Update Dockerfile

Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>

.github/workflows/image-pr.yml

@@ -0,0 +1,86 @@
+---
+name: 'build container images tests'
+
+on:
+  pull_request:
+
+concurrency:
+  group: ci-${{ github.head_ref || github.ref }}-${{ github.repository }}
+  cancel-in-progress: true
+
+jobs:
+  extras-image-build:
+    uses: ./.github/workflows/image_build.yml
+    with:
+      tag-latest: ${{ matrix.tag-latest }}
+      tag-suffix: ${{ matrix.tag-suffix }}
+      ffmpeg: ${{ matrix.ffmpeg }}
+      image-type: ${{ matrix.image-type }}
+      build-type: ${{ matrix.build-type }}
+      cuda-major-version: ${{ matrix.cuda-major-version }}
+      cuda-minor-version: ${{ matrix.cuda-minor-version }}
+      platforms: ${{ matrix.platforms }}
+      runs-on: ${{ matrix.runs-on }}
+    secrets:
+      dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
+      dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
+      quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
+      quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
+    strategy:
+      # Pushing with all jobs in parallel
+      # eats the bandwidth of all the nodes
+      max-parallel: ${{ github.event_name != 'pull_request' && 2 || 4 }}
+      matrix:
+        include:
+          - build-type: ''
+            platforms: 'linux/amd64'
+            tag-latest: 'false'
+            tag-suffix: '-ffmpeg'
+            ffmpeg: 'true'
+            image-type: 'extras'
+            runs-on: 'arc-runner-set'
+          - build-type: 'cublas'
+            cuda-major-version: "12"
+            cuda-minor-version: "1"
+            platforms: 'linux/amd64'
+            tag-latest: 'false'
+            tag-suffix: '-cublas-cuda12-ffmpeg'
+            ffmpeg: 'true'
+            image-type: 'extras'
+            runs-on: 'arc-runner-set'
+  core-image-build:
+    uses: ./.github/workflows/image_build.yml
+    with:
+      tag-latest: ${{ matrix.tag-latest }}
+      tag-suffix: ${{ matrix.tag-suffix }}
+      ffmpeg: ${{ matrix.ffmpeg }}
+      image-type: ${{ matrix.image-type }}
+      build-type: ${{ matrix.build-type }}
+      cuda-major-version: ${{ matrix.cuda-major-version }}
+      cuda-minor-version: ${{ matrix.cuda-minor-version }}
+      platforms: ${{ matrix.platforms }}
+      runs-on: ${{ matrix.runs-on }}
+    secrets:
+      dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
+      dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
+      quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
+      quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
+    strategy:
+      matrix:
+        include:
+          - build-type: ''
+            platforms: 'linux/amd64'
+            tag-latest: 'false'
+            tag-suffix: '-ffmpeg-core'
+            ffmpeg: 'true'
+            image-type: 'core'
+            runs-on: 'ubuntu-latest'
+          - build-type: 'cublas'
+            cuda-major-version: "12"
+            cuda-minor-version: "1"
+            platforms: 'linux/amd64'
+            tag-latest: 'false'
+            tag-suffix: '-cublas-cuda12-ffmpeg-core'
+            ffmpeg: 'true'
+            image-type: 'core'
+            runs-on: 'ubuntu-latest'

.github/workflows/image.yml

@@ -2,7 +2,6 @@
 name: 'build container images'
 
 on:
-  pull_request:
   push:
     branches:
       - master
@@ -27,8 +26,10 @@ jobs:
       platforms: ${{ matrix.platforms }}
       runs-on: ${{ matrix.runs-on }}
     secrets:
-      dockerUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
-      dockerPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
+      dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
+      dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
+      quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
+      quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
     strategy:
       # Pushing with all jobs in parallel
       # eats the bandwidth of all the nodes
@@ -107,8 +108,10 @@ jobs:
       platforms: ${{ matrix.platforms }}
       runs-on: ${{ matrix.runs-on }}
     secrets:
-      dockerUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
-      dockerPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
+      dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
+      dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
+      quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
+      quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
     strategy:
       matrix:
         include:

.github/workflows/image_build.yml

@@ -46,6 +46,10 @@ on:
         required: true
       dockerPassword:
         required: true
+      quayUsername:
+        required: true
+      quayPassword:
+        required: true
 jobs:
   reusable_image-build:
     runs-on: ${{ inputs.runs-on }}
@@ -100,7 +104,9 @@ jobs:
         id: meta
         uses: docker/metadata-action@v5
         with:
-          images: quay.io/go-skynet/local-ai
+          images: |
+            quay.io/go-skynet/local-ai
+            localai/localai
           tags: |
             type=ref,event=branch
             type=semver,pattern={{raw}}
@@ -122,10 +128,17 @@ jobs:
         if: github.event_name != 'pull_request'
         uses: docker/login-action@v3
         with:
-          registry: quay.io
           username: ${{ secrets.dockerUsername }}
           password: ${{ secrets.dockerPassword }}
 
+      - name: Login to DockerHub
+        if: github.event_name != 'pull_request'
+        uses: docker/login-action@v3
+        with:
+          registry: quay.io
+          username: ${{ secrets.quayUsername }}
+          password: ${{ secrets.quayPassword }}
+
       - name: Build and push
         uses: docker/build-push-action@v5
         with:

.github/workflows/release.yaml

@@ -34,10 +34,22 @@ jobs:
           sudo apt-get update
           sudo apt-get install build-essential ffmpeg
 
+      - name: Cache grpc
+        id: cache-grpc
+        uses: actions/cache@v3
+        with:
+          path: grpc
+          key: ${{ runner.os }}-grpc
+      - name: Build grpc
+        if: steps.cache-grpc.outputs.cache-hit != 'true'
+        run: |
           git clone --recurse-submodules -b v1.58.0 --depth 1 --shallow-submodules https://github.com/grpc/grpc && \
-              cd grpc && mkdir -p cmake/build && cd cmake/build && cmake -DgRPC_INSTALL=ON \
-                -DgRPC_BUILD_TESTS=OFF \
-                ../.. && sudo make -j12 install
+          cd grpc && mkdir -p cmake/build && cd cmake/build && cmake -DgRPC_INSTALL=ON \
+            -DgRPC_BUILD_TESTS=OFF \
+            ../.. && sudo make -j12
+      - name: Install gRPC
+        run: |
+          cd grpc && cd cmake/build && sudo make -j12 install
 
       - name: Build
         id: build

.github/workflows/test.yml

@@ -86,11 +86,22 @@ jobs:
           sudo cp -rfv sources/go-piper/piper-phonemize/pi/lib/. /usr/lib/ && \
           # Pre-build stable diffusion before we install a newer version of abseil (not compatible with stablediffusion-ncn)
           GO_TAGS="stablediffusion tts" GRPC_BACKENDS=backend-assets/grpc/stablediffusion make build
-
+      - name: Cache grpc
+        id: cache-grpc
+        uses: actions/cache@v3
+        with:
+          path: grpc
+          key: ${{ runner.os }}-grpc
+      - name: Build grpc
+        if: steps.cache-grpc.outputs.cache-hit != 'true'
+        run: |
           git clone --recurse-submodules -b v1.58.0 --depth 1 --shallow-submodules https://github.com/grpc/grpc && \
-              cd grpc && mkdir -p cmake/build && cd cmake/build && cmake -DgRPC_INSTALL=ON \
-                -DgRPC_BUILD_TESTS=OFF \
-                ../.. && sudo make -j12 install
+          cd grpc && mkdir -p cmake/build && cd cmake/build && cmake -DgRPC_INSTALL=ON \
+            -DgRPC_BUILD_TESTS=OFF \
+            ../.. && sudo make -j12
+      - name: Install gRPC
+        run: |
+          cd grpc && cd cmake/build && sudo make -j12 install
       - name: Test
         run: |
           GO_TAGS="stablediffusion tts" make test

Dockerfile

@@ -15,7 +15,6 @@ ENV BUILD_TYPE=${BUILD_TYPE}
 
 ENV EXTERNAL_GRPC_BACKENDS="coqui:/build/backend/python/coqui/run.sh,huggingface-embeddings:/build/backend/python/sentencetransformers/run.sh,petals:/build/backend/python/petals/run.sh,transformers:/build/backend/python/transformers/run.sh,sentencetransformers:/build/backend/python/sentencetransformers/run.sh,autogptq:/build/backend/python/autogptq/run.sh,bark:/build/backend/python/bark/run.sh,diffusers:/build/backend/python/diffusers/run.sh,exllama:/build/backend/python/exllama/run.sh,vall-e-x:/build/backend/python/vall-e-x/run.sh,vllm:/build/backend/python/vllm/run.sh,exllama2:/build/backend/python/exllama2/run.sh,transformers-musicgen:/build/backend/python/transformers-musicgen/run.sh"
 
-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 tinydream tts"
 
 RUN apt-get update && \
@@ -64,12 +63,12 @@ RUN curl https://repo.anaconda.com/pkgs/misc/gpgkeys/anaconda.asc | gpg --dearmo
     echo "deb [arch=amd64 signed-by=/usr/share/keyrings/conda-archive-keyring.gpg] https://repo.anaconda.com/pkgs/misc/debrepo/conda stable main" > /etc/apt/sources.list.d/conda.list && \
     echo "deb [arch=amd64 signed-by=/usr/share/keyrings/conda-archive-keyring.gpg] https://repo.anaconda.com/pkgs/misc/debrepo/conda stable main" | tee -a /etc/apt/sources.list.d/conda.list && \
     apt-get update && \
-    apt-get install -y conda
+    apt-get install -y conda && apt-get clean
 
 ENV PATH="/root/.cargo/bin:${PATH}"
 RUN pip install --upgrade pip
 RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
-RUN apt-get install -y espeak-ng espeak
+RUN apt-get install -y espeak-ng espeak && apt-get clean
 
 ###################################
 ###################################
@@ -127,10 +126,11 @@ ARG CUDA_MAJOR_VERSION=11
 ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
 ENV NVIDIA_REQUIRE_CUDA="cuda>=${CUDA_MAJOR_VERSION}.0"
 ENV NVIDIA_VISIBLE_DEVICES=all
+ENV PIP_CACHE_PURGE=true
 
 # Add FFmpeg
 RUN if [ "${FFMPEG}" = "true" ]; then \
-    apt-get install -y ffmpeg \
+    apt-get install -y ffmpeg && apt-get clean \
     ; fi
 
 WORKDIR /build
@@ -193,6 +193,9 @@ RUN if [ "${IMAGE_TYPE}" = "extras" ]; then \
 	PATH=$PATH:/opt/conda/bin make -C backend/python/coqui \
     ; fi
 
+# Make sure the models directory exists
+RUN mkdir -p /build/models
+
 # Define the health check command
 HEALTHCHECK --interval=1m --timeout=10m --retries=10 \
   CMD curl -f $HEALTHCHECK_ENDPOINT || exit 1

Makefile

@@ -8,7 +8,7 @@ GOLLAMA_VERSION?=aeba71ee842819da681ea537e78846dc75949ac0
 
 GOLLAMA_STABLE_VERSION?=50cee7712066d9e38306eccadcfbb44ea87df4b7
 
-CPPLLAMA_VERSION?=0235b9b571f3cc7d2b8836409a5404b41ce1379c
+CPPLLAMA_VERSION?=1fc2f265ff9377a37fd2c61eae9cd813a3491bea
 
 # gpt4all version
 GPT4ALL_REPO?=https://github.com/nomic-ai/gpt4all

README.md

@@ -20,6 +20,9 @@
 </a>
 </p>
 
+[<img src="https://img.shields.io/badge/dockerhub-images-important.svg?logo=Docker">](https://hub.docker.com/r/localai/localai)
+[<img src="https://img.shields.io/badge/quay.io-images-important.svg?">](https://quay.io/repository/go-skynet/local-ai?tab=tags&tag=latest)
+
 > :bulb: Get help - [❓FAQ](https://localai.io/faq/) [💭Discussions](https://github.com/go-skynet/LocalAI/discussions) [:speech_balloon: Discord](https://discord.gg/uJAeKSAGDy) [:book: Documentation website](https://localai.io/)
 >
 > [💻 Quickstart](https://localai.io/basics/getting_started/) [📣 News](https://localai.io/basics/news/) [ 🛫 Examples ](https://github.com/go-skynet/LocalAI/tree/master/examples/) [ 🖼️ Models ](https://localai.io/models/) [ 🚀 Roadmap ](https://github.com/mudler/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3Aroadmap)
@@ -40,6 +43,7 @@
 
 [Roadmap](https://github.com/mudler/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3Aroadmap)
 
+- Start and share models with config file: https://github.com/mudler/LocalAI/pull/1522
 - 🐸 Coqui: https://github.com/mudler/LocalAI/pull/1489
 - Inline templates: https://github.com/mudler/LocalAI/pull/1452
 - Mixtral: https://github.com/mudler/LocalAI/pull/1449

api/api.go

@@ -16,6 +16,7 @@ import (
 	"github.com/go-skynet/LocalAI/metrics"
 	"github.com/go-skynet/LocalAI/pkg/assets"
 	"github.com/go-skynet/LocalAI/pkg/model"
+	"github.com/go-skynet/LocalAI/pkg/startup"
 
 	"github.com/gofiber/fiber/v2"
 	"github.com/gofiber/fiber/v2/middleware/cors"
@@ -36,6 +37,8 @@ func Startup(opts ...options.AppOption) (*options.Option, *config.ConfigLoader,
 	log.Info().Msgf("Starting LocalAI using %d threads, with models path: %s", options.Threads, options.Loader.ModelPath)
 	log.Info().Msgf("LocalAI version: %s", internal.PrintableVersion())
 
+	startup.PreloadModelsConfigurations(options.Loader.ModelPath, options.ModelsURL...)
+
 	cl := config.NewConfigLoader()
 	if err := cl.LoadConfigs(options.Loader.ModelPath); err != nil {
 		log.Error().Msgf("error loading config files: %s", err.Error())
@@ -51,6 +54,18 @@ func Startup(opts ...options.AppOption) (*options.Option, *config.ConfigLoader,
 		log.Error().Msgf("error downloading models: %s", err.Error())
 	}
 
+	if options.PreloadJSONModels != "" {
+		if err := localai.ApplyGalleryFromString(options.Loader.ModelPath, options.PreloadJSONModels, cl, options.Galleries); err != nil {
+			return nil, nil, err
+		}
+	}
+
+	if options.PreloadModelsFromPath != "" {
+		if err := localai.ApplyGalleryFromFile(options.Loader.ModelPath, options.PreloadModelsFromPath, cl, options.Galleries); err != nil {
+			return nil, nil, err
+		}
+	}
+
 	if options.Debug {
 		for _, v := range cl.ListConfigs() {
 			cfg, _ := cl.GetConfig(v)
@@ -67,18 +82,6 @@ func Startup(opts ...options.AppOption) (*options.Option, *config.ConfigLoader,
 		}
 	}
 
