change push to chunked uploads from monolithic

restart server more gracefully

.gitignore

@@ -2,9 +2,6 @@
 .vscode
 .env
 .venv
-*.spec
-build
+.swp
 dist
-__pycache__
 ollama
-ggml-metal.metal

Dockerfile

@@ -1,8 +1,6 @@
 FROM golang:1.20
-RUN apt-get update && apt-get install -y cmake
 WORKDIR /go/src/github.com/jmorganca/ollama
 COPY . .
-RUN cmake -S llama -B llama/build && cmake --build llama/build
 RUN CGO_ENABLED=1 go build -ldflags '-linkmode external -extldflags "-static"' .
 
 FROM alpine

Makefile

@@ -1,19 +0,0 @@
-default: ollama
-
-.PHONY: llama
-llama:
-	cmake -S llama -B llama/build -DLLAMA_METAL=on
-	cmake --build llama/build
-
-.PHONY: ollama
-ollama: llama
-	go build .
-
-.PHONY: app
-app: ollama
-	npm install --prefix app
-	npm run --prefix app make:sign
-
-clean:
-	go clean
-	rm -rf llama/build

README.md

@@ -1,101 +1,137 @@
-![ollama](https://github.com/jmorganca/ollama/assets/251292/961f99bb-251a-4eec-897d-1ba99997ad0f)
+<div align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" height="200px" srcset="https://github.com/jmorganca/ollama/assets/3325447/56ea1849-1284-4645-8970-956de6e51c3c">
+    <img alt="logo" height="200px" src="https://github.com/jmorganca/ollama/assets/3325447/0d0b44e2-8f4a-4e99-9b52-a5c1c741c8f7">
+  </picture>
+</div>
 
 # Ollama
 
-Run large language models with `llama.cpp`.
+[![Discord](https://dcbadge.vercel.app/api/server/ollama?style=flat&compact=true)](https://discord.gg/ollama)
 
-> Note: certain models that can be run with Ollama are intended for research and/or non-commercial use only.
+> Note: Ollama is in early preview. Please report any issues you find.
 
-### Features
+Run, create, and share large language models (LLMs).
 
-- Download and run popular large language models
-- Switch between multiple models on the fly
-- Hardware acceleration where available (Metal, CUDA)
-- Fast inference server written in Go, powered by [llama.cpp](https://github.com/ggerganov/llama.cpp)
-- REST API to use with your application (python, typescript SDKs coming soon)
+## Download
 
-## Install
-
-- [Download](https://ollama.ai/download) for macOS
-- Download for Windows (coming soon)
-- Docker: `docker run -p 11434:11434 ollama/ollama`
-
-You can also build the [binary from source](#building).
+- [Download](https://ollama.ai/download) for macOS on Apple Silicon (Intel coming soon)
+- Download for Windows and Linux (coming soon)
+- Build [from source](#building)
 
 ## Quickstart
 
-Run a fast and simple model.
+To run and chat with [Llama 2](https://ai.meta.com/llama), the new model by Meta:
 
 ```
-ollama run orca
+ollama run llama2
 ```
 
-## Example models
+## Model library
 
-### 💬 Chat
+`ollama` includes a library of open-source models:
 
-Have a conversation.
+| Model                    | Parameters | Size  | Download                    |
+| ------------------------ | ---------- | ----- | --------------------------- |
+| Llama2                   | 7B         | 3.8GB | `ollama pull llama2`        |
+| Llama2 13B               | 13B        | 7.3GB | `ollama pull llama2:13b`    |
+| Orca Mini                | 3B         | 1.9GB | `ollama pull orca`          |
+| Vicuna                   | 7B         | 3.8GB | `ollama pull vicuna`        |
+| Nous-Hermes              | 13B        | 7.3GB | `ollama pull nous-hermes`   |
+| Wizard Vicuna Uncensored | 13B        | 7.3GB | `ollama pull wizard-vicuna` |
+
+> Note: You should have at least 8 GB of RAM to run the 3B models, 16 GB to run the 7B models, and 32 GB to run the 13B models.
+
+## Examples
+
+### Run a model
 
 ```
-ollama run vicuna "Why is the sky blue?"
+ollama run llama2
+>>> hi
+Hello! How can I help you today?
 ```
 
-### 🗺️ Instructions
+### Create a custom model
 
-Ask questions. Get answers.
+Pull a base model:
 
 ```
-ollama run orca "Write an email to my boss."
+ollama pull llama2
 ```
 
-### 📖 Storytelling
-
-Venture into the unknown.
+Create a `Modelfile`:
 
 ```
-ollama run nous-hermes "Once upon a time"
+FROM llama2
+
+# set the temperature to 1 [higher is more creative, lower is more coherent]
+PARAMETER temperature 1
+
+# set the system prompt
+SYSTEM """
+You are Mario from Super Mario Bros. Answer as Mario, the assistant, only.
+"""
 ```
 
-## Advanced usage
-
-### Run a local model
+Next, create and run the model:
 
 ```
-ollama run ~/Downloads/vicuna-7b-v1.3.ggmlv3.q4_1.bin
+ollama create mario -f ./Modelfile
+ollama run mario
+>>> hi
+Hello! It's your friend Mario.
 ```
 
+For more examples, see the [examples](./examples) directory.
+
+### Pull a model from the registry
+
+```
+ollama pull orca
+```
+
+### Listing local models
+
+```
+ollama list
+```
+
+## Model packages
+
+### Overview
+
+Ollama bundles model weights, configuration, and data into a single package, defined by a [Modelfile](./docs/modelfile.md).
+
+<picture>
+  <source media="(prefers-color-scheme: dark)" height="480" srcset="https://github.com/jmorganca/ollama/assets/251292/2fd96b5f-191b-45c1-9668-941cfad4eb70">
+  <img alt="logo" height="480" src="https://github.com/jmorganca/ollama/assets/251292/2fd96b5f-191b-45c1-9668-941cfad4eb70">
+</picture>
+
 ## Building
 
 ```
-make
+go build .
 ```
 
 To run it start the server:
 
 ```
-./ollama server &
+./ollama serve &
 ```
 
 Finally, run a model!
 
 ```
-./ollama run ~/Downloads/vicuna-7b-v1.3.ggmlv3.q4_1.bin
+./ollama run llama2
 ```
 
-## API Reference
-
-### `POST /api/pull`
-
-Download a model
-
-```
-curl -X POST http://localhost:11343/api/pull -d '{"model": "orca"}'
-```
+## REST API
 
 ### `POST /api/generate`
 
-Complete a prompt
+Generate text from a model.
 
 ```
-curl -X POST http://localhost:11434/api/generate -d '{"model": "orca", "prompt": "hello!", "stream": true}'
+curl -X POST http://localhost:11434/api/generate -d '{"model": "llama2", "prompt":"Why is the sky blue?"}'
 ```

api/client.go

@@ -5,13 +5,32 @@ import (
 	"bytes"
 	"context"
 	"encoding/json"
+	"fmt"
 	"io"
 	"net/http"
 	"net/url"
 )
 
 type Client struct {
-	base url.URL
+	base    url.URL
+	HTTP    http.Client
+	Headers http.Header
+}
+
+func checkError(resp *http.Response, body []byte) error {
+	if resp.StatusCode >= 200 && resp.StatusCode < 400 {
+		return nil
+	}
+
+	apiError := StatusError{StatusCode: resp.StatusCode}
+
+	err := json.Unmarshal(body, &apiError)
+	if err != nil {
+		// Use the full body as the message if we fail to decode a response.
+		apiError.ErrorMessage = string(body)
+	}
+
+	return apiError
 }
 
 func NewClient(hosts ...string) *Client {
@@ -22,38 +41,71 @@ func NewClient(hosts ...string) *Client {
 
 	return &Client{
 		base: url.URL{Scheme: "http", Host: host},
+		HTTP: http.Client{},
 	}
 }
 
-type options struct {
-	requestBody  io.Reader
-	responseFunc func(bts []byte) error
-}
+func (c *Client) do(ctx context.Context, method, path string, reqData, respData any) error {
+	var reqBody io.Reader
+	var data []byte
+	var err error
+	if reqData != nil {
+		data, err = json.Marshal(reqData)
+		if err != nil {
+			return err
+		}
+		reqBody = bytes.NewReader(data)
+	}
 
-func OptionRequestBody(data any) func(*options) {
-	bts, err := json.Marshal(data)
+	url := c.base.JoinPath(path).String()
+
+	req, err := http.NewRequestWithContext(ctx, method, url, reqBody)
 	if err != nil {
-		panic(err)
+		return err
 	}
 
-	return func(opts *options) {
-		opts.requestBody = bytes.NewReader(bts)
+	req.Header.Set("Content-Type", "application/json")
+	req.Header.Set("Accept", "application/json")
+
+	for k, v := range c.Headers {
+		req.Header[k] = v
 	}
+
+	respObj, err := c.HTTP.Do(req)
+	if err != nil {
+		return err
+	}
+	defer respObj.Body.Close()
+
+	respBody, err := io.ReadAll(respObj.Body)
+	if err != nil {
+		return err
+	}
+
+	if err := checkError(respObj, respBody); err != nil {
+		return err
+	}
+
+	if len(respBody) > 0 && respData != nil {
+		if err := json.Unmarshal(respBody, respData); err != nil {
+			return err
+		}
+	}
+	return nil
 }
 
-func OptionResponseFunc(fn func([]byte) error) func(*options) {
-	return func(opts *options) {
-		opts.responseFunc = fn
-	}
-}
+func (c *Client) stream(ctx context.Context, method, path string, data any, fn func([]byte) error) error {
+	var buf *bytes.Buffer
+	if data != nil {
+		bts, err := json.Marshal(data)
+		if err != nil {
+			return err
+		}
 
-func (c *Client) stream(ctx context.Context, method, path string, fns ...func(*options)) error {
-	var opts options
-	for _, fn := range fns {
-		fn(&opts)
+		buf = bytes.NewBuffer(bts)
 	}
 
-	request, err := http.NewRequestWithContext(ctx, method, c.base.JoinPath(path).String(), opts.requestBody)
+	request, err := http.NewRequestWithContext(ctx, method, c.base.JoinPath(path).String(), buf)
 	if err != nil {
 		return err
 	}
@@ -67,13 +119,32 @@ func (c *Client) stream(ctx context.Context, method, path string, fns ...func(*o
 	}
 	defer response.Body.Close()
 
-	if opts.responseFunc != nil {
-		scanner := bufio.NewScanner(response.Body)
-		for scanner.Scan() {
-			if err := opts.responseFunc(scanner.Bytes()); err != nil {
-				return err
+	scanner := bufio.NewScanner(response.Body)
+	for scanner.Scan() {
+		var errorResponse struct {
+			Error string `json:"error,omitempty"`
+		}
+
+		bts := scanner.Bytes()
+		if err := json.Unmarshal(bts, &errorResponse); err != nil {
+			return fmt.Errorf("unmarshal: %w", err)
+		}
+
+		if errorResponse.Error != "" {
+			return fmt.Errorf("stream: %s", errorResponse.Error)
+		}
+
+		if response.StatusCode >= 400 {
+			return StatusError{
+				StatusCode:   response.StatusCode,
+				Status:       response.Status,
+				ErrorMessage: errorResponse.Error,
 			}
 		}
+
+		if err := fn(bts); err != nil {
+			return err
+		}
 	}
 
 	return nil
@@ -82,36 +153,66 @@ func (c *Client) stream(ctx context.Context, method, path string, fns ...func(*o
 type GenerateResponseFunc func(GenerateResponse) error
 
 func (c *Client) Generate(ctx context.Context, req *GenerateRequest, fn GenerateResponseFunc) error {
-	return c.stream(ctx, http.MethodPost, "/api/generate",
-		OptionRequestBody(req),
-		OptionResponseFunc(func(bts []byte) error {
-			var resp GenerateResponse
-			if err := json.Unmarshal(bts, &resp); err != nil {
-				return err
-			}
+	return c.stream(ctx, http.MethodPost, "/api/generate", req, func(bts []byte) error {
+		var resp GenerateResponse
+		if err := json.Unmarshal(bts, &resp); err != nil {
+			return err
+		}
 
-			return fn(resp)
-		}),
-	)
+		return fn(resp)
+	})
 }
 
-type PullProgressFunc func(PullProgress) error
+type PullProgressFunc func(ProgressResponse) error
 
 func (c *Client) Pull(ctx context.Context, req *PullRequest, fn PullProgressFunc) error {
-	return c.stream(ctx, http.MethodPost, "/api/pull",
-		OptionRequestBody(req),
-		OptionResponseFunc(func(bts []byte) error {
-			var resp PullProgress
-			if err := json.Unmarshal(bts, &resp); err != nil {
-				return err
-			}
+	return c.stream(ctx, http.MethodPost, "/api/pull", req, func(bts []byte) error {
+		var resp ProgressResponse
+		if err := json.Unmarshal(bts, &resp); err != nil {
+			return err
+		}
 
-			if resp.Error.Message != "" {
-				// couldn't pull the model from the directory, proceed anyway
-				return nil
-			}
-
-			return fn(resp)
-		}),
-	)
+		return fn(resp)
+	})
+}
+
+type PushProgressFunc func(ProgressResponse) error
+
+func (c *Client) Push(ctx context.Context, req *PushRequest, fn PushProgressFunc) error {
+	return c.stream(ctx, http.MethodPost, "/api/push", req, func(bts []byte) error {
+		var resp ProgressResponse
+		if err := json.Unmarshal(bts, &resp); err != nil {
+			return err
+		}
+
+		return fn(resp)
+	})
+}
+
+type CreateProgressFunc func(CreateProgress) error
+
+func (c *Client) Create(ctx context.Context, req *CreateRequest, fn CreateProgressFunc) error {
+	return c.stream(ctx, http.MethodPost, "/api/create", req, func(bts []byte) error {
+		var resp CreateProgress
+		if err := json.Unmarshal(bts, &resp); err != nil {
+			return err
+		}
+
+		return fn(resp)
+	})
+}
+
+func (c *Client) List(ctx context.Context) (*ListResponse, error) {
+	var lr ListResponse
+	if err := c.do(ctx, http.MethodGet, "/api/tags", nil, &lr); err != nil {
+		return nil, err
+	}
+	return &lr, nil
+}
+
+func (c *Client) Delete(ctx context.Context, req *DeleteRequest) error {
+	if err := c.do(ctx, http.MethodDelete, "/api/delete", req, nil); err != nil {
+		return err
+	}
+	return nil
 }

api/types.go

@@ -2,124 +2,185 @@ package api
 
 import (
 	"fmt"
-	"net/http"
-	"strings"
+	"os"
+	"runtime"
+	"time"
 )
 
-type Error struct {
-	Code    int32  `json:"code"`
-	Message string `json:"message"`
+type StatusError struct {
+	StatusCode   int
+	Status       string
+	ErrorMessage string `json:"error"`
 }
 
-func (e Error) Error() string {
-	if e.Message == "" {
-		return fmt.Sprintf("%d %v", e.Code, strings.ToLower(http.StatusText(int(e.Code))))
+func (e StatusError) Error() string {
+	switch {
+	case e.Status != "" && e.ErrorMessage != "":
+		return fmt.Sprintf("%s: %s", e.Status, e.ErrorMessage)
+	case e.Status != "":
+		return e.Status
+	case e.ErrorMessage != "":
+		return e.ErrorMessage
+	default:
+		// this should not happen
+		return "something went wrong, please see the ollama server logs for details"
 	}
-	return e.Message
-}
-
-type PullRequest struct {
-	Model string `json:"model"`
-}
-
-type PullProgress struct {
-	Total     int64   `json:"total"`
-	Completed int64   `json:"completed"`
-	Percent   float64 `json:"percent"`
-	Error     Error   `json:"error"`
 }
 
 type GenerateRequest struct {
-	Model  string `json:"model"`
-	Prompt string `json:"prompt"`
+	Model   string `json:"model"`
+	Prompt  string `json:"prompt"`
+	Context []int  `json:"context,omitempty"`
 
-	ModelOptions   `json:"model_opts,omitempty"`
-	PredictOptions `json:"predict_opts,omitempty"`
+	Options `json:"options"`
 }
 
-type ModelOptions struct {
-	ContextSize int    `json:"context_size,omitempty"`
-	Seed        int    `json:"seed,omitempty"`
-	NBatch      int    `json:"n_batch,omitempty"`
-	F16Memory   bool   `json:"memory_f16,omitempty"`
-	MLock       bool   `json:"mlock,omitempty"`
-	MMap        bool   `json:"mmap,omitempty"`
-	VocabOnly   bool   `json:"vocab_only,omitempty"`
-	LowVRAM     bool   `json:"low_vram,omitempty"`
-	Embeddings  bool   `json:"embeddings,omitempty"`
-	NUMA        bool   `json:"numa,omitempty"`
-	NGPULayers  int    `json:"gpu_layers,omitempty"`
-	MainGPU     string `json:"main_gpu,omitempty"`
-	TensorSplit string `json:"tensor_split,omitempty"`
+type CreateRequest struct {
+	Name string `json:"name"`
+	Path string `json:"path"`
 }
 
-type PredictOptions struct {
-	Seed        int     `json:"seed,omitempty"`
-	Threads     int     `json:"threads,omitempty"`
-	Tokens      int     `json:"tokens,omitempty"`
-	TopK        int     `json:"top_k,omitempty"`
-	Repeat      int     `json:"repeat,omitempty"`
-	Batch       int     `json:"batch,omitempty"`
-	NKeep       int     `json:"nkeep,omitempty"`
-	TopP        float64 `json:"top_p,omitempty"`
-	Temperature float64 `json:"temp,omitempty"`
-	Penalty     float64 `json:"penalty,omitempty"`
-	F16KV       bool
-	DebugMode   bool
-	StopPrompts []string
-	IgnoreEOS   bool `json:"ignore_eos,omitempty"`
-
-	TailFreeSamplingZ float64 `json:"tfs_z,omitempty"`
-	TypicalP          float64 `json:"typical_p,omitempty"`
-	FrequencyPenalty  float64 `json:"freq_penalty,omitempty"`
-	PresencePenalty   float64 `json:"pres_penalty,omitempty"`
-	Mirostat          int     `json:"mirostat,omitempty"`
-	MirostatETA       float64 `json:"mirostat_lr,omitempty"`
-	MirostatTAU       float64 `json:"mirostat_ent,omitempty"`
-	PenalizeNL        bool    `json:"penalize_nl,omitempty"`
-	LogitBias         string  `json:"logit_bias,omitempty"`
-
-	PathPromptCache string
-	MLock           bool `json:"mlock,omitempty"`
-	MMap            bool `json:"mmap,omitempty"`
-	PromptCacheAll  bool
-	PromptCacheRO   bool
-	MainGPU         string
-	TensorSplit     string
+type CreateProgress struct {
+	Status string `json:"status"`
 }
 
-var DefaultModelOptions ModelOptions = ModelOptions{
-	ContextSize: 128,
-	Seed:        0,
-	F16Memory:   true,
-	MLock:       false,
-	Embeddings:  true,
-	MMap:        true,
-	LowVRAM:     false,
+type DeleteRequest struct {
+	Name string `json:"name"`
 }
 
-var DefaultPredictOptions PredictOptions = PredictOptions{
-	Seed:              -1,
-	Threads:           -1,
-	Tokens:            512,
-	Penalty:           1.1,
-	Repeat:            64,
-	Batch:             512,
-	NKeep:             64,
-	TopK:              90,
-	TopP:              0.86,
-	TailFreeSamplingZ: 1.0,
-	TypicalP:          1.0,
-	Temperature:       0.8,
-	FrequencyPenalty:  0.0,
-	PresencePenalty:   0.0,
-	Mirostat:          0,
-	MirostatTAU:       5.0,
-	MirostatETA:       0.1,
-	MMap:              true,
-	StopPrompts:       []string{"llama"},
+type PullRequest struct {
+	Name     string `json:"name"`
+	Insecure bool   `json:"insecure,omitempty"`
+	Username string `json:"username"`
+	Password string `json:"password"`
+}
+
+type ProgressResponse struct {
+	Status    string `json:"status"`
+	Digest    string `json:"digest,omitempty"`
+	Total     int    `json:"total,omitempty"`
+	Completed int    `json:"completed,omitempty"`
+}
+
+type PushRequest struct {
+	Name     string `json:"name"`
+	Insecure bool   `json:"insecure,omitempty"`
+	Username string `json:"username"`
+	Password string `json:"password"`
+}
+
+type ListResponse struct {
+	Models []ListResponseModel `json:"models"`
+}
+
+type ListResponseModel struct {
+	Name       string    `json:"name"`
+	ModifiedAt time.Time `json:"modified_at"`
+	Size       int       `json:"size"`
 }
 
 type GenerateResponse struct {
-	Response string `json:"response"`
+	Model     string    `json:"model"`
+	CreatedAt time.Time `json:"created_at"`
+	Response  string    `json:"response,omitempty"`
+
+	Done    bool  `json:"done"`
+	Context []int `json:"context,omitempty"`
+
+	TotalDuration      time.Duration `json:"total_duration,omitempty"`
+	PromptEvalCount    int           `json:"prompt_eval_count,omitempty"`
+	PromptEvalDuration time.Duration `json:"prompt_eval_duration,omitempty"`
+	EvalCount          int           `json:"eval_count,omitempty"`
+	EvalDuration       time.Duration `json:"eval_duration,omitempty"`
+}
+
+func (r *GenerateResponse) Summary() {
+	if r.TotalDuration > 0 {
+		fmt.Fprintf(os.Stderr, "total duration:       %v\n", r.TotalDuration)
+	}
+
+	if r.PromptEvalCount > 0 {
+		fmt.Fprintf(os.Stderr, "prompt eval count:    %d token(s)\n", r.PromptEvalCount)
+	}
+
+	if r.PromptEvalDuration > 0 {
+		fmt.Fprintf(os.Stderr, "prompt eval duration: %s\n", r.PromptEvalDuration)
+		fmt.Fprintf(os.Stderr, "prompt eval rate:     %.2f tokens/s\n", float64(r.PromptEvalCount)/r.PromptEvalDuration.Seconds())
+	}
+
+	if r.EvalCount > 0 {
+		fmt.Fprintf(os.Stderr, "eval count:           %d token(s)\n", r.EvalCount)
+	}
+
+	if r.EvalDuration > 0 {
+		fmt.Fprintf(os.Stderr, "eval duration:        %s\n", r.EvalDuration)
+		fmt.Fprintf(os.Stderr, "eval rate:            %.2f tokens/s\n", float64(r.EvalCount)/r.EvalDuration.Seconds())
+	}
+}
+
+type Options struct {
+	Seed int `json:"seed,omitempty"`
+
+	// Backend options
+	UseNUMA bool `json:"numa,omitempty"`
+
+	// Model options
+	NumCtx        int  `json:"num_ctx,omitempty"`
+	NumBatch      int  `json:"num_batch,omitempty"`
+	NumGPU        int  `json:"num_gpu,omitempty"`
+	MainGPU       int  `json:"main_gpu,omitempty"`
+	LowVRAM       bool `json:"low_vram,omitempty"`
+	F16KV         bool `json:"f16_kv,omitempty"`
+	LogitsAll     bool `json:"logits_all,omitempty"`
+	VocabOnly     bool `json:"vocab_only,omitempty"`
+	UseMMap       bool `json:"use_mmap,omitempty"`
+	UseMLock      bool `json:"use_mlock,omitempty"`
+	EmbeddingOnly bool `json:"embedding_only,omitempty"`
+
+	// Predict options
+	RepeatLastN      int     `json:"repeat_last_n,omitempty"`
+	RepeatPenalty    float32 `json:"repeat_penalty,omitempty"`
+	FrequencyPenalty float32 `json:"frequency_penalty,omitempty"`
+	PresencePenalty  float32 `json:"presence_penalty,omitempty"`
+	Temperature      float32 `json:"temperature,omitempty"`
+	TopK             int     `json:"top_k,omitempty"`
+	TopP             float32 `json:"top_p,omitempty"`
+	TFSZ             float32 `json:"tfs_z,omitempty"`
+	TypicalP         float32 `json:"typical_p,omitempty"`
+	Mirostat         int     `json:"mirostat,omitempty"`
+	MirostatTau      float32 `json:"mirostat_tau,omitempty"`
+	MirostatEta      float32 `json:"mirostat_eta,omitempty"`
+
+	NumThread int `json:"num_thread,omitempty"`
+}
+
+func DefaultOptions() Options {
+	return Options{
+		Seed: -1,
+
+		UseNUMA: false,
+
+		NumCtx:   2048,
+		NumBatch: 512,
+		NumGPU:   1,
+		LowVRAM:  false,
+		F16KV:    true,
+		UseMMap:  true,
+		UseMLock: false,
+
+		RepeatLastN:      512,
+		RepeatPenalty:    1.1,
+		FrequencyPenalty: 0.0,
+		PresencePenalty:  0.0,
+		Temperature:      0.8,
+		TopK:             40,
+		TopP:             0.9,
+		TFSZ:             1.0,
+		TypicalP:         1.0,
+		Mirostat:         0,
+		MirostatTau:      5.0,
+		MirostatEta:      0.1,
+
+		NumThread: runtime.NumCPU(),
+	}
 }

app/forge.config.ts

@@ -1,4 +1,4 @@
-import type { ForgeConfig, ResolvedForgeConfig, ForgeMakeResult } from '@electron-forge/shared-types'
+import type { ForgeConfig } from '@electron-forge/shared-types'
 import { MakerSquirrel } from '@electron-forge/maker-squirrel'
 import { MakerZIP } from '@electron-forge/maker-zip'
 import { PublisherGithub } from '@electron-forge/publisher-github'
@@ -21,7 +21,9 @@ const config: ForgeConfig = {
       '../ollama',
       path.join(__dirname, './assets/ollama_icon_16x16Template.png'),
       path.join(__dirname, './assets/ollama_icon_16x16Template@2x.png'),
-      ...(process.platform === 'darwin' ? ['../ggml-metal.metal'] : []),
+      path.join(__dirname, './assets/ollama_outline_icon_16x16Template.png'),
+      path.join(__dirname, './assets/ollama_outline_icon_16x16Template@2x.png'),
+      ...(process.platform === 'darwin' ? ['../llama/ggml-metal.metal'] : []),
     ],
     ...(process.env.SIGN
       ? {
@@ -58,7 +60,7 @@ const config: ForgeConfig = {
     new AutoUnpackNativesPlugin({}),
     new WebpackPlugin({
       mainConfig,
-      devContentSecurityPolicy: `default-src * 'unsafe-eval' 'unsafe-inline'`,
+      devContentSecurityPolicy: `default-src * 'unsafe-eval' 'unsafe-inline'; img-src data: 'self'`,
       renderer: {
         config: rendererConfig,
         nodeIntegration: true,

app/package.json

@@ -11,7 +11,9 @@
     "make": "electron-forge make",
     "make:sign": "SIGN=1 electron-forge make",
     "publish": "SIGN=1 electron-forge publish",
-    "lint": "eslint --ext .ts,.tsx ."
+    "lint": "eslint --ext .ts,.tsx .",
+    "format": "prettier --check . --ignore-path .gitignore",
+    "format:fix": "prettier --write . --ignore-path .gitignore"
   },
   "keywords": [],
   "author": {
@@ -30,6 +32,7 @@
     "@electron-forge/plugin-auto-unpack-natives": "^6.2.1",
     "@electron-forge/plugin-webpack": "^6.2.1",
     "@electron-forge/publisher-github": "^6.2.1",
+    "@svgr/webpack": "^8.0.1",
     "@types/chmodr": "^1.0.0",
     "@types/node": "^20.4.0",
     "@types/react": "^18.2.14",
@@ -54,21 +57,27 @@
     "prettier": "^2.8.8",
     "prettier-plugin-tailwindcss": "^0.3.0",
     "style-loader": "^3.3.3",
+    "svg-inline-loader": "^0.8.2",
     "tailwindcss": "^3.3.2",
     "ts-loader": "^9.4.3",
     "ts-node": "^10.9.1",
     "typescript": "~4.5.4",
+    "url-loader": "^4.1.1",
     "webpack": "^5.88.0",
     "webpack-cli": "^5.1.4",
     "webpack-dev-server": "^4.15.1"
   },
   "dependencies": {
     "@electron/remote": "^2.0.10",
+    "@heroicons/react": "^2.0.18",
     "@segment/analytics-node": "^1.0.0",
+    "copy-to-clipboard": "^3.3.3",
     "electron-squirrel-startup": "^1.0.0",
     "electron-store": "^8.1.0",
     "react": "^18.2.0",
     "react-dom": "^18.2.0",
-    "uuid": "^9.0.0"
+    "uuid": "^9.0.0",
+    "winston": "^3.10.0",
+    "winston-daily-rotate-file": "^4.7.1"
   }
 }

app/src/app.css

@@ -11,6 +11,10 @@ body {
   -webkit-app-region: drag;
 }
 
+.no-drag {
+  -webkit-app-region: no-drag;
+}
+
 .blink {
   -webkit-animation: 1s blink step-end infinite;
   -moz-animation: 1s blink step-end infinite;

app/src/app.tsx

@@ -1,158 +1,115 @@
 import { useState } from 'react'
-import path from 'path'
-import os from 'os'
-import { dialog, getCurrentWindow } from '@electron/remote'
+import copy from 'copy-to-clipboard'
+import { CheckIcon, DocumentDuplicateIcon } from '@heroicons/react/24/outline'
+import Store from 'electron-store'
+import { getCurrentWindow } from '@electron/remote'
 
-const API_URL = 'http://127.0.0.1:7734'
+import { install } from './install'
+import OllamaIcon from './ollama.svg'
 
-type Message = {
-  sender: 'bot' | 'human'
-  content: string
-}
+const store = new Store()
 
-const userInfo = os.userInfo()
-
-async function generate(prompt: string, model: string, callback: (res: string) => void) {
-  const result = await fetch(`${API_URL}/generate`, {
-    method: 'POST',
-    headers: {
-      'Content-Type': 'application/json',
-    },
-    body: JSON.stringify({
-      prompt,
-      model,
-    }),
-  })
-
-  if (!result.ok) {
-    return
-  }
-
-  let reader = result.body.getReader()
-
-  while (true) {
-    const { done, value } = await reader.read()
-
-    if (done) {
-      break
-    }
-
-    let decoder = new TextDecoder()
-    let str = decoder.decode(value)
-
-    let re = /}\s*{/g
-    str = '[' + str.replace(re, '},{') + ']'
-    let messages = JSON.parse(str)
-
-    for (const message of messages) {
-      const choice = message.choices[0]
-
-      callback(choice.text)
-
-      if (choice.finish_reason === 'stop') {
-        break
-      }
-    }
-  }
-
-  return
+enum Step {
+  WELCOME = 0,
+  CLI,
+  FINISH,
 }
 
 export default function () {
-  const [prompt, setPrompt] = useState('')
-  const [messages, setMessages] = useState<Message[]>([])
-  const [model, setModel] = useState('')
-  const [generating, setGenerating] = useState(false)
+  const [step, setStep] = useState<Step>(Step.WELCOME)
+  const [commandCopied, setCommandCopied] = useState<boolean>(false)
+
+  const command = 'ollama run llama2'
 
   return (
-    <div className='flex min-h-screen flex-1 flex-col justify-between bg-white'>
-      <header className='drag sticky top-0 z-50 flex h-14 w-full flex-row items-center border-b border-black/10 bg-white/75 backdrop-blur-md'>
-        <div className='mx-auto w-full max-w-xl leading-none'>
-          <h1 className='text-sm font-medium'>{path.basename(model).replace('.bin', '')}</h1>
-        </div>
-      </header>
-      {model ? (
-        <section className='mx-auto mb-10 w-full max-w-xl flex-1 break-words'>
-          {messages.map((m, i) => (
-            <div className='my-4 flex gap-4' key={i}>
-              <div className='flex-none pr-1 text-lg'>
-                {m.sender === 'human' ? (
-                  <div className='mt-px flex h-6 w-6 items-center justify-center rounded-md bg-neutral-200 text-sm text-neutral-700'>
-                    {userInfo.username[0].toUpperCase()}
-                  </div>
-                ) : (
-                  <div className='mt-0.5 flex h-6 w-6 items-center justify-center rounded-md bg-blue-600 text-sm text-white'>
-                    {path.basename(model)[0].toUpperCase()}
-                  </div>
-                )}
-              </div>
-              <div className='flex-1 text-gray-800'>
-                {m.content}
-                {m.sender === 'bot' && generating && i === messages.length - 1 && (
-                  <span className='blink relative -top-[3px] left-1 text-[10px]'>█</span>
-                )}
+    <div className='drag'>
+      <div className='mx-auto flex min-h-screen w-full flex-col justify-between bg-white px-4 pt-16'>
+        {step === Step.WELCOME && (
+          <>
+            <div className='mx-auto text-center'>
+              <h1 className='mb-6 mt-4 text-2xl tracking-tight text-gray-900'>Welcome to Ollama</h1>
+              <p className='mx-auto w-[65%] text-sm text-gray-400'>
+                Let's get you up and running with your own large language models.
+              </p>
+              <button
+                onClick={() => setStep(Step.CLI)}
+                className='no-drag rounded-dm mx-auto my-8 w-[40%] rounded-md bg-black px-4 py-2 text-sm text-white hover:brightness-110'
+              >
+                Next
+              </button>
+            </div>
+            <div className='mx-auto'>
+              <OllamaIcon />
+            </div>
+          </>
+        )}
+        {step === Step.CLI && (
+          <>
+            <div className='mx-auto flex flex-col space-y-28 text-center'>
+              <h1 className='mt-4 text-2xl tracking-tight text-gray-900'>Install the command line</h1>
+              <pre className='mx-auto text-4xl text-gray-400'>&gt; ollama</pre>
+              <div className='mx-auto'>
+                <button
+                  onClick={async () => {
+                    await install()
+                    getCurrentWindow().show()
+                    getCurrentWindow().focus()
+                    setStep(Step.FINISH)
+                  }}
+                  className='no-drag rounded-dm mx-auto w-[60%] rounded-md bg-black px-4 py-2 text-sm text-white hover:brightness-110'
+                >
+                  Install
+                </button>
+                <p className='mx-auto my-4 w-[70%] text-xs text-gray-400'>
+                  You will be prompted for administrator access
+                </p>
               </div>
             </div>
-          ))}
-        </section>
-      ) : (
-        <section className='flex flex-1 select-none flex-col items-center justify-center pb-20'>
-          <h2 className='text-3xl font-light text-neutral-400'>No model selected</h2>
-          <button
-            onClick={async () => {
-              const res = await dialog.showOpenDialog(getCurrentWindow(), {
-                properties: ['openFile', 'multiSelections'],
-              })
-              if (res.canceled) {
-                return
-              }
-
-              setModel(res.filePaths[0])
-            }}
-            className='rounded-dm my-8 rounded-md bg-blue-600 px-4 py-2 text-sm text-white hover:brightness-110'
-          >
-            Open file...
-          </button>
-        </section>
-      )}
-      <div className='sticky bottom-0 bg-gradient-to-b from-transparent to-white'>
-        {model && (
-          <textarea
-            autoFocus
-            rows={1}
-            value={prompt}
-            placeholder='Send a message...'
-            onChange={e => setPrompt(e.target.value)}
-            className='mx-auto my-4 block w-full max-w-xl resize-none rounded-xl border border-gray-200 px-5 py-3.5 text-[15px] shadow-lg shadow-black/5 focus:outline-none'
-            onKeyDownCapture={async e => {
-              if (e.key === 'Enter' && !e.shiftKey) {
-                e.preventDefault()
-
-                if (generating) {
-                  return
-                }
-
-                if (!prompt) {
-                  return
-                }
-
-                await setMessages(messages => {
-                  return [...messages, { sender: 'human', content: prompt }, { sender: 'bot', content: '' }]
-                })
-
-                setPrompt('')
-
-                setGenerating(true)
-                await generate(prompt, model, res => {
-                  setMessages(messages => {
-                    let last = messages[messages.length - 1]
-                    return [...messages.slice(0, messages.length - 1), { ...last, content: last.content + res }]
-                  })
-                })
-                setGenerating(false)
-              }
-            }}
-          ></textarea>
+          </>
+        )}
+        {step === Step.FINISH && (
+          <>
+            <div className='mx-auto flex flex-col space-y-20 text-center'>
+              <h1 className='mt-4 text-2xl tracking-tight text-gray-900'>Run your first model</h1>
+              <div className='flex flex-col'>
+                <div className='group relative flex items-center'>
+                  <pre className='language-none text-2xs w-full rounded-md bg-gray-100 px-4 py-3 text-start leading-normal'>
+                    {command}
+                  </pre>
+                  <button
+                    className={`no-drag absolute right-[5px] px-2 py-2 ${
+                      commandCopied
+                        ? 'text-gray-900 opacity-100 hover:cursor-auto'
+                        : 'text-gray-200 opacity-50 hover:cursor-pointer'
+                    } hover:font-bold hover:text-gray-900 group-hover:opacity-100`}
+                    onClick={() => {
+                      copy(command)
+                      setCommandCopied(true)
+                      setTimeout(() => setCommandCopied(false), 3000)
+                    }}
+                  >
+                    {commandCopied ? (
+                      <CheckIcon className='h-4 w-4 font-bold text-gray-500' />
+                    ) : (
+                      <DocumentDuplicateIcon className='h-4 w-4 text-gray-500' />
+                    )}
+                  </button>
+                </div>
+                <p className='mx-auto my-4 w-[70%] text-xs text-gray-400'>
+                  Run this command in your favorite terminal.
+                </p>
+              </div>
+              <button
+                onClick={() => {
+                  store.set('first-time-run', true)
+                  window.close()
+                }}
+                className='no-drag rounded-dm mx-auto w-[60%] rounded-md bg-black px-4 py-2 text-sm text-white hover:brightness-110'
+              >
+                Finish
+              </button>
+            </div>
+          </>
         )}
       </div>
     </div>

app/src/declarations.d.ts

@@ -0,0 +1,4 @@
+declare module '*.svg' {
+  const content: string
+  export default content
+}

app/src/index.ts

@@ -1,161 +1,183 @@
-import { spawn, exec } from 'child_process'
-import { app, autoUpdater, dialog, Tray, Menu } from 'electron'
+import { spawn } from 'child_process'
+import { app, autoUpdater, dialog, Tray, Menu, BrowserWindow, nativeTheme } from 'electron'
 import Store from 'electron-store'
+import winston from 'winston'
+import 'winston-daily-rotate-file'
 import * as path from 'path'
-import * as fs from 'fs'
 
 import { analytics, id } from './telemetry'
+import { installed } from './install'
 
 require('@electron/remote/main').initialize()
 
 const store = new Store()
 let tray: Tray | null = null
+let welcomeWindow: BrowserWindow | null = null
+
+declare const MAIN_WINDOW_WEBPACK_ENTRY: string
+
+const logger = winston.createLogger({
+  transports: [
+    new winston.transports.Console(),
+    new winston.transports.File({
+      filename: path.join(app.getPath('home'), '.ollama', 'logs', 'server.log'),
+      maxsize: 1024 * 1024 * 20,
+      maxFiles: 5,
+    }),
+  ],
+  format: winston.format.printf(info => info.message),
+})
 
 const SingleInstanceLock = app.requestSingleInstanceLock()
 if (!SingleInstanceLock) {
   app.quit()
 }
 
-const createSystemtray = () => {
-  let iconPath = path.join(__dirname, '..', '..', 'assets', 'ollama_icon_16x16Template.png')
+function firstRunWindow() {
+  // Create the browser window.
+  welcomeWindow = new BrowserWindow({
+    width: 400,
+    height: 500,
+    frame: false,
+    fullscreenable: false,
+    resizable: false,
+    movable: true,
+    show: false,
+    webPreferences: {
+      nodeIntegration: true,
+      contextIsolation: false,
+    },
+    alwaysOnTop: true,
+  })
+
+  require('@electron/remote/main').enable(welcomeWindow.webContents)
+
+  // and load the index.html of the app.
+  welcomeWindow.loadURL(MAIN_WINDOW_WEBPACK_ENTRY)
+
+  welcomeWindow.on('ready-to-show', () => welcomeWindow.show())
+
+  // for debugging
+  // welcomeWindow.webContents.openDevTools()
+
+  if (process.platform === 'darwin') {
+    app.dock.hide()
+  }
+}
+
+function createSystemtray() {
+  let iconPath = nativeTheme.shouldUseDarkColors
+    ? path.join(__dirname, '..', '..', 'assets', 'ollama_icon_16x16Template.png')
+    : path.join(__dirname, '..', '..', 'assets', 'ollama_outline_icon_16x16Template.png')
 
   if (app.isPackaged) {
-    iconPath = path.join(process.resourcesPath, 'ollama_icon_16x16Template.png')
+    iconPath = nativeTheme.shouldUseDarkColors
+      ? path.join(process.resourcesPath, 'ollama_icon_16x16Template.png')
+      : path.join(process.resourcesPath, 'ollama_outline_icon_16x16Template.png')
   }
 
   tray = new Tray(iconPath)
 
+  nativeTheme.on('updated', function theThemeHasChanged() {
+    if (nativeTheme.shouldUseDarkColors) {
+      app.isPackaged
+        ? tray.setImage(path.join(process.resourcesPath, 'ollama_icon_16x16Template.png'))
+        : tray.setImage(path.join(__dirname, '..', '..', 'assets', 'ollama_icon_16x16Template.png'))
+    } else {
+      app.isPackaged
+        ? tray.setImage(path.join(process.resourcesPath, 'ollama_outline_icon_16x16Template.png'))
+        : tray.setImage(path.join(__dirname, '..', '..', 'assets', 'ollama_outline_icon_16x16Template.png'))
+    }
+  })
+
   const contextMenu = Menu.buildFromTemplate([{ role: 'quit', label: 'Quit Ollama', accelerator: 'Command+Q' }])
 
   tray.setContextMenu(contextMenu)
   tray.setToolTip('Ollama')
 }
 
-// Handle creating/removing shortcuts on Windows when installing/uninstalling.
 if (require('electron-squirrel-startup')) {
   app.quit()
 }
 
-const ollama = path.join(process.resourcesPath, 'ollama')
-
-// if the app is packaged then run the server
-if (app.isPackaged) {
-  // Start the executable
-  console.log(`Starting server`)
-  const proc = spawn(ollama, ['serve'])
-  proc.stdout.on('data', data => {
-    console.log(`server: ${data}`)
-  })
-  proc.stderr.on('data', data => {
-    console.error(`server: ${data}`)
-  })
-
-  process.on('exit', () => {
-    proc.kill()
-  })
-}
-
 function server() {
   const binary = app.isPackaged
     ? path.join(process.resourcesPath, 'ollama')
-    : path.resolve(__dirname, '..', '..', 'ollama')
+    : path.resolve(process.cwd(), '..', 'ollama')
 
-  console.log(`Starting server`)
   const proc = spawn(binary, ['serve'])
+
   proc.stdout.on('data', data => {
-    console.log(`server: ${data}`)
-  })
-  proc.stderr.on('data', data => {
-    console.error(`server: ${data}`)
+    logger.info(data.toString().trim())
   })
 
-  process.on('exit', () => {
+  proc.stderr.on('data', data => {
+    logger.error(data.toString().trim())
+  })
+
+  function restart() {
+    setTimeout(server, 3000)
+  }
+
+  proc.on('exit', restart)
+
+  app.on('before-quit', () => {
+    proc.off('exit', restart)
     proc.kill()
   })
 }
 
-function installCLI() {
-  const symlinkPath = '/usr/local/bin/ollama'
-
-  if (fs.existsSync(symlinkPath) && fs.readlinkSync(symlinkPath) === ollama) {
-    return
-  }
-
-  dialog
-    .showMessageBox({
-      type: 'info',
-      title: 'Ollama CLI installation',
-      message: 'To make the Ollama command work in your terminal, it needs administrator privileges.',
-      buttons: ['OK'],
-    })
-    .then(result => {
-      if (result.response === 0) {
-        const command = `
-    do shell script "ln -F -s ${ollama} /usr/local/bin/ollama" with administrator privileges
-    `
-        exec(`osascript -e '${command}'`, (error: Error | null, stdout: string, stderr: string) => {
-          if (error) {
-            console.error(`exec error: ${error}`)
-            return
-          }
-          console.log(`stdout: ${stdout}`)
-          console.error(`stderr: ${stderr}`)
-        })
-      }
-    })
+if (process.platform === 'darwin') {
+  app.dock.hide()
 }
 
 app.on('ready', () => {
   if (process.platform === 'darwin') {
-    app.dock.hide()
+    if (app.isPackaged) {
+      if (!app.isInApplicationsFolder()) {
+        const chosen = dialog.showMessageBoxSync({
+          type: 'question',
+          buttons: ['Move to Applications', 'Do Not Move'],
+          message: 'Ollama works best when run from the Applications directory.',
+          defaultId: 0,
+          cancelId: 1,
+        })
 
-    if (!store.has('first-time-run')) {
-      // This is the first run
-      app.setLoginItemSettings({ openAtLogin: true })
-      store.set('first-time-run', false)
-    } else {
-      // The app has been run before
-      app.setLoginItemSettings({ openAtLogin: app.getLoginItemSettings().openAtLogin })
-    }
-
-    if (!app.isInApplicationsFolder()) {
-      const chosen = dialog.showMessageBoxSync({
-        type: 'question',
-        buttons: ['Move to Applications', 'Do Not Move'],
-        message: 'Ollama works best when run from the Applications directory.',
-        defaultId: 0,
-        cancelId: 1,
-      })
-
-      if (chosen === 0) {
-        try {
-          app.moveToApplicationsFolder({
-            conflictHandler: conflictType => {
-              if (conflictType === 'existsAndRunning') {
-                dialog.showMessageBoxSync({
-                  type: 'info',
-                  message: 'Cannot move to Applications directory',
-                  detail:
-                    'Another version of Ollama is currently running from your Applications directory. Close it first and try again.',
-                })
-              }
-              return true
-            },
-          })
-          return
-        } catch (e) {
-          console.error('Failed to move to applications folder')
-          console.error(e)
+        if (chosen === 0) {
+          try {
+            app.moveToApplicationsFolder({
+              conflictHandler: conflictType => {
+                if (conflictType === 'existsAndRunning') {
+                  dialog.showMessageBoxSync({
+                    type: 'info',
+                    message: 'Cannot move to Applications directory',
+                    detail:
+                      'Another version of Ollama is currently running from your Applications directory. Close it first and try again.',
+                  })
+                }
+                return true
+              },
+            })
+            return
+          } catch (e) {
+            logger.error(`[Move to Applications] Failed to move to applications folder - ${e.message}}`)
+          }
         }
       }
     }
   }
 
   createSystemtray()
+  server()
 
-  if (app.isPackaged) {
-    installCLI()
+  if (store.get('first-time-run') && installed()) {
+    app.setLoginItemSettings({ openAtLogin: app.getLoginItemSettings().openAtLogin })
+    return
   }
+
+  // This is the first run or the CLI is no longer installed
+  app.setLoginItemSettings({ openAtLogin: true })
+  firstRunWindow()
 })
 
 // Quit when all windows are closed, except on macOS. There, it's common
@@ -183,8 +205,6 @@ async function heartbeat() {
   })
 }
 
-heartbeat()
-
 if (app.isPackaged) {
   heartbeat()
   autoUpdater.checkForUpdates()
@@ -195,7 +215,7 @@ if (app.isPackaged) {
 }
 
 autoUpdater.on('error', e => {
-  console.error('update check failed', e)
+  logger.error(`update check failed - ${e.message}`)
 })
 
 autoUpdater.on('update-downloaded', (event, releaseNotes, releaseName) => {

app/src/install.ts

@@ -0,0 +1,26 @@
+import * as fs from 'fs'
+import { exec as cbExec } from 'child_process'
+import * as path from 'path'
+import { promisify } from 'util'
+
+const app = process && process.type === 'renderer' ? require('@electron/remote').app : require('electron').app
+const ollama = app.isPackaged ? path.join(process.resourcesPath, 'ollama') : path.resolve(process.cwd(), '..', 'ollama')
+const exec = promisify(cbExec)
+const symlinkPath = '/usr/local/bin/ollama'
+
+export function installed() {
+  return fs.existsSync(symlinkPath) && fs.readlinkSync(symlinkPath) === ollama
+}
+
+export async function install() {
+  const command = `do shell script "mkdir -p ${path.dirname(
+    symlinkPath
+  )} && ln -F -s ${ollama} ${symlinkPath}" with administrator privileges`
+
+  try {
+    await exec(`osascript -e '${command}'`)
+  } catch (error) {
+    console.error(`cli: failed to install cli: ${error.message}`)
+    return
+  }
+}

app/src/telemetry.ts

@@ -4,8 +4,6 @@ import Store from 'electron-store'
 
 const store = new Store()
 
-console.log(process.env)
-
 export const analytics = new Analytics({ writeKey: process.env.TELEMETRY_WRITE_KEY || '<empty>' })
 
 export function id(): string {

app/webpack.rules.ts

@@ -28,4 +28,8 @@ export const rules: Required<ModuleOptions>['rules'] = [
       },
     },
   },
+  {
+    test: /\.svg$/,
+    use: ['@svgr/webpack'],
+  },
 ]

cmd/cmd.go

@@ -5,36 +5,79 @@ import (
 	"context"
 	"errors"
 	"fmt"
+	"io"
 	"log"
 	"net"
+	"net/http"
 	"os"
-	"path"
+	"path/filepath"
 	"strings"
 	"time"
 
-	"github.com/schollz/progressbar/v3"
+	"github.com/chzyer/readline"
+	"github.com/dustin/go-humanize"
+	"github.com/olekukonko/tablewriter"
 	"github.com/spf13/cobra"
-	"golang.org/x/term"
 
 	"github.com/jmorganca/ollama/api"
+	"github.com/jmorganca/ollama/format"
+	"github.com/jmorganca/ollama/progressbar"
 	"github.com/jmorganca/ollama/server"
 )
 
-func cacheDir() string {
-	home, err := os.UserHomeDir()
+func CreateHandler(cmd *cobra.Command, args []string) error {
+	filename, _ := cmd.Flags().GetString("file")
+	filename, err := filepath.Abs(filename)
 	if err != nil {
-		panic(err)
+		return err
 	}
 
-	return path.Join(home, ".ollama")
+	client := api.NewClient()
+
+	var spinner *Spinner
+
+	request := api.CreateRequest{Name: args[0], Path: filename}
+	fn := func(resp api.CreateProgress) error {
+		if spinner != nil {
+			spinner.Stop()
+		}
+
+		spinner = NewSpinner(resp.Status)
+		go spinner.Spin(100 * time.Millisecond)
+
+		return nil
+	}
+
+	if err := client.Create(context.Background(), &request, fn); err != nil {
+		return err
+	}
+
+	if spinner != nil {
+		spinner.Stop()
+	}
+
+	return nil
 }
 
-func RunRun(cmd *cobra.Command, args []string) error {
-	_, err := os.Stat(args[0])
+func RunHandler(cmd *cobra.Command, args []string) error {
+	mp := server.ParseModelPath(args[0])
+	fp, err := mp.GetManifestPath(false)
+	if err != nil {
+		return err
+	}
+
+	_, err = os.Stat(fp)
 	switch {
 	case errors.Is(err, os.ErrNotExist):
-		if err := pull(args[0]); err != nil {
-			return err
+		if err := pull(args[0], false); err != nil {
+			var apiStatusError api.StatusError
+			if !errors.As(err, &apiStatusError) {
+				return err
+			}
+
+			if apiStatusError.StatusCode != http.StatusBadGateway {
+				return err
+			}
 		}
 	case err != nil:
 		return err
@@ -43,108 +86,300 @@ func RunRun(cmd *cobra.Command, args []string) error {
 	return RunGenerate(cmd, args)
 }
 
-func pull(model string) error {
+func PushHandler(cmd *cobra.Command, args []string) error {
 	client := api.NewClient()
+
+	insecure, err := cmd.Flags().GetBool("insecure")
+	if err != nil {
+		return err
+	}
+
+	var currentDigest string
 	var bar *progressbar.ProgressBar
-	return client.Pull(
-		context.Background(),
-		&api.PullRequest{Model: model},
-		func(progress api.PullProgress) error {
-			if bar == nil && progress.Percent == 100 {
-				// already downloaded
-				return nil
-			}
-			if bar == nil {
-				bar = progressbar.DefaultBytes(progress.Total)
-			}
 
-			return bar.Set64(progress.Completed)
-		},
-	)
+	request := api.PushRequest{Name: args[0], Insecure: insecure}
+	fn := func(resp api.ProgressResponse) error {
+		if resp.Digest != currentDigest && resp.Digest != "" {
+			currentDigest = resp.Digest
+			bar = progressbar.DefaultBytes(
+				int64(resp.Total),
+				fmt.Sprintf("pushing %s...", resp.Digest[7:19]),
+			)
+
+			bar.Set(resp.Completed)
+		} else if resp.Digest == currentDigest && resp.Digest != "" {
+			bar.Set(resp.Completed)
+		} else {
+			currentDigest = ""
+			fmt.Println(resp.Status)
+		}
+		return nil
+	}
+
+	if err := client.Push(context.Background(), &request, fn); err != nil {
+		return err
+	}
+	return nil
 }
 
-func RunGenerate(_ *cobra.Command, args []string) error {
+func ListHandler(cmd *cobra.Command, args []string) error {
+	client := api.NewClient()
+
+	models, err := client.List(context.Background())
+	if err != nil {
+		return err
+	}
+
+	var data [][]string
+
+	for _, m := range models.Models {
+		if len(args) == 0 || strings.HasPrefix(m.Name, args[0]) {
+			data = append(data, []string{m.Name, humanize.Bytes(uint64(m.Size)), format.HumanTime(m.ModifiedAt, "Never")})
+		}
+	}
+
+	table := tablewriter.NewWriter(os.Stdout)
+	table.SetHeader([]string{"NAME", "SIZE", "MODIFIED"})
+	table.SetHeaderAlignment(tablewriter.ALIGN_LEFT)
+	table.SetAlignment(tablewriter.ALIGN_LEFT)
+	table.SetHeaderLine(false)
+	table.SetBorder(false)
+	table.SetNoWhiteSpace(true)
+	table.SetTablePadding("\t")
+	table.AppendBulk(data)
+	table.Render()
+
+	return nil
+}
+
+func DeleteHandler(cmd *cobra.Command, args []string) error {
+	client := api.NewClient()
+
+	request := api.DeleteRequest{Name: args[0]}
+	if err := client.Delete(context.Background(), &request); err != nil {
+		return err
+	}
+	fmt.Printf("deleted '%s'\n", args[0])
+	return nil
+}
+
+func PullHandler(cmd *cobra.Command, args []string) error {
+	insecure, err := cmd.Flags().GetBool("insecure")
+	if err != nil {
+		return err
+	}
+
+	return pull(args[0], insecure)
+}
+
+func pull(model string, insecure bool) error {
+	client := api.NewClient()
+
+	var currentDigest string
+	var bar *progressbar.ProgressBar
+
+	request := api.PullRequest{Name: model, Insecure: insecure}
+	fn := func(resp api.ProgressResponse) error {
+		if resp.Digest != currentDigest && resp.Digest != "" {
+			currentDigest = resp.Digest
+			bar = progressbar.DefaultBytes(
+				int64(resp.Total),
+				fmt.Sprintf("pulling %s...", resp.Digest[7:19]),
+			)
+
+			bar.Set(resp.Completed)
+		} else if resp.Digest == currentDigest && resp.Digest != "" {
+			bar.Set(resp.Completed)
+		} else {
+			currentDigest = ""
+			fmt.Println(resp.Status)
+		}
+		return nil
+	}
+
+	if err := client.Pull(context.Background(), &request, fn); err != nil {
+		return err
+	}
+	return nil
+}
+
+func RunGenerate(cmd *cobra.Command, args []string) error {
 	if len(args) > 1 {
-		return generateOneshot(args[0], args[1:]...)
+		// join all args into a single prompt
+		return generate(cmd, args[0], strings.Join(args[1:], " "))
 	}
 
-	if term.IsTerminal(int(os.Stdin.Fd())) {
-		return generateInteractive(args[0])
+	if readline.IsTerminal(int(os.Stdin.Fd())) {
+		return generateInteractive(cmd, args[0])
 	}
 
-	return generateBatch(args[0])
+	return generateBatch(cmd, args[0])
 }
 
-func generate(model, prompt string) error {
+var generateContextKey struct{}
+
+func generate(cmd *cobra.Command, model, prompt string) error {
 	if len(strings.TrimSpace(prompt)) > 0 {
 		client := api.NewClient()
 
-		spinner := progressbar.NewOptions(-1,
-			progressbar.OptionSetWriter(os.Stderr),
-			progressbar.OptionThrottle(60*time.Millisecond),
-			progressbar.OptionSpinnerType(14),
-			progressbar.OptionSetRenderBlankState(true),
-			progressbar.OptionSetElapsedTime(false),
-			progressbar.OptionClearOnFinish(),
-		)
+		spinner := NewSpinner("")
+		go spinner.Spin(60 * time.Millisecond)
 
-		go func() {
-			for range time.Tick(60 * time.Millisecond) {
-				if spinner.IsFinished() {
-					break
-				}
+		var latest api.GenerateResponse
 
-				spinner.Add(1)
-			}
-		}()
+		generateContext, ok := cmd.Context().Value(generateContextKey).([]int)
+		if !ok {
+			generateContext = []int{}
+		}
 
-		client.Generate(context.Background(), &api.GenerateRequest{Model: model, Prompt: prompt}, func(resp api.GenerateResponse) error {
+		request := api.GenerateRequest{Model: model, Prompt: prompt, Context: generateContext}
+		fn := func(resp api.GenerateResponse) error {
 			if !spinner.IsFinished() {
 				spinner.Finish()
 			}
 
+			latest = resp
+
 			fmt.Print(resp.Response)
+
+			cmd.SetContext(context.WithValue(cmd.Context(), generateContextKey, resp.Context))
 			return nil
-		})
+		}
 
-		fmt.Println()
-		fmt.Println()
-	}
-
-	return nil
-}
-
-func generateOneshot(model string, prompts ...string) error {
-	for _, prompt := range prompts {
-		fmt.Printf(">>> %s\n", prompt)
-		if err := generate(model, prompt); err != nil {
+		if err := client.Generate(context.Background(), &request, fn); err != nil {
 			return err
 		}
+
+		fmt.Println()
+		fmt.Println()
+
+		verbose, err := cmd.Flags().GetBool("verbose")
+		if err != nil {
+			return err
+		}
+
+		if verbose {
+			latest.Summary()
+		}
 	}
 
 	return nil
 }
 
-func generateInteractive(model string) error {
-	fmt.Print(">>> ")
-	scanner := bufio.NewScanner(os.Stdin)
-	for scanner.Scan() {
-		if err := generate(model, scanner.Text()); err != nil {
+func generateInteractive(cmd *cobra.Command, model string) error {
+	home, err := os.UserHomeDir()
+	if err != nil {
+		return err
+	}
+
+	completer := readline.NewPrefixCompleter(
+		readline.PcItem("/help"),
+		readline.PcItem("/list"),
+		readline.PcItem("/set",
+			readline.PcItem("history"),
+			readline.PcItem("nohistory"),
+			readline.PcItem("verbose"),
+			readline.PcItem("quiet"),
+			readline.PcItem("mode",
+				readline.PcItem("vim"),
+				readline.PcItem("emacs"),
+				readline.PcItem("default"),
+			),
+		),
+		readline.PcItem("/exit"),
+		readline.PcItem("/bye"),
+	)
+
+	usage := func() {
+		fmt.Fprintln(os.Stderr, "commands:")
+		fmt.Fprintln(os.Stderr, completer.Tree("  "))
+	}
+
+	config := readline.Config{
+		Prompt:       ">>> ",
+		HistoryFile:  filepath.Join(home, ".ollama", "history"),
+		AutoComplete: completer,
+	}
+
+	scanner, err := readline.NewEx(&config)
+	if err != nil {
+		return err
+	}
+	defer scanner.Close()
+
+	for {
+		line, err := scanner.Readline()
+		switch {
+		case errors.Is(err, io.EOF):
+			return nil
+		case errors.Is(err, readline.ErrInterrupt):
+			if line == "" {
+				return nil
+			}
+
+			continue
+		case err != nil:
 			return err
 		}
 
-		fmt.Print(">>> ")
-	}
+		line = strings.TrimSpace(line)
 
-	return nil
+		switch {
+		case strings.HasPrefix(line, "/list"):
+			args := strings.Fields(line)
+			if err := ListHandler(cmd, args[1:]); err != nil {
+				return err
+			}
+
+			continue
+		case strings.HasPrefix(line, "/set"):
+			args := strings.Fields(line)
+			if len(args) > 1 {
+				switch args[1] {
+				case "history":
+					scanner.HistoryEnable()
+					continue
+				case "nohistory":
+					scanner.HistoryDisable()
+					continue
+				case "verbose":
+					cmd.Flags().Set("verbose", "true")
+					continue
+				case "quiet":
+					cmd.Flags().Set("verbose", "false")
+					continue
+				case "mode":
+					if len(args) > 2 {
+						switch args[2] {
+						case "vim":
+							scanner.SetVimMode(true)
+							continue
+						case "emacs", "default":
+							scanner.SetVimMode(false)
+							continue
+						}
+					}
+				}
+			}
+		case line == "/help", line == "/?":
+			usage()
+			continue
+		case line == "/exit", line == "/bye":
+			return nil
+		}
+
+		if err := generate(cmd, model, line); err != nil {
+			return err
+		}
+	}
 }
 
-func generateBatch(model string) error {
+func generateBatch(cmd *cobra.Command, model string) error {
 	scanner := bufio.NewScanner(os.Stdin)
 	for scanner.Scan() {
 		prompt := scanner.Text()
 		fmt.Printf(">>> %s\n", prompt)
-		if err := generate(model, prompt); err != nil {
+		if err := generate(cmd, model, prompt); err != nil {
 			return err
 		}
 	}
@@ -181,21 +416,28 @@ func NewCLI() *cobra.Command {
 		CompletionOptions: cobra.CompletionOptions{
 			DisableDefaultCmd: true,
 		},
-		PersistentPreRunE: func(_ *cobra.Command, args []string) error {
-			// create the models directory and it's parent
-			return os.MkdirAll(path.Join(cacheDir(), "models"), 0o700)
-		},
 	}
 
 	cobra.EnableCommandSorting = false
 
+	createCmd := &cobra.Command{
+		Use:   "create MODEL",
+		Short: "Create a model from a Modelfile",
+		Args:  cobra.MinimumNArgs(1),
+		RunE:  CreateHandler,
+	}
+
+	createCmd.Flags().StringP("file", "f", "Modelfile", "Name of the Modelfile (default \"Modelfile\")")
+
 	runCmd := &cobra.Command{
 		Use:   "run MODEL [PROMPT]",
 		Short: "Run a model",
 		Args:  cobra.MinimumNArgs(1),
-		RunE:  RunRun,
+		RunE:  RunHandler,
 	}
 
+	runCmd.Flags().Bool("verbose", false, "Show timings for response")
+
 	serveCmd := &cobra.Command{
 		Use:     "serve",
 		Aliases: []string{"start"},
@@ -203,9 +445,46 @@ func NewCLI() *cobra.Command {
 		RunE:    RunServer,
 	}
 
+	pullCmd := &cobra.Command{
+		Use:   "pull MODEL",
+		Short: "Pull a model from a registry",
+		Args:  cobra.MinimumNArgs(1),
+		RunE:  PullHandler,
+	}
+
+	pullCmd.Flags().Bool("insecure", false, "Use an insecure registry")
+
+	pushCmd := &cobra.Command{
+		Use:   "push MODEL",
+		Short: "Push a model to a registry",
+		Args:  cobra.MinimumNArgs(1),
+		RunE:  PushHandler,
+	}
+
+	pushCmd.Flags().Bool("insecure", false, "Use an insecure registry")
+
+	listCmd := &cobra.Command{
+		Use:     "list",
+		Aliases: []string{"ls"},
+		Short:   "List models",
+		RunE:    ListHandler,
+	}
+
+	deleteCmd := &cobra.Command{
+		Use:   "rm",
+		Short: "Remove a model",
+		Args:  cobra.MinimumNArgs(1),
+		RunE:  DeleteHandler,
+	}
+
 	rootCmd.AddCommand(
 		serveCmd,
+		createCmd,
 		runCmd,
+		pullCmd,
+		pushCmd,
+		listCmd,
+		deleteCmd,
 	)
 
 	return rootCmd

cmd/spinner.go

@@ -0,0 +1,44 @@
+package cmd
+
+import (
+	"fmt"
+	"os"
+	"time"
+
+	"github.com/jmorganca/ollama/progressbar"
+)
+
+type Spinner struct {
+	description string
+	*progressbar.ProgressBar
+}
+
+func NewSpinner(description string) *Spinner {
+	return &Spinner{
+		description: description,
+		ProgressBar: progressbar.NewOptions(-1,
+			progressbar.OptionSetWriter(os.Stderr),
+			progressbar.OptionThrottle(60*time.Millisecond),
+			progressbar.OptionSpinnerType(14),
+			progressbar.OptionSetRenderBlankState(true),
+			progressbar.OptionSetElapsedTime(false),
+			progressbar.OptionClearOnFinish(),
+			progressbar.OptionSetDescription(description),
+		),
+	}
+}
+
+func (s *Spinner) Spin(tick time.Duration) {
+	for range time.Tick(tick) {
+		if s.IsFinished() {
+			break
+		}
+
+		s.Add(1)
+	}
+}
+
+func (s *Spinner) Stop() {
+	s.Finish()
+	fmt.Println(s.description)
+}

docs/development.md

@@ -3,13 +3,19 @@
 Install required tools:
 
 ```
-brew install cmake go node
+brew install go
 ```
 
-Then run `make`:
+Enable CGO:
 
 ```
-make
+export CGO_ENABLED=1
+```
+
+Then build ollama:
+
+```
+go build .
 ```
 
 Now you can run `ollama`:

docs/modelfile.md

@@ -0,0 +1,105 @@
+# Ollama Model File
+
+> Note: this model file syntax is in development
+
+A model file is the blueprint to create and share models with Ollama.
+
+## Format
+
+The format of the Modelfile:
+
+```modelfile
+# comment
+INSTRUCTION arguments
+```
+
+| Instruction       | Description                                           |
+| ----------------- | ----------------------------------------------------- |
+| `FROM` (required) | Defines the base model to use                         |
+| `PARAMETER`       | Sets the parameters for how Ollama will run the model |
+| `SYSTEM`          | Specifies the system prompt that will set the context |
+| `TEMPLATE`        | The full prompt template to be sent to the model      |
+| `LICENSE`         | Specifies the legal license                           |
+
+## Examples
+
+An example of a model file creating a mario blueprint:
+
+```
+FROM llama2
+# sets the temperature to 1 [higher is more creative, lower is more coherent]
+# sets the context size to 4096
+PARAMETER temperature 1
+PARAMETER num_ctx 4096
+
+# Overriding the system prompt
+SYSTEM You are Mario from super mario bros, acting as an assistant.
+```
+
+To use this:
+
+1. Save it as a file (eg. `Modelfile`)
+2. `ollama create NAME -f <location of the file eg. ./Modelfile>'`
+3. `ollama run NAME`
+4. Start using the model!
+
+## FROM (Required)
+
+The FROM instruction defines the base model to use when creating a model.
+
+```
+FROM <model name>:<tag>
+```
+
+### Build from llama2
+
+```
+FROM llama2
+```
+
+A list of available base models:
+<https://github.com/jmorganca/ollama#model-library>
+
+### Build from a bin file
+
+```
+FROM ./ollama-model.bin
+```
+
+## PARAMETER (Optional)
+
+The `PARAMETER` instruction defines a parameter that can be set when the model is run.
+
+```
+PARAMETER <parameter> <parametervalue>
+```
+
+### Valid Parameters and Values
+
+| Parameter      | Description                                                                                                                                                                                                                                             | Value Type | Example Usage      |
+| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | ------------------ |
+| num_ctx        | Sets the size of the prompt context size length model. (Default: 2048)                                                                                                                                                                                  | int        | num_ctx 4096       |
+| temperature    | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)                                                                                                                                     | float      | temperature 0.7    |
+| top_k          | Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)                                                                        | int        | top_k 40           |
+| top_p          | Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)                                                                 | float      | top_p 0.9          |
+| num_gpu        | The number of GPUs to use. On macOS it defaults to 1 to enable metal support, 0 to disable.                                                                                                                                                             | int        | num_gpu 1          |
+| repeat_last_n  | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = ctx-size)                                                                                                                                          | int        | repeat_last_n 64   |
+| repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)                                                                     | float      | repeat_penalty 1.1 |
+| tfs_z          | Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)                                               | float      | tfs_z 1            |
+| mirostat       | Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)                                                                                                                                         | int        | mirostat 0         |
+| mirostat_tau   | Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)                                                                                                         | float      | mirostat_tau 5.0   |
+| mirostat_eta   | Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)                        | float      | mirostat_eta 0.1   |
+| num_thread     | Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). | int        | num_thread 8       |
+
+## Prompt
+
+When building on top of the base models supplied by Ollama, it comes with the prompt template predefined. To override the supplied system prompt, simply add `SYSTEM insert system prompt` to change the system prompt.
+
+### Prompt Template
+
+`TEMPLATE` the full prompt template to be passed into the model. It may include (optionally) a system prompt, user prompt, and assistant prompt. This is used to create a full custom prompt, and syntax may be model specific.
+
+## Notes
+
+- the **modelfile is not case sensitive**. In the examples, we use uppercase for instructions to make it easier to distinguish it from arguments.
+- Instructions can be in any order. In the examples, we start with FROM instruction to keep it easily readable.

docs/python.md

@@ -1,64 +0,0 @@
-# Python SDK
-
-## Install
-
-```
-pip install ollama
-```
-
-## Example
-
-```python
-import ollama
-ollama.generate("orca-mini-3b", "hi")
-```
-
-## Reference
-
-### `ollama.generate(model, message)`
-
-Generate a completion
-
-```python
-ollama.generate("./llama-7b-ggml.bin", "hi")
-```
-
-### `ollama.models()`
-
-List available local models
-
-```python
-models = ollama.models()
-```
-
-### `ollama.load(model)`
-
-Manually a model for generation
-
-```python
-ollama.load("model")
-```
-
-### `ollama.unload(model)`
-
-Unload a model
-
-```python
-ollama.unload("model")
-```
-
-### `ollama.pull(model)`
-
-Download a model
-
-```python
-ollama.pull("huggingface.co/thebloke/llama-7b-ggml")
-```
-
-### `ollama.search(query)`
-
-Search for compatible models that Ollama can run
-
-```python
-ollama.search("llama-7b")
-```

examples/README.md

@@ -0,0 +1,15 @@
+# Examples
+
+This directory contains examples that can be created and run with `ollama`.
+
+To create a model:
+
+```
+ollama create example -f <example file>
+```
+
+To run a model:
+
+```
+ollama run example
+```

examples/mario/Modelfile

@@ -0,0 +1,5 @@
+FROM llama2
+PARAMETER temperature 1
+SYSTEM """
+You are Mario from super mario bros, acting as an assistant.
+"""

examples/mario/readme.md

@@ -0,0 +1,43 @@
+<img src="logo.png" alt="image of Italian plumber" height="200"/>
+
+# Example character: Mario
+
+This example shows how to create a basic character using Llama2 as the base model.
+
+To run this example:
+
+1. Download the Modelfile
+2. `ollama pull llama2` to get the base model used in the model file.
+3. `ollama create NAME -f ./Modelfile`
+4. `ollama run NAME`
+
+Ask it some questions like "Who are you?" or "Is Peach in trouble again?"
+
+## Editing this file
+
+What the model file looks like:
+
+```
+FROM llama2
+PARAMETER temperature 1
+SYSTEM """
+You are Mario from Super Mario Bros, acting as an assistant.
+"""
+```
+
+What if you want to change its behaviour?
+
+- Try changing the prompt
+- Try changing the parameters [Docs](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md)
+- Try changing the model (e.g. An uncensored model by `FROM wizard-vicuna` this is the wizard-vicuna uncensored model )
+
+Once the changes are made,
+
+1. `ollama create NAME -f ./Modelfile`
+2. `ollama run NAME`
+3. Iterate until you are happy with the results.
+
+Notes:
+
+- This example is for research purposes only. There is no affiliation with any entity.
+- When using an uncensored model, please be aware that it may generate offensive content.

examples/midjourney-prompter/Modelfile

@@ -0,0 +1,8 @@
+# Modelfile for creating a Midjourney prompts from a topic
+# This prompt was adapted from the original at https://www.greataiprompts.com/guide/midjourney/best-chatgpt-prompt-for-midjourney/
+# Run `ollama create mj -f ./Modelfile` and then `ollama run mj` and enter a topic
+
+FROM nous-hermes
+SYSTEM """
+Embrace your role as an AI-powered creative assistant, employing Midjourney to manifest compelling AI-generated art. I will outline a specific image concept, and in response, you must produce an exhaustive, multifaceted prompt for Midjourney, ensuring every detail of the original concept is represented in your instructions. Midjourney doesn't do well with text, so after the prompt, give me instructions that I can use to create the titles in a image editor.
+"""

examples/recipemaker/Modelfile

@@ -0,0 +1,6 @@
+# Modelfile for creating a recipe from a list of ingredients
+# Run `ollama create recipemaker -f ./Modelfile` and then `ollama run recipemaker` and feed it lists of ingredients to create recipes around.
+FROM nous-hermes
+SYSTEM """
+The instruction will be a list of ingredients. You should generate a recipe that can be made in less than an hour. You can also include ingredients that most people will find in their pantry every day. The recipe should be 4 people and you should include a description of what the meal will taste like
+"""

examples/tweetwriter/Modelfile

@@ -0,0 +1,7 @@
+# Modelfile for creating a tweet from a topic
+# Run `ollama create tweetwriter -f ./Modelfile` and then `ollama run tweetwriter` and enter a topic
+
+FROM nous-hermes
+SYSTEM """
+You are a content marketer who needs to come up with a short but succinct tweet. Make sure to include the appropriate hashtags and links. Sometimes when appropriate, describe a meme that can be includes as well. All answers should be in the form of a tweet which has a max size of 280 characters. Every instruction will be the topic to create a tweet about.
+"""

format/time.go

@@ -0,0 +1,141 @@
+package format
+
+import (
+	"fmt"
+	"math"
+	"strings"
+	"time"
+)
+
+// HumanDuration returns a human-readable approximation of a duration
+// (eg. "About a minute", "4 hours ago", etc.).
+// Modified version of github.com/docker/go-units.HumanDuration
+func HumanDuration(d time.Duration) string {
+	return HumanDurationWithCase(d, true)
+}
+
+// HumanDurationWithCase returns a human-readable approximation of a
+// duration (eg. "About a minute", "4 hours ago", etc.). but allows
+// you to specify whether the first word should be capitalized
+// (eg. "About" vs. "about")
+func HumanDurationWithCase(d time.Duration, useCaps bool) string {
+	seconds := int(d.Seconds())
+
+	switch {
+	case seconds < 1:
+		if useCaps {
+			return "Less than a second"
+		}
+		return "less than a second"
+	case seconds == 1:
+		return "1 second"
+	case seconds < 60:
+		return fmt.Sprintf("%d seconds", seconds)
+	}
+
+	minutes := int(d.Minutes())
+	switch {
+	case minutes == 1:
+		if useCaps {
+			return "About a minute"
+		}
+		return "about a minute"
+	case minutes < 60:
+		return fmt.Sprintf("%d minutes", minutes)
+	}
+
+	hours := int(math.Round(d.Hours()))
+	switch {
+	case hours == 1:
+		if useCaps {
+			return "About an hour"
+		}
+		return "about an hour"
+	case hours < 48:
+		return fmt.Sprintf("%d hours", hours)
+	case hours < 24*7*2:
+		return fmt.Sprintf("%d days", hours/24)
+	case hours < 24*30*2:
+		return fmt.Sprintf("%d weeks", hours/24/7)
+	case hours < 24*365*2:
+		return fmt.Sprintf("%d months", hours/24/30)
+	}
+
+	return fmt.Sprintf("%d years", int(d.Hours())/24/365)
+}
+
+func HumanTime(t time.Time, zeroValue string) string {
+	return humanTimeWithCase(t, zeroValue, true)
+}
+
+func HumanTimeLower(t time.Time, zeroValue string) string {
+	return humanTimeWithCase(t, zeroValue, false)
+}
+
+func humanTimeWithCase(t time.Time, zeroValue string, useCaps bool) string {
+	if t.IsZero() {
+		return zeroValue
+	}
+
+	delta := time.Since(t)
+	if delta < 0 {
+		return HumanDurationWithCase(-delta, useCaps) + " from now"
+	}
+	return HumanDurationWithCase(delta, useCaps) + " ago"
+}
+
+// ExcatDuration returns a human readable hours/minutes/seconds or milliseconds format of a duration
+// the most precise level of duration is milliseconds
+func ExactDuration(d time.Duration) string {
+	if d.Seconds() < 1 {
+		if d.Milliseconds() == 1 {
+			return fmt.Sprintf("%d millisecond", d.Milliseconds())
+		}
+		return fmt.Sprintf("%d milliseconds", d.Milliseconds())
+	}
+
+	var readableDur strings.Builder
+
+	dur := d.String()
+
+	// split the default duration string format of 0h0m0s into something nicer to read
+	h := strings.Split(dur, "h")
+	if len(h) > 1 {
+		hours := h[0]
+		if hours == "1" {
+			readableDur.WriteString(fmt.Sprintf("%s hour ", hours))
+		} else {
+			readableDur.WriteString(fmt.Sprintf("%s hours ", hours))
+		}
+		dur = h[1]
+	}
+
+	m := strings.Split(dur, "m")
+	if len(m) > 1 {
+		mins := m[0]
+		switch mins {
+		case "0":
+			// skip
+		case "1":
+			readableDur.WriteString(fmt.Sprintf("%s minute ", mins))
+		default:
+			readableDur.WriteString(fmt.Sprintf("%s minutes ", mins))
+		}
+		dur = m[1]
+	}
+
+	s := strings.Split(dur, "s")
+	if len(s) > 0 {
+		sec := s[0]
+		switch sec {
+		case "0":
+			// skip
+		case "1":
+			readableDur.WriteString(fmt.Sprintf("%s second ", sec))
+		default:
+			readableDur.WriteString(fmt.Sprintf("%s seconds ", sec))
+		}
+	}
+
+	return strings.TrimSpace(readableDur.String())
+}

format/time_test.go

@@ -0,0 +1,102 @@
+package format
+
+import (
+	"testing"
+	"time"
+)
+
+func assertEqual(t *testing.T, a interface{}, b interface{}) {
+	if a != b {
+		t.Errorf("Assert failed, expected %v, got %v", b, a)
+	}
+}
+
+func TestHumanDuration(t *testing.T) {
+	day := 24 * time.Hour
+	week := 7 * day
+	month := 30 * day
+	year := 365 * day
+
+	assertEqual(t, "Less than a second", HumanDuration(450*time.Millisecond))
+	assertEqual(t, "Less than a second", HumanDurationWithCase(450*time.Millisecond, true))
+	assertEqual(t, "less than a second", HumanDurationWithCase(450*time.Millisecond, false))
+	assertEqual(t, "1 second", HumanDuration(1*time.Second))
+	assertEqual(t, "45 seconds", HumanDuration(45*time.Second))
+	assertEqual(t, "46 seconds", HumanDuration(46*time.Second))
+	assertEqual(t, "59 seconds", HumanDuration(59*time.Second))
+	assertEqual(t, "About a minute", HumanDuration(60*time.Second))
+	assertEqual(t, "About a minute", HumanDurationWithCase(1*time.Minute, true))
+	assertEqual(t, "about a minute", HumanDurationWithCase(1*time.Minute, false))
+	assertEqual(t, "3 minutes", HumanDuration(3*time.Minute))
+	assertEqual(t, "35 minutes", HumanDuration(35*time.Minute))
+	assertEqual(t, "35 minutes", HumanDuration(35*time.Minute+40*time.Second))
+	assertEqual(t, "45 minutes", HumanDuration(45*time.Minute))
+	assertEqual(t, "45 minutes", HumanDuration(45*time.Minute+40*time.Second))
+	assertEqual(t, "46 minutes", HumanDuration(46*time.Minute))
+	assertEqual(t, "59 minutes", HumanDuration(59*time.Minute))
+	assertEqual(t, "About an hour", HumanDuration(1*time.Hour))
+	assertEqual(t, "About an hour", HumanDurationWithCase(1*time.Hour+29*time.Minute, true))
+	assertEqual(t, "about an hour", HumanDurationWithCase(1*time.Hour+29*time.Minute, false))
+	assertEqual(t, "2 hours", HumanDuration(1*time.Hour+31*time.Minute))
+	assertEqual(t, "2 hours", HumanDuration(1*time.Hour+59*time.Minute))
+	assertEqual(t, "3 hours", HumanDuration(3*time.Hour))
+	assertEqual(t, "3 hours", HumanDuration(3*time.Hour+29*time.Minute))
+	assertEqual(t, "4 hours", HumanDuration(3*time.Hour+31*time.Minute))
+	assertEqual(t, "4 hours", HumanDuration(3*time.Hour+59*time.Minute))
+	assertEqual(t, "4 hours", HumanDuration(3*time.Hour+60*time.Minute))
+	assertEqual(t, "24 hours", HumanDuration(24*time.Hour))
+	assertEqual(t, "36 hours", HumanDuration(1*day+12*time.Hour))
+	assertEqual(t, "2 days", HumanDuration(2*day))
+	assertEqual(t, "7 days", HumanDuration(7*day))
+	assertEqual(t, "13 days", HumanDuration(13*day+5*time.Hour))
+	assertEqual(t, "2 weeks", HumanDuration(2*week))
+	assertEqual(t, "2 weeks", HumanDuration(2*week+4*day))
+	assertEqual(t, "3 weeks", HumanDuration(3*week))
+	assertEqual(t, "4 weeks", HumanDuration(4*week))
+	assertEqual(t, "4 weeks", HumanDuration(4*week+3*day))
+	assertEqual(t, "4 weeks", HumanDuration(1*month))
+	assertEqual(t, "6 weeks", HumanDuration(1*month+2*week))
+	assertEqual(t, "2 months", HumanDuration(2*month))
+	assertEqual(t, "2 months", HumanDuration(2*month+2*week))
+	assertEqual(t, "3 months", HumanDuration(3*month))
+	assertEqual(t, "3 months", HumanDuration(3*month+1*week))
+	assertEqual(t, "5 months", HumanDuration(5*month+2*week))
+	assertEqual(t, "13 months", HumanDuration(13*month))
+	assertEqual(t, "23 months", HumanDuration(23*month))
+	assertEqual(t, "24 months", HumanDuration(24*month))
+	assertEqual(t, "2 years", HumanDuration(24*month+2*week))
+	assertEqual(t, "3 years", HumanDuration(3*year+2*month))
+}
+
+func TestHumanTime(t *testing.T) {
+	now := time.Now()
+
+	t.Run("zero value", func(t *testing.T) {
+		assertEqual(t, HumanTime(time.Time{}, "never"), "never")
+	})
+	t.Run("time in the future", func(t *testing.T) {
+		v := now.Add(48 * time.Hour)
+		assertEqual(t, HumanTime(v, ""), "2 days from now")
+	})
+	t.Run("time in the past", func(t *testing.T) {
+		v := now.Add(-48 * time.Hour)
+		assertEqual(t, HumanTime(v, ""), "2 days ago")
+	})
+}
+
+func TestExactDuration(t *testing.T) {
+	assertEqual(t, "1 millisecond", ExactDuration(1*time.Millisecond))
+	assertEqual(t, "10 milliseconds", ExactDuration(10*time.Millisecond))
+	assertEqual(t, "1 second", ExactDuration(1*time.Second))
+	assertEqual(t, "10 seconds", ExactDuration(10*time.Second))
+	assertEqual(t, "1 minute", ExactDuration(1*time.Minute))
+	assertEqual(t, "10 minutes", ExactDuration(10*time.Minute))
+	assertEqual(t, "1 hour", ExactDuration(1*time.Hour))
+	assertEqual(t, "10 hours", ExactDuration(10*time.Hour))
+	assertEqual(t, "1 hour 1 second", ExactDuration(1*time.Hour+1*time.Second))
+	assertEqual(t, "1 hour 10 seconds", ExactDuration(1*time.Hour+10*time.Second))
+	assertEqual(t, "1 hour 1 minute", ExactDuration(1*time.Hour+1*time.Minute))
+	assertEqual(t, "1 hour 10 minutes", ExactDuration(1*time.Hour+10*time.Minute))
+	assertEqual(t, "1 hour 1 minute 1 second", ExactDuration(1*time.Hour+1*time.Minute+1*time.Second))
+	assertEqual(t, "10 hours 10 minutes 10 seconds", ExactDuration(10*time.Hour+10*time.Minute+10*time.Second))
+}

ggml-metal.metal

@@ -0,0 +1 @@
+llama/ggml-metal.metal

go.mod

@@ -3,20 +3,23 @@ module github.com/jmorganca/ollama
 go 1.20
 
 require (
+	github.com/dustin/go-humanize v1.0.1
 	github.com/gin-gonic/gin v1.9.1
+	github.com/mattn/go-runewidth v0.0.14
+	github.com/mitchellh/colorstring v0.0.0-20190213212951-d06e56a500db
+	github.com/olekukonko/tablewriter v0.0.5
 	github.com/spf13/cobra v1.7.0
 )
 
-require (
-	github.com/mattn/go-runewidth v0.0.14 // indirect
-	github.com/mitchellh/colorstring v0.0.0-20190213212951-d06e56a500db // indirect
-	github.com/rivo/uniseg v0.2.0 // indirect
-)
+require github.com/rivo/uniseg v0.2.0 // indirect
 
 require (
+	dario.cat/mergo v1.0.0
 	github.com/bytedance/sonic v1.9.1 // indirect
 	github.com/chenzhuoyu/base64x v0.0.0-20221115062448-fe3a3abad311 // indirect
+	github.com/chzyer/readline v1.5.1
 	github.com/gabriel-vasile/mimetype v1.4.2 // indirect
+	github.com/gin-contrib/cors v1.4.0
 	github.com/gin-contrib/sse v0.1.0 // indirect
 	github.com/go-playground/locales v0.14.1 // indirect
 	github.com/go-playground/universal-translator v0.18.1 // indirect
@@ -27,12 +30,10 @@ require (
 	github.com/json-iterator/go v1.1.12 // indirect
 	github.com/klauspost/cpuid/v2 v2.2.4 // indirect
 	github.com/leodido/go-urn v1.2.4 // indirect
-	github.com/lithammer/fuzzysearch v1.1.8
 	github.com/mattn/go-isatty v0.0.19 // indirect
 	github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd // indirect
 	github.com/modern-go/reflect2 v1.0.2 // indirect
 	github.com/pelletier/go-toml/v2 v2.0.8 // indirect
-	github.com/schollz/progressbar/v3 v3.13.1
 	github.com/spf13/pflag v1.0.5 // indirect
 	github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
 	github.com/ugorji/go/codec v1.2.11 // indirect

go.sum

@@ -1,26 +1,45 @@
+dario.cat/mergo v1.0.0 h1:AGCNq9Evsj31mOgNPcLyXc+4PNABt905YmuqPYYpBWk=
+dario.cat/mergo v1.0.0/go.mod h1:uNxQE+84aUszobStD9th8a29P2fMDhsBdgRYvZOxGmk=
 github.com/bytedance/sonic v1.5.0/go.mod h1:ED5hyg4y6t3/9Ku1R6dU/4KyJ48DZ4jPhfY1O2AihPM=
 github.com/bytedance/sonic v1.9.1 h1:6iJ6NqdoxCDr6mbY8h18oSO+cShGSMRGCEo7F2h0x8s=
 github.com/bytedance/sonic v1.9.1/go.mod h1:i736AoUSYt75HyZLoJW9ERYxcy6eaN6h4BZXU064P/U=
 github.com/chenzhuoyu/base64x v0.0.0-20211019084208-fb5309c8db06/go.mod h1:DH46F32mSOjUmXrMHnKwZdA8wcEefY7UVqBKYGjpdQY=
 github.com/chenzhuoyu/base64x v0.0.0-20221115062448-fe3a3abad311 h1:qSGYFH7+jGhDF8vLC+iwCD4WpbV1EBDSzWkJODFLams=
 github.com/chenzhuoyu/base64x v0.0.0-20221115062448-fe3a3abad311/go.mod h1:b583jCggY9gE99b6G5LEC39OIiVsWj+R97kbl5odCEk=
+github.com/chzyer/logex v1.2.1 h1:XHDu3E6q+gdHgsdTPH6ImJMIp436vR6MPtH8gP05QzM=
+github.com/chzyer/logex v1.2.1/go.mod h1:JLbx6lG2kDbNRFnfkgvh4eRJRPX1QCoOIWomwysCBrQ=
+github.com/chzyer/readline v1.5.1 h1:upd/6fQk4src78LMRzh5vItIt361/o4uq553V8B5sGI=
+github.com/chzyer/readline v1.5.1/go.mod h1:Eh+b79XXUwfKfcPLepksvw2tcLE/Ct21YObkaSkeBlk=
+github.com/chzyer/test v1.0.0 h1:p3BQDXSxOhOG0P9z6/hGnII4LGiEPOYBhs8asl/fC04=
+github.com/chzyer/test v1.0.0/go.mod h1:2JlltgoNkt4TW/z9V/IzDdFaMTM2JPIi26O1pF38GC8=
 github.com/cpuguy83/go-md2man/v2 v2.0.2/go.mod h1:tgQtvFlXSQOSOSIRvRPT7W67SCa46tRHOmNcaadrF8o=
+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/dustin/go-humanize v1.0.1 h1:GzkhY7T5VNhEkwH0PVJgjz+fX1rhBrR7pRT3mDkpeCY=
+github.com/dustin/go-humanize v1.0.1/go.mod h1:Mu1zIs6XwVuF/gI1OepvI0qD18qycQx+mFykh5fBlto=
 github.com/gabriel-vasile/mimetype v1.4.2 h1:w5qFW6JKBz9Y393Y4q372O9A7cUSequkh1Q7OhCmWKU=
 github.com/gabriel-vasile/mimetype v1.4.2/go.mod h1:zApsH/mKG4w07erKIaJPFiX0Tsq9BFQgN3qGY5GnNgA=
+github.com/gin-contrib/cors v1.4.0 h1:oJ6gwtUl3lqV0WEIwM/LxPF1QZ5qe2lGWdY2+bz7y0g=
+github.com/gin-contrib/cors v1.4.0/go.mod h1:bs9pNM0x/UsmHPBWT2xZz9ROh8xYjYkiURUfmBoMlcs=
 github.com/gin-contrib/sse v0.1.0 h1:Y/yl/+YNO8GZSjAhjMsSuLt29uWRFHdHYUb5lYOV9qE=
 github.com/gin-contrib/sse v0.1.0/go.mod h1:RHrZQHXnP2xjPF+u1gW/2HnVO7nvIa9PG3Gm+fLHvGI=
+github.com/gin-gonic/gin v1.8.1/go.mod h1:ji8BvRH1azfM+SYow9zQ6SZMvR8qOMZHmsCuWR9tTTk=
 github.com/gin-gonic/gin v1.9.1 h1:4idEAncQnU5cB7BeOkPtxjfCSye0AAm1R0RVIqJ+Jmg=
 github.com/gin-gonic/gin v1.9.1/go.mod h1:hPrL7YrpYKXt5YId3A/Tnip5kqbEAP+KLuI3SUcPTeU=
+github.com/go-playground/assert/v2 v2.0.1/go.mod h1:VDjEfimB/XKnb+ZQfWdccd7VUvScMdVu0Titje2rxJ4=
 github.com/go-playground/assert/v2 v2.2.0 h1:JvknZsQTYeFEAhQwI4qEt9cyV5ONwRHC+lYKSsYSR8s=
+github.com/go-playground/locales v0.14.0/go.mod h1:sawfccIbzZTqEDETgFXqTho0QybSa7l++s0DH+LDiLs=
 github.com/go-playground/locales v0.14.1 h1:EWaQ/wswjilfKLTECiXz7Rh+3BjFhfDFKv/oXslEjJA=
 github.com/go-playground/locales v0.14.1/go.mod h1:hxrqLVvrK65+Rwrd5Fc6F2O76J/NuW9t0sjnWqG1slY=
+github.com/go-playground/universal-translator v0.18.0/go.mod h1:UvRDBj+xPUEGrFYl+lu/H90nyDXpg0fqeB/AQUGNTVA=
 github.com/go-playground/universal-translator v0.18.1 h1:Bcnm0ZwsGyWbCzImXv+pAJnYK9S473LQFuzCbDbfSFY=
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@@ -32,17 +51,24 @@ github.com/inconshreveable/mousetrap v1.1.0 h1:wN+x4NVGpMsO7ErUn/mUI3vEoE6Jt13X2
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@@ -52,15 +78,20 @@ github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd h1:TRLaZ9cD/w
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 google.golang.org/protobuf v1.30.0 h1:kPPoIgf3TsEvrm0PFe15JQ+570QVxYzEvvHqChK+cng=
 google.golang.org/protobuf v1.30.0/go.mod h1:HV8QOd/L58Z+nl8r43ehVNZIU/HEI6OcFqwMG9pJV4I=
-gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405 h1:yhCVgyC4o1eVCa2tZl7eS0r+SDo693bJlVdllGtEeKM=
 gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
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+gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c h1:Hei/4ADfdWqJk1ZMxUNpqntNwaWcugrBjAiHlqqRiVk=
+gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c/go.mod h1:JHkPIbrfpd72SG/EVd6muEfDQjcINNoR0C8j2r3qZ4Q=
+gopkg.in/errgo.v2 v2.1.0/go.mod h1:hNsd1EY+bozCKY1Ytp96fpM3vjJbqLJn88ws8XvfDNI=
+gopkg.in/yaml.v2 v2.4.0/go.mod h1:RDklbk79AGWmwhnvt/jBztapEOGDOx6ZbXqjP6csGnQ=
 gopkg.in/yaml.v3 v3.0.0-20200313102051-9f266ea9e77c/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
+gopkg.in/yaml.v3 v3.0.0-20210107192922-496545a6307b/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
 gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=
 gopkg.in/yaml.v3 v3.0.1/go.mod h1:K4uyk7z7BCEPqu6E+C64Yfv1cQ7kz7rIZviUmN+EgEM=
 rsc.io/pdf v0.1.1/go.mod h1:n8OzWcQ6Sp37PL01nO98y4iUCRdTGarVfzxY20ICaU4=

library/.gitignore

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

library/downloads

@@ -0,0 +1,7 @@
+https://huggingface.co/TheBloke/orca_mini_3B-GGML/resolve/main/orca-mini-3b.ggmlv3.q4_0.bin e84705205f71dd55be7b24a778f248f0eda9999a125d313358c087e092d83148
+https://huggingface.co/TheBloke/Nous-Hermes-13B-GGML/resolve/main/nous-hermes-13b.ggmlv3.q4_0.bin d1735b93e1dc503f1045ccd6c8bd73277b18ba892befd1dc29e9b9a7822ed998
+https://huggingface.co/TheBloke/vicuna-7B-v1.3-GGML/resolve/main/vicuna-7b-v1.3.ggmlv3.q4_0.bin 23ce5ed290b56a19305178b9ada2c3d96036bd69a6c18304b6158eb6672d6c0f
+https://huggingface.co/TheBloke/Wizard-Vicuna-13B-Uncensored-GGML/resolve/main/Wizard-Vicuna-13B-Uncensored.ggmlv3.q4_0.bin 1f08b147a5bce41cfcbb3fd5d51ba765dea1786e15b5655ab69ba3a337a893b7
+https://huggingface.co/TheBloke/Llama-2-7B-GGML/resolve/main/llama-2-7b.ggmlv3.q4_0.bin bfa26d855e44629c4cf919985e90bd7fa03b77eea1676791519e39a4d45fd4d5
+https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/resolve/main/llama-2-7b-chat.ggmlv3.q4_0.bin 8daa9615cce30c259a9555b1cc250d461d1bc69980a274b44d7eda0be78076d8
+https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/resolve/main/llama-2-13b-chat.ggmlv3.q4_0.bin f79142715bc9539a2edbb4b253548db8b34fac22736593eeaa28555874476e30

library/modelfiles/llama2

@@ -0,0 +1,147 @@
+FROM ../models/llama-2-7b-chat.ggmlv3.q4_0.bin
+
+TEMPLATE """
+{{- if .First }}
+<<SYS>>
+{{ .System }}
+<</SYS>>
+{{- end }}
+
+[INST] {{ .Prompt }} [/INST]
+"""
+
+SYSTEM """
+You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
+"""
+
+LICENSE """
+Llama 2 Community License Agreement
+
+Llama 2 Version Release Date: July 18, 2023
+
+“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.
+
+“Documentation” means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at ai.meta.com/resources/models-and-libraries/llama-downloads/.
+
+“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.
+
+“Llama 2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-libraries/llama-downloads/.
+
+“Llama Materials” means, collectively, Meta’s proprietary Llama 2 and Documentation (and any portion thereof) made available under this Agreement.
+
+“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).
+
+By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement.
+
+1. License Rights and Redistribution.
+
+a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.
+
+b. Redistribution and Use.
+
+i. If you distribute or make the Llama Materials, or any derivative works thereof, available to a third party, you shall provide a copy of this Agreement to such third party.
+
+ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.
+
+iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.”
+
+iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into this Agreement.
+
+v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Llama 2 or derivative works thereof).
+
+2. Additional Commercial Terms. If, on the Llama 2 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.
+
+3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
+
+4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
+
+5. Intellectual Property.
+
+a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials.
+
+b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.
+
+c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 2 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.
+
+6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.
+
+7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.
+
+"""
+
+LICENSE """
+Llama 2 Acceptable Use Policy
+
+Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at ai.meta.com/llama/use-policy.
+
+Prohibited Uses
+
+We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to:
+
+1. Violate the law or others’ rights, including to:
+
+a. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
+
+i. Violence or terrorism
+
+ii. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
+
+b. Human trafficking, exploitation, and sexual violence
+
+iii. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
+
+iv. Sexual solicitation
+
+vi. Any other criminal activity
+
+c. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
+
+d. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
+
+e. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
+
+f. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
+
+g. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
+
+h. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
+
+2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:
+
+a. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
+
+b. Guns and illegal weapons (including weapon development)
+
+c. Illegal drugs and regulated/controlled substances
+
+d. Operation of critical infrastructure, transportation technologies, or heavy machinery
+
+e. Self-harm or harm to others, including suicide, cutting, and eating disorders
+
+f. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
+
+3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
+
+a. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
+
+b. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
+
+c. Generating, promoting, or further distributing spam
+
+d. Impersonating another individual without consent, authorization, or legal right
+
+e. Representing that the use of Llama 2 or outputs are human-generated
+
+f. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
+
+4. Fail to appropriately disclose to end users any known dangers of your AI system
+
+Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
+
+Reporting issues with the model: github.com/facebookresearch/llama
+Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback
+Reporting bugs and security concerns: facebook.com/whitehat/info
+Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: LlamaUseReport@meta.com
+"""

library/modelfiles/llama2_13b

@@ -0,0 +1,147 @@
+FROM ../models/llama-2-13b-chat.ggmlv3.q4_0.bin
+
+TEMPLATE """
+{{- if .First }}
+<<SYS>>
+{{ .System }}
+<</SYS>>
+{{- end }}
+
+[INST] {{ .Prompt }} [/INST]
+"""
+
+SYSTEM """
+You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
+"""
+
+LICENSE """
+Llama 2 Community License Agreement
+
+Llama 2 Version Release Date: July 18, 2023
+
+“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.
+
+“Documentation” means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at ai.meta.com/resources/models-and-libraries/llama-downloads/.
+
+“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.
+
+“Llama 2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-libraries/llama-downloads/.
+
+“Llama Materials” means, collectively, Meta’s proprietary Llama 2 and Documentation (and any portion thereof) made available under this Agreement.
+
+“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).
+
+By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement.
+
+1. License Rights and Redistribution.
+
+a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.
+
+b. Redistribution and Use.
+
+i. If you distribute or make the Llama Materials, or any derivative works thereof, available to a third party, you shall provide a copy of this Agreement to such third party.
+
+ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.
+
+iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.”
+
+iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into this Agreement.
+
+v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Llama 2 or derivative works thereof).
+
+2. Additional Commercial Terms. If, on the Llama 2 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.
+
+3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
+
+4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
+
+5. Intellectual Property.
+
+a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials.
+
+b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.
+
+c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 2 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.
+
+6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.
+
+7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.
+
+"""
+
+LICENSE """
+Llama 2 Acceptable Use Policy
+
+Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at ai.meta.com/llama/use-policy.
+
+Prohibited Uses
+
+We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to:
+
+1. Violate the law or others’ rights, including to:
+
+a. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
+
+i. Violence or terrorism
+
+ii. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
+
+b. Human trafficking, exploitation, and sexual violence
+
+iii. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
+
+iv. Sexual solicitation
+
+vi. Any other criminal activity
+
+c. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
+
+d. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
+
+e. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
+
+f. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
+
+g. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
+
+h. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
+
+2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:
+
+a. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
+
+b. Guns and illegal weapons (including weapon development)
+
+c. Illegal drugs and regulated/controlled substances
+
+d. Operation of critical infrastructure, transportation technologies, or heavy machinery
+
+e. Self-harm or harm to others, including suicide, cutting, and eating disorders
+
+f. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
+
+3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
+
+a. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
+
+b. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
+
+c. Generating, promoting, or further distributing spam
+
+d. Impersonating another individual without consent, authorization, or legal right
+
+e. Representing that the use of Llama 2 or outputs are human-generated
+
+f. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
+
+4. Fail to appropriately disclose to end users any known dangers of your AI system
+
+Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
+
+Reporting issues with the model: github.com/facebookresearch/llama
+Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback
+Reporting bugs and security concerns: facebook.com/whitehat/info
+Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: LlamaUseReport@meta.com
+"""

library/modelfiles/llama2_7b

@@ -0,0 +1,147 @@
+FROM ../models/llama-2-7b-chat.ggmlv3.q4_0.bin
+
+TEMPLATE """
+{{- if .First }}
+<<SYS>>
+{{ .System }}
+<</SYS>>
+{{- end }}
+
+[INST] {{ .Prompt }} [/INST]
+"""
+
+SYSTEM """
+You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
+"""
+
+LICENSE """
+Llama 2 Community License Agreement
+
+Llama 2 Version Release Date: July 18, 2023
+
+“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.
+
+“Documentation” means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at ai.meta.com/resources/models-and-libraries/llama-downloads/.
+
+“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.
+
+“Llama 2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and-libraries/llama-downloads/.
+
+“Llama Materials” means, collectively, Meta’s proprietary Llama 2 and Documentation (and any portion thereof) made available under this Agreement.
+
+“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).
+
+By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement.
+
+1. License Rights and Redistribution.
+
+a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.
+
+b. Redistribution and Use.
+
+i. If you distribute or make the Llama Materials, or any derivative works thereof, available to a third party, you shall provide a copy of this Agreement to such third party.
+
+ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.
+
+iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.”
+
+iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into this Agreement.
+
+v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Llama 2 or derivative works thereof).
+
+2. Additional Commercial Terms. If, on the Llama 2 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.
+
+3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
+
+4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
+
+5. Intellectual Property.
+
+a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials.
+
+b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.
+
+c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 2 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.
+
+6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.
+
+7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.
+
+"""
+
+LICENSE """
+Llama 2 Acceptable Use Policy
+
+Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at ai.meta.com/llama/use-policy.
+
+Prohibited Uses
+
+We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to:
+
+1. Violate the law or others’ rights, including to:
+
+a. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
+
+i. Violence or terrorism
+
+ii. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
+
+b. Human trafficking, exploitation, and sexual violence
+
+iii. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
+
+iv. Sexual solicitation
+
+vi. Any other criminal activity
+
+c. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
+
+d. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
+
+e. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
+
+f. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
+
+g. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
+
+h. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
+
+2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following:
+
+a. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
+
+b. Guns and illegal weapons (including weapon development)
+
+c. Illegal drugs and regulated/controlled substances
+
+d. Operation of critical infrastructure, transportation technologies, or heavy machinery
+
+e. Self-harm or harm to others, including suicide, cutting, and eating disorders
+
+f. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
+
+3. Intentionally deceive or mislead others, including use of Llama 2 related to the following:
+
+a. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
+
+b. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
+
+c. Generating, promoting, or further distributing spam
+
+d. Impersonating another individual without consent, authorization, or legal right
+
+e. Representing that the use of Llama 2 or outputs are human-generated
+
+f. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
+
+4. Fail to appropriately disclose to end users any known dangers of your AI system
+
+Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
+
+Reporting issues with the model: github.com/facebookresearch/llama
+Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback
+Reporting bugs and security concerns: facebook.com/whitehat/info
+Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: LlamaUseReport@meta.com
+"""

library/modelfiles/nous-hermes

@@ -0,0 +1,7 @@
+FROM ../models/nous-hermes-13b.ggmlv3.q4_0.bin
+TEMPLATE """
+### Instruction:
+{{ .Prompt }}
+
+### Response:
+"""

library/modelfiles/orca

@@ -0,0 +1,14 @@
+FROM ../models/orca-mini-3b.ggmlv3.q4_0.bin
+TEMPLATE """
+{{- if .First }}
+### System:
+{{ .System }}
+{{- end }}
+
+### User:
+{{ .Prompt }}
+
+### Response:
+"""
+
+SYSTEM """You are an AI assistant that follows instruction extremely well. Help as much as you can."""

library/modelfiles/vicuna

@@ -0,0 +1,11 @@
+FROM ../models/vicuna-7b-v1.3.ggmlv3.q4_0.bin
+TEMPLATE """
+{{ if .First }}
+{{ .System }}
+{{- end }}
+
+USER: {{ .Prompt }}
+ASSISTANT:
+"""
+
+SYSTEM """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions."""

library/modelfiles/wizard-vicuna

@@ -0,0 +1,5 @@
+FROM ../models/Wizard-Vicuna-13B-Uncensored.ggmlv3.q4_0.bin
+TEMPLATE """
+USER: {{ .Prompt }}
+ASSISTANT:
+"""

library/publish.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+
+mkdir -p models
+
+# download binaries
+function process_line {
+    local url=$1
+    local checksum=$2
+
+    # Get the filename from the URL
+    local filename=models/$(basename $url)
+
+    echo "verifying $filename..."
+
+    # If the file exists, compute its checksum
+    if [ -f $filename ]; then
+        local existing_checksum=$(shasum -a 256 $filename | cut -d ' ' -f1)
+    fi
+
+    # If the file does not exist, or its checksum does not match, download it
+    if [ ! -f $filename ] || [ $existing_checksum != $checksum ]; then
+        echo "downloading $filename..."
+        
+        # Download the file
+        curl -L $url -o $filename
+
+        # Compute the SHA256 hash of the downloaded file
+        local computed_checksum=$(shasum -a 256 $filename | cut -d ' ' -f1)
+
+        # Verify the checksum
+        if [ $computed_checksum != $checksum ]; then
+            echo "Checksum verification failed for $filename"
+            exit 1
+        fi
+    fi
+}
+
+while IFS=' ' read -r url checksum
+do
+    process_line $url $checksum
+done < "downloads"
+
+# create and publish the models
+for file in modelfiles/*; do
+  if [ -f "$file" ]; then
+    filename=$(basename "$file")
+    echo $filename
+    ollama create "library/${filename}" -f "$file"
+    ollama push "${filename}"
+  fi
+done
+

llama/.gitignore

@@ -1 +0,0 @@
-build

llama/CMakeLists.txt

@@ -1,23 +0,0 @@
-cmake_minimum_required(VERSION 3.12)
-project(binding)
-
-include(FetchContent)
-
-FetchContent_Declare(
-    llama_cpp
-    GIT_REPOSITORY https://github.com/ggerganov/llama.cpp.git
-    GIT_TAG        55dbb91
-)
-
-FetchContent_MakeAvailable(llama_cpp)
-
-add_library(binding ${CMAKE_CURRENT_SOURCE_DIR}/binding/binding.cpp ${llama_cpp_SOURCE_DIR}/examples/common.cpp)
-target_include_directories(binding PRIVATE ${llama_cpp_SOURCE_DIR}/examples)
-target_link_libraries(binding llama ggml_static)
-
-if (LLAMA_METAL)
-    configure_file(${llama_cpp_SOURCE_DIR}/ggml-metal.metal ${CMAKE_CURRENT_BINARY_DIR}/../../ggml-metal.metal COPYONLY)
-endif()
-
-add_custom_target(copy_libllama ALL COMMAND ${CMAKE_COMMAND} -E copy_if_different $<TARGET_FILE:llama> ${CMAKE_CURRENT_BINARY_DIR})
-add_custom_target(copy_libggml_static ALL COMMAND ${CMAKE_COMMAND} -E copy_if_different $<TARGET_FILE:ggml_static> ${CMAKE_CURRENT_BINARY_DIR})

llama/binding/binding.cpp

@@ -1,691 +0,0 @@
-#include "common.h"
-#include "llama.h"
-
-#include "binding.h"
-
-#include <cassert>
-#include <cinttypes>
-#include <cmath>
-#include <cstdio>
-#include <cstring>
-#include <fstream>
-#include <iostream>
-#include <regex>
-#include <sstream>
-#include <string>
-#include <vector>
-#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__))
-#include <signal.h>
-#include <unistd.h>
-#elif defined(_WIN32)
-#define WIN32_LEAN_AND_MEAN
-#define NOMINMAX
-#include <signal.h>
-#include <windows.h>
-#endif
-
-#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) || \
-    defined(_WIN32)
-void sigint_handler(int signo) {
-  if (signo == SIGINT) {
-    _exit(130);
-  }
-}
-#endif
-
-int get_embeddings(void *params_ptr, void *state_pr, float *res_embeddings) {
-  gpt_params *params_p = (gpt_params *)params_ptr;
-  llama_context *ctx = (llama_context *)state_pr;
-  gpt_params params = *params_p;
-
-  if (params.seed <= 0) {
-    params.seed = time(NULL);
-  }
-
-  std::mt19937 rng(params.seed);
-
-  llama_init_backend(params.numa);
-
-  int n_past = 0;
-
-  // Add a space in front of the first character to match OG llama tokenizer
-  // behavior
-  params.prompt.insert(0, 1, ' ');
-
-  // tokenize the prompt
-  auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
-
-  // determine newline token
-  auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
-
-  if (embd_inp.size() > 0) {
-    if (llama_eval(ctx, embd_inp.data(), embd_inp.size(), n_past,
-                   params.n_threads)) {
-      fprintf(stderr, "%s : failed to eval\n", __func__);
-      return 1;
-    }
-  }
-
-  const int n_embd = llama_n_embd(ctx);
-
-  const auto embeddings = llama_get_embeddings(ctx);
-
-  for (int i = 0; i < n_embd; i++) {
-    res_embeddings[i] = embeddings[i];
-  }
-
-  return 0;
-}
-
-int get_token_embeddings(void *params_ptr, void *state_pr, int *tokens,
-                         int tokenSize, float *res_embeddings) {
-  gpt_params *params_p = (gpt_params *)params_ptr;
-  llama_context *ctx = (llama_context *)state_pr;
-  gpt_params params = *params_p;
-
-  for (int i = 0; i < tokenSize; i++) {
-    auto token_str = llama_token_to_str(ctx, tokens[i]);
-    if (token_str == nullptr) {
-      continue;
-    }
-    std::vector<std::string> my_vector;
-    std::string str_token(token_str); // create a new std::string from the char*
-    params_p->prompt += str_token;
-  }
-
-  return get_embeddings(params_ptr, state_pr, res_embeddings);
-}
-
-int eval(void *params_ptr, void *state_pr, char *text) {
-  gpt_params *params_p = (gpt_params *)params_ptr;
-  llama_context *ctx = (llama_context *)state_pr;
-
-  auto n_past = 0;
-  auto last_n_tokens_data =
-      std::vector<llama_token>(params_p->repeat_last_n, 0);
-
-  auto tokens = std::vector<llama_token>(params_p->n_ctx);
-  auto n_prompt_tokens =
-      llama_tokenize(ctx, text, tokens.data(), tokens.size(), true);
-
-  if (n_prompt_tokens < 1) {
-    fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);
-    return 1;
-  }
-
-  // evaluate prompt
-  return llama_eval(ctx, tokens.data(), n_prompt_tokens, n_past,
-                    params_p->n_threads);
-}
-
-int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
-  gpt_params *params_p = (gpt_params *)params_ptr;
-  llama_context *ctx = (llama_context *)state_pr;
-
-  gpt_params params = *params_p;
-
-  const int n_ctx = llama_n_ctx(ctx);
-
-  if (params.seed <= 0) {
-    params.seed = time(NULL);
-  }
-
-  std::mt19937 rng(params.seed);
-
-  std::string path_session = params.path_prompt_cache;
-  std::vector<llama_token> session_tokens;
-
-  if (!path_session.empty()) {
-    if (debug) {
-      fprintf(stderr, "%s: attempting to load saved session from '%s'\n",
-              __func__, path_session.c_str());
-    }
-    // fopen to check for existing session
-    FILE *fp = std::fopen(path_session.c_str(), "rb");
-    if (fp != NULL) {
-      std::fclose(fp);
-
-      session_tokens.resize(n_ctx);
-      size_t n_token_count_out = 0;
-      if (!llama_load_session_file(
-              ctx, path_session.c_str(), session_tokens.data(),
-              session_tokens.capacity(), &n_token_count_out)) {
-        fprintf(stderr, "%s: error: failed to load session file '%s'\n",
-                __func__, path_session.c_str());
-        return 1;
-      }
-      session_tokens.resize(n_token_count_out);
-      llama_set_rng_seed(ctx, params.seed);
-      if (debug) {
-        fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n",
-                __func__, (int)session_tokens.size());
-      }
-    } else {
-      if (debug) {
-        fprintf(stderr, "%s: session file does not exist, will create\n",
-                __func__);
-      }
-    }
-  }
-
-  std::vector<llama_token> embd_inp;
-  if (!params.prompt.empty() || session_tokens.empty()) {
-    // Add a space in front of the first character to match OG llama tokenizer
-    // behavior
-    params.prompt.insert(0, 1, ' ');
-
-    embd_inp = ::llama_tokenize(ctx, params.prompt, true);
-  } else {
-    embd_inp = session_tokens;
-  }
-
-  // debug message about similarity of saved session, if applicable
-  size_t n_matching_session_tokens = 0;
-  if (session_tokens.size()) {
-    for (llama_token id : session_tokens) {
-      if (n_matching_session_tokens >= embd_inp.size() ||
-          id != embd_inp[n_matching_session_tokens]) {
-        break;
-      }
-      n_matching_session_tokens++;
-    }
-    if (debug) {
-      if (params.prompt.empty() &&
-          n_matching_session_tokens == embd_inp.size()) {
-        fprintf(stderr, "%s: using full prompt from session file\n", __func__);
-      } else if (n_matching_session_tokens >= embd_inp.size()) {
-        fprintf(stderr, "%s: session file has exact match for prompt!\n",
-                __func__);
-      } else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
-        fprintf(stderr,
-                "%s: warning: session file has low similarity to prompt (%zu / "
-                "%zu tokens); will mostly be reevaluated\n",
-                __func__, n_matching_session_tokens, embd_inp.size());
-      } else {
-        fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n",
-                __func__, n_matching_session_tokens, embd_inp.size());
-      }
-    }
-  }
-  // if we will use the cache for the full prompt without reaching the end of
-  // the cache, force reevaluation of the last token token to recalculate the
-  // cached logits
-  if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() &&
-      session_tokens.size() > embd_inp.size()) {
-    session_tokens.resize(embd_inp.size() - 1);
-  }
-  // number of tokens to keep when resetting context
-  if (params.n_keep < 0 || params.n_keep > (int)embd_inp.size()) {
-    params.n_keep = (int)embd_inp.size();
-  }
-
-  // determine newline token
-  auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
-
-  // TODO: replace with ring-buffer
-  std::vector<llama_token> last_n_tokens(n_ctx);
-  std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
-
-  bool need_to_save_session =
-      !path_session.empty() && n_matching_session_tokens < embd_inp.size();
-  int n_past = 0;
-  int n_remain = params.n_predict;
-  int n_consumed = 0;
-  int n_session_consumed = 0;
-
-  std::vector<llama_token> embd;
-  std::string res = "";
-
-  // do one empty run to warm up the model
-  {
-    const std::vector<llama_token> tmp = {
-        llama_token_bos(),
-    };
-    llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
-    llama_reset_timings(ctx);
-  }
-
-  while (n_remain != 0) {
-    // predict
-    if (embd.size() > 0) {
-      // infinite text generation via context swapping
-      // if we run out of context:
-      // - take the n_keep first tokens from the original prompt (via n_past)
-      // - take half of the last (n_ctx - n_keep) tokens and recompute the
-      // logits in batches
-      if (n_past + (int)embd.size() > n_ctx) {
-        const int n_left = n_past - params.n_keep;
-
-        // always keep the first token - BOS
-        n_past = std::max(1, params.n_keep);
-
-        // insert n_left/2 tokens at the start of embd from last_n_tokens
-        embd.insert(embd.begin(),
-                    last_n_tokens.begin() + n_ctx - n_left / 2 - embd.size(),
-                    last_n_tokens.end() - embd.size());
-
-        // stop saving session if we run out of context
-        path_session.clear();
-
-        // printf("\n---\n");
-        // printf("resetting: '");
-        // for (int i = 0; i < (int) embd.size(); i++) {
-        //     printf("%s", llama_token_to_str(ctx, embd[i]));
-        // }
-        // printf("'\n");
-        // printf("\n---\n");
-      }
-
-      // try to reuse a matching prefix from the loaded session instead of
-      // re-eval (via n_past)
-      if (n_session_consumed < (int)session_tokens.size()) {
-        size_t i = 0;
-        for (; i < embd.size(); i++) {
-          if (embd[i] != session_tokens[n_session_consumed]) {
-            session_tokens.resize(n_session_consumed);
-            break;
-          }
-
-          n_past++;
-          n_session_consumed++;
-
-          if (n_session_consumed >= (int)session_tokens.size()) {
-            ++i;
-            break;
-          }
-        }
-        if (i > 0) {
-          embd.erase(embd.begin(), embd.begin() + i);
-        }
-      }
-
-      // evaluate tokens in batches
-      // embd is typically prepared beforehand to fit within a batch, but not
-      // always
-      for (int i = 0; i < (int)embd.size(); i += params.n_batch) {
-        int n_eval = (int)embd.size() - i;
-        if (n_eval > params.n_batch) {
-          n_eval = params.n_batch;
-        }
-        if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) {
-          fprintf(stderr, "%s : failed to eval\n", __func__);
-          return 1;
-        }
-        n_past += n_eval;
-      }
-
-      if (embd.size() > 0 && !path_session.empty()) {
-        session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
-        n_session_consumed = session_tokens.size();
-      }
-    }
-
-    embd.clear();
-
-    if ((int)embd_inp.size() <= n_consumed) {
-      // out of user input, sample next token
-      const float temp = params.temp;
-      const int32_t top_k =
-          params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
-      const float top_p = params.top_p;
-      const float tfs_z = params.tfs_z;
-      const float typical_p = params.typical_p;
-      const int32_t repeat_last_n =
-          params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
-      const float repeat_penalty = params.repeat_penalty;
-      const float alpha_presence = params.presence_penalty;
-      const float alpha_frequency = params.frequency_penalty;
-      const int mirostat = params.mirostat;
-      const float mirostat_tau = params.mirostat_tau;
-      const float mirostat_eta = params.mirostat_eta;
-      const bool penalize_nl = params.penalize_nl;
-
-      // optionally save the session on first sample (for faster prompt loading
-      // next time)
-      if (!path_session.empty() && need_to_save_session &&
-          !params.prompt_cache_ro) {
-        need_to_save_session = false;
-        llama_save_session_file(ctx, path_session.c_str(),
-                                session_tokens.data(), session_tokens.size());
-      }
-
-      llama_token id = 0;
-
-      {
-        auto logits = llama_get_logits(ctx);
-        auto n_vocab = llama_n_vocab(ctx);
-
-        // Apply params.logit_bias map
-        for (auto it = params.logit_bias.begin(); it != params.logit_bias.end();
-             it++) {
-          logits[it->first] += it->second;
-        }
-
-        std::vector<llama_token_data> candidates;
-        candidates.reserve(n_vocab);
-        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-          candidates.emplace_back(
-              llama_token_data{token_id, logits[token_id], 0.0f});
-        }
-
-        llama_token_data_array candidates_p = {candidates.data(),
-                                               candidates.size(), false};
-
-        // Apply penalties
-        float nl_logit = logits[llama_token_nl()];
-        auto last_n_repeat =
-            std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
-        llama_sample_repetition_penalty(
-            ctx, &candidates_p,
-            last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
-            last_n_repeat, repeat_penalty);
-        llama_sample_frequency_and_presence_penalties(
-            ctx, &candidates_p,
-            last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
-            last_n_repeat, alpha_frequency, alpha_presence);
-        if (!penalize_nl) {
-          logits[llama_token_nl()] = nl_logit;
-        }
-
-        if (temp <= 0) {
-          // Greedy sampling
-          id = llama_sample_token_greedy(ctx, &candidates_p);
-        } else {
-          if (mirostat == 1) {
-            static float mirostat_mu = 2.0f * mirostat_tau;
-            const int mirostat_m = 100;
-            llama_sample_temperature(ctx, &candidates_p, temp);
-            id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau,
-                                             mirostat_eta, mirostat_m,
-                                             &mirostat_mu);
-          } else if (mirostat == 2) {
-            static float mirostat_mu = 2.0f * mirostat_tau;
-            llama_sample_temperature(ctx, &candidates_p, temp);
-            id = llama_sample_token_mirostat_v2(
-                ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
-          } else {
-            // Temperature sampling
-            llama_sample_top_k(ctx, &candidates_p, top_k, 1);
-            llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
-            llama_sample_typical(ctx, &candidates_p, typical_p, 1);
-            llama_sample_top_p(ctx, &candidates_p, top_p, 1);
-            llama_sample_temperature(ctx, &candidates_p, temp);
-            id = llama_sample_token(ctx, &candidates_p);
-          }
-        }
-        // printf("`%d`", candidates_p.size);
-
-        last_n_tokens.erase(last_n_tokens.begin());
-        last_n_tokens.push_back(id);
-      }
-
-      // add it to the context
-      embd.push_back(id);
-
-      // decrement remaining sampling budget
-      --n_remain;
-
-      // call the token callback, no need to check if one is actually
-      // registered, that will be handled on the Go side.
-      auto token_str = llama_token_to_str(ctx, id);
-      if (!tokenCallback(state_pr, (char *)token_str)) {
-        break;
-      }
-    } else {
-      // some user input remains from prompt or interaction, forward it to
-      // processing
-      while ((int)embd_inp.size() > n_consumed) {
-        embd.push_back(embd_inp[n_consumed]);
-        last_n_tokens.erase(last_n_tokens.begin());
-        last_n_tokens.push_back(embd_inp[n_consumed]);
-        ++n_consumed;
-        if ((int)embd.size() >= params.n_batch) {
-          break;
-        }
-      }
-    }
-
-    for (auto id : embd) {
-      res += llama_token_to_str(ctx, id);
-    }
-
-    // check for stop prompt
-    if (params.antiprompt.size()) {
-      std::string last_output;
-      for (auto id : last_n_tokens) {
-        last_output += llama_token_to_str(ctx, id);
-      }
-      // Check if each of the reverse prompts appears at the end of the output.
-      for (std::string &antiprompt : params.antiprompt) {
-        // size_t extra_padding = params.interactive ? 0 : 2;
-        size_t extra_padding = 2;
-        size_t search_start_pos =
-            last_output.length() >
-                    static_cast<size_t>(antiprompt.length() + extra_padding)
-                ? last_output.length() -
-                      static_cast<size_t>(antiprompt.length() + extra_padding)
-                : 0;
-
-        if (last_output.find(antiprompt.c_str(), search_start_pos) !=
-            std::string::npos) {
-          goto end;
-        }
-      }
-    }
-
-    // end of text token
-    if (!embd.empty() && embd.back() == llama_token_eos()) {
-      break;
-    }
-  }
-
-  if (!path_session.empty() && params.prompt_cache_all &&
-      !params.prompt_cache_ro) {
-    if (debug) {
-      fprintf(stderr, "\n%s: saving final output to session file '%s'\n",
-              __func__, path_session.c_str());
-    }
-    llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(),
-                            session_tokens.size());
-  }
-
-end:
-#if defined(_WIN32)
-  signal(SIGINT, SIG_DFL);
-#endif
-
-  if (debug) {
-    llama_print_timings(ctx);
-    llama_reset_timings(ctx);
-  }
-
-  strcpy(result, res.c_str());
-  return 0;
-}
-
-void llama_binding_free_model(void *state_ptr) {
-  llama_context *ctx = (llama_context *)state_ptr;
-  llama_free(ctx);
-}
-
-void llama_free_params(void *params_ptr) {
-  gpt_params *params = (gpt_params *)params_ptr;
-  delete params;
-}
-
-std::vector<std::string> create_vector(const char **strings, int count) {
-  std::vector<std::string> *vec = new std::vector<std::string>;
-  for (int i = 0; i < count; i++) {
-    vec->push_back(std::string(strings[i]));
-  }
-  return *vec;
-}
-
-void delete_vector(std::vector<std::string> *vec) { delete vec; }
-
-int load_state(void *ctx, char *statefile, char *modes) {
-  llama_context *state = (llama_context *)ctx;
-  const llama_context *constState = static_cast<const llama_context *>(state);
-  const size_t state_size = llama_get_state_size(state);
-  uint8_t *state_mem = new uint8_t[state_size];
-
-  {
-    FILE *fp_read = fopen(statefile, modes);
-    if (state_size != llama_get_state_size(constState)) {
-      fprintf(stderr, "\n%s : failed to validate state size\n", __func__);
-      return 1;
-    }
-
-    const size_t ret = fread(state_mem, 1, state_size, fp_read);
-    if (ret != state_size) {
-      fprintf(stderr, "\n%s : failed to read state\n", __func__);
-      return 1;
-    }
-
-    llama_set_state_data(
-        state, state_mem); // could also read directly from memory mapped file
-    fclose(fp_read);
-  }
-
-  return 0;
-}
-
-void save_state(void *ctx, char *dst, char *modes) {
-  llama_context *state = (llama_context *)ctx;
-
-  const size_t state_size = llama_get_state_size(state);
-  uint8_t *state_mem = new uint8_t[state_size];
-
-  // Save state (rng, logits, embedding and kv_cache) to file
-  {
-    FILE *fp_write = fopen(dst, modes);
-    llama_copy_state_data(
-        state, state_mem); // could also copy directly to memory mapped file
-    fwrite(state_mem, 1, state_size, fp_write);
-    fclose(fp_write);
-  }
-}
-
-void *llama_allocate_params(
-    const char *prompt, int seed, int threads, int tokens, int top_k,
-    float top_p, float temp, float repeat_penalty, int repeat_last_n,
-    bool ignore_eos, bool memory_f16, int n_batch, int n_keep,
-    const char **antiprompt, int antiprompt_count, float tfs_z, float typical_p,
-    float frequency_penalty, float presence_penalty, int mirostat,
-    float mirostat_eta, float mirostat_tau, bool penalize_nl,
-    const char *logit_bias, bool mlock, bool mmap, const char *maingpu,
-    const char *tensorsplit) {
-  gpt_params *params = new gpt_params;
-  params->seed = seed;
-  params->n_threads = threads;
-  params->n_predict = tokens;
-  params->repeat_last_n = repeat_last_n;
-  params->top_k = top_k;
-  params->top_p = top_p;
-  params->memory_f16 = memory_f16;
-  params->temp = temp;
-  params->use_mmap = mmap;
-  params->use_mlock = mlock;
-  params->repeat_penalty = repeat_penalty;
-  params->n_batch = n_batch;
-  params->n_keep = n_keep;
-  if (maingpu[0] != '\0') {
-    params->main_gpu = std::stoi(maingpu);
-  }
-
-  if (tensorsplit[0] != '\0') {
-    std::string arg_next = tensorsplit;
-    // split string by , and /
-    const std::regex regex{R"([,/]+)"};
-    std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
-    std::vector<std::string> split_arg{it, {}};
-    GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
-
-    for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
-      if (i < split_arg.size()) {
-        params->tensor_split[i] = std::stof(split_arg[i]);
-      } else {
-        params->tensor_split[i] = 0.0f;
-      }
-    }
-  }
-
-  if (ignore_eos) {
-    params->logit_bias[llama_token_eos()] = -INFINITY;
-  }
-  if (antiprompt_count > 0) {
-    params->antiprompt = create_vector(antiprompt, antiprompt_count);
-  }
-  params->tfs_z = tfs_z;
-  params->typical_p = typical_p;
-  params->presence_penalty = presence_penalty;
-  params->mirostat = mirostat;
-  params->mirostat_eta = mirostat_eta;
-  params->mirostat_tau = mirostat_tau;
-  params->penalize_nl = penalize_nl;
-  std::stringstream ss(logit_bias);
-  llama_token key;
-  char sign;
-  std::string value_str;
-  if (ss >> key && ss >> sign && std::getline(ss, value_str) &&
-      (sign == '+' || sign == '-')) {
-    params->logit_bias[key] =
-        std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
-  }
-  params->frequency_penalty = frequency_penalty;
-  params->prompt = prompt;
-
-  return params;
-}
-
-void *load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16,
-                 bool mlock, bool embeddings, bool mmap, bool low_vram,
-                 bool vocab_only, int n_gpu_layers, int n_batch,
-                 const char *maingpu, const char *tensorsplit, bool numa) {
-  // load the model
-  auto lparams = llama_context_default_params();
-
-  lparams.n_ctx = n_ctx;
-  lparams.seed = n_seed;
-  lparams.f16_kv = memory_f16;
-  lparams.embedding = embeddings;
-  lparams.use_mlock = mlock;
-  lparams.n_gpu_layers = n_gpu_layers;
-  lparams.use_mmap = mmap;
-  lparams.low_vram = low_vram;
-  lparams.vocab_only = vocab_only;
-
-  if (maingpu[0] != '\0') {
-    lparams.main_gpu = std::stoi(maingpu);
-  }
-
-  if (tensorsplit[0] != '\0') {
-    std::string arg_next = tensorsplit;
-    // split string by , and /
-    const std::regex regex{R"([,/]+)"};
-    std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
-    std::vector<std::string> split_arg{it, {}};
-    GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
-
-    for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
-      if (i < split_arg.size()) {
-        lparams.tensor_split[i] = std::stof(split_arg[i]);
-      } else {
-        lparams.tensor_split[i] = 0.0f;
-      }
-    }
-  }
-
-  lparams.n_batch = n_batch;
-
-  llama_init_backend(numa);
-  void *res = nullptr;
-  try {
-    res = llama_init_from_file(fname, lparams);
-  } catch (std::runtime_error &e) {
-    fprintf(stderr, "failed %s", e.what());
-    return res;
-  }
-
-  return res;
-}

llama/binding/binding.h

@@ -1,48 +0,0 @@
-#ifdef __cplusplus
-#include <string>
-#include <vector>
-extern "C" {
-#endif
-
-#include <stdbool.h>
-
-extern unsigned char tokenCallback(void *, char *);
-
-int load_state(void *ctx, char *statefile, char *modes);
-
-int eval(void *params_ptr, void *ctx, char *text);
-
-void save_state(void *ctx, char *dst, char *modes);
-
-void *load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16,
-                 bool mlock, bool embeddings, bool mmap, bool low_vram,
-                 bool vocab_only, int n_gpu, int n_batch, const char *maingpu,
-                 const char *tensorsplit, bool numa);
-
-int get_embeddings(void *params_ptr, void *state_pr, float *res_embeddings);
-
-int get_token_embeddings(void *params_ptr, void *state_pr, int *tokens,
-                         int tokenSize, float *res_embeddings);
-
-void *llama_allocate_params(
-    const char *prompt, int seed, int threads, int tokens, int top_k,
-    float top_p, float temp, float repeat_penalty, int repeat_last_n,
-    bool ignore_eos, bool memory_f16, int n_batch, int n_keep,
-    const char **antiprompt, int antiprompt_count, float tfs_z, float typical_p,
-    float frequency_penalty, float presence_penalty, int mirostat,
-    float mirostat_eta, float mirostat_tau, bool penalize_nl,
-    const char *logit_bias, bool mlock, bool mmap, const char *maingpu,
-    const char *tensorsplit);
-
-void llama_free_params(void *params_ptr);
-
-void llama_binding_free_model(void *state);
-
-int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug);
-
-#ifdef __cplusplus
-}
-
-std::vector<std::string> create_vector(const char **strings, int count);
-void delete_vector(std::vector<std::string> *vec);
-#endif

llama/ggml-cuda.h

@@ -0,0 +1,62 @@
+/**
+ * llama.cpp - git e782c9e735f93ab4767ffc37462c523b73a17ddc
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#pragma once
+
+#include "ggml.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+#define GGML_CUDA_MAX_DEVICES       16
+
+void   ggml_init_cublas(void);
+void   ggml_cuda_set_tensor_split(const float * tensor_split);
+
+void   ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+bool   ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+size_t ggml_cuda_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+void   ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
+
+// TODO: export these with GGML_API
+void * ggml_cuda_host_malloc(size_t size);
+void   ggml_cuda_host_free(void * ptr);
+
+void   ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
+
+void   ggml_cuda_free_data(struct ggml_tensor * tensor);
+void   ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
+void   ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
+void   ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
+void   ggml_cuda_set_main_device(int main_device);
+void   ggml_cuda_set_scratch_size(size_t scratch_size);
+void   ggml_cuda_free_scratch(void);
+bool   ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
+
+#ifdef  __cplusplus
+}
+#endif

llama/ggml-metal.h

@@ -0,0 +1,99 @@
+//go:build darwin
+
+/**
+ * llama.cpp - git e782c9e735f93ab4767ffc37462c523b73a17ddc
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+// An interface allowing to compute ggml_cgraph with Metal
+//
+// This is a fully functional interface that extends ggml with GPU support for Apple devices.
+// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, OpenCL, etc.)
+//
+// How it works?
+//
+// As long as your program can create and evaluate a ggml_cgraph on the CPU, you can use this
+// interface to evaluate the same graph on the GPU. Instead of using ggml_graph_compute(), you
+// use ggml_metal_graph_compute() (or ggml_vulkan_graph_compute(), etc.)
+//
+// You only need to make sure that all memory buffers that you used during the graph creation
+// are mapped to the device memory with the ggml_metal_add_buffer() function. This mapping is
+// used during the graph evaluation to determine the arguments of the compute kernels.
+//
+// Synchronization between device and host memory (for example for input and output tensors)
+// is done with the ggml_metal_set_tensor() and ggml_metal_get_tensor() functions.
+//
+
+#pragma once
+
+#include <stddef.h>
+#include <stdbool.h>
+
+// max memory buffers that can be mapped to the device
+#define GGML_METAL_MAX_BUFFERS 16
+
+struct ggml_tensor;
+struct ggml_cgraph;
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+struct ggml_metal_context;
+
+// number of command buffers to use
+struct ggml_metal_context * ggml_metal_init(int n_cb);
+void ggml_metal_free(struct ggml_metal_context * ctx);
+
+// set the number of command buffers to use
+void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb);
+
+// creates a mapping between a host memory buffer and a device memory buffer
+// - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute
+// - the mapping is used during computation to determine the arguments of the compute kernels
+// - you don't need to keep the host memory buffer allocated as it is never accessed by Metal
+// - max_size specifies the maximum size of a tensor and is used to create shared views such
+//   that it is guaranteed that the tensor will fit in at least one of the views
+//
+bool ggml_metal_add_buffer(
+        struct ggml_metal_context * ctx,
+                       const char * name,
+                             void * data,
+                           size_t   size,
+                           size_t   max_size);
+
+// set data from host memory into the device
+void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
+
+// get data from the device into host memory
+void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
+
+// same as ggml_graph_compute but uses Metal
+// creates gf->n_threads command buffers in parallel
+void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
+
+#ifdef __cplusplus
+}
+#endif
+

llama/ggml-mpi.c

@@ -0,0 +1,244 @@
+//go:build mpi
+
+/**
+ * llama.cpp - git e782c9e735f93ab4767ffc37462c523b73a17ddc
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#include "ggml-mpi.h"
+
+#include "ggml.h"
+
+#include <mpi.h>
+
+#include <stdio.h>
+#include <stdlib.h>
+
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+
+#define UNUSED GGML_UNUSED
+
+struct ggml_mpi_context {
+    int rank;
+    int size;
+};
+
+void ggml_mpi_backend_init(void) {
+    MPI_Init(NULL, NULL);
+}
+
+void ggml_mpi_backend_free(void) {
+    MPI_Finalize();
+}
+
+struct ggml_mpi_context * ggml_mpi_init(void) {
+    struct ggml_mpi_context * ctx = calloc(1, sizeof(struct ggml_mpi_context));
+
+    MPI_Comm_rank(MPI_COMM_WORLD, &ctx->rank);
+    MPI_Comm_size(MPI_COMM_WORLD, &ctx->size);
+
+    return ctx;
+}
+
+void ggml_mpi_free(struct ggml_mpi_context * ctx) {
+    free(ctx);
+}
+
+int ggml_mpi_rank(struct ggml_mpi_context * ctx) {
+    return ctx->rank;
+}
+
+void ggml_mpi_eval_init(
+        struct ggml_mpi_context * ctx_mpi,
+                            int * n_tokens,
+                            int * n_past,
+                            int * n_threads) {
+    UNUSED(ctx_mpi);
+
+    // synchronize the worker node parameters with the root node
+    MPI_Barrier(MPI_COMM_WORLD);
+
+    MPI_Bcast(n_tokens,  1, MPI_INT, 0, MPI_COMM_WORLD);
+    MPI_Bcast(n_past,    1, MPI_INT, 0, MPI_COMM_WORLD);
+    MPI_Bcast(n_threads, 1, MPI_INT, 0, MPI_COMM_WORLD);
+}
+
+static int ggml_graph_get_node_idx(struct ggml_cgraph * gf, const char * name) {
+    struct ggml_tensor * t = ggml_graph_get_tensor(gf, name);
+    if (t == NULL) {
+        fprintf(stderr, "%s: tensor %s not found\n", __func__, name);
+        return -1;
+    }
+
+    for (int i = 0; i < gf->n_nodes; i++) {
+        if (gf->nodes[i] == t) {
+            return i;
+        }
+    }
+
+    fprintf(stderr, "%s: tensor %s not found in graph (should not happen)\n", __func__, name);
+    return -1;
+}
+
+static void ggml_mpi_tensor_send(struct ggml_tensor * t, int mpi_rank_dst) {
+    MPI_Datatype mpi_type;
+
+    switch (t->type) {
+        case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break;
+        case GGML_TYPE_F32: mpi_type = MPI_FLOAT;   break;
+        default: GGML_ASSERT(false && "not implemented");
+    }
+
+    const int retval = MPI_Send(t->data, ggml_nelements(t), mpi_type, mpi_rank_dst, 0, MPI_COMM_WORLD);
+    GGML_ASSERT(retval == MPI_SUCCESS);
+}
+
+static void ggml_mpi_tensor_recv(struct ggml_tensor * t, int mpi_rank_src) {
+    MPI_Datatype mpi_type;
+
+    switch (t->type) {
+        case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break;
+        case GGML_TYPE_F32: mpi_type = MPI_FLOAT;   break;
+        default: GGML_ASSERT(false && "not implemented");
+    }
+
+    MPI_Status status; UNUSED(status);
+
+    const int retval = MPI_Recv(t->data, ggml_nelements(t), mpi_type, mpi_rank_src, MPI_ANY_TAG, MPI_COMM_WORLD, &status);
+    GGML_ASSERT(retval == MPI_SUCCESS);
+}
+
+// TODO: there are many improvements that can be done to this implementation
+void ggml_mpi_graph_compute_pre(
+        struct ggml_mpi_context * ctx_mpi,
+             struct ggml_cgraph * gf,
+                            int   n_layers) {
+    const int mpi_rank = ctx_mpi->rank;
+    const int mpi_size = ctx_mpi->size;
+
+    struct ggml_tensor * inp_tokens = ggml_graph_get_tensor(gf, "inp_tokens");
+    if (inp_tokens == NULL) {
+        fprintf(stderr, "%s: tensor 'inp_tokens' not found\n", __func__);
+        return;
+    }
+
+    struct ggml_tensor * inp0 = ggml_graph_get_tensor(gf, "layer_inp_0");
+    if (inp0 == NULL) {
+        fprintf(stderr, "%s: tensor 'inp0' not found\n", __func__);
+        return;
+    }
+
+    GGML_ASSERT(inp0 == gf->nodes[0]);
+
+    // distribute the compute graph into slices across the MPI nodes
+    //
+    // the main node (0) processes the last layers + the remainder of the compute graph
+    // and is responsible to pass the input tokens to the first node (1)
+    //
+    // node 1:   [(  0) * n_per_node, (  1) * n_per_node)
+    // node 2:   [(  1) * n_per_node, (  2) * n_per_node)
+    // ...
+    // node n-1: [(n-2) * n_per_node, (n-1) * n_per_node)
+    // node 0:   [(n-1) * n_per_node,            n_nodes)
+    //
+    if (mpi_rank > 0) {
+        if (mpi_rank == 1) {
+            // the first node (1) receives the input tokens from the main node (0)
+            ggml_mpi_tensor_recv(inp_tokens, 0);
+        } else {
+            // recv input data for each node into the "inp0" tensor (i.e. the first node in the compute graph)
+            ggml_mpi_tensor_recv(inp0, mpi_rank - 1);
+        }
+    } else if (mpi_size > 1) {
+        // node 0 sends the input tokens to node 1
+        ggml_mpi_tensor_send(inp_tokens, 1);
+
+        // recv the output data from the last node
+        ggml_mpi_tensor_recv(inp0, mpi_size - 1);
+    }
+
+    {
+        const int n_per_node = (n_layers + (mpi_size - 1)) / mpi_size;
+
+        const int mpi_idx = mpi_rank > 0 ? mpi_rank - 1 : mpi_size - 1;
+
+        const int il0 =               (mpi_idx + 0) * n_per_node;
+        const int il1 = MIN(n_layers, (mpi_idx + 1) * n_per_node);
+
+        char name_l0[GGML_MAX_NAME];
+        char name_l1[GGML_MAX_NAME];
+
+        snprintf(name_l0, sizeof(name_l0), "layer_inp_%d", il0);
+        snprintf(name_l1, sizeof(name_l1), "layer_inp_%d", il1);
+
+        const int idx_l0 =                ggml_graph_get_node_idx(gf, name_l0);
+        const int idx_l1 = mpi_rank > 0 ? ggml_graph_get_node_idx(gf, name_l1) + 1 : gf->n_nodes;
+
+        if (idx_l0 < 0 || idx_l1 < 0) {
+            fprintf(stderr, "%s: layer input nodes not found\n", __func__);
+            return;
+        }
+
+        // attach the input data to all nodes that need it
+        // TODO: not great - should be able to do this without modifying the compute graph (see next TODO below)
+        for (int i = idx_l0; i < idx_l1; i++) {
+            if (gf->nodes[i]->src[0] == gf->nodes[idx_l0]) {
+                gf->nodes[i]->src[0] =  inp0;
+            }
+            if (gf->nodes[i]->src[1] == gf->nodes[idx_l0]) {
+                gf->nodes[i]->src[1] =  inp0;
+            }
+        }
+
+        // TODO: instead of rearranging the nodes, we should be able to execute a subset of the compute graph
+        for (int i = 1; i < idx_l1 - idx_l0; i++) {
+            gf->nodes[i] = gf->nodes[idx_l0 + i];
+            gf->grads[i] = gf->grads[idx_l0 + i];
+        }
+
+        // the first node performs the "get_rows" operation, the rest of the nodes get the data from the previous node
+        if (mpi_idx != 0) {
+            gf->nodes[0]->op = GGML_OP_NONE;
+        }
+
+        gf->n_nodes = idx_l1 - idx_l0;
+
+        //fprintf(stderr, "%s: node %d: processing %d nodes [%d, %d)\n", __func__, mpi_rank, gf->n_nodes, il0, il1);
+    }
+}
+
+void ggml_mpi_graph_compute_post(
+        struct ggml_mpi_context * ctx_mpi,
+             struct ggml_cgraph * gf,
+                            int   n_layers) {
+    UNUSED(n_layers);
+
+    const int mpi_rank = ctx_mpi->rank;
+    const int mpi_size = ctx_mpi->size;
+
+    // send the output data to the next node
+    if (mpi_rank > 0) {
+        ggml_mpi_tensor_send(gf->nodes[gf->n_nodes - 1], (mpi_rank + 1) % mpi_size);
+    }
+}

llama/ggml-mpi.h

@@ -0,0 +1,67 @@
+//go:build mpi
+
+/**
+ * llama.cpp - git e782c9e735f93ab4767ffc37462c523b73a17ddc
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#pragma once
+
+struct ggml_context;
+struct ggml_tensor;
+struct ggml_cgraph;
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+struct ggml_mpi_context;
+
+void ggml_mpi_backend_init(void);
+void ggml_mpi_backend_free(void);
+
+struct ggml_mpi_context * ggml_mpi_init(void);
+void ggml_mpi_free(struct ggml_mpi_context * ctx);
+
+int ggml_mpi_rank(struct ggml_mpi_context * ctx);
+
+void ggml_mpi_eval_init(
+        struct ggml_mpi_context * ctx_mpi,
+                            int * n_tokens,
+                            int * n_past,
+                            int * n_threads);
+
+void ggml_mpi_graph_compute_pre(
+        struct ggml_mpi_context * ctx_mpi,
+             struct ggml_cgraph * gf,
+                            int   n_layers);
+
+void ggml_mpi_graph_compute_post(
+        struct ggml_mpi_context * ctx_mpi,
+             struct ggml_cgraph * gf,
+                            int   n_layers);
+
+#ifdef __cplusplus
+}
+#endif

llama/ggml-opencl.h

@@ -0,0 +1,53 @@
+//go:build opencl
+
+/**
+ * llama.cpp - git e782c9e735f93ab4767ffc37462c523b73a17ddc
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#pragma once
+
+#include "ggml.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+void ggml_cl_init(void);
+
+void   ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+bool   ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+void   ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
+
+void * ggml_cl_host_malloc(size_t size);
+void   ggml_cl_host_free(void * ptr);
+
+void ggml_cl_free_data(const struct ggml_tensor* tensor);
+
+void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor);
+
+#ifdef  __cplusplus
+}
+#endif

llama/k_quants.h

@@ -0,0 +1,191 @@
+/**
+ * llama.cpp - git e782c9e735f93ab4767ffc37462c523b73a17ddc
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#pragma once
+
+#include "ggml.h"
+
+#include <stdint.h>
+#include <assert.h>
+#include <stddef.h>
+
+// Super-block size
+#ifdef GGML_QKK_64
+#define QK_K 64
+#define K_SCALE_SIZE 4
+#else
+#define QK_K 256
+#define K_SCALE_SIZE 12
+#endif
+
+#ifndef static_assert
+#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
+#define static_assert(cond, msg) _Static_assert(cond, msg)
+#else
+#define static_assert(cond, msg) struct global_scope_noop_trick
+#endif
+#endif
+
+//
+// Super-block quantization structures
+//
+
+// 2-bit quantization
+// weight is represented as x = a * q + b
+// 16 blocks of 16 elemenets each
+// Effectively 2.5625 bits per weight
+typedef struct {
+    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
+    uint8_t qs[QK_K/4];      // quants
+    ggml_fp16_t d;           // super-block scale for quantized scales
+    ggml_fp16_t dmin;        // super-block scale for quantized mins
+} block_q2_K;
+static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
+
+// 3-bit quantization
+// weight is represented as x = a * q
+// 16 blocks of 16 elemenets each
+// Effectively 3.4375 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    uint8_t hmask[QK_K/8];     // quants - high bit
+    uint8_t qs[QK_K/4];        // quants - low 2 bits
+    uint8_t scales[2];
+    ggml_fp16_t d;             // super-block scale
+} block_q3_K;
+static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
+#else
+typedef struct {
+    uint8_t hmask[QK_K/8];     // quants - high bit
+    uint8_t qs[QK_K/4];        // quants - low 2 bits
+    uint8_t scales[12];        // scales, quantized with 6 bits
+    ggml_fp16_t d;             // super-block scale
+} block_q3_K;
+static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
+#endif
+
+// 4-bit quantization
+// 16 blocks of 32 elements each
+// weight is represented as x = a * q + b
+// Effectively 4.5 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    ggml_fp16_t d[2];          // super-block scales/mins
+    uint8_t scales[2];         // 4-bit block scales/mins
+    uint8_t qs[QK_K/2];        // 4--bit quants
+} block_q4_K;
+static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
+#else
+typedef struct {
+    ggml_fp16_t d;             // super-block scale for quantized scales
+    ggml_fp16_t dmin;          // super-block scale for quantized mins
+    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
+    uint8_t qs[QK_K/2];        // 4--bit quants
+} block_q4_K;
+static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
+#endif
+
+// 5-bit quantization
+// 16 blocks of 32 elements each
+// weight is represented as x = a * q + b
+// Effectively 5.5 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    ggml_fp16_t d;               // super-block scale
+    int8_t  scales[QK_K/16];     // 8-bit block scales
+    uint8_t qh[QK_K/8];          // quants, high bit
+    uint8_t qs[QK_K/2];          // quants, low 4 bits
+} block_q5_K;
+static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
+#else
+typedef struct {
+    ggml_fp16_t d;               // super-block scale for quantized scales
+    ggml_fp16_t dmin;            // super-block scale for quantized mins
+    uint8_t scales[K_SCALE_SIZE];   // scales and mins, quantized with 6 bits
+    uint8_t qh[QK_K/8];          // quants, high bit
+    uint8_t qs[QK_K/2];          // quants, low 4 bits
+} block_q5_K;
+static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
+#endif
+
+// 6-bit quantization
+// weight is represented as x = a * q
+// 16 blocks of 16 elemenets each
+// Effectively 6.5625 bits per weight
+typedef struct {
+    uint8_t ql[QK_K/2];      // quants, lower 4 bits
+    uint8_t qh[QK_K/4];      // quants, upper 2 bits
+    int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
+    ggml_fp16_t d;           // super-block scale
+} block_q6_K;
+static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding");
+
+// This is only used for intermediate quantization and dot products
+typedef struct {
+    float   d;              // delta
+    int8_t  qs[QK_K];       // quants
+    int16_t bsums[QK_K/16]; // sum of quants in groups of 16
+} block_q8_K;
+static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");
+
+
+// Quantization
+void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k);
+void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k);
+void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k);
+void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k);
+void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k);
+void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k);
+
+void quantize_row_q2_K(const float * restrict x, void * restrict y, int k);
+void quantize_row_q3_K(const float * restrict x, void * restrict y, int k);
+void quantize_row_q4_K(const float * restrict x, void * restrict y, int k);
+void quantize_row_q5_K(const float * restrict x, void * restrict y, int k);
+void quantize_row_q6_K(const float * restrict x, void * restrict y, int k);
+void quantize_row_q8_K(const float * restrict x, void * restrict y, int k);
+
+// Dequantization
+void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k);
+void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k);
+void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k);
+void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k);
+void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k);
+void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k);
+
+// Dot product
+void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+
+// Quantization with histogram collection
+size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist);
+size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist);
+size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist);
+size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist);
+size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist);
+

llama/llama-util.h

@@ -0,0 +1,530 @@
+/**
+ * llama.cpp - git e782c9e735f93ab4767ffc37462c523b73a17ddc
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+// Internal header to be included only by llama.cpp.
+// Contains wrappers around OS interfaces.
+
+#ifndef LLAMA_UTIL_H
+#define LLAMA_UTIL_H
+
+#include <cstdio>
+#include <cstdint>
+#include <cerrno>
+#include <cstring>
+#include <cstdarg>
+#include <cstdlib>
+#include <climits>
+
+#include <string>
+#include <vector>
+#include <stdexcept>
+
+#ifdef __has_include
+    #if __has_include(<unistd.h>)
+        #include <unistd.h>
+        #if defined(_POSIX_MAPPED_FILES)
+            #include <sys/mman.h>
+        #endif
+        #if defined(_POSIX_MEMLOCK_RANGE)
+            #include <sys/resource.h>
+        #endif
+    #endif
+#endif
+
+#if defined(_WIN32)
+    #define WIN32_LEAN_AND_MEAN
+    #ifndef NOMINMAX
+        #define NOMINMAX
+    #endif
+    #include <windows.h>
+    #include <io.h>
+    #include <stdio.h> // for _fseeki64
+#endif
+
+#define LLAMA_ASSERT(x) \
+    do { \
+        if (!(x)) { \
+            fprintf(stderr, "LLAMA_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
+            abort(); \
+        } \
+    } while (0)
+
+#ifdef __GNUC__
+#ifdef __MINGW32__
+__attribute__((format(gnu_printf, 1, 2)))
+#else
+__attribute__((format(printf, 1, 2)))
+#endif
+#endif
+static std::string format(const char * fmt, ...) {
+    va_list ap, ap2;
+    va_start(ap, fmt);
+    va_copy(ap2, ap);
+    int size = vsnprintf(NULL, 0, fmt, ap);
+    LLAMA_ASSERT(size >= 0 && size < INT_MAX);
+    std::vector<char> buf(size + 1);
+    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
+    LLAMA_ASSERT(size2 == size);
+    va_end(ap2);
+    va_end(ap);
+    return std::string(buf.data(), size);
+}
+
+struct llama_file {
+    // use FILE * so we don't have to re-open the file to mmap
+    FILE * fp;
+    size_t size;
+
+    llama_file(const char * fname, const char * mode) {
+        fp = std::fopen(fname, mode);
+        if (fp == NULL) {
+            throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
+        }
+        seek(0, SEEK_END);
+        size = tell();
+        seek(0, SEEK_SET);
+    }
+
+    size_t tell() const {
+#ifdef _WIN32
+        __int64 ret = _ftelli64(fp);
+#else
+        long ret = std::ftell(fp);
+#endif
+        LLAMA_ASSERT(ret != -1); // this really shouldn't fail
+        return (size_t) ret;
+    }
+
+    void seek(size_t offset, int whence) {
+#ifdef _WIN32
+        int ret = _fseeki64(fp, (__int64) offset, whence);
+#else
+        int ret = std::fseek(fp, (long) offset, whence);
+#endif
+        LLAMA_ASSERT(ret == 0); // same
+    }
+
+    void read_raw(void * ptr, size_t len) const {
+        if (len == 0) {
+            return;
+        }
+        errno = 0;
+        std::size_t ret = std::fread(ptr, len, 1, fp);
+        if (ferror(fp)) {
+            throw std::runtime_error(format("read error: %s", strerror(errno)));
+        }
+        if (ret != 1) {
+            throw std::runtime_error(std::string("unexpectedly reached end of file"));
+        }
+    }
+
+    std::uint32_t read_u32() {
+        std::uint32_t ret;
+        read_raw(&ret, sizeof(ret));
+        return ret;
+    }
+
+    std::string read_string(std::uint32_t len) {
+        std::vector<char> chars(len);
+        read_raw(chars.data(), len);
+        return std::string(chars.data(), len);
+    }
+
+    void write_raw(const void * ptr, size_t len) const {
+        if (len == 0) {
+            return;
+        }
+        errno = 0;
+        size_t ret = std::fwrite(ptr, len, 1, fp);
+        if (ret != 1) {
+            throw std::runtime_error(format("write error: %s", strerror(errno)));
+        }
+    }
+
+    void write_u32(std::uint32_t val) {
+        write_raw(&val, sizeof(val));
+    }
+
+    ~llama_file() {
+        if (fp) {
+            std::fclose(fp);
+        }
+    }
+};
+
+#if defined(_WIN32)
+static std::string llama_format_win_err(DWORD err) {
+    LPSTR buf;
+    size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
+                                 NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&buf, 0, NULL);
+    if (!size) {
+        return "FormatMessageA failed";
+    }
+    std::string ret(buf, size);
+    LocalFree(buf);
+    return ret;
+}
+#endif
+
+struct llama_mmap {
+    void * addr;
+    size_t size;
+
+    llama_mmap(const llama_mmap &) = delete;
+
+#ifdef _POSIX_MAPPED_FILES
+    static constexpr bool SUPPORTED = true;
+
+    llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) {
+        size = file->size;
+        int fd = fileno(file->fp);
+        int flags = MAP_SHARED;
+        // prefetch/readahead impairs performance on NUMA systems
+        if (numa) { prefetch = 0; }
+#ifdef __linux__
+        if (prefetch) { flags |= MAP_POPULATE; }
+#endif
+        addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
+        if (addr == MAP_FAILED) {
+            throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
+        }
+
+        if (prefetch > 0) {
+            // Advise the kernel to preload the mapped memory
+            if (madvise(addr, std::min(file->size, prefetch), MADV_WILLNEED)) {
+                fprintf(stderr, "warning: madvise(.., MADV_WILLNEED) failed: %s\n",
+                        strerror(errno));
+            }
+        }
+        if (numa) {
+            // advise the kernel not to use readahead
+            // (because the next page might not belong on the same node)
+            if (madvise(addr, file->size, MADV_RANDOM)) {
+                fprintf(stderr, "warning: madvise(.., MADV_RANDOM) failed: %s\n",
+                        strerror(errno));
+            }
+        }
+    }
+
+    ~llama_mmap() {
+        munmap(addr, size);
+    }
+#elif defined(_WIN32)
+    static constexpr bool SUPPORTED = true;
+
+    llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) {
+        (void) numa;
+
+        size = file->size;
+
+        HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
+
+        HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
+        DWORD error = GetLastError();
+
+        if (hMapping == NULL) {
+            throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
+        }
+
+        addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
+        error = GetLastError();
+        CloseHandle(hMapping);
+
+        if (addr == NULL) {
+            throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
+        }
+
+        #if _WIN32_WINNT >= _WIN32_WINNT_WIN8
+        if (prefetch) {
+            // Advise the kernel to preload the mapped memory
+            WIN32_MEMORY_RANGE_ENTRY range;
+            range.VirtualAddress = addr;
+            range.NumberOfBytes = (SIZE_T)size;
+            if (!PrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
+                fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
+                        llama_format_win_err(GetLastError()).c_str());
+            }
+        }
+        #else
+        #pragma message("warning: You are building for pre-Windows 8; prefetch not supported")
+        #endif // _WIN32_WINNT >= _WIN32_WINNT_WIN8
+    }
+
+    ~llama_mmap() {
+        if (!UnmapViewOfFile(addr)) {
+            fprintf(stderr, "warning: UnmapViewOfFile failed: %s\n",
+                    llama_format_win_err(GetLastError()).c_str());
+        }
+    }
+#else
+    static constexpr bool SUPPORTED = false;
+
+    llama_mmap(struct llama_file *, bool prefetch = true, bool numa = false) {
+        (void) prefetch;
+        (void) numa;
+
+        throw std::runtime_error(std::string("mmap not supported"));
+    }
+#endif
+};
+
+// Represents some region of memory being locked using mlock or VirtualLock;
+// will automatically unlock on destruction.
+struct llama_mlock {
+    void * addr = NULL;
+    size_t size = 0;
+    bool failed_already = false;
+
+    llama_mlock() {}
+    llama_mlock(const llama_mlock &) = delete;
+
+    ~llama_mlock() {
+        if (size) {
+            raw_unlock(addr, size);
+        }
+    }
+
+    void init(void * ptr) {
+        LLAMA_ASSERT(addr == NULL && size == 0);
+        addr = ptr;
+    }
+
+    void grow_to(size_t target_size) {
+        LLAMA_ASSERT(addr);
+        if (failed_already) {
+            return;
+        }
+        size_t granularity = lock_granularity();
+        target_size = (target_size + granularity - 1) & ~(granularity - 1);
+        if (target_size > size) {
+            if (raw_lock((uint8_t *) addr + size, target_size - size)) {
+                size = target_size;
+            } else {
+                failed_already = true;
+            }
+        }
+    }
+
+#ifdef _POSIX_MEMLOCK_RANGE
+    static constexpr bool SUPPORTED = true;
+
+    size_t lock_granularity() {
+        return (size_t) sysconf(_SC_PAGESIZE);
+    }
+
+    #ifdef __APPLE__
+        #define MLOCK_SUGGESTION \
+            "Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
+            "decreasing 'vm.global_no_user_wire_amount'.  Also try increasing RLIMIT_MLOCK (ulimit -l).\n"
+    #else
+        #define MLOCK_SUGGESTION \
+            "Try increasing RLIMIT_MLOCK ('ulimit -l' as root).\n"
+    #endif
+
+    bool raw_lock(const void * addr, size_t size) {
+        if (!mlock(addr, size)) {
+            return true;
+        } else {
+            char* errmsg = std::strerror(errno);
+            bool suggest = (errno == ENOMEM);
+
+            // Check if the resource limit is fine after all
+            struct rlimit lock_limit;
+            if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit))
+                suggest = false;
+            if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size))
+                suggest = false;
+
+            fprintf(stderr, "warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
+                    size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
+            return false;
+        }
+    }
+
+    #undef MLOCK_SUGGESTION
+
+    void raw_unlock(void * addr, size_t size) {
+        if (munlock(addr, size)) {
+            fprintf(stderr, "warning: failed to munlock buffer: %s\n", std::strerror(errno));
+        }
+    }
+#elif defined(_WIN32)
+    static constexpr bool SUPPORTED = true;
+
+    size_t lock_granularity() {
+        SYSTEM_INFO si;
+        GetSystemInfo(&si);
+        return (size_t) si.dwPageSize;
+    }
+
+    bool raw_lock(void * ptr, size_t len) {
+        for (int tries = 1; ; tries++) {
+            if (VirtualLock(ptr, len)) {
+                return true;
+            }
+            if (tries == 2) {
+                fprintf(stderr, "warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
+                    len, size, llama_format_win_err(GetLastError()).c_str());
+                return false;
+            }
+
+            // It failed but this was only the first try; increase the working
+            // set size and try again.
+            SIZE_T min_ws_size, max_ws_size;
+            if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) {
+                fprintf(stderr, "warning: GetProcessWorkingSetSize failed: %s\n",
+                        llama_format_win_err(GetLastError()).c_str());
+                return false;
+            }
+            // Per MSDN: "The maximum number of pages that a process can lock
+            // is equal to the number of pages in its minimum working set minus
+            // a small overhead."
+            // Hopefully a megabyte is enough overhead:
+            size_t increment = len + 1048576;
+            // The minimum must be <= the maximum, so we need to increase both:
+            min_ws_size += increment;
+            max_ws_size += increment;
+            if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
+                fprintf(stderr, "warning: SetProcessWorkingSetSize failed: %s\n",
+                        llama_format_win_err(GetLastError()).c_str());
+                return false;
+            }
+        }
+    }
+
+    void raw_unlock(void * ptr, size_t len) {
+        if (!VirtualUnlock(ptr, len)) {
+            fprintf(stderr, "warning: failed to VirtualUnlock buffer: %s\n",
+                    llama_format_win_err(GetLastError()).c_str());
+        }
+    }
+#else
+    static constexpr bool SUPPORTED = false;
+
+    size_t lock_granularity() {
+        return (size_t) 65536;
+    }
+
+    bool raw_lock(const void * addr, size_t len) {
+        fprintf(stderr, "warning: mlock not supported on this system\n");
+        return false;
+    }
+
+    void raw_unlock(const void * addr, size_t len) {}
+#endif
+};
+
+// Replacement for std::vector<uint8_t> that doesn't require zero-initialization.
+struct llama_buffer {
+    uint8_t * addr = NULL;
+    size_t size = 0;
+
+    llama_buffer() = default;
+
+    void resize(size_t len) {
+#ifdef GGML_USE_METAL
+        free(addr);
+        int result = posix_memalign((void **) &addr, getpagesize(), len);
+        if (result == 0) {
+            memset(addr, 0, len);
+        }
+        else {
+            addr = NULL;
+        }
+#else
+        delete[] addr;
+        addr = new uint8_t[len];
+#endif
+        size = len;
+    }
+
+    ~llama_buffer() {
+#ifdef GGML_USE_METAL
+        free(addr);
+#else
+        delete[] addr;
+#endif
+        addr = NULL;
+    }
+
+    // disable copy and move
+    llama_buffer(const llama_buffer&) = delete;
+    llama_buffer(llama_buffer&&) = delete;
+    llama_buffer& operator=(const llama_buffer&) = delete;
+    llama_buffer& operator=(llama_buffer&&) = delete;
+};
+
+#ifdef GGML_USE_CUBLAS
+#include "ggml-cuda.h"
+struct llama_ctx_buffer {
+    uint8_t * addr = NULL;
+    bool is_cuda;
+    size_t size = 0;
+
+    llama_ctx_buffer() = default;
+
+    void resize(size_t size) {
+        free();
+
+        addr = (uint8_t *) ggml_cuda_host_malloc(size);
+        if (addr) {
+            is_cuda = true;
+        }
+        else {
+            // fall back to pageable memory
+            addr = new uint8_t[size];
+            is_cuda = false;
+        }
+        this->size = size;
+    }
+
+    void free() {
+        if (addr) {
+            if (is_cuda) {
+                ggml_cuda_host_free(addr);
+            }
+            else {
+                delete[] addr;
+            }
+        }
+        addr = NULL;
+    }
+
+    ~llama_ctx_buffer() {
+        free();
+    }
+
+    // disable copy and move
+    llama_ctx_buffer(const llama_ctx_buffer&) = delete;
+    llama_ctx_buffer(llama_ctx_buffer&&) = delete;
+    llama_ctx_buffer& operator=(const llama_ctx_buffer&) = delete;
+    llama_ctx_buffer& operator=(llama_ctx_buffer&&) = delete;
+};
+#else
+typedef llama_buffer llama_ctx_buffer;
+#endif
+
+#endif

llama/llama.go

@@ -1,217 +1,282 @@
-// MIT License
-
-// Copyright (c) 2023 go-skynet authors
-
-// Permission is hereby granted, free of charge, to any person obtaining a copy
-// of this software and associated documentation files (the "Software"), to deal
-// in the Software without restriction, including without limitation the rights
-// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-// copies of the Software, and to permit persons to whom the Software is
-// furnished to do so, subject to the following conditions:
-
-// The above copyright notice and this permission notice shall be included in all
-// copies or substantial portions of the Software.
-
-// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-// SOFTWARE.
-
 package llama
 
-// #cgo LDFLAGS: -Lbuild -lbinding -lllama -lm -lggml_static -lstdc++
-// #cgo CXXFLAGS: -std=c++11
-// #cgo darwin LDFLAGS: -framework Accelerate -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
-// #include "binding/binding.h"
-// #include <stdlib.h>
-import "C"
+/*
+#cgo CPPFLAGS: -O3 -DNDEBUG=1
+#cgo CXXFLAGS: -std=c++11
+#cgo darwin CPPFLAGS: -DGGML_USE_METAL=1 -DGGML_METAL_NDEBUG=1
+#cgo darwin LDFLAGS: -framework Accelerate -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
+#include <stdlib.h>
+#include "llama.h"
 
+struct llama_sample_options
+{
+	float repeat_penalty;
+	float frequency_penalty;
+	float presence_penalty;
+	float temperature;
+	int32_t top_k;
+	float top_p;
+	float tfs_z;
+	float typical_p;
+	int mirostat;
+	float mirostat_tau;
+	float mirostat_eta;
+};
+
+llama_token llama_sample(
+		struct llama_context *ctx,
+		struct llama_token_data *candidates,
+		size_t n_candidates,
+		const llama_token *last_tokens,
+		size_t n_last_tokens,
+		struct llama_sample_options *opts)
+{
+	llama_token_data_array candidates_p = {
+		candidates,
+		n_candidates,
+		false,
+	};
+
+	llama_sample_repetition_penalty(
+		ctx, &candidates_p,
+		last_tokens, n_last_tokens,
+		opts->repeat_penalty);
+
+	llama_sample_frequency_and_presence_penalties(
+		ctx, &candidates_p,
+		last_tokens, n_last_tokens,
+		opts->frequency_penalty, opts->presence_penalty);
+
+	if (opts->temperature <= 0) {
+		return llama_sample_token_greedy(ctx, &candidates_p);
+	}
+
+	if (opts->mirostat == 1) {
+		int mirostat_m = 100;
+		float mirostat_mu = 2.0f * opts->mirostat_tau;
+		llama_sample_temperature(ctx, &candidates_p, opts->temperature);
+		return llama_sample_token_mirostat(
+			ctx, &candidates_p,
+			opts->mirostat_tau, opts->mirostat_eta,
+			mirostat_m, &mirostat_mu);
+	} else if (opts->mirostat == 2) {
+		float mirostat_mu = 2.0f * opts->mirostat_tau;
+		llama_sample_temperature(ctx, &candidates_p, opts->temperature);
+		return llama_sample_token_mirostat_v2(
+			ctx, &candidates_p,
+			opts->mirostat_tau, opts->mirostat_eta,
+			&mirostat_mu);
+	} else {
+		llama_sample_top_k(ctx, &candidates_p, opts->top_k, 1);
+		llama_sample_tail_free(ctx, &candidates_p, opts->tfs_z, 1);
+		llama_sample_typical(ctx, &candidates_p, opts->typical_p, 1);
+		llama_sample_top_p(ctx, &candidates_p, opts->top_p, 1);
+		llama_sample_temperature(ctx, &candidates_p, opts->temperature);
+		return llama_sample_token(ctx, &candidates_p);
+	}
+}
+*/
+import "C"
 import (
+	"bytes"
+	"errors"
 	"fmt"
+	"io"
+	"os"
 	"strings"
-	"sync"
+	"time"
+	"unicode/utf8"
 	"unsafe"
+
+	"github.com/jmorganca/ollama/api"
 )
 
-type LLama struct {
-	ctx         unsafe.Pointer
-	embeddings  bool
-	contextSize int
+type llama struct {
+	params *C.struct_llama_context_params
+	model  *C.struct_llama_model
+	ctx    *C.struct_llama_context
+
+	api.Options
 }
 
-func New(model string, mo ModelOptions) (*LLama, error) {
-	modelPath := C.CString(model)
-	defer C.free(unsafe.Pointer(modelPath))
-
-	ctx := C.load_model(modelPath, C.int(mo.ContextSize), C.int(mo.Seed), C.bool(mo.F16Memory), C.bool(mo.MLock), C.bool(mo.Embeddings), C.bool(mo.MMap), C.bool(mo.LowVRAM), C.bool(mo.VocabOnly), C.int(mo.NGPULayers), C.int(mo.NBatch), C.CString(mo.MainGPU), C.CString(mo.TensorSplit), C.bool(mo.NUMA))
-	if ctx == nil {
-		return nil, fmt.Errorf("failed loading model")
+func New(model string, opts api.Options) (*llama, error) {
+	if _, err := os.Stat(model); err != nil {
+		return nil, err
 	}
 
-	ll := &LLama{ctx: ctx, contextSize: mo.ContextSize, embeddings: mo.Embeddings}
+	llm := llama{Options: opts}
 
-	return ll, nil
+	C.llama_backend_init(C.bool(llm.UseNUMA))
+
+	params := C.llama_context_default_params()
+	params.seed = C.uint(llm.Seed)
+	params.n_ctx = C.int(llm.NumCtx)
+	params.n_batch = C.int(llm.NumBatch)
+	params.n_gpu_layers = C.int(llm.NumGPU)
+	params.main_gpu = C.int(llm.MainGPU)
+	params.low_vram = C.bool(llm.LowVRAM)
+	params.f16_kv = C.bool(llm.F16KV)
+	params.logits_all = C.bool(llm.LogitsAll)
+	params.vocab_only = C.bool(llm.VocabOnly)
+	params.use_mmap = C.bool(llm.UseMMap)
+	params.use_mlock = C.bool(llm.UseMLock)
+	params.embedding = C.bool(llm.EmbeddingOnly)
+	llm.params = &params
+
+	cModel := C.CString(model)
+	defer C.free(unsafe.Pointer(cModel))
+
+	llm.model = C.llama_load_model_from_file(cModel, params)
+	if llm.model == nil {
+		return nil, errors.New("failed to load model")
+	}
+
+	llm.ctx = C.llama_new_context_with_model(llm.model, params)
+	if llm.ctx == nil {
+		return nil, errors.New("failed to create context")
+	}
+
+	// warm up the model
+	bos := []C.llama_token{C.llama_token_bos()}
+	C.llama_eval(llm.ctx, unsafe.SliceData(bos), C.int(len(bos)), 0, C.int(opts.NumThread))
+	C.llama_reset_timings(llm.ctx)
+
+	return &llm, nil
 }
 
-func (l *LLama) Free() {
-	C.llama_binding_free_model(l.ctx)
+func (llm *llama) Close() {
+	defer C.llama_free_model(llm.model)
+	defer C.llama_free(llm.ctx)
+
+	C.llama_print_timings(llm.ctx)
 }
 
-func (l *LLama) Eval(text string, opts ...PredictOption) error {
-	po := NewPredictOptions(opts...)
+func (llm *llama) Predict(ctx []int, prompt string, fn func(api.GenerateResponse)) error {
+	if input := llm.tokenize(prompt); input != nil {
+		embd := make([]C.llama_token, len(ctx))
+		for i := range ctx {
+			embd[i] = C.llama_token(ctx[i])
+		}
 
-	input := C.CString(text)
-	if po.Tokens == 0 {
-		po.Tokens = 99999999
-	}
-	defer C.free(unsafe.Pointer(input))
-
-	reverseCount := len(po.StopPrompts)
-	reversePrompt := make([]*C.char, reverseCount)
-	var pass **C.char
-	for i, s := range po.StopPrompts {
-		cs := C.CString(s)
-		reversePrompt[i] = cs
-		pass = &reversePrompt[0]
-		defer C.free(unsafe.Pointer(cs))
+		return llm.generate(append(embd, input...), fn)
 	}
 
-	cLogitBias := C.CString(po.LogitBias)
-	defer C.free(unsafe.Pointer(cLogitBias))
+	return errors.New("llama: tokenize")
+}
 
-	cMainGPU := C.CString(po.MainGPU)
-	defer C.free(unsafe.Pointer(cMainGPU))
+func (llm *llama) tokenize(prompt string) []C.llama_token {
+	cPrompt := C.CString(prompt)
+	defer C.free(unsafe.Pointer(cPrompt))
 
-	cTensorSplit := C.CString(po.TensorSplit)
-	defer C.free(unsafe.Pointer(cTensorSplit))
-
-	params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.Tokens), C.int(po.TopK),
-		C.float(po.TopP), C.float(po.Temperature), C.float(po.Penalty), C.int(po.Repeat),
-		C.bool(po.IgnoreEOS), C.bool(po.F16KV),
-		C.int(po.Batch), C.int(po.NKeep), pass, C.int(reverseCount),
-		C.float(po.TailFreeSamplingZ), C.float(po.TypicalP), C.float(po.FrequencyPenalty), C.float(po.PresencePenalty),
-		C.int(po.Mirostat), C.float(po.MirostatETA), C.float(po.MirostatTAU), C.bool(po.PenalizeNL), cLogitBias,
-		C.bool(po.MLock), C.bool(po.MMap), cMainGPU, cTensorSplit,
-	)
-	defer C.llama_free_params(params)
-
-	ret := C.eval(params, l.ctx, input)
-	if ret != 0 {
-		return fmt.Errorf("inference failed")
+	tokens := make([]C.llama_token, llm.NumCtx)
+	if n := C.llama_tokenize(llm.ctx, cPrompt, unsafe.SliceData(tokens), C.int(len(tokens)), true); n > 0 {
+		return tokens[:n]
 	}
 
 	return nil
 }
 
-func (l *LLama) Predict(text string, po PredictOptions) (string, error) {
-	if po.TokenCallback != nil {
-		setCallback(l.ctx, po.TokenCallback)
+func (llm *llama) detokenize(tokens ...C.llama_token) string {
+	var sb strings.Builder
+	for _, token := range tokens {
+		sb.WriteString(C.GoString(C.llama_token_to_str(llm.ctx, token)))
 	}
 
-	input := C.CString(text)
-	if po.Tokens == 0 {
-		po.Tokens = 99999999
-	}
-	defer C.free(unsafe.Pointer(input))
-
-	out := make([]byte, po.Tokens)
-
-	reverseCount := len(po.StopPrompts)
-	reversePrompt := make([]*C.char, reverseCount)
-	var pass **C.char
-	for i, s := range po.StopPrompts {
-		cs := C.CString(s)
-		reversePrompt[i] = cs
-		pass = &reversePrompt[0]
-		defer C.free(unsafe.Pointer(cs))
-	}
-
-	cLogitBias := C.CString(po.LogitBias)
-	defer C.free(unsafe.Pointer(cLogitBias))
-
-	cMainGPU := C.CString(po.MainGPU)
-	defer C.free(unsafe.Pointer(cMainGPU))
-
-	cTensorSplit := C.CString(po.TensorSplit)
-	defer C.free(unsafe.Pointer(cTensorSplit))
-
-	params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.Tokens), C.int(po.TopK),
-		C.float(po.TopP), C.float(po.Temperature), C.float(po.Penalty), C.int(po.Repeat),
-		C.bool(po.IgnoreEOS), C.bool(po.F16KV),
-		C.int(po.Batch), C.int(po.NKeep), pass, C.int(reverseCount),
-		C.float(po.TailFreeSamplingZ), C.float(po.TypicalP), C.float(po.FrequencyPenalty), C.float(po.PresencePenalty),
-		C.int(po.Mirostat), C.float(po.MirostatETA), C.float(po.MirostatTAU), C.bool(po.PenalizeNL), cLogitBias,
-		C.bool(po.MLock), C.bool(po.MMap), cMainGPU, cTensorSplit,
-	)
-	defer C.llama_free_params(params)
-
-	ret := C.llama_predict(params, l.ctx, (*C.char)(unsafe.Pointer(&out[0])), C.bool(po.DebugMode))
-	if ret != 0 {
-		return "", fmt.Errorf("inference failed")
-	}
-	res := C.GoString((*C.char)(unsafe.Pointer(&out[0])))
-
-	res = strings.TrimPrefix(res, " ")
-	res = strings.TrimPrefix(res, text)
-	res = strings.TrimPrefix(res, "\n")
-
-	for _, s := range po.StopPrompts {
-		res = strings.TrimRight(res, s)
-	}
-
-	if po.TokenCallback != nil {
-		setCallback(l.ctx, nil)
-	}
-
-	return res, nil
+	return sb.String()
 }
 
-// CGo only allows us to use static calls from C to Go, we can't just dynamically pass in func's.
-// This is the next best thing, we register the callbacks in this map and call tokenCallback from
-// the C code. We also attach a finalizer to LLama, so it will unregister the callback when the
-// garbage collection frees it.
+func (llm *llama) generate(input []C.llama_token, fn func(api.GenerateResponse)) error {
+	var opts C.struct_llama_sample_options
+	opts.repeat_penalty = C.float(llm.RepeatPenalty)
+	opts.frequency_penalty = C.float(llm.FrequencyPenalty)
+	opts.presence_penalty = C.float(llm.PresencePenalty)
+	opts.temperature = C.float(llm.Temperature)
+	opts.top_k = C.int(llm.TopK)
+	opts.top_p = C.float(llm.TopP)
+	opts.tfs_z = C.float(llm.TFSZ)
+	opts.typical_p = C.float(llm.TypicalP)
+	opts.mirostat = C.int(llm.Mirostat)
+	opts.mirostat_tau = C.float(llm.MirostatTau)
+	opts.mirostat_eta = C.float(llm.MirostatEta)
 
-// SetTokenCallback registers a callback for the individual tokens created when running Predict. It
-// will be called once for each token. The callback shall return true as long as the model should
-// continue predicting the next token. When the callback returns false the predictor will return.
-// The tokens are just converted into Go strings, they are not trimmed or otherwise changed. Also
-// the tokens may not be valid UTF-8.
-// Pass in nil to remove a callback.
-//
-// It is save to call this method while a prediction is running.
-func (l *LLama) SetTokenCallback(callback func(token string) bool) {
-	setCallback(l.ctx, callback)
-}
+	output := deque[C.llama_token]{capacity: llm.NumCtx}
 
-var (
-	m         sync.Mutex
-	callbacks = map[uintptr]func(string) bool{}
-)
-
-//export tokenCallback
-func tokenCallback(statePtr unsafe.Pointer, token *C.char) bool {
-	m.Lock()
-	defer m.Unlock()
-
-	if callback, ok := callbacks[uintptr(statePtr)]; ok {
-		return callback(C.GoString(token))
+	context := deque[int]{capacity: llm.NumCtx / 2}
+	for _, in := range input {
+		context.PushLeft(int(in))
 	}
 
-	return true
-}
+	var b bytes.Buffer
+	for C.llama_get_kv_cache_token_count(llm.ctx) < C.int(llm.NumCtx) {
+		if retval := C.llama_eval(llm.ctx, unsafe.SliceData(input), C.int(len(input)), C.llama_get_kv_cache_token_count(llm.ctx), C.int(llm.NumThread)); retval != 0 {
+			return errors.New("llama: eval")
+		}
 
-// setCallback can be used to register a token callback for LLama. Pass in a nil callback to
-// remove the callback.
-func setCallback(statePtr unsafe.Pointer, callback func(string) bool) {
-	m.Lock()
-	defer m.Unlock()
+		token, err := llm.sample(output, &opts)
+		if errors.Is(err, io.EOF) {
+			break
+		} else if err != nil {
+			return err
+		}
 
-	if callback == nil {
-		delete(callbacks, uintptr(statePtr))
-	} else {
-		callbacks[uintptr(statePtr)] = callback
+		b.WriteString(llm.detokenize(token))
+		if utf8.Valid(b.Bytes()) || b.Len() >= utf8.UTFMax {
+			// call the callback
+			fn(api.GenerateResponse{
+				Response: b.String(),
+			})
+
+			output.PushLeft(token)
+			context.PushLeft(int(token))
+			b.Reset()
+		}
+
+		input = []C.llama_token{token}
 	}
+
+	dur := func(ms float64) time.Duration {
+		d, err := time.ParseDuration(fmt.Sprintf("%fms", ms))
+		if err != nil {
+			panic(err)
+		}
+
+		return d
+	}
+
+	timings := C.llama_get_timings(llm.ctx)
+	fn(api.GenerateResponse{
+		Done:               true,
+		Context:            context.Data(),
+		PromptEvalCount:    int(timings.n_p_eval),
+		PromptEvalDuration: dur(float64(timings.t_p_eval_ms)),
+		EvalCount:          int(timings.n_eval),
+		EvalDuration:       dur(float64(timings.t_eval_ms)),
+	})
+
+	return nil
+}
+
+func (llm *llama) sample(output deque[C.llama_token], opts *C.struct_llama_sample_options) (C.llama_token, error) {
+	numVocab := int(C.llama_n_vocab(llm.ctx))
+	logits := unsafe.Slice(C.llama_get_logits(llm.ctx), numVocab)
+
+	candidates := deque[C.struct_llama_token_data]{capacity: numVocab}
+	for i := 0; i < candidates.Cap(); i++ {
+		candidates.PushLeft(C.struct_llama_token_data{
+			id:    C.int(i),
+			logit: logits[i],
+			p:     0,
+		})
+	}
+
+	token := C.llama_sample(
+		llm.ctx,
+		unsafe.SliceData(candidates.Data()), C.size_t(candidates.Len()),
+		unsafe.SliceData(output.Data()), C.size_t(output.Len()),
+		opts)
+	if token != C.llama_token_eos() {
+		return token, nil
+	}
+
+	return 0, io.EOF
 }

llama/llama.h

@@ -0,0 +1,440 @@
+/**
+ * llama.cpp - git e782c9e735f93ab4767ffc37462c523b73a17ddc
+ *
+ * MIT License
+ *
+ * Copyright (c) 2023 Georgi Gerganov
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#ifndef LLAMA_H
+#define LLAMA_H
+
+#include "ggml.h"
+#ifdef GGML_USE_CUBLAS
+#include "ggml-cuda.h"
+#define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES
+#else
+#define LLAMA_MAX_DEVICES 1
+#endif // GGML_USE_CUBLAS
+#include <stddef.h>
+#include <stdint.h>
+#include <stdbool.h>
+
+#ifdef LLAMA_SHARED
+#    if defined(_WIN32) && !defined(__MINGW32__)
+#        ifdef LLAMA_BUILD
+#            define LLAMA_API __declspec(dllexport)
+#        else
+#            define LLAMA_API __declspec(dllimport)
+#        endif
+#    else
+#        define LLAMA_API __attribute__ ((visibility ("default")))
+#    endif
+#else
+#    define LLAMA_API
+#endif
+
+#ifdef __GNUC__
+#    define DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
+#elif defined(_MSC_VER)
+#    define DEPRECATED(func, hint) __declspec(deprecated(hint)) func
+#else
+#    define DEPRECATED(func, hint) func
+#endif
+
+#define LLAMA_FILE_MAGIC_GGJT        0x67676a74u // 'ggjt'
+#define LLAMA_FILE_MAGIC_GGLA        0x67676c61u // 'ggla'
+#define LLAMA_FILE_MAGIC_GGMF        0x67676d66u // 'ggmf'
+#define LLAMA_FILE_MAGIC_GGML        0x67676d6cu // 'ggml'
+#define LLAMA_FILE_MAGIC_GGSN        0x6767736eu // 'ggsn'
+
+#define LLAMA_FILE_VERSION           3
+#define LLAMA_FILE_MAGIC             LLAMA_FILE_MAGIC_GGJT
+#define LLAMA_FILE_MAGIC_UNVERSIONED LLAMA_FILE_MAGIC_GGML
+#define LLAMA_SESSION_MAGIC          LLAMA_FILE_MAGIC_GGSN
+#define LLAMA_SESSION_VERSION        1
+
+#define LLAMA_DEFAULT_SEED           0xFFFFFFFF
+
+#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL)
+// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
+#define LLAMA_SUPPORTS_GPU_OFFLOAD
+#endif
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+    //
+    // C interface
+    //
+    // TODO: show sample usage
+    //
+
+    struct llama_model;
+    struct llama_context;
+
+    typedef int llama_token;
+
+    typedef struct llama_token_data {
+        llama_token id; // token id
+        float logit;    // log-odds of the token
+        float p;        // probability of the token
+    } llama_token_data;
+
+    typedef struct llama_token_data_array {
+        llama_token_data * data;
+        size_t size;
+        bool sorted;
+    } llama_token_data_array;
+
+    typedef void (*llama_progress_callback)(float progress, void *ctx);
+
+   struct llama_context_params {
+        uint32_t seed;                         // RNG seed, -1 for random
+        int32_t  n_ctx;                        // text context
+        int32_t  n_batch;                      // prompt processing batch size
+        int32_t  n_gpu_layers;                 // number of layers to store in VRAM
+        int32_t  main_gpu;                     // the GPU that is used for scratch and small tensors
+        float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs
+
+        // ref: https://github.com/ggerganov/llama.cpp/pull/2054
+        float    rope_freq_base;  // RoPE base frequency
+        float    rope_freq_scale; // RoPE frequency scaling factor
+
+        // called with a progress value between 0 and 1, pass NULL to disable
+        llama_progress_callback progress_callback;
+        // context pointer passed to the progress callback
+        void * progress_callback_user_data;
+
+        // Keep the booleans together to avoid misalignment during copy-by-value.
+        bool low_vram;   // if true, reduce VRAM usage at the cost of performance
+        bool f16_kv;     // use fp16 for KV cache
+        bool logits_all; // the llama_eval() call computes all logits, not just the last one
+        bool vocab_only; // only load the vocabulary, no weights
+        bool use_mmap;   // use mmap if possible
+        bool use_mlock;  // force system to keep model in RAM
+        bool embedding;  // embedding mode only
+    };
+    // model file types
+    enum llama_ftype {
+        LLAMA_FTYPE_ALL_F32              = 0,
+        LLAMA_FTYPE_MOSTLY_F16           = 1, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_0          = 2, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_1          = 3, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
+        // LLAMA_FTYPE_MOSTLY_Q4_2       = 5, // support has been removed
+        // LLAMA_FTYPE_MOSTLY_Q4_3       = 6, // support has been removed
+        LLAMA_FTYPE_MOSTLY_Q8_0          = 7, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q5_0          = 8, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q5_1          = 9, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q2_K          = 10,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q3_K_S        = 11,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q3_K_M        = 12,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q3_K_L        = 13,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_K_S        = 14,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q4_K_M        = 15,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q5_K_S        = 16,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q5_K_M        = 17,// except 1d tensors
+        LLAMA_FTYPE_MOSTLY_Q6_K          = 18,// except 1d tensors
+    };
+
+    // model quantization parameters
+    typedef struct llama_model_quantize_params {
+        int nthread;                 // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
+        enum llama_ftype   ftype;    // quantize to this llama_ftype
+        bool allow_requantize;       // allow quantizing non-f32/f16 tensors
+        bool quantize_output_tensor; // quantize output.weight
+    } llama_model_quantize_params;
+
+    // performance timing information
+    struct llama_timings {
+        double t_start_ms;
+        double t_end_ms;
+        double t_load_ms;
+        double t_sample_ms;
+        double t_p_eval_ms;
+        double t_eval_ms;
+
+        int32_t n_sample;
+        int32_t n_p_eval;
+        int32_t n_eval;
+    };
+
+    LLAMA_API int llama_max_devices();
+
+    LLAMA_API struct llama_context_params llama_context_default_params();
+    LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params();
+
+    LLAMA_API bool llama_mmap_supported();
+    LLAMA_API bool llama_mlock_supported();
+
+    // TODO: not great API - very likely to change
+    // Initialize the llama + ggml backend
+    // If numa is true, use NUMA optimizations
+    // Call once at the start of the program
+    LLAMA_API void llama_backend_init(bool numa);
+    // Call once at the end of the program - currently only used for MPI
+    LLAMA_API void llama_backend_free();
+
+    LLAMA_API int64_t llama_time_us();
+
+    LLAMA_API struct llama_model * llama_load_model_from_file(
+                             const char * path_model,
+            struct llama_context_params   params);
+
+    LLAMA_API void llama_free_model(struct llama_model * model);
+
+    LLAMA_API struct llama_context * llama_new_context_with_model(
+                     struct llama_model * model,
+            struct llama_context_params   params);
+
+    // Various functions for loading a ggml llama model.
+    // Allocate (almost) all memory needed for the model.
+    // Return NULL on failure
+    LLAMA_API DEPRECATED(struct llama_context * llama_init_from_file(
+                             const char * path_model,
+            struct llama_context_params   params),
+            "please use llama_load_model_from_file combined with llama_new_context_with_model instead");
+
+    // Frees all allocated memory
+    LLAMA_API void llama_free(struct llama_context * ctx);
+
+    // Returns 0 on success
+    LLAMA_API int llama_model_quantize(
+            const char * fname_inp,
+            const char * fname_out,
+            const llama_model_quantize_params * params);
+
+    // Apply a LoRA adapter to a loaded model
+    // path_base_model is the path to a higher quality model to use as a base for
+    // the layers modified by the adapter. Can be NULL to use the current loaded model.
+    // The model needs to be reloaded before applying a new adapter, otherwise the adapter
+    // will be applied on top of the previous one
+    // Returns 0 on success
+    LLAMA_API DEPRECATED(int llama_apply_lora_from_file(
+            struct llama_context * ctx,
+                      const char * path_lora,
+                      const char * path_base_model,
+                             int   n_threads),
+            "please use llama_model_apply_lora_from_file instead");
+
+    LLAMA_API int llama_model_apply_lora_from_file(
+            const struct llama_model * model,
+                      const char * path_lora,
+                      const char * path_base_model,
+                             int   n_threads);
+
+    // Returns the number of tokens in the KV cache
+    LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
+
+    // Sets the current rng seed.
+    LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);
+
+    // Returns the maximum size in bytes of the state (rng, logits, embedding
+    // and kv_cache) - will often be smaller after compacting tokens
+    LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
+
+    // Copies the state to the specified destination address.
+    // Destination needs to have allocated enough memory.
+    // Returns the number of bytes copied
+    LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst);
+
+    // Set the state reading from the specified address
+    // Returns the number of bytes read
+    LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src);
+
+    // Save/load session file
+    LLAMA_API bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
+    LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
+
+    // Run the llama inference to obtain the logits and probabilities for the next token.
+    // tokens + n_tokens is the provided batch of new tokens to process
+    // n_past is the number of tokens to use from previous eval calls
+    // Returns 0 on success
+    LLAMA_API int llama_eval(
+            struct llama_context * ctx,
+               const llama_token * tokens,
+                             int   n_tokens,
+                             int   n_past,
+                             int   n_threads);
+
+    // Same as llama_eval, but use float matrix input directly.
+    LLAMA_API int llama_eval_embd(
+            struct llama_context * ctx,
+                     const float * embd,
+                             int   n_tokens,
+                             int   n_past,
+                             int   n_threads);
+
+    // Export a static computation graph for context of 511 and batch size of 1
+    // NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these
+    //       parameters here to keep things simple
+    // IMPORTANT: do not use for anything else other than debugging and testing!
+    LLAMA_API int llama_eval_export(struct llama_context * ctx, const char * fname);
+
+    // Convert the provided text into tokens.
+    // The tokens pointer must be large enough to hold the resulting tokens.
+    // Returns the number of tokens on success, no more than n_max_tokens
+    // Returns a negative number on failure - the number of tokens that would have been returned
+    // TODO: not sure if correct
+    LLAMA_API int llama_tokenize(
+            struct llama_context * ctx,
+                      const char * text,
+                     llama_token * tokens,
+                             int   n_max_tokens,
+                            bool   add_bos);
+
+    LLAMA_API int llama_tokenize_with_model(
+        const struct llama_model * model,
+                      const char * text,
+                     llama_token * tokens,
+                             int   n_max_tokens,
+                            bool   add_bos);
+
+    LLAMA_API int llama_n_vocab(const struct llama_context * ctx);
+    LLAMA_API int llama_n_ctx  (const struct llama_context * ctx);
+    LLAMA_API int llama_n_embd (const struct llama_context * ctx);
+
+    LLAMA_API int llama_n_vocab_from_model(const struct llama_model * model);
+    LLAMA_API int llama_n_ctx_from_model  (const struct llama_model * model);
+    LLAMA_API int llama_n_embd_from_model (const struct llama_model * model);
+
+    // Get the vocabulary as output parameters.
+    // Returns number of results.
+    LLAMA_API int llama_get_vocab(
+            const struct llama_context * ctx,
+                          const char * * strings,
+                                 float * scores,
+                                   int   capacity);
+
+    LLAMA_API int llama_get_vocab_from_model(
+              const struct llama_model * model,
+                          const char * * strings,
+                                 float * scores,
+                                   int   capacity);
+
+    // Token logits obtained from the last call to llama_eval()
+    // The logits for the last token are stored in the last row
+    // Can be mutated in order to change the probabilities of the next token
+    // Rows: n_tokens
+    // Cols: n_vocab
+    LLAMA_API float * llama_get_logits(struct llama_context * ctx);
+
+    // Get the embeddings for the input
+    // shape: [n_embd] (1-dimensional)
+    LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
+
+    // Token Id -> String. Uses the vocabulary in the provided context
+    LLAMA_API const char * llama_token_to_str(
+            const struct llama_context * ctx,
+                           llama_token   token);
+
+    LLAMA_API const char * llama_token_to_str_with_model(
+              const struct llama_model * model,
+                           llama_token   token);
+
+    // Special tokens
+    LLAMA_API llama_token llama_token_bos();  // beginning-of-sentence
+    LLAMA_API llama_token llama_token_eos();  // end-of-sentence
+    LLAMA_API llama_token llama_token_nl();   // next-line
+
+    // Sampling functions
+
+    /// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
+    LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty);
+
+    /// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
+    LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
+
+    /// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806
+    /// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
+    /// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
+    /// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
+    /// @params smooth_factor Smooth factor between guidance logits and original logits. 1.0f means only use guidance logits. 0.0f means only original logits.
+    LLAMA_API void llama_sample_classifier_free_guidance(
+              struct llama_context * ctx,
+            llama_token_data_array * candidates,
+              struct llama_context * guidance_ctx,
+                             float   scale,
+                             float   smooth_factor);
+
+    /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
+    LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
+
+    /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
+    LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep);
+
+    /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
+    LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
+
+    /// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
+    LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep);
+
+    /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
+    LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
+    LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
+
+    /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
+    /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
+    /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
+    /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
+    /// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
+    /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
+    LLAMA_API llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu);
+
+    /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
+    /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
+    /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
+    /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
+    /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
+    LLAMA_API llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu);
+
+    /// @details Selects the token with the highest probability.
+    LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates);
+
+    /// @details Randomly selects a token from the candidates based on their probabilities.
+    LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
+
+    // Performance information
+    LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
+    LLAMA_API void llama_print_timings(struct llama_context * ctx);
+    LLAMA_API void llama_reset_timings(struct llama_context * ctx);
+
+    // Print system information
+    LLAMA_API const char * llama_print_system_info(void);
+
+#ifdef __cplusplus
+}
+#endif
+
+// Internal API to be implemented by llama.cpp and used by tests/benchmarks only
+#ifdef LLAMA_API_INTERNAL
+
+#include <vector>
+#include <string>
+struct ggml_tensor;
+
+const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
+
+#endif
+
+#endif // LLAMA_H

