Merge pull request #22 from go-skynet/update-llama.cpp

⬆️ Update go-llama.cpp to `llama.cpp-2f7c8e0`

.dockerignore

@@ -0,0 +1 @@
+models/*.bin

.env

@@ -0,0 +1 @@
+THREADS=14

.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
+          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
@@ -81,7 +56,23 @@ jobs:
           registry: quay.io
           username: ${{ secrets.QUAY_USERNAME }}
           password: ${{ secrets.QUAY_PASSWORD }}
-      - uses: earthly/actions/setup-earthly@v1
       - name: Build
-        run: |
-            earthly --push +image-all --IMAGE=${{ 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 }}

.gitignore

@@ -0,0 +1,2 @@
+llama-cli
+models/*.bin

Dockerfile

@@ -0,0 +1,18 @@
+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 $GO_LLAMA_CPP_TAG --recurse-submodules https://github.com/go-skynet/go-llama.cpp
+RUN cd go-llama.cpp && make libbinding.a
+COPY go.mod ./
+COPY go.sum ./
+RUN go mod download
+RUN apt-get update
+COPY . .
+RUN go mod edit -replace github.com/go-skynet/go-llama.cpp=/build/go-llama.cpp
+RUN C_INCLUDE_PATH=/build/go-llama.cpp LIBRARY_PATH=/build/go-llama.cpp go build -o llama-cli ./
+
+FROM debian:$DEBIAN_VERSION
+COPY --from=builder /build/llama-cli /usr/bin/llama-cli
+ENTRYPOINT [ "/usr/bin/llama-cli" ]

Earthfile

@@ -1,40 +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
-
-alpaca-model:
-    FROM alpine
-    # This is the alpaca.cpp model https://github.com/antimatter15/alpaca.cpp
-    ARG MODEL_URL=https://ipfs.io/ipfs/QmQ1bf2BTnYxq73MFJWu1B7bQ2UD6qG7D7YDCxhTndVkPC
-    RUN wget -O model.bin -c https://ipfs.io/ipfs/QmQ1bf2BTnYxq73MFJWu1B7bQ2UD6qG7D7YDCxhTndVkPC
-    SAVE ARTIFACT model.bin AS LOCAL 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 +alpaca-model/model.bin /model.bin
-    COPY +build/llama-cli /llama-cli
-    ENV MODEL_PATH=/model.bin
-    ENTRYPOINT [ "/llama-cli" ]
-    SAVE IMAGE --push $IMAGE
-
-image-all:
-    BUILD --platform=linux/amd64 --platform=linux/arm64 +image
+    FROM DOCKERFILE -f Dockerfile .
+    SAVE ARTIFACT /usr/bin/llama-cli AS LOCAL llama-cli

README.md

@@ -1,14 +1,70 @@
 ## :camel: llama-cli
 
 
-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.
+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. 
+
+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`.
+
+`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).
+
+## Usage
+
+You can use `docker-compose`:
+
+```bash
+
+git clone https://github.com/go-skynet/llama-cli
+cd llama-cli
+
+# copy your models to models/
+cp your-model.bin models/
+
+# (optional) Edit the .env file to set the number of concurrent threads used for inference
+# echo "THREADS=14" > .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
+   }'
+
+
+```
+
+Note: You can use a default template for every model 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:
+
+```
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{{.Input}}
+
+### Response:
+```
 
 ## Container images
 
-The `llama-cli` [container images](https://quay.io/repository/go-skynet/llama-cli?tab=tags&tag=latest) come preloaded with the [alpaca.cpp](https://github.com/antimatter15/alpaca.cpp) model, enabling you to start making predictions immediately! To begin, run:
+`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
+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:
@@ -19,7 +75,7 @@ An alpaca is a member of the South American Camelid family, which includes the l
 
 ## 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:
+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>]
@@ -33,10 +89,10 @@ llama-cli --model <model_path> --instruction <instruction> [--input <input>] [--
 | 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.  |
+| 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`:
 
@@ -46,22 +102,15 @@ 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
+## API
 
-`llama-cli` also provides an API for running text generation as a service. You can start the API server using the following command:
+`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.
 
+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
 ```
-llama-cli api --model <model_path> [--address <address>] [--threads <num_threads>]
-```
-
-The API takes takes the following arguments:
-
-| 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. |
-| address      | ADDRESS              | :8080         | The address and port to listen on. |
-
 
 And you'll see:
 ```
@@ -75,28 +124,170 @@ And you'll see:
 └───────────────────────────────────────────────────┘ 
 ```
 
-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:
+Note: Models have to end up with `.bin`.
+
+You can control the API server options with command line arguments:
 
 ```
-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
-}'
+llama-cli api --models-path <model_path> [--address <address>] [--threads <num_threads>]
 ```
 
-Example of starting the API with `docker`:
+The API takes takes the following:
+
+| Parameter    | Environment Variable | Default Value | Description                            |
+| ------------ | -------------------- | ------------- | -------------------------------------- |
+| models-path        | MODELS_PATH           |               | The path where you have models (ending with `.bin`).      |
+| threads      | THREADS              | CPU 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. |
+
+Once the server is running, you can start making requests to it using HTTP, using the OpenAI API. 
+
+### 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.
+
+#### Chat completions
+
+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 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
+   }'
+```
+
+Available additional parameters: `top_p`, `top_k`, `max_tokens`
+
+#### Completions
+
+For example, to generate a comletion, 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`
+
+#### List models
+
+You can list all the models available with:
+
+```
+curl http://localhost:8080/v1/models
+```
+
+## Web interface
+
+There is also available a simple web interface (for instance, http://localhost:8080/) which can be used as a playground.
+
+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:
+
+```
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+### Response:
+```
+
+
+## Using other models
+
+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
-docker run -ti --rm quay.io/go-skynet/llama-cli:latest api
+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!
 ```
 
+### 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:port")
+
+	out, err := cli.Predict("What's an alpaca?")
+	if err != nil {
+		panic(err)
+	}
+
+	fmt.Println(out)
+}
+```
+
+### 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
+
 ### 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
-```
+```
+
+### 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`:
+
+```
+# build the image as "alpaca-image"
+docker build -t llama-cli .
+docker run llama-cli --instruction "What's an alpaca?"
+```
+
+Or build the binary with:
+
+```
+# 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?"
+```
+
+## 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)
+- Upstream our golang bindings to llama.cpp (https://github.com/ggerganov/llama.cpp/issues/351)
+- [x] Multi-model support
+- Have a webUI!
+
+## 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

@@ -1,83 +0,0 @@
-package main
-
-import (
-	"strconv"
-
-	llama "github.com/go-skynet/llama/go"
-	"github.com/gofiber/fiber/v2"
-)
-
-func api(model, listenAddr string, threads int) error {
-	app := fiber.New()
-
-	l, err := llama.New(model)
-	if err != nil {
-		return err
-	}
-
-	/*
-		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 +1,276 @@
+package api
+
+import (
+	"embed"
+	"fmt"
+	"net/http"
+	"strconv"
+	"strings"
+	"sync"
+
+	model "github.com/go-skynet/llama-cli/pkg/model"
+
+	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/filesystem"
+	"github.com/gofiber/fiber/v2/middleware/recover"
+)
+
+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"`
+	
+	// Messages is read only by chat/completion API calls
+	Messages []Message `json:"messages"`
+
+	// 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"`
+}
+
+//go:embed index.html
+var indexHTML embed.FS
+
+func openAIEndpoint(chat bool, defaultModel *llama.LLama, loader *model.ModelLoader, threads int, 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
+
+		input := new(OpenAIRequest)
+		// Get input data from the request body
+		if err := c.BodyParser(input); err != nil {
+			return err
+		}
+
+		if input.Model == "" {
+			if defaultModel == nil {
+				return fmt.Errorf("no default model loaded, and no model specified")
+			}
+			model = defaultModel
+		} else {
+			model, err = loader.LoadModel(input.Model)
+			if err != nil {
+				return err
+			}
+		}
+
+		// This is still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
+		if input.Model != "" {
+			mutexMap.Lock()
+			l, ok := mutexes[input.Model]
+			if !ok {
+				m := &sync.Mutex{}
+				mutexes[input.Model] = m
+				l = m
+			}
+			mutexMap.Unlock()
+			l.Lock()
+			defer l.Unlock()
+		} else {
+			defaultMutex.Lock()
+			defer defaultMutex.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{}
+			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(input.Model, struct {
+			Input string
+		}{Input: predInput})
+		if err == nil {
+			predInput = templatedInput
+		}
+
+		// Generate the prediction using the language model
+		prediction, err := model.Predict(
+			predInput,
+			llama.SetTemperature(temperature),
+			llama.SetTopP(topP),
+			llama.SetTopK(topK),
+			llama.SetTokens(tokens),
+			llama.SetThreads(threads),
+		)
+		if err != nil {
+			return err
+		}
+
+		if chat {
+			// Return the chat prediction in the response body
+			return c.JSON(OpenAIResponse{
+				Model:   input.Model,
+				Choices: []Choice{{Message: Message{Role: "assistant", Content: prediction}}},
+			})
+		}
+
+		// Return the prediction in the response body
+		return c.JSON(OpenAIResponse{
+			Model:   input.Model,
+			Choices: []Choice{{Text: prediction}},
+		})
+	}
+}
+
+func Start(defaultModel *llama.LLama, loader *model.ModelLoader, listenAddr string, threads int) error {
+	app := fiber.New()
+
+	// Default middleware config
+	app.Use(recover.New())
+	app.Use(cors.New())
+
+	// This is still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
+	var mutex = &sync.Mutex{}
+	mu := map[string]*sync.Mutex{}
+	var mumutex = &sync.Mutex{}
+
+	// openAI compatible API endpoint
+	app.Post("/v1/chat/completions", openAIEndpoint(true, defaultModel, loader, threads, mutex, mumutex, mu))
+	app.Post("/v1/completions", openAIEndpoint(false, defaultModel, loader, threads, mutex, mumutex, mu))
+	app.Get("/v1/models", 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,
+		})
+	})
+
+	app.Use("/", filesystem.New(filesystem.Config{
+		Root:         http.FS(indexHTML),
+		NotFoundFile: "index.html",
+	}))
+
+	/*
+		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 {
+		mutex.Lock()
+		defer mutex.Unlock()
+		// 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 := defaultModel.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(listenAddr)
+	return nil
+}

api/index.html

@@ -0,0 +1,120 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <title>llama-cli</title>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.3/css/all.min.css" crossorigin="anonymous" referrerpolicy="no-referrer" />
+    <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css">
+</head>
+<style>
+    @keyframes rotating {
+    from {
+        transform: rotate(0deg);
+    }
+    to {
+        transform: rotate(360deg);
+    }
+}
+
+.waiting {
+    animation: rotating 1s linear infinite;
+}
+
+</style>
+<body>
+
+<div class="container mt-5" x-data="{ templates:[
+    {
+      name: 'Alpaca: Instruction without input',
+      text: `Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{{.Instruction}}
+
+### Response:`,
+    },
+    {
+      name: 'Alpaca: Instruction with input',
+      text: `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:`,
+    }
+  ], selectedTemplate: '', selectedTemplateText: '' }">
+    <h1>llama-cli API</h1>
+    <div class="form-group">
+        <label for="inputText">Input Text:</label>
+        <textarea class="form-control" id="inputText" rows="6" placeholder="Your text input here..." x-text="selectedTemplateText"></textarea>
+    </div>
+    <div class="form-group">
+        <label for="templateSelect">Select Template:</label>
+        <select class="form-control" id="templateSelect" x-model="selectedTemplateText">
+            <option value="">None</option>
+            <template x-for="(template, index) in templates" :key="index">
+                <option :value="template.text" x-text="template.name"></option>
+            </template>
+        </select>
+    </div>
+    <div class="form-group">
+        <label for="topP">Top P:</label>
+        <input type="range" step="0.01" min="0" max="1" class="form-control" id="topP" value="0.20" name="topP" onchange="this.nextElementSibling.value = this.value" required>
+        <output>0.20</output>
+    </div>
+    <div class="form-group">
+        <label for="topK">Top K:</label>
+        <input type="number" class="form-control" id="topK" value="10000" name="topK"  required>
+    </div>
+    <div class="form-group">
+        <label for="temperature">Temperature:</label>
+        <input type="range" step="0.01" min="0" max="1" value="0.9" class="form-control" id="temperature" name="temperature" onchange="this.nextElementSibling.value = this.value"  required>
+        <output>0.9</output>
+    </div>
+    <div class="form-group">
+        <label for="tokens">Tokens:</label>
+        <input type="number" class="form-control" id="tokens" name="tokens" value="128" required>
+    </div>
+    <button class="btn btn-primary" x-on:click="submitRequest()">Submit <i class="fas fa-paper-plane"></i></button>
+    <hr>
+    <div class="form-group">
+        <label for="outputText">Output Text:</label>
+        <textarea class="form-control" id="outputText" rows="5" readonly></textarea>
+    </div>
+</div>
+
+<script defer src="https://cdn.jsdelivr.net/npm/alpinejs@3.x.x/dist/cdn.min.js"></script>
+<script>
+    function submitRequest() {
+        var button = document.querySelector("i.fa-paper-plane");
+        button.classList.add("waiting");
+        var text = document.getElementById("inputText").value;
+        var url = "/predict";
+        var data = {
+            "text": text,
+            "topP": document.getElementById("topP").value,
+            "topK": document.getElementById("topK").value,
+            "temperature": document.getElementById("temperature").value,
+            "tokens": document.getElementById("tokens").value
+        };
+        fetch(url, {
+            method: "POST",
+            headers: {
+                "Content-Type": "application/json"
+            },
+            body: JSON.stringify(data)
+        })
+        .then(response => response.json())
+        .then(data => {
+            document.getElementById("outputText").value = data.prediction;
+            button.classList.remove("waiting");
+        })
+        .catch(error => { console.error(error); button.classList.remove("waiting"); });
+    }
+</script>
+</body>
+</html>

client/client.go

@@ -0,0 +1,75 @@
+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

@@ -0,0 +1,51 @@
+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/llama-cli:latest
+    build: .
+    volumes:
+      - ./models:/models
+    ports:
+      - 8080:8080
+    environment:
+      - MODELS_PATH=/models
+      - CONTEXT_SIZE=700
+      - THREADS=$THREADS
+    command: api

go.mod

@@ -3,7 +3,7 @@ module github.com/go-skynet/llama-cli
 go 1.19
 
 require (
-	github.com/go-skynet/llama v0.0.0-20230319223917-0076188dd548
+	github.com/go-skynet/go-llama.cpp v0.0.0-20230415155049-9260bfd28bc4
 	github.com/gofiber/fiber/v2 v2.42.0
 	github.com/urfave/cli/v2 v2.25.0
 )
@@ -26,5 +26,5 @@ 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/sys v0.0.0-20220811171246-fbc7d0a398ab // indirect
+	golang.org/x/sys v0.6.0 // indirect
 )

go.sum

@@ -2,10 +2,14 @@ github.com/andybalholm/brotli v1.0.4 h1:V7DdXeJtZscaqfNuAdSRuRFzuiKlHSC/Zh3zl9qY
 github.com/andybalholm/brotli v1.0.4/go.mod h1:fO7iG3H7G2nSZ7m0zPUDn85XEX2GTukHGRSepvi9Eig=
 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 v0.0.0-20230319223917-0076188dd548 h1:wXcEESf+zNXidrSZoDKBoIJQDDMzUwVysLIbCkWGYvM=
-github.com/go-skynet/llama v0.0.0-20230319223917-0076188dd548/go.mod h1:ZtYsAIud4cvP9VTTI9uhdgR1uCwaO/gGKnZZ95h9i7w=
+github.com/go-logr/logr v1.2.3 h1:2DntVwHkVopvECVRSlL5PSo9eG+cAkDCuckLubN+rq0=
+github.com/go-skynet/go-llama.cpp v0.0.0-20230415155049-9260bfd28bc4 h1:u/y9MlPHOeIj636IQmrf9ptMjjdgCVIcsfb7lMFh39M=
+github.com/go-skynet/go-llama.cpp v0.0.0-20230415155049-9260bfd28bc4/go.mod h1:35AKIEMY+YTKCBJIa/8GZcNGJ2J+nQk1hQiWo/OnEWw=
+github.com/go-task/slim-sprig v0.0.0-20230315185526-52ccab3ef572 h1:tfuBGBXKqDEevZMzYi5KSi8KkcZtzBcTgAUUtapy0OI=
 github.com/gofiber/fiber/v2 v2.42.0 h1:Fnp7ybWvS+sjNQsFvkhf4G8OhXswvB6Vee8hM/LyS+8=
 github.com/gofiber/fiber/v2 v2.42.0/go.mod h1:3+SGNjqMh5VQH5Vz2Wdi43zTIV16ktlFd3x3R6O1Zlc=
+github.com/google/go-cmp v0.5.9 h1:O2Tfq5qg4qc4AmwVlvv0oLiVAGB7enBSJ2x2DqQFi38=
+github.com/google/pprof v0.0.0-20210407192527-94a9f03dee38 h1:yAJXTCF9TqKcTiHJAE8dj7HMvPfh66eeA2JYW7eFpSE=
 github.com/google/uuid v1.3.0 h1:t6JiXgmwXMjEs8VusXIJk2BXHsn+wx8BZdTaoZ5fu7I=
 github.com/google/uuid v1.3.0/go.mod h1:TIyPZe4MgqvfeYDBFedMoGGpEw/LqOeaOT+nhxU+yHo=
 github.com/klauspost/compress v1.15.9 h1:wKRjX6JRtDdrE9qwa4b/Cip7ACOshUI4smpCQanqjSY=
@@ -17,6 +21,8 @@ github.com/mattn/go-isatty v0.0.17 h1:BTarxUcIeDqL27Mc+vyvdWYSL28zpIhv3RoTdsLMPn
 github.com/mattn/go-isatty v0.0.17/go.mod h1:kYGgaQfpe5nmfYZH+SKPsOc2e4SrIfOl2e/yFXSvRLM=
 github.com/mattn/go-runewidth v0.0.14 h1:+xnbZSEeDbOIg5/mE6JF0w6n9duR1l3/WmbinWVwUuU=
 github.com/mattn/go-runewidth v0.0.14/go.mod h1:Jdepj2loyihRzMpdS35Xk/zdY8IAYHsh153qUoGf23w=
+github.com/onsi/ginkgo/v2 v2.9.2 h1:BA2GMJOtfGAfagzYtrAlufIP0lq6QERkFmHLMLPwFSU=
+github.com/onsi/gomega v1.27.6 h1:ENqfyGeS5AX/rlXDd/ETokDz93u0YufY1Pgxuy/PvWE=
 github.com/philhofer/fwd v1.1.1 h1:GdGcTjf5RNAxwS4QLsiMzJYj5KEvPJD3Abr261yRQXQ=
 github.com/philhofer/fwd v1.1.1/go.mod h1:gk3iGcWd9+svBvR0sR+KPcfE+RNWozjowpeBVG3ZVNU=
 github.com/rivo/uniseg v0.2.0 h1:S1pD9weZBuJdFmowNwbpi7BJ8TNftyUImj/0WQi72jY=
@@ -50,6 +56,7 @@ 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=
+golang.org/x/net v0.8.0 h1:Zrh2ngAOFYneWTAIAPethzeaQLuHwhuBkuV6ZiRnUaQ=
 golang.org/x/sync v0.0.0-20190423024810-112230192c58/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
 golang.org/x/sync v0.0.0-20201020160332-67f06af15bc9/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
 golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
@@ -59,17 +66,21 @@ golang.org/x/sys v0.0.0-20201119102817-f84b799fce68/go.mod h1:h1NjWce9XRLGQEsW7w
 golang.org/x/sys v0.0.0-20210423082822-04245dca01da/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
 golang.org/x/sys v0.0.0-20210615035016-665e8c7367d1/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.0.0-20220728004956-3c1f35247d10/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
-golang.org/x/sys v0.0.0-20220811171246-fbc7d0a398ab h1:2QkjZIsXupsJbJIdSjjUOgWK3aEtzyuh2mPt3l/CkeU=
 golang.org/x/sys v0.0.0-20220811171246-fbc7d0a398ab/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
+golang.org/x/sys v0.6.0 h1:MVltZSvRTcU2ljQOhs94SXPftV6DCNnZViHeQps87pQ=
+golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
 golang.org/x/term v0.0.0-20210927222741-03fcf44c2211/go.mod h1:jbD1KX2456YbFQfuXm/mYQcufACuNUgVhRMnK/tPxf8=
 golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
 golang.org/x/text v0.3.3/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
 golang.org/x/text v0.3.6/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
 golang.org/x/text v0.3.7/go.mod h1:u+2+/6zg+i71rQMx5EYifcz6MCKuco9NR6JIITiCfzQ=
+golang.org/x/text v0.8.0 h1:57P1ETyNKtuIjB4SRd15iJxuhj8Gc416Y78H3qgMh68=
 golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
 golang.org/x/tools v0.0.0-20191119224855-298f0cb1881e/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo=
 golang.org/x/tools v0.0.0-20201022035929-9cf592e881e9/go.mod h1:emZCQorbCU4vsT4fOWvOPXz4eW1wZW4PmDk9uLelYpA=
+golang.org/x/tools v0.7.0 h1:W4OVu8VVOaIO0yzWMNdepAulS7YfoS3Zabrm8DOXXU4=
 golang.org/x/xerrors v0.0.0-20190717185122-a985d3407aa7/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20191011141410-1b5146add898/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20200804184101-5ec99f83aff1/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
+gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=

kubernetes/deployment.yaml

@@ -39,4 +39,4 @@ spec:
   ports:
     - protocol: TCP
       port: 8080
-      targetPort: 8080
+      targetPort: 8080

main.go

@@ -8,7 +8,10 @@ import (
 	"runtime"
 	"text/template"
 
-	llama "github.com/go-skynet/llama/go"
+	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"
+
 	"github.com/urfave/cli/v2"
 )
 
@@ -31,6 +34,11 @@ var nonEmptyInput string = `Below is an instruction that describes a task, paire
 ### 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)
@@ -46,12 +54,49 @@ func templateString(t string, in interface{}) (string, error) {
 	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,
+	},
+}
+
 func main() {
 	app := &cli.App{
 		Name:    "llama-cli",
 		Version: "0.1",
 		Usage:   "llama-cli --model ... --instruction 'What is an alpaca?'",
-		Flags: []cli.Flag{
+		Flags: append(modelFlags,
 			&cli.StringFlag{
 				Name:    "template",
 				EnvVars: []string{"TEMPLATE"},
@@ -63,37 +108,7 @@ func main() {
 			&cli.StringFlag{
 				Name:    "input",
 				EnvVars: []string{"INPUT"},
-			},
-			&cli.StringFlag{
-				Name:    "model",
-				EnvVars: []string{"MODEL_PATH"},
-			},
-			&cli.IntFlag{
-				Name:    "tokens",
-				EnvVars: []string{"TOKENS"},
-				Value:   128,
-			},
-			&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,
-			},
-		},
+			}),
 		Description: `Run llama.cpp inference`,
 		UsageText: `
 llama-cli --model ~/ggml-alpaca-7b-q4.bin --instruction "What's an alpaca?"
@@ -107,6 +122,7 @@ echo "An Alpaca (Vicugna pacos) is a domesticated species of South American came
 		Copyright: "go-skynet authors",
 		Commands: []*cli.Command{
 			{
+
 				Name: "api",
 				Flags: []cli.Flag{
 					&cli.IntFlag{
@@ -115,17 +131,38 @@ echo "An Alpaca (Vicugna pacos) is a domesticated species of South American came
 						Value:   runtime.NumCPU(),
 					},
 					&cli.StringFlag{
-						Name:    "model",
-						EnvVars: []string{"MODEL_PATH"},
+						Name:    "models-path",
+						EnvVars: []string{"MODELS_PATH"},
+					},
+					&cli.StringFlag{
+						Name:    "default-model",
+						EnvVars: []string{"default-model"},
 					},
 					&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(ctx.String("model"), ctx.String("address"), ctx.Int("threads"))
+
+					var defaultModel *llama.LLama
+					defModel := ctx.String("default-model")
+					if defModel != "" {
+						opts := []llama.ModelOption{llama.SetContext(ctx.Int("context-size"))}
+						var err error
+						defaultModel, err = llama.New(ctx.String("default-model"), opts...)
+						if err != nil {
+							return err
+						}
+					}
+
+					return api.Start(defaultModel, model.NewModelLoader(ctx.String("models-path")), ctx.String("address"), ctx.Int("threads"))
 				},
 			},
 		},
@@ -179,11 +216,13 @@ echo "An Alpaca (Vicugna pacos) is a domesticated species of South American came
 				fmt.Println("Templating the input failed:", err.Error())
 				os.Exit(1)
 			}
-			l, err := llama.New(ctx.String("model"))
+
+			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")),

pkg/model/loader.go

@@ -0,0 +1,114 @@
+package model
+
+import (
+	"bytes"
+	"fmt"
+	"io/ioutil"
+	"os"
+	"path/filepath"
+	"strings"
+	"sync"
+	"text/template"
+
+	llama "github.com/go-skynet/go-llama.cpp"
+)
+
+type ModelLoader struct {
+	modelPath        string
+	mu               sync.Mutex
+	models           map[string]*llama.LLama
+	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)}
+}
+
+func (ml *ModelLoader) ListModels() ([]string, error) {
+	files, err := ioutil.ReadDir(ml.modelPath)
+	if err != nil {
+		return []string{}, err
+	}
+
+	models := []string{}
+	for _, file := range files {
+		if strings.HasSuffix(file.Name(), ".bin") {
+			models = append(models, strings.TrimRight(file.Name(), ".bin"))
+		}
+	}
+
+	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 {
+		// try to find a s.bin
+		modelBin := fmt.Sprintf("%s.bin", modelName)
+		m, ok = ml.promptsTemplates[modelBin]
+		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) LoadModel(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
+	}
+
+	// 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 := llama.New(modelFile, opts...)
+	if err != nil {
+		return nil, err
+	}
+
+	// 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
+	}
+
+	ml.models[modelFile] = model
+	return model, err
+}