-	if options.PreloadJSONModels != "" {
-		if err := localai.ApplyGalleryFromString(options.Loader.ModelPath, options.PreloadJSONModels, cl, options.Galleries); err != nil {
-			return nil, nil, err
-		}
-	}
-
-	if options.PreloadModelsFromPath != "" {
-		if err := localai.ApplyGalleryFromFile(options.Loader.ModelPath, options.PreloadModelsFromPath, cl, options.Galleries); err != nil {
-			return nil, nil, err
-		}
-	}
-
 	// turn off any process that was started by GRPC if the context is canceled
 	go func() {
 		<-options.Context.Done()

api/api_test.go

@@ -16,9 +16,9 @@ import (
 	. "github.com/go-skynet/LocalAI/api"
 	"github.com/go-skynet/LocalAI/api/options"
 	"github.com/go-skynet/LocalAI/metrics"
+	"github.com/go-skynet/LocalAI/pkg/downloader"
 	"github.com/go-skynet/LocalAI/pkg/gallery"
 	"github.com/go-skynet/LocalAI/pkg/model"
-	"github.com/go-skynet/LocalAI/pkg/utils"
 	"github.com/gofiber/fiber/v2"
 	. "github.com/onsi/ginkgo/v2"
 	. "github.com/onsi/gomega"
@@ -61,7 +61,7 @@ func getModelStatus(url string) (response map[string]interface{}) {
 }
 
 func getModels(url string) (response []gallery.GalleryModel) {
-	utils.GetURI(url, func(url string, i []byte) error {
+	downloader.GetURI(url, func(url string, i []byte) error {
 		// Unmarshal YAML data into a struct
 		return json.Unmarshal(i, &response)
 	})

api/backend/llm.go

@@ -159,6 +159,9 @@ func Finetune(config config.Config, input, prediction string) string {
 	for _, c := range config.TrimSpace {
 		prediction = strings.TrimSpace(strings.TrimPrefix(prediction, c))
 	}
-	return prediction
 
+	for _, c := range config.TrimSuffix {
+		prediction = strings.TrimSpace(strings.TrimSuffix(prediction, c))
+	}
+	return prediction
 }

api/config/config.go

@@ -9,6 +9,7 @@ import (
 	"strings"
 	"sync"
 
+	"github.com/go-skynet/LocalAI/pkg/downloader"
 	"github.com/go-skynet/LocalAI/pkg/utils"
 	"github.com/rs/zerolog/log"
 	"gopkg.in/yaml.v3"
@@ -52,6 +53,17 @@ type Config struct {
 	// CUDA
 	// Explicitly enable CUDA or not (some backends might need it)
 	CUDA bool `yaml:"cuda"`
+
+	DownloadFiles []File `yaml:"download_files"`
+
+	Description string `yaml:"description"`
+	Usage       string `yaml:"usage"`
+}
+
+type File struct {
+	Filename string `yaml:"filename" json:"filename"`
+	SHA256   string `yaml:"sha256" json:"sha256"`
+	URI      string `yaml:"uri" json:"uri"`
 }
 
 type VallE struct {
@@ -103,16 +115,18 @@ type LLMConfig struct {
 	StopWords       []string `yaml:"stopwords"`
 	Cutstrings      []string `yaml:"cutstrings"`
 	TrimSpace       []string `yaml:"trimspace"`
-	ContextSize     int      `yaml:"context_size"`
-	NUMA            bool     `yaml:"numa"`
-	LoraAdapter     string   `yaml:"lora_adapter"`
-	LoraBase        string   `yaml:"lora_base"`
-	LoraScale       float32  `yaml:"lora_scale"`
-	NoMulMatQ       bool     `yaml:"no_mulmatq"`
-	DraftModel      string   `yaml:"draft_model"`
-	NDraft          int32    `yaml:"n_draft"`
-	Quantization    string   `yaml:"quantization"`
-	MMProj          string   `yaml:"mmproj"`
+	TrimSuffix      []string `yaml:"trimsuffix"`
+
+	ContextSize  int     `yaml:"context_size"`
+	NUMA         bool    `yaml:"numa"`
+	LoraAdapter  string  `yaml:"lora_adapter"`
+	LoraBase     string  `yaml:"lora_base"`
+	LoraScale    float32 `yaml:"lora_scale"`
+	NoMulMatQ    bool    `yaml:"no_mulmatq"`
+	DraftModel   string  `yaml:"draft_model"`
+	NDraft       int32   `yaml:"n_draft"`
+	Quantization string  `yaml:"quantization"`
+	MMProj       string  `yaml:"mmproj"`
 
 	RopeScaling    string  `yaml:"rope_scaling"`
 	YarnExtFactor  float32 `yaml:"yarn_ext_factor"`
@@ -267,26 +281,44 @@ func (cm *ConfigLoader) ListConfigs() []string {
 	return res
 }
 
+// Preload prepare models if they are not local but url or huggingface repositories
 func (cm *ConfigLoader) Preload(modelPath string) error {
 	cm.Lock()
 	defer cm.Unlock()
 
+	status := func(fileName, current, total string, percent float64) {
+		utils.DisplayDownloadFunction(fileName, current, total, percent)
+	}
+
 	log.Info().Msgf("Preloading models from %s", modelPath)
 
 	for i, config := range cm.configs {
 
-		modelURL := config.PredictionOptions.Model
-		modelURL = utils.ConvertURL(modelURL)
+		// Download files and verify their SHA
+		for _, file := range config.DownloadFiles {
+			log.Debug().Msgf("Checking %q exists and matches SHA", file.Filename)
 
-		if utils.LooksLikeURL(modelURL) {
+			if err := utils.VerifyPath(file.Filename, modelPath); err != nil {
+				return err
+			}
+			// Create file path
+			filePath := filepath.Join(modelPath, file.Filename)
+
+			if err := downloader.DownloadFile(file.URI, filePath, file.SHA256, status); err != nil {
+				return err
+			}
+		}
+
+		modelURL := config.PredictionOptions.Model
+		modelURL = downloader.ConvertURL(modelURL)
+
+		if downloader.LooksLikeURL(modelURL) {
 			// md5 of model name
 			md5Name := utils.MD5(modelURL)
 
 			// check if file exists
 			if _, err := os.Stat(filepath.Join(modelPath, md5Name)); errors.Is(err, os.ErrNotExist) {
-				err := utils.DownloadFile(modelURL, filepath.Join(modelPath, md5Name), "", func(fileName, current, total string, percent float64) {
-					log.Info().Msgf("Downloading %s: %s/%s (%.2f%%)", fileName, current, total, percent)
-				})
+				err := downloader.DownloadFile(modelURL, filepath.Join(modelPath, md5Name), "", status)
 				if err != nil {
 					return err
 				}
@@ -297,6 +329,15 @@ func (cm *ConfigLoader) Preload(modelPath string) error {
 			c.PredictionOptions.Model = md5Name
 			cm.configs[i] = *c
 		}
+		if cm.configs[i].Name != "" {
+			log.Info().Msgf("Model name: %s", cm.configs[i].Name)
+		}
+		if cm.configs[i].Description != "" {
+			log.Info().Msgf("Model description: %s", cm.configs[i].Description)
+		}
+		if cm.configs[i].Usage != "" {
+			log.Info().Msgf("Model usage: \n%s", cm.configs[i].Usage)
+		}
 	}
 	return nil
 }

api/localai/gallery.go

@@ -130,6 +130,12 @@ func (g *galleryApplier) Start(c context.Context, cm *config.ConfigLoader) {
 					continue
 				}
 
+				err = cm.Preload(g.modelPath)
+				if err != nil {
+					updateError(err)
+					continue
+				}
+
 				g.updateStatus(op.id, &galleryOpStatus{Processed: true, Message: "completed", Progress: 100})
 			}
 		}

api/options/options.go

@@ -40,9 +40,12 @@ type Option struct {
 	SingleBackend           bool
 	ParallelBackendRequests bool
 
-	WatchDogIdle                             bool
-	WatchDogBusy                             bool
-	WatchDog                                 bool
+	WatchDogIdle bool
+	WatchDogBusy bool
+	WatchDog     bool
+
+	ModelsURL []string
+
 	WatchDogBusyTimeout, WatchDogIdleTimeout time.Duration
 }
 
@@ -63,6 +66,12 @@ func NewOptions(o ...AppOption) *Option {
 	return opt
 }
 
+func WithModelsURL(urls ...string) AppOption {
+	return func(o *Option) {
+		o.ModelsURL = urls
+	}
+}
+
 func WithCors(b bool) AppOption {
 	return func(o *Option) {
 		o.CORS = b

backend/cpp/llama/grpc-server.cpp

@@ -26,6 +26,7 @@
 #include <mutex>
 #include <chrono>
 #include <regex>
+#include <condition_variable>
 #include <grpcpp/ext/proto_server_reflection_plugin.h>
 #include <grpcpp/grpcpp.h>
 #include <grpcpp/health_check_service_interface.h>
@@ -40,12 +41,15 @@ using backend::HealthMessage;
 
 
 ///// LLAMA.CPP server code below
+
 #define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
+
 using json = nlohmann::json;
 
 struct server_params
 {
     std::string hostname = "127.0.0.1";
+    std::string api_key;
     std::string public_path = "examples/server/public";
     int32_t port = 8080;
     int32_t read_timeout = 600;
@@ -89,7 +93,7 @@ static inline bool is_base64(uint8_t c)
     return (isalnum(c) || (c == '+') || (c == '/'));
 }
 
-static std::vector<uint8_t> base64_decode(std::string const &encoded_string)
+static std::vector<uint8_t> base64_decode(const std::string & encoded_string)
 {
     int i = 0;
     int j = 0;
@@ -216,10 +220,10 @@ struct slot_image
     int32_t id;
 
     bool request_encode_image = false;
-    float* image_embedding = nullptr;
+    float * image_embedding = nullptr;
     int32_t image_tokens = 0;
 
-    clip_image_u8 img_data;
+    clip_image_u8 * img_data;
 
     std::string prefix_prompt; // before of this image
 };
@@ -441,15 +445,16 @@ struct llama_client_slot
 
         generated_token_probs.clear();
 
-        for (slot_image &img : images)
+        for (slot_image & img : images)
         {
             free(img.image_embedding);
-            delete[] img.img_data.data;
+            if (img.img_data) {
+                clip_image_u8_free(img.img_data);
+            }
             img.prefix_prompt = "";
         }
 
         images.clear();
-        // llama_set_rng_seed(ctx, params.seed); in batched the seed matter???????
     }
 
     bool has_budget(gpt_params &global_params) {
@@ -550,7 +555,9 @@ struct llama_server_context
     std::vector<task_result> queue_results;
     std::vector<task_multi>  queue_multitasks;
     std::mutex mutex_tasks; // also guards id_gen, and queue_multitasks
+    std::condition_variable condition_tasks;
     std::mutex mutex_results;
+    std::condition_variable condition_results;
 
     ~llama_server_context()
     {
@@ -769,6 +776,42 @@ struct llama_server_context
             slot->prompt = "";
         }
 
+        slot->sparams.penalty_prompt_tokens.clear();
+        slot->sparams.use_penalty_prompt_tokens = false;
+        const auto &penalty_prompt = data.find("penalty_prompt");
+        if (penalty_prompt != data.end())
+        {
+            if (penalty_prompt->is_string())
+            {
+                const auto penalty_prompt_string = penalty_prompt->get<std::string>();
+                auto penalty_tokens = llama_tokenize(model, penalty_prompt_string, false);
+                slot->sparams.penalty_prompt_tokens.swap(penalty_tokens);
+                if (slot->params.n_predict > 0)
+                {
+                    slot->sparams.penalty_prompt_tokens.reserve(slot->sparams.penalty_prompt_tokens.size() + slot->params.n_predict);
+                }
+                slot->sparams.use_penalty_prompt_tokens = true;
+            }
+            else if (penalty_prompt->is_array())
+            {
+                const auto n_tokens = penalty_prompt->size();
+                slot->sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot->params.n_predict));
+                const int n_vocab = llama_n_vocab(model);
+                for (const auto &penalty_token : *penalty_prompt)
+                {
+                    if (penalty_token.is_number_integer())
+                    {
+                        const auto tok = penalty_token.get<llama_token>();
+                        if (tok >= 0 && tok < n_vocab)
+                        {
+                            slot->sparams.penalty_prompt_tokens.push_back(tok);
+                        }
+                    }
+                }
+                slot->sparams.use_penalty_prompt_tokens = true;
+            }
+        }
+
         slot->sparams.logit_bias.clear();
 
         if (json_value(data, "ignore_eos", false))
@@ -821,24 +864,17 @@ struct llama_server_context
             {
                 for (const auto &img : *images_data)
                 {
-                    std::string data_b64 = img["data"].get<std::string>();
+                    const std::vector<uint8_t> image_buffer = base64_decode(img["data"].get<std::string>());
+
                     slot_image img_sl;
                     img_sl.id = img.count("id") != 0 ? img["id"].get<int>() : slot->images.size();
-                    int width, height, channels;
-                    std::vector<uint8_t> image_buffer = base64_decode(data_b64);
-                    data_b64.clear();
-                    auto data = stbi_load_from_memory(image_buffer.data(), image_buffer.size(), &width, &height, &channels, 3);
-                    if (!data) {
+                    img_sl.img_data = clip_image_u8_init();
+                    if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data))
+                    {
                         LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id);
                         return false;
                     }
-                    LOG_TEE("slot %i - image loaded [id: %i] resolution (%i x %i)\n", slot->id, img_sl.id, width, height);
-                    img_sl.img_data.nx = width;
-                    img_sl.img_data.ny = height;
-                    img_sl.img_data.size = width * height * 3;
-                    img_sl.img_data.data = new uint8_t[width * height * 3]();
-                    memcpy(img_sl.img_data.data, data, width * height * 3);
-                    stbi_image_free(data);
+                    LOG_TEE("slot %i - loaded image\n", slot->id);
                     img_sl.request_encode_image = true;
                     slot->images.push_back(img_sl);
                 }
@@ -893,6 +929,7 @@ struct llama_server_context
             llama_sampling_free(slot->ctx_sampling);
         }
         slot->ctx_sampling = llama_sampling_init(slot->sparams);
+        llama_set_rng_seed(ctx, slot->params.seed);
         slot->command = LOAD_PROMPT;
 
         all_slots_are_idle = false;
@@ -1000,6 +1037,12 @@ struct llama_server_context
         slot.generated_text += token_str;
         slot.has_next_token = true;
 
+        if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1)
+        {
+            // we can change penalty_prompt_tokens because it is always created from scratch each request
+            slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok);
+        }
+
         // check if there is incomplete UTF-8 character at the end
         bool incomplete = false;
         for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i)
@@ -1106,8 +1149,8 @@ struct llama_server_context
             {
                 continue;
             }
-            clip_image_f32 img_res;
-            if (!clip_image_preprocess(clp_ctx, &img.img_data, &img_res, /*pad2square =*/ true))
+            clip_image_f32 * img_res = clip_image_f32_init();
+            if (!clip_image_preprocess(clp_ctx, img.img_data, img_res, /*pad2square =*/ true))
             {
                 LOG_TEE("Error processing the given image");
                 clip_free(clp_ctx);
@@ -1122,11 +1165,12 @@ struct llama_server_context
                 return false;
             }
             LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id);
-            if (!clip_image_encode(clp_ctx, params.n_threads, &img_res, img.image_embedding))
+            if (!clip_image_encode(clp_ctx, params.n_threads, img_res, img.image_embedding))
             {
                 LOG_TEE("Unable to encode image\n");
                 return false;
             }
+            clip_image_f32_free(img_res);
             img.request_encode_image = false;
         }
 
@@ -1135,7 +1179,7 @@ struct llama_server_context
 
     void send_error(task_server& task, std::string error)
     {
-        std::lock_guard<std::mutex> lock(mutex_results);
+        std::unique_lock<std::mutex> lock(mutex_results);
         task_result res;
         res.id = task.id;
         res.multitask_id = task.multitask_id;
@@ -1143,6 +1187,7 @@ struct llama_server_context
         res.error = true;
         res.result_json = { { "content", error } };
         queue_results.push_back(res);
+        condition_results.notify_all();
     }
 
     void add_multi_task(int id, std::vector<int>& sub_ids)
@@ -1152,6 +1197,7 @@ struct llama_server_context
         multi.id = id;
         std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
         queue_multitasks.push_back(multi);
+        condition_tasks.notify_one();
     }
 
     void update_multi_task(int multitask_id, int subtask_id, task_result& result)
@@ -1163,6 +1209,7 @@ struct llama_server_context
             {
                 multitask.subtasks_remaining.erase(subtask_id);
                 multitask.results.push_back(result);
+                condition_tasks.notify_one();
             }
         }
     }
@@ -1181,7 +1228,7 @@ struct llama_server_context
             {"n_ctx",             slot.n_ctx},
             {"model",             params.model_alias},
             {"seed",              slot.params.seed},
-            {"temp",              slot.sparams.temp},
+            {"temperature",       slot.sparams.temp},
             {"top_k",             slot.sparams.top_k},
             {"top_p",             slot.sparams.top_p},
             {"min_p",             slot.sparams.min_p},
@@ -1191,6 +1238,8 @@ struct llama_server_context
             {"repeat_penalty",    slot.sparams.penalty_repeat},
             {"presence_penalty",  slot.sparams.penalty_present},
             {"frequency_penalty", slot.sparams.penalty_freq},
+            {"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens},
+            {"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens},
             {"mirostat",          slot.sparams.mirostat},
             {"mirostat_tau",      slot.sparams.mirostat_tau},
             {"mirostat_eta",      slot.sparams.mirostat_eta},
@@ -1208,7 +1257,7 @@ struct llama_server_context
 
     void send_partial_response(llama_client_slot &slot, completion_token_output tkn)
     {
-        std::lock_guard<std::mutex> lock(mutex_results);
+        std::unique_lock<std::mutex> lock(mutex_results);
         task_result res;
         res.id = slot.task_id;
         res.multitask_id = slot.multitask_id;
@@ -1244,11 +1293,12 @@ struct llama_server_context
         }
 
         queue_results.push_back(res);
+        condition_results.notify_all();
     }
 
     void send_final_response(llama_client_slot &slot)
     {
-        std::lock_guard<std::mutex> lock(mutex_results);
+        std::unique_lock<std::mutex> lock(mutex_results);
         task_result res;
         res.id = slot.task_id;
         res.multitask_id = slot.multitask_id;
@@ -1304,11 +1354,12 @@ struct llama_server_context
         }
 
         queue_results.push_back(res);
+        condition_results.notify_all();
     }
 
     void send_embedding(llama_client_slot &slot)
     {
-        std::lock_guard<std::mutex> lock(mutex_results);
+        std::unique_lock<std::mutex> lock(mutex_results);
         task_result res;
         res.id = slot.task_id;
         res.multitask_id = slot.multitask_id;
@@ -1336,6 +1387,7 @@ struct llama_server_context
             };
         }
         queue_results.push_back(res);
+        condition_results.notify_all();
     }
 
     int request_completion(json data, bool infill, bool embedding, int multitask_id)
@@ -1359,6 +1411,7 @@ struct llama_server_context
 
         // otherwise, it's a single-prompt task, we actually queue it
         queue_tasks.push_back(task);
+        condition_tasks.notify_one();
         return task.id;
     }
 
@@ -1366,13 +1419,10 @@ struct llama_server_context
     {
         while (true)
         {
-            std::this_thread::sleep_for(std::chrono::microseconds(5));
-            std::lock_guard<std::mutex> lock(mutex_results);
-
-            if (queue_results.empty())
-            {
-                continue;
-            }
+            std::unique_lock<std::mutex> lock(mutex_results);
+            condition_results.wait(lock, [&]{
+                return !queue_results.empty();
+            });
 
             for (int i = 0; i < (int) queue_results.size(); i++)
             {
@@ -1468,12 +1518,13 @@ struct llama_server_context
 
     void request_cancel(int task_id)
     {
-        std::lock_guard<std::mutex> lock(mutex_tasks);
+        std::unique_lock<std::mutex> lock(mutex_tasks);
         task_server task;
         task.id = id_gen++;
         task.type = CANCEL_TASK;
         task.target_id = task_id;
         queue_tasks.push_back(task);
+        condition_tasks.notify_one();
     }
 
     int split_multiprompt_task(task_server& multiprompt_task)
@@ -1499,7 +1550,7 @@ struct llama_server_context
 
     void process_tasks()
     {
-        std::lock_guard<std::mutex> lock(mutex_tasks);
+        std::unique_lock<std::mutex> lock(mutex_tasks);
         while (!queue_tasks.empty())
         {
             task_server task = queue_tasks.front();
@@ -1571,6 +1622,7 @@ struct llama_server_context
 
                 std::lock_guard<std::mutex> lock(mutex_results);
                 queue_results.push_back(aggregate_result);
+                condition_results.notify_all();
 
                 queue_iterator = queue_multitasks.erase(queue_iterator);
             }
@@ -1601,8 +1653,10 @@ struct llama_server_context
                 LOG_TEE("all slots are idle and system prompt is empty, clear the KV cache\n");
                 kv_cache_clear();
             }
-            // avoid 100% usage of cpu all time
-            std::this_thread::sleep_for(std::chrono::milliseconds(5));
+            std::unique_lock<std::mutex> lock(mutex_tasks);
+            condition_tasks.wait(lock, [&]{
+                return !queue_tasks.empty();
+            });
         }
 
         for (llama_client_slot &slot : slots)
@@ -1962,28 +2016,35 @@ json oaicompat_completion_params_parse(
     llama_params["__oaicompat"] = true;
 
     // Map OpenAI parameters to llama.cpp parameters
+    //
+    // For parameters that are defined by the OpenAI documentation (e.g.
+    // temperature), we explicitly specify OpenAI's intended default; we
+    // need to do that because sometimes OpenAI disagrees with llama.cpp
+    //
+    // https://platform.openai.com/docs/api-reference/chat/create
+    llama_sampling_params default_sparams;
     llama_params["model"]             = json_value(body, "model", std::string("uknown"));
     llama_params["prompt"]            = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt'
     llama_params["cache_prompt"]      = json_value(body, "cache_prompt", false);
-    llama_params["temperature"]       = json_value(body, "temperature", 0.8);
-    llama_params["top_k"]             = json_value(body, "top_k", 40);
-    llama_params["top_p"]             = json_value(body, "top_p", 0.95);
+    llama_params["temperature"]       = json_value(body, "temperature", 0.0);
+    llama_params["top_k"]             = json_value(body, "top_k", default_sparams.top_k);
+    llama_params["top_p"]             = json_value(body, "top_p", 1.0);
     llama_params["n_predict"]         = json_value(body, "max_tokens", -1);
     llama_params["logit_bias"]        = json_value(body, "logit_bias",json::object());
     llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
     llama_params["presence_penalty"]  = json_value(body, "presence_penalty", 0.0);
-    llama_params["seed"]              = json_value(body, "seed", 0);
+    llama_params["seed"]              = json_value(body, "seed", LLAMA_DEFAULT_SEED);
     llama_params["stream"]            = json_value(body, "stream", false);
-    llama_params["mirostat"]          = json_value(body, "mirostat", false);
-    llama_params["mirostat_tau"]      = json_value(body, "mirostat_tau", 0.0);
-    llama_params["mirostat_eta"]      = json_value(body, "mirostat_eta", 0.0);
-    llama_params["penalize_nl"]       = json_value(body, "penalize_nl", false);
-    llama_params["typical_p"]         = json_value(body, "typical_p", 0.0);
-    llama_params["repeat_last_n"]     = json_value(body, "repeat_last_n", 0);
+    llama_params["mirostat"]          = json_value(body, "mirostat", default_sparams.mirostat);
+    llama_params["mirostat_tau"]      = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
+    llama_params["mirostat_eta"]      = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
+    llama_params["penalize_nl"]       = json_value(body, "penalize_nl", default_sparams.penalize_nl);
+    llama_params["typical_p"]         = json_value(body, "typical_p", default_sparams.typical_p);
+    llama_params["repeat_last_n"]     = json_value(body, "repeat_last_n", default_sparams.penalty_last_n);
     llama_params["ignore_eos"]        = json_value(body, "ignore_eos", false);
-    llama_params["tfs_z"]             = json_value(body, "tfs_z", 0.0);
+    llama_params["tfs_z"]             = json_value(body, "tfs_z", default_sparams.tfs_z);
 
-    if (llama_params.count("grammar") != 0) {
+    if (body.count("grammar") != 0) {
         llama_params["grammar"] = json_value(body, "grammar", json::object());
     }
 

backend/python/autogptq/Makefile

@@ -1,5 +1,4 @@
 .PHONY: autogptq
 autogptq:
-	@echo "Creating virtual environment..."
-	@conda env create --name autogptq --file autogptq.yml
-	@echo "Virtual environment created."
+	$(MAKE) -C ../common-env/transformers
+

backend/python/autogptq/run.sh

@@ -6,7 +6,7 @@
 export PATH=$PATH:/opt/conda/bin
 
 # Activate conda environment
-source activate autogptq
+source activate transformers
 
 # get the directory where the bash script is located
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"

backend/python/common-env/transformers/install.sh

@@ -13,3 +13,12 @@ if conda_env_exists "transformers" ; then
 else 
     echo "Virtual environment already exists."
 fi
+
+if [ "$PIP_CACHE_PURGE" = true ] ; then
+    export PATH=$PATH:/opt/conda/bin
+
+    # Activate conda environment
+    source activate transformers
+
+    pip cache purge
+fi

backend/python/common-env/transformers/transformers-nvidia.yml

@@ -45,7 +45,7 @@ dependencies:
       - fsspec==2023.6.0
       - funcy==2.0
       - grpcio==1.59.0
-      - huggingface-hub==0.16.4
+      - huggingface-hub
       - idna==3.4
       - jinja2==3.1.2
       - jmespath==1.0.1
@@ -70,7 +70,6 @@ dependencies:
       - packaging==23.2
       - pandas
       - peft==0.5.0
-      - git+https://github.com/bigscience-workshop/petals
       - protobuf==4.24.4
       - psutil==5.9.5
       - pyarrow==13.0.0
@@ -85,17 +84,16 @@ dependencies:
       - scipy==1.11.3
       - six==1.16.0
       - sympy==1.12
-      - tokenizers==0.14.0
-      - torch==2.1.0
-      - torchaudio==2.1.0
+      - tokenizers
+      - torch==2.1.2
+      - torchaudio==2.1.2
       - tqdm==4.66.1
-      - transformers==4.34.0
-      - TTS==0.22.0
       - triton==2.1.0
       - typing-extensions==4.8.0
       - tzdata==2023.3
       - urllib3==1.26.17
       - xxhash==3.4.1
+      - auto-gptq==0.6.0
       - yarl==1.9.2
       - soundfile
       - langid
@@ -114,4 +112,7 @@ dependencies:
       - sudachipy
       - sudachidict_core
       - vocos
+      - vllm==0.2.7
+      - transformers>=4.36.0  # Required for Mixtral.
+      - xformers==0.0.23.post1  
 prefix: /opt/conda/envs/transformers

backend/python/common-env/transformers/transformers.yml

@@ -46,7 +46,7 @@ dependencies:
       - fsspec==2023.6.0
       - funcy==2.0
       - grpcio==1.59.0
-      - huggingface-hub==0.16.4
+      - huggingface-hub
       - idna==3.4
       - jinja2==3.1.2
       - jmespath==1.0.1
@@ -59,7 +59,6 @@ dependencies:
       - packaging==23.2
       - pandas
       - peft==0.5.0
-      - git+https://github.com/bigscience-workshop/petals
       - protobuf==4.24.4
       - psutil==5.9.5
       - pyarrow==13.0.0
@@ -74,14 +73,14 @@ dependencies:
       - scipy==1.11.3
       - six==1.16.0
       - sympy==1.12
-      - tokenizers==0.14.0
-      - torch==2.1.0
-      - torchaudio==2.1.0
+      - tokenizers
+      - torch==2.1.2
+      - torchaudio==2.1.2
       - tqdm==4.66.1
-      - transformers==4.34.0
       - triton==2.1.0
       - typing-extensions==4.8.0
       - tzdata==2023.3
+      - auto-gptq==0.6.0
       - urllib3==1.26.17
       - xxhash==3.4.1
       - yarl==1.9.2
@@ -102,4 +101,7 @@ dependencies:
       - sudachipy
       - sudachidict_core
       - vocos
+      - vllm==0.2.7
+      - transformers>=4.36.0  # Required for Mixtral.
+      - xformers==0.0.23.post1  
 prefix: /opt/conda/envs/transformers

backend/python/coqui/coqui_server.py

@@ -21,7 +21,7 @@ _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'))
-COQUI_LANGUAGE = os.environ.get('COQUI_LANGUAGE', 'en')
+COQUI_LANGUAGE = os.environ.get('COQUI_LANGUAGE', None)
 
 # Implement the BackendServicer class with the service methods
 class BackendServicer(backend_pb2_grpc.BackendServicer):
@@ -38,6 +38,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         if not torch.cuda.is_available() and request.CUDA:
             return backend_pb2.Result(success=False, message="CUDA is not available")
 
+        self.AudioPath = None
         # List available 🐸TTS models
         print(TTS().list_models())
         if os.path.isabs(request.AudioPath):

backend/python/diffusers/diffusers.yml

@@ -53,6 +53,7 @@ dependencies:
       - nvidia-nccl-cu12==2.18.1
       - nvidia-nvjitlink-cu12==12.2.140
       - nvidia-nvtx-cu12==12.1.105
+      - omegaconf
       - packaging==23.2
       - pillow==10.0.1
       - protobuf==4.24.4

backend/python/exllama/Makefile

@@ -1,8 +1,6 @@
 .PHONY: exllama
 exllama:
-	@echo "Creating virtual environment..."
-	@conda env create --name exllama --file exllama.yml
-	@echo "Virtual environment created."
+	$(MAKE) -C ../common-env/transformers
 	bash install.sh
 
 .PHONY: run

backend/python/exllama/install.sh

@@ -5,11 +5,15 @@
 export PATH=$PATH:/opt/conda/bin
 
 # Activate conda environment
-source activate exllama
+source activate transformers
 
 echo $CONDA_PREFIX
 
 
 git clone https://github.com/turboderp/exllama $CONDA_PREFIX/exllama && pushd $CONDA_PREFIX/exllama && pip install -r requirements.txt && popd
 
-cp -rfv $CONDA_PREFIX/exllama/* ./
+cp -rfv $CONDA_PREFIX/exllama/* ./
+
+if [ "$PIP_CACHE_PURGE" = true ] ; then
+    pip cache purge
+fi

backend/python/exllama/run.sh

@@ -6,9 +6,11 @@
 export PATH=$PATH:/opt/conda/bin
 
 # Activate conda environment
-source activate exllama
+source activate transformers
 
 # get the directory where the bash script is located
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 
+cd $DIR
+
 python $DIR/exllama.py $@

backend/python/exllama2/Makefile

@@ -1,8 +1,6 @@
 .PHONY: exllama2
 exllama2:
-	@echo "Creating virtual environment..."
-	@conda env create --name exllama2 --file exllama2.yml
-	@echo "Virtual environment created."
+	$(MAKE) -C ../common-env/transformers
 	bash install.sh
 
 .PHONY: run

backend/python/exllama2/install.sh

@@ -5,10 +5,14 @@
 export PATH=$PATH:/opt/conda/bin
 
 # Activate conda environment
-source activate exllama2
+source activate transformers
 
 echo $CONDA_PREFIX
 
 git clone https://github.com/turboderp/exllamav2 $CONDA_PREFIX/exllamav2 && pushd $CONDA_PREFIX/exllamav2 && pip install -r requirements.txt && popd
 
-cp -rfv $CONDA_PREFIX/exllamav2/* ./  
+cp -rfv $CONDA_PREFIX/exllamav2/* ./  
+
+if [ "$PIP_CACHE_PURGE" = true ] ; then
+    pip cache purge
+fi

backend/python/exllama2/run.sh

@@ -6,9 +6,11 @@
 export PATH=$PATH:/opt/conda/bin
 
 # Activate conda environment
-source activate exllama2
+source activate transformers
 
 # get the directory where the bash script is located
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 
+cd $DIR
+
 python $DIR/exllama2_backend.py $@

backend/python/petals/Makefile

@@ -1,6 +1,8 @@
 .PHONY: petals
 petals:
-	$(MAKE) -C ../common-env/transformers
+	@echo "Creating virtual environment..."
+	@conda env create --name petals --file petals.yml
+	@echo "Virtual environment created."
 
 .PHONY: run
 run:

backend/python/petals/run.sh

@@ -5,14 +5,16 @@
 
 export PATH=$PATH:/opt/conda/bin
 
+CONDA_ENV=petals
+
 # Activate conda environment
 # if source is available use it, or use conda
 #
 if [ -f /opt/conda/bin/activate ]; then
-    source activate transformers
+    source activate $CONDA_ENV
 else
     eval "$(conda shell.bash hook)"
-    conda activate transformers
+    conda activate $CONDA_ENV
 fi
 
 # get the directory where the bash script is located

backend/python/petals/test.sh

@@ -3,7 +3,16 @@
 ## A bash script wrapper that runs the transformers server with conda
 
 # Activate conda environment
-source activate transformers
+CONDA_ENV=petals
+# Activate conda environment
+# if source is available use it, or use conda
+#
+if [ -f /opt/conda/bin/activate ]; then
+    source activate $CONDA_ENV
+else
+    eval "$(conda shell.bash hook)"
+    conda activate $CONDA_ENV
+fi
 
 # get the directory where the bash script is located
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"

backend/python/vall-e-x/install.sh

@@ -12,4 +12,8 @@ echo $CONDA_PREFIX
 
 git clone https://github.com/Plachtaa/VALL-E-X.git $CONDA_PREFIX/vall-e-x && pushd $CONDA_PREFIX/vall-e-x && git checkout -b build $SHA && pip install -r requirements.txt && popd
 
-cp -rfv $CONDA_PREFIX/vall-e-x/* ./
+cp -rfv $CONDA_PREFIX/vall-e-x/* ./
+
+if [ "$PIP_CACHE_PURGE" = true ] ; then
+    pip cache purge
+fi

backend/python/vall-e-x/run.sh

@@ -10,4 +10,6 @@ source activate transformers
 # get the directory where the bash script is located
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 
+cd $DIR
+
 python $DIR/ttsvalle.py $@

backend/python/vllm/Makefile

@@ -1,8 +1,6 @@
 .PHONY: vllm
 vllm:
-	@echo "Creating virtual environment..."
-	@conda env create --name vllm --file vllm.yml
-	@echo "Virtual environment created."
+	$(MAKE) -C ../common-env/transformers
 
 .PHONY: run
 run:

backend/python/vllm/run.sh

@@ -6,7 +6,7 @@
 export PATH=$PATH:/opt/conda/bin
 
 # Activate conda environment
-source activate vllm
+source activate transformers
 
 # get the directory where the bash script is located
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"

backend/python/vllm/test.sh

@@ -3,7 +3,7 @@
 ## A bash script wrapper that runs the transformers server with conda
 
 # Activate conda environment
-source activate vllm
+source activate transformers
 
 # get the directory where the bash script is located
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"

backend/python/vllm/vllm.yml

@@ -1,99 +0,0 @@
-name: vllm
-channels:
-  - defaults
-dependencies:
-  - _libgcc_mutex=0.1=main
-  - _openmp_mutex=5.1=1_gnu
-  - bzip2=1.0.8=h7b6447c_0
-  - ca-certificates=2023.08.22=h06a4308_0
-  - ld_impl_linux-64=2.38=h1181459_1
-  - libffi=3.4.4=h6a678d5_0
-  - libgcc-ng=11.2.0=h1234567_1
-  - libgomp=11.2.0=h1234567_1
-  - libstdcxx-ng=11.2.0=h1234567_1
-  - libuuid=1.41.5=h5eee18b_0
-  - ncurses=6.4=h6a678d5_0
-  - openssl=3.0.11=h7f8727e_2
-  - pip=23.2.1=py311h06a4308_0
-  - python=3.11.5=h955ad1f_0
-  - readline=8.2=h5eee18b_0
-  - setuptools=68.0.0=py311h06a4308_0
-  - sqlite=3.41.2=h5eee18b_0
-  - tk=8.6.12=h1ccaba5_0
-  - wheel=0.41.2=py311h06a4308_0
-  - xz=5.4.2=h5eee18b_0
-  - zlib=1.2.13=h5eee18b_0
-  - pip:
-      - aiosignal==1.3.1
-      - anyio==3.7.1
-      - attrs==23.1.0
-      - certifi==2023.7.22
-      - charset-normalizer==3.3.0
-      - click==8.1.7
-      - cmake==3.27.6
-      - fastapi==0.103.2
-      - filelock==3.12.4
-      - frozenlist==1.4.0
-      - fsspec==2023.9.2
-      - grpcio==1.59.0
-      - h11==0.14.0
-      - httptools==0.6.0
-      - huggingface-hub==0.17.3
-      - idna==3.4
-      - jinja2==3.1.2
-      - jsonschema==4.19.1
-      - jsonschema-specifications==2023.7.1
-      - lit==17.0.2
-      - markupsafe==2.1.3
-      - mpmath==1.3.0
-      - msgpack==1.0.7
-      - networkx==3.1
-      - ninja==1.11.1
-      - numpy==1.26.0
-      - nvidia-cublas-cu11==11.10.3.66
-      - nvidia-cuda-cupti-cu11==11.7.101
-      - nvidia-cuda-nvrtc-cu11==11.7.99
-      - nvidia-cuda-runtime-cu11==11.7.99
-      - nvidia-cudnn-cu11==8.5.0.96
-      - nvidia-cufft-cu11==10.9.0.58
-      - nvidia-curand-cu11==10.2.10.91
-      - nvidia-cusolver-cu11==11.4.0.1
-      - nvidia-cusparse-cu11==11.7.4.91
-      - nvidia-nccl-cu11==2.14.3
-      - nvidia-nvtx-cu11==11.7.91
-      - packaging==23.2
-      - pandas==2.1.1
-      - protobuf==4.24.4
-      - psutil==5.9.5
-      - pyarrow==13.0.0
-      - pydantic==1.10.13
-      - python-dateutil==2.8.2
-      - python-dotenv==1.0.0
-      - pytz==2023.3.post1
-      - pyyaml==6.0.1
-      - ray==2.7.0
-      - referencing==0.30.2
-      - regex==2023.10.3
-      - requests==2.31.0
-      - rpds-py==0.10.4
-      - safetensors==0.4.0
-      - sentencepiece==0.1.99
-      - six==1.16.0
-      - sniffio==1.3.0
-      - starlette==0.27.0
-      - sympy==1.12
-      - tokenizers==0.14.1
-      - torch==2.0.1
-      - tqdm==4.66.1
-      - transformers==4.34.0
-      - triton==2.0.0
-      - typing-extensions==4.8.0
-      - tzdata==2023.3
-      - urllib3==2.0.6
-      - uvicorn==0.23.2
-      - uvloop==0.17.0
-      - vllm==0.2.0
-      - watchfiles==0.20.0
-      - websockets==11.0.3
-      - xformers==0.0.22
-prefix: /opt/conda/envs/vllm

docs/content/_index.en.md

@@ -18,6 +18,9 @@ title = "LocalAI"
 </a>
 </p>
 
+[<img src="https://img.shields.io/badge/dockerhub-images-important.svg?logo=Docker">](https://hub.docker.com/r/localai/localai)
+[<img src="https://img.shields.io/badge/quay.io-images-important.svg?">](https://quay.io/repository/go-skynet/local-ai?tab=tags&tag=latest)
+
 > 💡 Get help - [❓FAQ](https://localai.io/faq/) [❓How tos](https://localai.io/howtos/) [💭Discussions](https://github.com/go-skynet/LocalAI/discussions) [💭Discord](https://discord.gg/uJAeKSAGDy)
 >
 > [💻 Quickstart](https://localai.io/basics/getting_started/) [📣 News](https://localai.io/basics/news/) [ 🛫 Examples ](https://github.com/go-skynet/LocalAI/tree/master/examples/) [ 🖼️ Models ](https://localai.io/models/) [ 🚀 Roadmap ](https://github.com/mudler/LocalAI/issues?q=is%3Aissue+is%3Aopen+label%3Aroadmap)
@@ -36,10 +39,10 @@ In a nutshell:
 
 - Local, OpenAI drop-in alternative REST API. You own your data.
 - NO GPU required. NO Internet access is required either
-  - Optional, GPU Acceleration is available in `llama.cpp`-compatible LLMs. See also the [build section](https://localai.io/basics/build/index.html).
+  - Optional, GPU Acceleration is available. See also the [build section](https://localai.io/basics/build/index.html).
 - Supports multiple models
 - 🏃 Once loaded the first time, it keep models loaded in memory for faster inference
-- ⚡ Doesn't shell-out, but uses C++ bindings for a faster inference and better performance.
+- ⚡ Doesn't shell-out, but uses bindings for a faster inference and better performance.
 
 LocalAI is focused on making the AI accessible to anyone. Any contribution, feedback and PR is welcome!
 

docs/content/advanced/_index.en.md

@@ -9,7 +9,7 @@ weight = 6
 
 In order to define default prompts, model parameters (such as custom default `top_p` or `top_k`), LocalAI can be configured to serve user-defined models with a set of default parameters and templates.
 
-You can create multiple `yaml` files in the models path or either specify a single YAML configuration file. 
+In order to configure a model, you can create multiple `yaml` files in the models path or either specify a single YAML configuration file. 
 Consider the following `models` folder in the `example/chatbot-ui`:
 
 ```
@@ -96,6 +96,12 @@ Specifying a `config-file` via CLI allows to declare models in a single file as
 
 See also [chatbot-ui](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui) as an example on how to use config files.
 
+It is possible to specify a full URL or a short-hand URL to a YAML model configuration file and use it on start with local-ai, for example to use phi-2:
+
+```
+local-ai github://mudler/LocalAI/examples/configurations/phi-2.yaml@master
+```
+
 ### Full config model file reference
 
 ```yaml
@@ -359,15 +365,37 @@ docker run --env REBUILD=true localai
 docker run --env-file .env localai
 ```
 
-### Build only a single backend
+### CLI parameters
 
-You can control the backends that are built by setting the `GRPC_BACKENDS` environment variable. For instance, to build only the `llama-cpp` backend only:
+You can control LocalAI with command line arguments, to specify a binding address, or the number of threads.
 
-```bash
-make GRPC_BACKENDS=backend-assets/grpc/llama-cpp build
-```
 
-By default, all the backends are built.
+| Parameter                      | Environmental Variable          | Default Variable                                   | Description                                                         |
+| ------------------------------ | ------------------------------- | -------------------------------------------------- | ------------------------------------------------------------------- |
+| --f16                          | $F16                            | false                                              | Enable f16 mode                                                     |
+| --debug                        | $DEBUG                          | false                                              | Enable debug mode                                                   |
+| --cors                         | $CORS                           | false                                              | Enable CORS support                                                 |
+| --cors-allow-origins value     | $CORS_ALLOW_ORIGINS             |                                                    | Specify origins allowed for CORS                                     |
+| --threads value                | $THREADS                        | 4    | Number of threads to use for parallel computation                    |
+| --models-path value            | $MODELS_PATH                    | ./models       | Path to the directory containing models used for inferencing        |
+| --preload-models value         | $PRELOAD_MODELS                 |           | List of models to preload in JSON format at startup                  |
+| --preload-models-config value  | $PRELOAD_MODELS_CONFIG          |  | A config with a list of models to apply at startup. Specify the path to a YAML config file |
+| --config-file value            | $CONFIG_FILE                    |                                         | Path to the config file                                             |
+| --address value                | $ADDRESS                        | :8080                    | Specify the bind address for the API server                         |
+| --image-path value             | $IMAGE_PATH                     |                                     | Path to the directory used to store generated images                             |
+| --context-size value           | $CONTEXT_SIZE                   | 512                 | Default context size of the model                                   |
+| --upload-limit value           | $UPLOAD_LIMIT                   | 15                         | Default upload limit in megabytes (audio file upload)                                  |
+| --galleries                    | $GALLERIES                      |                                                    | Allows to set galleries from command line                           |
+|--parallel-requests              | $PARALLEL_REQUESTS     |   false |            Enable backends to handle multiple requests in parallel. This is for backends that supports multiple requests in parallel, like llama.cpp or vllm |
+| --single-active-backend   | $SINGLE_ACTIVE_BACKEND |  false |    Allow only one backend to be running |
+| --api-keys value |   $API_KEY | empty |  List of API Keys to enable API authentication. When this is set, all the requests must be authenticated with one of these API keys.
+| --enable-watchdog-idle | $WATCHDOG_IDLE | false | Enable watchdog for stopping idle backends. This will stop the backends if are in idle state for too long. (default: false) [$WATCHDOG_IDLE]
+| --enable-watchdog-busy   |     $WATCHDOG_BUSY | false |         Enable watchdog for stopping busy backends that exceed a defined threshold.|
+| --watchdog-busy-timeout value | $WATCHDOG_BUSY_TIMEOUT | 5m | Watchdog timeout. This will restart the backend if it crashes.  |
+| --watchdog-idle-timeout value | $WATCHDOG_IDLE_TIMEOUT | 15m | Watchdog idle timeout. This will restart the backend if it crashes. |
+| --preload-backend-only | $PRELOAD_BACKEND_ONLY | false | If set, the api is NOT launched, and only the preloaded models / backends are started. This is intended for multi-node setups. |
+| --external-grpc-backends | EXTERNAL_GRPC_BACKENDS | none | Comma separated list of external gRPC backends to use. Format: `name:host:port` or `name:/path/to/file` |
+
 
 ### Extra backends
 

docs/content/build/_index.en.md

@@ -7,16 +7,15 @@ url = '/basics/build/'
 
 +++
 
-### Build locally
+### Build
+
+#### Container image
 
 Requirements:
 
-Either Docker/podman, or
-- Golang >= 1.21
-- Cmake/make
-- GCC
+- Docker or podman, or a container engine
 
-In order to build the `LocalAI` container image locally you can use `docker`:
+In order to build the `LocalAI` container image locally you can use `docker`, for example:
 
 ```
 # build the image
@@ -24,7 +23,45 @@ docker build -t localai .
 docker run localai
 ```
 
-Or you can build the manually binary with `make`:
+#### Locally
+
+In order to build LocalAI locally, you need the following requirements:
+
+- Golang >= 1.21
+- Cmake/make
+- GCC
+- GRPC
+
+To install the dependencies follow the instructions below:
+
+{{< tabs >}}
+{{% tab name="Apple" %}}
+
+```bash
+brew install abseil cmake go grpc protobuf wget
+```
+
+{{% /tab %}}
+{{% tab name="Debian" %}}
+
+```bash
+apt install protobuf-compiler-grpc libgrpc-dev make cmake
+```
+
+{{% /tab %}}
+{{% tab name="From source" %}}
+
+Specify `BUILD_GRPC_FOR_BACKEND_LLAMA=true` to build automatically the gRPC dependencies
+
+```bash
+make ... BUILD_GRPC_FOR_BACKEND_LLAMA=true build
+```
+
+{{% /tab %}}
+{{< /tabs >}}
+
+
+To build LocalAI with `make`:
 
 ```
 git clone https://github.com/go-skynet/LocalAI
@@ -32,7 +69,7 @@ cd LocalAI
 make build
 ```
 
-To run: `./local-ai`
+This should produce the binary `local-ai`
 
 {{% notice note %}}
 
@@ -54,7 +91,7 @@ docker run --rm -ti -p 8080:8080 -e DEBUG=true -e MODELS_PATH=/models -e THREADS
 
 {{% /notice %}}
 
-### Build on mac
+### Example: Build on mac
 
 Building on Mac (M1 or M2) works, but you may need to install some prerequisites using `brew`. 
 
@@ -188,6 +225,24 @@ make BUILD_TYPE=metal build
 # Note: only models quantized with q4_0 are supported!
 ```
 
+### Build only a single backend
+
+You can control the backends that are built by setting the `GRPC_BACKENDS` environment variable. For instance, to build only the `llama-cpp` backend only:
+
+```bash
+make GRPC_BACKENDS=backend-assets/grpc/llama-cpp build
+```
+
+By default, all the backends are built.
+
+### Specific llama.cpp version
+
+To build with a specific version of llama.cpp, set `CPPLLAMA_VERSION` to the tag or wanted sha:
+
+```
+CPPLLAMA_VERSION=<sha> make build
+```
+
 ### Windows compatibility
 
 Make sure to give enough resources to the running container. See https://github.com/go-skynet/LocalAI/issues/2

docs/content/features/GPU-acceleration.md

@@ -15,11 +15,19 @@ This section contains instruction on how to use LocalAI with GPU acceleration.
 For accelleration for AMD or Metal HW there are no specific container images, see the [build]({{%relref "build/#acceleration" %}})
 {{% /notice %}}
 
-### CUDA
+### CUDA(NVIDIA) acceleration
 
 Requirement: nvidia-container-toolkit (installation instructions [1](https://www.server-world.info/en/note?os=Ubuntu_22.04&p=nvidia&f=2) [2](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html))
 
-To use CUDA, use the images with the `cublas` tag.
+To check what CUDA version do you need, you can either run `nvidia-smi` or `nvcc --version`. 
+
+Alternatively, you can also check nvidia-smi with docker:
+
+```
+docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi
+```
+
+To use CUDA, use the images with the `cublas` tag, for example.
 
 The image list is on [quay](https://quay.io/repository/go-skynet/local-ai?tab=tags):
 

docs/content/getting_started/_index.en.md

@@ -1,4 +1,4 @@
-
+ 
 +++
 disableToc = false
 title = "Getting started"
@@ -6,7 +6,11 @@ weight = 1
 url = '/basics/getting_started/'
 +++
 
-`LocalAI` is available as a container image and binary. It can be used with docker, podman, kubernetes and any container engine. You can check out all the available images with corresponding tags [here](https://quay.io/repository/go-skynet/local-ai?tab=tags&tag=latest).
+`LocalAI` is available as a container image and binary. It can be used with docker, podman, kubernetes and any container engine. 
+Container images are published to [quay.io](https://quay.io/repository/go-skynet/local-ai?tab=tags&tag=latest) and [Dockerhub](https://hub.docker.com/r/localai/localai).
+
+[<img src="https://img.shields.io/badge/dockerhub-images-important.svg?logo=Docker">](https://hub.docker.com/r/localai/localai)
+[<img src="https://img.shields.io/badge/quay.io-images-important.svg?">](https://quay.io/repository/go-skynet/local-ai?tab=tags&tag=latest)
 
 See also our [How to]({{%relref "howtos" %}}) section for end-to-end guided examples curated by the community.
 
@@ -14,6 +18,8 @@ See also our [How to]({{%relref "howtos" %}}) section for end-to-end guided exam
 
 The easiest way to run LocalAI is by using [`docker compose`](https://docs.docker.com/compose/install/) or with [Docker](https://docs.docker.com/engine/install/) (to build locally, see the [build section]({{%relref "build" %}})).
 
+LocalAI needs at least a model file to work, or a configuration YAML file, or both. You can customize further model defaults and specific settings with a configuration file (see [advanced]({{%relref "advanced" %}})).
+
 {{% notice note %}}
 To run with GPU Accelleration, see [GPU acceleration]({{%relref "features/gpu-acceleration" %}}).
 {{% /notice %}}
@@ -111,10 +117,108 @@ helm show values go-skynet/local-ai > values.yaml
 helm install local-ai go-skynet/local-ai -f values.yaml
 ```
 
+{{% /tab %}}
+{{% tab name="From binary" %}}
+
+LocalAI binary releases are available in [Github](https://github.com/go-skynet/LocalAI/releases).
+
+{{% /tab %}}
+
+{{% tab name="From source" %}}
+
+See the [build section]({{%relref "build" %}}).
+  
 {{% /tab %}}
 
 {{< /tabs >}}
 
+### Running Popular models (one-click!)
+
+You can run `local-ai` directly with a model name, and it will download the model and start the API with the model loaded.
+
+> Don't need GPU acceleration? use the CPU images which are lighter and do not have Nvidia dependencies
+> To know which version of CUDA do you have available, you can check with `nvidia-smi` or `nvcc --version`
+
+
+{{< tabs >}}
+{{% tab name="CPU-only" %}}
+
+| Model | Category | Docker command |
+| --- | --- | --- |
+| [phi-2](https://huggingface.co/microsoft/phi-2) | LLM | ```docker run -ti -p 8080:8080 localai/localai:{{< version >}}-ffmpeg-core phi-2``` |
+| [llava](https://github.com/SkunkworksAI/BakLLaVA) | Multimodal LLM | ```docker run -ti -p 8080:8080 localai/localai:{{< version >}}-ffmpeg-core llava``` |
+| [mistral-openorca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) | LLM | ```docker run -ti -p 8080:8080 localai/localai:{{< version >}}-ffmpeg-core mistral-openorca``` |
+| [bert-cpp](https://github.com/skeskinen/bert.cpp) | Embeddings | ```docker run -ti -p 8080:8080 localai/localai:{{< version >}}-ffmpeg-core bert-cpp``` |
+| all-minilm-l6-v2 | Embeddings | ```docker run -ti -p 8080:8080 localai/localai:{{< version >}}-ffmpeg all-minilm-l6-v2``` |
+| whisper-base | Audio to Text | ```docker run -ti -p 8080:8080 localai/localai:{{< version >}}-ffmpeg-core whisper-base``` |
+| rhasspy-voice-en-us-amy | Text to Audio | ```docker run -ti -p 8080:8080 localai/localai:{{< version >}}-ffmpeg-core rhasspy-voice-en-us-amy``` |
+| coqui | Text to Audio | ```docker run -ti -p 8080:8080 localai/localai:{{< version >}}-ffmpeg coqui``` |
+| bark | Text to Audio | ```docker run -ti -p 8080:8080 localai/localai:{{< version >}}-ffmpeg bark``` |
+| vall-e-x | Text to Audio | ```docker run -ti -p 8080:8080 localai/localai:{{< version >}}-ffmpeg vall-e-x``` |
+  
+{{% /tab %}}
+{{% tab name="GPU (CUDA 11)" %}}
+
+
+
+| Model | Category | Docker command |
+| --- | --- | --- |
+| [phi-2](https://huggingface.co/microsoft/phi-2) | LLM | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11-core phi-2``` |
+| [llava](https://github.com/SkunkworksAI/BakLLaVA) | Multimodal LLM | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11-core llava``` |
+| [mistral-openorca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) | LLM | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11-core mistral-openorca``` |
+| [bert-cpp](https://github.com/skeskinen/bert.cpp) | Embeddings | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11-core bert-cpp``` |
+| [all-minilm-l6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | Embeddings | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11 all-minilm-l6-v2``` |
+| whisper-base | Audio to Text | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11-core whisper-base``` |
+| rhasspy-voice-en-us-amy | Text to Audio | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11-core rhasspy-voice-en-us-amy``` |
+| coqui | Text to Audio | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11 coqui``` |
+| bark | Text to Audio | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11 bark``` |
+| vall-e-x | Text to Audio | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda11 vall-e-x``` |
+
+{{% /tab %}}
+
+
+{{% tab name="GPU (CUDA 12)" %}}
+
+| Model | Category | Docker command |
+| --- | --- | --- |
+| [phi-2](https://huggingface.co/microsoft/phi-2) | LLM | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12-core phi-2``` |
+| [llava](https://github.com/SkunkworksAI/BakLLaVA) | Multimodal LLM | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12-core llava``` |
+| [mistral-openorca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) | LLM | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12-core mistral-openorca``` |
+| bert-cpp | Embeddings | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12-core bert-cpp``` |
+| all-minilm-l6-v2 | Embeddings | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12 all-minilm-l6-v2``` |
+| whisper-base | Audio to Text | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12-core whisper-base``` |
+| rhasspy-voice-en-us-amy | Text to Audio | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12-core rhasspy-voice-en-us-amy``` |
+| coqui | Text to Audio | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12 coqui``` |
+| bark | Text to Audio | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12 bark``` |
+| vall-e-x | Text to Audio | ```docker run -ti -p 8080:8080 --gpus all localai/localai:{{< version >}}-cublas-cuda12 vall-e-x``` |
+
+{{% /tab %}}
+
+{{< /tabs >}}
+
+{{% notice note %}}
+
+LocalAI can be started (either the container image or the binary) with a list of model config files URLs or our short-handed format (e.g. `huggingface://`. `github://`). It works by passing the urls as arguments or environment variable, for example:
+
+```
+local-ai github://owner/repo/file.yaml@branch
+
+# Env
+MODELS="github://owner/repo/file.yaml@branch,github://owner/repo/file.yaml@branch" local-ai
+
+# Args
+local-ai --models github://owner/repo/file.yaml@branch --models github://owner/repo/file.yaml@branch
+```
+
+For example, to start localai with phi-2, it's possible for instance to also use a full config file from gists:
+
+```bash
+docker run -p 8080:8080 localai/localai:{{< version >}}-ffmpeg-core https://gist.githubusercontent.com/mudler/ad601a0488b497b69ec549150d9edd18/raw/a8a8869ef1bb7e3830bf5c0bae29a0cce991ff8d/phi-2.yaml
+```
+
+The file should be a valid LocalAI YAML configuration file, for the full syntax see [advanced]({{%relref "advanced" %}}).
+{{% /notice %}}
+
 ### Container images
 
 LocalAI has a set of images to support CUDA, ffmpeg and 'vanilla' (CPU-only). The image list is on [quay](https://quay.io/repository/go-skynet/local-ai?tab=tags):
@@ -131,6 +235,11 @@ Core Images - Smaller images without predownload python dependencies
 {{% /tab %}}
 
 {{% tab name="GPU Images CUDA 11" %}}
+
+Images with Nvidia accelleration support
+
+> If you do not know which version of CUDA do you have available, you can check with `nvidia-smi` or `nvcc --version`
+
 - `master-cublas-cuda11`
 - `master-cublas-cuda11-core`
 - `{{< version >}}-cublas-cuda11`
@@ -142,6 +251,11 @@ Core Images - Smaller images without predownload python dependencies
 {{% /tab %}}
 
 {{% tab name="GPU Images CUDA 12" %}}
+
+Images with Nvidia accelleration support
+
+> If you do not know which version of CUDA do you have available, you can check with `nvidia-smi` or `nvcc --version`
+
 - `master-cublas-cuda12`
 - `master-cublas-cuda12-core`
 - `{{< version >}}-cublas-cuda12`
@@ -201,212 +315,9 @@ curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/jso
 
 To see other model configurations, see also the example section [here](https://github.com/mudler/LocalAI/tree/master/examples/configurations).
 
-
-### From binaries
-
-LocalAI binary releases are available in [Github](https://github.com/go-skynet/LocalAI/releases).
-
-You can control LocalAI with command line arguments, to specify a binding address, or the number of threads.
-
-### CLI parameters
-
-| Parameter                      | Environmental Variable          | Default Variable                                   | Description                                                         |
-| ------------------------------ | ------------------------------- | -------------------------------------------------- | ------------------------------------------------------------------- |
-| --f16                          | $F16                            | false                                              | Enable f16 mode                                                     |
-| --debug                        | $DEBUG                          | false                                              | Enable debug mode                                                   |
-| --cors                         | $CORS                           | false                                              | Enable CORS support                                                 |
-| --cors-allow-origins value     | $CORS_ALLOW_ORIGINS             |                                                    | Specify origins allowed for CORS                                     |
-| --threads value                | $THREADS                        | 4    | Number of threads to use for parallel computation                    |
-| --models-path value            | $MODELS_PATH                    | ./models       | Path to the directory containing models used for inferencing        |
-| --preload-models value         | $PRELOAD_MODELS                 |           | List of models to preload in JSON format at startup                  |
-| --preload-models-config value  | $PRELOAD_MODELS_CONFIG          |  | A config with a list of models to apply at startup. Specify the path to a YAML config file |
-| --config-file value            | $CONFIG_FILE                    |                                         | Path to the config file                                             |
-| --address value                | $ADDRESS                        | :8080                    | Specify the bind address for the API server                         |
-| --image-path value             | $IMAGE_PATH                     |                                     | Path to the directory used to store generated images                             |
-| --context-size value           | $CONTEXT_SIZE                   | 512                 | Default context size of the model                                   |
-| --upload-limit value           | $UPLOAD_LIMIT                   | 15                         | Default upload limit in megabytes (audio file upload)                                  |
-| --galleries                    | $GALLERIES                      |                                                    | Allows to set galleries from command line                           |
-|--parallel-requests              | $PARALLEL_REQUESTS     |   false |            Enable backends to handle multiple requests in parallel. This is for backends that supports multiple requests in parallel, like llama.cpp or vllm |
-| --single-active-backend   | $SINGLE_ACTIVE_BACKEND |  false |    Allow only one backend to be running |
-| --api-keys value |   $API_KEY | empty |  List of API Keys to enable API authentication. When this is set, all the requests must be authenticated with one of these API keys.
-| --enable-watchdog-idle | $WATCHDOG_IDLE | false | Enable watchdog for stopping idle backends. This will stop the backends if are in idle state for too long. (default: false) [$WATCHDOG_IDLE]
-| --enable-watchdog-busy   |     $WATCHDOG_BUSY | false |         Enable watchdog for stopping busy backends that exceed a defined threshold.|
-| --watchdog-busy-timeout value | $WATCHDOG_BUSY_TIMEOUT | 5m | Watchdog timeout. This will restart the backend if it crashes.  |
-| --watchdog-idle-timeout value | $WATCHDOG_IDLE_TIMEOUT | 15m | Watchdog idle timeout. This will restart the backend if it crashes. |
-| --preload-backend-only | $PRELOAD_BACKEND_ONLY | false | If set, the api is NOT launched, and only the preloaded models / backends are started. This is intended for multi-node setups. |
-| --external-grpc-backends | EXTERNAL_GRPC_BACKENDS | none | Comma separated list of external gRPC backends to use. Format: `name:host:port` or `name:/path/to/file` |
-
-### Run LocalAI in Kubernetes
-
-LocalAI can be installed inside Kubernetes with helm.
-
-Requirements:
-- SSD storage class, or disable `mmap` to load the whole model in memory
-
-<details>
-By default, the helm chart will install LocalAI instance using the ggml-gpt4all-j model without persistent storage.
-
-1. Add the helm repo
-    ```bash
-    helm repo add go-skynet https://go-skynet.github.io/helm-charts/
-    ```
-2. Install the helm chart:
-    ```bash
-    helm repo update
-    helm install local-ai go-skynet/local-ai -f values.yaml
-    ```
-> **Note:** For further configuration options, see the [helm chart repository on GitHub](https://github.com/go-skynet/helm-charts).
-### Example values
-Deploy a single LocalAI pod with 6GB of persistent storage serving up a `ggml-gpt4all-j` model with custom prompt.
-```yaml
-### values.yaml
-
-replicaCount: 1
-
-deployment:
-  image: quay.io/go-skynet/local-ai:latest ##(This is for CPU only, to use GPU change it to a image that supports GPU IE "v2.0.0-cublas-cuda12-core")
-  env:
-    threads: 4
-    context_size: 512
-  modelsPath: "/models"
-
-resources:
-  {}
-  # We usually recommend not to specify default resources and to leave this as a conscious
-  # choice for the user. This also increases chances charts run on environments with little
-  # resources, such as Minikube. If you do want to specify resources, uncomment the following
-  # lines, adjust them as necessary, and remove the curly braces after 'resources:'.
-  # limits:
-  #   cpu: 100m
-  #   memory: 128Mi
-  # requests:
-  #   cpu: 100m
-  #   memory: 128Mi
-
-# Prompt templates to include
-# Note: the keys of this map will be the names of the prompt template files
-promptTemplates:
-  {}
-  # ggml-gpt4all-j.tmpl: |
-  #   The prompt below is a question to answer, a task to complete, or a conversation to respond to; decide which and write an appropriate response.
-  #   ### Prompt:
-  #   {{.Input}}
-  #   ### Response:
-
-# Models to download at runtime
-models:
-  # Whether to force download models even if they already exist
-  forceDownload: false
-
-  # The list of URLs to download models from
-  # Note: the name of the file will be the name of the loaded model
-  list:
-  - url: "https://gpt4all.io/models/ggml-gpt4all-j.bin"
-      # basicAuth: base64EncodedCredentials
-
-  # Persistent storage for models and prompt templates.
-  # PVC and HostPath are mutually exclusive. If both are enabled,
-  # PVC configuration takes precedence. If neither are enabled, ephemeral
-  # storage is used.
-  persistence:
-    pvc:
-      enabled: false
-      size: 6Gi
-      accessModes:
-        - ReadWriteOnce
-
-      annotations: {}
-
-      # Optional
-      storageClass: ~
-
-    hostPath:
-      enabled: false
-      path: "/models"
-
-service:
-  type: ClusterIP
-  port: 80
-  annotations: {}
-  # If using an AWS load balancer, you'll need to override the default 60s load balancer idle timeout
-  # service.beta.kubernetes.io/aws-load-balancer-connection-idle-timeout: "1200"
-
-ingress:
-  enabled: false
-  className: ""
-  annotations:
-    {}
-    # kubernetes.io/ingress.class: nginx
-    # kubernetes.io/tls-acme: "true"
-  hosts:
-    - host: chart-example.local
-      paths:
-        - path: /
-          pathType: ImplementationSpecific
-  tls: []
-  #  - secretName: chart-example-tls
-  #    hosts:
-  #      - chart-example.local
-
-nodeSelector: {}
-
-tolerations: []
-
-affinity: {}
-```
-</details>
-
-
-### Build from source
-
-See the [build section]({{%relref "build" %}}).
-
-### Other examples
+### Examples
 
 ![Screenshot from 2023-04-26 23-59-55](https://user-images.githubusercontent.com/2420543/234715439-98d12e03-d3ce-4f94-ab54-2b256808e05e.png)
 
 To see other examples on how to integrate with other projects for instance for question answering or for using it with chatbot-ui, see: [examples](https://github.com/go-skynet/LocalAI/tree/master/examples/).
 
-
-### Clients
-
-OpenAI clients are already compatible with LocalAI by overriding the basePath, or the target URL.
-
-## Javascript
-
-<details>
-
-https://github.com/openai/openai-node/
-
-```javascript
-import { Configuration, OpenAIApi } from 'openai';
-
-const configuration = new Configuration({
-  basePath: `http://localhost:8080/v1`
-});
-const openai = new OpenAIApi(configuration);
-```
-
-</details>
-
-## Python
-
-<details>
-
-https://github.com/openai/openai-python
-
-Set the `OPENAI_API_BASE` environment variable, or by code:
-
-```python
-import openai
-
-openai.api_base = "http://localhost:8080/v1"
-
-# create a chat completion
-chat_completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello world"}])
-
-# print the completion
-print(completion.choices[0].message.content)
-```
-
-</details>

docs/content/model-compatibility/_index.en.md

@@ -43,15 +43,18 @@ Besides llama based models, LocalAI is compatible also with other architectures.
 | [langchain-huggingface](https://github.com/tmc/langchaingo)                                                                    | Any text generators available on HuggingFace through API | yes                      | GPT                        | no                                | no                   | N/A |
 | [piper](https://github.com/rhasspy/piper) ([binding](https://github.com/mudler/go-piper))                                                                     | Any piper onnx model | no                      | Text to voice                        | no                                | no                   | N/A |
 | [falcon](https://github.com/cmp-nct/ggllm.cpp/tree/c12b2d65f732a0d8846db2244e070f0f3e73505c) ([binding](https://github.com/mudler/go-ggllm.cpp))                                                                      | Falcon *** | yes                      | GPT                        | no                                | yes                   | CUDA |
-| `huggingface-embeddings` [sentence-transformers](https://github.com/UKPLab/sentence-transformers) | BERT                   | no                       | Embeddings only                  | yes                               | no                   | N/A |
+| [sentencetransformers](https://github.com/UKPLab/sentence-transformers) | BERT                   | no                       | Embeddings only                  | yes                               | no                   | N/A |
 | `bark`  | bark                   | no                       | Audio generation                  | no                               | no                   | yes |
-| `AutoGPTQ` | GPTQ                   | yes                       | GPT                  | yes                               | no                   | N/A |
+| `autogptq` | GPTQ                   | yes                       | GPT                  | yes                               | no                   | N/A |
 | `exllama`  | GPTQ                   | yes                       | GPT only                  | no                               | no                   | N/A |
 | `diffusers`  | SD,...                   | no                       | Image generation    | no                               | no                   | N/A |
 | `vall-e-x` | Vall-E    | no                       | Audio generation and Voice cloning    | no                               | no                   | CPU/CUDA |
 | `vllm` | Various GPTs and quantization formats | yes                      | GPT             | no | no                  | CPU/CUDA |
 | `exllama2`  | GPTQ                   | yes                       | GPT only                  | no                               | no                   | N/A |
 | `transformers-musicgen`  |                    | no                       | Audio generation                | no                               | no                   | N/A |
+| [tinydream](https://github.com/symisc/tiny-dream#tiny-dreaman-embedded-header-only-stable-diffusion-inference-c-librarypixlabiotiny-dream)         | stablediffusion               | no                       | Image                 | no                                | no                   | N/A |
+| `coqui` | Coqui    | no                       | Audio generation and Voice cloning    | no                               | no                   | CPU/CUDA |
+| `petals` | Various GPTs and quantization formats | yes                      | GPT             | no | no                  | CPU/CUDA |
 
 Note: any backend name listed above can be used in the `backend` field of the model configuration file (See [the advanced section]({{%relref "advanced" %}})).
 

docs/content/model-compatibility/diffusers.md

@@ -167,11 +167,6 @@ curl -H "Content-Type: application/json" -d @-  http://localhost:8080/v1/images/
 
 ## img2vid
 
-{{% notice note %}}
-
-Experimental and available only on master builds. See: https://github.com/mudler/LocalAI/pull/1442
-
-{{% /notice %}}
 
 ```yaml
 name: img2vid
@@ -193,12 +188,6 @@ curl -H "Content-Type: application/json" -X POST -d @- http://localhost:8080/v1/
 
 ## txt2vid
 
-{{% notice note %}}
-
-Experimental and available only on master builds. See: https://github.com/mudler/LocalAI/pull/1442
-
-{{% /notice %}}
-
 ```yaml
 name: txt2vid
 parameters:

docs/data/version.json

@@ -1,3 +1,3 @@
 {
-  "version": "v2.3.0"
+  "version": "v2.5.0"
 }

embedded/embedded.go

@@ -0,0 +1,53 @@
+package embedded
+
+import (
+	"embed"
+	"fmt"
+	"slices"
+	"strings"
+
+	"github.com/go-skynet/LocalAI/pkg/assets"
+	"gopkg.in/yaml.v3"
+)
+
+var modelShorteners map[string]string
+
+//go:embed model_library.yaml
+var modelLibrary []byte
+
+//go:embed models/*
+var embeddedModels embed.FS
+
+func ModelShortURL(s string) string {
+	if _, ok := modelShorteners[s]; ok {
+		s = modelShorteners[s]
+	}
+
+	return s
+}
+
+func init() {
+	yaml.Unmarshal(modelLibrary, &modelShorteners)
+}
+
+// ExistsInModelsLibrary checks if a model exists in the embedded models library
+func ExistsInModelsLibrary(s string) bool {
+	f := fmt.Sprintf("%s.yaml", s)
+
+	a := []string{}
+
+	for _, j := range assets.ListFiles(embeddedModels) {
+		a = append(a, strings.TrimPrefix(j, "models/"))
+	}
+
+	return slices.Contains(a, f)
+}
+
+// ResolveContent returns the content in the embedded model library
+func ResolveContent(s string) ([]byte, error) {
+	if ExistsInModelsLibrary(s) {
+		return embeddedModels.ReadFile(fmt.Sprintf("models/%s.yaml", s))
+	}
+
+	return nil, fmt.Errorf("cannot find model %s", s)
+}

embedded/model_library.yaml

@@ -0,0 +1,9 @@
+### 
+###
+### This file contains the list of models that are available in the library
+### The URLs are automatically expanded when local-ai is being called with the key as argument
+###
+### For models with an entire YAML file to be embededd, put the file inside the `models`
+### directory, it will be automatically available with the file name as key (without the .yaml extension)
+
+phi-2:  "github://mudler/LocalAI/examples/configurations/phi-2.yaml@master"

embedded/models/all-minilm-l6-v2.yaml

@@ -0,0 +1,13 @@
+name: all-minilm-l6-v2
+backend: sentencetransformers
+embeddings: true
+parameters:
+  model: all-MiniLM-L6-v2
+
+usage: |
+    You can test this model with curl like this:
+
+    curl http://localhost:8080/embeddings -X POST -H "Content-Type: application/json" -d '{
+      "input": "Your text string goes here",
+      "model": "all-minilm-l6-v2"
+    }'

embedded/models/bark.yaml

@@ -0,0 +1,8 @@
+usage: |
+    bark works without any configuration, to test it, you can run the following curl command:
+
+    curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
+     "backend": "bark",
+     "input":"Hello, this is a test!"
+    }' | aplay
+# TODO: This is a placeholder until we manage to pre-load HF/Transformers models

embedded/models/bert-cpp.yaml

@@ -0,0 +1,23 @@
+backend: bert-embeddings
+embeddings: true
+f16: true
+
+gpu_layers: 90
+mmap: true
+name: bert-cpp-minilm-v6
+
+parameters:
+  model: bert-MiniLM-L6-v2q4_0.bin
+
+download_files:
+- filename: "bert-MiniLM-L6-v2q4_0.bin"
+  sha256: "a5a174d8772c8a569faf9f3136c441f2c3855b5bf35ed32274294219533feaad"
+  uri: "https://huggingface.co/mudler/all-MiniLM-L6-v2/resolve/main/ggml-model-q4_0.bin"
+
+usage: |
+    You can test this model with curl like this:
+
+    curl http://localhost:8080/embeddings -X POST -H "Content-Type: application/json" -d '{
+      "input": "Your text string goes here",
+      "model": "bert-cpp-minilm-v6"
+    }'

embedded/models/coqui.yaml

@@ -0,0 +1,9 @@
+usage: |
+    coqui works without any configuration, to test it, you can run the following curl command:
+
+    curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
+        "backend": "coqui",
+        "model": "tts_models/en/ljspeech/glow-tts",
+        "input":"Hello, this is a test!"
+        }'
+# TODO: This is a placeholder until we manage to pre-load HF/Transformers models

embedded/models/llava.yaml

@@ -0,0 +1,36 @@
+backend: llama-cpp
+context_size: 4096
+f16: true
+
+gpu_layers: 90
+mmap: true
+name: llava
+
+roles:
+  user: "USER:"
+  assistant: "ASSISTANT:"
+  system: "SYSTEM:"
+
+mmproj: bakllava-mmproj.gguf
+parameters:
+  model: bakllava.gguf
+  temperature: 0.2
+  top_k: 40
+  top_p: 0.95
+
+template:
+  chat: |
+    A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.
+    {{.Input}}
+    ASSISTANT:
+
+download_files:
+- filename: bakllava.gguf
+  uri: huggingface://mys/ggml_bakllava-1/ggml-model-q4_k.gguf
+- filename: bakllava-mmproj.gguf
+  uri: huggingface://mys/ggml_bakllava-1/mmproj-model-f16.gguf
+
+usage: |
+    curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
+        "model": "llava",
+        "messages": [{"role": "user", "content": [{"type":"text", "text": "What is in the image?"}, {"type": "image_url", "image_url": {"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" }}], "temperature": 0.9}]}'

embedded/models/mistral-openorca.yaml

@@ -0,0 +1,29 @@
+name: mistral-openorca
+mmap: true
+parameters:
+  model: huggingface://TheBloke/Mistral-7B-OpenOrca-GGUF/mistral-7b-openorca.Q6_K.gguf
+  temperature: 0.2
+  top_k: 40
+  top_p: 0.95
+template:
+  chat_message: |
+    <|im_start|>{{if eq .RoleName "assistant"}}assistant{{else if eq .RoleName "system"}}system{{else if eq .RoleName "user"}}user{{end}}
+    {{if .Content}}{{.Content}}{{end}}
+    <|im_end|>
+    
+  chat: |
+    {{.Input}}
+    <|im_start|>assistant
+    
+  completion: |
+    {{.Input}}
+context_size: 4096
+f16: true
+stopwords:
+- <|im_end|>
+
+usage: |
+      curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
+          "model": "mistral-openorca",
+          "messages": [{"role": "user", "content": "How are you doing?", "temperature": 0.1}]
+      }'

embedded/models/rhasspy-voice-en-us-amy.yaml

@@ -0,0 +1,13 @@
+name: voice-en-us-amy-low
+download_files:
+  - filename: voice-en-us-amy-low.tar.gz
+    uri: https://github.com/rhasspy/piper/releases/download/v0.0.2/voice-en-us-amy-low.tar.gz
+
+
+usage: |
+    To test if this model works as expected, you can use the following curl command:
+
+    curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{
+      "model":"en-us-amy-low.onnx",
+      "input": "Hi, this is a test."
+    }'

embedded/models/vall-e-x.yaml

@@ -0,0 +1,8 @@
+usage: |
+    Vall-e-x works without any configuration, to test it, you can run the following curl command:
+
+    curl http://localhost:8080/tts -H "Content-Type: application/json" -d '{         
+     "backend": "vall-e-x",
+     "input":"Hello, this is a test!"
+    }' | aplay
+# TODO: This is a placeholder until we manage to pre-load HF/Transformers models

embedded/models/whisper-base.yaml

@@ -0,0 +1,18 @@
+name: whisper
+backend: whisper
+parameters:
+  model: ggml-whisper-base.bin
+
+usage: |
+    ## example audio file
+    wget --quiet --show-progress -O gb1.ogg https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg
+
+    ## Send the example audio file to the transcriptions endpoint
+    curl http://localhost:8080/v1/audio/transcriptions \
+         -H "Content-Type: multipart/form-data" \
+         -F file="@$PWD/gb1.ogg" -F model="whisper"
+
+download_files:
+- filename: "ggml-whisper-base.bin"
+  sha256: "60ed5bc3dd14eea856493d334349b405782ddcaf0028d4b5df4088345fba2efe"
+  uri: "https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.bin"

examples/configurations/README.md

@@ -67,6 +67,17 @@ curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/jso
 
 ```
 
+### Phi-2
+
+```
+cp -r examples/configurations/phi-2.yaml models/
+
+curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
+     "model": "phi-2",
+     "messages": [{"role": "user", "content": "How are you doing?", "temperature": 0.1}]
+}'
+```
+
 ### Mixtral
 
 ```

examples/configurations/phi-2.yaml

@@ -0,0 +1,17 @@
+name: phi-2
+context_size: 2048
+f16: true
+gpu_layers: 90
+mmap: true
+trimsuffix: 
+- "\n"
+parameters:
+  model: huggingface://TheBloke/phi-2-GGUF/phi-2.Q8_0.gguf
+  temperature: 0.2
+  top_k: 40
+  top_p: 0.95
+template:
+  chat: &template |
+    Instruct: {{.Input}}
+    Output:
+  completion: *template

main.go

@@ -99,6 +99,11 @@ func main() {
 				Usage:   "A List of models to apply in JSON at start",
 				EnvVars: []string{"PRELOAD_MODELS"},
 			},
+			&cli.StringFlag{
+				Name:    "models",
+				Usage:   "A List of models URLs configurations.",
+				EnvVars: []string{"MODELS"},
+			},
 			&cli.StringFlag{
 				Name:    "preload-models-config",
 				Usage:   "A List of models to apply at startup. Path to a YAML config file",
@@ -222,6 +227,7 @@ For a list of compatible model, check out: https://localai.io/model-compatibilit
 				options.WithBackendAssetsOutput(ctx.String("backend-assets-path")),
 				options.WithUploadLimitMB(ctx.Int("upload-limit")),
 				options.WithApiKeys(ctx.StringSlice("api-keys")),
+				options.WithModelsURL(append(ctx.StringSlice("models"), ctx.Args().Slice()...)...),
 			}
 
 			idleWatchDog := ctx.Bool("enable-watchdog-idle")

pkg/assets/list.go

@@ -0,0 +1,22 @@
+package assets
+
+import (
+	"embed"
+	"io/fs"
+)
+
+func ListFiles(content embed.FS) (files []string) {
+	fs.WalkDir(content, ".", func(path string, d fs.DirEntry, err error) error {
+		if err != nil {
+			return err
+		}
+
+		if d.IsDir() {
+			return nil
+		}
+
+		files = append(files, path)
+		return nil
+	})
+	return
+}

pkg/downloader/progress.go

@@ -0,0 +1,26 @@
+package downloader
+
+import "hash"
+
+type progressWriter struct {
+	fileName       string
+	total          int64
+	written        int64
+	downloadStatus func(string, string, string, float64)
+	hash           hash.Hash
+}
+
+func (pw *progressWriter) Write(p []byte) (n int, err error) {
+	n, err = pw.hash.Write(p)
+	pw.written += int64(n)
+
+	if pw.total > 0 {
+		percentage := float64(pw.written) / float64(pw.total) * 100
+		//log.Debug().Msgf("Downloading %s: %s/%s (%.2f%%)", pw.fileName, formatBytes(pw.written), formatBytes(pw.total), percentage)
+		pw.downloadStatus(pw.fileName, formatBytes(pw.written), formatBytes(pw.total), percentage)
+	} else {
+		pw.downloadStatus(pw.fileName, formatBytes(pw.written), "", 0)
+	}
+
+	return
+}

pkg/downloader/uri.go

@@ -1,10 +1,9 @@
-package utils
+package downloader
 
 import (
-	"crypto/md5"
 	"crypto/sha256"
+	"encoding/base64"
 	"fmt"
-	"hash"
 	"io"
 	"net/http"
 	"os"
@@ -12,30 +11,20 @@ import (
 	"strconv"
 	"strings"
 
+	"github.com/go-skynet/LocalAI/pkg/utils"
 	"github.com/rs/zerolog/log"
 )
 
 const (
-	githubURI = "github:"
+	HuggingFacePrefix = "huggingface://"
+	HTTPPrefix        = "http://"
+	HTTPSPrefix       = "https://"
+	GithubURI         = "github:"
+	GithubURI2        = "github://"
 )
 
 func GetURI(url string, f func(url string, i []byte) error) error {
-	if strings.HasPrefix(url, githubURI) {
-		parts := strings.Split(url, ":")
-		repoParts := strings.Split(parts[1], "@")
-		branch := "main"
-
-		if len(repoParts) > 1 {
-			branch = repoParts[1]
-		}
-
-		repoPath := strings.Split(repoParts[0], "/")
-		org := repoPath[0]
-		project := repoPath[1]
-		projectPath := strings.Join(repoPath[2:], "/")
-
-		url = fmt.Sprintf("https://raw.githubusercontent.com/%s/%s/%s/%s", org, project, branch, projectPath)
-	}
+	url = ConvertURL(url)
 
 	if strings.HasPrefix(url, "file://") {
 		rawURL := strings.TrimPrefix(url, "file://")
@@ -71,16 +60,47 @@ func GetURI(url string, f func(url string, i []byte) error) error {
 	return f(url, body)
 }
 
-const (
-	HuggingFacePrefix = "huggingface://"
-)
-
 func LooksLikeURL(s string) bool {
-	return strings.HasPrefix(s, "http://") || strings.HasPrefix(s, "https://") || strings.HasPrefix(s, HuggingFacePrefix)
+	return strings.HasPrefix(s, HTTPPrefix) ||
+		strings.HasPrefix(s, HTTPSPrefix) ||
+		strings.HasPrefix(s, HuggingFacePrefix) ||
+		strings.HasPrefix(s, GithubURI) ||
+		strings.HasPrefix(s, GithubURI2)
 }
 
 func ConvertURL(s string) string {
 	switch {
+	case strings.HasPrefix(s, GithubURI2):
+		repository := strings.Replace(s, GithubURI2, "", 1)
+
+		repoParts := strings.Split(repository, "@")
+		branch := "main"
+
+		if len(repoParts) > 1 {
+			branch = repoParts[1]
+		}
+
+		repoPath := strings.Split(repoParts[0], "/")
+		org := repoPath[0]
+		project := repoPath[1]
+		projectPath := strings.Join(repoPath[2:], "/")
+
+		return fmt.Sprintf("https://raw.githubusercontent.com/%s/%s/%s/%s", org, project, branch, projectPath)
+	case strings.HasPrefix(s, GithubURI):
+		parts := strings.Split(s, ":")
+		repoParts := strings.Split(parts[1], "@")
+		branch := "main"
+
+		if len(repoParts) > 1 {
+			branch = repoParts[1]
+		}
+
+		repoPath := strings.Split(repoParts[0], "/")
+		org := repoPath[0]
+		project := repoPath[1]
+		projectPath := strings.Join(repoPath[2:], "/")
+
+		return fmt.Sprintf("https://raw.githubusercontent.com/%s/%s/%s/%s", org, project, branch, projectPath)
 	case strings.HasPrefix(s, HuggingFacePrefix):
 		repository := strings.Replace(s, HuggingFacePrefix, "", 1)
 		// convert repository to a full URL.
@@ -209,10 +229,10 @@ func DownloadFile(url string, filePath, sha string, downloadStatus func(string,
 	}
 
 	log.Info().Msgf("File %q downloaded and verified", filePath)
-	if IsArchive(filePath) {
+	if utils.IsArchive(filePath) {
 		basePath := filepath.Dir(filePath)
 		log.Info().Msgf("File %q is an archive, uncompressing to %s", filePath, basePath)
-		if err := ExtractArchive(filePath, basePath); err != nil {
+		if err := utils.ExtractArchive(filePath, basePath); err != nil {
 			log.Debug().Msgf("Failed decompressing %q: %s", filePath, err.Error())
 			return err
 		}
@@ -221,32 +241,35 @@ func DownloadFile(url string, filePath, sha string, downloadStatus func(string,
 	return nil
 }
 
-type progressWriter struct {
-	fileName       string
-	total          int64
-	written        int64
-	downloadStatus func(string, string, string, float64)
-	hash           hash.Hash
-}
+// this function check if the string is an URL, if it's an URL downloads the image in memory
+// encodes it in base64 and returns the base64 string
+func GetBase64Image(s string) (string, error) {
+	if strings.HasPrefix(s, "http") {
+		// download the image
+		resp, err := http.Get(s)
+		if err != nil {
+			return "", err
+		}
+		defer resp.Body.Close()
 
-func (pw *progressWriter) Write(p []byte) (n int, err error) {
-	n, err = pw.hash.Write(p)
-	pw.written += int64(n)
+		// read the image data into memory
+		data, err := io.ReadAll(resp.Body)
+		if err != nil {
+			return "", err
+		}
 
-	if pw.total > 0 {
-		percentage := float64(pw.written) / float64(pw.total) * 100
-		//log.Debug().Msgf("Downloading %s: %s/%s (%.2f%%)", pw.fileName, formatBytes(pw.written), formatBytes(pw.total), percentage)
-		pw.downloadStatus(pw.fileName, formatBytes(pw.written), formatBytes(pw.total), percentage)
-	} else {
-		pw.downloadStatus(pw.fileName, formatBytes(pw.written), "", 0)
+		// encode the image data in base64
+		encoded := base64.StdEncoding.EncodeToString(data)
+
+		// return the base64 string
+		return encoded, nil
 	}
 
-	return
-}
-
-// MD5 of a string
-func MD5(s string) string {
-	return fmt.Sprintf("%x", md5.Sum([]byte(s)))
+	// if the string instead is prefixed with "data:image/jpeg;base64,", drop it
+	if strings.HasPrefix(s, "data:image/jpeg;base64,") {
+		return strings.ReplaceAll(s, "data:image/jpeg;base64,", ""), nil
+	}
+	return "", fmt.Errorf("not valid string")
 }
 
 func formatBytes(bytes int64) string {

pkg/downloader/uri_test.go

@@ -1,7 +1,7 @@
-package utils_test
+package downloader_test
 
 import (
-	. "github.com/go-skynet/LocalAI/pkg/utils"
+	. "github.com/go-skynet/LocalAI/pkg/downloader"
 	. "github.com/onsi/ginkgo/v2"
 	. "github.com/onsi/gomega"
 )

pkg/gallery/gallery.go

@@ -6,7 +6,7 @@ import (
 	"path/filepath"
 	"strings"
 
-	"github.com/go-skynet/LocalAI/pkg/utils"
+	"github.com/go-skynet/LocalAI/pkg/downloader"
 	"github.com/imdario/mergo"
 	"github.com/rs/zerolog/log"
 	"gopkg.in/yaml.v2"
@@ -140,7 +140,7 @@ func AvailableGalleryModels(galleries []Gallery, basePath string) ([]*GalleryMod
 
 func findGalleryURLFromReferenceURL(url string) (string, error) {
 	var refFile string
-	err := utils.GetURI(url, func(url string, d []byte) error {
+	err := downloader.GetURI(url, func(url string, d []byte) error {
 		refFile = string(d)
 		if len(refFile) == 0 {
 			return fmt.Errorf("invalid reference file at url %s: %s", url, d)
@@ -163,7 +163,7 @@ func getGalleryModels(gallery Gallery, basePath string) ([]*GalleryModel, error)
 		}
 	}
 
-	err := utils.GetURI(gallery.URL, func(url string, d []byte) error {
+	err := downloader.GetURI(gallery.URL, func(url string, d []byte) error {
 		return yaml.Unmarshal(d, &models)
 	})
 	if err != nil {

pkg/gallery/models.go

@@ -1,14 +1,11 @@
 package gallery
 
 import (
-	"crypto/sha256"
 	"fmt"
-	"hash"
-	"io"
 	"os"
 	"path/filepath"
-	"strconv"
 
+	"github.com/go-skynet/LocalAI/pkg/downloader"
 	"github.com/go-skynet/LocalAI/pkg/utils"
 	"github.com/imdario/mergo"
 	"github.com/rs/zerolog/log"
@@ -66,7 +63,7 @@ type PromptTemplate struct {
 
 func GetGalleryConfigFromURL(url string) (Config, error) {
 	var config Config
-	err := utils.GetURI(url, func(url string, d []byte) error {
+	err := downloader.GetURI(url, func(url string, d []byte) error {
 		return yaml.Unmarshal(d, &config)
 	})
 	if err != nil {
@@ -114,7 +111,7 @@ func InstallModel(basePath, nameOverride string, config *Config, configOverrides
 		// Create file path
 		filePath := filepath.Join(basePath, file.Filename)
 
-		if err := utils.DownloadFile(file.URI, filePath, file.SHA256, downloadStatus); err != nil {
+		if err := downloader.DownloadFile(file.URI, filePath, file.SHA256, downloadStatus); err != nil {
 			return err
 		}
 	}
@@ -183,54 +180,3 @@ func InstallModel(basePath, nameOverride string, config *Config, configOverrides
 
 	return nil
 }
-
-type progressWriter struct {
-	fileName       string
-	total          int64
-	written        int64
-	downloadStatus func(string, string, string, float64)
-	hash           hash.Hash
-}
-
-func (pw *progressWriter) Write(p []byte) (n int, err error) {
-	n, err = pw.hash.Write(p)
-	pw.written += int64(n)
-
-	if pw.total > 0 {
-		percentage := float64(pw.written) / float64(pw.total) * 100
-		//log.Debug().Msgf("Downloading %s: %s/%s (%.2f%%)", pw.fileName, formatBytes(pw.written), formatBytes(pw.total), percentage)
-		pw.downloadStatus(pw.fileName, formatBytes(pw.written), formatBytes(pw.total), percentage)
-	} else {
-		pw.downloadStatus(pw.fileName, formatBytes(pw.written), "", 0)
-	}
-
-	return
-}
-
-func formatBytes(bytes int64) string {
-	const unit = 1024
-	if bytes < unit {
-		return strconv.FormatInt(bytes, 10) + " B"
-	}
-	div, exp := int64(unit), 0
-	for n := bytes / unit; n >= unit; n /= unit {
-		div *= unit
-		exp++
-	}
-	return fmt.Sprintf("%.1f %ciB", float64(bytes)/float64(div), "KMGTPE"[exp])
-}
-
-func calculateSHA(filePath string) (string, error) {
-	file, err := os.Open(filePath)
-	if err != nil {
-		return "", err
-	}
-	defer file.Close()
-
-	hash := sha256.New()
-	if _, err := io.Copy(hash, file); err != nil {
-		return "", err
-	}
-
-	return fmt.Sprintf("%x", hash.Sum(nil)), nil
-}

pkg/grpc/client.go

@@ -50,7 +50,7 @@ func (c *Client) setBusy(v bool) {
 	c.Unlock()
 }
 
-func (c *Client) HealthCheck(ctx context.Context) bool {
+func (c *Client) HealthCheck(ctx context.Context) (bool, error) {
 	if !c.parallel {
 		c.opMutex.Lock()
 		defer c.opMutex.Unlock()
@@ -59,8 +59,7 @@ func (c *Client) HealthCheck(ctx context.Context) bool {
 	defer c.setBusy(false)
 	conn, err := grpc.Dial(c.address, grpc.WithTransportCredentials(insecure.NewCredentials()))
 	if err != nil {
-		fmt.Println(err)
-		return false
+		return false, err
 	}
 	defer conn.Close()
 	client := pb.NewBackendClient(conn)
@@ -71,15 +70,14 @@ func (c *Client) HealthCheck(ctx context.Context) bool {
 
 	res, err := client.Health(ctx, &pb.HealthMessage{})
 	if err != nil {
-		fmt.Println(err)
-
-		return false
+		return false, err
 	}
 
 	if string(res.Message) == "OK" {
-		return true
+		return true, nil
 	}
-	return false
+
+	return false, fmt.Errorf("health check failed: %s", res.Message)
 }
 
 func (c *Client) Embeddings(ctx context.Context, in *pb.PredictOptions, opts ...grpc.CallOption) (*pb.EmbeddingResult, error) {

pkg/model/initializers.go

@@ -131,11 +131,15 @@ func (ml *ModelLoader) grpcModel(backend string, o *Options) func(string, string
 		// Wait for the service to start up
 		ready := false
 		for i := 0; i < o.grpcAttempts; i++ {
-			if client.GRPC(o.parallelRequests, ml.wd).HealthCheck(context.Background()) {
+			alive, err := client.GRPC(o.parallelRequests, ml.wd).HealthCheck(context.Background())
+			if alive {
 				log.Debug().Msgf("GRPC Service Ready")
 				ready = true
 				break
 			}
+			if err != nil && i == o.grpcAttempts-1 {
+				log.Error().Msgf("Failed starting/connecting to the gRPC service: %s", err.Error())
+			}
 			time.Sleep(time.Duration(o.grpcAttemptsDelay) * time.Second)
 		}
 
@@ -176,7 +180,11 @@ func (ml *ModelLoader) resolveAddress(addr ModelAddress, parallel bool) (*grpc.C
 func (ml *ModelLoader) BackendLoader(opts ...Option) (client *grpc.Client, err error) {
 	o := NewOptions(opts...)
 
-	log.Info().Msgf("Loading model '%s' with backend %s", o.model, o.backendString)
+	if o.model != "" {
+		log.Info().Msgf("Loading model '%s' with backend %s", o.model, o.backendString)
+	} else {
+		log.Info().Msgf("Loading model with backend %s", o.backendString)
+	}
 
 	backend := strings.ToLower(o.backendString)
 	if realBackend, exists := Aliases[backend]; exists {
@@ -239,10 +247,13 @@ func (ml *ModelLoader) GreedyLoader(opts ...Option) (*grpc.Client, error) {
 	for _, b := range o.externalBackends {
 		allBackendsToAutoLoad = append(allBackendsToAutoLoad, b)
 	}
-	log.Debug().Msgf("Loading model '%s' greedly from all the available backends: %s", o.model, strings.Join(allBackendsToAutoLoad, ", "))
+
+	if o.model != "" {
+		log.Info().Msgf("Trying to load the model '%s' with all the available backends: %s", o.model, strings.Join(allBackendsToAutoLoad, ", "))
+	}
 
 	for _, b := range allBackendsToAutoLoad {
-		log.Debug().Msgf("[%s] Attempting to load", b)
+		log.Info().Msgf("[%s] Attempting to load", b)
 		options := []Option{
 			WithBackendString(b),
 			WithModel(o.model),
@@ -257,14 +268,14 @@ func (ml *ModelLoader) GreedyLoader(opts ...Option) (*grpc.Client, error) {
 
 		model, modelerr := ml.BackendLoader(options...)
 		if modelerr == nil && model != nil {
-			log.Debug().Msgf("[%s] Loads OK", b)
+			log.Info().Msgf("[%s] Loads OK", b)
 			return model, nil
 		} else if modelerr != nil {
 			err = multierror.Append(err, modelerr)
-			log.Debug().Msgf("[%s] Fails: %s", b, modelerr.Error())
+			log.Info().Msgf("[%s] Fails: %s", b, modelerr.Error())
 		} else if model == nil {
 			err = multierror.Append(err, fmt.Errorf("backend returned no usable model"))
-			log.Debug().Msgf("[%s] Fails: %s", b, "backend returned no usable model")
+			log.Info().Msgf("[%s] Fails: %s", b, "backend returned no usable model")
 		}
 	}
 

pkg/model/loader.go

@@ -171,9 +171,10 @@ func (ml *ModelLoader) CheckIsLoaded(s string) ModelAddress {
 		} else {
 			client = m.GRPC(false, ml.wd)
 		}
-
-		if !client.HealthCheck(context.Background()) {
-			log.Debug().Msgf("GRPC Model not responding: %s", s)
+		alive, err := client.HealthCheck(context.Background())
+		if !alive {
+			log.Warn().Msgf("GRPC Model not responding: %s", err.Error())
+			log.Warn().Msgf("Deleting the process in order to recreate it")
 			if !ml.grpcProcesses[s].IsAlive() {
 				log.Debug().Msgf("GRPC Process is not responding: %s", s)
 				// stop and delete the process, this forces to re-load the model and re-create again the service

pkg/startup/model_preload.go

@@ -0,0 +1,54 @@
+package startup
+
+import (
+	"errors"
+	"os"
+	"path/filepath"
+
+	"github.com/go-skynet/LocalAI/embedded"
+	"github.com/go-skynet/LocalAI/pkg/downloader"
+	"github.com/go-skynet/LocalAI/pkg/utils"
+	"github.com/rs/zerolog/log"
+)
+
+// PreloadModelsConfigurations will preload models from the given list of URLs
+// It will download the model if it is not already present in the model path
+// It will also try to resolve if the model is an embedded model YAML configuration
+func PreloadModelsConfigurations(modelPath string, models ...string) {
+	for _, url := range models {
+		url = embedded.ModelShortURL(url)
+
+		switch {
+		case embedded.ExistsInModelsLibrary(url):
+			modelYAML, err := embedded.ResolveContent(url)
+			// If we resolve something, just save it to disk and continue
+			if err != nil {
+				log.Error().Msgf("error loading model: %s", err.Error())
+				continue
+			}
+
+			log.Debug().Msgf("[startup] resolved embedded model: %s", url)
+			md5Name := utils.MD5(url)
+			if err := os.WriteFile(filepath.Join(modelPath, md5Name)+".yaml", modelYAML, os.ModePerm); err != nil {
+				log.Error().Msgf("error loading model: %s", err.Error())
+			}
+		case downloader.LooksLikeURL(url):
+			log.Debug().Msgf("[startup] resolved model to download: %s", url)
+
+			// md5 of model name
+			md5Name := utils.MD5(url)
+
+			// check if file exists
+			if _, err := os.Stat(filepath.Join(modelPath, md5Name)); errors.Is(err, os.ErrNotExist) {
+				err := downloader.DownloadFile(url, filepath.Join(modelPath, md5Name)+".yaml", "", func(fileName, current, total string, percent float64) {
+					utils.DisplayDownloadFunction(fileName, current, total, percent)
+				})
+				if err != nil {
+					log.Error().Msgf("error loading model: %s", err.Error())
+				}
+			}
+		default:
+			log.Warn().Msgf("[startup] failed resolving model '%s'", url)
+		}
+	}
+}

pkg/startup/model_preload_test.go

@@ -0,0 +1,66 @@
+package startup_test
+
+import (
+	"fmt"
+	"os"
+	"path/filepath"
+
+	. "github.com/go-skynet/LocalAI/pkg/startup"
+	"github.com/go-skynet/LocalAI/pkg/utils"
+
+	. "github.com/onsi/ginkgo/v2"
+	. "github.com/onsi/gomega"
+)
+
+var _ = Describe("Preload test", func() {
+
+	Context("Preloading from strings", func() {
+		It("loads from embedded full-urls", func() {
+			tmpdir, err := os.MkdirTemp("", "")
+			Expect(err).ToNot(HaveOccurred())
+			url := "https://raw.githubusercontent.com/mudler/LocalAI/master/examples/configurations/phi-2.yaml"
+			fileName := fmt.Sprintf("%s.yaml", utils.MD5(url))
+
+			PreloadModelsConfigurations(tmpdir, url)
+
+			resultFile := filepath.Join(tmpdir, fileName)
+
+			content, err := os.ReadFile(resultFile)
+			Expect(err).ToNot(HaveOccurred())
+
+			Expect(string(content)).To(ContainSubstring("name: phi-2"))
+		})
+		It("loads from embedded short-urls", func() {
+			tmpdir, err := os.MkdirTemp("", "")
+			Expect(err).ToNot(HaveOccurred())
+			url := "phi-2"
+
+			PreloadModelsConfigurations(tmpdir, url)
+
+			entry, err := os.ReadDir(tmpdir)
+			Expect(err).ToNot(HaveOccurred())
+			Expect(entry).To(HaveLen(1))
+			resultFile := entry[0].Name()
+
+			content, err := os.ReadFile(filepath.Join(tmpdir, resultFile))
+			Expect(err).ToNot(HaveOccurred())
+
+			Expect(string(content)).To(ContainSubstring("name: phi-2"))
+		})
+		It("loads from embedded models", func() {
+			tmpdir, err := os.MkdirTemp("", "")
+			Expect(err).ToNot(HaveOccurred())
+			url := "mistral-openorca"
+			fileName := fmt.Sprintf("%s.yaml", utils.MD5(url))
+
+			PreloadModelsConfigurations(tmpdir, url)
+
+			resultFile := filepath.Join(tmpdir, fileName)
+
+			content, err := os.ReadFile(resultFile)
+			Expect(err).ToNot(HaveOccurred())
+
+			Expect(string(content)).To(ContainSubstring("name: mistral-openorca"))
+		})
+	})
+})

pkg/startup/startup_suite_test.go

@@ -0,0 +1,13 @@
+package startup_test
+
+import (
+	"testing"
+
+	. "github.com/onsi/ginkgo/v2"
+	. "github.com/onsi/gomega"
+)
+
+func TestStartup(t *testing.T) {
+	RegisterFailHandler(Fail)
+	RunSpecs(t, "LocalAI startup test")
+}

pkg/utils/hash.go

@@ -0,0 +1,10 @@
+package utils
+
+import (
+	"crypto/md5"
+	"fmt"
+)
+
+func MD5(s string) string {
+	return fmt.Sprintf("%x", md5.Sum([]byte(s)))
+}

pkg/utils/logging.go

@@ -29,9 +29,9 @@ func DisplayDownloadFunction(fileName string, current string, total string, perc
 		}
 
 		if total != "" {
-			log.Debug().Msgf("Downloading %s: %s/%s (%.2f%%) ETA: %s", fileName, current, total, percentage, eta)
+			log.Info().Msgf("Downloading %s: %s/%s (%.2f%%) ETA: %s", fileName, current, total, percentage, eta)
 		} else {
-			log.Debug().Msgf("Downloading: %s", current)
+			log.Info().Msgf("Downloading: %s", current)
 		}
 	}
 }