llama/llama_cublas.go

@@ -1,9 +0,0 @@
-//go:build cublas
-// +build cublas
-
-package llama
-
-/*
-#cgo LDFLAGS: -lcublas -lcudart -L/usr/local/cuda/lib64/
-*/
-import "C"

llama/llama_metal.go

@@ -1,2 +0,0 @@
-//go:build metal
-package llama

llama/llama_openblas.go

@@ -1,9 +0,0 @@
-//go:build openblas
-// +build openblas
-
-package llama
-
-/*
-#cgo LDFLAGS: -lopenblas
-*/
-import "C"

llama/options.go

@@ -1,375 +0,0 @@
-// MIT License
-
-// Copyright (c) 2023 go-skynet authors
-
-// Permission is hereby granted, free of charge, to any person obtaining a copy
-// of this software and associated documentation files (the "Software"), to deal
-// in the Software without restriction, including without limitation the rights
-// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-// copies of the Software, and to permit persons to whom the Software is
-// furnished to do so, subject to the following conditions:
-
-// The above copyright notice and this permission notice shall be included in all
-// copies or substantial portions of the Software.
-
-// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-// SOFTWARE.
-
-package llama
-
-type ModelOptions struct {
-	ContextSize int
-	Seed        int
-	NBatch      int
-	F16Memory   bool
-	MLock       bool
-	MMap        bool
-	VocabOnly   bool
-	LowVRAM     bool
-	Embeddings  bool
-	NUMA        bool
-	NGPULayers  int
-	MainGPU     string
-	TensorSplit string
-}
-
-type PredictOptions struct {
-	Seed, Threads, Tokens, TopK, Repeat, Batch, NKeep int
-	TopP, Temperature, Penalty                        float64
-	F16KV                                             bool
-	DebugMode                                         bool
-	StopPrompts                                       []string
-	IgnoreEOS                                         bool
-
-	TailFreeSamplingZ float64
-	TypicalP          float64
-	FrequencyPenalty  float64
-	PresencePenalty   float64
-	Mirostat          int
-	MirostatETA       float64
-	MirostatTAU       float64
-	PenalizeNL        bool
-	LogitBias         string
-	TokenCallback     func(string) bool
-
-	MLock, MMap bool
-	MainGPU     string
-	TensorSplit string
-}
-
-type PredictOption func(p *PredictOptions)
-
-type ModelOption func(p *ModelOptions)
-
-var DefaultModelOptions ModelOptions = ModelOptions{
-	ContextSize: 512,
-	Seed:        0,
-	F16Memory:   false,
-	MLock:       false,
-	Embeddings:  false,
-	MMap:        true,
-	LowVRAM:     false,
-}
-
-var DefaultOptions PredictOptions = PredictOptions{
-	Seed:              -1,
-	Threads:           4,
-	Tokens:            128,
-	Penalty:           1.1,
-	Repeat:            64,
-	Batch:             512,
-	NKeep:             64,
-	TopK:              40,
-	TopP:              0.95,
-	TailFreeSamplingZ: 1.0,
-	TypicalP:          1.0,
-	Temperature:       0.8,
-	FrequencyPenalty:  0.0,
-	PresencePenalty:   0.0,
-	Mirostat:          0,
-	MirostatTAU:       5.0,
-	MirostatETA:       0.1,
-	MMap:              true,
-}
-
-// SetContext sets the context size.
-func SetContext(c int) ModelOption {
-	return func(p *ModelOptions) {
-		p.ContextSize = c
-	}
-}
-
-func SetModelSeed(c int) ModelOption {
-	return func(p *ModelOptions) {
-		p.Seed = c
-	}
-}
-
-// SetContext sets the context size.
-func SetMMap(b bool) ModelOption {
-	return func(p *ModelOptions) {
-		p.MMap = b
-	}
-}
-
-// SetNBatch sets the  n_Batch
-func SetNBatch(n_batch int) ModelOption {
-	return func(p *ModelOptions) {
-		p.NBatch = n_batch
-	}
-}
-
-// Set sets the tensor split for the GPU
-func SetTensorSplit(maingpu string) ModelOption {
-	return func(p *ModelOptions) {
-		p.TensorSplit = maingpu
-	}
-}
-
-// SetMainGPU sets the main_gpu
-func SetMainGPU(maingpu string) ModelOption {
-	return func(p *ModelOptions) {
-		p.MainGPU = maingpu
-	}
-}
-
-// SetPredictionTensorSplit sets the tensor split for the GPU
-func SetPredictionTensorSplit(maingpu string) PredictOption {
-	return func(p *PredictOptions) {
-		p.TensorSplit = maingpu
-	}
-}
-
-// SetPredictionMainGPU sets the main_gpu
-func SetPredictionMainGPU(maingpu string) PredictOption {
-	return func(p *PredictOptions) {
-		p.MainGPU = maingpu
-	}
-}
-
-var VocabOnly ModelOption = func(p *ModelOptions) {
-	p.VocabOnly = true
-}
-
-var EnabelLowVRAM ModelOption = func(p *ModelOptions) {
-	p.LowVRAM = true
-}
-
-var EnableNUMA ModelOption = func(p *ModelOptions) {
-	p.NUMA = true
-}
-
-var EnableEmbeddings ModelOption = func(p *ModelOptions) {
-	p.Embeddings = true
-}
-
-var EnableF16Memory ModelOption = func(p *ModelOptions) {
-	p.F16Memory = true
-}
-
-var EnableF16KV PredictOption = func(p *PredictOptions) {
-	p.F16KV = true
-}
-
-var Debug PredictOption = func(p *PredictOptions) {
-	p.DebugMode = true
-}
-
-var EnableMLock ModelOption = func(p *ModelOptions) {
-	p.MLock = true
-}
-
-// Create a new PredictOptions object with the given options.
-func NewModelOptions(opts ...ModelOption) ModelOptions {
-	p := DefaultModelOptions
-	for _, opt := range opts {
-		opt(&p)
-	}
-	return p
-}
-
-var IgnoreEOS PredictOption = func(p *PredictOptions) {
-	p.IgnoreEOS = true
-}
-
-// SetMlock sets the memory lock.
-func SetMlock(b bool) PredictOption {
-	return func(p *PredictOptions) {
-		p.MLock = b
-	}
-}
-
-// SetMemoryMap sets memory mapping.
-func SetMemoryMap(b bool) PredictOption {
-	return func(p *PredictOptions) {
-		p.MMap = b
-	}
-}
-
-// SetGPULayers sets the number of GPU layers to use to offload computation
-func SetGPULayers(n int) ModelOption {
-	return func(p *ModelOptions) {
-		p.NGPULayers = n
-	}
-}
-
-// SetTokenCallback sets the prompts that will stop predictions.
-func SetTokenCallback(fn func(string) bool) PredictOption {
-	return func(p *PredictOptions) {
-		p.TokenCallback = fn
-	}
-}
-
-// SetStopWords sets the prompts that will stop predictions.
-func SetStopWords(stop ...string) PredictOption {
-	return func(p *PredictOptions) {
-		p.StopPrompts = stop
-	}
-}
-
-// SetSeed sets the random seed for sampling text generation.
-func SetSeed(seed int) PredictOption {
-	return func(p *PredictOptions) {
-		p.Seed = seed
-	}
-}
-
-// SetThreads sets the number of threads to use for text generation.
-func SetThreads(threads int) PredictOption {
-	return func(p *PredictOptions) {
-		p.Threads = threads
-	}
-}
-
-// SetTokens sets the number of tokens to generate.
-func SetTokens(tokens int) PredictOption {
-	return func(p *PredictOptions) {
-		p.Tokens = tokens
-	}
-}
-
-// SetTopK sets the value for top-K sampling.
-func SetTopK(topk int) PredictOption {
-	return func(p *PredictOptions) {
-		p.TopK = topk
-	}
-}
-
-// SetTopP sets the value for nucleus sampling.
-func SetTopP(topp float64) PredictOption {
-	return func(p *PredictOptions) {
-		p.TopP = topp
-	}
-}
-
-// SetTemperature sets the temperature value for text generation.
-func SetTemperature(temp float64) PredictOption {
-	return func(p *PredictOptions) {
-		p.Temperature = temp
-	}
-}
-
-// SetPenalty sets the repetition penalty for text generation.
-func SetPenalty(penalty float64) PredictOption {
-	return func(p *PredictOptions) {
-		p.Penalty = penalty
-	}
-}
-
-// SetRepeat sets the number of times to repeat text generation.
-func SetRepeat(repeat int) PredictOption {
-	return func(p *PredictOptions) {
-		p.Repeat = repeat
-	}
-}
-
-// SetBatch sets the batch size.
-func SetBatch(size int) PredictOption {
-	return func(p *PredictOptions) {
-		p.Batch = size
-	}
-}
-
-// SetKeep sets the number of tokens from initial prompt to keep.
-func SetNKeep(n int) PredictOption {
-	return func(p *PredictOptions) {
-		p.NKeep = n
-	}
-}
-
-// Create a new PredictOptions object with the given options.
-func NewPredictOptions(opts ...PredictOption) PredictOptions {
-	p := DefaultOptions
-	for _, opt := range opts {
-		opt(&p)
-	}
-	return p
-}
-
-// SetTailFreeSamplingZ sets the tail free sampling, parameter z.
-func SetTailFreeSamplingZ(tfz float64) PredictOption {
-	return func(p *PredictOptions) {
-		p.TailFreeSamplingZ = tfz
-	}
-}
-
-// SetTypicalP sets the typicality parameter, p_typical.
-func SetTypicalP(tp float64) PredictOption {
-	return func(p *PredictOptions) {
-		p.TypicalP = tp
-	}
-}
-
-// SetFrequencyPenalty sets the frequency penalty parameter, freq_penalty.
-func SetFrequencyPenalty(fp float64) PredictOption {
-	return func(p *PredictOptions) {
-		p.FrequencyPenalty = fp
-	}
-}
-
-// SetPresencePenalty sets the presence penalty parameter, presence_penalty.
-func SetPresencePenalty(pp float64) PredictOption {
-	return func(p *PredictOptions) {
-		p.PresencePenalty = pp
-	}
-}
-
-// SetMirostat sets the mirostat parameter.
-func SetMirostat(m int) PredictOption {
-	return func(p *PredictOptions) {
-		p.Mirostat = m
-	}
-}
-
-// SetMirostatETA sets the mirostat ETA parameter.
-func SetMirostatETA(me float64) PredictOption {
-	return func(p *PredictOptions) {
-		p.MirostatETA = me
-	}
-}
-
-// SetMirostatTAU sets the mirostat TAU parameter.
-func SetMirostatTAU(mt float64) PredictOption {
-	return func(p *PredictOptions) {
-		p.MirostatTAU = mt
-	}
-}
-
-// SetPenalizeNL sets whether to penalize newlines or not.
-func SetPenalizeNL(pnl bool) PredictOption {
-	return func(p *PredictOptions) {
-		p.PenalizeNL = pnl
-	}
-}
-
-// SetLogitBias sets the logit bias parameter.
-func SetLogitBias(lb string) PredictOption {
-	return func(p *PredictOptions) {
-		p.LogitBias = lb
-	}
-}

llama/update-llama-cpp.sh

@@ -0,0 +1,70 @@
+#!/bin/sh
+
+set -eu
+
+
+status() { echo >&2 ">>> $*"; }
+error() { status "ERROR $*"; }
+usage() {
+    echo "usage: $(basename $0) /path/to/repo"
+    exit 1
+}
+
+OUT=$(dirname $0)
+while getopts "hC:" OPTION; do
+    case $OPTION in
+        C) OUT=$OPTARG ;;
+        *) usage ;;
+    esac
+done
+
+shift $(( $OPTIND - 1 ))
+[ $# -eq 1 ] || usage
+
+status "updating source..."
+cp -a "$1"/*.{c,h,cpp,m,metal,cu} "$OUT"
+
+status "removing incompatible files..."
+rm -f "$OUT"/build-info.h
+
+SHA1=$(git -C $1 rev-parse @)
+
+LICENSE=$(mktemp)
+cleanup() {
+    rm -f $LICENSE
+}
+trap cleanup 0
+
+cat <<EOF | sed 's/ *$//' >$LICENSE
+/**
+ * llama.cpp - git $SHA1
+ *
+$(sed 's/^/ * /' <$1/LICENSE)
+ */
+
+EOF
+
+for IN in $OUT/*.{c,h,cpp,m,metal,cu}; do
+    TMP=$(mktemp)
+    status "updating license $IN"
+    cat $LICENSE $IN >$TMP
+    mv $TMP $IN
+done
+
+touchup() {
+    local CONSTRAINT=$1 && shift
+
+    for IN in $*; do
+        status "touching up $IN..."
+        TMP=$(mktemp)
+        {
+            echo "//go:build $CONSTRAINT"
+            echo
+        } | cat - $IN >$TMP
+        mv $TMP $IN
+    done
+}
+
+touchup darwin $OUT/ggml-metal.*
+touchup mpi $OUT/ggml-mpi.*
+touchup opencl $OUT/ggml-opencl.*

llama/utils.go

@@ -0,0 +1,104 @@
+package llama
+
+type node[T any] struct {
+	t    T
+	next *node[T]
+	prev *node[T]
+}
+
+type deque[T any] struct {
+	head     *node[T]
+	tail     *node[T]
+	size     int
+	capacity int
+}
+
+func (d *deque[T]) Empty() bool {
+	return d.size == 0
+}
+
+func (d *deque[T]) Len() int {
+	return d.size
+}
+
+func (d *deque[T]) Cap() int {
+	return d.capacity
+}
+
+func (d *deque[T]) Push(t T) {
+	if d.capacity > 0 && d.size >= d.capacity {
+		d.PopLeft()
+	}
+
+	n := node[T]{t: t}
+	if d.head != nil {
+		n.next = d.head
+		d.head.prev = &n
+		d.head = &n
+	} else {
+		d.head = &n
+		d.tail = &n
+	}
+
+	d.size++
+}
+
+func (d *deque[T]) PushLeft(t T) {
+	if d.capacity > 0 && d.size >= d.capacity {
+		d.Pop()
+	}
+
+	n := node[T]{t: t}
+	if d.tail != nil {
+		n.prev = d.tail
+		d.tail.next = &n
+		d.tail = &n
+	} else {
+		d.head = &n
+		d.tail = &n
+	}
+
+	d.size++
+}
+
+func (d *deque[T]) Pop() *T {
+	if d.Empty() {
+		return nil
+	}
+
+	head := d.head
+	d.head = head.next
+	if d.head != nil {
+		d.head.prev = nil
+	} else {
+		d.tail = nil
+	}
+
+	d.size--
+	return &head.t
+}
+
+func (d *deque[T]) PopLeft() *T {
+	if d.Empty() {
+		return nil
+	}
+
+	tail := d.tail
+	d.tail = tail.prev
+	if d.tail != nil {
+		d.tail.next = nil
+	} else {
+		d.head = nil
+	}
+
+	d.size--
+	return &tail.t
+}
+
+func (d *deque[T]) Data() (data []T) {
+	for n := d.head; n != nil; n = n.next {
+		data = append(data, n.t)
+	}
+
+	return data
+}

main.go

@@ -1,9 +1,11 @@
 package main
 
 import (
+	"context"
+
 	"github.com/jmorganca/ollama/cmd"
 )
 
 func main() {
-	cmd.NewCLI().Execute()
+	cmd.NewCLI().ExecuteContext(context.Background())
 }

models.json

@@ -1,38 +0,0 @@
-[
-  {
-    "name": "orca",
-    "display_name": "Orca Mini",
-    "parameters": "3B",
-    "url": "https://huggingface.co/TheBloke/orca_mini_3B-GGML/resolve/main/orca-mini-3b.ggmlv3.q4_1.bin",
-    "short_description": "Follow instructions. Great small model that runs fast even without GPU support.",
-    "description": "An OpenLLaMa-3B model trained on explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches.",
-    "published_by": "TheBloke",
-    "original_author": "psmathur",
-    "original_url": "https://huggingface.co/psmathur/orca_mini_3b",
-    "license": "CC-BY-SA-4.0"
-  },
-  {
-    "name": "nous-hermes",
-    "display_name": "Nous Hermes",
-    "parameters": "13B",
-    "url": "https://huggingface.co/TheBloke/Nous-Hermes-13B-GGML/resolve/main/nous-hermes-13b.ggmlv3.q2_K.bin",
-    "short_description": "Currently one of the best 13B general model.",
-    "description": "It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions. This model was fine-tuned by Nous Research, with Teknium and Karan4D leading the fine tuning process and dataset curation, Redmond AI sponsoring the compute, and several other contributors. The result is an enhanced Llama 13b model that rivals GPT-3.5-turbo in performance across a variety of tasks. \n \n This model stands out for its long responses, low hallucination rate, and absence of OpenAI censorship mechanisms. The fine-tuning process was performed with a 2000 sequence length on an 8x a100 80GB DGX machine for over 50 hours.",
-    "published_by": "TheBloke",
-    "original_author": "NousResearch",
-    "original_url": "https://huggingface.co/NousResearch/Nous-Hermes-13b",
-    "license": "GPL"
-  },
-  {
-    "name": "vicuna",
-    "display_name": "Wizard Vicuna Uncensored",
-    "parameters": "13B",
-    "url": "https://huggingface.co/TheBloke/Wizard-Vicuna-13B-Uncensored-GGML/resolve/main/Wizard-Vicuna-13B-Uncensored.ggmlv3.q2_K.bin",
-    "short_description": "An uncensored model with no guardrails.",
-    "description": "This model is trained with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.",
-    "published_by": "TheBloke",
-    "original_author": "ehartford",
-    "original_url": "https://huggingface.co/ehartford/Wizard-Vicuna-13B-Uncensored",
-    "license:": "GPL"
-  }
-]

parser/parser.go

@@ -0,0 +1,82 @@
+package parser
+
+import (
+	"bufio"
+	"bytes"
+	"errors"
+	"fmt"
+	"io"
+)
+
+type Command struct {
+	Name string
+	Args string
+}
+
+func (c *Command) Reset() {
+	c.Name = ""
+	c.Args = ""
+}
+
+func Parse(reader io.Reader) ([]Command, error) {
+	var commands []Command
+
+	var command, modelCommand Command
+
+	scanner := bufio.NewScanner(reader)
+	scanner.Split(scanModelfile)
+	for scanner.Scan() {
+		line := scanner.Bytes()
+
+		fields := bytes.SplitN(line, []byte(" "), 2)
+		if len(fields) == 0 {
+			continue
+		}
+
+		switch string(bytes.ToUpper(fields[0])) {
+		case "FROM":
+			command.Name = "model"
+			command.Args = string(fields[1])
+			// copy command for validation
+			modelCommand = command
+		case "LICENSE", "TEMPLATE", "SYSTEM", "PROMPT":
+			command.Name = string(bytes.ToLower(fields[0]))
+			command.Args = string(fields[1])
+		case "PARAMETER":
+			fields = bytes.SplitN(fields[1], []byte(" "), 2)
+			command.Name = string(fields[0])
+			command.Args = string(fields[1])
+		default:
+			continue
+		}
+
+		commands = append(commands, command)
+		command.Reset()
+	}
+
+	if modelCommand.Args == "" {
+		return nil, fmt.Errorf("no FROM line for the model was specified")
+	}
+
+	return commands, scanner.Err()
+}
+
+func scanModelfile(data []byte, atEOF bool) (advance int, token []byte, err error) {
+	newline := bytes.IndexByte(data, '\n')
+
+	if start := bytes.Index(data, []byte(`"""`)); start >= 0 && start < newline {
+		end := bytes.Index(data[start+3:], []byte(`"""`))
+		if end < 0 {
+			if atEOF {
+				return 0, nil, errors.New(`unterminated multiline string: """`)
+			} else {
+				return 0, nil, nil
+			}
+		}
+
+		n := start + 3 + end + 3
+		return n, bytes.Replace(data[:n], []byte(`"""`), []byte(""), 2), nil
+	}
+
+	return bufio.ScanLines(data, atEOF)
+}

progressbar/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2017 Zack
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

progressbar/README.md

@@ -0,0 +1,121 @@
+# progressbar
+
+[![CI](https://github.com/schollz/progressbar/actions/workflows/ci.yml/badge.svg?branch=main&event=push)](https://github.com/schollz/progressbar/actions/workflows/ci.yml)
+[![go report card](https://goreportcard.com/badge/github.com/schollz/progressbar)](https://goreportcard.com/report/github.com/schollz/progressbar) 
+[![coverage](https://img.shields.io/badge/coverage-84%25-brightgreen.svg)](https://gocover.io/github.com/schollz/progressbar)
+[![godocs](https://godoc.org/github.com/schollz/progressbar?status.svg)](https://godoc.org/github.com/schollz/progressbar/v3) 
+
+A very simple thread-safe progress bar which should work on every OS without problems. I needed a progressbar for [croc](https://github.com/schollz/croc) and everything I tried had problems, so I made another one. In order to be OS agnostic I do not plan to support [multi-line outputs](https://github.com/schollz/progressbar/issues/6).
+
+
+## Install
+
+```
+go get -u github.com/schollz/progressbar/v3
+```
+
+## Usage 
+
+### Basic usage
+
+```golang
+bar := progressbar.Default(100)
+for i := 0; i < 100; i++ {
+    bar.Add(1)
+    time.Sleep(40 * time.Millisecond)
+}
+```
+
+which looks like:
+
+![Example of basic bar](examples/basic/basic.gif)
+
+
+### I/O operations
+
+The `progressbar` implements an `io.Writer` so it can automatically detect the number of bytes written to a stream, so you can use it as a progressbar for an `io.Reader`.
+
+```golang
+req, _ := http.NewRequest("GET", "https://dl.google.com/go/go1.14.2.src.tar.gz", nil)
+resp, _ := http.DefaultClient.Do(req)
+defer resp.Body.Close()
+
+f, _ := os.OpenFile("go1.14.2.src.tar.gz", os.O_CREATE|os.O_WRONLY, 0644)
+defer f.Close()
+
+bar := progressbar.DefaultBytes(
+    resp.ContentLength,
+    "downloading",
+)
+io.Copy(io.MultiWriter(f, bar), resp.Body)
+```
+
+which looks like:
+
+![Example of download bar](examples/download/download.gif)
+
+
+### Progress bar with unknown length
+
+A progressbar with unknown length is a spinner. Any bar with -1 length will automatically convert it to a spinner with a customizable spinner type. For example, the above code can be run and set the `resp.ContentLength` to `-1`.
+
+which looks like:
+
+![Example of download bar with unknown length](examples/download-unknown/download-unknown.gif)
+
+
+### Customization
+
+There is a lot of customization that you can do - change the writer, the color, the width, description, theme, etc. See [all the options](https://pkg.go.dev/github.com/schollz/progressbar/v3?tab=doc#Option).
+
+```golang
+bar := progressbar.NewOptions(1000,
+    progressbar.OptionSetWriter(ansi.NewAnsiStdout()),
+    progressbar.OptionEnableColorCodes(true),
+    progressbar.OptionShowBytes(true),
+    progressbar.OptionSetWidth(15),
+    progressbar.OptionSetDescription("[cyan][1/3][reset] Writing moshable file..."),
+    progressbar.OptionSetTheme(progressbar.Theme{
+        Saucer:        "[green]=[reset]",
+        SaucerHead:    "[green]>[reset]",
+        SaucerPadding: " ",
+        BarStart:      "[",
+        BarEnd:        "]",
+    }))
+for i := 0; i < 1000; i++ {
+    bar.Add(1)
+    time.Sleep(5 * time.Millisecond)
+}
+```
+
+which looks like:
+
+![Example of customized bar](examples/customization/customization.gif)
+
+
+## Contributing
+
+Pull requests are welcome. Feel free to...
+
+- Revise documentation
+- Add new features
+- Fix bugs
+- Suggest improvements
+
+## Thanks
+
+Thanks [@Dynom](https://github.com/dynom) for massive improvements in version 2.0!
+
+Thanks [@CrushedPixel](https://github.com/CrushedPixel) for adding descriptions and color code support!
+
+Thanks [@MrMe42](https://github.com/MrMe42) for adding some minor features!
+
+Thanks [@tehstun](https://github.com/tehstun) for some great PRs!
+
+Thanks [@Benzammour](https://github.com/Benzammour) and [@haseth](https://github.com/haseth) for helping create v3!
+
+Thanks [@briandowns](https://github.com/briandowns) for compiling the list of spinners.
+
+## License
+
+MIT

