feat: Return OpenAI errors and update docs (#80)

Signed-off-by: mudler <mudler@mocaccino.org>

.devcontainer/Dockerfile

@@ -0,0 +1,3 @@
+ARG GO_VERSION=1.20
+FROM mcr.microsoft.com/devcontainers/go:0-$GO_VERSION-bullseye
+RUN apt-get update && apt-get install -y cmake

.devcontainer/devcontainer.json

@@ -0,0 +1,46 @@
+// For format details, see https://aka.ms/devcontainer.json. For config options, see the
+// README at: https://github.com/devcontainers/templates/tree/main/src/docker-existing-docker-compose
+{
+	"name": "Existing Docker Compose (Extend)",
+
+	// Update the 'dockerComposeFile' list if you have more compose files or use different names.
+	// The .devcontainer/docker-compose.yml file contains any overrides you need/want to make.
+	"dockerComposeFile": [
+		"../docker-compose.yaml",
+		"docker-compose.yml"
+	],
+
+	// The 'service' property is the name of the service for the container that VS Code should
+	// use. Update this value and .devcontainer/docker-compose.yml to the real service name.
+	"service": "api",
+
+	// The optional 'workspaceFolder' property is the path VS Code should open by default when
+	// connected. This is typically a file mount in .devcontainer/docker-compose.yml
+	"workspaceFolder": "/workspace",
+
+	"features": {
+		"ghcr.io/devcontainers/features/go:1": {},
+		"ghcr.io/azutake/devcontainer-features/go-packages-install:0": {}
+	},
+
+	// Features to add to the dev container. More info: https://containers.dev/features.
+	// "features": {},
+
+	// Use 'forwardPorts' to make a list of ports inside the container available locally.
+	// "forwardPorts": [],
+
+	// Uncomment the next line if you want start specific services in your Docker Compose config.
+	// "runServices": [],
+
+	// Uncomment the next line if you want to keep your containers running after VS Code shuts down.
+	// "shutdownAction": "none",
+
+	// Uncomment the next line to run commands after the container is created.
+	"postCreateCommand": "make prepare"
+
+	// Configure tool-specific properties.
+	// "customizations": {},
+
+	// Uncomment to connect as an existing user other than the container default. More info: https://aka.ms/dev-containers-non-root.
+	// "remoteUser": "devcontainer"
+}

.devcontainer/docker-compose.yml

@@ -0,0 +1,26 @@
+version: '3.6'
+services:
+  # Update this to the name of the service you want to work with in your docker-compose.yml file
+  api:
+    # Uncomment if you want to override the service's Dockerfile to one in the .devcontainer 
+    # folder. Note that the path of the Dockerfile and context is relative to the *primary* 
+    # docker-compose.yml file (the first in the devcontainer.json "dockerComposeFile"
+    # array). The sample below assumes your primary file is in the root of your project.
+    #
+    build:
+      context: .
+      dockerfile: .devcontainer/Dockerfile
+
+    volumes:
+      # Update this to wherever you want VS Code to mount the folder of your project
+      - .:/workspace:cached
+
+    # Uncomment the next four lines if you will use a ptrace-based debugger like C++, Go, and Rust.
+    # cap_add:
+    #   - SYS_PTRACE
+    # security_opt:
+    #   - seccomp:unconfined
+
+    # Overrides default command so things don't shut down after the process ends.
+    command: /bin/sh -c "while sleep 1000; do :; done"
+ 

.dockerignore

@@ -0,0 +1 @@
+models

.env

@@ -0,0 +1,5 @@
+# THREADS=14
+# CONTEXT_SIZE=512
+MODELS_PATH=/models
+# DEBUG=true
+# BUILD_TYPE=generic

.github/workflows/image.yml

@@ -2,6 +2,7 @@
 name: 'build container images'
 
 on:
+  pull_request:
   push:
     branches:
       - master
@@ -12,68 +13,42 @@ jobs:
   docker:
     runs-on: ubuntu-latest
     steps:
-      - name: Release space from worker
-        run: |
-          echo "Listing top largest packages"
-          pkgs=$(dpkg-query -Wf '${Installed-Size}\t${Package}\t${Status}\n' | awk '$NF == "installed"{print $1 "\t" $2}' | sort -nr)
-          head -n 30 <<< "${pkgs}"
-          echo
-          df -h
-          echo
-          sudo apt-get remove -y '^llvm-.*|^libllvm.*' || true
-          sudo apt-get remove --auto-remove android-sdk-platform-tools || true
-          sudo apt-get purge --auto-remove android-sdk-platform-tools || true
-          sudo rm -rf /usr/local/lib/android
-          sudo apt-get remove -y '^dotnet-.*|^aspnetcore-.*' || true
-          sudo rm -rf /usr/share/dotnet
-          sudo apt-get remove -y '^mono-.*' || true
-          sudo apt-get remove -y '^ghc-.*' || true
-          sudo apt-get remove -y '.*jdk.*|.*jre.*' || true
-          sudo apt-get remove -y 'php.*' || true
-          sudo apt-get remove -y hhvm powershell firefox monodoc-manual msbuild || true
-          sudo apt-get remove -y '^google-.*' || true
-          sudo apt-get remove -y azure-cli || true
-          sudo apt-get remove -y '^mongo.*-.*|^postgresql-.*|^mysql-.*|^mssql-.*' || true
-          sudo apt-get remove -y '^gfortran-.*' || true
-          sudo apt-get autoremove -y
-          sudo apt-get clean
-          echo
-          echo "Listing top largest packages"
-          pkgs=$(dpkg-query -Wf '${Installed-Size}\t${Package}\t${Status}\n' | awk '$NF == "installed"{print $1 "\t" $2}' | sort -nr)
-          head -n 30 <<< "${pkgs}"
-          echo
-          sudo rm -rfv build || true
-          df -h
       - name: Checkout
         uses: actions/checkout@v3
+
       - name: Prepare
         id: prep
         run: |
-          DOCKER_IMAGE=quay.io/go-skynet/llama-cli
-          VERSION=latest
+          DOCKER_IMAGE=quay.io/go-skynet/local-ai
+          VERSION=master
           SHORTREF=${GITHUB_SHA::8}
+
           # If this is git tag, use the tag name as a docker tag
           if [[ $GITHUB_REF == refs/tags/* ]]; then
             VERSION=${GITHUB_REF#refs/tags/}
           fi
           TAGS="${DOCKER_IMAGE}:${VERSION},${DOCKER_IMAGE}:${SHORTREF}"
+
           # If the VERSION looks like a version number, assume that
           # this is the most recent version of the image and also
           # tag it 'latest'.
-          if [[ $VERSION =~ ^[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}$ ]]; then
+          if [[ $VERSION =~ ^v[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}$ ]]; then
             TAGS="$TAGS,${DOCKER_IMAGE}:latest"
           fi
+
           # Set output parameters.
           echo ::set-output name=tags::${TAGS}
           echo ::set-output name=docker_image::${DOCKER_IMAGE}
-          echo ::set-output name=image::${DOCKER_IMAGE}:${VERSION}
+
       - name: Set up QEMU
         uses: docker/setup-qemu-action@master
         with:
           platforms: all
+
       - name: Set up Docker Buildx
         id: buildx
         uses: docker/setup-buildx-action@master
+
       - name: Login to DockerHub
         if: github.event_name != 'pull_request'
         uses: docker/login-action@v2
@@ -81,9 +56,23 @@ jobs:
           registry: quay.io
           username: ${{ secrets.QUAY_USERNAME }}
           password: ${{ secrets.QUAY_PASSWORD }}
-      - uses: earthly/actions/setup-earthly@v1
       - name: Build
-        run: |
-            earthly config "global.conversion_parallelism" "1"
-            earthly config "global.buildkit_max_parallelism" "1"
-            earthly --push +image-all --IMAGE=${{ steps.prep.outputs.image }}
+        if: github.event_name != 'pull_request'
+        uses: docker/build-push-action@v4
+        with:
+          builder: ${{ steps.buildx.outputs.name }}
+          context: .
+          file: ./Dockerfile
+          platforms: linux/amd64,linux/arm64
+          push: true
+          tags: ${{ steps.prep.outputs.tags }}
+      - name: Build PRs
+        if: github.event_name == 'pull_request'
+        uses: docker/build-push-action@v4
+        with:
+          builder: ${{ steps.buildx.outputs.name }}
+          context: .
+          file: ./Dockerfile
+          platforms: linux/amd64
+          push: false
+          tags: ${{ steps.prep.outputs.tags }}

.github/workflows/test.yml

@@ -0,0 +1,44 @@
+---
+name: 'tests'
+
+on:
+  pull_request:
+  push:
+    branches:
+      - master
+    tags:
+      - '*'
+
+jobs:
+  ubuntu-latest:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Clone
+        uses: actions/checkout@v1
+        with: 
+          submodules: true
+      - name: Dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential
+      - name: Test
+        run: |
+          make test
+
+  macOS-latest:
+    runs-on: macOS-latest
+
+    steps:
+      - name: Clone
+        uses: actions/checkout@v1
+        with: 
+          submodules: true
+
+      - name: Dependencies
+        run: |
+          brew update
+          brew install sdl2
+      - name: Test
+        run: |
+          make test

.gitignore

@@ -0,0 +1,15 @@
+# go-llama build artifacts
+go-llama
+go-gpt4all-j
+go-gpt2
+
+# LocalAI build binary
+LocalAI
+local-ai
+# prevent above rules from omitting the helm chart
+!charts/*
+
+# Ignore models
+models/*.bin
+models/ggml-*
+test-models/

.goreleaser.yaml

@@ -1,5 +1,5 @@
 # Make sure to check the documentation at http://goreleaser.com
-project_name: llama-cli
+project_name: local-ai
 builds:
   - ldflags:
       - -w -s

.vscode/launch.json

@@ -0,0 +1,20 @@
+{
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Launch Go",
+            "type": "go",
+            "request": "launch",
+            "mode": "debug",
+            "program": "${workspaceFolder}/main.go",
+            "args": [
+                "api"
+            ],
+            "env": {
+                "C_INCLUDE_PATH": "/workspace/go-llama:/workspace/go-gpt4all-j:/workspace/go-gpt2",
+                "LIBRARY_PATH": "/workspace/go-llama:/workspace/go-gpt4all-j:/workspace/go-gpt2",
+                "DEBUG": "true"
+            }
+        }
+    ]
+}

Dockerfile

@@ -0,0 +1,13 @@
+ARG GO_VERSION=1.20
+ARG DEBIAN_VERSION=11
+ARG BUILD_TYPE=
+
+FROM golang:$GO_VERSION as builder
+WORKDIR /build
+RUN apt-get update && apt-get install -y cmake
+COPY . .
+RUN make build
+
+FROM debian:$DEBIAN_VERSION
+COPY --from=builder /build/local-ai /usr/bin/local-ai
+ENTRYPOINT [ "/usr/bin/local-ai" ]

Earthfile

@@ -1,47 +1,5 @@
 VERSION 0.7
 
-go-deps:
-    ARG GO_VERSION=1.20
-    FROM golang:$GO_VERSION
-    WORKDIR /build
-    COPY go.mod ./
-    COPY go.sum ./
-    RUN go mod download
-    RUN apt-get update
-    SAVE ARTIFACT go.mod AS LOCAL go.mod
-    SAVE ARTIFACT go.sum AS LOCAL go.sum
-
-model-image:
-    ARG MODEL_IMAGE=quay.io/go-skynet/models:ggml2-alpaca-7b-v0.2
-    FROM $MODEL_IMAGE
-    SAVE ARTIFACT /models/model.bin
-
 build:
-    FROM +go-deps
-    WORKDIR /build
-    RUN git clone https://github.com/go-skynet/llama
-    RUN cd llama && make libllama.a
-    COPY . .
-    RUN C_INCLUDE_PATH=/build/llama LIBRARY_PATH=/build/llama go build -o llama-cli ./
-    SAVE ARTIFACT llama-cli AS LOCAL llama-cli
-
-image:
-    FROM +go-deps
-    ARG IMAGE=alpaca-cli
-    COPY +model-image/model.bin /model.bin
-    COPY +build/llama-cli /llama-cli
-    ENV MODEL_PATH=/model.bin
-    ENTRYPOINT [ "/llama-cli" ]
-    SAVE IMAGE --push $IMAGE
-
-lite-image:
-    FROM +go-deps
-    ARG IMAGE=alpaca-cli-nomodel
-    COPY +build/llama-cli /llama-cli
-    ENV MODEL_PATH=/model.bin
-    ENTRYPOINT [ "/llama-cli" ]
-    SAVE IMAGE --push $IMAGE-lite
-
-image-all:
-    BUILD --platform=linux/amd64 --platform=linux/arm64 +image
-    BUILD --platform=linux/amd64 --platform=linux/arm64 +lite-image
+    FROM DOCKERFILE -f Dockerfile .
+    SAVE ARTIFACT /usr/bin/local-ai AS LOCAL local-ai

Makefile

@@ -0,0 +1,119 @@
+GOCMD=go
+GOTEST=$(GOCMD) test
+GOVET=$(GOCMD) vet
+BINARY_NAME=local-ai
+# renovate: datasource=github-tags depName=go-skynet/go-llama.cpp
+GOLLAMA_VERSION?=llama.cpp-25d7abb
+# renovate: datasource=git-refs packageNameTemplate=https://github.com/go-skynet/go-gpt4all-j.cpp currentValueTemplate=master depNameTemplate=go-gpt4all-j.cpp
+GOGPT4ALLJ_VERSION?=1f7bff57f66cb7062e40d0ac3abd2217815e5109
+# renovate: datasource=git-refs packageNameTemplate=https://github.com/go-skynet/go-gpt2.cpp currentValueTemplate=master depNameTemplate=go-gpt2.cpp
+GOGPT2_VERSION?=245a5bfe6708ab80dc5c733dcdbfbe3cfd2acdaa
+
+GREEN  := $(shell tput -Txterm setaf 2)
+YELLOW := $(shell tput -Txterm setaf 3)
+WHITE  := $(shell tput -Txterm setaf 7)
+CYAN   := $(shell tput -Txterm setaf 6)
+RESET  := $(shell tput -Txterm sgr0)
+
+C_INCLUDE_PATH=$(shell pwd)/go-llama:$(shell pwd)/go-gpt4all-j:$(shell pwd)/go-gpt2
+LIBRARY_PATH=$(shell pwd)/go-llama:$(shell pwd)/go-gpt4all-j:$(shell pwd)/go-gpt2
+
+# Use this if you want to set the default behavior
+ifndef BUILD_TYPE
+	BUILD_TYPE:=default
+endif
+
+ifeq ($(BUILD_TYPE), "generic")
+	GENERIC_PREFIX:=generic-
+else
+	GENERIC_PREFIX:=
+endif
+
+.PHONY: all test build vendor
+
+all: help
+
+## Build:
+
+build: prepare ## Build the project
+	$(info ${GREEN}I local-ai build info:${RESET})
+	$(info ${GREEN}I BUILD_TYPE: ${YELLOW}$(BUILD_TYPE)${RESET})
+	C_INCLUDE_PATH=${C_INCLUDE_PATH} LIBRARY_PATH=${LIBRARY_PATH} $(GOCMD) build -o $(BINARY_NAME) ./
+
+generic-build: ## Build the project using generic
+	BUILD_TYPE="generic" $(MAKE) build
+
+## GPT4ALL-J
+go-gpt4all-j:
+	git clone --recurse-submodules https://github.com/go-skynet/go-gpt4all-j.cpp go-gpt4all-j
+	cd go-gpt4all-j && git checkout -b build $(GOGPT4ALLJ_VERSION)
+	# This is hackish, but needed as both go-llama and go-gpt4allj have their own version of ggml..
+	@find ./go-gpt4all-j -type f -name "*.c" -exec sed -i'' -e 's/ggml_/ggml_gptj_/g' {} +
+	@find ./go-gpt4all-j -type f -name "*.cpp" -exec sed -i'' -e 's/ggml_/ggml_gptj_/g' {} +
+	@find ./go-gpt4all-j -type f -name "*.h" -exec sed -i'' -e 's/ggml_/ggml_gptj_/g' {} +
+	@find ./go-gpt4all-j -type f -name "*.cpp" -exec sed -i'' -e 's/gpt_/gptj_/g' {} +
+	@find ./go-gpt4all-j -type f -name "*.h" -exec sed -i'' -e 's/gpt_/gptj_/g' {} +
+	@find ./go-gpt4all-j -type f -name "*.cpp" -exec sed -i'' -e 's/json_/json_gptj_/g' {} +
+	@find ./go-gpt4all-j -type f -name "*.cpp" -exec sed -i'' -e 's/void replace/void json_gptj_replace/g' {} +
+	@find ./go-gpt4all-j -type f -name "*.cpp" -exec sed -i'' -e 's/::replace/::json_gptj_replace/g' {} +
+
+go-gpt4all-j/libgptj.a: go-gpt4all-j
+	$(MAKE) -C go-gpt4all-j $(GENERIC_PREFIX)libgptj.a
+
+# CEREBRAS GPT
+go-gpt2:
+	git clone --recurse-submodules https://github.com/go-skynet/go-gpt2.cpp go-gpt2
+	cd go-gpt2 && git checkout -b build $(GOGPT2_VERSION)
+	# This is hackish, but needed as both go-llama and go-gpt4allj have their own version of ggml..
+	@find ./go-gpt2 -type f -name "*.c" -exec sed -i'' -e 's/ggml_/ggml_gpt2_/g' {} +
+	@find ./go-gpt2 -type f -name "*.cpp" -exec sed -i'' -e 's/ggml_/ggml_gpt2_/g' {} +
+	@find ./go-gpt2 -type f -name "*.h" -exec sed -i'' -e 's/ggml_/ggml_gpt2_/g' {} +
+	@find ./go-gpt2 -type f -name "*.cpp" -exec sed -i'' -e 's/gpt_/gpt2_/g' {} +
+	@find ./go-gpt2 -type f -name "*.h" -exec sed -i'' -e 's/gpt_/gpt2_/g' {} +
+	@find ./go-gpt2 -type f -name "*.cpp" -exec sed -i'' -e 's/json_/json_gpt2_/g' {} +
+
+go-gpt2/libgpt2.a: go-gpt2
+	$(MAKE) -C go-gpt2 $(GENERIC_PREFIX)libgpt2.a
+	
+
+go-llama:
+	git clone -b $(GOLLAMA_VERSION) --recurse-submodules https://github.com/go-skynet/go-llama.cpp go-llama
+
+go-llama/libbinding.a: go-llama
+	$(MAKE) -C go-llama $(GENERIC_PREFIX)libbinding.a
+
+replace:
+	$(GOCMD) mod edit -replace github.com/go-skynet/go-llama.cpp=$(shell pwd)/go-llama
+	$(GOCMD) mod edit -replace github.com/go-skynet/go-gpt4all-j.cpp=$(shell pwd)/go-gpt4all-j
+	$(GOCMD) mod edit -replace github.com/go-skynet/go-gpt2.cpp=$(shell pwd)/go-gpt2
+
+prepare: go-llama/libbinding.a go-gpt4all-j/libgptj.a go-gpt2/libgpt2.a replace
+
+clean: ## Remove build related file
+	rm -fr ./go-llama
+	rm -rf ./go-gpt4all-j
+	rm -rf ./go-gpt2
+	rm -rf $(BINARY_NAME)
+
+## Run:
+run: prepare
+	C_INCLUDE_PATH=${C_INCLUDE_PATH} LIBRARY_PATH=${LIBRARY_PATH} $(GOCMD) run ./main.go
+
+test-models/testmodel:
+	mkdir test-models
+	wget https://huggingface.co/concedo/cerebras-111M-ggml/resolve/main/cerberas-111m-q4_0.bin -O test-models/testmodel
+
+test: prepare test-models/testmodel
+	@C_INCLUDE_PATH=${C_INCLUDE_PATH} LIBRARY_PATH=${LIBRARY_PATH} MODELS_PATH=$(abspath ./)/test-models $(GOCMD) test -v ./...
+
+## Help:
+help: ## Show this help.
+	@echo ''
+	@echo 'Usage:'
+	@echo '  ${YELLOW}make${RESET} ${GREEN}<target>${RESET}'
+	@echo ''
+	@echo 'Targets:'
+	@awk 'BEGIN {FS = ":.*?## "} { \
+		if (/^[a-zA-Z_-]+:.*?##.*$$/) {printf "    ${YELLOW}%-20s${GREEN}%s${RESET}\n", $$1, $$2} \
+		else if (/^## .*$$/) {printf "  ${CYAN}%s${RESET}\n", substr($$1,4)} \
+		}' $(MAKEFILE_LIST)

README.md

@@ -1,60 +1,140 @@
-## :camel: llama-cli
+<h1 align="center">
+  <br>
+  <img height="300" src="https://user-images.githubusercontent.com/2420543/233147843-88697415-6dbf-4368-a862-ab217f9f7342.jpeg"> <br>
+    LocalAI
+<br>
+</h1>
+
+> :warning: This project has been renamed from `llama-cli` to `LocalAI` to reflect the fact that we are focusing on a fast drop-in OpenAI API rather on the CLI interface. We think that there are already many projects that can be used as a CLI interface already, for instance  [llama.cpp](https://github.com/ggerganov/llama.cpp) and [gpt4all](https://github.com/nomic-ai/gpt4all). If you are were using `llama-cli` for CLI interactions and want to keep using it, use older versions or please open up an issue - contributions are welcome!
 
 
-llama-cli is a straightforward golang CLI interface for [llama.cpp](https://github.com/ggerganov/llama.cpp), providing a simple API and a command line interface that allows text generation using a GPT-based model like llama directly from the terminal.
+[![tests](https://github.com/go-skynet/LocalAI/actions/workflows/test.yml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/test.yml) [![build container images](https://github.com/go-skynet/LocalAI/actions/workflows/image.yml/badge.svg)](https://github.com/go-skynet/LocalAI/actions/workflows/image.yml)
 
-## Container images
+[![](https://dcbadge.vercel.app/api/server/uJAeKSAGDy?style=flat-square&theme=default-inverted)](https://discord.gg/uJAeKSAGDy) 
 
-The `llama-cli` [container images](https://quay.io/repository/go-skynet/llama-cli?tab=tags&tag=latest) come preloaded with the [alpaca.cpp 7B](https://github.com/antimatter15/alpaca.cpp) model, enabling you to start making predictions immediately! To begin, run:
+**LocalAI** is a straightforward, drop-in replacement API compatible with OpenAI for local CPU inferencing, based on [llama.cpp](https://github.com/ggerganov/llama.cpp), [gpt4all](https://github.com/nomic-ai/gpt4all) and [ggml](https://github.com/ggerganov/ggml), including support GPT4ALL-J which is Apache 2.0 Licensed and can be used for commercial purposes.
 
-```
-docker run -ti --rm quay.io/go-skynet/llama-cli:v0.2  --instruction "What's an alpaca?" --topk 10000
+- OpenAI compatible API
+- Supports multiple-models
+- Once loaded the first time, it keep models loaded in memory for faster inference
+- Support for prompt templates
+- Doesn't shell-out, but uses C bindings for a faster inference and better performance. Uses [go-llama.cpp](https://github.com/go-skynet/go-llama.cpp) and [go-gpt4all-j.cpp](https://github.com/go-skynet/go-gpt4all-j.cpp).
+
+Reddit post: https://www.reddit.com/r/selfhosted/comments/12w4p2f/localai_openai_compatible_api_to_run_llm_models/
+
+## Model compatibility
+
+It is compatible with the models supported by [llama.cpp](https://github.com/ggerganov/llama.cpp) supports also [GPT4ALL-J](https://github.com/nomic-ai/gpt4all) and [cerebras-GPT with ggml](https://huggingface.co/lxe/Cerebras-GPT-2.7B-Alpaca-SP-ggml).
+
+Tested with:
+- Vicuna
+- Alpaca
+- [GPT4ALL](https://github.com/nomic-ai/gpt4all)
+- [GPT4ALL-J](https://gpt4all.io/models/ggml-gpt4all-j.bin)
+- Koala
+- [cerebras-GPT with ggml](https://huggingface.co/lxe/Cerebras-GPT-2.7B-Alpaca-SP-ggml)
+
+It should also be compatible with StableLM and GPTNeoX ggml models (untested)
+
+Note: You might need to convert older models to the new format, see [here](https://github.com/ggerganov/llama.cpp#using-gpt4all) for instance to run `gpt4all`.
+
+## Usage
+
+> `LocalAI` comes by default as a container image. You can check out all the available images with corresponding tags [here](https://quay.io/repository/go-skynet/local-ai?tab=tags&tag=latest).
+
+The easiest way to run LocalAI is by using `docker-compose`:
+
+```bash
+
+git clone https://github.com/go-skynet/LocalAI
+
+cd LocalAI
+
+# copy your models to models/
+cp your-model.bin models/
+
+# (optional) Edit the .env file to set things like context size and threads
+# vim .env
+
+# start with docker-compose
+docker-compose up -d --build
+
+# Now API is accessible at localhost:8080
+curl http://localhost:8080/v1/models
+# {"object":"list","data":[{"id":"your-model.bin","object":"model"}]}
+
+curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
+     "model": "your-model.bin",            
+     "prompt": "A long time ago in a galaxy far, far away",
+     "temperature": 0.7
+   }'
 ```
 
-You will receive a response like the following:
+### Example: Use GPT4ALL-J model
+
+<details>
+
+```bash
+# Clone LocalAI
+git clone https://github.com/go-skynet/LocalAI
+
+cd LocalAI
+
+# Download gpt4all-j to models/
+wget https://gpt4all.io/models/ggml-gpt4all-j.bin -O models/ggml-gpt4all-j
+
+# Use a template from the examples
+cp -rf prompt-templates/ggml-gpt4all-j.tmpl models/
+
+# (optional) Edit the .env file to set things like context size and threads
+# vim .env
+
+# start with docker-compose
+docker-compose up -d --build
+
+# Now API is accessible at localhost:8080
+curl http://localhost:8080/v1/models
+# {"object":"list","data":[{"id":"ggml-gpt4all-j","object":"model"}]}
+
+curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
+     "model": "ggml-gpt4all-j",
+     "messages": [{"role": "user", "content": "How are you?"}],
+     "temperature": 0.9 
+   }'
+
+# {"model":"ggml-gpt4all-j","choices":[{"message":{"role":"assistant","content":"I'm doing well, thanks. How about you?"}}]}
+```
+</details>
+
+## Prompt templates 
+
+The API doesn't inject a default prompt for talking to the model. You have to use a prompt similar to what's described in the standford-alpaca docs: https://github.com/tatsu-lab/stanford_alpaca#data-release.
+
+<details>
+You can use a default template for every model present in your model path, by creating a corresponding file with the `.tmpl` suffix next to your model. For instance, if the model is called `foo.bin`, you can create a sibiling file, `foo.bin.tmpl` which will be used as a default prompt, for instance this can be used with alpaca:
 
 ```
-An alpaca is a member of the South American Camelid family, which includes the llama, guanaco and vicuña. It is a domesticated species that originates from the Andes mountain range in South America. Alpacas are used in the textile industry for their fleece, which is much softer than wool. Alpacas are also used for meat, milk, and fiber.
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{{.Input}}
+
+### Response:
 ```
 
-## Basic usage
+See the [prompt-templates](https://github.com/go-skynet/LocalAI/tree/master/prompt-templates) directory in this repository for templates for most popular models.
 
-To use llama-cli, specify a pre-trained GPT-based model, an input text, and an instruction for text generation. llama-cli takes the following arguments when running from the CLI:
+</details>
 
-```
-llama-cli --model <model_path> --instruction <instruction> [--input <input>] [--template <template_path>] [--tokens <num_tokens>] [--threads <num_threads>] [--temperature <temperature>] [--topp <top_p>] [--topk <top_k>]
-```
+## API
 
-| Parameter    | Environment Variable | Default Value | Description                            |
-| ------------ | -------------------- | ------------- | -------------------------------------- |
-| template     | TEMPLATE             |               | A file containing a template for output formatting (optional).  |
-| instruction  | INSTRUCTION          |               | Input prompt text or instruction. "-" for STDIN.   |
-| input        | INPUT                | -             | Path to text or "-" for STDIN.                    |
-| model        | MODEL_PATH           |               | The path to the pre-trained GPT-based model.      |
-| tokens       | TOKENS               | 128           | The maximum number of tokens to generate. |
-| threads      | THREADS              | NumCPU()      | The number of threads to use for text generation. |
-| temperature  | TEMPERATURE          | 0.95          | Sampling temperature for model output. ( values between `0.1` and `1.0` )  |
-| top_p        | TOP_P                | 0.85          | The cumulative probability for top-p sampling. |
-| top_k        | TOP_K                | 20            | The number of top-k tokens to consider for text generation.  |
-| context-size | CONTEXT_SIZE         | 512           | Default token context size. |
-| alpaca       | ALPACA               | true          | Set to true for alpaca models. |
-
-Here's an example of using `llama-cli`:
-
-```
-llama-cli --model ~/ggml-alpaca-7b-q4.bin --instruction "What's an alpaca?"
-```
-
-This will generate text based on the given model and instruction.
-
-## Advanced usage
-
-`llama-cli` also provides an API for running text generation as a service. 
+`LocalAI` provides an API for running text generation as a service, that follows the OpenAI reference and can be used as a drop-in. The models once loaded the first time will be kept in memory.
 
+<details>
 Example of starting the API with `docker`:
 
 ```bash
-docker run -p 8080:8080 -ti --rm quay.io/go-skynet/llama-cli:v0.2 api
+docker run -p 8080:8080 -ti --rm quay.io/go-skynet/local-ai:latest --models-path /path/to/models --context-size 700 --threads 4
 ```
 
 And you'll see:
@@ -72,101 +152,207 @@ And you'll see:
 You can control the API server options with command line arguments:
 
 ```
-llama-cli api --model <model_path> [--address <address>] [--threads <num_threads>]
+local-api --models-path <model_path> [--address <address>] [--threads <num_threads>]
 ```
 
-The API takes takes the following:
+The API takes takes the following parameters:
 
 | Parameter    | Environment Variable | Default Value | Description                            |
 | ------------ | -------------------- | ------------- | -------------------------------------- |
-| model        | MODEL_PATH           |               | The path to the pre-trained GPT-based model.      |
-| threads      | THREADS              | CPU cores     | The number of threads to use for text generation. |
+| models-path        | MODELS_PATH           |               | The path where you have models (ending with `.bin`).      |
+| threads      | THREADS              | Number of Physical cores     | The number of threads to use for text generation. |
 | address      | ADDRESS              | :8080         | The address and port to listen on. |
 | context-size | CONTEXT_SIZE         | 512           | Default token context size. |
-| alpaca       | ALPACA               | true          | Set to true for alpaca models. |
+| debug | DEBUG         | false           | Enable debug mode. |
 
+Once the server is running, you can start making requests to it using HTTP, using the OpenAI API. 
 
-Once the server is running, you can make requests to it using HTTP. For example, to generate text based on an instruction, you can send a POST request to the `/predict` endpoint with the instruction as the request body:
+</details>
+
+### Supported OpenAI API endpoints
+
+You can check out the [OpenAI API reference](https://platform.openai.com/docs/api-reference/chat/create). 
+
+Following the list of endpoints/parameters supported. 
+
+Note:
+
+- You can also specify the model a part of the OpenAI token.
+- If only one model is available, the API will use it for all the requests.
+
+#### Chat completions
+
+<details>
+For example, to generate a chat completion, you can send a POST request to the `/v1/chat/completions` endpoint with the instruction as the request body:
 
 ```
-curl --location --request POST 'http://localhost:8080/predict' --header 'Content-Type: application/json' --data-raw '{
-    "text": "What is an alpaca?",
-    "topP": 0.8,
-    "topK": 50,
-    "temperature": 0.7,
-    "tokens": 100
-}'
+curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
+     "model": "ggml-koala-7b-model-q4_0-r2.bin",
+     "messages": [{"role": "user", "content": "Say this is a test!"}],
+     "temperature": 0.7
+   }'
 ```
 
-Note: The API doesn't inject a template for talking to the instance, while the CLI does. You have to use a prompt similar to what's described in the standford-alpaca docs: https://github.com/tatsu-lab/stanford_alpaca#data-release, for instance:
+Available additional parameters: `top_p`, `top_k`, `max_tokens`
+</details>
+
+#### Completions
+
+<details>
+For example, to generate a completion, you can send a POST request to the `/v1/completions` endpoint with the instruction as the request body:
+```
+curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
+     "model": "ggml-koala-7b-model-q4_0-r2.bin",
+     "prompt": "A long time ago in a galaxy far, far away",
+     "temperature": 0.7
+   }'
+```
+
+Available additional parameters: `top_p`, `top_k`, `max_tokens`
+
+</details>
+
+#### List models
+
+<details>
+You can list all the models available with:
 
 ```
-Below is an instruction that describes a task. Write a response that appropriately completes the request.
-
-### Instruction:
-{instruction}
-
-### Response:
+curl http://localhost:8080/v1/models
 ```
 
+</details>
+
 ## Using other models
 
-You can use the lite images ( for example `quay.io/go-skynet/llama-cli:v0.2-lite`) that don't ship any model, and specify a model binary to be used for inference with `--model`.
-
-13B and 30B models are known to work:
-
-### 13B
-
-```
-# Download the model image, extract the model
-docker run --name model --entrypoint /models quay.io/go-skynet/models:ggml2-alpaca-13b-v0.2
-docker cp model:/models/model.bin ./
-
-# Use the model with llama-cli
-docker run -v $PWD:/models -p 8080:8080 -ti --rm quay.io/go-skynet/llama-cli:v0.2-lite api --model /models/model.bin
-```
-
-### 30B
-
-```
-# Download the model image, extract the model
-docker run --name model --entrypoint /models quay.io/go-skynet/models:ggml2-alpaca-30b-v0.2
-docker cp model:/models/model.bin ./
-
-# Use the model with llama-cli
-docker run -v $PWD:/models -p 8080:8080 -ti --rm quay.io/go-skynet/llama-cli:v0.2-lite api --model /models/model.bin
-```
-
-### Golang client API
-
-The `llama-cli` codebase has also a small client in go that can be used alongside with the api:
-
-```golang
-package main
-
-import (
-	"fmt"
-
-	client "github.com/go-skynet/llama-cli/client"
-)
-
-func main() {
-
-	cli := client.NewClient("http://ip:30007")
-
-	out, err := cli.Predict("What's an alpaca?")
-	if err != nil {
-		panic(err)
-	}
-
-	fmt.Println(out)
-}
-```
-
-### Kubernetes
-
-You can run the API directly in Kubernetes:
+gpt4all (https://github.com/nomic-ai/gpt4all) works as well, however the original model needs to be converted (same applies for old alpaca models, too):
 
 ```bash
-kubectl apply -f https://raw.githubusercontent.com/go-skynet/llama-cli/master/kubernetes/deployment.yaml
-```
+wget -O tokenizer.model https://huggingface.co/decapoda-research/llama-30b-hf/resolve/main/tokenizer.model
+mkdir models
+cp gpt4all.. models/
+git clone https://gist.github.com/eiz/828bddec6162a023114ce19146cb2b82
+pip install sentencepiece
+python 828bddec6162a023114ce19146cb2b82/gistfile1.txt models tokenizer.model
+# There will be a new model with the ".tmp" extension, you have to use that one!
+```
+
+
+## Helm Chart Installation (run LocalAI in Kubernetes)
+The local-ai Helm chart supports two options for the LocalAI server's models directory:
+1. Basic deployment with no persistent volume. You must manually update the Deployment to configure your own models directory.
+
+    Install the chart with `.Values.deployment.volumes.enabled == false` and `.Values.dataVolume.enabled == false`.
+
+2. Advanced, two-phase deployment to provision the models directory using a DataVolume. Requires [Containerized Data Importer CDI](https://github.com/kubevirt/containerized-data-importer) to be pre-installed in your cluster.
+
+    First, install the chart with `.Values.deployment.volumes.enabled == false` and `.Values.dataVolume.enabled == true`:
+    ```bash
+    helm install local-ai charts/local-ai -n local-ai --create-namespace
+    ```
+    Wait for CDI to create an importer Pod for the DataVolume and for the importer pod to finish provisioning the model archive inside the PV.
+
+    Once the PV is provisioned and the importer Pod removed, set `.Values.deployment.volumes.enabled == true` and `.Values.dataVolume.enabled == false` and upgrade the chart:
+    ```bash
+    helm upgrade local-ai -n local-ai charts/local-ai
+    ```
+    This will update the local-ai Deployment to mount the PV that was provisioned by the DataVolume.
+
+## Windows compatibility
+
+It should work, however you need to make sure you give enough resources to the container. See https://github.com/go-skynet/LocalAI/issues/2
+
+## Build locally
+
+Pre-built images might fit well for most of the modern hardware, however you can and might need to build the images manually.
+
+In order to build the `LocalAI` container image locally you can use `docker`:
+
+```
+# build the image
+docker build -t LocalAI .
+docker run LocalAI
+```
+
+Or build the binary with `make`:
+
+```
+make build
+```
+
+## Frequently asked questions
+
+Here are answers to some of the most common questions.
+
+
+### How do I get models? 
+
+<details>
+
+Most ggml-based models should work, but newer models may require additions to the API. If a model doesn't work, please feel free to open up issues. However, be cautious about downloading models from the internet and directly onto your machine, as there may be security vulnerabilities in lama.cpp or ggml that could be maliciously exploited. Some models can be found on Hugging Face: https://huggingface.co/models?search=ggml, or models from gpt4all should also work: https://github.com/nomic-ai/gpt4all.
+
+</details>
+
+### What's the difference with Serge, or XXX?
+
+
+<details>
+
+LocalAI is a multi-model solution that doesn't focus on a specific model type (e.g., llama.cpp or alpaca.cpp), and it handles all of these internally for faster inference,  easy to set up locally and deploy to Kubernetes.
+
+</details>
+
+
+### Can I use it with a Discord bot, or XXX?
+
+<details>
+
+Yes! If the client uses OpenAI and supports setting a different base URL to send requests to, you can use the LocalAI endpoint. This allows to use this with every application that was supposed to work with OpenAI, but without changing the application!
+
+</details>
+
+
+### Can this leverage GPUs? 
+
+<details>
+
+Not currently, as ggml doesn't support GPUs yet: https://github.com/ggerganov/llama.cpp/discussions/915.
+
+</details>
+
+### Where is the webUI? 
+
+<details> 
+We are working on to have a good out of the box experience - however as LocalAI is an API you can already plug it into existing projects that provides are UI interfaces to OpenAI's APIs. There are several already on github, and should be compatible with LocalAI already (as it mimics the OpenAI API)
+
+</details>
+
+### Does it work with AutoGPT? 
+
+<details>
+
+AutoGPT currently doesn't allow to set a different API URL, but there is a PR open for it, so this should be possible soon!
+
+</details>
+
+
+## Short-term roadmap
+
+- [x] Mimic OpenAI API (https://github.com/go-skynet/LocalAI/issues/10)
+- [ ] Binary releases (https://github.com/go-skynet/LocalAI/issues/6)
+- [ ] Upstream our golang bindings to llama.cpp (https://github.com/ggerganov/llama.cpp/issues/351)
+- [x] Multi-model support
+- [ ] Have a webUI!
+- [ ] Allow configuration of defaults for models.
+- [ ] Enable automatic downloading of models from a curated gallery, with only free-licensed models.
+
+## License
+
+MIT
+
+## Acknowledgements
+
+- [llama.cpp](https://github.com/ggerganov/llama.cpp)
+- https://github.com/tatsu-lab/stanford_alpaca
+- https://github.com/cornelk/llama-go for the initial ideas
+- https://github.com/antimatter15/alpaca.cpp for the light model version (this is compatible and tested only with that checkpoint model!)

api.go

@@ -1,78 +0,0 @@
-package main
-
-import (
-	"strconv"
-
-	llama "github.com/go-skynet/llama/go"
-	"github.com/gofiber/fiber/v2"
-)
-
-func api(l *llama.LLama, listenAddr string, threads int) error {
-	app := fiber.New()
-
-	/*
-		curl --location --request POST 'http://localhost:8080/predict' --header 'Content-Type: application/json' --data-raw '{
-		    "text": "What is an alpaca?",
-		    "topP": 0.8,
-		    "topK": 50,
-		    "temperature": 0.7,
-		    "tokens": 100
-		}'
-	*/
-
-	// Endpoint to generate the prediction
-	app.Post("/predict", func(c *fiber.Ctx) error {
-		// Get input data from the request body
-		input := new(struct {
-			Text string `json:"text"`
-		})
-		if err := c.BodyParser(input); err != nil {
-			return err
-		}
-
-		// Set the parameters for the language model prediction
-		topP, err := strconv.ParseFloat(c.Query("topP", "0.9"), 64) // Default value of topP is 0.9
-		if err != nil {
-			return err
-		}
-
-		topK, err := strconv.Atoi(c.Query("topK", "40")) // Default value of topK is 40
-		if err != nil {
-			return err
-		}
-
-		temperature, err := strconv.ParseFloat(c.Query("temperature", "0.5"), 64) // Default value of temperature is 0.5
-		if err != nil {
-			return err
-		}
-
-		tokens, err := strconv.Atoi(c.Query("tokens", "128")) // Default value of tokens is 128
-		if err != nil {
-			return err
-		}
-
-		// Generate the prediction using the language model
-		prediction, err := l.Predict(
-			input.Text,
-			llama.SetTemperature(temperature),
-			llama.SetTopP(topP),
-			llama.SetTopK(topK),
-			llama.SetTokens(tokens),
-			llama.SetThreads(threads),
-		)
-		if err != nil {
-			return err
-		}
-
-		// Return the prediction in the response body
-		return c.JSON(struct {
-			Prediction string `json:"prediction"`
-		}{
-			Prediction: prediction,
-		})
-	})
-
-	// Start the server
-	app.Listen(":8080")
-	return nil
-}

api/api.go

@@ -0,0 +1,437 @@
+package api
+
+import (
+	"encoding/json"
+	"errors"
+	"fmt"
+	"strings"
+	"sync"
+
+	model "github.com/go-skynet/LocalAI/pkg/model"
+	gpt2 "github.com/go-skynet/go-gpt2.cpp"
+	gptj "github.com/go-skynet/go-gpt4all-j.cpp"
+	llama "github.com/go-skynet/go-llama.cpp"
+	"github.com/gofiber/fiber/v2"
+	"github.com/gofiber/fiber/v2/middleware/cors"
+	"github.com/gofiber/fiber/v2/middleware/recover"
+	"github.com/rs/zerolog"
+	"github.com/rs/zerolog/log"
+)
+
+// APIError provides error information returned by the OpenAI API.
+type APIError struct {
+	Code    any     `json:"code,omitempty"`
+	Message string  `json:"message"`
+	Param   *string `json:"param,omitempty"`
+	Type    string  `json:"type"`
+}
+
+type ErrorResponse struct {
+	Error *APIError `json:"error,omitempty"`
+}
+
+type OpenAIResponse struct {
+	Created int      `json:"created,omitempty"`
+	Object  string   `json:"chat.completion,omitempty"`
+	ID      string   `json:"id,omitempty"`
+	Model   string   `json:"model,omitempty"`
+	Choices []Choice `json:"choices,omitempty"`
+}
+
+type Choice struct {
+	Index        int      `json:"index,omitempty"`
+	FinishReason string   `json:"finish_reason,omitempty"`
+	Message      *Message `json:"message,omitempty"`
+	Text         string   `json:"text,omitempty"`
+}
+
+type Message struct {
+	Role    string `json:"role,omitempty"`
+	Content string `json:"content,omitempty"`
+}
+
+type OpenAIModel struct {
+	ID     string `json:"id"`
+	Object string `json:"object"`
+}
+
+type OpenAIRequest struct {
+	Model string `json:"model"`
+
+	// Prompt is read only by completion API calls
+	Prompt string `json:"prompt"`
+
+	Stop string `json:"stop"`
+
+	// Messages is read only by chat/completion API calls
+	Messages []Message `json:"messages"`
+
+	Echo bool `json:"echo"`
+	// Common options between all the API calls
+	TopP        float64 `json:"top_p"`
+	TopK        int     `json:"top_k"`
+	Temperature float64 `json:"temperature"`
+	Maxtokens   int     `json:"max_tokens"`
+
+	N int `json:"n"`
+
+	// Custom parameters - not present in the OpenAI API
+	Batch         int     `json:"batch"`
+	F16           bool    `json:"f16kv"`
+	IgnoreEOS     bool    `json:"ignore_eos"`
+	RepeatPenalty float64 `json:"repeat_penalty"`
+	Keep          int     `json:"n_keep"`
+
+	Seed int `json:"seed"`
+}
+
+// https://platform.openai.com/docs/api-reference/completions
+func openAIEndpoint(chat, debug bool, loader *model.ModelLoader, threads, ctx int, f16 bool, mutexMap *sync.Mutex, mutexes map[string]*sync.Mutex) func(c *fiber.Ctx) error {
+	return func(c *fiber.Ctx) error {
+		var err error
+		var model *llama.LLama
+		var gptModel *gptj.GPTJ
+		var gpt2Model *gpt2.GPT2
+		var stableLMModel *gpt2.StableLM
+
+		input := new(OpenAIRequest)
+		// Get input data from the request body
+		if err := c.BodyParser(input); err != nil {
+			return err
+		}
+		modelFile := input.Model
+		received, _ := json.Marshal(input)
+
+		log.Debug().Msgf("Request received: %s", string(received))
+
+		// Set model from bearer token, if available
+		bearer := strings.TrimLeft(c.Get("authorization"), "Bearer ")
+		bearerExists := bearer != "" && loader.ExistsInModelPath(bearer)
+
+		// If no model was specified, take the first available
+		if modelFile == "" {
+			models, _ := loader.ListModels()
+			if len(models) > 0 {
+				modelFile = models[0]
+				log.Debug().Msgf("No model specified, using: %s", modelFile)
+			}
+		}
+
+		// If no model is found or specified, we bail out
+		if modelFile == "" && !bearerExists {
+			return fmt.Errorf("no model specified")
+		}
+
+		// If a model is found in bearer token takes precedence
+		if bearerExists {
+			log.Debug().Msgf("Using model from bearer token: %s", bearer)
+			modelFile = bearer
+		}
+
+		// Try to load the model
+		var llamaerr, gpt2err, gptjerr, stableerr error
+		llamaOpts := []llama.ModelOption{}
+		if ctx != 0 {
+			llamaOpts = append(llamaOpts, llama.SetContext(ctx))
+		}
+		if f16 {
+			llamaOpts = append(llamaOpts, llama.EnableF16Memory)
+		}
+
+		// TODO: this is ugly, better identifying the model somehow! however, it is a good stab for a first implementation..
+		model, llamaerr = loader.LoadLLaMAModel(modelFile, llamaOpts...)
+		if llamaerr != nil {
+			gptModel, gptjerr = loader.LoadGPTJModel(modelFile)
+			if gptjerr != nil {
+				gpt2Model, gpt2err = loader.LoadGPT2Model(modelFile)
+				if gpt2err != nil {
+					stableLMModel, stableerr = loader.LoadStableLMModel(modelFile)
+					if stableerr != nil {
+						return fmt.Errorf("llama: %s gpt: %s gpt2: %s stableLM: %s", llamaerr.Error(), gptjerr.Error(), gpt2err.Error(), stableerr.Error()) // llama failed first, so we want to catch both errors
+					}
+				}
+			}
+		}
+
+		// This is still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
+		mutexMap.Lock()
+		l, ok := mutexes[modelFile]
+		if !ok {
+			m := &sync.Mutex{}
+			mutexes[modelFile] = m
+			l = m
+		}
+		mutexMap.Unlock()
+		l.Lock()
+		defer l.Unlock()
+
+		// Set the parameters for the language model prediction
+		topP := input.TopP
+		if topP == 0 {
+			topP = 0.7
+		}
+		topK := input.TopK
+		if topK == 0 {
+			topK = 80
+		}
+
+		temperature := input.Temperature
+		if temperature == 0 {
+			temperature = 0.9
+		}
+
+		tokens := input.Maxtokens
+		if tokens == 0 {
+			tokens = 512
+		}
+
+		predInput := input.Prompt
+		if chat {
+			mess := []string{}
+			// TODO: encode roles
+			for _, i := range input.Messages {
+				mess = append(mess, i.Content)
+			}
+
+			predInput = strings.Join(mess, "\n")
+		}
+
+		// A model can have a "file.bin.tmpl" file associated with a prompt template prefix
+		templatedInput, err := loader.TemplatePrefix(modelFile, struct {
+			Input string
+		}{Input: predInput})
+		if err == nil {
+			predInput = templatedInput
+			log.Debug().Msgf("Template found, input modified to: %s", predInput)
+		}
+
+		result := []Choice{}
+
+		n := input.N
+
+		if input.N == 0 {
+			n = 1
+		}
+
+		var predFunc func() (string, error)
+		switch {
+		case stableLMModel != nil:
+			predFunc = func() (string, error) {
+				// Generate the prediction using the language model
+				predictOptions := []gpt2.PredictOption{
+					gpt2.SetTemperature(temperature),
+					gpt2.SetTopP(topP),
+					gpt2.SetTopK(topK),
+					gpt2.SetTokens(tokens),
+					gpt2.SetThreads(threads),
+				}
+
+				if input.Batch != 0 {
+					predictOptions = append(predictOptions, gpt2.SetBatch(input.Batch))
+				}
+
+				if input.Seed != 0 {
+					predictOptions = append(predictOptions, gpt2.SetSeed(input.Seed))
+				}
+
+				return stableLMModel.Predict(
+					predInput,
+					predictOptions...,
+				)
+			}
+		case gpt2Model != nil:
+			predFunc = func() (string, error) {
+				// Generate the prediction using the language model
+				predictOptions := []gpt2.PredictOption{
+					gpt2.SetTemperature(temperature),
+					gpt2.SetTopP(topP),
+					gpt2.SetTopK(topK),
+					gpt2.SetTokens(tokens),
+					gpt2.SetThreads(threads),
+				}
+
+				if input.Batch != 0 {
+					predictOptions = append(predictOptions, gpt2.SetBatch(input.Batch))
+				}
+
+				if input.Seed != 0 {
+					predictOptions = append(predictOptions, gpt2.SetSeed(input.Seed))
+				}
+
+				return gpt2Model.Predict(
+					predInput,
+					predictOptions...,
+				)
+			}
+		case gptModel != nil:
+			predFunc = func() (string, error) {
+				// Generate the prediction using the language model
+				predictOptions := []gptj.PredictOption{
+					gptj.SetTemperature(temperature),
+					gptj.SetTopP(topP),
+					gptj.SetTopK(topK),
+					gptj.SetTokens(tokens),
+					gptj.SetThreads(threads),
+				}
+
+				if input.Batch != 0 {
+					predictOptions = append(predictOptions, gptj.SetBatch(input.Batch))
+				}
+
+				if input.Seed != 0 {
+					predictOptions = append(predictOptions, gptj.SetSeed(input.Seed))
+				}
+
+				return gptModel.Predict(
+					predInput,
+					predictOptions...,
+				)
+			}
+		case model != nil:
+			predFunc = func() (string, error) {
+				// Generate the prediction using the language model
+				predictOptions := []llama.PredictOption{
+					llama.SetTemperature(temperature),
+					llama.SetTopP(topP),
+					llama.SetTopK(topK),
+					llama.SetTokens(tokens),
+					llama.SetThreads(threads),
+				}
+
+				if debug {
+					predictOptions = append(predictOptions, llama.Debug)
+				}
+
+				if input.Stop != "" {
+					predictOptions = append(predictOptions, llama.SetStopWords(input.Stop))
+				}
+
+				if input.RepeatPenalty != 0 {
+					predictOptions = append(predictOptions, llama.SetPenalty(input.RepeatPenalty))
+				}
+
+				if input.Keep != 0 {
+					predictOptions = append(predictOptions, llama.SetNKeep(input.Keep))
+				}
+
+				if input.Batch != 0 {
+					predictOptions = append(predictOptions, llama.SetBatch(input.Batch))
+				}
+
+				if input.F16 {
+					predictOptions = append(predictOptions, llama.EnableF16KV)
+				}
+
+				if input.IgnoreEOS {
+					predictOptions = append(predictOptions, llama.IgnoreEOS)
+				}
+
+				if input.Seed != 0 {
+					predictOptions = append(predictOptions, llama.SetSeed(input.Seed))
+				}
+
+				return model.Predict(
+					predInput,
+					predictOptions...,
+				)
+			}
+		}
+
+		for i := 0; i < n; i++ {
+			prediction, err := predFunc()
+			if err != nil {
+				return err
+			}
+
+			if input.Echo {
+				prediction = predInput + prediction
+			}
+
+			if chat {
+				result = append(result, Choice{Message: &Message{Role: "assistant", Content: prediction}})
+			} else {
+				result = append(result, Choice{Text: prediction})
+			}
+		}
+
+		jsonResult, _ := json.Marshal(result)
+		log.Debug().Msgf("Response: %s", jsonResult)
+
+		// Return the prediction in the response body
+		return c.JSON(OpenAIResponse{
+			Model:   input.Model, // we have to return what the user sent here, due to OpenAI spec.
+			Choices: result,
+		})
+	}
+}
+
+func listModels(loader *model.ModelLoader) func(ctx *fiber.Ctx) error {
+	return func(c *fiber.Ctx) error {
+		models, err := loader.ListModels()
+		if err != nil {
+			return err
+		}
+
+		dataModels := []OpenAIModel{}
+		for _, m := range models {
+			dataModels = append(dataModels, OpenAIModel{ID: m, Object: "model"})
+		}
+		return c.JSON(struct {
+			Object string        `json:"object"`
+			Data   []OpenAIModel `json:"data"`
+		}{
+			Object: "list",
+			Data:   dataModels,
+		})
+	}
+}
+
+func App(loader *model.ModelLoader, threads, ctxSize int, f16 bool, debug, disableMessage bool) *fiber.App {
+	zerolog.SetGlobalLevel(zerolog.InfoLevel)
+	if debug {
+		zerolog.SetGlobalLevel(zerolog.DebugLevel)
+	}
+
+	// Return errors as JSON responses
+	app := fiber.New(fiber.Config{
+		DisableStartupMessage: disableMessage,
+		// Override default error handler
+		ErrorHandler: func(ctx *fiber.Ctx, err error) error {
+			// Status code defaults to 500
+			code := fiber.StatusInternalServerError
+
+			// Retrieve the custom status code if it's a *fiber.Error
+			var e *fiber.Error
+			if errors.As(err, &e) {
+				code = e.Code
+			}
+
+			// Send custom error page
+			return ctx.Status(code).JSON(
+				ErrorResponse{
+					Error: &APIError{Message: err.Error(), Code: code},
+				},
+			)
+		},
+	})
+
+	// Default middleware config
+	app.Use(recover.New())
+	app.Use(cors.New())
+
+	// This is still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
+	mu := map[string]*sync.Mutex{}
+	var mumutex = &sync.Mutex{}
+
+	// openAI compatible API endpoint
+	app.Post("/v1/chat/completions", openAIEndpoint(true, debug, loader, threads, ctxSize, f16, mumutex, mu))
+	app.Post("/chat/completions", openAIEndpoint(true, debug, loader, threads, ctxSize, f16, mumutex, mu))
+
+	app.Post("/v1/completions", openAIEndpoint(false, debug, loader, threads, ctxSize, f16, mumutex, mu))
+	app.Post("/completions", openAIEndpoint(false, debug, loader, threads, ctxSize, f16, mumutex, mu))
+
+	app.Get("/v1/models", listModels(loader))
+	app.Get("/models", listModels(loader))
+
+	return app
+}

api/api_test.go

@@ -0,0 +1,58 @@
+package api_test
+
+import (
+	"context"
+	"os"
+
+	. "github.com/go-skynet/LocalAI/api"
+	"github.com/go-skynet/LocalAI/pkg/model"
+	"github.com/gofiber/fiber/v2"
+	. "github.com/onsi/ginkgo/v2"
+	. "github.com/onsi/gomega"
+
+	"github.com/sashabaranov/go-openai"
+)
+
+var _ = Describe("API test", func() {
+
+	var app *fiber.App
+	var modelLoader *model.ModelLoader
+	var client *openai.Client
+	Context("API query", func() {
+		BeforeEach(func() {
+			modelLoader = model.NewModelLoader(os.Getenv("MODELS_PATH"))
+			app = App(modelLoader, 1, 512, false, false, true)
+			go app.Listen("127.0.0.1:9090")
+
+			defaultConfig := openai.DefaultConfig("")
+			defaultConfig.BaseURL = "http://127.0.0.1:9090/v1"
+
+			// Wait for API to be ready
+			client = openai.NewClientWithConfig(defaultConfig)
+			Eventually(func() error {
+				_, err := client.ListModels(context.TODO())
+				return err
+			}, "2m").ShouldNot(HaveOccurred())
+		})
+		AfterEach(func() {
+			app.Shutdown()
+		})
+		It("returns the models list", func() {
+			models, err := client.ListModels(context.TODO())
+			Expect(err).ToNot(HaveOccurred())
+			Expect(len(models.Models)).To(Equal(1))
+			Expect(models.Models[0].ID).To(Equal("testmodel"))
+		})
+		It("can generate completions", func() {
+			resp, err := client.CreateCompletion(context.TODO(), openai.CompletionRequest{Model: "testmodel", Prompt: "abcdedfghikl"})
+			Expect(err).ToNot(HaveOccurred())
+			Expect(len(resp.Choices)).To(Equal(1))
+			Expect(resp.Choices[0].Text).ToNot(BeEmpty())
+		})
+		It("returns errors", func() {
+			_, err := client.CreateCompletion(context.TODO(), openai.CompletionRequest{Model: "foomodel", Prompt: "abcdedfghikl"})
+			Expect(err).To(HaveOccurred())
+			Expect(err.Error()).To(ContainSubstring("error, status code: 500, message: llama: model does not exist"))
+		})
+	})
+})

api/apt_suite_test.go

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

charts/local-ai/Chart.yaml

@@ -0,0 +1,6 @@
+apiVersion: v2
+appVersion: 0.1.0
+description: A Helm chart for LocalAI
+name: local-ai
+type: application
+version: 1.0.0

charts/local-ai/templates/_helpers.tpl

@@ -0,0 +1,44 @@
+{{/*
+Expand the name of the chart.
+*/}}
+{{- define "local-ai.name" -}}
+{{- default .Chart.Name .Values.nameOverride | trunc 63 | trimSuffix "-" }}
+{{- end }}
+
+{{/*
+Create a default fully qualified app name.
+We truncate at 63 chars because some Kubernetes name fields are limited to this (by the DNS naming spec).
+If release name contains chart name it will be used as a full name.
+*/}}
+{{- define "local-ai.fullname" -}}
+{{- if .Values.fullnameOverride }}
+{{- .Values.fullnameOverride | trunc 63 | trimSuffix "-" }}
+{{- else }}
+{{- $name := default .Chart.Name .Values.nameOverride }}
+{{- if contains $name .Release.Name }}
+{{- .Release.Name | trunc 63 | trimSuffix "-" }}
+{{- else }}
+{{- printf "%s-%s" .Release.Name $name | trunc 63 | trimSuffix "-" }}
+{{- end }}
+{{- end }}
+{{- end }}
+
+{{/*
+Create chart name and version as used by the chart label.
+*/}}
+{{- define "local-ai.chart" -}}
+{{- printf "%s-%s" .Chart.Name .Chart.Version | replace "+" "_" | trunc 63 | trimSuffix "-" }}
+{{- end }}
+
+{{/*
+Common labels
+*/}}
+{{- define "local-ai.labels" -}}
+helm.sh/chart: {{ include "local-ai.chart" . }}
+app.kubernetes.io/name: {{ include "local-ai.name" . }}
+app.kubernetes.io/instance: "{{ .Release.Name }}"
+app.kubernetes.io/managed-by: {{ .Release.Service }}
+{{- if .Chart.AppVersion }}
+app.kubernetes.io/version: {{ .Chart.AppVersion | quote }}
+{{- end }}
+{{- end }}

charts/local-ai/templates/data-volume.yaml

@@ -0,0 +1,39 @@
+{{- if .Values.dataVolume.enabled }}
+apiVersion: cdi.kubevirt.io/v1beta1
+kind: DataVolume
+metadata:
+  name: {{ template "local-ai.fullname" . }}
+  namespace: {{ .Release.Namespace | quote }}
+  labels:
+    {{- include "local-ai.labels" . | nindent 4 }}
+spec:
+  contentType: archive
+  source:
+    {{ .Values.dataVolume.source.type }}:
+      url: {{ .Values.dataVolume.source.url }}
+      secretRef: {{ template "local-ai.fullname" . }}
+      {{- if and (eq .Values.dataVolume.source.type "http") .Values.dataVolume.source.secretExtraHeaders }}
+      secretExtraHeaders: {{ .Values.dataVolume.source.secretExtraHeaders }}
+      {{- end }}
+      {{- if .Values.dataVolume.source.caCertConfigMap }}
+      caCertConfigMap: {{ .Values.dataVolume.source.caCertConfigMap }}
+      {{- end }}
+  pvc:
+    accessModes: {{ .Values.dataVolume.pvc.accessModes }}
+    resources:
+      requests:
+        storage: {{ .Values.dataVolume.pvc.size }}
+---
+{{- if .Values.dataVolume.secret.enabled }}
+apiVersion: v1
+kind: Secret
+metadata:
+  name: {{ template "local-ai.fullname" . }}
+  namespace: {{ .Release.Namespace | quote }}
+  labels:
+    {{- include "local-ai.labels" . | nindent 4 }}
+data:
+  accessKeyId: {{ .Values.dataVolume.secret.username }}
+  secretKey: {{ .Values.dataVolume.secret.password }}
+{{- end }}
+{{- end }}

charts/local-ai/templates/deployment.yaml

@@ -0,0 +1,39 @@
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: {{ template "local-ai.fullname" . }}
+  namespace: {{ .Release.Namespace | quote }}
+  labels:
+    {{- include "local-ai.labels" . | nindent 4 }}
+spec:
+  selector:
+    matchLabels:
+      app.kubernetes.io/name: {{ include "local-ai.name" . }}
+      app.kubernetes.io/instance: {{ .Release.Name }}
+  replicas: 1
+  template:
+    metadata:
+      name: {{ template "local-ai.fullname" . }}
+      labels:
+        app.kubernetes.io/name: {{ include "local-ai.name" . }}
+        app.kubernetes.io/instance: {{ .Release.Name }}
+    spec:
+      containers:
+        - name: {{ template "local-ai.fullname" . }}
+          image: {{ .Values.deployment.image }}
+          env:
+          - name: THREADS
+            value: {{ .Values.deployment.env.threads | quote }}
+          - name: CONTEXT_SIZE
+            value: {{ .Values.deployment.env.contextSize | quote }}
+          - name: MODELS_PATH
+            value: {{ .Values.deployment.env.modelsPath }}
+{{- if .Values.deployment.volume.enabled }}
+          volumeMounts:
+          - mountPath: {{ .Values.deployment.env.modelsPath }}
+            name: models
+      volumes:
+      - name: models
+        persistentVolumeClaim:
+          claimName: {{ template "local-ai.fullname" . }}
+{{- end }}

charts/local-ai/templates/service.yaml

@@ -0,0 +1,19 @@
+apiVersion: v1
+kind: Service
+metadata:
+  name: {{ template "local-ai.fullname" . }}
+  namespace: {{ .Release.Namespace | quote }}
+  labels:
+    {{- include "local-ai.labels" . | nindent 4 }}
+{{- if .Values.service.annotations }}
+  annotations:
+  {{ toYaml .Values.service.annotations | indent 4 }}
+{{- end }}
+spec:
+  selector:
+    app.kubernetes.io/name: {{ include "local-ai.name" . }}
+  type: "{{ .Values.service.type }}"
+  ports:
+    - protocol: TCP
+      port: 8080
+      targetPort: 8080

charts/local-ai/values.yaml

@@ -0,0 +1,38 @@
+deployment:
+  image: quay.io/go-skynet/local-ai:latest
+  env:
+    threads: 14
+    contextSize: 512
+    modelsPath: "/models"
+  volume:
+    enabled: false
+
+service:
+  type: ClusterIP
+  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"
+
+# Optionally create a PVC containing a model binary, sourced from an arbitrary HTTP server or S3 bucket
+# (requires https://github.com/kubevirt/containerized-data-importer)
+dataVolume:
+  enabled: false
+  source:
+    type: "http" # Source type. One of: [ http | s3 ]
+    url: "http://<model_server>/<model_archive>" # e.g. koala-7B-4bit-128g.GGML.tar
+
+    # CertConfigMap is an optional ConfigMap reference, containing a Certificate Authority (CA) public key
+    # and a base64 encoded pem certificate
+    caCertConfigMap: ""
+
+    # SecretExtraHeaders is an optional list of Secret references, each containing an extra HTTP header
+    # that may include sensitive information. Only applicable for the http source type.
+    secretExtraHeaders: []
+  pvc:
+    accessModes:
+    - ReadWriteOnce
+    size: 5Gi
+  secret:
+    enabled: false
+    username: "" # base64 encoded
+    password: "" # base64 encoded

client/client.go

@@ -1,75 +0,0 @@
-package client
-
-import (
-	"bytes"
-	"encoding/json"
-	"fmt"
-	"net/http"
-)
-
-type Prediction struct {
-	Prediction string `json:"prediction"`
-}
-
-type Client struct {
-	baseURL  string
-	client   *http.Client
-	endpoint string
-}
-
-func NewClient(baseURL string) *Client {
-	return &Client{
-		baseURL:  baseURL,
-		client:   &http.Client{},
-		endpoint: "/predict",
-	}
-}
-
-type InputData struct {
-	Text        string  `json:"text"`
-	TopP        float64 `json:"topP,omitempty"`
-	TopK        int     `json:"topK,omitempty"`
-	Temperature float64 `json:"temperature,omitempty"`
-	Tokens      int     `json:"tokens,omitempty"`
-}
-
-func (c *Client) Predict(text string, opts ...InputOption) (string, error) {
-	input := NewInputData(opts...)
-	input.Text = text
-
-	// encode input data to JSON format
-	inputBytes, err := json.Marshal(input)
-	if err != nil {
-		return "", err
-	}
-
-	// create HTTP request
-	url := c.baseURL + c.endpoint
-	req, err := http.NewRequest("POST", url, bytes.NewBuffer(inputBytes))
-	if err != nil {
-		return "", err
-	}
-
-	// set request headers
-	req.Header.Set("Content-Type", "application/json")
-
-	// send request and get response
-	resp, err := c.client.Do(req)
-	if err != nil {
-		return "", err
-	}
-	defer resp.Body.Close()
-
-	if resp.StatusCode != http.StatusOK {
-		return "", fmt.Errorf("request failed with status %d", resp.StatusCode)
-	}
-
-	// decode response body to Prediction struct
-	var prediction Prediction
-	err = json.NewDecoder(resp.Body).Decode(&prediction)
-	if err != nil {
-		return "", err
-	}
-
-	return prediction.Prediction, nil
-}

client/options.go

@@ -1,51 +0,0 @@
-package client
-
-import "net/http"
-
-type ClientOption func(c *Client)
-
-func WithHTTPClient(httpClient *http.Client) ClientOption {
-	return func(c *Client) {
-		c.client = httpClient
-	}
-}
-
-func WithEndpoint(endpoint string) ClientOption {
-	return func(c *Client) {
-		c.endpoint = endpoint
-	}
-}
-
-type InputOption func(d *InputData)
-
-func NewInputData(opts ...InputOption) *InputData {
-	data := &InputData{}
-	for _, opt := range opts {
-		opt(data)
-	}
-	return data
-}
-
-func WithTopP(topP float64) InputOption {
-	return func(d *InputData) {
-		d.TopP = topP
-	}
-}
-
-func WithTopK(topK int) InputOption {
-	return func(d *InputData) {
-		d.TopK = topK
-	}
-}
-
-func WithTemperature(temperature float64) InputOption {
-	return func(d *InputData) {
-		d.Temperature = temperature
-	}
-}
-
-func WithTokens(tokens int) InputOption {
-	return func(d *InputData) {
-		d.Tokens = tokens
-	}
-}

docker-compose.yaml

@@ -0,0 +1,15 @@
+version: '3.6'
+
+services:
+  api:
+    image: quay.io/go-skynet/local-ai:latest
+    build:
+      context: .
+      dockerfile: Dockerfile
+    ports:
+      - 8080:8080
+    env_file:
+      - .env
+    volumes:
+      - ./models:/models:cached
+    command: ["/usr/bin/local-ai" ]

go.mod

@@ -1,34 +1,40 @@
-module github.com/go-skynet/llama-cli
+module github.com/go-skynet/LocalAI
 
 go 1.19
 
 require (
-	github.com/charmbracelet/bubbles v0.15.0
-	github.com/charmbracelet/bubbletea v0.23.2
-	github.com/charmbracelet/lipgloss v0.7.1
-	github.com/go-skynet/llama v0.0.0-20230321172246-7be5326e18cc
+	github.com/go-skynet/go-gpt2.cpp v0.0.0-20230422085954-245a5bfe6708
+	github.com/go-skynet/go-gpt4all-j.cpp v0.0.0-20230422090028-1f7bff57f66c
+	github.com/go-skynet/go-llama.cpp v0.0.0-20230424120713-e45cebe33c04
 	github.com/gofiber/fiber/v2 v2.42.0
-	github.com/urfave/cli/v2 v2.25.0
+	github.com/jaypipes/ghw v0.10.0
+	github.com/onsi/ginkgo/v2 v2.9.2
+	github.com/onsi/gomega v1.27.6
+	github.com/rs/zerolog v1.29.1
+	github.com/sashabaranov/go-openai v1.9.0
+	github.com/urfave/cli/v2 v2.25.1
 )
 
 require (
+	github.com/StackExchange/wmi v1.2.1 // indirect
 	github.com/andybalholm/brotli v1.0.4 // indirect
-	github.com/atotto/clipboard v0.1.4 // indirect
-	github.com/aymanbagabas/go-osc52/v2 v2.0.1 // indirect
-	github.com/containerd/console v1.0.3 // indirect
 	github.com/cpuguy83/go-md2man/v2 v2.0.2 // indirect
+	github.com/ghodss/yaml v1.0.0 // indirect
+	github.com/go-logr/logr v1.2.3 // indirect
+	github.com/go-ole/go-ole v1.2.6 // indirect
+	github.com/go-task/slim-sprig v0.0.0-20230315185526-52ccab3ef572 // indirect
+	github.com/google/go-cmp v0.5.9 // indirect
+	github.com/google/pprof v0.0.0-20210407192527-94a9f03dee38 // indirect
 	github.com/google/uuid v1.3.0 // indirect
+	github.com/jaypipes/pcidb v1.0.0 // indirect
 	github.com/klauspost/compress v1.15.9 // indirect
-	github.com/lucasb-eyer/go-colorful v1.2.0 // indirect
+	github.com/kr/text v0.2.0 // indirect
 	github.com/mattn/go-colorable v0.1.13 // indirect
 	github.com/mattn/go-isatty v0.0.17 // indirect
-	github.com/mattn/go-localereader v0.0.1 // indirect
 	github.com/mattn/go-runewidth v0.0.14 // indirect
-	github.com/muesli/ansi v0.0.0-20211018074035-2e021307bc4b // indirect
-	github.com/muesli/cancelreader v0.2.2 // indirect
-	github.com/muesli/reflow v0.3.0 // indirect
-	github.com/muesli/termenv v0.15.1 // indirect
+	github.com/mitchellh/go-homedir v1.1.0 // indirect
 	github.com/philhofer/fwd v1.1.1 // indirect
+	github.com/pkg/errors v0.9.1 // indirect
 	github.com/rivo/uniseg v0.2.0 // indirect
 	github.com/russross/blackfriday/v2 v2.1.0 // indirect
 	github.com/savsgio/dictpool v0.0.0-20221023140959-7bf2e61cea94 // indirect
@@ -38,8 +44,11 @@ require (
 	github.com/valyala/fasthttp v1.44.0 // indirect
 	github.com/valyala/tcplisten v1.0.0 // indirect
 	github.com/xrash/smetrics v0.0.0-20201216005158-039620a65673 // indirect
-	golang.org/x/sync v0.1.0 // indirect
+	golang.org/x/net v0.8.0 // indirect
 	golang.org/x/sys v0.6.0 // indirect
-	golang.org/x/term v0.0.0-20210927222741-03fcf44c2211 // indirect
-	golang.org/x/text v0.3.7 // indirect
+	golang.org/x/text v0.8.0 // indirect
+	golang.org/x/tools v0.7.0 // indirect
+	gopkg.in/yaml.v2 v2.4.0 // indirect
+	gopkg.in/yaml.v3 v3.0.1 // indirect
+	howett.net/plist v1.0.0 // indirect
 )

go.sum

@@ -1,76 +1,102 @@
+github.com/StackExchange/wmi v1.2.1 h1:VIkavFPXSjcnS+O8yTq7NI32k0R5Aj+v39y29VYDOSA=
+github.com/StackExchange/wmi v1.2.1/go.mod h1:rcmrprowKIVzvc+NUiLncP2uuArMWLCbu9SBzvHz7e8=
 github.com/andybalholm/brotli v1.0.4 h1:V7DdXeJtZscaqfNuAdSRuRFzuiKlHSC/Zh3zl9qY3JY=
 github.com/andybalholm/brotli v1.0.4/go.mod h1:fO7iG3H7G2nSZ7m0zPUDn85XEX2GTukHGRSepvi9Eig=
-github.com/atotto/clipboard v0.1.4 h1:EH0zSVneZPSuFR11BlR9YppQTVDbh5+16AmcJi4g1z4=
-github.com/atotto/clipboard v0.1.4/go.mod h1:ZY9tmq7sm5xIbd9bOK4onWV4S6X0u6GY7Vn0Yu86PYI=
-github.com/aymanbagabas/go-osc52 v1.0.3/go.mod h1:zT8H+Rk4VSabYN90pWyugflM3ZhpTZNC7cASDfUCdT4=
-github.com/aymanbagabas/go-osc52 v1.2.1/go.mod h1:zT8H+Rk4VSabYN90pWyugflM3ZhpTZNC7cASDfUCdT4=
-github.com/aymanbagabas/go-osc52/v2 v2.0.1 h1:HwpRHbFMcZLEVr42D4p7XBqjyuxQH5SMiErDT4WkJ2k=
-github.com/aymanbagabas/go-osc52/v2 v2.0.1/go.mod h1:uYgXzlJ7ZpABp8OJ+exZzJJhRNQ2ASbcXHWsFqH8hp8=
-github.com/charmbracelet/bubbles v0.15.0 h1:c5vZ3woHV5W2b8YZI1q7v4ZNQaPetfHuoHzx+56Z6TI=
-github.com/charmbracelet/bubbles v0.15.0/go.mod h1:Y7gSFbBzlMpUDR/XM9MhZI374Q+1p1kluf1uLl8iK74=
-github.com/charmbracelet/bubbletea v0.23.1/go.mod h1:JAfGK/3/pPKHTnAS8JIE2u9f61BjWTQY57RbT25aMXU=
-github.com/charmbracelet/bubbletea v0.23.2 h1:vuUJ9HJ7b/COy4I30e8xDVQ+VRDUEFykIjryPfgsdps=
-github.com/charmbracelet/bubbletea v0.23.2/go.mod h1:FaP3WUivcTM0xOKNmhciz60M6I+weYLF76mr1JyI7sM=
-github.com/charmbracelet/harmonica v0.2.0/go.mod h1:KSri/1RMQOZLbw7AHqgcBycp8pgJnQMYYT8QZRqZ1Ao=
-github.com/charmbracelet/lipgloss v0.6.0/go.mod h1:tHh2wr34xcHjC2HCXIlGSG1jaDF0S0atAUvBMP6Ppuk=
-github.com/charmbracelet/lipgloss v0.7.1 h1:17WMwi7N1b1rVWOjMT+rCh7sQkvDU75B2hbZpc5Kc1E=
-github.com/charmbracelet/lipgloss v0.7.1/go.mod h1:yG0k3giv8Qj8edTCbbg6AlQ5e8KNWpFujkNawKNhE2c=
-github.com/containerd/console v1.0.3 h1:lIr7SlA5PxZyMV30bDW0MGbiOPXwc63yRuCP0ARubLw=
-github.com/containerd/console v1.0.3/go.mod h1:7LqA/THxQ86k76b8c/EMSiaJ3h1eZkMkXar0TQ1gf3U=
+github.com/chzyer/logex v1.1.10/go.mod h1:+Ywpsq7O8HXn0nuIou7OrIPyXbp3wmkHB+jjWRnGsAI=
+github.com/chzyer/readline v0.0.0-20180603132655-2972be24d48e/go.mod h1:nSuG5e5PlCu98SY8svDHJxuZscDgtXS6KTTbou5AhLI=
+github.com/chzyer/test v0.0.0-20180213035817-a1ea475d72b1/go.mod h1:Q3SI9o4m/ZMnBNeIyt5eFwwo7qiLfzFZmjNmxjkiQlU=
+github.com/coreos/go-systemd/v22 v22.5.0/go.mod h1:Y58oyj3AT4RCenI/lSvhwexgC+NSVTIJ3seZv2GcEnc=
 github.com/cpuguy83/go-md2man/v2 v2.0.2 h1:p1EgwI/C7NhT0JmVkwCD2ZBK8j4aeHQX2pMHHBfMQ6w=
 github.com/cpuguy83/go-md2man/v2 v2.0.2/go.mod h1:tgQtvFlXSQOSOSIRvRPT7W67SCa46tRHOmNcaadrF8o=
-github.com/go-skynet/llama v0.0.0-20230321172246-7be5326e18cc h1:NcmO8mA7iRZIX0Qy2SjcsSaV14+g87MiTey1neUJaFQ=
-github.com/go-skynet/llama v0.0.0-20230321172246-7be5326e18cc/go.mod h1:ZtYsAIud4cvP9VTTI9uhdgR1uCwaO/gGKnZZ95h9i7w=
+github.com/creack/pty v1.1.9/go.mod h1:oKZEueFk5CKHvIhNR5MUki03XCEU+Q6VDXinZuGJ33E=
+github.com/davecgh/go-spew v1.1.0/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
+github.com/davecgh/go-spew v1.1.1 h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c=
+github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38=
+github.com/ghodss/yaml v1.0.0 h1:wQHKEahhL6wmXdzwWG11gIVCkOv05bNOh+Rxn0yngAk=
+github.com/ghodss/yaml v1.0.0/go.mod h1:4dBDuWmgqj2HViK6kFavaiC9ZROes6MMH2rRYeMEF04=
+github.com/go-logr/logr v1.2.3 h1:2DntVwHkVopvECVRSlL5PSo9eG+cAkDCuckLubN+rq0=
+github.com/go-logr/logr v1.2.3/go.mod h1:jdQByPbusPIv2/zmleS9BjJVeZ6kBagPoEUsqbVz/1A=
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 github.com/valyala/bytebufferpool v1.0.0/go.mod h1:6bBcMArwyJ5K/AmCkWv1jt77kVWyCJ6HpOuEn7z0Csc=
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@@ -90,34 +116,48 @@ golang.org/x/net v0.0.0-20190620200207-3b0461eec859/go.mod h1:z5CRVTTTmAJ677TzLL
 golang.org/x/net v0.0.0-20201021035429-f5854403a974/go.mod h1:sp8m0HH+o8qH0wwXwYZr8TS3Oi6o0r6Gce1SSxlDquU=
 golang.org/x/net v0.0.0-20211112202133-69e39bad7dc2/go.mod h1:9nx3DQGgdP8bBQD5qxJ1jj9UTztislL4KSBs9R2vV5Y=
 golang.org/x/net v0.0.0-20220906165146-f3363e06e74c/go.mod h1:YDH+HFinaLZZlnHAfSS6ZXJJ9M9t4Dl22yv3iI2vPwk=
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 golang.org/x/sync v0.0.0-20201020160332-67f06af15bc9/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
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interactive.go

@@ -1,142 +0,0 @@
-package main
-
-// A simple program demonstrating the text area component from the Bubbles
-// component library.
-
-import (
-	"fmt"
-	"strings"
-
-	"github.com/charmbracelet/bubbles/textarea"
-	"github.com/charmbracelet/bubbles/viewport"
-	tea "github.com/charmbracelet/bubbletea"
-	"github.com/charmbracelet/lipgloss"
-	llama "github.com/go-skynet/llama/go"
-)
-
-func startInteractive(l *llama.LLama, opts ...llama.PredictOption) error {
-	p := tea.NewProgram(initialModel(l, opts...))
-
-	_, err := p.Run()
-	return err
-}
-
-type (
-	errMsg error
-)
-
-type model struct {
-	viewport    viewport.Model
-	messages    *[]string
-	textarea    textarea.Model
-	senderStyle lipgloss.Style
-	err         error
-	l           *llama.LLama
-	opts        []llama.PredictOption
-
-	predictC chan string
-}
-
-func initialModel(l *llama.LLama, opts ...llama.PredictOption) model {
-	ta := textarea.New()
-	ta.Placeholder = "Send a message..."
-	ta.Focus()
-
-	ta.Prompt = "┃ "
-	ta.CharLimit = 280
-
-	ta.SetWidth(200)
-	ta.SetHeight(3)
-
-	// Remove cursor line styling
-	ta.FocusedStyle.CursorLine = lipgloss.NewStyle()
-
-	ta.ShowLineNumbers = false
-
-	vp := viewport.New(200, 5)
-	vp.SetContent(`Welcome to llama-cli. Type a message and press Enter to send. Alpaca doesn't keep context of the whole chat (yet).`)
-
-	ta.KeyMap.InsertNewline.SetEnabled(false)
-
-	predictChannel := make(chan string)
-	messages := []string{}
-	m := model{
-		textarea:    ta,
-		messages:    &messages,
-		viewport:    vp,
-		senderStyle: lipgloss.NewStyle().Foreground(lipgloss.Color("5")),
-		err:         nil,
-		l:           l,
-		opts:        opts,
-		predictC:    predictChannel,
-	}
-	go func() {
-		for p := range predictChannel {
-			str, _ := templateString(emptyInput, struct {
-				Instruction string
-				Input       string
-			}{Instruction: p})
-			res, _ := l.Predict(
-				str,
-				opts...,
-			)
-
-			mm := *m.messages
-			*m.messages = mm[:len(mm)-1]
-			*m.messages = append(*m.messages, m.senderStyle.Render("llama: ")+res)
-			m.viewport.SetContent(strings.Join(*m.messages, "\n"))
-			ta.Reset()
-			m.viewport.GotoBottom()
-		}
-	}()
-
-	return m
-}
-
-func (m model) Init() tea.Cmd {
-	return textarea.Blink
-}
-
-func (m model) Update(msg tea.Msg) (tea.Model, tea.Cmd) {
-	var (
-		tiCmd tea.Cmd
-		vpCmd tea.Cmd
-	)
-
-	m.textarea, tiCmd = m.textarea.Update(msg)
-	m.viewport, vpCmd = m.viewport.Update(msg)
-
-	switch msg := msg.(type) {
-	case tea.WindowSizeMsg:
-
-	//	m.viewport.Width = msg.Width
-	//	m.viewport.Height = msg.Height
-	case tea.KeyMsg:
-		switch msg.Type {
-		case tea.KeyCtrlC, tea.KeyEsc:
-			fmt.Println(m.textarea.Value())
-			return m, tea.Quit
-		case tea.KeyEnter:
-			*m.messages = append(*m.messages, m.senderStyle.Render("You: ")+m.textarea.Value(), m.senderStyle.Render("Loading response..."))
-			m.predictC <- m.textarea.Value()
-			m.viewport.SetContent(strings.Join(*m.messages, "\n"))
-			m.textarea.Reset()
-			m.viewport.GotoBottom()
-		}
-
-	// We handle errors just like any other message
-	case errMsg:
-		m.err = msg
-		return m, nil
-	}
-
-	return m, tea.Batch(tiCmd, vpCmd)
-}
-
-func (m model) View() string {
-	return fmt.Sprintf(
-		"%s\n\n%s",
-		m.viewport.View(),
-		m.textarea.View(),
-	) + "\n\n"
-}

kubernetes/deployment.yaml

@@ -1,42 +0,0 @@
-apiVersion: v1
-kind: Namespace
-metadata:
-  name: llama
----
-apiVersion: apps/v1
-kind: Deployment
-metadata:
-  name: llama
-  namespace: llama
-  labels:
-    app: llama
-spec:
-  selector:
-    matchLabels:
-      app: llama
-  replicas: 1
-  template:
-    metadata:
-      labels:
-        app: llama
-      name: llama
-    spec:
-      containers:
-        - name: llama
-          args:
-          - api
-          image: quay.io/go-skynet/llama-cli:v0.1
----
-apiVersion: v1
-kind: Service
-metadata:
-  name: llama
-  namespace: llama
-spec:
-  selector:
-    app: llama
-  type: LoadBalancer
-  ports:
-    - protocol: TCP
-      port: 8080
-      targetPort: 8080

main.go

@@ -1,267 +1,92 @@
 package main
 
 import (
-	"bytes"
-	"fmt"
-	"io/ioutil"
 	"os"
-	"runtime"
-	"text/template"
 
-	llama "github.com/go-skynet/llama/go"
+	api "github.com/go-skynet/LocalAI/api"
+	model "github.com/go-skynet/LocalAI/pkg/model"
+	"github.com/jaypipes/ghw"
+	"github.com/rs/zerolog"
+	"github.com/rs/zerolog/log"
 	"github.com/urfave/cli/v2"
 )
 
-// Define the template string
-var emptyInput string = `Below is an instruction that describes a task. Write a response that appropriately completes the request.
-
-### Instruction:
-{{.Instruction}}
-
-### Response:`
-
-var nonEmptyInput string = `Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
-
-### Instruction:
-{{.Instruction}}
-
-### Input:
-{{.Input}}
-
-### Response:
-`
-
-func llamaFromOptions(ctx *cli.Context) (*llama.LLama, error) {
-	opts := []llama.ModelOption{llama.SetContext(ctx.Int("context-size"))}
-	if ctx.Bool("alpaca") {
-		opts = append(opts, llama.EnableAlpaca)
-	}
-
-	return llama.New(ctx.String("model"), opts...)
-}
-
-func templateString(t string, in interface{}) (string, error) {
-	// Parse the template
-	tmpl, err := template.New("prompt").Parse(t)
-	if err != nil {
-		return "", err
-	}
-
-	var buf bytes.Buffer
-	err = tmpl.Execute(&buf, in)
-	if err != nil {
-		return "", err
-	}
-	return buf.String(), nil
-}
-
-var modelFlags = []cli.Flag{
-	&cli.StringFlag{
-		Name:    "model",
-		EnvVars: []string{"MODEL_PATH"},
-	},
-	&cli.IntFlag{
-		Name:    "tokens",
-		EnvVars: []string{"TOKENS"},
-		Value:   128,
-	},
-	&cli.IntFlag{
-		Name:    "context-size",
-		EnvVars: []string{"CONTEXT_SIZE"},
-		Value:   512,
-	},
-	&cli.IntFlag{
-		Name:    "threads",
-		EnvVars: []string{"THREADS"},
-		Value:   runtime.NumCPU(),
-	},
-	&cli.Float64Flag{
-		Name:    "temperature",
-		EnvVars: []string{"TEMPERATURE"},
-		Value:   0.95,
-	},
-	&cli.Float64Flag{
-		Name:    "topp",
-		EnvVars: []string{"TOP_P"},
-		Value:   0.85,
-	},
-	&cli.IntFlag{
-		Name:    "topk",
-		EnvVars: []string{"TOP_K"},
-		Value:   20,
-	},
-	&cli.BoolFlag{
-		Name:    "alpaca",
-		EnvVars: []string{"ALPACA"},
-		Value:   true,
-	},
-}
-
 func main() {
+	log.Logger = log.Output(zerolog.ConsoleWriter{Out: os.Stderr})
+
+	path, err := os.Getwd()
+	if err != nil {
+		log.Error().Msgf("error: %s", err.Error())
+		os.Exit(1)
+	}
+
+	threads := 4
+	cpu, err := ghw.CPU()
+	if err == nil {
+		threads = int(cpu.TotalCores)
+	}
+
 	app := &cli.App{
-		Name:    "llama-cli",
-		Version: "0.1",
-		Usage:   "llama-cli --model ... --instruction 'What is an alpaca?'",
-		Flags: append(modelFlags,
-			&cli.StringFlag{
-				Name:    "template",
-				EnvVars: []string{"TEMPLATE"},
+		Name:  "LocalAI",
+		Usage: "OpenAI compatible API for running LLaMA/GPT models locally on CPU with consumer grade hardware.",
+		Flags: []cli.Flag{
+			&cli.BoolFlag{
+				Name:    "f16",
+				EnvVars: []string{"F16"},
+			},
+			&cli.BoolFlag{
+				Name:    "debug",
+				EnvVars: []string{"DEBUG"},
+			},
+			&cli.IntFlag{
+				Name:        "threads",
+				DefaultText: "Number of threads used for parallel computation. Usage of the number of physical cores in the system is suggested.",
+				EnvVars:     []string{"THREADS"},
+				Value:       threads,
 			},
 			&cli.StringFlag{
-				Name:    "instruction",
-				EnvVars: []string{"INSTRUCTION"},
+				Name:        "models-path",
+				DefaultText: "Path containing models used for inferencing",
+				EnvVars:     []string{"MODELS_PATH"},
+				Value:       path,
 			},
 			&cli.StringFlag{
-				Name:    "input",
-				EnvVars: []string{"INPUT"},
-			}),
-		Description: `Run llama.cpp inference`,
-		UsageText: `
-llama-cli --model ~/ggml-alpaca-7b-q4.bin --instruction "What's an alpaca?"
-
-	An Alpaca (Vicugna pacos) is a domesticated species of South American camelid, related to llamas and originally from Peru but now found throughout much of Andean region. They are bred for their fleeces which can be spun into wool or knitted items such as hats, sweaters, blankets etc
-		
-echo "An Alpaca (Vicugna pacos) is a domesticated species of South American camelid, related to llamas and originally from Peru but now found throughout much of Andean region. They are bred for their fleeces which can be spun into wool or knitted items such as hats, sweaters, blankets etc" | llama-cli --model ~/ggml-alpaca-7b-q4.bin --instruction "Proofread, improving clarity and flow" --input "-"
-
-	An Alpaca (Vicugna pacos) is a domesticated species from South America that's related to llamas. Originating in Peru but now found throughout the Andean region, they are bred for their fleeces which can be spun into wool or knitted items such as hats and sweaters—blankets too!
-`,
-		Copyright: "go-skynet authors",
-		Commands: []*cli.Command{
-			{
-				Flags: modelFlags,
-				Name:  "interactive",
-				Action: func(ctx *cli.Context) error {
-
-					l, err := llamaFromOptions(ctx)
-					if err != nil {
-						fmt.Println("Loading the model failed:", err.Error())
-						os.Exit(1)
-					}
-
-					return startInteractive(l, llama.SetTemperature(ctx.Float64("temperature")),
-						llama.SetTopP(ctx.Float64("topp")),
-						llama.SetTopK(ctx.Int("topk")),
-						llama.SetTokens(ctx.Int("tokens")),
-						llama.SetThreads(ctx.Int("threads")))
-				},
+				Name:        "address",
+				DefaultText: "Bind address for the API server.",
+				EnvVars:     []string{"ADDRESS"},
+				Value:       ":8080",
 			},
-			{
-
-				Name: "api",
-				Flags: []cli.Flag{
-					&cli.IntFlag{
-						Name:    "threads",
-						EnvVars: []string{"THREADS"},
-						Value:   runtime.NumCPU(),
-					},
-					&cli.StringFlag{
-						Name:    "model",
-						EnvVars: []string{"MODEL_PATH"},
-					},
-					&cli.StringFlag{
-						Name:    "address",
-						EnvVars: []string{"ADDRESS"},
-						Value:   ":8080",
-					},
-					&cli.BoolFlag{
-						Name:    "alpaca",
-						EnvVars: []string{"ALPACA"},
-						Value:   true,
-					},
-					&cli.IntFlag{
-						Name:    "context-size",
-						EnvVars: []string{"CONTEXT_SIZE"},
-						Value:   512,
-					},
-				},
-				Action: func(ctx *cli.Context) error {
-					l, err := llamaFromOptions(ctx)
-					if err != nil {
-						fmt.Println("Loading the model failed:", err.Error())
-						os.Exit(1)
-					}
-
-					return api(l, ctx.String("address"), ctx.Int("threads"))
-				},
+			&cli.IntFlag{
+				Name:        "context-size",
+				DefaultText: "Default context size of the model",
+				EnvVars:     []string{"CONTEXT_SIZE"},
+				Value:       512,
 			},
 		},
+		Description: `
+LocalAI is a drop-in replacement OpenAI API which runs inference locally.
+
+Some of the models compatible are:
+- Vicuna
+- Koala
+- GPT4ALL
+- GPT4ALL-J
+- Cerebras
+- Alpaca
+- StableLM (ggml quantized)
+
+It uses llama.cpp, ggml and gpt4all as backend with golang c bindings.
+`,
+		UsageText: `local-ai [options]`,
+		Copyright: "go-skynet authors",
 		Action: func(ctx *cli.Context) error {
-
-			instruction := ctx.String("instruction")
-			input := ctx.String("input")
-			templ := ctx.String("template")
-
-			promptTemplate := ""
-
-			if input != "" {
-				promptTemplate = nonEmptyInput
-			} else {
-				promptTemplate = emptyInput
-			}
-
-			if templ != "" {
-				dat, err := os.ReadFile(templ)
-				if err != nil {
-					fmt.Printf("Failed reading file: %s", err.Error())
-					os.Exit(1)
-				}
-				promptTemplate = string(dat)
-			}
-
-			if instruction == "-" {
-				dat, err := ioutil.ReadAll(os.Stdin)
-				if err != nil {
-					fmt.Printf("reading stdin failed: %s", err)
-					os.Exit(1)
-				}
-				instruction = string(dat)
-			}
-
-			if input == "-" {
-				dat, err := ioutil.ReadAll(os.Stdin)
-				if err != nil {
-					fmt.Printf("reading stdin failed: %s", err)
-					os.Exit(1)
-				}
-				input = string(dat)
-			}
-
-			str, err := templateString(promptTemplate, struct {
-				Instruction string
-				Input       string
-			}{Instruction: instruction, Input: input})
-
-			if err != nil {
-				fmt.Println("Templating the input failed:", err.Error())
-				os.Exit(1)
-			}
-
-			l, err := llamaFromOptions(ctx)
-			if err != nil {
-				fmt.Println("Loading the model failed:", err.Error())
-				os.Exit(1)
-			}
-
-			res, err := l.Predict(
-				str,
-				llama.SetTemperature(ctx.Float64("temperature")),
-				llama.SetTopP(ctx.Float64("topp")),
-				llama.SetTopK(ctx.Int("topk")),
-				llama.SetTokens(ctx.Int("tokens")),
-				llama.SetThreads(ctx.Int("threads")),
-			)
-			if err != nil {
-				fmt.Printf("predicting failed: %s", err)
-				os.Exit(1)
-			}
-			fmt.Println(res)
-			return nil
+			return api.App(model.NewModelLoader(ctx.String("models-path")), ctx.Int("threads"), ctx.Int("context-size"), ctx.Bool("f16"), ctx.Bool("debug"), false).Listen(ctx.String("address"))
 		},
 	}
 
-	err := app.Run(os.Args)
+	err = app.Run(os.Args)
 	if err != nil {
-		fmt.Println(err)
+		log.Error().Msgf("error: %s", err.Error())
 		os.Exit(1)
 	}
 }

pkg/model/loader.go

@@ -0,0 +1,274 @@
+package model
+
+import (
+	"bytes"
+	"fmt"
+	"io/ioutil"
+	"os"
+	"path/filepath"
+	"strings"
+	"sync"
+	"text/template"
+
+	"github.com/rs/zerolog/log"
+
+	gpt2 "github.com/go-skynet/go-gpt2.cpp"
+	gptj "github.com/go-skynet/go-gpt4all-j.cpp"
+	llama "github.com/go-skynet/go-llama.cpp"
+)
+
+type ModelLoader struct {
+	modelPath string
+	mu        sync.Mutex
+
+	models            map[string]*llama.LLama
+	gptmodels         map[string]*gptj.GPTJ
+	gpt2models        map[string]*gpt2.GPT2
+	gptstablelmmodels map[string]*gpt2.StableLM
+
+	promptsTemplates map[string]*template.Template
+}
+
+func NewModelLoader(modelPath string) *ModelLoader {
+	return &ModelLoader{
+		modelPath:         modelPath,
+		gpt2models:        make(map[string]*gpt2.GPT2),
+		gptmodels:         make(map[string]*gptj.GPTJ),
+		gptstablelmmodels: make(map[string]*gpt2.StableLM),
+		models:            make(map[string]*llama.LLama),
+		promptsTemplates:  make(map[string]*template.Template),
+	}
+}
+
+func (ml *ModelLoader) ExistsInModelPath(s string) bool {
+	_, err := os.Stat(filepath.Join(ml.modelPath, s))
+	return err == nil
+}
+
+func (ml *ModelLoader) ListModels() ([]string, error) {
+	files, err := ioutil.ReadDir(ml.modelPath)
+	if err != nil {
+		return []string{}, err
+	}
+
+	models := []string{}
+	for _, file := range files {
+		// Skip templates, YAML and .keep files
+		if strings.HasSuffix(file.Name(), ".tmpl") || strings.HasSuffix(file.Name(), ".keep") || strings.HasSuffix(file.Name(), ".yaml") || strings.HasSuffix(file.Name(), ".yml") {
+			continue
+		}
+
+		models = append(models, file.Name())
+	}
+
+	return models, nil
+}
+
+func (ml *ModelLoader) TemplatePrefix(modelName string, in interface{}) (string, error) {
+	ml.mu.Lock()
+	defer ml.mu.Unlock()
+
+	m, ok := ml.promptsTemplates[modelName]
+	if !ok {
+		return "", fmt.Errorf("no prompt template available")
+	}
+
+	var buf bytes.Buffer
+
+	if err := m.Execute(&buf, in); err != nil {
+		return "", err
+	}
+	return buf.String(), nil
+}
+
+func (ml *ModelLoader) loadTemplateIfExists(modelName, modelFile string) error {
+	// Check if the template was already loaded
+	if _, ok := ml.promptsTemplates[modelName]; ok {
+		return nil
+	}
+
+	// Check if the model path exists
+	// skip any error here - we run anyway if a template is not exist
+	modelTemplateFile := fmt.Sprintf("%s.tmpl", modelName)
+
+	if !ml.ExistsInModelPath(modelTemplateFile) {
+		return nil
+	}
+
+	dat, err := os.ReadFile(filepath.Join(ml.modelPath, modelTemplateFile))
+	if err != nil {
+		return err
+	}
+
+	// Parse the template
+	tmpl, err := template.New("prompt").Parse(string(dat))
+	if err != nil {
+		return err
+	}
+	ml.promptsTemplates[modelName] = tmpl
+
+	return nil
+}
+
+func (ml *ModelLoader) LoadStableLMModel(modelName string) (*gpt2.StableLM, error) {
+	ml.mu.Lock()
+	defer ml.mu.Unlock()
+
+	// Check if we already have a loaded model
+	if !ml.ExistsInModelPath(modelName) {
+		return nil, fmt.Errorf("model does not exist")
+	}
+
+	if m, ok := ml.gptstablelmmodels[modelName]; ok {
+		log.Debug().Msgf("Model already loaded in memory: %s", modelName)
+		return m, nil
+	}
+
+	// Load the model and keep it in memory for later use
+	modelFile := filepath.Join(ml.modelPath, modelName)
+	log.Debug().Msgf("Loading model in memory from file: %s", modelFile)
+
+	model, err := gpt2.NewStableLM(modelFile)
+	if err != nil {
+		return nil, err
+	}
+
+	// If there is a prompt template, load it
+	if err := ml.loadTemplateIfExists(modelName, modelFile); err != nil {
+		return nil, err
+	}
+
+	ml.gptstablelmmodels[modelName] = model
+	return model, err
+}
+
+func (ml *ModelLoader) LoadGPT2Model(modelName string) (*gpt2.GPT2, error) {
+	ml.mu.Lock()
+	defer ml.mu.Unlock()
+
+	// Check if we already have a loaded model
+	if !ml.ExistsInModelPath(modelName) {
+		return nil, fmt.Errorf("model does not exist")
+	}
+
+	if m, ok := ml.gpt2models[modelName]; ok {
+		log.Debug().Msgf("Model already loaded in memory: %s", modelName)
+		return m, nil
+	}
+
+	// TODO: This needs refactoring, it's really bad to have it in here
+	// Check if we have a GPTStable model loaded instead - if we do we return an error so the API tries with StableLM
+	if _, ok := ml.gptstablelmmodels[modelName]; ok {
+		log.Debug().Msgf("Model is GPTStableLM: %s", modelName)
+		return nil, fmt.Errorf("this model is a GPTStableLM one")
+	}
+
+	// Load the model and keep it in memory for later use
+	modelFile := filepath.Join(ml.modelPath, modelName)
+	log.Debug().Msgf("Loading model in memory from file: %s", modelFile)
+
+	model, err := gpt2.New(modelFile)
+	if err != nil {
+		return nil, err
+	}
+
+	// If there is a prompt template, load it
+	if err := ml.loadTemplateIfExists(modelName, modelFile); err != nil {
+		return nil, err
+	}
+
+	ml.gpt2models[modelName] = model
+	return model, err
+}
+
+func (ml *ModelLoader) LoadGPTJModel(modelName string) (*gptj.GPTJ, error) {
+	ml.mu.Lock()
+	defer ml.mu.Unlock()
+
+	// Check if we already have a loaded model
+	if !ml.ExistsInModelPath(modelName) {
+		return nil, fmt.Errorf("model does not exist")
+	}
+
+	if m, ok := ml.gptmodels[modelName]; ok {
+		log.Debug().Msgf("Model already loaded in memory: %s", modelName)
+		return m, nil
+	}
+
+	// TODO: This needs refactoring, it's really bad to have it in here
+	// Check if we have a GPT2 model loaded instead - if we do we return an error so the API tries with GPT2
+	if _, ok := ml.gpt2models[modelName]; ok {
+		log.Debug().Msgf("Model is GPT2: %s", modelName)
+		return nil, fmt.Errorf("this model is a GPT2 one")
+	}
+	if _, ok := ml.gptstablelmmodels[modelName]; ok {
+		log.Debug().Msgf("Model is GPTStableLM: %s", modelName)
+		return nil, fmt.Errorf("this model is a GPTStableLM one")
+	}
+
+	// Load the model and keep it in memory for later use
+	modelFile := filepath.Join(ml.modelPath, modelName)
+	log.Debug().Msgf("Loading model in memory from file: %s", modelFile)
+
+	model, err := gptj.New(modelFile)
+	if err != nil {
+		return nil, err
+	}
+
+	// If there is a prompt template, load it
+	if err := ml.loadTemplateIfExists(modelName, modelFile); err != nil {
+		return nil, err
+	}
+
+	ml.gptmodels[modelName] = model
+	return model, err
+}
+
+func (ml *ModelLoader) LoadLLaMAModel(modelName string, opts ...llama.ModelOption) (*llama.LLama, error) {
+	ml.mu.Lock()
+	defer ml.mu.Unlock()
+
+	log.Debug().Msgf("Loading model name: %s", modelName)
+
+	// Check if we already have a loaded model
+	if !ml.ExistsInModelPath(modelName) {
+		return nil, fmt.Errorf("model does not exist")
+	}
+
+	if m, ok := ml.models[modelName]; ok {
+		log.Debug().Msgf("Model already loaded in memory: %s", modelName)
+		return m, nil
+	}
+
+	// TODO: This needs refactoring, it's really bad to have it in here
+	// Check if we have a GPTJ model loaded instead - if we do we return an error so the API tries with GPTJ
+	if _, ok := ml.gptmodels[modelName]; ok {
+		log.Debug().Msgf("Model is GPTJ: %s", modelName)
+		return nil, fmt.Errorf("this model is a GPTJ one")
+	}
+	if _, ok := ml.gpt2models[modelName]; ok {
+		log.Debug().Msgf("Model is GPT2: %s", modelName)
+		return nil, fmt.Errorf("this model is a GPT2 one")
+	}
+	if _, ok := ml.gptstablelmmodels[modelName]; ok {
+		log.Debug().Msgf("Model is GPTStableLM: %s", modelName)
+		return nil, fmt.Errorf("this model is a GPTStableLM one")
+	}
+
+	// Load the model and keep it in memory for later use
+	modelFile := filepath.Join(ml.modelPath, modelName)
+	log.Debug().Msgf("Loading model in memory from file: %s", modelFile)
+
+	model, err := llama.New(modelFile, opts...)
+	if err != nil {
+		return nil, err
+	}
+
+	// If there is a prompt template, load it
+	if err := ml.loadTemplateIfExists(modelName, modelFile); err != nil {
+		return nil, err
+	}
+
+	ml.models[modelName] = model
+	return model, err
+}

prompt-templates/alpaca.tmpl

@@ -0,0 +1,6 @@
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{{.Input}}
+
+### Response:

prompt-templates/ggml-gpt4all-j.tmpl

@@ -0,0 +1,4 @@
+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:

prompt-templates/koala.tmpl

@@ -0,0 +1 @@
+BEGINNING OF CONVERSATION: USER: {{.Input}} GPT:

prompt-templates/vicuna.tmpl

@@ -0,0 +1,6 @@
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{{.Input}}
+
+### Response:

renovate.json

@@ -0,0 +1,17 @@
+{
+  "$schema": "https://docs.renovatebot.com/renovate-schema.json",
+  "extends": [
+    "config:base"
+  ],
+  "regexManagers": [
+    {
+      "fileMatch": [
+        "^Makefile$"
+      ],
+      "matchStrings": [
+        "#\\s*renovate:\\s*datasource=(?<datasource>.*?) depName=(?<depName>.*?)( datasourceTemplate=(?<datasourceTemplate>.*?))?( packageNameTemplate=(?<packageNameTemplate>.*?))?( depNameTemplate=(?<depNameTemplate>.*?))?( valueTemplate=(?<currentValueTemplate>.*?))?( versioning=(?<versioning>.*?))?\\s+.+_VERSION=(?<currentValue>.*?)\\s"
+      ],
+      "versioningTemplate": "{{#if versioning}}{{versioning}}{{/if}}"
+    }
+  ]
+}