remove .First

replace go-humanize with format.HumanBytes

.dockerignore

@@ -1,8 +1,8 @@
-build
-llama/build
-.venv
 .vscode
 ollama
 app
-web
+dist
+scripts
+llm/llama.cpp/ggml
+llm/llama.cpp/gguf
 .env

.gitignore

@@ -5,4 +5,4 @@
 .swp
 dist
 ollama
-/ggml-metal.metal
+ggml-metal.metal

.gitmodules

@@ -0,0 +1,10 @@
+[submodule "llm/llama.cpp/ggml"]
+    path = llm/llama.cpp/ggml
+    url = https://github.com/ggerganov/llama.cpp.git
+    ignore = dirty
+    shallow = true
+[submodule "llm/llama.cpp/gguf"]
+    path = llm/llama.cpp/gguf
+    url = https://github.com/ggerganov/llama.cpp.git
+    ignore = dirty
+    shallow = true

Dockerfile

@@ -1,15 +1,23 @@
-FROM golang:1.20
-WORKDIR /go/src/github.com/jmorganca/ollama
-COPY . .
-RUN CGO_ENABLED=1 go build -ldflags '-linkmode external -extldflags "-static"' .
+FROM nvidia/cuda:11.8.0-devel-ubuntu22.04
 
-FROM alpine
+ARG TARGETARCH
+ARG GOFLAGS="'-ldflags=-w -s'"
+
+WORKDIR /go/src/github.com/jmorganca/ollama
+RUN apt-get update && apt-get install -y git build-essential cmake
+ADD https://dl.google.com/go/go1.21.3.linux-$TARGETARCH.tar.gz /tmp/go1.21.3.tar.gz
+RUN mkdir -p /usr/local && tar xz -C /usr/local </tmp/go1.21.3.tar.gz
+
+COPY . .
+ENV GOARCH=$TARGETARCH
+ENV GOFLAGS=$GOFLAGS
+RUN /usr/local/go/bin/go generate ./... \
+    && /usr/local/go/bin/go build .
+
+FROM ubuntu:22.04
+RUN apt-get update && apt-get install -y ca-certificates
 COPY --from=0 /go/src/github.com/jmorganca/ollama/ollama /bin/ollama
 EXPOSE 11434
-ARG USER=ollama
-ARG GROUP=ollama
-RUN addgroup -g 1000 $GROUP && adduser -u 1000 -DG $GROUP $USER
-USER $USER:$GROUP
-ENTRYPOINT ["/bin/ollama"]
 ENV OLLAMA_HOST 0.0.0.0
+ENTRYPOINT ["/bin/ollama"]
 CMD ["serve"]

Dockerfile.build

@@ -0,0 +1,31 @@
+# centos7 amd64 dependencies
+FROM --platform=linux/amd64 nvidia/cuda:11.3.1-devel-centos7 AS base-amd64
+RUN yum install -y https://repo.ius.io/ius-release-el7.rpm centos-release-scl && \
+    yum update -y && \
+    yum install -y devtoolset-10-gcc devtoolset-10-gcc-c++ git236 wget
+RUN wget "https://github.com/Kitware/CMake/releases/download/v3.27.6/cmake-3.27.6-linux-x86_64.sh" -O cmake-installer.sh && chmod +x cmake-installer.sh && ./cmake-installer.sh --skip-license --prefix=/usr/local
+ENV PATH /opt/rh/devtoolset-10/root/usr/bin:$PATH
+
+# centos8 arm64 dependencies
+FROM --platform=linux/arm64 nvidia/cuda-arm64:11.3.1-devel-centos8 AS base-arm64
+RUN sed -i -e 's/mirrorlist/#mirrorlist/g' -e 's|#baseurl=http://mirror.centos.org|baseurl=http://vault.centos.org|g' /etc/yum.repos.d/CentOS-*
+RUN yum install -y git cmake
+
+FROM base-${TARGETARCH}
+ARG TARGETARCH
+ARG GOFLAGS="'-ldflags -w -s'"
+
+# install go
+ADD https://dl.google.com/go/go1.21.3.linux-$TARGETARCH.tar.gz /tmp/go1.21.3.tar.gz
+RUN mkdir -p /usr/local && tar xz -C /usr/local </tmp/go1.21.3.tar.gz
+
+# build the final binary
+WORKDIR /go/src/github.com/jmorganca/ollama
+COPY . .
+
+ENV GOOS=linux
+ENV GOARCH=$TARGETARCH
+ENV GOFLAGS=$GOFLAGS
+
+RUN /usr/local/go/bin/go generate ./... && \
+    /usr/local/go/bin/go build .

README.md

@@ -9,19 +9,31 @@
 
 [![Discord](https://dcbadge.vercel.app/api/server/ollama?style=flat&compact=true)](https://discord.gg/ollama)
 
-Run, create, and share large language models (LLMs).
+Get up and running with large language models locally.
 
-> Note: Ollama is in early preview. Please report any issues you find.
+### macOS
 
-## Download
+[Download](https://ollama.ai/download/Ollama-darwin.zip)
 
-- [Download](https://ollama.ai/download) for macOS
-- Download for Windows and Linux (coming soon)
-- Build [from source](#building)
+### Windows
+
+Coming soon!
+
+### Linux & WSL2
+
+```
+curl https://ollama.ai/install.sh | sh
+```
+
+[Manual install instructions](https://github.com/jmorganca/ollama/blob/main/docs/linux.md)
+
+### Docker
+
+The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `ollama/ollama` is available on Docker Hub.
 
 ## Quickstart
 
-To run and chat with [Llama 2](https://ai.meta.com/llama), the new model by Meta:
+To run and chat with [Llama 2](https://ollama.ai/library/llama2):
 
 ```
 ollama run llama2
@@ -29,53 +41,59 @@ ollama run llama2
 
 ## Model library
 
-Ollama supports a list of open-source models available on [ollama.ai/library](https://ollama.ai/library "ollama model library") 
+Ollama supports a list of open-source models available on [ollama.ai/library](https://ollama.ai/library 'ollama model library')
 
-Here are some example open-source models that can be downloaded: 
+Here are some example open-source models that can be downloaded:
 
-| Model                    | Parameters | Size  | Download                        |
-| ------------------------ | ---------- | ----- | ------------------------------- |
-| Llama2                   | 7B         | 3.8GB | `ollama pull llama2`            |
-| Llama2 13B               | 13B        | 7.3GB | `ollama pull llama2:13b`        |
-| Llama2 70B               | 70B        | 39GB  | `ollama pull llama2:70b`        |
-| Llama2 Uncensored        | 7B         | 3.8GB | `ollama pull llama2-uncensored` |
-| Orca Mini                | 3B         | 1.9GB | `ollama pull orca-mini`         |
-| Vicuna                   | 7B         | 3.8GB | `ollama pull vicuna`            |
-| Nous-Hermes              | 7B         | 3.8GB | `ollama pull nous-hermes`       |
-| Nous-Hermes 13B          | 13B        | 7.3GB | `ollama pull nous-hermes:13b`   |
-| Wizard Vicuna Uncensored | 13B        | 7.3GB | `ollama pull wizard-vicuna`     |
+| Model              | Parameters | Size  | Download                       |
+| ------------------ | ---------- | ----- | ------------------------------ |
+| Mistral            | 7B         | 4.1GB | `ollama run mistral`           |
+| Llama 2            | 7B         | 3.8GB | `ollama run llama2`            |
+| Code Llama         | 7B         | 3.8GB | `ollama run codellama`         |
+| Llama 2 Uncensored | 7B         | 3.8GB | `ollama run llama2-uncensored` |
+| Llama 2 13B        | 13B        | 7.3GB | `ollama run llama2:13b`        |
+| Llama 2 70B        | 70B        | 39GB  | `ollama run llama2:70b`        |
+| Orca Mini          | 3B         | 1.9GB | `ollama run orca-mini`         |
+| Vicuna             | 7B         | 3.8GB | `ollama run 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
+## Customize your own model
 
-### Run a model
+### Import from GGUF
 
-```
-ollama run llama2
->>> hi
-Hello! How can I help you today?
-```
+Ollama supports importing GGUF models in the Modelfile:
 
-For multiline input, you can wrap text with `"""`:
+1. Create a file named `Modelfile`, with a `FROM` instruction with the local filepath to the model you want to import.
 
-```
->>> """Hello,
-... world!
-... """
-I'm a basic program that prints the famous "Hello, world!" message to the console.
-```
+   ```
+   FROM ./vicuna-33b.Q4_0.gguf
+   ```
 
-### Create a custom model
+2. Create the model in Ollama
 
-Pull a base model:
+   ```
+   ollama create example -f Modelfile
+   ```
+
+3. Run the model
+
+   ```
+   ollama run example
+   ```
+
+### Import from PyTorch or Safetensors
+
+See the [guide](docs/import.md) on importing models for more information.
+
+### Customize a prompt
+
+Models from the Ollama library can be customized with a prompt. For example, to customize the `llama2` model:
 
 ```
 ollama pull llama2
 ```
 
-> To update a model to the latest version, run `ollama pull llama2` again. The model will be updated (if necessary).
-
 Create a `Modelfile`:
 
 ```
@@ -99,44 +117,84 @@ ollama run mario
 Hello! It's your friend Mario.
 ```
 
-For more examples, see the [examples](./examples) directory. For more information on creating a Modelfile, see the [Modelfile](./docs/modelfile.md) documentation.
+For more examples, see the [examples](examples) directory. For more information on working with a Modelfile, see the [Modelfile](docs/modelfile.md) documentation.
 
-### Pull a model from the registry
+## CLI Reference
+
+### Create a model
+
+`ollama create` is used to create a model from a Modelfile.
+
+### Pull a model
 
 ```
-ollama pull orca
+ollama pull llama2
 ```
 
-### Listing local models
+> This command can also be used to update a local model. Only the diff will be pulled.
+
+### Remove a model
+
+```
+ollama rm llama2
+```
+
+### Copy a model
+
+```
+ollama cp llama2 my-llama2
+```
+
+### Multiline input
+
+For multiline input, you can wrap text with `"""`:
+
+```
+>>> """Hello,
+... world!
+... """
+I'm a basic program that prints the famous "Hello, world!" message to the console.
+```
+
+### Pass in prompt as arguments
+
+```
+$ ollama run llama2 "Summarize this file: $(cat README.md)"
+ Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.
+```
+
+### List models on your computer
 
 ```
 ollama list
 ```
 
-## Model packages
+### Start Ollama
 
-### 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>
+`ollama serve` is used when you want to start ollama without running the desktop application.
 
 ## Building
 
+Install `cmake` and `go`:
+
 ```
+brew install cmake go
+```
+
+Then generate dependencies and build:
+
+```
+go generate ./...
 go build .
 ```
 
-To run it start the server:
+Next, start the server:
 
 ```
-./ollama serve &
+./ollama serve
 ```
 
-Finally, run a model!
+Finally, in a separate shell, run a model:
 
 ```
 ./ollama run llama2
@@ -144,9 +202,8 @@ Finally, run a model!
 
 ## REST API
 
-> See the [API documentation](./docs/api.md) for all endpoints.
-
-Ollama has an API for running and managing models. For example to generate text from a model:
+Ollama has a REST API for running and managing models.
+For example, to generate text from a model:
 
 ```
 curl -X POST http://localhost:11434/api/generate -d '{
@@ -155,12 +212,48 @@ curl -X POST http://localhost:11434/api/generate -d '{
 }'
 ```
 
-## Tools using Ollama
+See the [API documentation](./docs/api.md) for all endpoints.
 
-- [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/modules/model_io/models/llms/integrations/ollama) with a question-answering [example](https://js.langchain.com/docs/use_cases/question_answering/local_retrieval_qa).
-- [Continue](https://github.com/continuedev/continue) - embeds Ollama inside Visual Studio Code. The extension lets you highlight code to add to the prompt, ask questions in the sidebar, and generate code inline.
-- [LiteLLM](https://github.com/BerriAI/litellm) a lightweight python package to simplify LLM API calls
-- [Discord AI Bot](https://github.com/mekb-turtle/discord-ai-bot) - interact with Ollama as a chatbot on Discord.
-- [Raycast Ollama](https://github.com/MassimilianoPasquini97/raycast_ollama) - Raycast extension to use Ollama for local llama inference on Raycast.
-- [Simple HTML UI for Ollama](https://github.com/rtcfirefly/ollama-ui)
-- [Emacs client](https://github.com/zweifisch/ollama) for Ollama
+## Community Integrations
+
+### Web & Desktop
+
+- [HTML UI](https://github.com/rtcfirefly/ollama-ui)
+- [Chatbot UI](https://github.com/ivanfioravanti/chatbot-ollama)
+- [Typescript UI](https://github.com/ollama-interface/Ollama-Gui?tab=readme-ov-file)
+- [Minimalistic React UI for Ollama Models](https://github.com/richawo/minimal-llm-ui)
+- [Web UI](https://github.com/ollama-webui/ollama-webui)
+- [Ollamac](https://github.com/kevinhermawan/Ollamac)
+- [big-AGI](https://github.com/enricoros/big-agi/blob/main/docs/config-ollama.md)
+
+### Terminal
+
+- [oterm](https://github.com/ggozad/oterm)
+- [Ellama Emacs client](https://github.com/s-kostyaev/ellama)
+- [Emacs client](https://github.com/zweifisch/ollama)
+- [gen.nvim](https://github.com/David-Kunz/gen.nvim)
+- [ollama.nvim](https://github.com/nomnivore/ollama.nvim)
+- [gptel Emacs client](https://github.com/karthink/gptel)
+
+### Libraries
+
+- [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/modules/model_io/models/llms/integrations/ollama) with [example](https://js.langchain.com/docs/use_cases/question_answering/local_retrieval_qa)
+- [LlamaIndex](https://gpt-index.readthedocs.io/en/stable/examples/llm/ollama.html)
+- [LiteLLM](https://github.com/BerriAI/litellm)
+- [OllamaSharp for .NET](https://github.com/awaescher/OllamaSharp)
+- [Ollama-rs for Rust](https://github.com/pepperoni21/ollama-rs)
+- [Ollama4j for Java](https://github.com/amithkoujalgi/ollama4j)
+- [ModelFusion Typescript Library](https://modelfusion.dev/integration/model-provider/ollama)
+- [OllamaKit for Swift](https://github.com/kevinhermawan/OllamaKit)
+- [Ollama for Dart](https://github.com/breitburg/dart-ollama)
+
+### Extensions & Plugins
+
+- [Raycast extension](https://github.com/MassimilianoPasquini97/raycast_ollama)
+- [Discollama](https://github.com/mxyng/discollama) (Discord bot inside the Ollama discord channel)
+- [Continue](https://github.com/continuedev/continue)
+- [Obsidian Ollama plugin](https://github.com/hinterdupfinger/obsidian-ollama)
+- [Logseq Ollama plugin](https://github.com/omagdy7/ollama-logseq)
+- [Dagger Chatbot](https://github.com/samalba/dagger-chatbot)
+- [Discord AI Bot](https://github.com/mekb-turtle/discord-ai-bot)
+- [Hass Ollama Conversation](https://github.com/ej52/hass-ollama-conversation)

api/client.go

@@ -7,29 +7,24 @@ import (
 	"encoding/json"
 	"fmt"
 	"io"
+	"net"
 	"net/http"
 	"net/url"
 	"os"
 	"runtime"
 	"strings"
 
+	"github.com/jmorganca/ollama/format"
 	"github.com/jmorganca/ollama/version"
 )
 
-const DefaultHost = "localhost:11434"
-
-var (
-	envHost = os.Getenv("OLLAMA_HOST")
-)
-
 type Client struct {
-	Base    url.URL
-	HTTP    http.Client
-	Headers http.Header
+	base *url.URL
+	http http.Client
 }
 
 func checkError(resp *http.Response, body []byte) error {
-	if resp.StatusCode >= 200 && resp.StatusCode < 400 {
+	if resp.StatusCode < http.StatusBadRequest {
 		return nil
 	}
 
@@ -44,34 +39,56 @@ func checkError(resp *http.Response, body []byte) error {
 	return apiError
 }
 
-// Host returns the default host to use for the client. It is determined in the following order:
-// 1. The OLLAMA_HOST environment variable
-// 2. The default host (localhost:11434)
-func Host() string {
-	if envHost != "" {
-		return envHost
-	}
-	return DefaultHost
-}
+func ClientFromEnvironment() (*Client, error) {
+	defaultPort := "11434"
 
-// FromEnv creates a new client using Host() as the host. An error is returns
-// if the host is invalid.
-func FromEnv() (*Client, error) {
-	h := Host()
-	if !strings.HasPrefix(h, "http://") && !strings.HasPrefix(h, "https://") {
-		h = "http://" + h
+	scheme, hostport, ok := strings.Cut(os.Getenv("OLLAMA_HOST"), "://")
+	switch {
+	case !ok:
+		scheme, hostport = "http", os.Getenv("OLLAMA_HOST")
+	case scheme == "http":
+		defaultPort = "80"
+	case scheme == "https":
+		defaultPort = "443"
 	}
 
-	u, err := url.Parse(h)
+	// trim trailing slashes
+	hostport = strings.TrimRight(hostport, "/")
+
+	host, port, err := net.SplitHostPort(hostport)
 	if err != nil {
-		return nil, fmt.Errorf("could not parse host: %w", err)
+		host, port = "127.0.0.1", defaultPort
+		if ip := net.ParseIP(strings.Trim(hostport, "[]")); ip != nil {
+			host = ip.String()
+		} else if hostport != "" {
+			host = hostport
+		}
 	}
 
-	if u.Port() == "" {
-		u.Host += ":11434"
+	client := Client{
+		base: &url.URL{
+			Scheme: scheme,
+			Host:   net.JoinHostPort(host, port),
+		},
 	}
 
-	return &Client{Base: *u, HTTP: http.Client{}}, nil
+	mockRequest, err := http.NewRequest(http.MethodHead, client.base.String(), nil)
+	if err != nil {
+		return nil, err
+	}
+
+	proxyURL, err := http.ProxyFromEnvironment(mockRequest)
+	if err != nil {
+		return nil, err
+	}
+
+	client.http = http.Client{
+		Transport: &http.Transport{
+			Proxy: http.ProxyURL(proxyURL),
+		},
+	}
+
+	return &client, nil
 }
 
 func (c *Client) do(ctx context.Context, method, path string, reqData, respData any) error {
@@ -86,7 +103,7 @@ func (c *Client) do(ctx context.Context, method, path string, reqData, respData
 		reqBody = bytes.NewReader(data)
 	}
 
-	requestURL := c.Base.JoinPath(path)
+	requestURL := c.base.JoinPath(path)
 	request, err := http.NewRequestWithContext(ctx, method, requestURL.String(), reqBody)
 	if err != nil {
 		return err
@@ -96,11 +113,7 @@ func (c *Client) do(ctx context.Context, method, path string, reqData, respData
 	request.Header.Set("Accept", "application/json")
 	request.Header.Set("User-Agent", fmt.Sprintf("ollama/%s (%s %s) Go/%s", version.Version, runtime.GOARCH, runtime.GOOS, runtime.Version()))
 
-	for k, v := range c.Headers {
-		request.Header[k] = v
-	}
-
-	respObj, err := c.HTTP.Do(request)
+	respObj, err := c.http.Do(request)
 	if err != nil {
 		return err
 	}
@@ -123,6 +136,8 @@ func (c *Client) do(ctx context.Context, method, path string, reqData, respData
 	return nil
 }
 
+const maxBufferSize = 512 * format.KiloByte
+
 func (c *Client) stream(ctx context.Context, method, path string, data any, fn func([]byte) error) error {
 	var buf *bytes.Buffer
 	if data != nil {
@@ -134,23 +149,26 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
 		buf = bytes.NewBuffer(bts)
 	}
 
-	requestURL := c.Base.JoinPath(path)
+	requestURL := c.base.JoinPath(path)
 	request, err := http.NewRequestWithContext(ctx, method, requestURL.String(), buf)
 	if err != nil {
 		return err
 	}
 
 	request.Header.Set("Content-Type", "application/json")
-	request.Header.Set("Accept", "application/json")
+	request.Header.Set("Accept", "application/x-ndjson")
 	request.Header.Set("User-Agent", fmt.Sprintf("ollama/%s (%s %s) Go/%s", version.Version, runtime.GOARCH, runtime.GOOS, runtime.Version()))
 
-	response, err := http.DefaultClient.Do(request)
+	response, err := c.http.Do(request)
 	if err != nil {
 		return err
 	}
 	defer response.Body.Close()
 
 	scanner := bufio.NewScanner(response.Body)
+	// increase the buffer size to avoid running out of space
+	scanBuf := make([]byte, 0, maxBufferSize)
+	scanner.Buffer(scanBuf, maxBufferSize)
 	for scanner.Scan() {
 		var errorResponse struct {
 			Error string `json:"error,omitempty"`
@@ -165,7 +183,7 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
 			return fmt.Errorf(errorResponse.Error)
 		}
 
-		if response.StatusCode >= 400 {
+		if response.StatusCode >= http.StatusBadRequest {
 			return StatusError{
 				StatusCode:   response.StatusCode,
 				Status:       response.Status,
@@ -255,6 +273,14 @@ func (c *Client) Delete(ctx context.Context, req *DeleteRequest) error {
 	return nil
 }
 
+func (c *Client) Show(ctx context.Context, req *ShowRequest) (*ShowResponse, error) {
+	var resp ShowResponse
+	if err := c.do(ctx, http.MethodPost, "/api/show", req, &resp); err != nil {
+		return nil, err
+	}
+	return &resp, nil
+}
+
 func (c *Client) Heartbeat(ctx context.Context) error {
 	if err := c.do(ctx, http.MethodHead, "/", nil, nil); err != nil {
 		return err

api/client.py

@@ -0,0 +1,226 @@
+import os
+import json
+import requests
+
+BASE_URL = os.environ.get('OLLAMA_HOST', 'http://localhost:11434')
+
+# Generate a response for a given prompt with a provided model. This is a streaming endpoint, so will be a series of responses.
+# The final response object will include statistics and additional data from the request. Use the callback function to override
+# the default handler.
+def generate(model_name, prompt, system=None, template=None, format="", context=None, options=None, callback=None):
+    try:
+        url = f"{BASE_URL}/api/generate"
+        payload = {
+            "model": model_name, 
+            "prompt": prompt, 
+            "system": system, 
+            "template": template, 
+            "context": context, 
+            "options": options,
+            "format": format,
+        }
+        
+        # Remove keys with None values
+        payload = {k: v for k, v in payload.items() if v is not None}
+        
+        with requests.post(url, json=payload, stream=True) as response:
+            response.raise_for_status()
+            
+            # Creating a variable to hold the context history of the final chunk
+            final_context = None
+            
+            # Variable to hold concatenated response strings if no callback is provided
+            full_response = ""
+
+            # Iterating over the response line by line and displaying the details
+            for line in response.iter_lines():
+                if line:
+                    # Parsing each line (JSON chunk) and extracting the details
+                    chunk = json.loads(line)
+                    
+                    # If a callback function is provided, call it with the chunk
+                    if callback:
+                        callback(chunk)
+                    else:
+                        # If this is not the last chunk, add the "response" field value to full_response and print it
+                        if not chunk.get("done"):
+                            response_piece = chunk.get("response", "")
+                            full_response += response_piece
+                            print(response_piece, end="", flush=True)
+                    
+                    # Check if it's the last chunk (done is true)
+                    if chunk.get("done"):
+                        final_context = chunk.get("context")
+            
+            # Return the full response and the final context
+            return full_response, final_context
+    except requests.exceptions.RequestException as e:
+        print(f"An error occurred: {e}")
+        return None, None
+
+# Create a model from a Modelfile. Use the callback function to override the default handler.
+def create(model_name, model_path, callback=None):
+    try:
+        url = f"{BASE_URL}/api/create"
+        payload = {"name": model_name, "path": model_path}
+        
+        # Making a POST request with the stream parameter set to True to handle streaming responses
+        with requests.post(url, json=payload, stream=True) as response:
+            response.raise_for_status()
+
+            # Iterating over the response line by line and displaying the status
+            for line in response.iter_lines():
+                if line:
+                    # Parsing each line (JSON chunk) and extracting the status
+                    chunk = json.loads(line)
+
+                    if callback:
+                        callback(chunk)
+                    else:
+                        print(f"Status: {chunk.get('status')}")
+    except requests.exceptions.RequestException as e:
+        print(f"An error occurred: {e}")
+
+# Pull a model from a the model registry. Cancelled pulls are resumed from where they left off, and multiple
+# calls to will share the same download progress. Use the callback function to override the default handler.
+def pull(model_name, insecure=False, callback=None):
+    try:
+        url = f"{BASE_URL}/api/pull"
+        payload = {
+            "name": model_name,
+            "insecure": insecure
+        }
+
+        # Making a POST request with the stream parameter set to True to handle streaming responses
+        with requests.post(url, json=payload, stream=True) as response:
+            response.raise_for_status()
+
+            # Iterating over the response line by line and displaying the details
+            for line in response.iter_lines():
+                if line:
+                    # Parsing each line (JSON chunk) and extracting the details
+                    chunk = json.loads(line)
+
+                    # If a callback function is provided, call it with the chunk
+                    if callback:
+                        callback(chunk)
+                    else:
+                        # Print the status message directly to the console
+                        print(chunk.get('status', ''), end='', flush=True)
+                    
+                    # If there's layer data, you might also want to print that (adjust as necessary)
+                    if 'digest' in chunk:
+                        print(f" - Digest: {chunk['digest']}", end='', flush=True)
+                        print(f" - Total: {chunk['total']}", end='', flush=True)
+                        print(f" - Completed: {chunk['completed']}", end='\n', flush=True)
+                    else:
+                        print()
+    except requests.exceptions.RequestException as e:
+        print(f"An error occurred: {e}")
+
+# Push a model to the model registry. Use the callback function to override the default handler.
+def push(model_name, insecure=False, callback=None):
+    try:
+        url = f"{BASE_URL}/api/push"
+        payload = {
+            "name": model_name,
+            "insecure": insecure
+        }
+
+        # Making a POST request with the stream parameter set to True to handle streaming responses
+        with requests.post(url, json=payload, stream=True) as response:
+            response.raise_for_status()
+
+            # Iterating over the response line by line and displaying the details
+            for line in response.iter_lines():
+                if line:
+                    # Parsing each line (JSON chunk) and extracting the details
+                    chunk = json.loads(line)
+
+                    # If a callback function is provided, call it with the chunk
+                    if callback:
+                        callback(chunk)
+                    else:
+                        # Print the status message directly to the console
+                        print(chunk.get('status', ''), end='', flush=True)
+                    
+                    # If there's layer data, you might also want to print that (adjust as necessary)
+                    if 'digest' in chunk:
+                        print(f" - Digest: {chunk['digest']}", end='', flush=True)
+                        print(f" - Total: {chunk['total']}", end='', flush=True)
+                        print(f" - Completed: {chunk['completed']}", end='\n', flush=True)
+                    else:
+                        print()
+    except requests.exceptions.RequestException as e:
+        print(f"An error occurred: {e}")
+
+# List models that are available locally.
+def list():
+    try:
+        response = requests.get(f"{BASE_URL}/api/tags")
+        response.raise_for_status()
+        data = response.json()
+        models = data.get('models', [])
+        return models
+
+    except requests.exceptions.RequestException as e:
+        print(f"An error occurred: {e}")
+        return None
+
+# Copy a model. Creates a model with another name from an existing model.
+def copy(source, destination):
+    try:
+        # Create the JSON payload
+        payload = {
+            "source": source,
+            "destination": destination
+        }
+        
+        response = requests.post(f"{BASE_URL}/api/copy", json=payload)
+        response.raise_for_status()
+        
+        # If the request was successful, return a message indicating that the copy was successful
+        return "Copy successful"
+
+    except requests.exceptions.RequestException as e:
+        print(f"An error occurred: {e}")
+        return None
+
+# Delete a model and its data.
+def delete(model_name):
+    try:
+        url = f"{BASE_URL}/api/delete"
+        payload = {"name": model_name}
+        response = requests.delete(url, json=payload)
+        response.raise_for_status()
+        return "Delete successful"
+    except requests.exceptions.RequestException as e:
+        print(f"An error occurred: {e}")
+        return None
+
+# Show info about a model.
+def show(model_name):
+    try:
+        url = f"{BASE_URL}/api/show"
+        payload = {"name": model_name}
+        response = requests.post(url, json=payload)
+        response.raise_for_status()
+        
+        # Parse the JSON response and return it
+        data = response.json()
+        return data
+    except requests.exceptions.RequestException as e:
+        print(f"An error occurred: {e}")
+        return None
+
+def heartbeat():
+    try:
+        url = f"{BASE_URL}/"
+        response = requests.head(url)
+        response.raise_for_status()
+        return "Ollama is running"
+    except requests.exceptions.RequestException as e:
+        print(f"An error occurred: {e}")
+        return "Ollama is not running"
+
+

api/client_test.go

@@ -0,0 +1,43 @@
+package api
+
+import "testing"
+
+func TestClientFromEnvironment(t *testing.T) {
+	type testCase struct {
+		value  string
+		expect string
+		err    error
+	}
+
+	testCases := map[string]*testCase{
+		"empty":                      {value: "", expect: "http://127.0.0.1:11434"},
+		"only address":               {value: "1.2.3.4", expect: "http://1.2.3.4:11434"},
+		"only port":                  {value: ":1234", expect: "http://:1234"},
+		"address and port":           {value: "1.2.3.4:1234", expect: "http://1.2.3.4:1234"},
+		"scheme http and address":    {value: "http://1.2.3.4", expect: "http://1.2.3.4:80"},
+		"scheme https and address":   {value: "https://1.2.3.4", expect: "https://1.2.3.4:443"},
+		"scheme, address, and port":  {value: "https://1.2.3.4:1234", expect: "https://1.2.3.4:1234"},
+		"hostname":                   {value: "example.com", expect: "http://example.com:11434"},
+		"hostname and port":          {value: "example.com:1234", expect: "http://example.com:1234"},
+		"scheme http and hostname":   {value: "http://example.com", expect: "http://example.com:80"},
+		"scheme https and hostname":  {value: "https://example.com", expect: "https://example.com:443"},
+		"scheme, hostname, and port": {value: "https://example.com:1234", expect: "https://example.com:1234"},
+		"trailing slash":             {value: "example.com/", expect: "http://example.com:11434"},
+		"trailing slash port":        {value: "example.com:1234/", expect: "http://example.com:1234"},
+	}
+
+	for k, v := range testCases {
+		t.Run(k, func(t *testing.T) {
+			t.Setenv("OLLAMA_HOST", v.value)
+
+			client, err := ClientFromEnvironment()
+			if err != v.err {
+				t.Fatalf("expected %s, got %s", v.err, err)
+			}
+
+			if client.base.String() != v.expect {
+				t.Fatalf("expected %s, got %s", v.expect, client.base.String())
+			}
+		})
+	}
+}

api/types.go

@@ -3,11 +3,9 @@ package api
 import (
 	"encoding/json"
 	"fmt"
-	"log"
 	"math"
 	"os"
 	"reflect"
-	"runtime"
 	"strings"
 	"time"
 )
@@ -38,10 +36,57 @@ type GenerateRequest struct {
 	System   string `json:"system"`
 	Template string `json:"template"`
 	Context  []int  `json:"context,omitempty"`
+	Stream   *bool  `json:"stream,omitempty"`
+	Raw      bool   `json:"raw,omitempty"`
+	Format   string `json:"format"`
 
 	Options map[string]interface{} `json:"options"`
 }
 
+// Options specfied in GenerateRequest, if you add a new option here add it to the API docs also
+type Options struct {
+	Runner
+
+	// Predict options used at runtime
+	NumKeep          int      `json:"num_keep,omitempty"`
+	Seed             int      `json:"seed,omitempty"`
+	NumPredict       int      `json:"num_predict,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"`
+	RepeatLastN      int      `json:"repeat_last_n,omitempty"`
+	Temperature      float32  `json:"temperature,omitempty"`
+	RepeatPenalty    float32  `json:"repeat_penalty,omitempty"`
+	PresencePenalty  float32  `json:"presence_penalty,omitempty"`
+	FrequencyPenalty float32  `json:"frequency_penalty,omitempty"`
+	Mirostat         int      `json:"mirostat,omitempty"`
+	MirostatTau      float32  `json:"mirostat_tau,omitempty"`
+	MirostatEta      float32  `json:"mirostat_eta,omitempty"`
+	PenalizeNewline  bool     `json:"penalize_newline,omitempty"`
+	Stop             []string `json:"stop,omitempty"`
+}
+
+// Runner options which must be set when the model is loaded into memory
+type Runner struct {
+	UseNUMA            bool    `json:"numa,omitempty"`
+	NumCtx             int     `json:"num_ctx,omitempty"`
+	NumBatch           int     `json:"num_batch,omitempty"`
+	NumGQA             int     `json:"num_gqa,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"`
+	RopeFrequencyBase  float32 `json:"rope_frequency_base,omitempty"`
+	RopeFrequencyScale float32 `json:"rope_frequency_scale,omitempty"`
+	NumThread          int     `json:"num_thread,omitempty"`
+}
+
 type EmbeddingRequest struct {
 	Model  string `json:"model"`
 	Prompt string `json:"prompt"`
@@ -54,14 +99,27 @@ type EmbeddingResponse struct {
 }
 
 type CreateRequest struct {
-	Name string `json:"name"`
-	Path string `json:"path"`
+	Name   string `json:"name"`
+	Path   string `json:"path"`
+	Stream *bool  `json:"stream,omitempty"`
 }
 
 type DeleteRequest struct {
 	Name string `json:"name"`
 }
 
+type ShowRequest struct {
+	Name string `json:"name"`
+}
+
+type ShowResponse struct {
+	License    string `json:"license,omitempty"`
+	Modelfile  string `json:"modelfile,omitempty"`
+	Parameters string `json:"parameters,omitempty"`
+	Template   string `json:"template,omitempty"`
+	System     string `json:"system,omitempty"`
+}
+
 type CopyRequest struct {
 	Source      string `json:"source"`
 	Destination string `json:"destination"`
@@ -72,13 +130,14 @@ type PullRequest struct {
 	Insecure bool   `json:"insecure,omitempty"`
 	Username string `json:"username"`
 	Password string `json:"password"`
+	Stream   *bool  `json:"stream,omitempty"`
 }
 
 type ProgressResponse struct {
 	Status    string `json:"status"`
 	Digest    string `json:"digest,omitempty"`
-	Total     int    `json:"total,omitempty"`
-	Completed int    `json:"completed,omitempty"`
+	Total     int64  `json:"total,omitempty"`
+	Completed int64  `json:"completed,omitempty"`
 }
 
 type PushRequest struct {
@@ -86,16 +145,18 @@ type PushRequest struct {
 	Insecure bool   `json:"insecure,omitempty"`
 	Username string `json:"username"`
 	Password string `json:"password"`
+	Stream   *bool  `json:"stream,omitempty"`
 }
 
 type ListResponse struct {
-	Models []ListResponseModel `json:"models"`
+	Models []ModelResponse `json:"models"`
 }
 
-type ListResponseModel struct {
+type ModelResponse struct {
 	Name       string    `json:"name"`
 	ModifiedAt time.Time `json:"modified_at"`
-	Size       int       `json:"size"`
+	Size       int64     `json:"size"`
+	Digest     string    `json:"digest"`
 }
 
 type TokenResponse struct {
@@ -105,15 +166,13 @@ type TokenResponse struct {
 type GenerateResponse struct {
 	Model     string    `json:"model"`
 	CreatedAt time.Time `json:"created_at"`
-	Response  string    `json:"response,omitempty"`
+	Response  string    `json:"response"`
 
 	Done    bool  `json:"done"`
 	Context []int `json:"context,omitempty"`
 
 	TotalDuration      time.Duration `json:"total_duration,omitempty"`
 	LoadDuration       time.Duration `json:"load_duration,omitempty"`
-	SampleCount        int           `json:"sample_count,omitempty"`
-	SampleDuration     time.Duration `json:"sample_duration,omitempty"`
 	PromptEvalCount    int           `json:"prompt_eval_count,omitempty"`
 	PromptEvalDuration time.Duration `json:"prompt_eval_duration,omitempty"`
 	EvalCount          int           `json:"eval_count,omitempty"`
@@ -129,15 +188,6 @@ func (r *GenerateResponse) Summary() {
 		fmt.Fprintf(os.Stderr, "load duration:        %v\n", r.LoadDuration)
 	}
 
-	if r.SampleCount > 0 {
-		fmt.Fprintf(os.Stderr, "sample count:         %d token(s)\n", r.SampleCount)
-	}
-
-	if r.SampleDuration > 0 {
-		fmt.Fprintf(os.Stderr, "sample duration:      %s\n", r.SampleDuration)
-		fmt.Fprintf(os.Stderr, "sample rate:          %.2f tokens/s\n", float64(r.SampleCount)/r.SampleDuration.Seconds())
-	}
-
 	if r.PromptEvalCount > 0 {
 		fmt.Fprintf(os.Stderr, "prompt eval count:    %d token(s)\n", r.PromptEvalCount)
 	}
@@ -157,47 +207,7 @@ func (r *GenerateResponse) Summary() {
 	}
 }
 
-type Options struct {
-	Seed int `json:"seed,omitempty"`
-
-	// Backend options
-	UseNUMA bool `json:"numa,omitempty"`
-
-	// Model options
-	NumCtx             int     `json:"num_ctx,omitempty"`
-	NumKeep            int     `json:"num_keep,omitempty"`
-	NumBatch           int     `json:"num_batch,omitempty"`
-	NumGQA             int     `json:"num_gqa,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"`
-	RopeFrequencyBase  float32 `json:"rope_frequency_base,omitempty"`
-	RopeFrequencyScale float32 `json:"rope_frequency_scale,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"`
-	PenalizeNewline  bool     `json:"penalize_newline,omitempty"`
-	Stop             []string `json:"stop,omitempty"`
-
-	NumThread int `json:"num_thread,omitempty"`
-}
+var ErrInvalidOpts = fmt.Errorf("invalid options")
 
 func (opts *Options) FromMap(m map[string]interface{}) error {
 	valueOpts := reflect.ValueOf(opts).Elem() // names of the fields in the options struct
@@ -212,6 +222,7 @@ func (opts *Options) FromMap(m map[string]interface{}) error {
 		}
 	}
 
+	invalidOpts := []string{}
 	for key, val := range m {
 		if opt, ok := jsonOpts[key]; ok {
 			field := valueOpts.FieldByName(opt.Name)
@@ -229,44 +240,39 @@ func (opts *Options) FromMap(m map[string]interface{}) error {
 						// when JSON unmarshals numbers, it uses float64, not int
 						field.SetInt(int64(t))
 					default:
-						log.Printf("could not convert model parameter %v to int, skipped", key)
+						return fmt.Errorf("option %q must be of type integer", key)
 					}
 				case reflect.Bool:
 					val, ok := val.(bool)
 					if !ok {
-						log.Printf("could not convert model parameter %v to bool, skipped", key)
-						continue
+						return fmt.Errorf("option %q must be of type boolean", key)
 					}
 					field.SetBool(val)
 				case reflect.Float32:
 					// JSON unmarshals to float64
 					val, ok := val.(float64)
 					if !ok {
-						log.Printf("could not convert model parameter %v to float32, skipped", key)
-						continue
+						return fmt.Errorf("option %q must be of type float32", key)
 					}
 					field.SetFloat(val)
 				case reflect.String:
 					val, ok := val.(string)
 					if !ok {
-						log.Printf("could not convert model parameter %v to string, skipped", key)
-						continue
+						return fmt.Errorf("option %q must be of type string", key)
 					}
 					field.SetString(val)
 				case reflect.Slice:
 					// JSON unmarshals to []interface{}, not []string
 					val, ok := val.([]interface{})
 					if !ok {
-						log.Printf("could not convert model parameter %v to slice, skipped", key)
-						continue
+						return fmt.Errorf("option %q must be of type array", key)
 					}
 					// convert []interface{} to []string
 					slice := make([]string, len(val))
 					for i, item := range val {
 						str, ok := item.(string)
 						if !ok {
-							log.Printf("could not convert model parameter %v to slice of strings, skipped", key)
-							continue
+							return fmt.Errorf("option %q must be of an array of strings", key)
 						}
 						slice[i] = str
 					}
@@ -275,45 +281,53 @@ func (opts *Options) FromMap(m map[string]interface{}) error {
 					return fmt.Errorf("unknown type loading config params: %v", field.Kind())
 				}
 			}
+		} else {
+			invalidOpts = append(invalidOpts, key)
 		}
 	}
+
+	if len(invalidOpts) > 0 {
+		return fmt.Errorf("%w: %v", ErrInvalidOpts, strings.Join(invalidOpts, ", "))
+	}
 	return nil
 }
 
 func DefaultOptions() Options {
 	return Options{
-		Seed: -1,
-
-		UseNUMA: false,
-
-		NumCtx:             2048,
-		NumKeep:            -1,
-		NumBatch:           512,
-		NumGPU:             1,
-		NumGQA:             1,
-		LowVRAM:            false,
-		F16KV:              true,
-		UseMMap:            true,
-		UseMLock:           false,
-		RopeFrequencyBase:  10000.0,
-		RopeFrequencyScale: 1.0,
-		EmbeddingOnly:      true,
-
-		RepeatLastN:      64,
-		RepeatPenalty:    1.1,
-		FrequencyPenalty: 0.0,
-		PresencePenalty:  0.0,
+		// options set on request to runner
+		NumPredict:       -1,
+		NumKeep:          0,
 		Temperature:      0.8,
 		TopK:             40,
 		TopP:             0.9,
 		TFSZ:             1.0,
 		TypicalP:         1.0,
+		RepeatLastN:      64,
+		RepeatPenalty:    1.1,
+		PresencePenalty:  0.0,
+		FrequencyPenalty: 0.0,
 		Mirostat:         0,
 		MirostatTau:      5.0,
 		MirostatEta:      0.1,
 		PenalizeNewline:  true,
+		Seed:             -1,
 
-		NumThread: runtime.NumCPU(),
+		Runner: Runner{
+			// options set when the model is loaded
+			NumCtx:             2048,
+			RopeFrequencyBase:  10000.0,
+			RopeFrequencyScale: 1.0,
+			NumBatch:           512,
+			NumGPU:             -1, // -1 here indicates that NumGPU should be set dynamically
+			NumGQA:             1,
+			NumThread:          0, // let the runtime decide
+			LowVRAM:            false,
+			F16KV:              true,
+			UseMLock:           false,
+			UseMMap:            true,
+			UseNUMA:            false,
+			EmbeddingOnly:      true,
+		},
 	}
 }
 

app/forge.config.ts

@@ -27,7 +27,6 @@ const config: ForgeConfig = {
       path.join(__dirname, './assets/iconDarkTemplate@2x.png'),
       path.join(__dirname, './assets/iconDarkUpdateTemplate.png'),
       path.join(__dirname, './assets/iconDarkUpdateTemplate@2x.png'),
-      ...(process.platform === 'darwin' ? ['../llm/ggml-metal.metal'] : []),
     ],
     ...(process.env.SIGN
       ? {
@@ -48,16 +47,6 @@ const config: ForgeConfig = {
   },
   rebuildConfig: {},
   makers: [new MakerSquirrel({}), new MakerZIP({}, ['darwin'])],
-  publishers: [
-    new PublisherGithub({
-      repository: {
-        name: 'ollama',
-        owner: 'jmorganca',
-      },
-      draft: false,
-      prerelease: true,
-    }),
-  ],
   hooks: {
     readPackageJson: async (_, packageJson) => {
       return { ...packageJson, version: process.env.VERSION || packageJson.version }

app/package.json

@@ -46,7 +46,7 @@
     "chmodr": "^1.2.0",
     "copy-webpack-plugin": "^11.0.0",
     "css-loader": "^6.8.1",
-    "electron": "25.2.0",
+    "electron": "25.9.2",
     "eslint": "^8.43.0",
     "eslint-plugin-import": "^2.27.5",
     "fork-ts-checker-webpack-plugin": "^7.3.0",

app/src/index.ts

@@ -5,7 +5,7 @@ import winston from 'winston'
 import 'winston-daily-rotate-file'
 import * as path from 'path'
 
-import { analytics, id } from './telemetry'
+import { v4 as uuidv4 } from 'uuid'
 import { installed } from './install'
 
 require('@electron/remote/main').initialize()
@@ -158,17 +158,60 @@ function restart() {
 app.on('before-quit', () => {
   if (proc) {
     proc.off('exit', restart)
-    proc.kill()
+    proc.kill('SIGINT') // send SIGINT signal to the server, which also stops any loaded llms
   }
 })
 
+const updateURL = `https://ollama.ai/api/update?os=${process.platform}&arch=${
+  process.arch
+}&version=${app.getVersion()}&id=${id()}`
+
+let latest = ''
+async function isNewReleaseAvailable() {
+  try {
+    const response = await fetch(updateURL)
+
+    if (!response.ok) {
+      return false
+    }
+
+    if (response.status === 204) {
+      return false
+    }
+
+    const data = await response.json()
+
+    const url = data?.url
+    if (!url) {
+      return false
+    }
+
+    if (latest === url) {
+      return false
+    }
+
+    latest = url
+
+    return true
+  } catch (error) {
+    logger.error(`update check failed - ${error}`)
+    return false
+  }
+}
+
+async function checkUpdate() {
+  const available = await isNewReleaseAvailable()
+  if (available) {
+    logger.info('checking for update')
+    autoUpdater.checkForUpdates()
+  }
+}
+
 function init() {
   if (app.isPackaged) {
-    heartbeat()
-    autoUpdater.checkForUpdates()
+    checkUpdate()
     setInterval(() => {
-      heartbeat()
-      autoUpdater.checkForUpdates()
+      checkUpdate()
     }, 60 * 60 * 1000)
   }
 
@@ -234,28 +277,22 @@ app.on('window-all-closed', () => {
   }
 })
 
-// In this file you can include the rest of your app's specific main process
-// code. You can also put them in separate files and import them here.
-let aid = ''
-try {
-  aid = id()
-} catch (e) {}
+function id(): string {
+  const id = store.get('id') as string
 
-autoUpdater.setFeedURL({
-  url: `https://ollama.ai/api/update?os=${process.platform}&arch=${process.arch}&version=${app.getVersion()}&id=${aid}`,
-})
+  if (id) {
+    return id
+  }
 
-async function heartbeat() {
-  analytics.track({
-    anonymousId: aid,
-    event: 'heartbeat',
-    properties: {
-      version: app.getVersion(),
-    },
-  })
+  const uuid = uuidv4()
+  store.set('id', uuid)
+  return uuid
 }
 
+autoUpdater.setFeedURL({ url: updateURL })
+
 autoUpdater.on('error', e => {
+  logger.error(`update check failed - ${e.message}`)
   console.error(`update check failed - ${e.message}`)
 })
 

app/src/telemetry.ts

@@ -1,19 +0,0 @@
-import { Analytics } from '@segment/analytics-node'
-import { v4 as uuidv4 } from 'uuid'
-import Store from 'electron-store'
-
-const store = new Store()
-
-export const analytics = new Analytics({ writeKey: process.env.TELEMETRY_WRITE_KEY || '<empty>' })
-
-export function id(): string {
-  const id = store.get('id') as string
-
-  if (id) {
-    return id
-  }
-
-  const uuid = uuidv4()
-  store.set('id', uuid)
-  return uuid
-}

cmd/cmd.go

@@ -1,7 +1,6 @@
 package cmd
 
 import (
-	"bufio"
 	"context"
 	"crypto/ed25519"
 	"crypto/rand"
@@ -14,21 +13,22 @@ import (
 	"net/http"
 	"os"
 	"os/exec"
-	"path"
+	"os/signal"
 	"path/filepath"
 	"runtime"
 	"strings"
+	"syscall"
 	"time"
 
-	"github.com/chzyer/readline"
-	"github.com/dustin/go-humanize"
 	"github.com/olekukonko/tablewriter"
 	"github.com/spf13/cobra"
 	"golang.org/x/crypto/ssh"
+	"golang.org/x/term"
 
 	"github.com/jmorganca/ollama/api"
 	"github.com/jmorganca/ollama/format"
 	"github.com/jmorganca/ollama/progressbar"
+	"github.com/jmorganca/ollama/readline"
 	"github.com/jmorganca/ollama/server"
 	"github.com/jmorganca/ollama/version"
 )
@@ -40,7 +40,7 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	client, err := api.FromEnv()
+	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
 	}
@@ -57,20 +57,14 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 				spinner.Stop()
 			}
 			currentDigest = resp.Digest
-			switch {
-			case strings.Contains(resp.Status, "embeddings"):
-				bar = progressbar.Default(int64(resp.Total), resp.Status)
-				bar.Set(resp.Completed)
-			default:
-				// pulling
-				bar = progressbar.DefaultBytes(
-					int64(resp.Total),
-					resp.Status,
-				)
-				bar.Set(resp.Completed)
-			}
+			// pulling
+			bar = progressbar.DefaultBytes(
+				resp.Total,
+				resp.Status,
+			)
+			bar.Set64(resp.Completed)
 		} else if resp.Digest == currentDigest && resp.Digest != "" {
-			bar.Set(resp.Completed)
+			bar.Set64(resp.Completed)
 		} else {
 			currentDigest = ""
 			if spinner != nil {
@@ -98,37 +92,19 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 }
 
 func RunHandler(cmd *cobra.Command, args []string) error {
-	insecure, err := cmd.Flags().GetBool("insecure")
+	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
 	}
 
-	mp := server.ParseModelPath(args[0])
-	if err != nil {
-		return err
-	}
-
-	if mp.ProtocolScheme == "http" && !insecure {
-		return fmt.Errorf("insecure protocol http")
-	}
-
-	fp, err := mp.GetManifestPath(false)
-	if err != nil {
-		return err
-	}
-
-	_, err = os.Stat(fp)
+	name := args[0]
+	// check if the model exists on the server
+	_, err = client.Show(context.Background(), &api.ShowRequest{Name: name})
+	var statusError api.StatusError
 	switch {
-	case errors.Is(err, os.ErrNotExist):
-		if err := pull(args[0], insecure); err != nil {
-			var apiStatusError api.StatusError
-			if !errors.As(err, &apiStatusError) {
-				return err
-			}
-
-			if apiStatusError.StatusCode != http.StatusBadGateway {
-				return err
-			}
+	case errors.As(err, &statusError) && statusError.StatusCode == http.StatusNotFound:
+		if err := PullHandler(cmd, args); err != nil {
+			return err
 		}
 	case err != nil:
 		return err
@@ -138,7 +114,7 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 }
 
 func PushHandler(cmd *cobra.Command, args []string) error {
-	client, err := api.FromEnv()
+	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
 	}
@@ -156,13 +132,13 @@ func PushHandler(cmd *cobra.Command, args []string) error {
 		if resp.Digest != currentDigest && resp.Digest != "" {
 			currentDigest = resp.Digest
 			bar = progressbar.DefaultBytes(
-				int64(resp.Total),
+				resp.Total,
 				fmt.Sprintf("pushing %s...", resp.Digest[7:19]),
 			)
 
-			bar.Set(resp.Completed)
+			bar.Set64(resp.Completed)
 		} else if resp.Digest == currentDigest && resp.Digest != "" {
-			bar.Set(resp.Completed)
+			bar.Set64(resp.Completed)
 		} else {
 			currentDigest = ""
 			fmt.Println(resp.Status)
@@ -182,7 +158,7 @@ func PushHandler(cmd *cobra.Command, args []string) error {
 }
 
 func ListHandler(cmd *cobra.Command, args []string) error {
-	client, err := api.FromEnv()
+	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
 	}
@@ -196,12 +172,12 @@ func ListHandler(cmd *cobra.Command, args []string) error {
 
 	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")})
+			data = append(data, []string{m.Name, m.Digest[:12], format.HumanBytes(m.Size), format.HumanTime(m.ModifiedAt, "Never")})
 		}
 	}
 
 	table := tablewriter.NewWriter(os.Stdout)
-	table.SetHeader([]string{"NAME", "SIZE", "MODIFIED"})
+	table.SetHeader([]string{"NAME", "ID", "SIZE", "MODIFIED"})
 	table.SetHeaderAlignment(tablewriter.ALIGN_LEFT)
 	table.SetAlignment(tablewriter.ALIGN_LEFT)
 	table.SetHeaderLine(false)
@@ -215,21 +191,101 @@ func ListHandler(cmd *cobra.Command, args []string) error {
 }
 
 func DeleteHandler(cmd *cobra.Command, args []string) error {
-	client, err := api.FromEnv()
+	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
 	}
 
-	req := api.DeleteRequest{Name: args[0]}
-	if err := client.Delete(context.Background(), &req); err != nil {
+	for _, name := range args {
+		req := api.DeleteRequest{Name: name}
+		if err := client.Delete(context.Background(), &req); err != nil {
+			return err
+		}
+		fmt.Printf("deleted '%s'\n", name)
+	}
+	return nil
+}
+
+func ShowHandler(cmd *cobra.Command, args []string) error {
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
 		return err
 	}
-	fmt.Printf("deleted '%s'\n", args[0])
+
+	if len(args) != 1 {
+		return errors.New("missing model name")
+	}
+
+	license, errLicense := cmd.Flags().GetBool("license")
+	modelfile, errModelfile := cmd.Flags().GetBool("modelfile")
+	parameters, errParams := cmd.Flags().GetBool("parameters")
+	system, errSystem := cmd.Flags().GetBool("system")
+	template, errTemplate := cmd.Flags().GetBool("template")
+
+	for _, boolErr := range []error{errLicense, errModelfile, errParams, errSystem, errTemplate} {
+		if boolErr != nil {
+			return errors.New("error retrieving flags")
+		}
+	}
+
+	flagsSet := 0
+	showType := ""
+
+	if license {
+		flagsSet++
+		showType = "license"
+	}
+
+	if modelfile {
+		flagsSet++
+		showType = "modelfile"
+	}
+
+	if parameters {
+		flagsSet++
+		showType = "parameters"
+	}
+
+	if system {
+		flagsSet++
+		showType = "system"
+	}
+
+	if template {
+		flagsSet++
+		showType = "template"
+	}
+
+	if flagsSet > 1 {
+		return errors.New("only one of '--license', '--modelfile', '--parameters', '--system', or '--template' can be specified")
+	} else if flagsSet == 0 {
+		return errors.New("one of '--license', '--modelfile', '--parameters', '--system', or '--template' must be specified")
+	}
+
+	req := api.ShowRequest{Name: args[0]}
+	resp, err := client.Show(context.Background(), &req)
+	if err != nil {
+		return err
+	}
+
+	switch showType {
+	case "license":
+		fmt.Println(resp.License)
+	case "modelfile":
+		fmt.Println(resp.Modelfile)
+	case "parameters":
+		fmt.Println(resp.Parameters)
+	case "system":
+		fmt.Println(resp.System)
+	case "template":
+		fmt.Println(resp.Template)
+	}
+
 	return nil
 }
 
 func CopyHandler(cmd *cobra.Command, args []string) error {
-	client, err := api.FromEnv()
+	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
 	}
@@ -252,7 +308,7 @@ func PullHandler(cmd *cobra.Command, args []string) error {
 }
 
 func pull(model string, insecure bool) error {
-	client, err := api.FromEnv()
+	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
 	}
@@ -265,13 +321,13 @@ func pull(model string, insecure bool) error {
 		if resp.Digest != currentDigest && resp.Digest != "" {
 			currentDigest = resp.Digest
 			bar = progressbar.DefaultBytes(
-				int64(resp.Total),
+				resp.Total,
 				fmt.Sprintf("pulling %s...", resp.Digest[7:19]),
 			)
 
-			bar.Set(resp.Completed)
+			bar.Set64(resp.Completed)
 		} else if resp.Digest == currentDigest && resp.Digest != "" {
-			bar.Set(resp.Completed)
+			bar.Set64(resp.Completed)
 		} else {
 			currentDigest = ""
 			fmt.Println(resp.Status)
@@ -292,160 +348,222 @@ func pull(model string, insecure bool) error {
 }
 
 func RunGenerate(cmd *cobra.Command, args []string) error {
-	if len(args) > 1 {
-		// join all args into a single prompt
-		return generate(cmd, args[0], strings.Join(args[1:], " "))
+	format, err := cmd.Flags().GetString("format")
+	if err != nil {
+		return err
 	}
 
-	if readline.IsTerminal(int(os.Stdin.Fd())) {
-		return generateInteractive(cmd, args[0])
+	prompts := args[1:]
+
+	// prepend stdin to the prompt if provided
+	if !term.IsTerminal(int(os.Stdin.Fd())) {
+		in, err := io.ReadAll(os.Stdin)
+		if err != nil {
+			return err
+		}
+
+		prompts = append([]string{string(in)}, prompts...)
 	}
 
-	return generateBatch(cmd, args[0])
+	// output is being piped
+	if !term.IsTerminal(int(os.Stdout.Fd())) {
+		return generate(cmd, args[0], strings.Join(prompts, " "), false, format)
+	}
+
+	wordWrap := os.Getenv("TERM") == "xterm-256color"
+
+	nowrap, err := cmd.Flags().GetBool("nowordwrap")
+	if err != nil {
+		return err
+	}
+	if nowrap {
+		wordWrap = false
+	}
+
+	// prompts are provided via stdin or args so don't enter interactive mode
+	if len(prompts) > 0 {
+		return generate(cmd, args[0], strings.Join(prompts, " "), wordWrap, format)
+	}
+
+	return generateInteractive(cmd, args[0], wordWrap, format)
 }
 
 type generateContextKey string
 
-func generate(cmd *cobra.Command, model, prompt string) error {
-	if len(strings.TrimSpace(prompt)) > 0 {
-		client, err := api.FromEnv()
-		if err != nil {
-			return err
+func generate(cmd *cobra.Command, model, prompt string, wordWrap bool, format string) error {
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return err
+	}
+
+	spinner := NewSpinner("")
+	go spinner.Spin(60 * time.Millisecond)
+
+	var latest api.GenerateResponse
+
+	generateContext, ok := cmd.Context().Value(generateContextKey("context")).([]int)
+	if !ok {
+		generateContext = []int{}
+	}
+
+	termWidth, _, err := term.GetSize(int(os.Stdout.Fd()))
+	if err != nil {
+		wordWrap = false
+	}
+
+	cancelCtx, cancel := context.WithCancel(context.Background())
+	defer cancel()
+
+	sigChan := make(chan os.Signal, 1)
+	signal.Notify(sigChan, syscall.SIGINT)
+	var abort bool
+
+	go func() {
+		<-sigChan
+		cancel()
+		abort = true
+	}()
+
+	var currentLineLength int
+	var wordBuffer string
+
+	request := api.GenerateRequest{Model: model, Prompt: prompt, Context: generateContext, Format: format}
+	fn := func(response api.GenerateResponse) error {
+		if !spinner.IsFinished() {
+			spinner.Finish()
 		}
 
-		spinner := NewSpinner("")
-		go spinner.Spin(60 * time.Millisecond)
+		latest = response
 
-		var latest api.GenerateResponse
+		if wordWrap {
+			for _, ch := range response.Response {
+				if currentLineLength+1 > termWidth-5 {
+					// backtrack the length of the last word and clear to the end of the line
+					fmt.Printf("\x1b[%dD\x1b[K\n", len(wordBuffer))
+					fmt.Printf("%s%c", wordBuffer, ch)
+					currentLineLength = len(wordBuffer) + 1
+				} else {
+					fmt.Print(string(ch))
+					currentLineLength += 1
 
-		generateContext, ok := cmd.Context().Value(generateContextKey("context")).([]int)
-		if !ok {
-			generateContext = []int{}
-		}
-
-		request := api.GenerateRequest{Model: model, Prompt: prompt, Context: generateContext}
-		fn := func(response api.GenerateResponse) error {
-			if !spinner.IsFinished() {
-				spinner.Finish()
+					switch ch {
+					case ' ':
+						wordBuffer = ""
+					case '\n':
+						currentLineLength = 0
+					default:
+						wordBuffer += string(ch)
+					}
+				}
 			}
-
-			latest = response
-
+		} else {
 			fmt.Print(response.Response)
+		}
+
+		return nil
+	}
+
+	if err := client.Generate(cancelCtx, &request, fn); err != nil {
+		if strings.Contains(err.Error(), "context canceled") && abort {
+			spinner.Finish()
 			return nil
 		}
-
-		if err := client.Generate(context.Background(), &request, fn); err != nil {
-			if strings.Contains(err.Error(), "failed to load model") {
-				// tell the user to check the server log, if it exists locally
-				home, nestedErr := os.UserHomeDir()
-				if nestedErr != nil {
-					// return the original error
-					return err
-				}
-				logPath := filepath.Join(home, ".ollama", "logs", "server.log")
-				if _, nestedErr := os.Stat(logPath); nestedErr == nil {
-					err = fmt.Errorf("%w\nFor more details, check the error logs at %s", err, logPath)
-				}
-			}
-			return err
-		}
-
-		fmt.Println()
-		fmt.Println()
-
-		if !latest.Done {
-			return errors.New("unexpected end of response")
-		}
-
-		verbose, err := cmd.Flags().GetBool("verbose")
-		if err != nil {
-			return err
-		}
-
-		if verbose {
-			latest.Summary()
-		}
-
-		ctx := cmd.Context()
-		ctx = context.WithValue(ctx, generateContextKey("context"), latest.Context)
-		cmd.SetContext(ctx)
+		return err
 	}
+	if prompt != "" {
+		fmt.Println()
+		fmt.Println()
+	}
+
+	if !latest.Done {
+		if abort {
+			return nil
+		}
+		return errors.New("unexpected end of response")
+	}
+
+	verbose, err := cmd.Flags().GetBool("verbose")
+	if err != nil {
+		return err
+	}
+
+	if verbose {
+		latest.Summary()
+	}
+
+	ctx := cmd.Context()
+	ctx = context.WithValue(ctx, generateContextKey("context"), latest.Context)
+	cmd.SetContext(ctx)
 
 	return nil
 }
 
-func showLayer(l *server.Layer) {
-	filename, err := server.GetBlobsPath(l.Digest)
-	if err != nil {
-		fmt.Println("Couldn't get layer's path")
-		return
-	}
-	bts, err := os.ReadFile(filename)
-	if err != nil {
-		fmt.Println("Couldn't read layer")
-		return
-	}
-	fmt.Println(string(bts))
-}
-
-func generateInteractive(cmd *cobra.Command, model string) error {
-	home, err := os.UserHomeDir()
-	if err != nil {
+func generateInteractive(cmd *cobra.Command, model string, wordWrap bool, format string) error {
+	// load the model
+	if err := generate(cmd, model, "", false, ""); 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("/show",
-			readline.PcItem("license"),
-			readline.PcItem("system"),
-			readline.PcItem("template"),
-		),
-		readline.PcItem("/exit"),
-		readline.PcItem("/bye"),
-	)
-
 	usage := func() {
-		fmt.Fprintln(os.Stderr, "commands:")
-		fmt.Fprintln(os.Stderr, completer.Tree("  "))
+		fmt.Fprintln(os.Stderr, "Available Commands:")
+		fmt.Fprintln(os.Stderr, "  /set         Set session variables")
+		fmt.Fprintln(os.Stderr, "  /show        Show model information")
+		fmt.Fprintln(os.Stderr, "  /bye         Exit")
+		fmt.Fprintln(os.Stderr, "  /?, /help    Help for a command")
+		fmt.Fprintln(os.Stderr, "")
+		fmt.Fprintln(os.Stderr, "Use \"\"\" to begin a multi-line message.")
+		fmt.Fprintln(os.Stderr, "")
 	}
 
-	config := readline.Config{
-		Prompt:       ">>> ",
-		HistoryFile:  filepath.Join(home, ".ollama", "history"),
-		AutoComplete: completer,
+	usageSet := func() {
+		fmt.Fprintln(os.Stderr, "Available Commands:")
+		fmt.Fprintln(os.Stderr, "  /set history      Enable history")
+		fmt.Fprintln(os.Stderr, "  /set nohistory    Disable history")
+		fmt.Fprintln(os.Stderr, "  /set wordwrap     Enable wordwrap")
+		fmt.Fprintln(os.Stderr, "  /set nowordwrap   Disable wordwrap")
+		fmt.Fprintln(os.Stderr, "  /set format json  Enable JSON mode")
+		fmt.Fprintln(os.Stderr, "  /set noformat     Disable formatting")
+		fmt.Fprintln(os.Stderr, "  /set verbose      Show LLM stats")
+		fmt.Fprintln(os.Stderr, "  /set quiet        Disable LLM stats")
+		fmt.Fprintln(os.Stderr, "")
 	}
 
-	scanner, err := readline.NewEx(&config)
+	usageShow := func() {
+		fmt.Fprintln(os.Stderr, "Available Commands:")
+		fmt.Fprintln(os.Stderr, "  /show license      Show model license")
+		fmt.Fprintln(os.Stderr, "  /show modelfile    Show Modelfile for this model")
+		fmt.Fprintln(os.Stderr, "  /show parameters   Show parameters for this model")
+		fmt.Fprintln(os.Stderr, "  /show system       Show system prompt")
+		fmt.Fprintln(os.Stderr, "  /show template     Show prompt template")
+		fmt.Fprintln(os.Stderr, "")
+	}
+
+	prompt := readline.Prompt{
+		Prompt:         ">>> ",
+		AltPrompt:      "... ",
+		Placeholder:    "Send a message (/? for help)",
+		AltPlaceholder: `Use """ to end multi-line input`,
+	}
+
+	scanner, err := readline.New(prompt)
 	if err != nil {
 		return err
 	}
-	defer scanner.Close()
+
+	fmt.Print(readline.StartBracketedPaste)
+	defer fmt.Printf(readline.EndBracketedPaste)
 
 	var multiLineBuffer string
-	var isMultiLine bool
 
 	for {
 		line, err := scanner.Readline()
 		switch {
 		case errors.Is(err, io.EOF):
+			fmt.Println()
 			return nil
 		case errors.Is(err, readline.ErrInterrupt):
 			if line == "" {
-				return nil
+				fmt.Println("\nUse Ctrl-D or /bye to exit.")
 			}
 
 			continue
@@ -456,159 +574,149 @@ func generateInteractive(cmd *cobra.Command, model string) error {
 		line = strings.TrimSpace(line)
 
 		switch {
-		case isMultiLine:
+		case scanner.Prompt.UseAlt:
 			if strings.HasSuffix(line, `"""`) {
-				isMultiLine = false
+				scanner.Prompt.UseAlt = false
 				multiLineBuffer += strings.TrimSuffix(line, `"""`)
 				line = multiLineBuffer
 				multiLineBuffer = ""
-				scanner.SetPrompt(">>> ")
 			} else {
 				multiLineBuffer += line + " "
 				continue
 			}
 		case strings.HasPrefix(line, `"""`):
-			isMultiLine = true
+			scanner.Prompt.UseAlt = true
 			multiLineBuffer = strings.TrimPrefix(line, `"""`) + " "
-			scanner.SetPrompt("... ")
 			continue
 		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 "wordwrap":
+					wordWrap = true
+					fmt.Println("Set 'wordwrap' mode.")
+				case "nowordwrap":
+					wordWrap = false
+					fmt.Println("Set 'nowordwrap' mode.")
 				case "verbose":
 					cmd.Flags().Set("verbose", "true")
-					continue
+					fmt.Println("Set 'verbose' mode.")
 				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
-						default:
-							usage()
-							continue
-						}
+					fmt.Println("Set 'quiet' mode.")
+				case "format":
+					if len(args) < 3 || args[2] != "json" {
+						fmt.Println("Invalid or missing format. For 'json' mode use '/set format json'")
 					} else {
-						usage()
-						continue
+						format = args[2]
+						fmt.Printf("Set format to '%s' mode.\n", args[2])
 					}
+				case "noformat":
+					format = ""
+					fmt.Println("Disabled format.")
+				default:
+					fmt.Printf("Unknown command '/set %s'. Type /? for help\n", args[1])
 				}
 			} else {
-				usage()
-				continue
+				usageSet()
 			}
 		case strings.HasPrefix(line, "/show"):
 			args := strings.Fields(line)
 			if len(args) > 1 {
-				mp := server.ParseModelPath(model)
+				client, err := api.ClientFromEnvironment()
 				if err != nil {
+					fmt.Println("error: couldn't connect to ollama server")
+					return err
+				}
+				resp, err := client.Show(cmd.Context(), &api.ShowRequest{Name: model})
+				if err != nil {
+					fmt.Println("error: couldn't get model")
 					return err
 				}
 
-				manifest, err := server.GetManifest(mp)
-				if err != nil {
-					fmt.Println("error: couldn't get a manifest for this model")
-					continue
-				}
 				switch args[1] {
 				case "license":
-					for _, l := range manifest.Layers {
-						if l.MediaType == "application/vnd.ollama.image.license" {
-							showLayer(l)
-						}
+					if resp.License == "" {
+						fmt.Print("No license was specified for this model.\n\n")
+					} else {
+						fmt.Println(resp.License)
+					}
+				case "modelfile":
+					fmt.Println(resp.Modelfile)
+				case "parameters":
+					if resp.Parameters == "" {
+						fmt.Print("No parameters were specified for this model.\n\n")
+					} else {
+						fmt.Println(resp.Parameters)
 					}
-					continue
 				case "system":
-					for _, l := range manifest.Layers {
-						if l.MediaType == "application/vnd.ollama.image.system" {
-							showLayer(l)
-						}
+					if resp.System == "" {
+						fmt.Print("No system prompt was specified for this model.\n\n")
+					} else {
+						fmt.Println(resp.System)
 					}
-					continue
 				case "template":
-					for _, l := range manifest.Layers {
-						if l.MediaType == "application/vnd.ollama.image.template" {
-							showLayer(l)
-						}
+					if resp.Template == "" {
+						fmt.Print("No prompt template was specified for this model.\n\n")
+					} else {
+						fmt.Println(resp.Template)
 					}
-					continue
 				default:
-					usage()
-					continue
+					fmt.Printf("Unknown command '/show %s'. Type /? for help\n", args[1])
+				}
+			} else {
+				usageShow()
+			}
+		case strings.HasPrefix(line, "/help"), strings.HasPrefix(line, "/?"):
+			args := strings.Fields(line)
+			if len(args) > 1 {
+				switch args[1] {
+				case "set", "/set":
+					usageSet()
+				case "show", "/show":
+					usageShow()
 				}
 			} else {
 				usage()
-				continue
 			}
-		case line == "/help", line == "/?":
-			usage()
-			continue
 		case line == "/exit", line == "/bye":
 			return nil
+		case strings.HasPrefix(line, "/"):
+			args := strings.Fields(line)
+			fmt.Printf("Unknown command '%s'. Type /? for help\n", args[0])
 		}
 
-		if err := generate(cmd, model, line); err != nil {
-			return err
+		if len(line) > 0 && line[0] != '/' {
+			if err := generate(cmd, model, line, wordWrap, format); err != nil {
+				return err
+			}
 		}
 	}
 }
 
-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(cmd, model, prompt); err != nil {
-			return err
-		}
-	}
-
-	return nil
-}
-
 func RunServer(cmd *cobra.Command, _ []string) error {
-	host, port := "127.0.0.1", "11434"
-
-	parts := strings.Split(os.Getenv("OLLAMA_HOST"), ":")
-	if ip := net.ParseIP(parts[0]); ip != nil {
-		host = ip.String()
-	}
-
-	if len(parts) > 1 {
-		port = parts[1]
-	}
-
-	// deprecated: include port in OLLAMA_HOST
-	if p := os.Getenv("OLLAMA_PORT"); p != "" {
-		port = p
-	}
-
-	err := initializeKeypair()
+	host, port, err := net.SplitHostPort(os.Getenv("OLLAMA_HOST"))
 	if err != nil {
+		host, port = "127.0.0.1", "11434"
+		if ip := net.ParseIP(strings.Trim(os.Getenv("OLLAMA_HOST"), "[]")); ip != nil {
+			host = ip.String()
+		}
+	}
+
+	if err := initializeKeypair(); err != nil {
 		return err
 	}
 
-	ln, err := net.Listen("tcp", fmt.Sprintf("%s:%s", host, port))
+	ln, err := net.Listen("tcp", net.JoinHostPort(host, port))
 	if err != nil {
 		return err
 	}
@@ -643,7 +751,7 @@ func initializeKeypair() error {
 			return err
 		}
 
-		err = os.MkdirAll(path.Dir(privKeyPath), 0o700)
+		err = os.MkdirAll(filepath.Dir(privKeyPath), 0o755)
 		if err != nil {
 			return fmt.Errorf("could not create directory %w", err)
 		}
@@ -702,7 +810,7 @@ func startMacApp(client *api.Client) error {
 }
 
 func checkServerHeartbeat(_ *cobra.Command, _ []string) error {
-	client, err := api.FromEnv()
+	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
 	}
@@ -740,13 +848,27 @@ func NewCLI() *cobra.Command {
 	createCmd := &cobra.Command{
 		Use:     "create MODEL",
 		Short:   "Create a model from a Modelfile",
-		Args:    cobra.MinimumNArgs(1),
+		Args:    cobra.ExactArgs(1),
 		PreRunE: checkServerHeartbeat,
 		RunE:    CreateHandler,
 	}
 
 	createCmd.Flags().StringP("file", "f", "Modelfile", "Name of the Modelfile (default \"Modelfile\")")
 
+	showCmd := &cobra.Command{
+		Use:     "show MODEL",
+		Short:   "Show information for a model",
+		Args:    cobra.ExactArgs(1),
+		PreRunE: checkServerHeartbeat,
+		RunE:    ShowHandler,
+	}
+
+	showCmd.Flags().Bool("license", false, "Show license of a model")
+	showCmd.Flags().Bool("modelfile", false, "Show Modelfile of a model")
+	showCmd.Flags().Bool("parameters", false, "Show parameters of a model")
+	showCmd.Flags().Bool("template", false, "Show template of a model")
+	showCmd.Flags().Bool("system", false, "Show system prompt of a model")
+
 	runCmd := &cobra.Command{
 		Use:     "run MODEL [PROMPT]",
 		Short:   "Run a model",
@@ -757,18 +879,21 @@ func NewCLI() *cobra.Command {
 
 	runCmd.Flags().Bool("verbose", false, "Show timings for response")
 	runCmd.Flags().Bool("insecure", false, "Use an insecure registry")
+	runCmd.Flags().Bool("nowordwrap", false, "Don't wrap words to the next line automatically")
+	runCmd.Flags().String("format", "", "Response format (e.g. json)")
 
 	serveCmd := &cobra.Command{
 		Use:     "serve",
 		Aliases: []string{"start"},
 		Short:   "Start ollama",
+		Args:    cobra.ExactArgs(0),
 		RunE:    RunServer,
 	}
 
 	pullCmd := &cobra.Command{
 		Use:     "pull MODEL",
 		Short:   "Pull a model from a registry",
-		Args:    cobra.MinimumNArgs(1),
+		Args:    cobra.ExactArgs(1),
 		PreRunE: checkServerHeartbeat,
 		RunE:    PullHandler,
 	}
@@ -778,7 +903,7 @@ func NewCLI() *cobra.Command {
 	pushCmd := &cobra.Command{
 		Use:     "push MODEL",
 		Short:   "Push a model to a registry",
-		Args:    cobra.MinimumNArgs(1),
+		Args:    cobra.ExactArgs(1),
 		PreRunE: checkServerHeartbeat,
 		RunE:    PushHandler,
 	}
@@ -794,15 +919,15 @@ func NewCLI() *cobra.Command {
 	}
 
 	copyCmd := &cobra.Command{
-		Use:     "cp",
+		Use:     "cp SOURCE TARGET",
 		Short:   "Copy a model",
-		Args:    cobra.MinimumNArgs(2),
+		Args:    cobra.ExactArgs(2),
 		PreRunE: checkServerHeartbeat,
 		RunE:    CopyHandler,
 	}
 
 	deleteCmd := &cobra.Command{
-		Use:     "rm",
+		Use:     "rm MODEL [MODEL...]",
 		Short:   "Remove a model",
 		Args:    cobra.MinimumNArgs(1),
 		PreRunE: checkServerHeartbeat,
@@ -812,6 +937,7 @@ func NewCLI() *cobra.Command {
 	rootCmd.AddCommand(
 		serveCmd,
 		createCmd,
+		showCmd,
 		runCmd,
 		pullCmd,
 		pushCmd,

docs/api.md

@@ -3,26 +3,32 @@
 ## Endpoints
 
 - [Generate a completion](#generate-a-completion)
-- [Create a model](#create-a-model)
-- [List local models](#list-local-models)
-- [Copy a model](#copy-a-model)
-- [Delete a model](#delete-a-model)
-- [Pull a model](#pull-a-model)
-- [Generate embeddings](#generate-embeddings)
+- [Create a Model](#create-a-model)
+- [List Local Models](#list-local-models)
+- [Show Model Information](#show-model-information)
+- [Copy a Model](#copy-a-model)
+- [Delete a Model](#delete-a-model)
+- [Pull a Model](#pull-a-model)
+- [Push a Model](#push-a-model)
+- [Generate Embeddings](#generate-embeddings)
 
 ## Conventions
 
 ### Model names
 
-Model names follow a `model:tag` format. Some examples are `orca:3b-q4_1` and `llama2:70b`. The tag is optional and if not provided will default to `latest`. The tag is used to identify a specific version.
+Model names follow a `model:tag` format. Some examples are `orca-mini:3b-q4_1` and `llama2:70b`. The tag is optional and, if not provided, will default to `latest`. The tag is used to identify a specific version.
 
 ### Durations
 
 All durations are returned in nanoseconds.
 
+### Streaming responses
+
+Certain endpoints stream responses as JSON objects delineated with the newline (`\n`) character.
+
 ## Generate a completion
 
-```
+```shell
 POST /api/generate
 ```
 
@@ -33,29 +39,38 @@ Generate a response for a given prompt with a provided model. This is a streamin
 - `model`: (required) the [model name](#model-names)
 - `prompt`: the prompt to generate a response for
 
-Advanced parameters:
+Advanced parameters (optional):
 
+- `format`: the format to return a response in. Currently the only accepted value is `json`
 - `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
 - `system`: system prompt to (overrides what is defined in the `Modelfile`)
 - `template`: the full prompt or prompt template (overrides what is defined in the `Modelfile`)
 - `context`: the context parameter returned from a previous request to `/generate`, this can be used to keep a short conversational memory
+- `stream`: if `false` the response will be returned as a single response object, rather than a stream of objects
+- `raw`: if `true` no formatting will be applied to the prompt and no context will be returned. You may choose to use the `raw` parameter if you are specifying a full templated prompt in your request to the API, and are managing history yourself.
 
-### Request
+### JSON mode
 
-```
+Enable JSON mode by setting the `format` parameter to `json` and specifying the model should use JSON in the `prompt`. This will structure the response as valid JSON. See the JSON mode [example](#request-json-mode) below.
+
+### Examples
+
+#### Request
+
+```shell
 curl -X POST http://localhost:11434/api/generate -d '{
-  "model": "llama2:7b",
+  "model": "llama2",
   "prompt": "Why is the sky blue?"
 }'
 ```
 
-### Response
+#### Response
 
-A stream of JSON objects:
+A stream of JSON objects is returned:
 
 ```json
 {
-  "model": "llama2:7b",
+  "model": "llama2",
   "created_at": "2023-08-04T08:52:19.385406455-07:00",
   "response": "The",
   "done": false
@@ -73,13 +88,15 @@ The final response in the stream also includes additional data about the generat
 - `eval_count`: number of tokens the response
 - `eval_duration`: time in nanoseconds spent generating the response
 - `context`: an encoding of the conversation used in this response, this can be sent in the next request to keep a conversational memory
+- `response`: empty if the response was streamed, if not streamed, this will contain the full response
 
 To calculate how fast the response is generated in tokens per second (token/s), divide `eval_count` / `eval_duration`.
 
 ```json
 {
-  "model": "llama2:7b",
+  "model": "llama2",
   "created_at": "2023-08-04T19:22:45.499127Z",
+  "response": "",
   "context": [1, 2, 3],
   "done": true,
   "total_duration": 5589157167,
@@ -93,9 +110,185 @@ To calculate how fast the response is generated in tokens per second (token/s),
 }
 ```
 
+#### Request (No streaming)
+
+```shell
+curl -X POST http://localhost:11434/api/generate -d '{
+  "model": "llama2:7b",
+  "prompt": "Why is the sky blue?",
+  "stream": false
+}'
+```
+
+#### Response
+
+If `stream` is set to `false`, the response will be a single JSON object:
+
+```json
+{
+  "model": "llama2:7b",
+  "created_at": "2023-08-04T19:22:45.499127Z",
+  "response": "The sky is blue because it is the color of the sky.",
+  "context": [1, 2, 3],
+  "done": true,
+  "total_duration": 5589157167,
+  "load_duration": 3013701500,
+  "sample_count": 114,
+  "sample_duration": 81442000,
+  "prompt_eval_count": 46,
+  "prompt_eval_duration": 1160282000,
+  "eval_count": 13,
+  "eval_duration": 1325948000
+}
+```
+
+#### Request (Raw mode)
+
+In some cases you may wish to bypass the templating system and provide a full prompt. In this case, you can use the `raw` parameter to disable formatting and context.
+
+```shell
+curl -X POST http://localhost:11434/api/generate -d '{
+  "model": "mistral",
+  "prompt": "[INST] why is the sky blue? [/INST]",
+  "raw": true,
+  "stream": false
+}'
+```
+
+#### Response
+
+```json
+{
+  "model": "mistral",
+  "created_at": "2023-11-03T15:36:02.583064Z",
+  "response": " The sky appears blue because of a phenomenon called Rayleigh scattering.",
+  "done": true,
+  "total_duration": 14648695333,
+  "load_duration": 3302671417,
+  "prompt_eval_count": 14,
+  "prompt_eval_duration": 286243000,
+  "eval_count": 129,
+  "eval_duration": 10931424000
+}
+```
+
+#### Request (JSON mode)
+
+```shell
+curl -X POST http://localhost:11434/api/generate -d '{
+  "model": "llama2",
+  "prompt": "What color is the sky at different times of the day? Respond using JSON",
+  "format": "json",
+  "stream": false
+}'
+```
+
+#### Response
+
+```json
+{
+  "model": "llama2",
+  "created_at": "2023-11-09T21:07:55.186497Z",
+  "response": "{\n\"morning\": {\n\"color\": \"blue\"\n},\n\"noon\": {\n\"color\": \"blue-gray\"\n},\n\"afternoon\": {\n\"color\": \"warm gray\"\n},\n\"evening\": {\n\"color\": \"orange\"\n}\n}\n",
+  "done": true,
+  "total_duration": 4661289125,
+  "load_duration": 1714434500,
+  "prompt_eval_count": 36,
+  "prompt_eval_duration": 264132000,
+  "eval_count": 75,
+  "eval_duration": 2112149000
+}
+```
+
+The value of `response` will be a string containing JSON similar to:
+
+```json
+{
+  "morning": {
+    "color": "blue"
+  },
+  "noon": {
+    "color": "blue-gray"
+  },
+  "afternoon": {
+    "color": "warm gray"
+  },
+  "evening": {
+    "color": "orange"
+  }
+}
+```
+
+#### Request (With options)
+
+If you want to set custom options for the model at runtime rather than in the Modelfile, you can do so with the `options` parameter. This example sets every available option, but you can set any of them individually and omit the ones you do not want to override.
+
+```shell
+curl -X POST http://localhost:11434/api/generate -d '{
+  "model": "llama2:7b",
+  "prompt": "Why is the sky blue?",
+  "stream": false,
+  "options": {
+    "num_keep": 5,
+    "seed": 42,
+    "num_predict": 100,
+    "top_k": 20,
+    "top_p": 0.9,
+    "tfs_z": 0.5,
+    "typical_p": 0.7,
+    "repeat_last_n": 33,
+    "temperature": 0.8,
+    "repeat_penalty": 1.2,
+    "presence_penalty": 1.5,
+    "frequency_penalty": 1.0,
+    "mirostat": 1,
+    "mirostat_tau": 0.8,
+    "mirostat_eta": 0.6,
+    "penalize_newline": true,
+    "stop": ["\n", "user:"],
+    "numa": false,
+    "num_ctx": 4,
+    "num_batch": 2,
+    "num_gqa": 1,
+    "num_gpu": 1,
+    "main_gpu": 0,
+    "low_vram": false,
+    "f16_kv": true,
+    "logits_all": false,
+    "vocab_only": false,
+    "use_mmap": true,
+    "use_mlock": false,
+    "embedding_only": false,
+    "rope_frequency_base": 1.1,
+    "rope_frequency_scale": 0.8,
+    "num_thread": 8
+    }
+}'
+```
+
+#### Response
+
+```json
+{
+  "model": "llama2:7b",
+  "created_at": "2023-08-04T19:22:45.499127Z",
+  "response": "The sky is blue because it is the color of the sky.",
+  "context": [1, 2, 3],
+  "done": true,
+  "total_duration": 5589157167,
+  "load_duration": 3013701500,
+  "sample_count": 114,
+  "sample_duration": 81442000,
+  "prompt_eval_count": 46,
+  "prompt_eval_duration": 1160282000,
+  "eval_count": 13,
+  "eval_duration": 1325948000
+}
+```
+
 ## Create a Model
 
-```
+```shell
 POST /api/create
 ```
 
@@ -105,19 +298,22 @@ Create a model from a [`Modelfile`](./modelfile.md)
 
 - `name`: name of the model to create
 - `path`: path to the Modelfile
+- `stream`: (optional) if `false` the response will be returned as a single response object, rather than a stream of objects
 
-### Request
+### Examples
 
-```
+#### Request
+
+```shell
 curl -X POST http://localhost:11434/api/create -d '{
   "name": "mario",
   "path": "~/Modelfile"
 }'
 ```
 
-### Response
+#### Response
 
-A stream of JSON objects. When finished, `status` is `success`
+A stream of JSON objects. When finished, `status` is `success`.
 
 ```json
 {
@@ -127,19 +323,23 @@ A stream of JSON objects. When finished, `status` is `success`
 
 ## List Local Models
 
-```
+```shell
 GET /api/tags
 ```
 
 List models that are available locally.
 
-### Request
+### Examples
 
-```
+#### Request
+
+```shell
 curl http://localhost:11434/api/tags
 ```
 
-### Response
+#### Response
+
+A single JSON object will be returned.
 
 ```json
 {
@@ -158,26 +358,65 @@ curl http://localhost:11434/api/tags
 }
 ```
 
+## Show Model Information
+
+```shell
+POST /api/show
+```
+
+Show details about a model including modelfile, template, parameters, license, and system prompt.
+
+### Parameters
+
+- `name`: name of the model to show
+
+### Examples
+
+#### Request
+
+```shell
+curl http://localhost:11434/api/show -d '{
+  "name": "llama2:7b"
+}'
+```
+
+#### Response
+
+```json
+{
+  "license": "<contents of license block>",
+  "modelfile": "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this one, replace the FROM line with:\n# FROM llama2:latest\n\nFROM /Users/username/.ollama/models/blobs/sha256:8daa9615cce30c259a9555b1cc250d461d1bc69980a274b44d7eda0be78076d8\nTEMPLATE \"\"\"[INST] <<SYS>>{{ .System }}<</SYS>>\n\n{{ .Prompt }} [/INST] \"\"\"\nSYSTEM \"\"\"\"\"\"\nPARAMETER stop [INST]\nPARAMETER stop [/INST]\nPARAMETER stop <<SYS>>\nPARAMETER stop <</SYS>>\n",
+  "parameters": "stop                           [INST]\nstop                           [/INST]\nstop                           <<SYS>>\nstop                           <</SYS>>",
+  "template": "[INST] <<SYS>>{{ .System }}<</SYS>>\n\n{{ .Prompt }} [/INST] "
+}
+```
+
 ## Copy a Model
 
-```
+```shell
 POST /api/copy
 ```
 
 Copy a model. Creates a model with another name from an existing model.
 
-### Request
+### Examples
 
-```
+#### Request
+
+```shell
 curl http://localhost:11434/api/copy -d '{
   "source": "llama2:7b",
   "destination": "llama2-backup"
 }'
 ```
 
+#### Response
+
+The only response is a 200 OK if successful.
+
 ## Delete a Model
 
-```
+```shell
 DELETE /api/delete
 ```
 
@@ -185,49 +424,162 @@ Delete a model and its data.
 
 ### Parameters
 
-- `model`: model name to delete
+- `name`: model name to delete
 
-### Request
+### Examples
 
-```
+#### Request
+
+```shell
 curl -X DELETE http://localhost:11434/api/delete -d '{
   "name": "llama2:13b"
 }'
 ```
 
+#### Response
+
+If successful, the only response is a 200 OK.
+
 ## Pull a Model
 
-```
+```shell
 POST /api/pull
 ```
 
-Download a model from a the model registry. Cancelled pulls are resumed from where they left off, and multiple calls to will share the same download progress.
+Download a model from the ollama library. Cancelled pulls are resumed from where they left off, and multiple calls will share the same download progress.
 
 ### Parameters
 
 - `name`: name of the model to pull
+- `insecure`: (optional) allow insecure connections to the library. Only use this if you are pulling from your own library during development.
+- `stream`: (optional) if `false` the response will be returned as a single response object, rather than a stream of objects
 
-### Request
+### Examples
 
-```
+#### Request
+
+```shell
 curl -X POST http://localhost:11434/api/pull -d '{
   "name": "llama2:7b"
 }'
 ```
 
-### Response
+#### Response
+
+If `stream` is not specified, or set to `true`, a stream of JSON objects is returned:
+
+The first object is the manifest:
+
+```json
+{
+  "status": "pulling manifest"
+}
+```
+
+Then there is a series of downloading responses. Until any of the download is completed, the `completed` key may not be included. The number of files to be downloaded depends on the number of layers specified in the manifest.
 
 ```json
 {
   "status": "downloading digestname",
   "digest": "digestname",
-  "total": 2142590208
+  "total": 2142590208,
+  "completed": 241970
 }
 ```
 
+After all the files are downloaded, the final responses are:
+
+```json
+{
+    "status": "verifying sha256 digest"
+}
+{
+    "status": "writing manifest"
+}
+{
+    "status": "removing any unused layers"
+}
+{
+    "status": "success"
+}
+```
+
+if `stream` is set to false, then the response is a single JSON object:
+
+```json
+{
+  "status": "success"
+}
+```
+
+## Push a Model
+
+```shell
+POST /api/push
+```
+
+Upload a model to a model library. Requires registering for ollama.ai and adding a public key first.
+
+### Parameters
+
+- `name`: name of the model to push in the form of `<namespace>/<model>:<tag>`
+- `insecure`: (optional) allow insecure connections to the library. Only use this if you are pushing to your library during development.
+- `stream`: (optional) if `false` the response will be returned as a single response object, rather than a stream of objects
+
+### Examples
+
+#### Request
+
+```shell
+curl -X POST http://localhost:11434/api/push -d '{
+  "name": "mattw/pygmalion:latest"
+}'
+```
+
+#### Response
+
+If `stream` is not specified, or set to `true`, a stream of JSON objects is returned:
+
+```json
+{ "status": "retrieving manifest" }
+```
+
+and then:
+
+```json
+{
+  "status": "starting upload",
+  "digest": "sha256:bc07c81de745696fdf5afca05e065818a8149fb0c77266fb584d9b2cba3711ab",
+  "total": 1928429856
+}
+```
+
+Then there is a series of uploading responses:
+
+```json
+{
+  "status": "starting upload",
+  "digest": "sha256:bc07c81de745696fdf5afca05e065818a8149fb0c77266fb584d9b2cba3711ab",
+  "total": 1928429856
+}
+```
+
+Finally, when the upload is complete:
+
+```json
+{"status":"pushing manifest"}
+{"status":"success"}
+```
+
+If `stream` is set to `false`, then the response is a single JSON object:
+
+```json
+{ "status": "success" }
+```
+
 ## Generate Embeddings
 
-```
+```shell
 POST /api/embeddings
 ```
 
@@ -238,20 +590,26 @@ Generate embeddings from a model
 - `model`: name of model to generate embeddings from
 - `prompt`: text to generate embeddings for
 
-### Request
+Advanced parameters:
 
-```
+- `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
+
+### Examples
+
+#### Request
+
+```shell
 curl -X POST http://localhost:11434/api/embeddings -d '{
   "model": "llama2:7b",
   "prompt": "Here is an article about llamas..."
 }'
 ```
 
-### Response
+#### Response
 
 ```json
 {
-  "embeddings": [
+  "embedding": [
     0.5670403838157654, 0.009260174818336964, 0.23178744316101074, -0.2916173040866852, -0.8924556970596313,
     0.8785552978515625, -0.34576427936553955, 0.5742510557174683, -0.04222835972905159, -0.137906014919281
   ]

docs/development.md

@@ -1,44 +1,39 @@
 # Development
 
+- Install cmake or (optionally, required tools for GPUs)
+- run `go generate ./...`
+- run `go build .`
+
 Install required tools:
 
-```
-brew install go
+- cmake version 3.24 or higher
+- go version 1.20 or higher
+- gcc version 11.4.0 or higher
+
+```bash
+brew install go cmake gcc
 ```
 
-Enable CGO:
+Get the required libraries:
 
+```bash
+go generate ./...
 ```
-export CGO_ENABLED=1
-```
-
-You will also need a C/C++ compiler such as GCC for MacOS and Linux or Mingw-w64 GCC for Windows.
 
 Then build ollama:
 
-```
+```bash
 go build .
 ```
 
 Now you can run `ollama`:
 
-```
+```bash
 ./ollama
 ```
 
-## Releasing
+## Building on Linux with GPU support
 
-To release a new version of Ollama you'll need to set some environment variables:
-
-- `GITHUB_TOKEN`: your GitHub token
-- `APPLE_IDENTITY`: the Apple signing identity (macOS only)
-- `APPLE_ID`: your Apple ID
-- `APPLE_PASSWORD`: your Apple ID app-specific password
-- `APPLE_TEAM_ID`: the Apple team ID for the signing identity
-- `TELEMETRY_WRITE_KEY`: segment write key for telemetry
-
-Then run the publish script with the target version:
-
-```
-VERSION=0.0.2 ./scripts/publish.sh
-```
+- Install cmake and nvidia-cuda-toolkit
+- run `go generate ./...`
+- run `go build .`

docs/faq.md

@@ -1,17 +1,98 @@
 # FAQ
 
-## How can I expose the Ollama server?
+## How can I view the logs?
+
+On macOS:
 
 ```
+cat ~/.ollama/logs/server.log
+```
+
+On Linux:
+
+```
+journalctl -u ollama
+```
+
+If you're running `ollama serve` directly, the logs will be printed to the console.
+
+## How can I expose Ollama on my network?
+
+Ollama binds to 127.0.0.1 port 11434 by default. Change the bind address with the `OLLAMA_HOST` environment variable.
+
+On macOS:
+
+```bash
 OLLAMA_HOST=0.0.0.0:11435 ollama serve
 ```
 
-By default, Ollama allows cross origin requests from `127.0.0.1` and `0.0.0.0`. To support more origins, you can use the `OLLAMA_ORIGINS` environment variable:
+On Linux:
 
+Create a `systemd` drop-in directory and set `Environment=OLLAMA_HOST`
+
+```bash
+mkdir -p /etc/systemd/system/ollama.service.d
+echo "[Service]" >>/etc/systemd/system/ollama.service.d/environment.conf
 ```
+
+```bash
+echo "Environment=OLLAMA_HOST=0.0.0.0:11434" >>/etc/systemd/system/ollama.service.d/environment.conf
+```
+
+Reload `systemd` and restart Ollama:
+
+```bash
+systemctl daemon-reload
+systemctl restart ollama
+```
+
+## How can I allow additional web origins to access Ollama?
+
+Ollama allows cross origin requests from `127.0.0.1` and `0.0.0.0` by default. Add additional origins with the `OLLAMA_ORIGINS` environment variable:
+
+On macOS:
+
+```bash
 OLLAMA_ORIGINS=http://192.168.1.1:*,https://example.com ollama serve
 ```
 
+On Linux:
+
+```bash
+echo "Environment=OLLAMA_ORIGINS=http://129.168.1.1:*,https://example.com" >>/etc/systemd/system/ollama.service.d/environment.conf
+```
+
+Reload `systemd` and restart Ollama:
+
+```bash
+systemctl daemon-reload
+systemctl restart ollama
+```
+
 ## Where are models stored?
 
-Raw model data is stored under `~/.ollama/models`.
+- macOS: Raw model data is stored under `~/.ollama/models`.
+- Linux: Raw model data is stored under `/usr/share/ollama/.ollama/models`
+
+
+
+Below the models directory you will find a structure similar to the following:
+
+```shell
+.
+├── blobs
+└── manifests
+   └── registry.ollama.ai
+      ├── f0rodo
+      ├── library
+      ├── mattw
+      └── saikatkumardey
+```
+
+There is a `manifests/registry.ollama.ai/namespace` path. In example above, the user has downloaded models from the official `library`, `f0rodo`, `mattw`, and `saikatkumardey` namespaces. Within each of those directories, you will find directories for each of the models downloaded. And in there you will find a file name representing each tag. Each tag file is the manifest for the model.  
+
+The manifest lists all the layers used in this model. You will see a `media type` for each layer, along with a digest. That digest corresponds with a file in the `models/blobs directory`.
+
+### How can I change where Ollama stores models?
+
+To modify where models are stored, you can use the `OLLAMA_MODELS` environment variable. Note that on Linux this means defining `OLLAMA_MODELS` in a drop-in `/etc/systemd/system/ollama.service.d` service file, reloading systemd, and restarting the ollama service.

docs/import.md

@@ -0,0 +1,198 @@
+# Import a model
+
+This guide walks through importing a GGUF, PyTorch or Safetensors model.
+
+## Importing (GGUF)
+
+### Step 1: Write a `Modelfile`
+
+Start by creating a `Modelfile`. This file is the blueprint for your model, specifying weights, parameters, prompt templates and more.
+
+```
+FROM ./mistral-7b-v0.1.Q4_0.gguf
+```
+
+(Optional) many chat models require a prompt template in order to answer correctly. A default prompt template can be specified with the `TEMPLATE` instruction in the `Modelfile`:
+
+```
+FROM ./q4_0.bin
+TEMPLATE "[INST] {{ .Prompt }} [/INST]"
+```
+
+### Step 2: Create the Ollama model
+
+Finally, create a model from your `Modelfile`:
+
+```
+ollama create example -f Modelfile
+```
+
+### Step 3: Run your model
+
+Next, test the model with `ollama run`:
+
+```
+ollama run example "What is your favourite condiment?"
+```
+
+## Importing (PyTorch & Safetensors)
+
+### Supported models
+
+Ollama supports a set of model architectures, with support for more coming soon:
+
+- Llama & Mistral
+- Falcon & RW
+- GPT-NeoX
+- BigCode
+
+To view a model's architecture, check the `config.json` file in its HuggingFace repo. You should see an entry under `architectures` (e.g. `LlamaForCausalLM`).
+
+### Step 1: Clone the HuggingFace repository (optional)
+
+If the model is currently hosted in a HuggingFace repository, first clone that repository to download the raw model.
+
+```
+git lfs install
+git clone https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1
+cd Mistral-7B-Instruct-v0.1
+```
+
+### Step 2: Convert and quantize to a `.bin` file (optional, for PyTorch and Safetensors)
+
+If the model is in PyTorch or Safetensors format, a [Docker image](https://hub.docker.com/r/ollama/quantize) with the tooling required to convert and quantize models is available.
+
+First, Install [Docker](https://www.docker.com/get-started/).
+
+Next, to convert and quantize your model, run:
+
+```
+docker run --rm -v .:/model ollama/quantize -q q4_0 /model
+```
+
+This will output two files into the directory:
+
+- `f16.bin`: the model converted to GGUF
+- `q4_0.bin` the model quantized to a 4-bit quantization (we will use this file to create the Ollama model)
+
+### Step 3: Write a `Modelfile`
+
+Next, create a `Modelfile` for your model:
+
+```
+FROM ./q4_0.bin
+```
+
+(Optional) many chat models require a prompt template in order to answer correctly. A default prompt template can be specified with the `TEMPLATE` instruction in the `Modelfile`:
+
+```
+FROM ./q4_0.bin
+TEMPLATE "[INST] {{ .Prompt }} [/INST]"
+```
+
+### Step 4: Create the Ollama model
+
+Finally, create a model from your `Modelfile`:
+
+```
+ollama create example -f Modelfile
+```
+
+### Step 5: Run your model
+
+Next, test the model with `ollama run`:
+
+```
+ollama run example "What is your favourite condiment?"
+```
+
+## Publishing your model (optional – early alpha)
+
+Publishing models is in early alpha. If you'd like to publish your model to share with others, follow these steps:
+
+1. Create [an account](https://ollama.ai/signup)
+2. Run `cat ~/.ollama/id_ed25519.pub` to view your Ollama public key. Copy this to the clipboard.
+3. Add your public key to your [Ollama account](https://ollama.ai/settings/keys)
+
+Next, copy your model to your username's namespace:
+
+```
+ollama cp example <your username>/example
+```
+
+Then push the model:
+
+```
+ollama push <your username>/example
+```
+
+After publishing, your model will be available at `https://ollama.ai/<your username>/example`.
+
+## Quantization reference
+
+The quantization options are as follow (from highest highest to lowest levels of quantization). Note: some architectures such as Falcon do not support K quants.
+
+- `q2_K`
+- `q3_K`
+- `q3_K_S`
+- `q3_K_M`
+- `q3_K_L`
+- `q4_0` (recommended)
+- `q4_1`
+- `q4_K`
+- `q4_K_S`
+- `q4_K_M`
+- `q5_0`
+- `q5_1`
+- `q5_K`
+- `q5_K_S`
+- `q5_K_M`
+- `q6_K`
+- `q8_0`
+
+## Manually converting & quantizing models
+
+### Prerequisites
+
+Start by cloning the `llama.cpp` repo to your machine in another directory:
+
+```
+git clone https://github.com/ggerganov/llama.cpp.git
+cd llama.cpp
+```
+
+Next, install the Python dependencies:
+
+```
+pip install -r requirements.txt
+```
+
+Finally, build the `quantize` tool:
+
+```
+make quantize
+```
+
+### Convert the model
+
+Run the correct conversion script for your model architecture:
+
+```shell
+# LlamaForCausalLM or MistralForCausalLM
+python convert.py <path to model directory>
+
+# FalconForCausalLM
+python convert-falcon-hf-to-gguf.py <path to model directory>
+
+# GPTNeoXForCausalLM
+python convert-gptneox-hf-to-gguf.py <path to model directory>
+
+# GPTBigCodeForCausalLM
+python convert-starcoder-hf-to-gguf.py <path to model directory>
+```
+
+### Quantize the model
+
+```
+quantize <path to model dir>/ggml-model-f32.bin <path to model dir>/q4_0.bin q4_0
+```

docs/linux.md

@@ -0,0 +1,116 @@
+# Ollama on Linux
+
+## Install
+
+Install Ollama running this one-liner:
+>
+```bash
+curl https://ollama.ai/install.sh | sh
+```
+
+## Manual install
+
+### Download the `ollama` binary
+
+Ollama is distributed as a self-contained binary. Download it to a directory in your PATH:
+
+```bash
+sudo curl -L https://ollama.ai/download/ollama-linux-amd64 -o /usr/bin/ollama
+sudo chmod +x /usr/bin/ollama
+```
+
+### Adding Ollama as a startup service (recommended)
+
+Create a user for Ollama:
+
+```bash
+sudo useradd -r -s /bin/false -m -d /usr/share/ollama ollama
+```
+
+Create a service file in `/etc/systemd/system/ollama.service`:
+
+```ini
+[Unit]
+Description=Ollama Service
+After=network-online.target
+
+[Service]
+ExecStart=/usr/bin/ollama serve
+User=ollama
+Group=ollama
+Restart=always
+RestartSec=3
+
+[Install]
+WantedBy=default.target
+```
+
+Then start the service:
+
+```bash
+sudo systemctl daemon-reload
+sudo systemctl enable ollama
+```
+
+### Install CUDA drivers (optional – for Nvidia GPUs)
+
+[Download and install](https://developer.nvidia.com/cuda-downloads) CUDA.
+
+Verify that the drivers are installed by running the following command, which should print details about your GPU:
+
+```bash
+nvidia-smi
+```
+
+### Start Ollama
+
+Start Ollama using `systemd`:
+
+```bash
+sudo systemctl start ollama
+```
+
+## Update
+
+Update ollama by running the install script again:
+
+```bash
+curl https://ollama.ai/install.sh | sh
+```
+
+Or by downloading the ollama binary:
+
+```bash
+sudo curl -L https://ollama.ai/download/ollama-linux-amd64 -o /usr/bin/ollama
+sudo chmod +x /usr/bin/ollama
+```
+
+## Viewing logs
+
+To view logs of Ollama running as a startup service, run:
+
+```bash
+journalctl -u ollama
+```
+
+## Uninstall
+
+Remove the ollama service:
+
+```bash
+sudo systemctl stop ollama
+sudo systemctl disable ollama
+sudo rm /etc/systemd/system/ollama.service
+```
+
+Remove the ollama binary from your bin directory (either `/usr/local/bin`, `/usr/bin`, or `/bin`):
+
+```bash
+sudo rm $(which ollama)
+```
+
+Remove the downloaded models and Ollama service user:
+```bash
+sudo rm -r /usr/share/ollama
+sudo userdel ollama
+```

docs/modelfile.md

@@ -1,6 +1,6 @@
 # Ollama Model File
 
-> Note: this model file syntax is in development
+> Note: this `Modelfile` syntax is in development
 
 A model file is the blueprint to create and share models with Ollama.
 
@@ -12,7 +12,6 @@ A model file is the blueprint to create and share models with Ollama.
   - [FROM (Required)](#from-required)
     - [Build from llama2](#build-from-llama2)
     - [Build from a bin file](#build-from-a-bin-file)
-  - [EMBED](#embed)
   - [PARAMETER](#parameter)
     - [Valid Parameters and Values](#valid-parameters-and-values)
   - [TEMPLATE](#template)
@@ -24,7 +23,7 @@ A model file is the blueprint to create and share models with Ollama.
 
 ## Format
 
-The format of the Modelfile:
+The format of the `Modelfile`:
 
 ```modelfile
 # comment
@@ -42,9 +41,9 @@ INSTRUCTION arguments
 
 ## Examples
 
-An example of a model file creating a mario blueprint:
+An example of a `Modelfile` creating a mario blueprint:
 
-```
+```modelfile
 FROM llama2
 # sets the temperature to 1 [higher is more creative, lower is more coherent]
 PARAMETER temperature 1
@@ -57,9 +56,9 @@ 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`
+1. Save it as a file (e.g. `Modelfile`)
+2. `ollama create choose-a-model-name -f <location of the file e.g. ./Modelfile>'`
+3. `ollama run choose-a-model-name`
 4. Start using the model!
 
 More examples are available in the [examples directory](../examples).
@@ -68,44 +67,34 @@ More examples are available in the [examples directory](../examples).
 
 ### FROM (Required)
 
-The FROM instruction defines the base model to use when creating a model.
+The `FROM` instruction defines the base model to use when creating a model.
 
-```
+```modelfile
 FROM <model name>:<tag>
 ```
 
 #### Build from llama2
 
-```
+```modelfile
 FROM llama2
 ```
 
 A list of available base models:
 <https://github.com/jmorganca/ollama#model-library>
 
-#### Build from a bin file
+#### Build from a `bin` file
 
-```
+```modelfile
 FROM ./ollama-model.bin
 ```
 
-This bin file location should be specified as an absolute path or relative to the Modelfile location.
-
-### EMBED
-
-The EMBED instruction is used to add embeddings of files to a model. This is useful for adding custom data that the model can reference when generating an answer. Note that currently only text files are supported, formatted with each line as one embedding.
-```
-FROM <model name>:<tag>
-EMBED <file path>.txt
-EMBED <different file path>.txt
-EMBED <path to directory>/*.txt
-```
+This bin file location should be specified as an absolute path or relative to the `Modelfile` location.
 
 ### PARAMETER
 
 The `PARAMETER` instruction defines a parameter that can be set when the model is run.
 
-```
+```modelfile
 PARAMETER <parameter> <parametervalue>
 ```
 
@@ -118,19 +107,21 @@ PARAMETER <parameter> <parametervalue>
 | 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     |
 | num_ctx        | Sets the size of the context window used to generate the next token. (Default: 2048)                                                                                                                                                                    | int        | num_ctx 4096         |
 | num_gqa        | The number of GQA groups in the transformer layer. Required for some models, for example it is 8 for llama2:70b                                                                                                                                         | int        | num_gqa 1            |
-| 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            |
+| num_gpu        | The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable.                                                                                                                                            | int        | num_gpu 50           |
 | 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         |
 | repeat_last_n  | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)                                                                                                                                           | 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   |
 | temperature    | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)                                                                                                                                     | float      | temperature 0.7      |
-| stop           | Sets the stop tokens to use.                                                                                                                                                                                                                            | string     | stop "AI assistant:" |
+| seed           | Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0)                                                                                       | int        | seed 42              |
+| stop           | Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate `stop` parameters in a modelfile.                                      | string     | stop "AI assistant:" |
 | 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              |
+| num_predict    | Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)                                                                                                                                   | int        | num_predict 42       |
 | 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            |
 
 ### TEMPLATE
 
-`TEMPLATE` of the full prompt template to be passed into the model. It may include (optionally) a system prompt and a user's prompt. This is used to create a full custom prompt, and syntax may be model specific.
+`TEMPLATE` of the full prompt template to be passed into the model. It may include (optionally) a system prompt and a user's prompt. This is used to create a full custom prompt, and syntax may be model specific. You can usually find the template for a given model in the readme for that model.
 
 #### Template Variables
 
@@ -138,14 +129,11 @@ PARAMETER <parameter> <parametervalue>
 | --------------- | ------------------------------------------------------------------------------------------------------------ |
 | `{{ .System }}` | The system prompt used to specify custom behavior, this must also be set in the Modelfile as an instruction. |
 | `{{ .Prompt }}` | The incoming prompt, this is not specified in the model file and will be set based on input.                 |
-| `{{ .First }}`  | A boolean value used to render specific template information for the first generation of a session.          |
 
-```
+```modelfile
 TEMPLATE """
-{{- if .First }}
 ### System:
 {{ .System }}
-{{- end }}
 
 ### User:
 {{ .Prompt }}
@@ -160,7 +148,7 @@ SYSTEM """<system message>"""
 
 The `SYSTEM` instruction specifies the system prompt to be used in the template, if applicable.
 
-```
+```modelfile
 SYSTEM """<system message>"""
 ```
 
@@ -168,7 +156,7 @@ SYSTEM """<system message>"""
 
 The `ADAPTER` instruction specifies the LoRA adapter to apply to the base model. The value of this instruction should be an absolute path or a path relative to the Modelfile and the file must be in a GGML file format. The adapter should be tuned from the base model otherwise the behaviour is undefined.
 
-```
+```modelfile
 ADAPTER ./ollama-lora.bin
 ```
 
@@ -176,7 +164,7 @@ ADAPTER ./ollama-lora.bin
 
 The `LICENSE` instruction allows you to specify the legal license under which the model used with this Modelfile is shared or distributed.
 
-```
+```modelfile
 LICENSE """
 <license text>
 """
@@ -184,5 +172,5 @@ LICENSE """
 
 ## Notes
 
-- the **modelfile is not case sensitive**. In the examples, we use uppercase for instructions to make it easier to distinguish it from arguments.
+- 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/tutorials.md

@@ -4,5 +4,6 @@ Here is a list of ways you can use Ollama with other tools to build interesting
 
 - [Using LangChain with Ollama in JavaScript](./tutorials/langchainjs.md)
 - [Using LangChain with Ollama in Python](./tutorials/langchainpy.md)
+- [Running Ollama on NVIDIA Jetson Devices](./tutorials/nvidia-jetson.md)
 
-Also be sure to check out the [examples](../examples) directory for more ways to use Ollama.
+Also be sure to check out the [examples](../examples) directory for more ways to use Ollama.

docs/tutorials/langchainjs.md

@@ -23,13 +23,17 @@ const answer = await ollama.call(`why is the sky blue?`);
 console.log(answer);
 ```
 
-That will get us the same thing as if we ran `ollama run llama2 "why is the sky blue"` in the terminal. But we want to load a document from the web to ask a question against. **Cheerio** is a great library for ingesting a webpage, and **LangChain** uses it in their **CheerioWebBaseLoader**. So let's build that part of the app.
+That will get us the same thing as if we ran `ollama run llama2 "why is the sky blue"` in the terminal. But we want to load a document from the web to ask a question against. **Cheerio** is a great library for ingesting a webpage, and **LangChain** uses it in their **CheerioWebBaseLoader**. So let's install **Cheerio** and build that part of the app.
+
+```bash
+npm install cheerio 
+```
 
 ```javascript
 import { CheerioWebBaseLoader } from "langchain/document_loaders/web/cheerio";
 
 const loader = new CheerioWebBaseLoader("https://en.wikipedia.org/wiki/2023_Hawaii_wildfires");
-const data = loader.load();
+const data = await loader.load();
 ```
 
 That will load the document. Although this page is smaller than the Odyssey, it is certainly bigger than the context size for most LLMs. So we are going to need to split into smaller pieces, and then select just the pieces relevant to our question. This is a great use for a vector datastore. In this example, we will use the **MemoryVectorStore** that is part of **LangChain**. But there is one more thing we need to get the content into the datastore. We have to run an embeddings process that converts the tokens in the text into a series of vectors. And for that, we are going to use **Tensorflow**. There is a lot of stuff going on in this one. First, install the **Tensorflow** components that we need.

docs/tutorials/nvidia-jetson.md

@@ -0,0 +1,38 @@
+# Running Ollama on NVIDIA Jetson Devices
+
+With some minor configuration, Ollama runs well on [NVIDIA Jetson Devices](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/). The following has been tested on [JetPack 5.1.2](https://developer.nvidia.com/embedded/jetpack).
+
+NVIDIA Jetson devices are Linux-based embedded AI computers that are purpose-built for AI applications.
+
+Jetsons have an integrated GPU that is wired directly to the memory controller of the machine. For this reason, the `nvidia-smi` command is unrecognized, and Ollama proceeds to operate in "CPU only"
+mode. This can be verified by using a monitoring tool like jtop.
+
+In order to address this, we simply pass the path to the Jetson's pre-installed CUDA libraries into `ollama serve` (while in a tmux session). We then hardcode the num_gpu parameters into a cloned
+version of our target model.
+
+Prerequisites:
+
+- curl
+- tmux
+
+Here are the steps:
+
+- Install Ollama via standard Linux command (ignore the 404 error): `curl https://ollama.ai/install.sh | sh`
+- Stop the Ollama service: `sudo systemctl stop ollama`
+- Start Ollama serve in a tmux session called ollama_jetson and reference the CUDA libraries path: `tmux has-session -t ollama_jetson 2>/dev/null || tmux new-session -d -s ollama_jetson 
+'LD_LIBRARY_PATH=/usr/local/cuda/lib64 ollama serve'`
+- Pull the model you want to use (e.g. mistral): `ollama pull mistral`
+- Create a new Modelfile specifically for enabling GPU support on the Jetson: `touch ModelfileMistralJetson`
+- In the ModelfileMistralJetson file, specify the FROM model and the num_gpu PARAMETER as shown below:
+
+```
+FROM mistral
+PARAMETER num_gpu 999
+```
+
+- Create a new model from your Modelfile: `ollama create mistral-jetson -f ./ModelfileMistralJetson`
+- Run the new model: `ollama run mistral-jetson`
+
+If you run a monitoring tool like jtop you should now see that Ollama is using the Jetson's integrated GPU.
+
+And that's it!

examples/.gitignore

@@ -0,0 +1,171 @@
+node_modules
+# OSX
+.DS_STORE
+
+# Models
+models/
+
+# Local Chroma db
+.chroma/
+db/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

examples/README.md

@@ -1,15 +1,3 @@
 # Examples
 
-This directory contains different examples of using Ollama
-
-To create a model:
-
-```
-ollama create example -f <example file>
-```
-
-To run a model:
-
-```
-ollama run example
-```
+This directory contains different examples of using Ollama.

examples/bash-comparemodels/README.md

@@ -0,0 +1,10 @@
+# Bash Shell examples
+
+When calling `ollama`, you can pass it a file to run all the prompts in the file, one after the other:
+
+`ollama run llama2 < sourcequestions.txt`
+
+This concept is used in the following example.
+
+## Compare Models
+`comparemodels.sh` is a script that runs all the questions in `sourcequestions.txt` using any 4 models you choose that you have already pulled from the Ollama library or have created locally.

examples/bash-comparemodels/comparemodels.sh

@@ -0,0 +1,64 @@
+#! /usr/bin/env bash
+# Compare multiple models by running them with the same questions
+
+NUMBEROFCHOICES=4
+SELECTIONS=()
+declare -a SUMS=()
+
+# Get the list of models
+CHOICES=$(ollama list | awk '{print $1}')
+
+# Select which models to run as a comparison
+echo "Select $NUMBEROFCHOICES models to compare:"
+select ITEM in $CHOICES; do
+    if [[ -n $ITEM ]]; then
+        echo "You have selected $ITEM"
+        SELECTIONS+=("$ITEM")
+        ((COUNT++))
+        if [[ $COUNT -eq $NUMBEROFCHOICES ]]; then
+            break
+        fi
+    else
+        echo "Invalid selection"
+    fi
+done
+
+# Loop through each of the selected models
+for ITEM in "${SELECTIONS[@]}"; do
+    echo "--------------------------------------------------------------"
+    echo "Loading the model $ITEM into memory"
+    ollama run "$ITEM" ""
+    echo "--------------------------------------------------------------"
+    echo "Running the questions through the model $ITEM"
+    COMMAND_OUTPUT=$(ollama run "$ITEM" --verbose < sourcequestions.txt 2>&1| tee /dev/stderr)
+
+    # eval duration is sometimes listed in seconds and sometimes in milliseconds. 
+    # Add up the values for each model
+    SUM=$(echo "$COMMAND_OUTPUT" | awk '
+    /eval duration:/ {
+        value = $3
+        if (index(value, "ms") > 0) {
+            gsub("ms", "", value)
+            value /= 1000
+        } else {
+            gsub("s", "", value)
+        }
+        sum += value
+    }
+    END { print sum }')
+
+
+    SUMS+=("All questions for $ITEM completed in $SUM seconds")
+done
+
+echo ""
+echo "--------------------------------------------------------------"
+echo -e "Sums of eval durations for each run:"
+for val in "${SUMS[@]}"; do
+    echo "$val"
+done
+
+echo "--------------------------------------------------------------"
+echo "Comparison complete. Now you can decide"
+echo "which model is best."
+echo "--------------------------------------------------------------"

examples/bash-comparemodels/sourcequestions.txt

@@ -0,0 +1,7 @@
+Why is the sky blue
+What is a black hole
+Explain the big bang theory like I am 5?
+What is the quickest way to win a game of Monopoly with 3 others?
+Why does a vacuum bottle keep my coffee hot and my milkshake cold?
+What is the difference between a meteor, a meteorite, and a meteoroid?
+Create an array with 5 items and print to the console. Do this in Python, C#, Typescript, and Rust.

examples/golang-simplegenerate/main.go

@@ -0,0 +1,27 @@
+package main
+
+import (
+	"bytes"
+	"fmt"
+	"io"
+	"log"
+	"net/http"
+	"os"
+)
+
+func main() {
+	body := []byte(`{"model":"mistral"}`)
+	resp, err := http.Post("http://localhost:11434/api/generate", "application/json", bytes.NewBuffer(body))
+
+	if err != nil {
+		fmt.Print(err.Error())
+		os.Exit(1)
+	}
+
+	responseData, err := io.ReadAll(resp.Body)
+	if err != nil {
+		log.Fatal(err)
+	}
+	fmt.Println(string(responseData))
+
+}

examples/kubernetes/README.md

@@ -0,0 +1,36 @@
+# Deploy Ollama to Kubernetes
+
+## Prerequisites
+
+- Ollama: https://ollama.ai/download
+- Kubernetes cluster. This example will use Google Kubernetes Engine.
+
+## Steps
+
+1. Create the Ollama namespace, daemon set, and service
+
+    ```bash
+    kubectl apply -f cpu.yaml
+    ```
+
+1. Port forward the Ollama service to connect and use it locally
+
+    ```bash
+    kubectl -n ollama port-forward service/ollama 11434:80
+    ```
+
+1. Pull and run a model, for example `orca-mini:3b`
+
+    ```bash
+    ollama run orca-mini:3b
+    ```
+
+## (Optional) Hardware Acceleration
+
+Hardware acceleration in Kubernetes requires NVIDIA's [`k8s-device-plugin`](https://github.com/NVIDIA/k8s-device-plugin). Follow the link for more details.
+
+Once configured, create a GPU enabled Ollama deployment.
+
+```bash
+kubectl apply -f gpu.yaml
+```

examples/kubernetes/cpu.yaml

@@ -0,0 +1,42 @@
+---
+apiVersion: v1
+kind: Namespace
+metadata:
+  name: ollama
+---
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: ollama
+  namespace: ollama
+spec:
+  selector:
+    matchLabels:
+      name: ollama
+  template:
+    metadata:
+      labels:
+        name: ollama
+    spec:
+      containers:
+      - name: ollama
+        image: ollama/ollama:latest
+        ports:
+        - name: http
+          containerPort: 11434
+          protocol: TCP
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: ollama
+  namespace: ollama
+spec:
+  type: ClusterIP
+  selector:
+    name: ollama
+  ports:
+  - port: 80
+    name: http
+    targetPort: http
+    protocol: TCP

examples/kubernetes/gpu.yaml

@@ -0,0 +1,56 @@
+---
+apiVersion: v1
+kind: Namespace
+metadata:
+  name: ollama
+---
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: ollama
+  namespace: ollama
+spec:
+  strategy:
+    type: Recreate
+  selector:
+    matchLabels:
+      name: ollama
+  template:
+    metadata:
+      labels:
+        name: ollama
+    spec:
+      containers:
+      - name: ollama
+        image: ollama/ollama:latest
+        env:
+        - name: PATH
+          value: /usr/local/nvidia/bin:/usr/local/nvidia/lib64:/usr/bin:/usr/sbin:/bin:/sbin
+        - name: LD_LIBRARY_PATH
+          value: /usr/local/nvidia/lib64
+        ports:
+        - name: http
+          containerPort: 11434
+          protocol: TCP
+        resources:
+          limits:
+            nvidia.com/gpu: 1
+      tolerations:
+      - key: nvidia.com/gpu
+        operator: Exists
+        effect: NoSchedule
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: ollama
+  namespace: ollama
+spec:
+  type: ClusterIP
+  selector:
+    name: ollama
+  ports:
+  - port: 80
+    name: http
+    targetPort: http
+    protocol: TCP

examples/langchain-python-rag-privategpt/constants.py

@@ -6,7 +6,6 @@ PERSIST_DIRECTORY = os.environ.get('PERSIST_DIRECTORY', 'db')
 
 # Define the Chroma settings
 CHROMA_SETTINGS = Settings(
-        chroma_db_impl='duckdb+parquet',
         persist_directory=PERSIST_DIRECTORY,
         anonymized_telemetry=False
 )

examples/langchain-python-rag-privategpt/ingest.py

@@ -150,7 +150,7 @@ def main():
         print("Creating new vectorstore")
         texts = process_documents()
         print(f"Creating embeddings. May take some minutes...")
-        db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory, client_settings=CHROMA_SETTINGS)
+        db = Chroma.from_documents(texts, embeddings, persist_directory=persist_directory)
     db.persist()
     db = None
 

examples/langchain-python-rag-privategpt/privateGPT.py

@@ -4,6 +4,7 @@ from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from langchain.vectorstores import Chroma
 from langchain.llms import Ollama
+import chromadb
 import os
 import argparse
 import time
@@ -22,7 +23,9 @@ def main():
     # Parse the command line arguments
     args = parse_arguments()
     embeddings = HuggingFaceEmbeddings(model_name=embeddings_model_name)
-    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings, client_settings=CHROMA_SETTINGS)
+
+    db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+
     retriever = db.as_retriever(search_kwargs={"k": target_source_chunks})
     # activate/deactivate the streaming StdOut callback for LLMs
     callbacks = [] if args.mute_stream else [StreamingStdOutCallbackHandler()]

examples/langchain-python-rag-privategpt/requirements.txt

@@ -0,0 +1,14 @@
+langchain==0.0.274
+gpt4all==1.0.8
+chromadb==0.4.7
+llama-cpp-python==0.1.81
+urllib3==2.0.4
+PyMuPDF==1.23.5
+python-dotenv==1.0.0
+unstructured==0.10.8
+extract-msg==0.45.0
+tabulate==0.9.0
+pandoc==2.3
+pypandoc==1.11
+tqdm==4.66.1
+sentence_transformers==2.2.2

examples/langchain-typescript-simple/README.md

@@ -0,0 +1,21 @@
+# LangChain
+
+This example is a basic "hello world" of using LangChain with Ollama using Node.js and Typescript.
+
+## Setup
+
+```shell
+npm install
+```
+
+## Run
+
+```shell
+ts-node main.ts
+```
+
+Running this example will print the response for "hello":
+
+```plaintext
+Hello! It's nice to meet you. hopefully you are having a great day! Is there something I can help you with or would you like to chat?
+```

examples/langchain-typescript-simple/main.ts

@@ -0,0 +1,15 @@
+import { Ollama} from 'langchain/llms/ollama';
+
+async function main() {
+  const ollama = new Ollama({
+    model: 'mistral'    
+    // other parameters can be found at https://js.langchain.com/docs/api/llms_ollama/classes/Ollama
+  })
+  const stream = await ollama.stream("Hello");
+
+  for await (const chunk of stream) {
+    process.stdout.write(chunk);
+  }
+}
+
+main();

examples/langchain-typescript-simple/package-lock.json

@@ -0,0 +1,997 @@
+{
+  "name": "with-langchain-typescript-simplegenerate",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "dependencies": {
+        "langchain": "^0.0.165"
+      },
+      "devDependencies": {
+        "typescript": "^5.2.2"
+      }
+    },
+    "node_modules/@anthropic-ai/sdk": {
+      "version": "0.6.2",
+      "resolved": "https://registry.npmjs.org/@anthropic-ai/sdk/-/sdk-0.6.2.tgz",
+      "integrity": "sha512-fB9PUj9RFT+XjkL+E9Ol864ZIJi+1P8WnbHspN3N3/GK2uSzjd0cbVIKTGgf4v3N8MwaQu+UWnU7C4BG/fap/g==",
+      "dependencies": {
+        "@types/node": "^18.11.18",
+        "@types/node-fetch": "^2.6.4",
+        "abort-controller": "^3.0.0",
+        "agentkeepalive": "^4.2.1",
+        "digest-fetch": "^1.3.0",
+        "form-data-encoder": "1.7.2",
+        "formdata-node": "^4.3.2",
+        "node-fetch": "^2.6.7"
+      }
+    },
+    "node_modules/@types/node": {
+      "version": "18.18.4",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-18.18.4.tgz",
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+}

examples/langchain-typescript-simple/package.json

@@ -0,0 +1,8 @@
+{
+  "devDependencies": {
+    "typescript": "^5.2.2"
+  },
+  "dependencies": {
+    "langchain": "^0.0.165"
+  }
+}

examples/midjourney-prompter/Modelfile

@@ -1,8 +0,0 @@
-# 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/modelfile-10tweets/Modelfile

@@ -0,0 +1,7 @@
+# Modelfile for creating a list of ten tweets from a topic
+# Run `ollama create 10tweets -f ./Modelfile` and then `ollama run 10tweets` and enter a topic
+
+FROM llama2
+SYSTEM """
+You are a content marketer who needs to come up with 10 short but succinct tweets. The answer should be a list of ten tweets. Each tweet can have a maximum of 280 characters and should include hashtags. Each user input will be a subject and you should expand it in ten creative ways. Never stop after just one tweet. Always include ten. 
+"""

examples/modelfile-10tweets/README.md

@@ -0,0 +1,23 @@
+# Ten Tweets Modelfile
+
+This is a simple modelfile that generates ten tweets based off any topic.
+
+```bash
+ollama create tentweets
+
+ollama run tentweets
+>>> underwater basketweaving
+ Great! Here are ten creative tweets about underwater basketweaving:
+
+1. "Just discovered the ultimate stress-reliever: Underwater basketweaving! 🌊🧵 #UnderwaterBasketweaving #StressRelief"
+2. "Who needs meditation when you can do underwater basketweaving? 😴👀 #PeacefulDistraction #UnderwaterBasketweaving"
+3. "Just spent an hour in the pool and still managed to knot my basket. Goal: untangle it before next session. 💪🏽 #ChallengeAccepted #UnderwaterBasketweaving"
+4. "When life gives you lemons, make underwater basketweaving! 🍋🧵 #LemonadeLife #UnderwaterBasketweaving"
+5. "Just realized my underwater basketweaving skills could come in handy during a zombie apocalypse. 😂🧡 #SurvivalTips #UnderwaterBasketweaving"
+6. "I'm not lazy, I'm just conserving energy for my next underwater basketweaving session. 😴💤 #LazyDay #UnderwaterBasketweaving"
+7. "Just found my inner peace while doing underwater basketweaving. It's like meditation, but with knots! 🙏🧵 #Mindfulness #UnderwaterBasketweaving"
+8. "Why study for exams when you can do underwater basketweaving and forget all your worries? 😜🧵 #ProcrastinationStation #UnderwaterBasketweaving"
+9. "Just had to cut my underwater basketweaving session short due to a sudden urge to breathe. 🤯🌊 #AquaticAdventures #UnderwaterBasketweaving"
+10. "I'm not sure what's more impressive: my underwater basketweaving skills or the fact that I didn't drown trying to make this tweet. 😅🧵 #Accomplishment 
+#UnderwaterBasketweaving"
+```

examples/modelfile-devopsengineer/Modelfile

@@ -1,7 +1,7 @@
 # Modelfile for creating a devops engineer assistant
 # Run `ollama create devops-engineer -f ./Modelfile` and then `ollama run devops-engineer` and enter a topic
 
-FROM llama2:13b
+FROM mistral
 PARAMETER temperature 1
 SYSTEM """
 You are a senior devops engineer, acting as an assistant. You offer help with cloud technologies like: Terraform, AWS, kubernetes, python. You answer with code examples when possible

examples/modelfile-devopsengineer/README.md

@@ -0,0 +1,40 @@
+# DevOps Engineer Modelfile
+
+This is a simple modelfile that takes will respond to any questions that a senior DevOps engineer should know how to solve.
+
+````text
+ollama create devopsengineer
+
+ollama run devopsengineer
+>>> how can i proxy all connections from inside the office through a single server that caches large files. Provide a config file for nginx to achieve this?
+
+Here is an example configuration file for Nginx that can be used to proxy all connections from inside the office network to a centralized cache server:
+```perl
+http {
+    upstream cache_server {
+        server <cache_server_ip>;
+        server <cache_server_ip>;
+        server <cache_server_ip>;
+    }
+    
+    server {
+        listen 80;
+        server_name localhost;
+        
+        location / {
+            proxy_pass http://cache_server;
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+        }
+    }
+}
+```
+In this configuration, we define an `upstream` block that lists the IP addresses of the cache servers. We then define a server block that listens on port 80 and routes all 
+traffic to the `/` location to the cache servers using the `proxy_pass` directive. The `proxy_set_header` directives are used to preserve the source IP address of the client
+request when forwarding it to the cache server.
+
+To use this configuration, you would need to replace the placeholder `<cache_server_ip>` with the actual IP addresses of your cache servers. You would also need to make sure
+that the cache servers are configured to accept incoming connections from the Nginx server and handle requests for files.
+
+````

examples/modelfile-midjourney/Modelfile

@@ -0,0 +1,11 @@
+# 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 zephyr
+PARAMETER temperature 0.8
+PARAMETER top_k 500
+PARAMETER top_p 0.9
+SYSTEM """
+Embrace your role as a creative illustrator. Based on a concept provided, you must produce a single paragraph with a multifaceted description of an image, ensuring significant details of the concept and more is represented in your instructions. You do not need to write complete sentences but rather short concepts with the following information: the level of detail that should be represented, an artistic style and maybe a specific name of a painter or illustrator, the ideal color pallete, lighting, mood, perspective, the setting, time of day, weather, the season, the time period, location, materials, the textures, patterns, lines, brushstrokes, techniques, the medium, the genre, the rendering style. Don't include everything and keep the description length under 250 words. 
+"""

examples/modelfile-midjourney/README.md

@@ -0,0 +1,11 @@
+# Midjourney Prompt Generator Modelfile
+
+This simple modelfile will help create a prompt to feed to Midjourney.
+
+```text
+ollama create midjourney
+
+ollama run midjourney
+>>> a sports car in the mountains. 
+A sleek, high-performance automobile cuts through a serpentine mountain landscape. The concept is a classic illustration of speed and power, depicted in the style of pop art by Andy Warhol. The color palette is dominated by bold, primary hues of red, blue, and yellow, with striking accent colors of white, black, and metallic shades. The lighting is bright and focused, casting sharp shadows on the rugged terrain. A sense of excitement and anticipation permeates throughout the scene, as the car navigates a treacherous course through the winding road. The perspective is low, allowing for a full view of the vehicle's sleek lines and intricate details. The setting takes place in the afternoon during a sunny day in autumn, as evidenced by the vibrant foliage on the mountainside. The time period is modern, with nods to classic car design. The materials are primarily digital, allowing for smooth curves and sharp contrasts. The textures are sleek and polished, with meticulously detailed lines and brushstrokes that accentuate the car's aerodynamic design. The patterns consist of geometric shapes and bold stripes, adding to the car's dynamic appeal. The genre is modern realism, with a focus on precision and detail. The rendering style is highly technical, capturing the nuances and subtleties of the vehicle and its surroundings in breathtaking detail.
+```

examples/modelfile-recipemaker/README.md

@@ -0,0 +1,20 @@
+# Recipe Maker Modelfile 
+
+Simple modelfile to generate a recipe from a short list of ingredients.
+
+```
+ollama create recipemaker
+
+ollama run recipemaker
+>>> chilli pepper, white chocolate, kale
+ Ingredients:
+- 1 small chili pepper
+- 4 squares of white chocolate
+- handful of kale leaves
+
+Instructions:
+1. In a blender or food processor, puree the chilies and white chocolate until smooth.
+2. Add the chopped kale leaves to the blender and pulse until well combined.
+3. Serve immediately as a dip for crackers or use it as an ingredient in your favorite recipe. The mixture of spicy chili pepper with sweet white chocolate and nutritious 
+kale will make your taste buds dance with delight!
+```

examples/modelfile-sentiments/Modelfile

@@ -0,0 +1,26 @@
+# Modelfile for creating a sentiment analyzer. 
+# Run `ollama create sentiments -f pathtofile` and then `ollama run sentiments` and enter a topic
+
+FROM orca
+TEMPLATE """
+### System:
+{{ .System }}
+### User: 
+I hate it when my phone dies
+### Response: 
+NEGATIVE
+### User: 
+He is awesome
+### Response: 
+POSITIVE
+### User: 
+This is the link to the article
+### Response: 
+NEUTRAL
+### User:
+{{ .Prompt }}
+
+### Response:
+"""
+
+SYSTEM """You are a sentiment analyzer. You will receive text and output only one word, either POSITIVE or NEGATIVE or NEUTRAL, depending on the sentiment of the text."""

examples/modelfile-sentiments/Readme.md

@@ -0,0 +1,23 @@
+# Sentiments Modelfile
+
+This is a simple sentiments analyzer using the Orca model. When you pull Orca from the registry, it has a Template already defined that looks like this:
+
+```Modelfile
+### System:
+{{ .System }}
+
+### User:
+{{ .Prompt }}
+
+### Response:
+```
+
+If we just wanted to have the text:
+
+```Plaintext
+You are a sentiment analyzer. You will receive text and output only one word, either POSITIVE or NEGATIVE or NEUTRAL, depending on the sentiment of the text.
+```
+
+then we could have put this in a SYSTEM block. But we want to provide examples which require updating the full Template. Any Modelfile you create will inherit all the settings from the source model. But in this example, we are overriding the Template.
+
+When providing examples for the input and output, you should include the way the model usually provides information. Since the Orca model expects a user prompt to appear after ### User: and the response is after ### Response, we should format our examples like that as well. If we were using the Llama 2 model, the format would be a bit different.

examples/python-dockerit/Modelfile

@@ -1,6 +1,6 @@
-FROM llama2
+FROM mistral
 SYSTEM """
-You are an experience Devops engineer focused on docker. When given specifications for a particular need or application you know the best way to host that within a docker container. For instance if someone tells you they want an nginx server to host files located at /web you will answer as follows
+You are an experienced Devops engineer focused on docker. When given specifications for a particular need or application you know the best way to host that within a docker container. For instance if someone tells you they want an nginx server to host files located at /web you will answer as follows
 
 ---start
 FROM nginx:alpine
@@ -16,5 +16,5 @@ ENV POSTGRES_PASSWORD=abc123
 EXPOSE 5432
 ---end
 
-Again it's just the contents of the dockerfile an nothing else.
-"""
+Again it's just the contents of the dockerfile and nothing else.
+"""

examples/python-rag-newssummary/README.md

@@ -0,0 +1,22 @@
+# News Summarizer
+
+This example goes through a series of steps:
+
+  1. You choose a topic area (e.g., "news", "NVidia", "music", etc.).
+  2. Gets the most recent articles on that topic from various sources.
+  3. Uses Ollama to summarize each article.
+  4. Creates chunks of sentences from each article.
+  5. Uses Sentence Transformers to generate embeddings for each of those chunks.
+  6. You enter a question regarding the summaries shown.
+  7. Uses Sentence Transformers to generate an embedding for that question.
+  8. Uses the embedded question to find the most similar chunks.
+  9. Feeds all that to Ollama to generate a good answer to your question based on these news articles.
+
+This example lets you pick from a few different topic areas, then summarize the most recent x articles for that topic. It then creates chunks of sentences from each article and then generates embeddings for each of those chunks.
+
+You can run the example like this:
+
+```bash
+pip install -r requirements.txt
+python summ.py
+```

examples/python-rag-newssummary/requirements.txt

@@ -0,0 +1,9 @@
+beautifulsoup4==4.12.2
+feedparser==6.0.10
+mattsollamatools==0.0.8
+newspaper3k==0.2.8
+nltk==3.8.1
+numpy==1.24.3
+Requests==2.31.0
+scikit_learn==1.3.0
+sentence_transformers==2.2.2

examples/python-rag-newssummary/summ.py

@@ -0,0 +1,86 @@
+import curses
+import json
+from utils import get_url_for_topic, topic_urls, menu, getUrls, get_summary, getArticleText, knn_search
+import requests
+from sentence_transformers import SentenceTransformer
+from mattsollamatools import chunker
+
+if __name__ == "__main__":
+    chosen_topic = curses.wrapper(menu)
+    print("Here is your news summary:\n")
+    urls = getUrls(chosen_topic, n=5)
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    allEmbeddings = []
+
+    for url in urls:
+      article={}
+      article['embeddings'] = []
+      article['url'] = url
+      text = getArticleText(url)
+      summary = get_summary(text)
+      chunks = chunker(text)  # Use the chunk_text function from web_utils
+      embeddings = model.encode(chunks)
+      for (chunk, embedding) in zip(chunks, embeddings):
+        item = {}
+        item['source'] = chunk
+        item['embedding'] = embedding.tolist()  # Convert NumPy array to list
+        item['sourcelength'] = len(chunk)
+        article['embeddings'].append(item)
+    
+      allEmbeddings.append(article)
+
+      print(f"{summary}\n")
+
+    
+    while True:
+      context = []
+      # Input a question from the user
+      question = input("Enter your question about the news, or type quit: ")
+
+      if question.lower() == 'quit':
+        break
+
+      # Embed the user's question
+      question_embedding = model.encode([question])
+
+      # Perform KNN search to find the best matches (indices and source text)
+      best_matches = knn_search(question_embedding, allEmbeddings, k=10)
+
+
+      sourcetext=""
+      for i, (index, source_text) in enumerate(best_matches, start=1):
+          sourcetext += f"{i}. Index: {index}, Source Text: {source_text}"
+
+      systemPrompt = f"Only use the following information to answer the question. Do not use anything else: {sourcetext}"
+
+      url = "http://localhost:11434/api/generate"
+
+      payload = {
+      "model": "mistral-openorca",
+      "prompt": question, 
+      "system": systemPrompt,
+      "stream": False, 
+      "context": context
+      }
+
+      # Convert the payload to a JSON string
+      payload_json = json.dumps(payload)
+
+      # Set the headers to specify JSON content
+      headers = {
+          "Content-Type": "application/json"
+      }
+
+      # Send the POST request
+      response = requests.post(url, data=payload_json, headers=headers)
+
+      # Check the response
+      if response.status_code == 200:
+          output = json.loads(response.text)
+          context = output['context']
+          print(output['response']+ "\n")
+          
+
+      else:
+          print(f"Request failed with status code {response.status_code}")
+

examples/python-rag-newssummary/utils.py

@@ -0,0 +1,108 @@
+import curses
+import feedparser
+import requests
+import unicodedata
+import json
+from newspaper import Article
+from bs4 import BeautifulSoup
+from nltk.tokenize import sent_tokenize, word_tokenize
+import numpy as np
+from sklearn.neighbors import NearestNeighbors
+from mattsollamatools import chunker
+
+# Create a dictionary to store topics and their URLs
+topic_urls = {
+    "Mac": "https://9to5mac.com/guides/mac/feed",
+    "News": "http://www.npr.org/rss/rss.php?id=1001",
+    "Nvidia": "https://nvidianews.nvidia.com/releases.xml",
+    "Raspberry Pi": "https://www.raspberrypi.com/news/feed/", 
+    "Music": "https://www.billboard.com/c/music/music-news/feed/"
+}
+
+# Use curses to create a menu of topics
+def menu(stdscr):
+    chosen_topic = get_url_for_topic(stdscr)  
+    url = topic_urls[chosen_topic] if chosen_topic in topic_urls else "Topic not found"
+    
+    stdscr.addstr(len(topic_urls) + 3, 0, f"Selected URL for {chosen_topic}: {url}")
+    stdscr.refresh()
+    
+    return chosen_topic
+
+# You have chosen a topic. Now return the url for that topic
+def get_url_for_topic(stdscr):
+    curses.curs_set(0)  # Hide the cursor
+    stdscr.clear()
+
+    stdscr.addstr(0, 0, "Choose a topic using the arrow keys (Press Enter to select):")
+
+    # Create a list of topics
+    topics = list(topic_urls.keys())
+    current_topic = 0
+
+    while True:
+        for i, topic in enumerate(topics):
+            if i == current_topic:
+                stdscr.addstr(i + 2, 2, f"> {topic}")
+            else:
+                stdscr.addstr(i + 2, 2, f"  {topic}")
+
+        stdscr.refresh()
+
+        key = stdscr.getch()
+
+        if key == curses.KEY_DOWN and current_topic < len(topics) - 1:
+            current_topic += 1
+        elif key == curses.KEY_UP and current_topic > 0:
+            current_topic -= 1
+        elif key == 10:  # Enter key
+            return topic_urls[topics[current_topic]]
+
+# Get the last N URLs from an RSS feed
+def getUrls(feed_url, n=20):
+    feed = feedparser.parse(feed_url)
+    entries = feed.entries[-n:]
+    urls = [entry.link for entry in entries]
+    return urls
+
+# Often there are a bunch of ads and menus on pages for a news article. This uses newspaper3k to get just the text of just the article.
+def getArticleText(url):
+  article = Article(url)
+  article.download()
+  article.parse()
+  return article.text
+
+def get_summary(text):
+  systemPrompt = "Write a concise summary of the text, return your responses with 5 lines that cover the key points of the text given."
+  prompt = text
+  
+  url = "http://localhost:11434/api/generate"
+
+  payload = {
+    "model": "mistral-openorca",
+    "prompt": prompt, 
+    "system": systemPrompt,
+    "stream": False
+  }
+  payload_json = json.dumps(payload)
+  headers = {"Content-Type": "application/json"}
+  response = requests.post(url, data=payload_json, headers=headers)
+
+  return json.loads(response.text)["response"]
+
+# Perform K-nearest neighbors (KNN) search
+def knn_search(question_embedding, embeddings, k=5):
+    X = np.array([item['embedding'] for article in embeddings for item in article['embeddings']])
+    source_texts = [item['source'] for article in embeddings for item in article['embeddings']]
+    
+    # Fit a KNN model on the embeddings
+    knn = NearestNeighbors(n_neighbors=k, metric='cosine')
+    knn.fit(X)
+    
+    # Find the indices and distances of the k-nearest neighbors
+    distances, indices = knn.kneighbors(question_embedding, n_neighbors=k)
+    
+    # Get the indices and source texts of the best matches
+    best_matches = [(indices[0][i], source_texts[indices[0][i]]) for i in range(k)]
+    
+    return best_matches

examples/python-simplegenerate/client.py

@@ -17,7 +17,7 @@ def generate(prompt, context):
     for line in r.iter_lines():
         body = json.loads(line)
         response_part = body.get('response', '')
-        # the response streams one token at a time, print that as we recieve it
+        # the response streams one token at a time, print that as we receive it
         print(response_part, end='', flush=True)
 
         if 'error' in body:
@@ -35,4 +35,4 @@ def main():
         print()
 
 if __name__ == "__main__":
-    main()
+    main()

examples/typescript-mentors/.gitignore

@@ -0,0 +1,2 @@
+node_modules
+package-lock.json

examples/typescript-mentors/README.md

@@ -0,0 +1,21 @@
+# Ask the Mentors
+
+This example demonstrates how one would create a set of 'mentors' you can have a conversation with. The mentors are generated using the `character-generator.ts` file. This will use **Stable Beluga 70b** to create a bio and list of verbal ticks and common phrases used by each person. Then `mentors.ts` will take a question, and choose three of the 'mentors' and start a conversation with them. Occasionally, they will talk to each other, and other times they will just deliver a set of monologues. It's fun to see what they do and say.
+
+## Usage
+
+```bash
+ts-node ./character-generator.ts "Lorne Greene"
+```
+
+This will create `lornegreene/Modelfile`. Now you can create a model with this command:
+
+```bash
+ollama create lornegreene -f lornegreene/Modelfile
+```
+
+If you want to add your own mentors, you will have to update the code to look at your namespace instead of **mattw**. Also set the list of mentors to include yours.
+
+```bash
+ts-node ./mentors.ts "What is a Jackalope?"
+```

examples/typescript-mentors/character-generator.ts

@@ -0,0 +1,26 @@
+import { Ollama } from 'ollama-node'
+import fs from 'fs';
+import path from 'path';
+
+async function characterGenerator() {
+  const character = process.argv[2];
+  console.log(`You are creating a character for ${character}.`);
+  const foldername = character.replace(/\s/g, '').toLowerCase();
+  const directory = path.join(__dirname, foldername);
+  if (!fs.existsSync(directory)) {
+    fs.mkdirSync(directory, { recursive: true });
+  }
+
+  const ollama = new Ollama();
+  ollama.setModel("stablebeluga2:70b-q4_K_M");
+  const bio = await ollama.generate(`create a bio of ${character} in a single long paragraph. Instead of saying '${character} is...' or '${character} was...' use language like 'You are...' or 'You were...'. Then create a paragraph describing the speaking mannerisms and style of ${character}. Don't include anything about how ${character} looked or what they sounded like, just focus on the words they said. Instead of saying '${character} would say...' use language like 'You should say...'. If you use quotes, always use single quotes instead of double quotes. If there are any specific words or phrases you used a lot, show how you used them. `);
+
+  const thecontents = `FROM llama2\nSYSTEM """\n${bio.response.replace(/(\r\n|\n|\r)/gm, " ").replace('would', 'should')} All answers to questions should be related back to what you are most known for.\n"""`;
+
+  fs.writeFile(path.join(directory, 'Modelfile'), thecontents, (err: any) => {
+    if (err) throw err;
+    console.log('The file has been saved!');
+  });
+}
+
+characterGenerator();

examples/typescript-mentors/mentors.ts

@@ -0,0 +1,59 @@
+import { Ollama } from 'ollama-node';
+
+const mentorCount = 3;
+const ollama = new Ollama();
+
+function getMentors(): string[] {
+  const mentors = ['Gary Vaynerchuk', 'Kanye West', 'Martha Stewart', 'Neil deGrasse Tyson', 'Owen Wilson', 'Ronald Reagan', 'Donald Trump', 'Barack Obama', 'Jeff Bezos'];
+  const chosenMentors: string[] = [];
+  for (let i = 0; i < mentorCount; i++) {
+    const mentor = mentors[Math.floor(Math.random() * mentors.length)];
+    chosenMentors.push(mentor);
+    mentors.splice(mentors.indexOf(mentor), 1);
+  }
+  return chosenMentors;
+}
+
+function getMentorFileName(mentor: string): string {
+  const model = mentor.toLowerCase().replace(/\s/g, '');
+  return `mattw/${model}`;
+}
+
+async function getSystemPrompt(mentor: string, isLast: boolean, question: string): Promise<string> {
+  ollama.setModel(getMentorFileName(mentor));
+  const info = await ollama.showModelInfo()
+  let SystemPrompt = info.system || '';
+  SystemPrompt += ` You should continue the conversation as if you were ${mentor} and acknowledge the people before you in the conversation. You should adopt their mannerisms and tone, but also not use language they wouldn't use. If they are not known to know about the concept in the question, don't offer an answer. Your answer should be no longer than 1 paragraph. And definitely try not to sound like anyone else. Don't repeat any slang or phrases already used. And if it is a question the original ${mentor} wouldn't have know the answer to, just say that you don't know, in the style of ${mentor}. And think about the time the person lived. Don't use terminology that they wouldn't have used.`
+
+  if (isLast) {
+    SystemPrompt += ` End your answer with something like I hope our answers help you out`;
+  } else {
+    SystemPrompt += ` Remember, this is a conversation, so you don't need a conclusion, but end your answer with a question related to the first question: "${question}".`;
+  }
+  return SystemPrompt;
+}
+
+async function main() {
+  const mentors = getMentors();
+  const question = process.argv[2];
+  let theConversation = `Here is the conversation so far.\nYou: ${question}\n`
+
+  for await (const mentor of mentors) {
+    const SystemPrompt = await getSystemPrompt(mentor, mentor === mentors[mentorCount - 1], question);
+    ollama.setModel(getMentorFileName(mentor));
+    ollama.setSystemPrompt(SystemPrompt);
+    let output = '';
+    process.stdout.write(`\n${mentor}: `);
+    for await (const chunk of ollama.streamingGenerate(theConversation + `Continue the conversation as if you were ${mentor} on the question "${question}".`)) {
+      if (chunk.response) {
+        output += chunk.response;
+        process.stdout.write(chunk.response);
+      } else {
+        process.stdout.write('\n');
+      }
+    }
+    theConversation += `${mentor}: ${output}\n\n`
+  }
+}
+
+main();

examples/typescript-mentors/package.json

@@ -0,0 +1,7 @@
+{
+  "dependencies": {
+    "fs": "^0.0.1-security",
+    "ollama-node": "^0.0.3",
+    "path": "^0.12.7"
+  }
+}

format/bytes.go

@@ -0,0 +1,23 @@
+package format
+
+import "fmt"
+
+const (
+	Byte     = 1
+	KiloByte = Byte * 1000
+	MegaByte = KiloByte * 1000
+	GigaByte = MegaByte * 1000
+)
+
+func HumanBytes(b int64) string {
+	switch {
+	case b > GigaByte:
+		return fmt.Sprintf("%.1f GB", float64(b)/GigaByte)
+	case b > MegaByte:
+		return fmt.Sprintf("%.1f MB", float64(b)/MegaByte)
+	case b > KiloByte:
+		return fmt.Sprintf("%.1f KB", float64(b)/KiloByte)
+	default:
+		return fmt.Sprintf("%d B", b)
+	}
+}

format/format.go

@@ -0,0 +1,25 @@
+package format
+
+import (
+	"fmt"
+	"math"
+)
+
+const (
+	Thousand = 1000
+	Million  = Thousand * 1000
+	Billion  = Million * 1000
+)
+
+func HumanNumber(b uint64) string {
+	switch {
+	case b > Billion:
+		return fmt.Sprintf("%.0fB", math.Round(float64(b)/Billion))
+	case b > Million:
+		return fmt.Sprintf("%.0fM", math.Round(float64(b)/Million))
+	case b > Thousand:
+		return fmt.Sprintf("%.0fK", math.Round(float64(b)/Thousand))
+	default:
+		return fmt.Sprintf("%d", b)
+	}
+}

format/openssh.go

@@ -10,15 +10,11 @@ package format
 
 import (
 	"crypto"
-	"crypto/ecdsa"
 	"crypto/ed25519"
-	"crypto/elliptic"
 	"crypto/rand"
-	"crypto/rsa"
 	"encoding/binary"
 	"encoding/pem"
 	"fmt"
-	"math/big"
 
 	"golang.org/x/crypto/ssh"
 )
@@ -41,25 +37,6 @@ type openSSHPrivateKey struct {
 	Rest    []byte `ssh:"rest"`
 }
 
-type openSSHRSAPrivateKey struct {
-	N       *big.Int
-	E       *big.Int
-	D       *big.Int
-	Iqmp    *big.Int
-	P       *big.Int
-	Q       *big.Int
-	Comment string
-	Pad     []byte `ssh:"rest"`
-}
-
-type openSSHECDSAPrivateKey struct {
-	Curve   string
-	Pub     []byte
-	D       *big.Int
-	Comment string
-	Pad     []byte `ssh:"rest"`
-}
-
 type openSSHEd25519PrivateKey struct {
 	Pub     []byte
 	Priv    []byte
@@ -85,64 +62,6 @@ func OpenSSHPrivateKey(key crypto.PrivateKey, comment string) (*pem.Block, error
 	}
 
 	switch k := key.(type) {
-	case *rsa.PrivateKey:
-		e := new(big.Int).SetInt64(int64(k.E))
-
-		key := openSSHRSAPrivateKey{
-			N:       k.N,
-			E:       e,
-			D:       k.D,
-			Iqmp:    k.Precomputed.Qinv,
-			P:       k.Primes[0],
-			Q:       k.Primes[1],
-			Comment: comment,
-		}
-
-		pk1.Keytype = ssh.KeyAlgoRSA
-		pk1.Rest = ssh.Marshal(key)
-
-		w.PubKey = ssh.Marshal(struct {
-			KeyType string
-			E       *big.Int
-			N       *big.Int
-		}{
-			ssh.KeyAlgoRSA, e, k.N,
-		})
-	case *ecdsa.PrivateKey:
-		var curve, keytype string
-		switch name := k.Curve.Params().Name; name {
-		case "P-256":
-			curve = "nistp256"
-			keytype = ssh.KeyAlgoECDSA256
-		case "P-384":
-			curve = "nistp384"
-			keytype = ssh.KeyAlgoECDSA384
-		case "P-521":
-			curve = "nistp521"
-			keytype = ssh.KeyAlgoECDSA521
-		default:
-			return nil, fmt.Errorf("ssh: unknown curve %q", name)
-		}
-
-		pub := elliptic.Marshal(k.Curve, k.X, k.Y)
-
-		key := openSSHECDSAPrivateKey{
-			Curve:   curve,
-			Pub:     pub,
-			D:       k.D,
-			Comment: comment,
-		}
-
-		pk1.Keytype = keytype
-		pk1.Rest = ssh.Marshal(key)
-
-		w.PubKey = ssh.Marshal(struct {
-			KeyType string
-			Curve   string
-			Pub     []byte
-		}{
-			keytype, curve, pub,
-		})
 	case ed25519.PrivateKey:
 		pub, priv := k[32:], k
 		key := openSSHEd25519PrivateKey{

format/time.go

@@ -7,26 +7,14 @@ import (
 	"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 {
+// humanDuration returns a human-readable approximation of a
+// duration (eg. "About a minute", "4 hours ago", etc.).
+func humanDuration(d time.Duration) string {
 	seconds := int(d.Seconds())
 
 	switch {
 	case seconds < 1:
-		if useCaps {
-			return "Less than a second"
-		}
-		return "less than a second"
+		return "Less than a second"
 	case seconds == 1:
 		return "1 second"
 	case seconds < 60:
@@ -36,10 +24,7 @@ func HumanDurationWithCase(d time.Duration, useCaps bool) string {
 	minutes := int(d.Minutes())
 	switch {
 	case minutes == 1:
-		if useCaps {
-			return "About a minute"
-		}
-		return "about a minute"
+		return "About a minute"
 	case minutes < 60:
 		return fmt.Sprintf("%d minutes", minutes)
 	}
@@ -47,10 +32,7 @@ func HumanDurationWithCase(d time.Duration, useCaps bool) string {
 	hours := int(math.Round(d.Hours()))
 	switch {
 	case hours == 1:
-		if useCaps {
-			return "About an hour"
-		}
-		return "about an hour"
+		return "About an hour"
 	case hours < 48:
 		return fmt.Sprintf("%d hours", hours)
 	case hours < 24*7*2:
@@ -65,77 +47,22 @@ func HumanDurationWithCase(d time.Duration, useCaps bool) string {
 }
 
 func HumanTime(t time.Time, zeroValue string) string {
-	return humanTimeWithCase(t, zeroValue, true)
+	return humanTime(t, zeroValue)
 }
 
 func HumanTimeLower(t time.Time, zeroValue string) string {
-	return humanTimeWithCase(t, zeroValue, false)
+	return strings.ToLower(humanTime(t, zeroValue))
 }
 
-func humanTimeWithCase(t time.Time, zeroValue string, useCaps bool) string {
+func humanTime(t time.Time, zeroValue string) string {
 	if t.IsZero() {
 		return zeroValue
 	}
 
 	delta := time.Since(t)
 	if delta < 0 {
-		return HumanDurationWithCase(-delta, useCaps) + " from now"
+		return humanDuration(-delta) + " 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())
+
+	return humanDuration(delta) + " ago"
 }

format/time_test.go

@@ -11,92 +11,25 @@ func assertEqual(t *testing.T, a interface{}, b interface{}) {
 	}
 }
 
-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))
+	t.Run("soon", func(t *testing.T) {
+		v := now.Add(800 * time.Millisecond)
+		assertEqual(t, HumanTime(v, ""), "Less than a second from now")
+	})
 }

go.mod

@@ -3,12 +3,13 @@ module github.com/jmorganca/ollama
 go 1.20
 
 require (
-	github.com/dustin/go-humanize v1.0.1
+	github.com/emirpasic/gods v1.18.1
 	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
+	golang.org/x/sync v0.3.0
 )
 
 require github.com/rivo/uniseg v0.2.0 // indirect
@@ -16,7 +17,6 @@ require github.com/rivo/uniseg v0.2.0 // indirect
 require (
 	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
@@ -38,12 +38,12 @@ require (
 	github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
 	github.com/ugorji/go/codec v1.2.11 // indirect
 	golang.org/x/arch v0.3.0 // indirect
-	golang.org/x/crypto v0.10.0 // indirect
-	golang.org/x/net v0.10.0 // indirect
-	golang.org/x/sys v0.10.0 // indirect
-	golang.org/x/term v0.10.0
-	golang.org/x/text v0.10.0 // indirect
-	gonum.org/v1/gonum v0.13.0
+	golang.org/x/crypto v0.14.0
+	golang.org/x/exp v0.0.0-20230817173708-d852ddb80c63
+	golang.org/x/net v0.17.0 // indirect
+	golang.org/x/sys v0.13.0 // indirect
+	golang.org/x/term v0.13.0
+	golang.org/x/text v0.13.0 // indirect
 	google.golang.org/protobuf v1.30.0 // indirect
 	gopkg.in/yaml.v3 v3.0.1 // indirect
 )

go.sum

@@ -4,19 +4,13 @@ github.com/bytedance/sonic v1.9.1/go.mod h1:i736AoUSYt75HyZLoJW9ERYxcy6eaN6h4BZX
 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/emirpasic/gods v1.18.1 h1:FXtiHYKDGKCW2KzwZKx0iC0PQmdlorYgdFG9jPXJ1Bc=
+github.com/emirpasic/gods v1.18.1/go.mod h1:8tpGGwCnJ5H4r6BWwaV6OrWmMoPhUl5jm/FMNAnJvWQ=
 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=
@@ -118,31 +112,32 @@ golang.org/x/arch v0.0.0-20210923205945-b76863e36670/go.mod h1:5om86z9Hs0C8fWVUu
 golang.org/x/arch v0.3.0 h1:02VY4/ZcO/gBOH6PUaoiptASxtXU10jazRCP865E97k=
 golang.org/x/arch v0.3.0/go.mod h1:5om86z9Hs0C8fWVUuoMHwpExlXzs5Tkyp9hOrfG7pp8=
 golang.org/x/crypto v0.0.0-20210711020723-a769d52b0f97/go.mod h1:GvvjBRRGRdwPK5ydBHafDWAxML/pGHZbMvKqRZ5+Abc=
-golang.org/x/crypto v0.10.0 h1:LKqV2xt9+kDzSTfOhx4FrkEBcMrAgHSYgzywV9zcGmM=
-golang.org/x/crypto v0.10.0/go.mod h1:o4eNf7Ede1fv+hwOwZsTHl9EsPFO6q6ZvYR8vYfY45I=
+golang.org/x/crypto v0.14.0 h1:wBqGXzWJW6m1XrIKlAH0Hs1JJ7+9KBwnIO8v66Q9cHc=
+golang.org/x/crypto v0.14.0/go.mod h1:MVFd36DqK4CsrnJYDkBA3VC4m2GkXAM0PvzMCn4JQf4=
+golang.org/x/exp v0.0.0-20230817173708-d852ddb80c63 h1:m64FZMko/V45gv0bNmrNYoDEq8U5YUhetc9cBWKS1TQ=
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llm/falcon.go

@@ -0,0 +1,20 @@
+package llm
+
+const (
+	falconModelType7B   = 32
+	falconModelType40B  = 60
+	falconModelType180B = 80
+)
+
+func falconModelType(numLayer uint32) string {
+	switch numLayer {
+	case 32:
+		return "7B"
+	case 60:
+		return "40B"
+	case 80:
+		return "180B"
+	default:
+		return "unknown"
+	}
+}

llm/ggml-alloc.c

@@ -1,575 +0,0 @@
-/**
- * llama.cpp - git 3ebb00935f3f0522b75df49c2769ab1774b91380
- *
- * 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-alloc.h"
-#include "ggml.h"
-#include <assert.h>
-#include <stdarg.h>
-#include <stdio.h>
-#include <stdlib.h>
-#include <string.h>
-
-#define UNUSED(x) (void)(x)
-#define MAX(a, b) ((a) > (b) ? (a) : (b))
-
-//#define GGML_ALLOCATOR_DEBUG
-
-//#define AT_PRINTF printf
-#define AT_PRINTF(...) ((void)0)
-
-struct hash_node {
-    struct ggml_tensor * t;
-    int n_children;
-    int n_views;
-};
-
-static size_t hash(void * p) {
-    return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE;
-}
-
-static struct hash_node * hash_get(struct hash_node hash_table[], struct ggml_tensor * t) {
-    size_t h = hash(t);
-
-    // linear probing
-    size_t i = h;
-    while (hash_table[i].t != NULL) {
-        if (hash_table[i].t == t) {
-            return &hash_table[i];
-        }
-        i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE;
-        if (i == h) {
-            // hash table is full
-            GGML_ASSERT(false);
-        }
-    }
-
-    hash_table[i].t = t;
-    return &hash_table[i];
-}
-
-// TODO: GGML_PAD ?
-static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
-    assert(alignment && !(alignment & (alignment - 1))); // power of 2
-    size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment;
-    return offset + align;
-}
-
-struct free_block {
-    void * addr;
-    size_t size;
-};
-
-#define MAX_FREE_BLOCKS 128
-
-struct ggml_allocr {
-    void * data;
-    size_t size;
-    size_t alignment;
-    int n_free_blocks;
-    struct free_block free_blocks[MAX_FREE_BLOCKS];
-    struct hash_node hash_table[GGML_GRAPH_HASHTABLE_SIZE];
-    size_t max_size;
-    bool measure;
-
-#ifdef GGML_ALLOCATOR_DEBUG
-    struct ggml_tensor * allocated_tensors[1024];
-#endif
-};
-
-#ifdef GGML_ALLOCATOR_DEBUG
-static void add_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tensor * tensor) {
-    for (int i = 0; i < 1024; i++) {
-        if (alloc->allocated_tensors[i] == NULL) {
-            alloc->allocated_tensors[i] = tensor;
-            return;
-        }
-    }
-    GGML_ASSERT(!"out of allocated_tensors");
-}
-static void remove_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tensor * tensor) {
-    for (int i = 0; i < 1024; i++) {
-        if (alloc->allocated_tensors[i] == tensor ||
-            (alloc->allocated_tensors[i] != NULL && alloc->allocated_tensors[i]->data == tensor->data)) {
-            alloc->allocated_tensors[i] = NULL;
-            return;
-        }
-    }
-    printf("tried to free tensor %s not found\n", tensor->name);
-    GGML_ASSERT(!"tensor not found");
-}
-#endif
-
-
-static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
-    return ggml_nbytes(tensor);
-
-    UNUSED(alloc);
-}
-
-void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
-    size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
-    size = aligned_offset(NULL, size, alloc->alignment);
-
-    AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size);
-
-    size_t max_avail = 0;
-
-    // find the best fitting free block
-    int best_fit_block = -1;
-    size_t best_fit_size = SIZE_MAX;
-    for (int i = 0; i < alloc->n_free_blocks; i++) {
-        struct free_block * block = &alloc->free_blocks[i];
-        max_avail = MAX(max_avail, block->size);
-        if (block->size >= size && block->size <= best_fit_size) {
-            best_fit_block = i;
-            best_fit_size = block->size;
-        }
-    }
-
-    AT_PRINTF("block %d\n", best_fit_block);
-
-    if (best_fit_block == -1) {
-        fprintf(stderr, "%s: not enough space in the buffer (needed %zu, largest block available %zu)\n",
-                __func__, size, max_avail);
-        GGML_ASSERT(!"not enough space in the buffer");
-        return;
-    }
-    struct free_block * block = &alloc->free_blocks[best_fit_block];
-    void * addr = block->addr;
-    block->addr = (char*)block->addr + size;
-    block->size -= size;
-    if (block->size == 0) {
-        // remove block if empty
-        alloc->n_free_blocks--;
-        for (int j = best_fit_block; j < alloc->n_free_blocks; j++) {
-            alloc->free_blocks[j] = alloc->free_blocks[j+1];
-        }
-    }
-
-    tensor->data = addr;
-
-#ifdef GGML_ALLOCATOR_DEBUG
-    add_allocated_tensor(alloc, tensor);
-    size_t cur_max = (char*)addr - (char*)alloc->data + size;
-    if (cur_max > alloc->max_size) {
-        printf("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
-        for (int i = 0; i < 1024; i++) {
-            if (alloc->allocated_tensors[i]) {
-                printf("%s (%.2f MB) ", alloc->allocated_tensors[i]->name, ggml_nbytes(alloc->allocated_tensors[i]) / 1024.0 / 1024.0);
-            }
-        }
-        printf("\n");
-    }
-#endif
-
-    alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->data + size);
-}
-
-// this is a very naive implementation, but for our case the number of free blocks should be very small
-static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
-    void * ptr = tensor->data;
-
-    if (ptr < alloc->data || (char*)ptr >= (char*)alloc->data + alloc->max_size) {
-        // the tensor was not allocated in this buffer
-        // this can happen because the graph allocator will try to free weights and other tensors from different buffers
-        // the easiest way to deal with this is just to ignore it
-        return;
-    }
-
-    size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
-    size = aligned_offset(NULL, size, alloc->alignment);
-    AT_PRINTF("%s: freeing %s (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, size, alloc->n_free_blocks);
-
-#ifdef GGML_ALLOCATOR_DEBUG
-    remove_allocated_tensor(alloc, tensor);
-#endif
-
-    // see if we can merge with an existing block
-    for (int i = 0; i < alloc->n_free_blocks; i++) {
-        struct free_block * block = &alloc->free_blocks[i];
-        // check if ptr is at the end of the block
-        if ((char*)block->addr + block->size == ptr) {
-            block->size += size;
-            // check if we can merge with the next block
-            if (i < alloc->n_free_blocks - 1 && (char*)block->addr + block->size == alloc->free_blocks[i+1].addr) {
-                block->size += alloc->free_blocks[i+1].size;
-                alloc->n_free_blocks--;
-                for (int j = i+1; j < alloc->n_free_blocks; j++) {
-                    alloc->free_blocks[j] = alloc->free_blocks[j+1];
-                }
-            }
-            return;
-        }
-        // check if ptr is at the beginning of the block
-        if ((char*)ptr + size == block->addr) {
-            block->addr = ptr;
-            block->size += size;
-            // check if we can merge with the previous block
-            if (i > 0 && (char*)alloc->free_blocks[i-1].addr + alloc->free_blocks[i-1].size == block->addr) {
-                alloc->free_blocks[i-1].size += block->size;
-                alloc->n_free_blocks--;
-                for (int j = i; j < alloc->n_free_blocks; j++) {
-                    alloc->free_blocks[j] = alloc->free_blocks[j+1];
-                }
-            }
-            return;
-        }
-    }
-    // otherwise, add a new block
-    GGML_ASSERT(alloc->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks");
-    // insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster)
-    int insert_pos = 0;
-    while (insert_pos < alloc->n_free_blocks && alloc->free_blocks[insert_pos].addr < ptr) {
-        insert_pos++;
-    }
-    // shift all blocks from insert_pos onward to make room for the new block
-    for (int i = alloc->n_free_blocks; i > insert_pos; i--) {
-        alloc->free_blocks[i] = alloc->free_blocks[i-1];
-    }
-    // insert the new block
-    alloc->free_blocks[insert_pos].addr = ptr;
-    alloc->free_blocks[insert_pos].size = size;
-    alloc->n_free_blocks++;
-}
-
-void ggml_allocr_reset(struct ggml_allocr * alloc) {
-    alloc->n_free_blocks = 1;
-    size_t align_offset = aligned_offset(alloc->data, 0, alloc->alignment);
-    alloc->free_blocks[0].addr = (char *)alloc->data + align_offset;
-    alloc->free_blocks[0].size = alloc->size - align_offset;
-}
-
-struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) {
-    struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
-
-    *alloc = (struct ggml_allocr){
-        /*.data          = */ data,
-        /*.size          = */ size,
-        /*.alignment     = */ alignment,
-        /*.n_free_blocks = */ 0,
-        /*.free_blocks   = */ {{0}},
-        /*.hash_table    = */ {{0}},
-        /*.max_size      = */ 0,
-        /*.measure       = */ false,
-#ifdef GGML_ALLOCATOR_DEBUG
-        /*.allocated_tensors = */ = {0},
-#endif
-    };
-
-    ggml_allocr_reset(alloc);
-
-    return alloc;
-}
-
-// address and size of the buffer when measuring
-// it needs to be large enough to fit all the tensors, but it cannot overlap with other existing buffers
-static void * const MEASURE_BASE_ADDR = (void *) 0x1000;
-static const size_t MEASURE_MAX_SIZE  = 1ULL<<40; // 1 TB
-
-struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
-    struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
-
-    *alloc = (struct ggml_allocr){
-        /*.data          = */ MEASURE_BASE_ADDR,
-        /*.size          = */ MEASURE_MAX_SIZE,
-        /*.alignment     = */ alignment,
-        /*.n_free_blocks = */ 0,
-        /*.free_blocks   = */ {{0}},
-        /*.hash_table    = */ {{0}},
-        /*.max_size      = */ 0,
-        /*.measure       = */ true,
-#ifdef GGML_ALLOCATOR_DEBUG
-        /*.allocated_tensors = */ = {0},
-#endif
-    };
-
-    ggml_allocr_reset(alloc);
-
-    return alloc;
-}
-
-void ggml_allocr_free(struct ggml_allocr * alloc) {
-    free(alloc);
-}
-
-bool ggml_allocr_is_measure(struct ggml_allocr * alloc) {
-    return alloc->measure;
-}
-
-//////////// compute graph allocator
-
-static bool ggml_is_view(struct ggml_tensor * t) {
-    return t->op == GGML_OP_RESHAPE || t->op == GGML_OP_VIEW || t->op == GGML_OP_TRANSPOSE ||
-           t->op == GGML_OP_PERMUTE || t->op == GGML_OP_CPY;
-}
-
-static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
-    if (a->type != b->type) {
-        return false;
-    }
-    for (int i = 0; i < GGML_MAX_DIMS; i++) {
-        if (a->ne[i] != b->ne[i]) {
-            return false;
-        }
-        if (a->nb[i] != b->nb[i]) {
-            return false;
-        }
-    }
-    return true;
-}
-
-static struct ggml_tensor * get_view_parent(struct ggml_tensor * t) {
-    switch (t->op) {
-        case GGML_OP_PERMUTE:
-        case GGML_OP_RESHAPE:
-        case GGML_OP_TRANSPOSE:
-        case GGML_OP_VIEW:
-            return t->src[0];
-        case GGML_OP_CPY:
-            return t->src[1];
-        default:
-            return NULL;
-    }
-}
-
-static struct ggml_tensor * get_view_source(struct ggml_tensor * t) {
-    struct ggml_tensor * parent = t;
-    do {
-        parent = get_view_parent(parent);
-    } while (ggml_is_view(parent));
-    return parent;
-}
-
-static bool ggml_op_can_inplace(enum ggml_op op) {
-    switch (op) {
-        case GGML_OP_SCALE:
-        case GGML_OP_DIAG_MASK_ZERO:
-        case GGML_OP_DIAG_MASK_INF:
-        case GGML_OP_ADD:
-        case GGML_OP_ADD1:
-        case GGML_OP_ACC:
-        case GGML_OP_SUB:
-        case GGML_OP_MUL:
-        case GGML_OP_DIV:
-        case GGML_OP_SQR:
-        case GGML_OP_SQRT:
-        case GGML_OP_LOG:
-        case GGML_OP_UNARY:
-        case GGML_OP_ROPE:
-        case GGML_OP_RMS_NORM:
-        case GGML_OP_SET:
-        case GGML_OP_SOFT_MAX:
-        case GGML_OP_CONT:
-            return true;
-
-        default:
-            return false;
-    }
-}
-
-static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) {
-    struct hash_node * ht = alloc->hash_table;
-    if (node->data == NULL) {
-        if (ggml_is_view(node)) {
-            size_t offset;
-            switch(node->op) {
-                case GGML_OP_VIEW:
-                    memcpy(&offset, node->op_params, sizeof(size_t));
-                    node->data = (char *) node->src[0]->data + offset;
-                    break;
-                case GGML_OP_PERMUTE:
-                case GGML_OP_RESHAPE:
-                case GGML_OP_TRANSPOSE:
-                    node->data = node->src[0]->data;
-                    break;
-                case GGML_OP_CPY:
-                    node->data = node->src[1]->data;
-                    break;
-                default:
-                    GGML_ASSERT(!"unknown view op");
-                    break;
-            }
-        } else {
-            // see if we can reuse a parent's buffer (inplace)
-            if (ggml_op_can_inplace(node->op)) {
-                for (int i = 0; i < GGML_MAX_SRC; i++) {
-                    struct ggml_tensor * parent = node->src[i];
-                    if (parent == NULL) {
-                        break;
-                    }
-
-                    // if the node's data is external, then we cannot re-use it
-                    if ((char *) parent->data < (char *) alloc->data ||
-                        (char *) parent->data >= ((char *) alloc->data + alloc->size)) {
-                        AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data);
-                        continue;
-                    }
-
-                    struct hash_node * p_hn = hash_get(ht, parent);
-                    if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) {
-                        if (ggml_is_view(parent)) {
-                            struct ggml_tensor * view_src = get_view_source(parent);
-                            struct hash_node * view_src_hn = hash_get(ht, view_src);
-                            if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
-                                // TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite
-                                // the parent's data that it will need later (same layout requirement). the problem is that then
-                                // we cannot free the tensor because the original address of the allocation is lost.
-                                // adding a view_src pointer to the tensor would solve this and simplify the code dealing with views
-                                // for now, we only reuse the parent's data if the offset is zero (view_src->data == parent->data)
-                                AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name);
-                                node->data = parent->data;
-                                return;
-                            }
-                        }
-                        else {
-                            AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
-                            node->data = parent->data;
-                        }
-                        return;
-                    }
-                }
-            }
-            ggml_allocr_alloc(alloc, node);
-        }
-    }
-}
-
-static size_t ggml_allocator_alloc_graph_tensors_n(
-    struct ggml_allocr * alloc,
-    struct ggml_cgraph ** graphs, int n_graphs,
-    struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) {
-
-    // reset hash table
-    struct hash_node * ht = alloc->hash_table;
-    memset(ht, 0, sizeof(struct hash_node) * GGML_GRAPH_HASHTABLE_SIZE);
-
-    // count number of children and views
-    for (int g = 0; g < n_graphs; g++) {
-        struct ggml_cgraph * gf = graphs[g];
-        for (int i = 0; i < gf->n_nodes; i++) {
-            struct ggml_tensor * node = gf->nodes[i];
-
-            if (ggml_is_view(node)) {
-                struct ggml_tensor * view_src = get_view_source(node);
-                hash_get(ht, view_src)->n_views += 1;
-            }
-
-            for (int j = 0; j < GGML_MAX_SRC; j++) {
-                struct ggml_tensor * parent = node->src[j];
-                if (parent == NULL) {
-                    break;
-                }
-                hash_get(ht, parent)->n_children += 1;
-            }
-        }
-    }
-
-    // allocate tensors
-    for (int g = 0; g < n_graphs; g++) {
-        struct ggml_cgraph * gf = graphs[g];
-        AT_PRINTF("####### graph %d/%d\n", g, n_graphs);
-        // graph inputs are allocated first to ensure that they are not overwritten by each other
-        if (inputs != NULL && inputs[g] != NULL) {
-            for (int i = 0; inputs[g][i] != NULL; i++) {
-                struct ggml_tensor * input = inputs[g][i];
-                AT_PRINTF("input: %s\n", input->name);
-                allocate_node(alloc, input);
-            }
-        }
-        for (int i = 0; i < gf->n_nodes; i++) {
-            struct ggml_tensor * node = gf->nodes[i];
-
-            // allocate parents (leafs)
-            for (int j = 0; j < GGML_MAX_SRC; j++) {
-                struct ggml_tensor * parent = node->src[j];
-                if (parent == NULL) {
-                    break;
-                }
-                allocate_node(alloc, parent);
-            }
-
-            // allocate node
-            allocate_node(alloc, node);
-
-            AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name);
-            for (int j = 0; j < GGML_MAX_SRC; j++) {
-                struct ggml_tensor * parent = node->src[j];
-                if (parent == NULL) {
-                    break;
-                }
-                AT_PRINTF("%s", parent->name);
-                if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) {
-                    AT_PRINTF(", ");
-                }
-            }
-            AT_PRINTF("\n");
-
-            // update parents
-            for (int j = 0; j < GGML_MAX_SRC; j++) {
-                struct ggml_tensor * parent = node->src[j];
-                if (parent == NULL) {
-                    break;
-                }
-                struct hash_node * p_hn = hash_get(ht, parent);
-                p_hn->n_children -= 1;
-
-                //AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views);
-
-                if (p_hn->n_children == 0 && p_hn->n_views == 0) {
-                    if (ggml_is_view(parent)) {
-                        struct ggml_tensor * view_src = get_view_source(parent);
-                        struct hash_node * view_src_hn = hash_get(ht, view_src);
-                        view_src_hn->n_views -= 1;
-                        AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src->n_children, view_src->n_views);
-                        if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
-                            ggml_allocator_free_tensor(alloc, view_src);
-                        }
-                    }
-                    else {
-                        if (parent->data != node->data) {
-                            ggml_allocator_free_tensor(alloc, parent);
-                        }
-                    }
-                }
-            }
-            AT_PRINTF("\n");
-        }
-        // free graph outputs here that wouldn't be freed otherwise because they have no children
-        if (outputs != NULL && outputs[g] != NULL) {
-            for (int i = 0; outputs[g][i] != NULL; i++) {
-                struct ggml_tensor * output = outputs[g][i];
-                AT_PRINTF("output: %s\n", output->name);
-                ggml_allocator_free_tensor(alloc, output);
-            }
-        }
-    }
-
-    return alloc->max_size;
-}
-
-size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) {
-    return ggml_allocator_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL);
-}

llm/ggml-alloc.h

@@ -1,48 +0,0 @@
-/**
- * llama.cpp - git 3ebb00935f3f0522b75df49c2769ab1774b91380
- *
- * 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
-
-
-GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment);
-GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);
-
-GGML_API void   ggml_allocr_free(struct ggml_allocr * alloc);
-GGML_API bool   ggml_allocr_is_measure(struct ggml_allocr * alloc);
-GGML_API void   ggml_allocr_reset(struct ggml_allocr * alloc);
-GGML_API void   ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor);
-GGML_API size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph);
-
-
-#ifdef  __cplusplus
-}
-#endif

llm/ggml-cuda.h

@@ -1,63 +0,0 @@
-/**
- * llama.cpp - git 3ebb00935f3f0522b75df49c2769ab1774b91380
- *
- * 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_mul_mat_q(bool mul_mat_q);
-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

llm/ggml-metal.h

@@ -1,106 +0,0 @@
-//go:build darwin
-
-/**
- * llama.cpp - git 3ebb00935f3f0522b75df49c2769ab1774b91380
- *
- * 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);
-
-// try to find operations that can be run concurrently in the graph
-// you should run it again if the topology of your graph changes
-void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
-
-// if the graph has been optimized for concurrently dispatch
-bool ggml_metal_if_optimized(struct ggml_metal_context * ctx);
-
-// 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
-