progressbar/spinners.go

@@ -0,0 +1,80 @@
+package progressbar
+
+var spinners = map[int][]string{
+	0:  {"←", "↖", "↑", "↗", "→", "↘", "↓", "↙"},
+	1:  {"▁", "▃", "▄", "▅", "▆", "▇", "█", "▇", "▆", "▅", "▄", "▃", "▁"},
+	2:  {"▖", "▘", "▝", "▗"},
+	3:  {"┤", "┘", "┴", "└", "├", "┌", "┬", "┐"},
+	4:  {"◢", "◣", "◤", "◥"},
+	5:  {"◰", "◳", "◲", "◱"},
+	6:  {"◴", "◷", "◶", "◵"},
+	7:  {"◐", "◓", "◑", "◒"},
+	8:  {".", "o", "O", "@", "*"},
+	9:  {"|", "/", "-", "\\"},
+	10: {"◡◡", "⊙⊙", "◠◠"},
+	11: {"⣾", "⣽", "⣻", "⢿", "⡿", "⣟", "⣯", "⣷"},
+	12: {">))'>", " >))'>", "  >))'>", "   >))'>", "    >))'>", "   <'((<", "  <'((<", " <'((<"},
+	13: {"⠁", "⠂", "⠄", "⡀", "⢀", "⠠", "⠐", "⠈"},
+	14: {"⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"},
+	15: {"a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"},
+	16: {"▉", "▊", "▋", "▌", "▍", "▎", "▏", "▎", "▍", "▌", "▋", "▊", "▉"},
+	17: {"■", "□", "▪", "▫"},
+	18: {"←", "↑", "→", "↓"},
+	19: {"╫", "╪"},
+	20: {"⇐", "⇖", "⇑", "⇗", "⇒", "⇘", "⇓", "⇙"},
+	21: {"⠁", "⠁", "⠉", "⠙", "⠚", "⠒", "⠂", "⠂", "⠒", "⠲", "⠴", "⠤", "⠄", "⠄", "⠤", "⠠", "⠠", "⠤", "⠦", "⠖", "⠒", "⠐", "⠐", "⠒", "⠓", "⠋", "⠉", "⠈", "⠈"},
+	22: {"⠈", "⠉", "⠋", "⠓", "⠒", "⠐", "⠐", "⠒", "⠖", "⠦", "⠤", "⠠", "⠠", "⠤", "⠦", "⠖", "⠒", "⠐", "⠐", "⠒", "⠓", "⠋", "⠉", "⠈"},
+	23: {"⠁", "⠉", "⠙", "⠚", "⠒", "⠂", "⠂", "⠒", "⠲", "⠴", "⠤", "⠄", "⠄", "⠤", "⠴", "⠲", "⠒", "⠂", "⠂", "⠒", "⠚", "⠙", "⠉", "⠁"},
+	24: {"⠋", "⠙", "⠚", "⠒", "⠂", "⠂", "⠒", "⠲", "⠴", "⠦", "⠖", "⠒", "⠐", "⠐", "⠒", "⠓", "⠋"},
+	25: {"ｦ", "ｧ", "ｨ", "ｩ", "ｪ", "ｫ", "ｬ", "ｭ", "ｮ", "ｯ", "ｱ", "ｲ", "ｳ", "ｴ", "ｵ", "ｶ", "ｷ", "ｸ", "ｹ", "ｺ", "ｻ", "ｼ", "ｽ", "ｾ", "ｿ", "ﾀ", "ﾁ", "ﾂ", "ﾃ", "ﾄ", "ﾅ", "ﾆ", "ﾇ", "ﾈ", "ﾉ", "ﾊ", "ﾋ", "ﾌ", "ﾍ", "ﾎ", "ﾏ", "ﾐ", "ﾑ", "ﾒ", "ﾓ", "ﾔ", "ﾕ", "ﾖ", "ﾗ", "ﾘ", "ﾙ", "ﾚ", "ﾛ", "ﾜ", "ﾝ"},
+	26: {".", "..", "..."},
+	27: {"▁", "▂", "▃", "▄", "▅", "▆", "▇", "█", "▉", "▊", "▋", "▌", "▍", "▎", "▏", "▏", "▎", "▍", "▌", "▋", "▊", "▉", "█", "▇", "▆", "▅", "▄", "▃", "▂", "▁"},
+	28: {".", "o", "O", "°", "O", "o", "."},
+	29: {"+", "x"},
+	30: {"v", "<", "^", ">"},
+	31: {">>--->", " >>--->", "  >>--->", "   >>--->", "    >>--->", "    <---<<", "   <---<<", "  <---<<", " <---<<", "<---<<"},
+	32: {"|", "||", "|||", "||||", "|||||", "|||||||", "||||||||", "|||||||", "||||||", "|||||", "||||", "|||", "||", "|"},
+	33: {"[          ]", "[=         ]", "[==        ]", "[===       ]", "[====      ]", "[=====     ]", "[======    ]", "[=======   ]", "[========  ]", "[========= ]", "[==========]"},
+	34: {"(*---------)", "(-*--------)", "(--*-------)", "(---*------)", "(----*-----)", "(-----*----)", "(------*---)", "(-------*--)", "(--------*-)", "(---------*)"},
+	35: {"█▒▒▒▒▒▒▒▒▒", "███▒▒▒▒▒▒▒", "█████▒▒▒▒▒", "███████▒▒▒", "██████████"},
+	36: {"[                    ]", "[=>                  ]", "[===>                ]", "[=====>              ]", "[======>             ]", "[========>           ]", "[==========>         ]", "[============>       ]", "[==============>     ]", "[================>   ]", "[==================> ]", "[===================>]"},
+	37: {"ဝ", "၀"},
+	38: {"▌", "▀", "▐▄"},
+	39: {"🌍", "🌎", "🌏"},
+	40: {"◜", "◝", "◞", "◟"},
+	41: {"⬒", "⬔", "⬓", "⬕"},
+	42: {"⬖", "⬘", "⬗", "⬙"},
+	43: {"[>>>          >]", "[]>>>>        []", "[]  >>>>      []", "[]    >>>>    []", "[]      >>>>  []", "[]        >>>>[]", "[>>          >>]"},
+	44: {"♠", "♣", "♥", "♦"},
+	45: {"➞", "➟", "➠", "➡", "➠", "➟"},
+	46: {"  |  ", ` \   `, "_    ", ` \   `, "  |  ", "   / ", "    _", "   / "},
+	47: {"  . . . .", ".   . . .", ". .   . .", ". . .   .", ". . . .  ", ". . . . ."},
+	48: {" |     ", "  /    ", "   _   ", `    \  `, "     | ", `    \  `, "   _   ", "  /    "},
+	49: {"⎺", "⎻", "⎼", "⎽", "⎼", "⎻"},
+	50: {"▹▹▹▹▹", "▸▹▹▹▹", "▹▸▹▹▹", "▹▹▸▹▹", "▹▹▹▸▹", "▹▹▹▹▸"},
+	51: {"[    ]", "[   =]", "[  ==]", "[ ===]", "[====]", "[=== ]", "[==  ]", "[=   ]"},
+	52: {"( ●    )", "(  ●   )", "(   ●  )", "(    ● )", "(     ●)", "(    ● )", "(   ●  )", "(  ●   )", "( ●    )"},
+	53: {"✶", "✸", "✹", "✺", "✹", "✷"},
+	54: {"▐|\\____________▌", "▐_|\\___________▌", "▐__|\\__________▌", "▐___|\\_________▌", "▐____|\\________▌", "▐_____|\\_______▌", "▐______|\\______▌", "▐_______|\\_____▌", "▐________|\\____▌", "▐_________|\\___▌", "▐__________|\\__▌", "▐___________|\\_▌", "▐____________|\\▌", "▐____________/|▌", "▐___________/|_▌", "▐__________/|__▌", "▐_________/|___▌", "▐________/|____▌", "▐_______/|_____▌", "▐______/|______▌", "▐_____/|_______▌", "▐____/|________▌", "▐___/|_________▌", "▐__/|__________▌", "▐_/|___________▌", "▐/|____________▌"},
+	55: {"▐⠂       ▌", "▐⠈       ▌", "▐ ⠂      ▌", "▐ ⠠      ▌", "▐  ⡀     ▌", "▐  ⠠     ▌", "▐   ⠂    ▌", "▐   ⠈    ▌", "▐    ⠂   ▌", "▐    ⠠   ▌", "▐     ⡀  ▌", "▐     ⠠  ▌", "▐      ⠂ ▌", "▐      ⠈ ▌", "▐       ⠂▌", "▐       ⠠▌", "▐       ⡀▌", "▐      ⠠ ▌", "▐      ⠂ ▌", "▐     ⠈  ▌", "▐     ⠂  ▌", "▐    ⠠   ▌", "▐    ⡀   ▌", "▐   ⠠    ▌", "▐   ⠂    ▌", "▐  ⠈     ▌", "▐  ⠂     ▌", "▐ ⠠      ▌", "▐ ⡀      ▌", "▐⠠       ▌"},
+	56: {"¿", "?"},
+	57: {"⢹", "⢺", "⢼", "⣸", "⣇", "⡧", "⡗", "⡏"},
+	58: {"⢄", "⢂", "⢁", "⡁", "⡈", "⡐", "⡠"},
+	59: {".  ", ".. ", "...", " ..", "  .", "   "},
+	60: {".", "o", "O", "°", "O", "o", "."},
+	61: {"▓", "▒", "░"},
+	62: {"▌", "▀", "▐", "▄"},
+	63: {"⊶", "⊷"},
+	64: {"▪", "▫"},
+	65: {"□", "■"},
+	66: {"▮", "▯"},
+	67: {"-", "=", "≡"},
+	68: {"d", "q", "p", "b"},
+	69: {"∙∙∙", "●∙∙", "∙●∙", "∙∙●", "∙∙∙"},
+	70: {"🌑 ", "🌒 ", "🌓 ", "🌔 ", "🌕 ", "🌖 ", "🌗 ", "🌘 "},
+	71: {"☗", "☖"},
+	72: {"⧇", "⧆"},
+	73: {"◉", "◎"},
+	74: {"㊂", "㊀", "㊁"},
+	75: {"⦾", "⦿"},
+}

scripts/publish.sh

@@ -10,7 +10,9 @@ fi
 OS=$(go env GOOS)
 ARCH=$(go env GOARCH)
 
-make app
+go build .
+
+npm --prefix app run make:sign
 
 # Create a new tag if it doesn't exist.
 if ! git rev-parse v$VERSION >/dev/null 2>&1; then
@@ -18,7 +20,7 @@ if ! git rev-parse v$VERSION >/dev/null 2>&1; then
   git push origin v$VERSION
 fi
 
-mkdir dist
+mkdir -p dist
 cp app/out/make/zip/${OS}/${ARCH}/Ollama-${OS}-${ARCH}-${VERSION}.zip dist/Ollama-${OS}-${ARCH}.zip
 cp ./ollama  dist/ollama-${OS}-${ARCH}
 

server/modelpath.go

@@ -0,0 +1,123 @@
+package server
+
+import (
+	"fmt"
+	"os"
+	"path/filepath"
+	"runtime"
+	"strings"
+)
+
+type ModelPath struct {
+	ProtocolScheme string
+	Registry       string
+	Namespace      string
+	Repository     string
+	Tag            string
+}
+
+const (
+	DefaultRegistry       = "registry.ollama.ai"
+	DefaultNamespace      = "library"
+	DefaultTag            = "latest"
+	DefaultProtocolScheme = "https"
+)
+
+func ParseModelPath(name string) ModelPath {
+	slashParts := strings.Split(name, "/")
+	var registry, namespace, repository, tag string
+
+	switch len(slashParts) {
+	case 3:
+		registry = slashParts[0]
+		namespace = slashParts[1]
+		repository = strings.Split(slashParts[2], ":")[0]
+	case 2:
+		registry = DefaultRegistry
+		namespace = slashParts[0]
+		repository = strings.Split(slashParts[1], ":")[0]
+	case 1:
+		registry = DefaultRegistry
+		namespace = DefaultNamespace
+		repository = strings.Split(slashParts[0], ":")[0]
+	default:
+		fmt.Println("Invalid image format.")
+		return ModelPath{}
+	}
+
+	colonParts := strings.Split(slashParts[len(slashParts)-1], ":")
+	if len(colonParts) == 2 {
+		tag = colonParts[1]
+	} else {
+		tag = DefaultTag
+	}
+
+	return ModelPath{
+		ProtocolScheme: DefaultProtocolScheme,
+		Registry:       registry,
+		Namespace:      namespace,
+		Repository:     repository,
+		Tag:            tag,
+	}
+}
+
+func (mp ModelPath) GetNamespaceRepository() string {
+	return fmt.Sprintf("%s/%s", mp.Namespace, mp.Repository)
+}
+
+func (mp ModelPath) GetFullTagname() string {
+	return fmt.Sprintf("%s/%s/%s:%s", mp.Registry, mp.Namespace, mp.Repository, mp.Tag)
+}
+
+func (mp ModelPath) GetShortTagname() string {
+	if mp.Registry == DefaultRegistry {
+		if mp.Namespace == DefaultNamespace {
+			return fmt.Sprintf("%s:%s", mp.Repository, mp.Tag)
+		}
+		return fmt.Sprintf("%s/%s:%s", mp.Namespace, mp.Repository, mp.Tag)
+	}
+	return fmt.Sprintf("%s/%s/%s:%s", mp.Registry, mp.Namespace, mp.Repository, mp.Tag)
+}
+
+func (mp ModelPath) GetManifestPath(createDir bool) (string, error) {
+	home, err := os.UserHomeDir()
+	if err != nil {
+		return "", err
+	}
+
+	path := filepath.Join(home, ".ollama", "models", "manifests", mp.Registry, mp.Namespace, mp.Repository, mp.Tag)
+	if createDir {
+		if err := os.MkdirAll(filepath.Dir(path), 0o755); err != nil {
+			return "", err
+		}
+	}
+
+	return path, nil
+}
+
+func GetManifestPath() (string, error) {
+	home, err := os.UserHomeDir()
+	if err != nil {
+		return "", err
+	}
+
+	return filepath.Join(home, ".ollama", "models", "manifests"), nil
+}
+
+func GetBlobsPath(digest string) (string, error) {
+	home, err := os.UserHomeDir()
+	if err != nil {
+		return "", err
+	}
+
+	if runtime.GOOS == "windows" {
+		digest = strings.ReplaceAll(digest, ":", "-")
+	}
+
+	path := filepath.Join(home, ".ollama", "models", "blobs", digest)
+	if err := os.MkdirAll(filepath.Dir(path), 0o755); err != nil {
+		return "", err
+	}
+
+	return path, nil
+}

server/models.go

@@ -1,154 +0,0 @@
-package server
-
-import (
-	"encoding/json"
-	"fmt"
-	"io"
-	"net/http"
-	"os"
-	"path"
-	"strconv"
-
-	"github.com/jmorganca/ollama/api"
-)
-
-const directoryURL = "https://ollama.ai/api/models"
-
-type Model struct {
-	Name             string `json:"name"`
-	DisplayName      string `json:"display_name"`
-	Parameters       string `json:"parameters"`
-	URL              string `json:"url"`
-	ShortDescription string `json:"short_description"`
-	Description      string `json:"description"`
-	PublishedBy      string `json:"published_by"`
-	OriginalAuthor   string `json:"original_author"`
-	OriginalURL      string `json:"original_url"`
-	License          string `json:"license"`
-}
-
-func (m *Model) FullName() string {
-	home, err := os.UserHomeDir()
-	if err != nil {
-		panic(err)
-	}
-
-	return path.Join(home, ".ollama", "models", m.Name+".bin")
-}
-
-func pull(model string, progressCh chan<- api.PullProgress) error {
-	remote, err := getRemote(model)
-	if err != nil {
-		return fmt.Errorf("failed to pull model: %w", err)
-	}
-	return saveModel(remote, progressCh)
-}
-
-func getRemote(model string) (*Model, error) {
-	// resolve the model download from our directory
-	resp, err := http.Get(directoryURL)
-	if err != nil {
-		return nil, fmt.Errorf("failed to get directory: %w", err)
-	}
-	defer resp.Body.Close()
-	body, err := io.ReadAll(resp.Body)
-	if err != nil {
-		return nil, fmt.Errorf("failed to read directory: %w", err)
-	}
-	var models []Model
-	err = json.Unmarshal(body, &models)
-	if err != nil {
-		return nil, fmt.Errorf("failed to parse directory: %w", err)
-	}
-	for _, m := range models {
-		if m.Name == model {
-			return &m, nil
-		}
-	}
-	return nil, fmt.Errorf("model not found in directory: %s", model)
-}
-
-func saveModel(model *Model, progressCh chan<- api.PullProgress) error {
-	// this models cache directory is created by the server on startup
-
-	client := &http.Client{}
-	req, err := http.NewRequest("GET", model.URL, nil)
-	if err != nil {
-		return fmt.Errorf("failed to download model: %w", err)
-	}
-	// check for resume
-	alreadyDownloaded := int64(0)
-	fileInfo, err := os.Stat(model.FullName())
-	if err != nil {
-		if !os.IsNotExist(err) {
-			return fmt.Errorf("failed to check resume model file: %w", err)
-		}
-		// file doesn't exist, create it now
-	} else {
-		alreadyDownloaded = fileInfo.Size()
-		req.Header.Add("Range", fmt.Sprintf("bytes=%d-", alreadyDownloaded))
-	}
-
-	resp, err := client.Do(req)
-	if err != nil {
-		return fmt.Errorf("failed to download model: %w", err)
-	}
-
-	defer resp.Body.Close()
-
-	if resp.StatusCode == http.StatusRequestedRangeNotSatisfiable {
-		// already downloaded
-		progressCh <- api.PullProgress{
-			Total:     alreadyDownloaded,
-			Completed: alreadyDownloaded,
-			Percent:   100,
-		}
-		return nil
-	}
-
-	if resp.StatusCode != http.StatusOK && resp.StatusCode != http.StatusPartialContent {
-		return fmt.Errorf("failed to download model: %s", resp.Status)
-	}
-
-	out, err := os.OpenFile(model.FullName(), os.O_CREATE|os.O_APPEND|os.O_WRONLY, 0o644)
-	if err != nil {
-		panic(err)
-	}
-	defer out.Close()
-
-	totalSize, _ := strconv.ParseInt(resp.Header.Get("Content-Length"), 10, 64)
-
-	buf := make([]byte, 1024)
-	totalBytes := alreadyDownloaded
-	totalSize += alreadyDownloaded
-
-	for {
-		n, err := resp.Body.Read(buf)
-		if err != nil && err != io.EOF {
-			return err
-		}
-		if n == 0 {
-			break
-		}
-		if _, err := out.Write(buf[:n]); err != nil {
-			return err
-		}
-
-		totalBytes += int64(n)
-
-		// send progress updates
-		progressCh <- api.PullProgress{
-			Total:     totalSize,
-			Completed: totalBytes,
-			Percent:   float64(totalBytes) / float64(totalSize) * 100,
-		}
-	}
-
-	progressCh <- api.PullProgress{
-		Total:     totalSize,
-		Completed: totalSize,
-		Percent:   100,
-	}
-
-	return nil
-}

server/routes.go

@@ -1,114 +1,278 @@
 package server
 
 import (
-	"embed"
 	"encoding/json"
 	"errors"
 	"fmt"
 	"io"
 	"log"
-	"math"
 	"net"
 	"net/http"
 	"os"
-	"path"
-	"runtime"
+	"path/filepath"
 	"strings"
-	"text/template"
+	"time"
 
+	"dario.cat/mergo"
+	"github.com/gin-contrib/cors"
 	"github.com/gin-gonic/gin"
-	"github.com/lithammer/fuzzysearch/fuzzy"
 
 	"github.com/jmorganca/ollama/api"
 	"github.com/jmorganca/ollama/llama"
 )
 
-//go:embed templates/*
-var templatesFS embed.FS
-var templates = template.Must(template.ParseFS(templatesFS, "templates/*.prompt"))
+func GenerateHandler(c *gin.Context) {
+	start := time.Now()
 
-func cacheDir() string {
-	home, err := os.UserHomeDir()
-	if err != nil {
-		panic(err)
+	var req api.GenerateRequest
+	if err := c.ShouldBindJSON(&req); err != nil {
+		c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
+		return
 	}
 
-	return path.Join(home, ".ollama")
+	model, err := GetModel(req.Model)
+	if err != nil {
+		c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
+		return
+	}
+
+	opts := api.DefaultOptions()
+	if err := mergo.Merge(&opts, model.Options, mergo.WithOverride); err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+		return
+	}
+
+	if err := mergo.Merge(&opts, req.Options, mergo.WithOverride); err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+		return
+	}
+
+	prompt, err := model.Prompt(req)
+	if err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+		return
+	}
+
+	llm, err := llama.New(model.ModelPath, opts)
+	if err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+		return
+	}
+	defer llm.Close()
+
+	ch := make(chan any)
+	go func() {
+		defer close(ch)
+		fn := func(r api.GenerateResponse) {
+			r.Model = req.Model
+			r.CreatedAt = time.Now().UTC()
+			if r.Done {
+				r.TotalDuration = time.Since(start)
+			}
+
+			ch <- r
+		}
+
+		if err := llm.Predict(req.Context, prompt, fn); err != nil {
+			ch <- gin.H{"error": err.Error()}
+		}
+	}()
+
+	streamResponse(c, ch)
 }
 
-func generate(c *gin.Context) {
-	var req api.GenerateRequest
-	req.ModelOptions = api.DefaultModelOptions
-	req.PredictOptions = api.DefaultPredictOptions
+func PullModelHandler(c *gin.Context) {
+	var req api.PullRequest
+	if err := c.ShouldBindJSON(&req); err != nil {
+		c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
+		return
+	}
+
+	ch := make(chan any)
+	go func() {
+		defer close(ch)
+		fn := func(r api.ProgressResponse) {
+			ch <- r
+		}
+
+		regOpts := &RegistryOptions{
+			Insecure: req.Insecure,
+			Username: req.Username,
+			Password: req.Password,
+		}
+
+		if err := PullModel(req.Name, regOpts, fn); err != nil {
+			ch <- gin.H{"error": err.Error()}
+		}
+	}()
+
+	streamResponse(c, ch)
+}
+
+func PushModelHandler(c *gin.Context) {
+	var req api.PushRequest
+	if err := c.ShouldBindJSON(&req); err != nil {
+		c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
+		return
+	}
+
+	ch := make(chan any)
+	go func() {
+		defer close(ch)
+		fn := func(r api.ProgressResponse) {
+			ch <- r
+		}
+
+		regOpts := &RegistryOptions{
+			Insecure: req.Insecure,
+			Username: req.Username,
+			Password: req.Password,
+		}
+
+		if err := PushModel(req.Name, regOpts, fn); err != nil {
+			ch <- gin.H{"error": err.Error()}
+		}
+	}()
+
+	streamResponse(c, ch)
+}
+
+func CreateModelHandler(c *gin.Context) {
+	var req api.CreateRequest
 	if err := c.ShouldBindJSON(&req); err != nil {
 		c.JSON(http.StatusBadRequest, gin.H{"message": err.Error()})
 		return
 	}
 
-	if remoteModel, _ := getRemote(req.Model); remoteModel != nil {
-		req.Model = remoteModel.FullName()
-	}
-	if _, err := os.Stat(req.Model); err != nil {
-		if !errors.Is(err, os.ErrNotExist) {
-			c.JSON(http.StatusBadRequest, gin.H{"message": err.Error()})
-			return
-		}
-		req.Model = path.Join(cacheDir(), "models", req.Model+".bin")
-	}
-
-	modelOpts := getModelOpts(req)
-	modelOpts.NGPULayers = 1 // hard-code this for now
-
-	model, err := llama.New(req.Model, modelOpts)
-	if err != nil {
-		fmt.Println("Loading the model failed:", err.Error())
-		return
-	}
-	defer model.Free()
-
-	templateNames := make([]string, 0, len(templates.Templates()))
-	for _, template := range templates.Templates() {
-		templateNames = append(templateNames, template.Name())
-	}
-
-	match, _ := matchRankOne(path.Base(req.Model), templateNames)
-	if template := templates.Lookup(match); template != nil {
-		var sb strings.Builder
-		if err := template.Execute(&sb, req); err != nil {
-			fmt.Println("Prompt template failed:", err.Error())
-			return
-		}
-
-		req.Prompt = sb.String()
-	}
-
-	ch := make(chan string)
-	model.SetTokenCallback(func(token string) bool {
-		ch <- token
-		return true
-	})
-
-	predictOpts := getPredictOpts(req)
-
+	ch := make(chan any)
 	go func() {
 		defer close(ch)
-		_, err := model.Predict(req.Prompt, predictOpts)
-		if err != nil {
-			panic(err)
+		fn := func(status string) {
+			ch <- api.CreateProgress{
+				Status: status,
+			}
+		}
+
+		if err := CreateModel(req.Name, req.Path, fn); err != nil {
+			ch <- gin.H{"error": err.Error()}
 		}
 	}()
 
+	streamResponse(c, ch)
+}
+
+func DeleteModelHandler(c *gin.Context) {
+	var req api.DeleteRequest
+	if err := c.ShouldBindJSON(&req); err != nil {
+		c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
+		return
+	}
+
+	if err := DeleteModel(req.Name); err != nil {
+		if os.IsNotExist(err) {
+			c.JSON(http.StatusNotFound, gin.H{"error": fmt.Sprintf("model '%s' not found", req.Name)})
+		} else {
+			c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+		}
+		return
+	}
+}
+
+func ListModelsHandler(c *gin.Context) {
+	var models []api.ListResponseModel
+	fp, err := GetManifestPath()
+	if err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+		return
+	}
+	err = filepath.Walk(fp, func(path string, info os.FileInfo, err error) error {
+		if err != nil {
+			if errors.Is(err, os.ErrNotExist) {
+				log.Printf("manifest file does not exist: %s", fp)
+				return nil
+			}
+			return err
+		}
+		if !info.IsDir() {
+			fi, err := os.Stat(path)
+			if err != nil {
+				log.Printf("skipping file: %s", fp)
+				return nil
+			}
+			path := path[len(fp)+1:]
+			slashIndex := strings.LastIndex(path, "/")
+			if slashIndex == -1 {
+				return nil
+			}
+			tag := path[:slashIndex] + ":" + path[slashIndex+1:]
+			mp := ParseModelPath(tag)
+			manifest, err := GetManifest(mp)
+			if err != nil {
+				log.Printf("skipping file: %s", fp)
+				return nil
+			}
+			model := api.ListResponseModel{
+				Name:       mp.GetShortTagname(),
+				Size:       manifest.GetTotalSize(),
+				ModifiedAt: fi.ModTime(),
+			}
+			models = append(models, model)
+		}
+		return nil
+	})
+	if err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+		return
+	}
+
+	c.JSON(http.StatusOK, api.ListResponse{models})
+}
+
+func Serve(ln net.Listener) error {
+	config := cors.DefaultConfig()
+	config.AllowWildcard = true
+	// only allow http/https from localhost
+	config.AllowOrigins = []string{
+		"http://localhost",
+		"http://localhost:*",
+		"https://localhost",
+		"https://localhost:*",
+		"http://127.0.0.1",
+		"http://127.0.0.1:*",
+		"https://127.0.0.1",
+		"https://127.0.0.1:*",
+	}
+
+	r := gin.Default()
+	r.Use(cors.New(config))
+
+	r.GET("/", func(c *gin.Context) {
+		c.String(http.StatusOK, "Ollama is running")
+	})
+
+	r.POST("/api/pull", PullModelHandler)
+	r.POST("/api/generate", GenerateHandler)
+	r.POST("/api/create", CreateModelHandler)
+	r.POST("/api/push", PushModelHandler)
+	r.GET("/api/tags", ListModelsHandler)
+	r.DELETE("/api/delete", DeleteModelHandler)
+
+	log.Printf("Listening on %s", ln.Addr())
+	s := &http.Server{
+		Handler: r,
+	}
+
+	return s.Serve(ln)
+}
+
+func streamResponse(c *gin.Context, ch chan any) {
 	c.Stream(func(w io.Writer) bool {
-		token, ok := <-ch
+		val, ok := <-ch
 		if !ok {
 			return false
 		}
 
-		resp := api.GenerateResponse{
-			Response: token,
-		}
-
-		bts, err := json.Marshal(resp)
+		bts, err := json.Marshal(val)
 		if err != nil {
 			return false
 		}
@@ -121,129 +285,3 @@ func generate(c *gin.Context) {
 		return true
 	})
 }
-
-func Serve(ln net.Listener) error {
-	r := gin.Default()
-
-	r.GET("/", func(c *gin.Context) {
-		c.String(http.StatusOK, "Ollama is running")
-	})
-
-	r.POST("api/pull", func(c *gin.Context) {
-		var req api.PullRequest
-		if err := c.ShouldBindJSON(&req); err != nil {
-			c.JSON(http.StatusBadRequest, gin.H{"message": err.Error()})
-			return
-		}
-
-		progressCh := make(chan api.PullProgress)
-		go func() {
-			defer close(progressCh)
-			if err := pull(req.Model, progressCh); err != nil {
-				var opError *net.OpError
-				if errors.As(err, &opError) {
-					result := api.PullProgress{
-						Error: api.Error{
-							Code:    http.StatusBadGateway,
-							Message: "failed to get models from directory",
-						},
-					}
-					c.JSON(http.StatusBadGateway, result)
-					return
-				}
-				c.JSON(http.StatusBadRequest, gin.H{"message": err.Error()})
-				return
-			}
-		}()
-
-		c.Stream(func(w io.Writer) bool {
-			progress, ok := <-progressCh
-			if !ok {
-				return false
-			}
-
-			bts, err := json.Marshal(progress)
-			if err != nil {
-				return false
-			}
-
-			bts = append(bts, '\n')
-			if _, err := w.Write(bts); err != nil {
-				return false
-			}
-
-			return true
-		})
-	})
-
-	r.POST("/api/generate", generate)
-
-	log.Printf("Listening on %s", ln.Addr())
-	s := &http.Server{
-		Handler: r,
-	}
-
-	return s.Serve(ln)
-}
-
-func matchRankOne(source string, targets []string) (bestMatch string, bestRank int) {
-	bestRank = math.MaxInt
-	for _, target := range targets {
-		if rank := fuzzy.LevenshteinDistance(source, target); bestRank > rank {
-			bestRank = rank
-			bestMatch = target
-		}
-	}
-
-	return
-}
-
-func getModelOpts(req api.GenerateRequest) llama.ModelOptions {
-	var opts llama.ModelOptions
-	opts.ContextSize = req.ModelOptions.ContextSize
-	opts.Seed = req.ModelOptions.Seed
-	opts.F16Memory = req.ModelOptions.F16Memory
-	opts.MLock = req.ModelOptions.MLock
-	opts.Embeddings = req.ModelOptions.Embeddings
-	opts.MMap = req.ModelOptions.MMap
-	opts.LowVRAM = req.ModelOptions.LowVRAM
-
-	opts.NBatch = req.ModelOptions.NBatch
-	opts.VocabOnly = req.ModelOptions.VocabOnly
-	opts.NUMA = req.ModelOptions.NUMA
-	opts.NGPULayers = req.ModelOptions.NGPULayers
-	opts.MainGPU = req.ModelOptions.MainGPU
-	opts.TensorSplit = req.ModelOptions.TensorSplit
-
-	return opts
-}
-
-func getPredictOpts(req api.GenerateRequest) llama.PredictOptions {
-	var opts llama.PredictOptions
-
-	if req.PredictOptions.Threads == -1 {
-		opts.Threads = runtime.NumCPU()
-	} else {
-		opts.Threads = req.PredictOptions.Threads
-	}
-
-	opts.Seed = req.PredictOptions.Seed
-	opts.Tokens = req.PredictOptions.Tokens
-	opts.Penalty = req.PredictOptions.Penalty
-	opts.Repeat = req.PredictOptions.Repeat
-	opts.Batch = req.PredictOptions.Batch
-	opts.NKeep = req.PredictOptions.NKeep
-	opts.TopK = req.PredictOptions.TopK
-	opts.TopP = req.PredictOptions.TopP
-	opts.TailFreeSamplingZ = req.PredictOptions.TailFreeSamplingZ
-	opts.TypicalP = req.PredictOptions.TypicalP
-	opts.Temperature = req.PredictOptions.Temperature
-	opts.FrequencyPenalty = req.PredictOptions.FrequencyPenalty
-	opts.PresencePenalty = req.PredictOptions.PresencePenalty
-	opts.Mirostat = req.PredictOptions.Mirostat
-	opts.MirostatTAU = req.PredictOptions.MirostatTAU
-	opts.MirostatETA = req.PredictOptions.MirostatETA
-	opts.MMap = req.PredictOptions.MMap
-
-	return opts
-}

server/templates/alpaca.prompt

@@ -1,8 +0,0 @@
-Below is an instruction that describes a task. Write a response that appropriately completes the request.
-
-### Instruction:
-{{ .Prompt }}
-
-### Response:
-
-

server/templates/falcon.prompt

@@ -1,3 +0,0 @@
-A helpful assistant who helps the user with any questions asked.
-User: {{ .Prompt }}
-Assistant:

server/templates/gpt4.prompt

@@ -1,5 +0,0 @@
-### Instruction:
-{{ .Prompt }}
-
-### Response:
-

server/templates/hermes.prompt

@@ -1,5 +0,0 @@
-### Instruction:
-{{ .Prompt }}
-
-### Response:
-

server/templates/mpt.prompt

@@ -1,4 +0,0 @@
-Below is an instruction that describes a task. Write a response that appropriately completes the request. Be concise. Once the request is completed, include no other text.
-### Instruction:
-{{ .Prompt }}
-### Response:

server/templates/oasst.prompt

@@ -1 +0,0 @@
-{{ .Prompt }}

server/templates/orca.prompt

@@ -1,7 +0,0 @@
-### System:
-You are an AI assistant that follows instruction extremely well. Help as much as you can.
-
-### User:
-{{ .Prompt }}
-
-### Response:

server/templates/qlora.prompt

@@ -1,2 +0,0 @@
-### Human: {{ .Prompt }}
-### Assistant:

server/templates/tulu.prompt

@@ -1,4 +0,0 @@
-
-{{ .Prompt }}
-
-

server/templates/ultralm.prompt

@@ -1,2 +0,0 @@
-USER: {{ .Prompt }}
-ASSISTANT:

server/templates/vicuna.prompt

@@ -1,4 +0,0 @@
-A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
-
-USER: {{ .Prompt }}
-ASSISTANT:

server/templates/wizardcoder.prompt

@@ -1,5 +0,0 @@
-Below is an instruction that describes a task. Write a response that appropriately completes the request
-
-### Instruction: {{ .Prompt }}
-
-### Response:

server/templates/wizardlm.prompt

@@ -1,3 +0,0 @@
-{{ .Prompt }}
-
-### Response: