *fiber.Ctx) error {
 		input := new(GalleryModel)
 		// Get input data from the request body
@@ -207,11 +233,11 @@ func ApplyModelGalleryEndpoint(modelPath string, cm *config.ConfigLoader, g chan
 		if err != nil {
 			return err
 		}
-		g <- galleryOp{
+		mgs.galleryApplier.C <- galleryOp{
 			req:         input.GalleryModel,
 			id:          uuid.String(),
 			galleryName: input.ID,
-			galleries:   galleries,
+			galleries:   mgs.galleries,
 		}
 		return c.JSON(struct {
 			ID        string `json:"uuid"`
@@ -220,11 +246,11 @@ func ApplyModelGalleryEndpoint(modelPath string, cm *config.ConfigLoader, g chan
 	}
 }

-func ListModelFromGalleryEndpoint(galleries []gallery.Gallery, basePath string) func(c *fiber.Ctx) error {
+func (mgs *ModelGalleryService) ListModelFromGalleryEndpoint() func(c *fiber.Ctx) error {
 	return func(c *fiber.Ctx) error {
-		log.Debug().Msgf("Listing models from galleries: %+v", galleries)
+		log.Debug().Msgf("Listing models from galleries: %+v", mgs.galleries)

-		models, err := gallery.AvailableGalleryModels(galleries, basePath)
+		models, err := gallery.AvailableGalleryModels(mgs.galleries, mgs.modelPath)
 		if err != nil {
 			return err
 		}
@@ -239,3 +265,56 @@ func ListModelFromGalleryEndpoint(galleries []gallery.Gallery, basePath string)
 		return c.Send(dat)
 	}
 }
+
+// NOTE: This is different (and much simpler!) than above! This JUST lists the model galleries that have been loaded, not their contents!
+func (mgs *ModelGalleryService) ListModelGalleriesEndpoint() func(c *fiber.Ctx) error {
+	return func(c *fiber.Ctx) error {
+		log.Debug().Msgf("Listing model galleries %+v", mgs.galleries)
+		dat, err := json.Marshal(mgs.galleries)
+		if err != nil {
+			return err
+		}
+		return c.Send(dat)
+	}
+}
+
+func (mgs *ModelGalleryService) AddModelGalleryEndpoint() func(c *fiber.Ctx) error {
+	return func(c *fiber.Ctx) error {
+		input := new(gallery.Gallery)
+		// Get input data from the request body
+		if err := c.BodyParser(input); err != nil {
+			return err
+		}
+		if slices.ContainsFunc(mgs.galleries, func(gallery gallery.Gallery) bool {
+			return gallery.Name == input.Name
+		}) {
+			return fmt.Errorf("%s already exists", input.Name)
+		}
+		dat, err := json.Marshal(mgs.galleries)
+		if err != nil {
+			return err
+		}
+		log.Debug().Msgf("Adding %+v to gallery list", *input)
+		mgs.galleries = append(mgs.galleries, *input)
+		return c.Send(dat)
+	}
+}
+
+func (mgs *ModelGalleryService) RemoveModelGalleryEndpoint() func(c *fiber.Ctx) error {
+	return func(c *fiber.Ctx) error {
+		input := new(gallery.Gallery)
+		// Get input data from the request body
+		if err := c.BodyParser(input); err != nil {
+			return err
+		}
+		if !slices.ContainsFunc(mgs.galleries, func(gallery gallery.Gallery) bool {
+			return gallery.Name == input.Name
+		}) {
+			return fmt.Errorf("%s is not currently registered", input.Name)
+		}
+		mgs.galleries = slices.DeleteFunc(mgs.galleries, func(gallery gallery.Gallery) bool {
+			return gallery.Name == input.Name
+		})
+		return c.Send(nil)
+	}
+}
--- a/api/options/options.go
+++ b/api/options/options.go
@@ -99,6 +99,7 @@ func WithStringGalleries(galls string) AppOption {
 	return func(o *Option) {
 		if galls == "" {
 			log.Debug().Msgf("no galleries to load")
+			o.Galleries = []gallery.Gallery{}
 			return
 		}
 		var galleries []gallery.Gallery
--- a/examples/README.md
+++ b/examples/README.md
@@ -159,14 +159,24 @@ Allows to run any LocalAI-compatible model as a backend on the servers of https:

 ### Continue

-<img src="continue/img/screen.png" width="600" height="200" alt="Screenshot">
-
 _by [@gruberdev](https://github.com/gruberdev)_

+<img src="continue/img/screen.png" width="600" height="200" alt="Screenshot">
+
 Demonstrates how to integrate an open-source copilot alternative that enhances code analysis, completion, and improvements. This approach seamlessly integrates with any LocalAI model, offering a more user-friendly experience.

 [Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/continue/)

+### Streamlit bot
+
+_by [@majoshi1](https://github.com/majoshi1)_
+
+![Screenshot](streamlit-bot/streamlit-bot.png)
+
+A chat bot made using `Streamlit` & LocalAI.
+
+[Check it out here](https://github.com/go-skynet/LocalAI/tree/master/examples/streamlit-bot/)
+
 ## Want to contribute?

 Create an issue, and put `Example: <description>` in the title! We will post your examples here.
--- a/examples/chatbot-ui-manual/README.md
+++ b/examples/chatbot-ui-manual/README.md
@@ -24,10 +24,13 @@ docker-compose up -d --pull always
 # docker-compose up -d --build
 ```

+Then browse to `http://localhost:3000` to view the Web UI.
+
 ## Pointing chatbot-ui to a separately managed LocalAI service

-If you want to use the [chatbot-ui example](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui) with an externally managed LocalAI service, you can alter the `docker-compose` file so that it looks like the below. You will notice the file is smaller, because we have removed the section that would normally start the LocalAI service. Take care to update the IP address (or FQDN) that the chatbot-ui service tries to access (marked `<<LOCALAI_IP>>` below):
-```
+If you want to use the [chatbot-ui example](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui) with an externally managed LocalAI service, you can alter the `docker-compose.yaml` file so that it looks like the below. You will notice the file is smaller, because we have removed the section that would normally start the LocalAI service. Take care to update the IP address (or FQDN) that the chatbot-ui service tries to access (marked `<<LOCALAI_IP>>` below):
+
+```yaml
 version: '3.6'

 services:
@@ -40,9 +43,8 @@ services:
      - 'OPENAI_API_HOST=http://<<LOCALAI_IP>>:8080'
 ```

-Once you've edited the Dockerfile, you can start it with `docker compose up`, then browse to `http://localhost:3000`.
+Once you've edited the `docker-compose.yaml`, you can start it with `docker compose up`, then browse to `http://localhost:3000` to view the Web UI.

 ## Accessing chatbot-ui

 Open http://localhost:3000 for the Web UI.
-
--- a/examples/chatbot-ui/README.md
+++ b/examples/chatbot-ui/README.md
@@ -20,10 +20,13 @@ docker-compose up --pull always
 # docker-compose up -d --build
 ```

+Then browse to `http://localhost:3000` to view the Web UI.
+
 ## Pointing chatbot-ui to a separately managed LocalAI service

-If you want to use the [chatbot-ui example](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui) with an externally managed LocalAI service, you can alter the `docker-compose` file so that it looks like the below. You will notice the file is smaller, because we have removed the section that would normally start the LocalAI service. Take care to update the IP address (or FQDN) that the chatbot-ui service tries to access (marked `<<LOCALAI_IP>>` below):
-```
+If you want to use the [chatbot-ui example](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui) with an externally managed LocalAI service, you can alter the `docker-compose.yaml` file so that it looks like the below. You will notice the file is smaller, because we have removed the section that would normally start the LocalAI service. Take care to update the IP address (or FQDN) that the chatbot-ui service tries to access (marked `<<LOCALAI_IP>>` below):
+
+```yaml
 version: '3.6'

 services:
@@ -36,9 +39,8 @@ services:
      - 'OPENAI_API_HOST=http://<<LOCALAI_IP>>:8080'
 ```

-Once you've edited the Dockerfile, you can start it with `docker compose up`, then browse to `http://localhost:3000`.
+Once you've edited the `docker-compose.yaml`, you can start it with `docker compose up`, then browse to `http://localhost:3000` to view the Web UI.

 ## Accessing chatbot-ui

 Open http://localhost:3000 for the Web UI.
-
--- a/examples/continue/README.md
+++ b/examples/continue/README.md
@@ -28,7 +28,7 @@ For a live demonstration, please click on the link below:
 3. Type `/config` within Continue's VSCode extension, or edit the file located at `~/.continue/config.py` on your system with the following configuration:

    ```py
-    from continuedev.src.continuedev.libs.llm.openai import OpenAI, OpenAIServerInfo
+    from continuedev.src.continuedev.libs.llm.openai import OpenAI

    config = ContinueConfig(
       ...
@@ -36,10 +36,7 @@ For a live demonstration, please click on the link below:
            default=OpenAI(
               api_key="my-api-key",
               model="gpt-3.5-turbo",
-               openai_server_info=OpenAIServerInfo(
-                  api_base="http://localhost:8080",
-                  model="gpt-3.5-turbo"
-               )
+               api_base="http://localhost:8080",
            )
       ),
    )
--- a/examples/insomnia/Insomnia_LocalAI.json
+++ b/examples/insomnia/Insomnia_LocalAI.json
--- a/examples/query_data/query.py
+++ b/examples/query_data/query.py
@@ -15,7 +15,7 @@ llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo
 # Configure prompt parameters and initialise helper
 max_input_size = 500
 num_output = 256
-max_chunk_overlap = 20
+max_chunk_overlap = 0.2

 prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap)

--- a/examples/query_data/store.py
+++ b/examples/query_data/store.py
@@ -15,7 +15,7 @@ llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo
 # Configure prompt parameters and initialise helper
 max_input_size = 400
 num_output = 400
-max_chunk_overlap = 30
+max_chunk_overlap = 0.3

 prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap)

--- a/examples/streamlit-bot/.gitignore
+++ b/examples/streamlit-bot/.gitignore
@@ -0,0 +1 @@
+installer_files
--- a/examples/streamlit-bot/LICENSE
+++ b/examples/streamlit-bot/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Manohar Joshi
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
--- a/examples/streamlit-bot/Main.py
+++ b/examples/streamlit-bot/Main.py
@@ -0,0 +1,70 @@
+import streamlit as st
+import time
+import requests
+import json
+
+def ask(prompt):
+    url = 'http://localhost:8080/v1/chat/completions'
+    myobj = {
+        "model": "ggml-gpt4all-j.bin",
+        "messages": [{"role": "user", "content": prompt}],
+        "temperature": 0.9
+    }
+    myheaders = { "Content-Type" : "application/json" }  
+
+    x = requests.post(url, json = myobj, headers=myheaders)
+    
+    print(x.text)
+    
+    json_data = json.loads(x.text)
+
+    return json_data["choices"][0]["message"]["content"]
+
+
+def main():
+    # Page setup
+    st.set_page_config(page_title="Ask your LLM")
+    st.header("Ask your Question 💬")
+
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # Display chat messages from history on app rerun
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # Scroll to bottom
+    st.markdown(
+        """
+        <script>
+        var element = document.getElementById("end-of-chat");
+        element.scrollIntoView({behavior: "smooth"});
+        </script>
+        """,
+        unsafe_allow_html=True,
+    )   
+
+    # React to user input
+    if prompt := st.chat_input("What is up?"):
+        # Display user message in chat message container
+        st.chat_message("user").markdown(prompt)
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        print(f"User has asked the following question: {prompt}")
+        
+        # Process
+        response = ""
+        with st.spinner('Processing...'):
+            response = ask(prompt)
+            
+        #response = f"Echo: {prompt}"
+        # Display assistant response in chat message container
+        with st.chat_message("assistant"):
+            st.markdown(response)
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": response})     
+
+if __name__ == "__main__":
+    main()        
--- a/examples/streamlit-bot/README.md
+++ b/examples/streamlit-bot/README.md
@@ -0,0 +1,54 @@
+## Streamlit bot
+
+![Screenshot](streamlit-bot.png)
+
+This is an example to deploy a Streamlit bot with LocalAI instead of OpenAI. Instructions are for Windows.
+
+```bash
+# Install & run Git Bash
+
+# Clone LocalAI
+git clone https://github.com/go-skynet/LocalAI.git
+cd LocalAI
+
+# (optional) Checkout a specific LocalAI tag
+# git checkout -b build <TAG>
+
+# 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
+# Download model
+curl --progress-bar -C - -O https://gpt4all.io/models/ggml-gpt4all-j.bin > models/ggml-gpt4all-j.bin
+
+# Install & Run Docker Desktop for Windows
+https://www.docker.com/products/docker-desktop/
+
+# 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?"}}]}
+
+cd examples/streamlit-bot
+
+install_requirements.bat
+
+# run the bot
+start_windows.bat
+
+# UI will be launched automatically (http://localhost:8501/) in browser.
+
+```
+
--- a/examples/streamlit-bot/cmd_windows.bat
+++ b/examples/streamlit-bot/cmd_windows.bat
@@ -0,0 +1,31 @@
+@echo off
+
+cd /D "%~dp0"
+
+set PATH=%PATH%;%SystemRoot%\system32
+
+echo "%CD%"| findstr /C:" " >nul && echo This script relies on Miniconda which can not be silently installed under a path with spaces. && goto end
+
+@rem fix failed install when installing to a separate drive
+set TMP=%cd%\installer_files
+set TEMP=%cd%\installer_files
+
+@rem config
+set CONDA_ROOT_PREFIX=%cd%\installer_files\conda
+set INSTALL_ENV_DIR=%cd%\installer_files\env
+
+@rem environment isolation
+set PYTHONNOUSERSITE=1
+set PYTHONPATH=
+set PYTHONHOME=
+set "CUDA_PATH=%INSTALL_ENV_DIR%"
+set "CUDA_HOME=%CUDA_PATH%"
+
+@rem activate installer env
+call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo Miniconda hook not found. && goto end )
+
+@rem enter commands
+cmd /k "%*"
+
+:end
+pause
--- a/examples/streamlit-bot/install_requirements.bat
+++ b/examples/streamlit-bot/install_requirements.bat
@@ -0,0 +1,81 @@
+@echo off
+
+cd /D "%~dp0"
+
+set PATH=%PATH%;%SystemRoot%\system32
+
+echo "%CD%"| findstr /C:" " >nul && echo This script relies on Miniconda which can not be silently installed under a path with spaces. && goto end
+
+@rem Check for special characters in installation path
+set "SPCHARMESSAGE="WARNING: Special characters were detected in the installation path!" "         This can cause the installation to fail!""
+echo "%CD%"| findstr /R /C:"[!#\$%&()\*+,;<=>?@\[\]\^`{|}~]" >nul && (
+	call :PrintBigMessage %SPCHARMESSAGE%
+)
+set SPCHARMESSAGE=
+
+@rem fix failed install when installing to a separate drive
+set TMP=%cd%\installer_files
+set TEMP=%cd%\installer_files
+
+@rem config
+set INSTALL_DIR=%cd%\installer_files
+set CONDA_ROOT_PREFIX=%cd%\installer_files\conda
+set INSTALL_ENV_DIR=%cd%\installer_files\env
+set MINICONDA_DOWNLOAD_URL=https://repo.anaconda.com/miniconda/Miniconda3-py310_23.3.1-0-Windows-x86_64.exe
+set conda_exists=F
+
+@rem figure out whether git and conda needs to be installed
+call "%CONDA_ROOT_PREFIX%\_conda.exe" --version >nul 2>&1
+if "%ERRORLEVEL%" EQU "0" set conda_exists=T
+
+@rem (if necessary) install git and conda into a contained environment
+@rem download conda
+if "%conda_exists%" == "F" (
+	echo Downloading Miniconda from %MINICONDA_DOWNLOAD_URL% to %INSTALL_DIR%\miniconda_installer.exe
+
+	mkdir "%INSTALL_DIR%"
+	call curl -Lk "%MINICONDA_DOWNLOAD_URL%" > "%INSTALL_DIR%\miniconda_installer.exe" || ( echo. && echo Miniconda failed to download. && goto end )
+
+	echo Installing Miniconda to %CONDA_ROOT_PREFIX%
+	start /wait "" "%INSTALL_DIR%\miniconda_installer.exe" /InstallationType=JustMe /NoShortcuts=1 /AddToPath=0 /RegisterPython=0 /NoRegistry=1 /S /D=%CONDA_ROOT_PREFIX%
+
+	@rem test the conda binary
+	echo Miniconda version:
+	call "%CONDA_ROOT_PREFIX%\_conda.exe" --version || ( echo. && echo Miniconda not found. && goto end )
+)
+
+@rem create the installer env
+if not exist "%INSTALL_ENV_DIR%" (
+  echo Packages to install: %PACKAGES_TO_INSTALL%
+  call "%CONDA_ROOT_PREFIX%\_conda.exe" create --no-shortcuts -y -k --prefix "%INSTALL_ENV_DIR%" python=3.10 || ( echo. && echo Conda environment creation failed. && goto end )
+)
+
+@rem check if conda environment was actually created
+if not exist "%INSTALL_ENV_DIR%\python.exe" ( echo. && echo Conda environment is empty. && goto end )
+
+@rem environment isolation
+set PYTHONNOUSERSITE=1
+set PYTHONPATH=
+set PYTHONHOME=
+set "CUDA_PATH=%INSTALL_ENV_DIR%"
+set "CUDA_HOME=%CUDA_PATH%"
+
+@rem activate installer env
+call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo Miniconda hook not found. && goto end )
+
+@rem setup installer env
+call pip install -r requirements.txt
+
+@rem below are functions for the script   next line skips these during normal execution
+goto end
+
+:PrintBigMessage
+echo. && echo.
+echo *******************************************************************
+for %%M in (%*) do echo * %%~M
+echo *******************************************************************
+echo. && echo.
+exit /b
+
+:end
+pause
--- a/examples/streamlit-bot/requirements.txt
+++ b/examples/streamlit-bot/requirements.txt
@@ -0,0 +1,2 @@
+streamlit==1.26.0
+requests
--- a/examples/streamlit-bot/start_windows.bat
+++ b/examples/streamlit-bot/start_windows.bat
@@ -0,0 +1,81 @@
+@echo off
+
+cd /D "%~dp0"
+
+set PATH=%PATH%;%SystemRoot%\system32
+
+echo "%CD%"| findstr /C:" " >nul && echo This script relies on Miniconda which can not be silently installed under a path with spaces. && goto end
+
+@rem Check for special characters in installation path
+set "SPCHARMESSAGE="WARNING: Special characters were detected in the installation path!" "         This can cause the installation to fail!""
+echo "%CD%"| findstr /R /C:"[!#\$%&()\*+,;<=>?@\[\]\^`{|}~]" >nul && (
+	call :PrintBigMessage %SPCHARMESSAGE%
+)
+set SPCHARMESSAGE=
+
+@rem fix failed install when installing to a separate drive
+set TMP=%cd%\installer_files
+set TEMP=%cd%\installer_files
+
+@rem config
+set INSTALL_DIR=%cd%\installer_files
+set CONDA_ROOT_PREFIX=%cd%\installer_files\conda
+set INSTALL_ENV_DIR=%cd%\installer_files\env
+set MINICONDA_DOWNLOAD_URL=https://repo.anaconda.com/miniconda/Miniconda3-py310_23.3.1-0-Windows-x86_64.exe
+set conda_exists=F
+
+@rem figure out whether git and conda needs to be installed
+call "%CONDA_ROOT_PREFIX%\_conda.exe" --version >nul 2>&1
+if "%ERRORLEVEL%" EQU "0" set conda_exists=T
+
+@rem (if necessary) install git and conda into a contained environment
+@rem download conda
+if "%conda_exists%" == "F" (
+	echo Downloading Miniconda from %MINICONDA_DOWNLOAD_URL% to %INSTALL_DIR%\miniconda_installer.exe
+
+	mkdir "%INSTALL_DIR%"
+	call curl -Lk "%MINICONDA_DOWNLOAD_URL%" > "%INSTALL_DIR%\miniconda_installer.exe" || ( echo. && echo Miniconda failed to download. && goto end )
+
+	echo Installing Miniconda to %CONDA_ROOT_PREFIX%
+	start /wait "" "%INSTALL_DIR%\miniconda_installer.exe" /InstallationType=JustMe /NoShortcuts=1 /AddToPath=0 /RegisterPython=0 /NoRegistry=1 /S /D=%CONDA_ROOT_PREFIX%
+
+	@rem test the conda binary
+	echo Miniconda version:
+	call "%CONDA_ROOT_PREFIX%\_conda.exe" --version || ( echo. && echo Miniconda not found. && goto end )
+)
+
+@rem create the installer env
+if not exist "%INSTALL_ENV_DIR%" (
+  echo Packages to install: %PACKAGES_TO_INSTALL%
+  call "%CONDA_ROOT_PREFIX%\_conda.exe" create --no-shortcuts -y -k --prefix "%INSTALL_ENV_DIR%" python=3.10 || ( echo. && echo Conda environment creation failed. && goto end )
+)
+
+@rem check if conda environment was actually created
+if not exist "%INSTALL_ENV_DIR%\python.exe" ( echo. && echo Conda environment is empty. && goto end )
+
+@rem environment isolation
+set PYTHONNOUSERSITE=1
+set PYTHONPATH=
+set PYTHONHOME=
+set "CUDA_PATH=%INSTALL_ENV_DIR%"
+set "CUDA_HOME=%CUDA_PATH%"
+
+@rem activate installer env
+call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo Miniconda hook not found. && goto end )
+
+@rem setup installer env
+streamlit run Main.py
+
+@rem below are functions for the script   next line skips these during normal execution
+goto end
+
+:PrintBigMessage
+echo. && echo.
+echo *******************************************************************
+for %%M in (%*) do echo * %%~M
+echo *******************************************************************
+echo. && echo.
+exit /b
+
+:end
+pause
--- a/examples/streamlit-bot/streamlit-bot.png
+++ b/examples/streamlit-bot/streamlit-bot.png
--- a/extra/grpc/autogptq/autogptq.py
+++ b/extra/grpc/autogptq/autogptq.py
@@ -15,6 +15,9 @@ from transformers import TextGenerationPipeline

 _ONE_DAY_IN_SECONDS = 60 * 60 * 24

+# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
+MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
+
 # Implement the BackendServicer class with the service methods
 class BackendServicer(backend_pb2_grpc.BackendServicer):
    def Health(self, request, context):
@@ -77,7 +80,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):


 def serve(address):
-    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
    backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
    server.add_insecure_port(address)
    server.start()
--- a/extra/grpc/autogptq/backend_pb2.py
+++ b/extra/grpc/autogptq/backend_pb2.py
--- a/extra/grpc/bark/backend_pb2.py
+++ b/extra/grpc/bark/backend_pb2.py
--- a/extra/grpc/bark/ttsbark.py
+++ b/extra/grpc/bark/ttsbark.py
@@ -15,6 +15,9 @@ from scipy.io.wavfile import write as write_wav

 _ONE_DAY_IN_SECONDS = 60 * 60 * 24

+# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
+MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
+
 # Implement the BackendServicer class with the service methods
 class BackendServicer(backend_pb2_grpc.BackendServicer):
    def Health(self, request, context):
@@ -51,7 +54,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
        return backend_pb2.Result(success=True)

 def serve(address):
-    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
    backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
    server.add_insecure_port(address)
    server.start()
--- a/extra/grpc/diffusers/backend_diffusers.py
+++ b/extra/grpc/diffusers/backend_diffusers.py
@@ -20,11 +20,15 @@ from io import BytesIO
 from diffusers import StableDiffusionImg2ImgPipeline
 from transformers import CLIPTextModel
 from enum import Enum
-
+from collections import defaultdict
+from safetensors.torch import load_file
 _ONE_DAY_IN_SECONDS = 60 * 60 * 24
 COMPEL=os.environ.get("COMPEL", "1") == "1"
 CLIPSKIP=os.environ.get("CLIPSKIP", "1") == "1"

+# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
+MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
+
 # https://github.com/CompVis/stable-diffusion/issues/239#issuecomment-1627615287
 def sc(self, clip_input, images) : return images, [False for i in images]
 # edit the StableDiffusionSafetyChecker class so that, when called, it just returns the images and an array of True values
@@ -213,11 +217,83 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
            self.compel = Compel(tokenizer=self.pipe.tokenizer, text_encoder=self.pipe.text_encoder)
            if request.CUDA:
                self.pipe.to('cuda')
+            # Assume directory from request.ModelFile.
+            # Only if request.LoraAdapter it's not an absolute path
+            if request.LoraAdapter and request.ModelFile != "" and not os.path.isabs(request.LoraAdapter) and request.LoraAdapter:
+                # get base path of modelFile
+                modelFileBase = os.path.dirname(request.ModelFile)
+                # modify LoraAdapter to be relative to modelFileBase
+                request.LoraAdapter = os.path.join(modelFileBase, request.LoraAdapter)
+            device = "cpu" if not request.CUDA else "cuda"
+            self.device = device
+            if request.LoraAdapter:
+                # Check if its a local file and not a directory ( we load lora differently for a safetensor file )
+                if os.path.exists(request.LoraAdapter) and not os.path.isdir(request.LoraAdapter):
+                    self.load_lora_weights(request.LoraAdapter, 1, device, torchType)
+                else:
+                    self.pipe.unet.load_attn_procs(request.LoraAdapter)
+
        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)
+
+    # https://github.com/huggingface/diffusers/issues/3064
+    def load_lora_weights(self, checkpoint_path, multiplier, device, dtype):
+        LORA_PREFIX_UNET = "lora_unet"
+        LORA_PREFIX_TEXT_ENCODER = "lora_te"
+        # load LoRA weight from .safetensors
+        state_dict = load_file(checkpoint_path, device=device)
+
+        updates = defaultdict(dict)
+        for key, value in state_dict.items():
+            # it is suggested to print out the key, it usually will be something like below
+            # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
+
+            layer, elem = key.split('.', 1)
+            updates[layer][elem] = value
+
+        # directly update weight in diffusers model
+        for layer, elems in updates.items():
+
+            if "text" in layer:
+                layer_infos = layer.split(LORA_PREFIX_TEXT_ENCODER + "_")[-1].split("_")
+                curr_layer = self.pipe.text_encoder
+            else:
+                layer_infos = layer.split(LORA_PREFIX_UNET + "_")[-1].split("_")
+                curr_layer = self.pipe.unet
+
+            # find the target layer
+            temp_name = layer_infos.pop(0)
+            while len(layer_infos) > -1:
+                try:
+                    curr_layer = curr_layer.__getattr__(temp_name)
+                    if len(layer_infos) > 0:
+                        temp_name = layer_infos.pop(0)
+                    elif len(layer_infos) == 0:
+                        break
+                except Exception:
+                    if len(temp_name) > 0:
+                        temp_name += "_" + layer_infos.pop(0)
+                    else:
+                        temp_name = layer_infos.pop(0)
+
+            # get elements for this layer
+            weight_up = elems['lora_up.weight'].to(dtype)
+            weight_down = elems['lora_down.weight'].to(dtype)
+            alpha = elems['alpha'] if 'alpha' in elems else None
+            if alpha:
+                alpha = alpha.item() / weight_up.shape[1]
+            else:
+                alpha = 1.0
+
+            # update weight
+            if len(weight_up.shape) == 4:
+                curr_layer.weight.data += multiplier * alpha * torch.mm(weight_up.squeeze(3).squeeze(2), weight_down.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
+            else:
+                curr_layer.weight.data += multiplier * alpha * torch.mm(weight_up, weight_down)
+
    def GenerateImage(self, request, context):

        prompt = request.positive_prompt
@@ -246,6 +322,12 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
        # create a dictionary of parameters by using the keys from EnableParameters and the values from defaults
        kwargs = {key: options[key] for key in keys}

+        # Set seed
+        if request.seed > 0:
+            kwargs["generator"] = torch.Generator(device=self.device).manual_seed(
+                request.seed
+            )
+
        image = {}
        if COMPEL:
            conditioning = self.compel.build_conditioning_tensor(prompt)
@@ -267,7 +349,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
        return backend_pb2.Result(message="Model loaded successfully", success=True)

 def serve(address):
-    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
    backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
    server.add_insecure_port(address)
    server.start()
--- a/extra/grpc/diffusers/backend_pb2.py
+++ b/extra/grpc/diffusers/backend_pb2.py
--- a/extra/grpc/exllama/backend_pb2.py
+++ b/extra/grpc/exllama/backend_pb2.py
--- a/extra/grpc/exllama/exllama.py
+++ b/extra/grpc/exllama/exllama.py
@@ -19,6 +19,9 @@ from exllama.tokenizer import ExLlamaTokenizer

 _ONE_DAY_IN_SECONDS = 60 * 60 * 24

+# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
+MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
+
 # Implement the BackendServicer class with the service methods
 class BackendServicer(backend_pb2_grpc.BackendServicer):
    def generate(self,prompt, max_new_tokens):
@@ -110,7 +113,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):


 def serve(address):
-    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
    backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
    server.add_insecure_port(address)
    server.start()
--- a/extra/grpc/huggingface/backend_pb2.py
+++ b/extra/grpc/huggingface/backend_pb2.py
--- a/extra/grpc/huggingface/huggingface.py
+++ b/extra/grpc/huggingface/huggingface.py
@@ -12,6 +12,9 @@ from sentence_transformers import SentenceTransformer

 _ONE_DAY_IN_SECONDS = 60 * 60 * 24

+# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
+MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
+
 # Implement the BackendServicer class with the service methods
 class BackendServicer(backend_pb2_grpc.BackendServicer):
    def Health(self, request, context):
@@ -34,7 +37,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):


 def serve(address):
-    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
    backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
    server.add_insecure_port(address)
    server.start()
--- a/extra/grpc/vall-e-x/backend_pb2.py
+++ b/extra/grpc/vall-e-x/backend_pb2.py
--- a/extra/grpc/vall-e-x/backend_pb2_grpc.py
+++ b/extra/grpc/vall-e-x/backend_pb2_grpc.py
@@ -0,0 +1,363 @@
+# 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,
+                )
+        self.TokenizeString = channel.unary_unary(
+                '/backend.Backend/TokenizeString',
+                request_serializer=backend__pb2.PredictOptions.SerializeToString,
+                response_deserializer=backend__pb2.TokenizationResponse.FromString,
+                )
+        self.Status = channel.unary_unary(
+                '/backend.Backend/Status',
+                request_serializer=backend__pb2.HealthMessage.SerializeToString,
+                response_deserializer=backend__pb2.StatusResponse.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 TokenizeString(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 Status(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,
+            ),
+            'TokenizeString': grpc.unary_unary_rpc_method_handler(
+                    servicer.TokenizeString,
+                    request_deserializer=backend__pb2.PredictOptions.FromString,
+                    response_serializer=backend__pb2.TokenizationResponse.SerializeToString,
+            ),
+            'Status': grpc.unary_unary_rpc_method_handler(
+                    servicer.Status,
+                    request_deserializer=backend__pb2.HealthMessage.FromString,
+                    response_serializer=backend__pb2.StatusResponse.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)
+
+    @staticmethod
+    def TokenizeString(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/TokenizeString',
+            backend__pb2.PredictOptions.SerializeToString,
+            backend__pb2.TokenizationResponse.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def Status(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/Status',
+            backend__pb2.HealthMessage.SerializeToString,
+            backend__pb2.StatusResponse.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
--- a/extra/grpc/vall-e-x/ttsvalle.py
+++ b/extra/grpc/vall-e-x/ttsvalle.py
@@ -0,0 +1,100 @@
+#!/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 pathlib import Path
+
+from utils.generation import SAMPLE_RATE, generate_audio, preload_models
+from scipy.io.wavfile import write as write_wav
+from utils.prompt_making import make_prompt
+
+_ONE_DAY_IN_SECONDS = 60 * 60 * 24
+
+# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
+MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
+
+# 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()
+            # Assume directory from request.ModelFile.
+            # Only if request.LoraAdapter it's not an absolute path
+            if request.AudioPath and request.ModelFile != "" and not os.path.isabs(request.AudioPath):
+                # get base path of modelFile
+                modelFileBase = os.path.dirname(request.ModelFile)
+                # modify LoraAdapter to be relative to modelFileBase
+                request.AudioPath = os.path.join(modelFileBase, request.AudioPath)
+            if request.AudioPath != "":
+                print("Generating model", file=sys.stderr)
+                make_prompt(name=model_name, audio_prompt_path=request.AudioPath)
+                ### Use given transcript
+                ##make_prompt(name=model_name, audio_prompt_path="paimon_prompt.wav",
+                ##                transcript="Just, what was that? Paimon thought we were gonna get eaten.")
+        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, 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=MAX_WORKERS))
+    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/vllm/backend_pb2.py
+++ b/extra/grpc/vllm/backend_pb2.py
--- a/extra/grpc/vllm/backend_pb2_grpc.py
+++ b/extra/grpc/vllm/backend_pb2_grpc.py
@@ -0,0 +1,363 @@
+# 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,
+                )
+        self.TokenizeString = channel.unary_unary(
+                '/backend.Backend/TokenizeString',
+                request_serializer=backend__pb2.PredictOptions.SerializeToString,
+                response_deserializer=backend__pb2.TokenizationResponse.FromString,
+                )
+        self.Status = channel.unary_unary(
+                '/backend.Backend/Status',
+                request_serializer=backend__pb2.HealthMessage.SerializeToString,
+                response_deserializer=backend__pb2.StatusResponse.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 TokenizeString(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 Status(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,
+            ),
+            'TokenizeString': grpc.unary_unary_rpc_method_handler(
+                    servicer.TokenizeString,
+                    request_deserializer=backend__pb2.PredictOptions.FromString,
+                    response_serializer=backend__pb2.TokenizationResponse.SerializeToString,
+            ),
+            'Status': grpc.unary_unary_rpc_method_handler(
+                    servicer.Status,
+                    request_deserializer=backend__pb2.HealthMessage.FromString,
+                    response_serializer=backend__pb2.StatusResponse.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)
+
+    @staticmethod
+    def TokenizeString(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/TokenizeString',
+            backend__pb2.PredictOptions.SerializeToString,
+            backend__pb2.TokenizationResponse.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+
+    @staticmethod
+    def Status(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/Status',
+            backend__pb2.HealthMessage.SerializeToString,
+            backend__pb2.StatusResponse.FromString,
+            options, channel_credentials,
+            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
--- a/extra/grpc/vllm/backend_vllm.py
+++ b/extra/grpc/vllm/backend_vllm.py
@@ -0,0 +1,107 @@
+#!/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
+from vllm import LLM, SamplingParams
+
+_ONE_DAY_IN_SECONDS = 60 * 60 * 24
+
+# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
+MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
+
+# 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:
+            if request.Quantization != "":
+                self.llm = LLM(model=request.Model, quantization=request.Quantization)
+            else:
+                self.llm = LLM(model=request.Model)
+        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):
+        if request.TopP == 0:
+            request.TopP = 0.9
+
+        sampling_params = SamplingParams(temperature=request.Temperature, top_p=request.TopP)
+        outputs = self.llm.generate([request.Prompt], sampling_params)
+
+        generated_text = outputs[0].outputs[0].text
+        # Remove prompt from response if present
+        if request.Prompt in generated_text:
+            generated_text = generated_text.replace(request.Prompt, "")
+
+        return backend_pb2.Result(message=bytes(generated_text, 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=MAX_WORKERS))
+    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/go.mod
+++ b/go.mod
@@ -9,8 +9,8 @@ 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 v0.0.0-20230825165210-bf63302a2be7
-	github.com/gofiber/fiber/v2 v2.48.0
+	github.com/go-skynet/go-llama.cpp v0.0.0-20230916071220-b471eb7d8c93
+	github.com/gofiber/fiber/v2 v2.49.2
 	github.com/google/uuid v1.3.1
 	github.com/hashicorp/go-multierror v1.1.1
 	github.com/hpcloud/tail v1.0.0
@@ -20,17 +20,17 @@ require (
 	github.com/mudler/go-ggllm.cpp v0.0.0-20230709223052-862477d16eef
 	github.com/mudler/go-processmanager v0.0.0-20230818213616-f204007f963c
 	github.com/mudler/go-stable-diffusion v0.0.0-20230605122230-d89260f598af
-	github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230823205330-27a8b020c36b
-	github.com/onsi/ginkgo/v2 v2.12.0
+	github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230916184256-e86c63750de2
+	github.com/onsi/ginkgo/v2 v2.12.1
 	github.com/onsi/gomega v1.27.10
 	github.com/otiai10/openaigo v1.6.0
 	github.com/phayes/freeport v0.0.0-20220201140144-74d24b5ae9f5
 	github.com/rs/zerolog v1.30.0
-	github.com/sashabaranov/go-openai v1.14.2
-	github.com/tmc/langchaingo v0.0.0-20230826022619-1e2a401097d6
+	github.com/sashabaranov/go-openai v1.15.3
+	github.com/tmc/langchaingo v0.0.0-20230923172951-2c309cf27956
 	github.com/urfave/cli/v2 v2.25.7
-	github.com/valyala/fasthttp v1.48.0
-	google.golang.org/grpc v1.57.0
+	github.com/valyala/fasthttp v1.50.0
+	google.golang.org/grpc v1.58.2
 	google.golang.org/protobuf v1.31.0
 	gopkg.in/yaml.v2 v2.4.0
 	gopkg.in/yaml.v3 v3.0.1
@@ -40,10 +40,10 @@ require (
 	github.com/go-ole/go-ole v1.2.6 // indirect
 	github.com/lufia/plan9stats v0.0.0-20211012122336-39d0f177ccd0 // indirect
 	github.com/power-devops/perfstat v0.0.0-20210106213030-5aafc221ea8c // indirect
-	github.com/shirou/gopsutil/v3 v3.23.7
+	github.com/shirou/gopsutil/v3 v3.23.8
 	github.com/shoenig/go-m1cpu v0.1.6 // indirect
-	github.com/tklauser/go-sysconf v0.3.11 // indirect
-	github.com/tklauser/numcpus v0.6.0 // indirect
+	github.com/tklauser/go-sysconf v0.3.12 // indirect
+	github.com/tklauser/numcpus v0.6.1 // indirect
 	github.com/yusufpapurcu/wmi v1.2.3 // indirect
 )

@@ -61,7 +61,7 @@ require (
 	github.com/pkoukk/tiktoken-go v0.1.2 // indirect
 	github.com/ulikunitz/xz v0.5.9 // indirect
 	github.com/xi2/xz v0.0.0-20171230120015-48954b6210f8 // indirect
-	google.golang.org/genproto/googleapis/rpc v0.0.0-20230525234030-28d5490b6b19 // indirect
+	google.golang.org/genproto/googleapis/rpc v0.0.0-20230711160842-782d3b101e98 // indirect
 	gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c // indirect
 	gopkg.in/fsnotify.v1 v1.4.7 // indirect
 	gopkg.in/tomb.v1 v1.0.0-20141024135613-dd632973f1e7 // indirect
@@ -77,10 +77,10 @@ require (
 	github.com/google/go-cmp v0.5.9 // indirect
 	github.com/google/pprof v0.0.0-20210407192527-94a9f03dee38 // indirect
 	github.com/hashicorp/errwrap v1.0.0 // indirect
-	github.com/klauspost/compress v1.16.3 // indirect
+	github.com/klauspost/compress v1.16.7 // indirect
 	github.com/mattn/go-colorable v0.1.13 // indirect
 	github.com/mattn/go-isatty v0.0.19 // indirect
-	github.com/mattn/go-runewidth v0.0.14 // indirect
+	github.com/mattn/go-runewidth v0.0.15 // indirect
 	github.com/mudler/go-piper v0.0.0-20230621222733-56b8a81b4760
 	github.com/rivo/uniseg v0.2.0 // indirect
 	github.com/russross/blackfriday/v2 v2.1.0 // indirect
@@ -88,7 +88,7 @@ require (
 	github.com/valyala/tcplisten v1.0.0 // indirect
 	github.com/xrash/smetrics v0.0.0-20201216005158-039620a65673 // indirect
 	golang.org/x/net v0.14.0 // indirect
-	golang.org/x/sys v0.11.0 // indirect
+	golang.org/x/sys v0.12.0 // indirect
 	golang.org/x/text v0.12.0 // indirect
 	golang.org/x/tools v0.12.0 // indirect
 )
--- a/go.sum
+++ b/go.sum
@@ -39,16 +39,14 @@ github.com/go-skynet/go-bert.cpp v0.0.0-20230716133540-6abe312cded1 h1:yXvc7QfGt
 github.com/go-skynet/go-bert.cpp v0.0.0-20230716133540-6abe312cded1/go.mod h1:fYjkCDRzC+oRLHSjQoajmYK6AmeJnmEanV27CClAcDc=
 github.com/go-skynet/go-ggml-transformers.cpp v0.0.0-20230714203132-ffb09d7dd71e h1:4reMY29i1eOZaRaSTMPNyXI7X8RMNxCTfDDBXYzrbr0=
 github.com/go-skynet/go-ggml-transformers.cpp v0.0.0-20230714203132-ffb09d7dd71e/go.mod h1:31j1odgFXP8hDSUVfH0zErKI5aYVP18ddYnPkwCso2A=
-github.com/go-skynet/go-llama.cpp v0.0.0-20230815201253-f03869d188b7 h1:d/FXe1a55gCLf124uRYYtlYg6KvI7OI33xaFejQUAws=
-github.com/go-skynet/go-llama.cpp v0.0.0-20230815201253-f03869d188b7/go.mod h1:fiJBto+Le1XLtD/cID5SAKs8cKE7wFXJKfTT3wvPQRA=
-github.com/go-skynet/go-llama.cpp v0.0.0-20230825165210-bf63302a2be7 h1:tWzT53oxcuw1xKCd058IX19eItt96WPdQPeqi574CHo=
-github.com/go-skynet/go-llama.cpp v0.0.0-20230825165210-bf63302a2be7/go.mod h1:yEf77qqgs/YqcrFmgzLiPIvaeteeII/y/YfcQ66b1rM=
+github.com/go-skynet/go-llama.cpp v0.0.0-20230916071220-b471eb7d8c93 h1:aJQn0Lb5TQtSXw5hK+kAUuaUjsXwDXyEItzI9U+GiOA=
+github.com/go-skynet/go-llama.cpp v0.0.0-20230916071220-b471eb7d8c93/go.mod h1:VGPpMv2UMxRgBiDIFrs7AGMzrX884K0Vy8tqYu3if/Q=
 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=
 github.com/godbus/dbus/v5 v5.0.4/go.mod h1:xhWf0FNVPg57R7Z0UbKHbJfkEywrmjJnf7w5xrFpKfA=
-github.com/gofiber/fiber/v2 v2.48.0 h1:cRVMCb9aUJDsyHxGFLwz/sGzDggdailZZyptU9F9cU0=
-github.com/gofiber/fiber/v2 v2.48.0/go.mod h1:xqJgfqrc23FJuqGOW6DVgi3HyZEm2Mn9pRqUb2kHSX8=
+github.com/gofiber/fiber/v2 v2.49.2 h1:ONEN3/Vc+dUCxxDgZZwpqvhISgHqb+bu+isBiEyKEQs=
+github.com/gofiber/fiber/v2 v2.49.2/go.mod h1:gNsKnyrmfEWFpJxQAV0qvW6l70K1dZGno12oLtukcts=
 github.com/golang/protobuf v1.2.0/go.mod h1:6lQm79b+lXiMfvg/cZm0SGofjICqVBUtrP5yJMmIC1U=
 github.com/golang/protobuf v1.4.0-rc.1/go.mod h1:ceaxUfeHdC40wWswd/P6IGgMaK3YpKi5j83Wpe3EHw8=
 github.com/golang/protobuf v1.4.0-rc.1.0.20200221234624-67d41d38c208/go.mod h1:xKAWHe0F5eneWXFV3EuXVDTCmh+JuBKY0li0aMyXATA=
@@ -72,8 +70,6 @@ github.com/google/go-cmp v0.5.9/go.mod h1:17dUlkBOakJ0+DkrSSNjCkIjxS6bF9zb3elmeN
 github.com/google/gofuzz v1.0.0/go.mod h1:dBl0BpW6vV/+mYPU4Po3pmUjxk6FQPldtuIdl/M65Eg=
 github.com/google/pprof v0.0.0-20210407192527-94a9f03dee38 h1:yAJXTCF9TqKcTiHJAE8dj7HMvPfh66eeA2JYW7eFpSE=
 github.com/google/pprof v0.0.0-20210407192527-94a9f03dee38/go.mod h1:kpwsk12EmLew5upagYY7GY0pfYCcupk39gWOCRROcvE=
-github.com/google/uuid v1.3.0 h1:t6JiXgmwXMjEs8VusXIJk2BXHsn+wx8BZdTaoZ5fu7I=
-github.com/google/uuid v1.3.0/go.mod h1:TIyPZe4MgqvfeYDBFedMoGGpEw/LqOeaOT+nhxU+yHo=
 github.com/google/uuid v1.3.1 h1:KjJaJ9iWZ3jOFZIf1Lqf4laDRCasjl0BCmnEGxkdLb4=
 github.com/google/uuid v1.3.1/go.mod h1:TIyPZe4MgqvfeYDBFedMoGGpEw/LqOeaOT+nhxU+yHo=
 github.com/hashicorp/errwrap v1.0.0 h1:hLrqtEDnRye3+sgx6z4qVLNuviH3MR5aQ0ykNJa/UYA=
@@ -89,8 +85,8 @@ github.com/json-iterator/go v1.1.12 h1:PV8peI4a0ysnczrg+LtxykD8LfKY9ML6u2jnxaEnr
 github.com/json-iterator/go v1.1.12/go.mod h1:e30LSqwooZae/UwlEbR2852Gd8hjQvJoHmT4TnhNGBo=
 github.com/klauspost/compress v1.4.1/go.mod h1:RyIbtBH6LamlWaDj8nUwkbUhJ87Yi3uG0guNDohfE1A=
 github.com/klauspost/compress v1.11.4/go.mod h1:aoV0uJVorq1K+umq18yTdKaF57EivdYsUV+/s2qKfXs=
-github.com/klauspost/compress v1.16.3 h1:XuJt9zzcnaz6a16/OU53ZjWp/v7/42WcR5t2a0PcNQY=
-github.com/klauspost/compress v1.16.3/go.mod h1:ntbaceVETuRiXiv4DpjP66DpAtAGkEQskQzEyD//IeE=
+github.com/klauspost/compress v1.16.7 h1:2mk3MPGNzKyxErAw8YaohYh69+pa4sIQSC0fPGCFR9I=
+github.com/klauspost/compress v1.16.7/go.mod h1:ntbaceVETuRiXiv4DpjP66DpAtAGkEQskQzEyD//IeE=
 github.com/klauspost/cpuid v1.2.0/go.mod h1:Pj4uuM528wm8OyEC2QMXAi2YiTZ96dNQPGgoMS4s3ek=
 github.com/klauspost/pgzip v1.2.5 h1:qnWYvvKqedOF2ulHpMG72XQol4ILEJ8k2wwRl/Km8oE=
 github.com/klauspost/pgzip v1.2.5/go.mod h1:Ch1tH69qFZu15pkjo5kYi6mth2Zzwzt50oCQKQE9RUs=
@@ -109,8 +105,8 @@ github.com/mattn/go-isatty v0.0.14/go.mod h1:7GGIvUiUoEMVVmxf/4nioHXj79iQHKdU27k
 github.com/mattn/go-isatty v0.0.16/go.mod h1:kYGgaQfpe5nmfYZH+SKPsOc2e4SrIfOl2e/yFXSvRLM=
 github.com/mattn/go-isatty v0.0.19 h1:JITubQf0MOLdlGRuRq+jtsDlekdYPia9ZFsB8h/APPA=
 github.com/mattn/go-isatty v0.0.19/go.mod h1:W+V8PltTTMOvKvAeJH7IuucS94S2C6jfK/D7dTCTo3Y=
-github.com/mattn/go-runewidth v0.0.14 h1:+xnbZSEeDbOIg5/mE6JF0w6n9duR1l3/WmbinWVwUuU=
-github.com/mattn/go-runewidth v0.0.14/go.mod h1:Jdepj2loyihRzMpdS35Xk/zdY8IAYHsh153qUoGf23w=
+github.com/mattn/go-runewidth v0.0.15 h1:UNAjwbU9l54TA3KzvqLGxwWjHmMgBUVhBiTjelZgg3U=
+github.com/mattn/go-runewidth v0.0.15/go.mod h1:Jdepj2loyihRzMpdS35Xk/zdY8IAYHsh153qUoGf23w=
 github.com/mholt/archiver/v3 v3.5.1 h1:rDjOBX9JSF5BvoJGvjqK479aL70qh9DIpZCl+k7Clwo=
 github.com/mholt/archiver/v3 v3.5.1/go.mod h1:e3dqJ7H78uzsRSEACH1joayhuSyhnonssnDhppzS1L4=
 github.com/modern-go/concurrent v0.0.0-20180228061459-e0a39a4cb421 h1:ZqeYNhU3OHLH3mGKHDcjJRFFRrJa6eAM5H+CtDdOsPc=
@@ -121,18 +117,12 @@ github.com/mudler/go-ggllm.cpp v0.0.0-20230709223052-862477d16eef h1:OJZtJ5vYhlk
 github.com/mudler/go-ggllm.cpp v0.0.0-20230709223052-862477d16eef/go.mod h1:00giAi/vwF8LX29JBjkPQhtASsivPnGNzB6sdmk8JGE=
 github.com/mudler/go-piper v0.0.0-20230621222733-56b8a81b4760 h1:OFVkSxR7CRSRSNm5dvpMRZwmSwWa8EMMnHbc84fW5tU=
 github.com/mudler/go-piper v0.0.0-20230621222733-56b8a81b4760/go.mod h1:O7SwdSWMilAWhBZMK9N9Y/oBDyMMzshE3ju8Xkexwig=
-github.com/mudler/go-processmanager v0.0.0-20220724164624-c45b5c61312d h1:/lAg9vPAAU+s35cDMCx1IyeMn+4OYfCBPqi08Q8vXDg=
-github.com/mudler/go-processmanager v0.0.0-20220724164624-c45b5c61312d/go.mod h1:HGGAOJhipApckwNV8ZTliRJqxctUv3xRY+zbQEwuytc=
 github.com/mudler/go-processmanager v0.0.0-20230818213616-f204007f963c h1:CI5uGwqBpN8N7BrSKC+nmdfw+9nPQIDyjHHlaIiitZI=
 github.com/mudler/go-processmanager v0.0.0-20230818213616-f204007f963c/go.mod h1:gY3wyrhkRySJtmtI/JPt4a2mKv48h/M9pEZIW+SjeC0=
 github.com/mudler/go-stable-diffusion v0.0.0-20230605122230-d89260f598af h1:XFq6OUqsWQam0OrEr05okXsJK/TQur3zoZTHbiZD3Ks=
 github.com/mudler/go-stable-diffusion v0.0.0-20230605122230-d89260f598af/go.mod h1:8ufRkpz/S/9ahkaxzZ5i4WMgO9w4InEhuRoT7vK5Rnw=
-github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230815171941-a63093554fb5 h1:b4EeYDaGxOLNlNm5LOVEmrUhaw1v6xq/V79ZwWVlY6I=
-github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230815171941-a63093554fb5/go.mod h1:4T3CHXyrt+7FQHXaxULZfPjHbD8/99WuDDJa0YVZARI=
-github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230817205628-36f7fb584824 h1:Q8CV2o6XPucrhu3jFhQPZj5OzJT3Mgtv8JdoCpQVEgg=
-github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230817205628-36f7fb584824/go.mod h1:4T3CHXyrt+7FQHXaxULZfPjHbD8/99WuDDJa0YVZARI=
-github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230823205330-27a8b020c36b h1:M6rq7NEbunl3NecCDkPcp7U27PTRdKS6ujtZxcstDjs=
-github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230823205330-27a8b020c36b/go.mod h1:4T3CHXyrt+7FQHXaxULZfPjHbD8/99WuDDJa0YVZARI=
+github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230916184256-e86c63750de2 h1:nBbgd4ORk4pubuh/kugHw5Sdytt+SxtJLEQ+vJfK6Ko=
+github.com/nomic-ai/gpt4all/gpt4all-bindings/golang v0.0.0-20230916184256-e86c63750de2/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=
@@ -142,18 +132,15 @@ github.com/onsi/ginkgo v1.6.0/go.mod h1:lLunBs/Ym6LB5Z9jYTR76FiuTmxDTDusOGeTQH+W
 github.com/onsi/ginkgo v1.12.1/go.mod h1:zj2OWP4+oCPe1qIXoGWkgMRwljMUYCdkwsT2108oapk=
 github.com/onsi/ginkgo v1.16.4 h1:29JGrr5oVBm5ulCWet69zQkzWipVXIol6ygQUe/EzNc=
 github.com/onsi/ginkgo v1.16.4/go.mod h1:dX+/inL/fNMqNlz0e9LfyB9TswhZpCVdJM/Z6Vvnwo0=
-github.com/onsi/ginkgo/v2 v2.11.0 h1:WgqUCUt/lT6yXoQ8Wef0fsNn5cAuMK7+KT9UFRz2tcU=
-github.com/onsi/ginkgo/v2 v2.11.0/go.mod h1:ZhrRA5XmEE3x3rhlzamx/JJvujdZoJ2uvgI7kR0iZvM=
-github.com/onsi/ginkgo/v2 v2.12.0 h1:UIVDowFPwpg6yMUpPjGkYvf06K3RAiJXUhCxEwQVHRI=
-github.com/onsi/ginkgo/v2 v2.12.0/go.mod h1:ZNEzXISYlqpb8S36iN71ifqLi3vVD1rVJGvWRCJOUpQ=
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 github.com/otiai10/mint v1.6.1 h1:kgbTJmOpp/0ce7hk3H8jiSuR0MXmpwWRfqUdKww17qg=
-github.com/otiai10/openaigo v1.5.2 h1:YnNDisZmA4syArF3IxMCIrfgZOq30PLV219gPY7n2z8=
-github.com/otiai10/openaigo v1.5.2/go.mod h1:kIaXc3V+Xy5JLplcBxehVyGYDtufHp3PFPy04jOwOAI=
+github.com/otiai10/mint v1.6.1/go.mod h1:MJm72SBthJjz8qhefc4z1PYEieWmy8Bku7CjcAqyUSM=
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@@ -174,12 +161,11 @@ github.com/rs/zerolog v1.30.0 h1:SymVODrcRsaRaSInD9yQtKbtWqwsfoPcRff/oRXLj4c=
 github.com/rs/zerolog v1.30.0/go.mod h1:/tk+P47gFdPXq4QYjvCmT5/Gsug2nagsFWBWhAiSi1w=
 github.com/russross/blackfriday/v2 v2.1.0 h1:JIOH55/0cWyOuilr9/qlrm0BSXldqnqwMsf35Ld67mk=
 github.com/russross/blackfriday/v2 v2.1.0/go.mod h1:+Rmxgy9KzJVeS9/2gXHxylqXiyQDYRxCVz55jmeOWTM=
-github.com/sashabaranov/go-openai v1.14.2 h1:5DPTtR9JBjKPJS008/A409I5ntFhUPPGCmaAihcPRyo=
-github.com/sashabaranov/go-openai v1.14.2/go.mod h1:lj5b/K+zjTSFxVLijLSTDZuP7adOgerWeFyZLUhAKRg=
-github.com/shirou/gopsutil/v3 v3.23.6 h1:5y46WPI9QBKBbK7EEccUPNXpJpNrvPuTD0O2zHEHT08=
-github.com/shirou/gopsutil/v3 v3.23.6/go.mod h1:j7QX50DrXYggrpN30W0Mo+I4/8U2UUIQrnrhqUeWrAU=
-github.com/shirou/gopsutil/v3 v3.23.7 h1:C+fHO8hfIppoJ1WdsVm1RoI0RwXoNdfTK7yWXV0wVj4=
+github.com/sashabaranov/go-openai v1.15.3 h1:rzoNK9n+Cak+PM6OQ9puxDmFllxfnVea9StlmhglXqA=
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 github.com/shirou/gopsutil/v3 v3.23.7/go.mod h1:c4gnmoRC0hQuaLqvxnx1//VXQ0Ms/X9UnJF8pddY5z4=
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+github.com/shirou/gopsutil/v3 v3.23.8/go.mod h1:7hmCaBn+2ZwaZOr6jmPBZDfawwMGuo1id3C6aM8EDqQ=
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@@ -194,16 +180,16 @@ github.com/stretchr/testify v1.7.1/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/
 github.com/stretchr/testify v1.8.0/go.mod h1:yNjHg4UonilssWZ8iaSj1OCr/vHnekPRkoO+kdMU+MU=
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-github.com/tklauser/go-sysconf v0.3.11 h1:89WgdJhk5SNwJfu+GKyYveZ4IaJ7xAkecBo+KdJV0CM=
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-github.com/tklauser/numcpus v0.6.0 h1:kebhY2Qt+3U6RNK7UqpYNA+tJ23IBEGKkB7JQBfDYms=
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@@ -211,8 +197,8 @@ github.com/urfave/cli/v2 v2.25.7 h1:VAzn5oq403l5pHjc4OhD54+XGO9cdKVL/7lDjF+iKUs=
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@@ -226,15 +212,14 @@ golang.org/x/crypto v0.0.0-20190308221718-c2843e01d9a2/go.mod h1:djNgcEr1/C05ACk
 golang.org/x/crypto v0.0.0-20191011191535-87dc89f01550/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
 golang.org/x/crypto v0.0.0-20200622213623-75b288015ac9/go.mod h1:LzIPMQfyMNhhGPhUkYOs5KpL4U8rLKemX1yGLhDgUto=
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-golang.org/x/mod v0.10.0 h1:lFO9qtOdlre5W1jxS3r/4szv2/6iXxScdzjoBMXNhYk=
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 golang.org/x/net v0.0.0-20210428140749-89ef3d95e781/go.mod h1:OJAsFXCWl8Ukc7SiCT/9KSuxbyM7479/AVlXFRxuMCk=
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@@ -259,34 +244,32 @@ golang.org/x/sys v0.0.0-20210927094055-39ccf1dd6fa6/go.mod h1:oPkhp1MJrh7nUepCBc
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-golang.org/x/sys v0.9.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
-golang.org/x/sys v0.10.0 h1:SqMFp9UcQJZa+pmYuAKjd9xq1f0j5rLcDIk0mj4qAsA=
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 google.golang.org/protobuf v0.0.0-20200228230310-ab0ca4ff8a60/go.mod h1:cfTl7dwQJ+fmap5saPgwCLgHXTUD7jkjRqWcaiX5VyM=
--- a/pkg/backend/llm/llama/llama.go
+++ b/pkg/backend/llm/llama/llama.go
@@ -4,6 +4,7 @@ package llama
 // It is meant to be used by the main executable that is the server for the specific backend type (falcon, gpt3, etc)
 import (
 	"fmt"
+	"path/filepath"

 	"github.com/go-skynet/LocalAI/pkg/grpc/base"
 	pb "github.com/go-skynet/LocalAI/pkg/grpc/proto"
@@ -13,7 +14,8 @@ import (
 type LLM struct {
 	base.SingleThread

-	llama *llama.LLama
+	llama      *llama.LLama
+	draftModel *llama.LLama
 }

 func (llm *LLM) Load(opts *pb.ModelOptions) error {
@@ -36,12 +38,15 @@ func (llm *LLM) Load(opts *pb.ModelOptions) error {
 		llamaOpts = append(llamaOpts, llama.SetMulMatQ(false))
 	}

+	// Get base path of opts.ModelFile and use the same for lora (assume the same path)
+	basePath := filepath.Dir(opts.ModelFile)
+
 	if opts.LoraAdapter != "" {
-		llamaOpts = append(llamaOpts, llama.SetLoraAdapter(opts.LoraAdapter))
+		llamaOpts = append(llamaOpts, llama.SetLoraAdapter(filepath.Join(basePath, opts.LoraAdapter)))
 	}

 	if opts.LoraBase != "" {
-		llamaOpts = append(llamaOpts, llama.SetLoraBase(opts.LoraBase))
+		llamaOpts = append(llamaOpts, llama.SetLoraBase(filepath.Join(basePath, opts.LoraBase)))
 	}

 	if opts.ContextSize != 0 {
@@ -74,7 +79,27 @@ func (llm *LLM) Load(opts *pb.ModelOptions) error {
 		llamaOpts = append(llamaOpts, llama.EnabelLowVRAM)
 	}

+	if opts.DraftModel != "" {
+		// https://github.com/ggerganov/llama.cpp/blob/71ca2fad7d6c0ef95ef9944fb3a1a843e481f314/examples/speculative/speculative.cpp#L40
+		llamaOpts = append(llamaOpts, llama.SetPerplexity(true))
+	}
+
 	model, err := llama.New(opts.ModelFile, llamaOpts...)
+
+	if opts.DraftModel != "" {
+		// opts.DraftModel is relative to opts.ModelFile, so we need to get the basepath of opts.ModelFile
+		if !filepath.IsAbs(opts.DraftModel) {
+			dir := filepath.Dir(opts.ModelFile)
+			opts.DraftModel = filepath.Join(dir, opts.DraftModel)
+		}
+
+		draftModel, err := llama.New(opts.DraftModel, llamaOpts...)
+		if err != nil {
+			return err
+		}
+		llm.draftModel = draftModel
+	}
+
 	llm.llama = model

 	return err
@@ -158,6 +183,9 @@ func buildPredictOptions(opts *pb.PredictOptions) []llama.PredictOption {
 		predictOptions = append(predictOptions, llama.SetSeed(int(opts.Seed)))
 	}

+	if opts.NDraft != 0 {
+		predictOptions = append(predictOptions, llama.SetNDraft(int(opts.NDraft)))
+	}
 	//predictOptions = append(predictOptions, llama.SetLogitBias(c.Seed))

 	predictOptions = append(predictOptions, llama.SetFrequencyPenalty(opts.FrequencyPenalty))
@@ -171,6 +199,9 @@ func buildPredictOptions(opts *pb.PredictOptions) []llama.PredictOption {
 }

 func (llm *LLM) Predict(opts *pb.PredictOptions) (string, error) {
+	if llm.draftModel != nil {
+		return llm.llama.SpeculativeSampling(llm.draftModel, opts.Prompt, buildPredictOptions(opts)...)
+	}
 	return llm.llama.Predict(opts.Prompt, buildPredictOptions(opts)...)
 }

@@ -183,7 +214,13 @@ func (llm *LLM) PredictStream(opts *pb.PredictOptions, results chan string) erro
 	}))

 	go func() {
-		_, err := llm.llama.Predict(opts.Prompt, predictOptions...)
+		var err error
+		if llm.draftModel != nil {
+			_, err = llm.llama.SpeculativeSampling(llm.draftModel, opts.Prompt, buildPredictOptions(opts)...)
+		} else {
+			_, err = llm.llama.Predict(opts.Prompt, predictOptions...)
+		}
+
 		if err != nil {
 			fmt.Println("err: ", err)
 		}
--- a/pkg/gallery/gallery.go
+++ b/pkg/gallery/gallery.go
@@ -98,8 +98,8 @@ func InstallModelFromGalleryByName(galleries []Gallery, name string, basePath st
 }

 // List available models
-// Models galleries are a list of json files that are hosted on a remote server (for example github).
-// Each json file contains a list of models that can be downloaded and optionally overrides to define a new model setting.
+// Models galleries are a list of yaml files that are hosted on a remote server (for example github).
+// Each yaml file contains a list of models that can be downloaded and optionally overrides to define a new model setting.
 func AvailableGalleryModels(galleries []Gallery, basePath string) ([]*GalleryModel, error) {
 	var models []*GalleryModel

--- a/pkg/grpc/proto/backend.pb.go
+++ b/pkg/grpc/proto/backend.pb.go
@@ -155,6 +155,7 @@ type PredictOptions struct {
 	RopeFreqScale       float32  `protobuf:"fixed32,38,opt,name=RopeFreqScale,proto3" json:"RopeFreqScale,omitempty"`
 	NegativePromptScale float32  `protobuf:"fixed32,39,opt,name=NegativePromptScale,proto3" json:"NegativePromptScale,omitempty"`
 	NegativePrompt      string   `protobuf:"bytes,40,opt,name=NegativePrompt,proto3" json:"NegativePrompt,omitempty"`
+	NDraft              int32    `protobuf:"varint,41,opt,name=NDraft,proto3" json:"NDraft,omitempty"`
 }

 func (x *PredictOptions) Reset() {
@@ -462,6 +463,13 @@ func (x *PredictOptions) GetNegativePrompt() string {
 	return ""
 }

+func (x *PredictOptions) GetNDraft() int32 {
+	if x != nil {
+		return x.NDraft
+	}
+	return 0
+}
+
 // The response message containing the result
 type Reply struct {
 	state         protoimpl.MessageState
@@ -553,9 +561,12 @@ type ModelOptions struct {
 	// RWKV
 	Tokenizer string `protobuf:"bytes,34,opt,name=Tokenizer,proto3" json:"Tokenizer,omitempty"`
 	// LLM (llama.cpp)
-	LoraBase    string `protobuf:"bytes,35,opt,name=LoraBase,proto3" json:"LoraBase,omitempty"`
-	LoraAdapter string `protobuf:"bytes,36,opt,name=LoraAdapter,proto3" json:"LoraAdapter,omitempty"`
-	NoMulMatQ   bool   `protobuf:"varint,37,opt,name=NoMulMatQ,proto3" json:"NoMulMatQ,omitempty"`
+	LoraBase     string `protobuf:"bytes,35,opt,name=LoraBase,proto3" json:"LoraBase,omitempty"`
+	LoraAdapter  string `protobuf:"bytes,36,opt,name=LoraAdapter,proto3" json:"LoraAdapter,omitempty"`
+	NoMulMatQ    bool   `protobuf:"varint,37,opt,name=NoMulMatQ,proto3" json:"NoMulMatQ,omitempty"`
+	DraftModel   string `protobuf:"bytes,39,opt,name=DraftModel,proto3" json:"DraftModel,omitempty"`
+	AudioPath    string `protobuf:"bytes,38,opt,name=AudioPath,proto3" json:"AudioPath,omitempty"`
+	Quantization string `protobuf:"bytes,40,opt,name=Quantization,proto3" json:"Quantization,omitempty"`
 }

 func (x *ModelOptions) Reset() {
@@ -849,6 +860,27 @@ func (x *ModelOptions) GetNoMulMatQ() bool {
 	return false
 }

+func (x *ModelOptions) GetDraftModel() string {
+	if x != nil {
+		return x.DraftModel
+	}
+	return ""
+}
+
+func (x *ModelOptions) GetAudioPath() string {
+	if x != nil {
+		return x.AudioPath
+	}
+	return ""
+}
+
+func (x *ModelOptions) GetQuantization() string {
+	if x != nil {
+		return x.Quantization
+	}
+	return ""
+}
+
 type Result struct {
 	state         protoimpl.MessageState
 	sizeCache     protoimpl.SizeCache
@@ -1510,7 +1542,7 @@ var file_pkg_grpc_proto_backend_proto_rawDesc = []byte{
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@@ -1586,204 +1618,211 @@ var file_pkg_grpc_proto_backend_proto_rawDesc = []byte{
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 }

 var (
--- a/pkg/grpc/proto/backend.proto
+++ b/pkg/grpc/proto/backend.proto
@@ -63,6 +63,7 @@ message PredictOptions {
  float RopeFreqScale = 38;
  float NegativePromptScale = 39;
  string NegativePrompt = 40;
+  int32 NDraft = 41;
 }

 // The response message containing the result
@@ -116,6 +117,12 @@ message ModelOptions {
  string LoraBase = 35;
  string LoraAdapter = 36;
  bool NoMulMatQ = 37;
+  string DraftModel = 39;
+  
+  string AudioPath = 38;
+
+  // vllm
+  string Quantization = 40;
 }

 message Result {
--- a/pkg/utils/uri.go
+++ b/pkg/utils/uri.go
@@ -5,6 +5,7 @@ import (
 	"io"
 	"net/http"
 	"os"
+	"path/filepath"
 	"strings"
 )

@@ -32,8 +33,13 @@ func GetURI(url string, f func(url string, i []byte) error) error {

 	if strings.HasPrefix(url, "file://") {
 		rawURL := strings.TrimPrefix(url, "file://")
+		// checks if the file is symbolic, and resolve if so - otherwise, this function returns the path unmodified.
+		resolvedFile, err := filepath.EvalSymlinks(rawURL)
+		if err != nil {
+			return err
+		}
 		// Read the response body
-		body, err := os.ReadFile(rawURL)
+		body, err := os.ReadFile(resolvedFile)
 		if err != nil {
 			return err
 		}
--- a/prompt-templates/getting_started.tmpl
+++ b/prompt-templates/getting_started.tmpl
@@ -0,0 +1,2 @@
+{{.Input}}
+### Response: