Rename project to LocalAI (#35)

Signed-off-by: mudler <mudler@c3os.io>

.env

@@ -1,3 +1,3 @@
 THREADS=14
-CONTEXT_SIZE=700
+CONTEXT_SIZE=512
 MODELS_PATH=/models

.github/workflows/image.yml

@@ -19,7 +19,7 @@ jobs:
       - name: Prepare
         id: prep
         run: |
-          DOCKER_IMAGE=quay.io/go-skynet/llama-cli
+          DOCKER_IMAGE=quay.io/go-skynet/local-ai
           VERSION=master
           SHORTREF=${GITHUB_SHA::8}
 

.gitignore

@@ -1,8 +1,10 @@
 # go-llama build artifacts
 go-llama
+go-gpt4all-j
 
-# llama-cli build binary
-llama-cli
+# LocalAI build binary
+LocalAI
+local-ai
 
 # Ignore models
 models/*.bin

.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

Dockerfile

@@ -2,17 +2,11 @@ ARG GO_VERSION=1.20
 ARG DEBIAN_VERSION=11
 FROM golang:$GO_VERSION as builder
 WORKDIR /build
-ARG GO_LLAMA_CPP_TAG=llama.cpp-2f7c8e0
-RUN git clone -b $GO_LLAMA_CPP_TAG --recurse-submodules https://github.com/go-skynet/go-llama.cpp
-RUN cd go-llama.cpp && make libbinding.a
-COPY go.mod ./
-COPY go.sum ./
-RUN go mod download
-RUN apt-get update
+RUN apt-get update && apt-get install -y cmake
 COPY . .
-RUN go mod edit -replace github.com/go-skynet/go-llama.cpp=/build/go-llama.cpp
-RUN C_INCLUDE_PATH=/build/go-llama.cpp LIBRARY_PATH=/build/go-llama.cpp go build -o llama-cli ./
+ARG BUILD_TYPE=
+RUN make build${BUILD_TYPE}
 
 FROM debian:$DEBIAN_VERSION
-COPY --from=builder /build/llama-cli /usr/bin/llama-cli
-ENTRYPOINT [ "/usr/bin/llama-cli" ]
+COPY --from=builder /build/local-ai /usr/bin/local-ai
+ENTRYPOINT [ "/usr/bin/local-ai" ]

Earthfile

@@ -2,4 +2,4 @@ VERSION 0.7
 
 build:
     FROM DOCKERFILE -f Dockerfile .
-    SAVE ARTIFACT /usr/bin/llama-cli AS LOCAL llama-cli
+    SAVE ARTIFACT /usr/bin/local-ai AS LOCAL local-ai

Makefile

@@ -1,8 +1,8 @@
 GOCMD=go
 GOTEST=$(GOCMD) test
 GOVET=$(GOCMD) vet
-BINARY_NAME=llama-cli
-GOLLAMA_VERSION?=llama.cpp-8b67998
+BINARY_NAME=local-ai
+GOLLAMA_VERSION?=llama.cpp-5ecff35
 
 GREEN  := $(shell tput -Txterm setaf 2)
 YELLOW := $(shell tput -Txterm setaf 3)
@@ -17,23 +17,50 @@ all: help
 ## Build:
 
 build: prepare ## Build the project
-	$(GOCMD) build -o $(BINARY_NAME) ./
+	C_INCLUDE_PATH=$(shell pwd)/go-llama.cpp:$(shell pwd)/go-gpt4all-j LIBRARY_PATH=$(shell pwd)/go-llama.cpp:$(shell pwd)/go-gpt4all-j $(GOCMD) build -o $(BINARY_NAME) ./
+
+buildgeneric: prepare-generic ## Build the project
+	C_INCLUDE_PATH=$(shell pwd)/go-llama.cpp:$(shell pwd)/go-gpt4all-j LIBRARY_PATH=$(shell pwd)/go-llama.cpp:$(shell pwd)/go-gpt4all-j $(GOCMD) build -o $(BINARY_NAME) ./
+
+go-gpt4all-j:
+	git clone --recurse-submodules https://github.com/go-skynet/go-gpt4all-j.cpp go-gpt4all-j
+# 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' {} +
+
+go-gpt4all-j/libgptj.a: go-gpt4all-j
+	$(MAKE) -C go-gpt4all-j libgptj.a
+
+go-gpt4all-j/libgptj.a-generic: go-gpt4all-j
+	$(MAKE) -C go-gpt4all-j generic-libgptj.a
 
 go-llama:
 	git clone -b $(GOLLAMA_VERSION) --recurse-submodules https://github.com/go-skynet/go-llama.cpp go-llama
-
-prepare: go-llama
 	$(MAKE) -C go-llama libbinding.a
+
+go-llama-generic:
+	git clone -b $(GOLLAMA_VERSION) --recurse-submodules https://github.com/go-skynet/go-llama.cpp go-llama
+	$(MAKE) -C go-llama generic-libbinding.a
+
+prepare: go-llama go-gpt4all-j/libgptj.a
 	$(GOCMD) mod edit -replace github.com/go-skynet/go-llama.cpp=$(shell pwd)/go-llama
+	$(GOCMD) mod edit -replace github.com/go-skynet/go-gpt4all-j.cpp=$(shell pwd)/go-gpt4all-j
+
+prepare-generic: go-llama-generic go-gpt4all-j/libgptj.a-generic
+	$(GOCMD) mod edit -replace github.com/go-skynet/go-llama.cpp=$(shell pwd)/go-llama
+	$(GOCMD) mod edit -replace github.com/go-skynet/go-gpt4all-j.cpp=$(shell pwd)/go-gpt4all-j
 	
 clean: ## Remove build related file
-	$(MAKE) -C go-llama clean
 	rm -fr ./go-llama
-	rm -f $(BINARY_NAME)
+	rm -rf ./go-gpt4all-j
+	rm -rf $(BINARY_NAME)
 
 ## Run:
 run: prepare
-	C_INCLUDE_PATH=$(shell pwd)/go-llama.cpp LIBRARY_PATH=$(shell pwd)/go-llama.cpp $(GOCMD) run ./ api
+	$(GOCMD) run ./ api
 
 ## Test:
 test: ## Run the tests of the project
@@ -49,4 +76,4 @@ help: ## Show this help.
 	@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)
+		}' $(MAKEFILE_LIST)

README.md

@@ -1,20 +1,33 @@
-## :camel: llama-cli
+## :camel: LocalAI
 
+> :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 and API compatible with OpenAI for [llama.cpp](https://github.com/ggerganov/llama.cpp), it supports multiple-models and also provides a simple command line interface that allows text generation using a GPT-based model like llama directly from the terminal. 
+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.
 
-It is compatible with the models supported by `llama.cpp`. 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`.
+- OpenAI compatible API
+- Supports multiple-models
+- Once loaded the first time, it keep models loaded in memory for faster inference
+- Provides a simple command line interface that allows text generation directly from the terminal
+- 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).
 
-`llama-cli` doesn't shell-out, it uses https://github.com/go-skynet/go-llama.cpp, which is a golang binding of [llama.cpp](https://github.com/ggerganov/llama.cpp).
+## Model compatibility
+
+It is compatible with the models supported by [llama.cpp](https://github.com/ggerganov/llama.cpp) and also [GPT4ALL-J](https://github.com/nomic-ai/gpt4all).
+
+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
 
-You can use `docker-compose`:
+> `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/llama-cli
-cd llama-cli
+git clone https://github.com/go-skynet/LocalAI
+
+cd LocalAI
 
 # copy your models to models/
 cp your-model.bin models/
@@ -27,19 +40,20 @@ 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
    }'
-
-
 ```
 
-Note: The API doesn't inject a default prompt for talking to the model, 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.
+## 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:
 
 ```
@@ -51,70 +65,19 @@ Below is an instruction that describes a task. Write a response that appropriate
 ### Response:
 ```
 
-See the [prompt-templates](https://github.com/go-skynet/llama-cli/tree/master/prompt-templates) directory in this repository for templates for most popular models.
+See the [prompt-templates](https://github.com/go-skynet/LocalAI/tree/master/prompt-templates) directory in this repository for templates for most popular models.
 
-## Container images
-
-`llama-cli` 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/llama-cli?tab=tags&tag=latest)
-
-To begin, run:
-
-```
-docker run -ti --rm quay.io/go-skynet/llama-cli:latest  --instruction "What's an alpaca?" --topk 10000 --model ...
-```
-
-Where `--model` is the path of the model you want to use. 
-
-Note: you need to mount a volume to the docker container in order to load a model, for instance:
-
-```
-# assuming your model is in /path/to/your/models/foo.bin
-docker run -v /path/to/your/models:/models -ti --rm quay.io/go-skynet/llama-cli:latest  --instruction "What's an alpaca?" --topk 10000 --model /models/foo.bin
-```
-
-You will receive a response like the following:
-
-```
-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.
-```
-
-## Basic usage
-
-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:
-
-```
-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>]
-```
-
-| 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           |               | 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. |
-
-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.
+</details>
 
 ## API
 
-`llama-cli` also provides an API for running text generation as a service. The models once loaded the first time will be kept in memory.
+`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:latest api --models-path /path/to/models --context-size 700 --threads 4
+docker run -p 8080:8080 -ti --rm quay.io/go-skynet/local-api:latest --models-path /path/to/models --context-size 700 --threads 4
 ```
 
 And you'll see:
@@ -129,15 +92,15 @@ And you'll see:
 └───────────────────────────────────────────────────┘ 
 ```
 
-Note: Models have to end up with `.bin`.
+Note: Models have to end up with `.bin` so can be listed by the `/models` endpoint.
 
 You can control the API server options with command line arguments:
 
 ```
-llama-cli api --models-path <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                            |
 | ------------ | -------------------- | ------------- | -------------------------------------- |
@@ -148,6 +111,8 @@ The API takes takes the following:
 
 Once the server is running, you can start making requests to it using HTTP, using the OpenAI API. 
 
+</details>
+
 ### Supported OpenAI API endpoints
 
 You can check out the [OpenAI API reference](https://platform.openai.com/docs/api-reference/chat/create). 
@@ -205,41 +170,34 @@ python 828bddec6162a023114ce19146cb2b82/gistfile1.txt models tokenizer.model
 
 ### Windows compatibility
 
-It should work, however you need to make sure you give enough resources to the container. See https://github.com/go-skynet/llama-cli/issues/2
+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
 
 ### Kubernetes
 
-You can run the API directly in Kubernetes:
-
-```bash
-kubectl apply -f https://raw.githubusercontent.com/go-skynet/llama-cli/master/kubernetes/deployment.yaml
-```
+You can run the API in Kubernetes, see an example deployment in [kubernetes](https://github.com/go-skynet/LocalAI/tree/master/kubernetes)
 
 ### 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 `llama-cli` container image locally you can use `docker`:
+In order to build the `LocalAI` container image locally you can use `docker`:
 
 ```
-# build the image as "alpaca-image"
-docker build -t llama-cli .
-docker run llama-cli --instruction "What's an alpaca?"
+# build the image
+docker build -t LocalAI .
+docker run LocalAI
 ```
 
-Or build the binary with:
+Or build the binary with `make`:
 
 ```
-# build the image as "alpaca-image"
-docker run --privileged -v /var/run/docker.sock:/var/run/docker.sock --rm -t -v "$(pwd)":/workspace -v earthly-tmp:/tmp/earthly:rw earthly/earthly:v0.7.2 +build
-# run the binary
-./llama-cli --instruction "What's an alpaca?"
+make build
 ```
 
 ## Short-term roadmap
 
-- [x] Mimic OpenAI API (https://github.com/go-skynet/llama-cli/issues/10)
-- Binary releases (https://github.com/go-skynet/llama-cli/issues/6)
+- [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!

api/api.go

@@ -5,8 +5,8 @@ import (
 	"strings"
 	"sync"
 
-	model "github.com/go-skynet/llama-cli/pkg/model"
-
+	model "github.com/go-skynet/LocalAI/pkg/model"
+	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"
@@ -60,13 +60,16 @@ type OpenAIRequest struct {
 	Batch     int  `json:"batch"`
 	F16       bool `json:"f16kv"`
 	IgnoreEOS bool `json:"ignore_eos"`
+
+	Seed int `json:"seed"`
 }
 
 // https://platform.openai.com/docs/api-reference/completions
-func openAIEndpoint(chat bool, loader *model.ModelLoader, threads int, defaultMutex *sync.Mutex, mutexMap *sync.Mutex, mutexes map[string]*sync.Mutex) func(c *fiber.Ctx) error {
+func openAIEndpoint(chat bool, loader *model.ModelLoader, threads, ctx int, f16 bool, defaultMutex *sync.Mutex, 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
 
 		input := new(OpenAIRequest)
 		// Get input data from the request body
@@ -77,9 +80,22 @@ func openAIEndpoint(chat bool, loader *model.ModelLoader, threads int, defaultMu
 		if input.Model == "" {
 			return fmt.Errorf("no model specified")
 		} else {
-			model, err = loader.LoadModel(input.Model)
-			if err != nil {
-				return err
+			// Try to load the model with both
+			var llamaerr error
+			llamaOpts := []llama.ModelOption{}
+			if ctx != 0 {
+				llamaOpts = append(llamaOpts, llama.SetContext(ctx))
+			}
+			if f16 {
+				llamaOpts = append(llamaOpts, llama.EnableF16Memory)
+			}
+
+			model, llamaerr = loader.LoadLLaMAModel(input.Model, llamaOpts...)
+			if llamaerr != nil {
+				gptModel, err = loader.LoadGPTJModel(input.Model)
+				if err != nil {
+					return fmt.Errorf("llama: %s gpt: %s", llamaerr.Error(), err.Error()) // llama failed first, so we want to catch both errors
+				}
 			}
 		}
 
@@ -146,32 +162,70 @@ func openAIEndpoint(chat bool, loader *model.ModelLoader, threads int, defaultMu
 			n = 1
 		}
 
+		var predFunc func() (string, error)
+		switch {
+		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 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++ {
-			// 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),
-			}
+			var prediction string
 
-			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)
-			}
-
-			prediction, err := model.Predict(
-				predInput,
-				predictOptions...,
-			)
+			prediction, err := predFunc()
 			if err != nil {
 				return err
 			}
@@ -179,6 +233,7 @@ func openAIEndpoint(chat bool, loader *model.ModelLoader, threads int, defaultMu
 			if input.Echo {
 				prediction = predInput + prediction
 			}
+
 			if chat {
 				result = append(result, Choice{Message: &Message{Role: "assistant", Content: prediction}})
 			} else {
@@ -194,7 +249,7 @@ func openAIEndpoint(chat bool, loader *model.ModelLoader, threads int, defaultMu
 	}
 }
 
-func Start(loader *model.ModelLoader, listenAddr string, threads int) error {
+func Start(loader *model.ModelLoader, listenAddr string, threads, ctxSize int, f16 bool) error {
 	app := fiber.New()
 
 	// Default middleware config
@@ -207,8 +262,8 @@ func Start(loader *model.ModelLoader, listenAddr string, threads int) error {
 	var mumutex = &sync.Mutex{}
 
 	// openAI compatible API endpoint
-	app.Post("/v1/chat/completions", openAIEndpoint(true, loader, threads, mutex, mumutex, mu))
-	app.Post("/v1/completions", openAIEndpoint(false, loader, threads, mutex, mumutex, mu))
+	app.Post("/v1/chat/completions", openAIEndpoint(true, loader, threads, ctxSize, f16, mutex, mumutex, mu))
+	app.Post("/v1/completions", openAIEndpoint(false, loader, threads, ctxSize, f16, mutex, mumutex, mu))
 	app.Get("/v1/models", func(c *fiber.Ctx) error {
 		models, err := loader.ListModels()
 		if err != nil {

docker-compose.yaml

@@ -1,21 +1,13 @@
 version: '3.6'
 
 services:
-
-  # chatgpt:
-  #   image: ghcr.io/mckaywrigley/chatbot-ui:main
-  #   # platform: linux/amd64
-  #   ports:
-  #     - 3000:3000
-  #   environment:
-  #     - 'OPENAI_API_KEY=sk-000000000000000'
-  #     - 'OPENAI_API_HOST=http://api:8080'
-
   api:
-    image: quay.io/go-skynet/llama-cli:latest
+    image: quay.io/go-skynet/local-ai:latest
     build:
       context: .
       dockerfile: Dockerfile
+      # args:
+        # BUILD_TYPE: generic # Uncomment to build CPU generic code that works on most HW
     ports:
       - 8080:8080
     environment:
@@ -23,6 +15,4 @@ services:
       - CONTEXT_SIZE=$CONTEXT_SIZE
       - THREADS=$THREADS
     volumes:
-      - ./models:/models:cached
-    command: api
-    
+      - ./models:/models:cached

go.mod

@@ -1,4 +1,4 @@
-module github.com/go-skynet/llama-cli
+module github.com/go-skynet/LocalAI
 
 go 1.19
 
@@ -11,6 +11,7 @@ require (
 require (
 	github.com/andybalholm/brotli v1.0.4 // indirect
 	github.com/cpuguy83/go-md2man/v2 v2.0.2 // indirect
+	github.com/go-skynet/go-gpt4all-j.cpp v0.0.0-20230419091210-303cf2a59a94 // indirect
 	github.com/google/uuid v1.3.0 // indirect
 	github.com/klauspost/compress v1.15.9 // indirect
 	github.com/mattn/go-colorable v0.1.13 // indirect

go.sum

@@ -3,6 +3,8 @@ github.com/andybalholm/brotli v1.0.4/go.mod h1:fO7iG3H7G2nSZ7m0zPUDn85XEX2GTukHG
 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-logr/logr v1.2.3 h1:2DntVwHkVopvECVRSlL5PSo9eG+cAkDCuckLubN+rq0=
+github.com/go-skynet/go-gpt4all-j.cpp v0.0.0-20230419091210-303cf2a59a94 h1:rtrrMvlIq+g0/ltXjDdLeNtz0uc4wJ4Qs15GFU4ba4c=
+github.com/go-skynet/go-gpt4all-j.cpp v0.0.0-20230419091210-303cf2a59a94/go.mod h1:5VZ9XbcINI0XcHhkcX8GPK8TplFGAzu1Hrg4tNiMCtI=
 github.com/go-skynet/go-llama.cpp v0.0.0-20230415213228-bac222030640 h1:8SSVbQ3yvq7JnfLCLF4USV0PkQnnduUkaNCv/hHDa3E=
 github.com/go-skynet/go-llama.cpp v0.0.0-20230415213228-bac222030640/go.mod h1:35AKIEMY+YTKCBJIa/8GZcNGJ2J+nQk1hQiWo/OnEWw=
 github.com/go-task/slim-sprig v0.0.0-20230315185526-52ccab3ef572 h1:tfuBGBXKqDEevZMzYi5KSi8KkcZtzBcTgAUUtapy0OI=

kubernetes/deployment.yaml

@@ -23,9 +23,7 @@ spec:
     spec:
       containers:
         - name: llama
-          args:
-          - api
-          image: quay.io/go-skynet/llama-cli:latest
+          image: quay.io/go-skynet/local-ai:latest
 ---
 apiVersion: v1
 kind: Service

main.go

@@ -1,230 +1,76 @@
 package main
 
 import (
-	"bytes"
 	"fmt"
-	"io/ioutil"
 	"os"
 	"runtime"
-	"text/template"
 
-	llama "github.com/go-skynet/go-llama.cpp"
-	api "github.com/go-skynet/llama-cli/api"
-	model "github.com/go-skynet/llama-cli/pkg/model"
+	api "github.com/go-skynet/LocalAI/api"
+	model "github.com/go-skynet/LocalAI/pkg/model"
 
 	"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"))}
-	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"},
-	},
-	&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,
-	},
-}
-
 func main() {
+	path, err := os.Getwd()
+	if err != nil {
+		fmt.Println(err)
+		os.Exit(1)
+	}
+
 	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.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:       runtime.NumCPU(),
 			},
 			&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{
-			{
-
-				Name: "api",
-				Flags: []cli.Flag{
-					&cli.IntFlag{
-						Name:    "threads",
-						EnvVars: []string{"THREADS"},
-						Value:   runtime.NumCPU(),
-					},
-					&cli.StringFlag{
-						Name:    "models-path",
-						EnvVars: []string{"MODELS_PATH"},
-					},
-					&cli.StringFlag{
-						Name:    "address",
-						EnvVars: []string{"ADDRESS"},
-						Value:   ":8080",
-					},
-					&cli.IntFlag{
-						Name:    "context-size",
-						EnvVars: []string{"CONTEXT_SIZE"},
-						Value:   512,
-					},
-				},
-				Action: func(ctx *cli.Context) error {
-					return api.Start(model.NewModelLoader(ctx.String("models-path")), ctx.String("address"), ctx.Int("threads"))
-				},
+				Name:        "address",
+				DefaultText: "Bind address for the API server.",
+				EnvVars:     []string{"ADDRESS"},
+				Value:       ":8080",
+			},
+			&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
+- Alpaca
+
+It uses llama.cpp and gpt4all as backend, supporting all the models supported by both.
+`,
+		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.Start(model.NewModelLoader(ctx.String("models-path")), ctx.String("address"), ctx.Int("threads"), ctx.Int("context-size"), ctx.Bool("f16"))
 		},
 	}
 
-	err := app.Run(os.Args)
+	err = app.Run(os.Args)
 	if err != nil {
 		fmt.Println(err)
 		os.Exit(1)

pkg/model/loader.go

@@ -10,6 +10,7 @@ import (
 	"sync"
 	"text/template"
 
+	gptj "github.com/go-skynet/go-gpt4all-j.cpp"
 	llama "github.com/go-skynet/go-llama.cpp"
 )
 
@@ -17,11 +18,12 @@ type ModelLoader struct {
 	modelPath        string
 	mu               sync.Mutex
 	models           map[string]*llama.LLama
+	gptmodels        map[string]*gptj.GPTJ
 	promptsTemplates map[string]*template.Template
 }
 
 func NewModelLoader(modelPath string) *ModelLoader {
-	return &ModelLoader{modelPath: modelPath, models: make(map[string]*llama.LLama), promptsTemplates: make(map[string]*template.Template)}
+	return &ModelLoader{modelPath: modelPath, gptmodels: make(map[string]*gptj.GPTJ), models: make(map[string]*llama.LLama), promptsTemplates: make(map[string]*template.Template)}
 }
 
 func (ml *ModelLoader) ListModels() ([]string, error) {
@@ -62,16 +64,81 @@ func (ml *ModelLoader) TemplatePrefix(modelName string, in interface{}) (string,
 	return buf.String(), nil
 }
 
-func (ml *ModelLoader) LoadModel(modelName string, opts ...llama.ModelOption) (*llama.LLama, error) {
+func (ml *ModelLoader) loadTemplate(modelName, modelFile string) error {
+	modelTemplateFile := fmt.Sprintf("%s.tmpl", modelFile)
+
+	// Check if the model path exists
+	if _, err := os.Stat(modelTemplateFile); err != nil {
+		return nil
+	}
+
+	dat, err := os.ReadFile(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) LoadGPTJModel(modelName string) (*gptj.GPTJ, error) {
 	ml.mu.Lock()
 	defer ml.mu.Unlock()
 
 	// Check if we already have a loaded model
 	modelFile := filepath.Join(ml.modelPath, modelName)
 
+	if m, ok := ml.gptmodels[modelFile]; ok {
+		return m, nil
+	}
+
+	// Check if the model path exists
+	if _, err := os.Stat(modelFile); os.IsNotExist(err) {
+		// try to find a s.bin
+		modelBin := fmt.Sprintf("%s.bin", modelFile)
+		if _, err := os.Stat(modelBin); os.IsNotExist(err) {
+			return nil, err
+		} else {
+			modelName = fmt.Sprintf("%s.bin", modelName)
+			modelFile = modelBin
+		}
+	}
+
+	// Load the model and keep it in memory for later use
+	model, err := gptj.New(modelFile)
+	if err != nil {
+		return nil, err
+	}
+
+	// If there is a prompt template, load it
+	if err := ml.loadTemplate(modelName, modelFile); err != nil {
+		return nil, err
+	}
+
+	ml.gptmodels[modelFile] = model
+	return model, err
+}
+
+func (ml *ModelLoader) LoadLLaMAModel(modelName string, opts ...llama.ModelOption) (*llama.LLama, error) {
+	ml.mu.Lock()
+	defer ml.mu.Unlock()
+
+	// Check if we already have a loaded model
+	modelFile := filepath.Join(ml.modelPath, modelName)
 	if m, ok := ml.models[modelFile]; ok {
 		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 _, ok := ml.gptmodels[modelFile]; ok {
+		return nil, fmt.Errorf("this model is a GPTJ one")
+	}
 
 	// Check if the model path exists
 	if _, err := os.Stat(modelFile); os.IsNotExist(err) {
@@ -92,21 +159,8 @@ func (ml *ModelLoader) LoadModel(modelName string, opts ...llama.ModelOption) (*
 	}
 
 	// If there is a prompt template, load it
-
-	modelTemplateFile := fmt.Sprintf("%s.tmpl", modelFile)
-	// Check if the model path exists
-	if _, err := os.Stat(modelTemplateFile); err == nil {
-		dat, err := os.ReadFile(modelTemplateFile)
-		if err != nil {
-			return nil, err
-		}
-
-		// Parse the template
-		tmpl, err := template.New("prompt").Parse(string(dat))
-		if err != nil {
-			return nil, err
-		}
-		ml.promptsTemplates[modelName] = tmpl
+	if err := ml.loadTemplate(modelName, modelFile); err != nil {
+		return nil, err
 	}
 
 	ml.models[modelFile] = model

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: