punch the linter again

.github/workflows/release.yaml

@@ -31,7 +31,7 @@ jobs:
           security set-keychain-settings -lut 3600 build.keychain
       - uses: actions/setup-go@v5
         with:
-          go-version: "stable"
+          go-version-file: go.mod
           cache: true
       - name: Build Darwin
         env:
@@ -87,7 +87,7 @@ jobs:
           write-host "plugin installed"
       - uses: actions/setup-go@v5
         with:
-          go-version: "stable"
+          go-version-file: go.mod
           cache: true
       - run: go get ./...
       - run: |
@@ -141,13 +141,13 @@ jobs:
           write-host "plugin installed"
       - uses: actions/setup-go@v5
         with:
-          go-version: "stable"
+          go-version-file: go.mod
           cache: true
       - name: 'Install ROCm'
         run: |
           $ErrorActionPreference = "Stop"
           write-host "downloading AMD HIP Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-23.Q4-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
           write-host "Installing AMD HIP"
           Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
           write-host "Completed AMD HIP"
@@ -218,7 +218,7 @@ jobs:
           write-host "plugin installed"
       - uses: actions/setup-go@v5
         with:
-          go-version: "stable"
+          go-version-file: go.mod
           cache: true
       - name: 'Install CUDA'
         run: |
@@ -304,9 +304,14 @@ jobs:
           write-host "Installing plugin"
           & "${env:RUNNER_TEMP}\plugin\*\kmscng.msi" /quiet
           write-host "plugin installed"
+      - name: remove unwanted mingw dll.a files
+        run: |
+          Get-ChildItem -Path "C:\mingw64" -Recurse -Filter "libpthread.dll.a" -File | Remove-Item -Force
+          Get-ChildItem -Path "C:\mingw64" -Recurse -Filter "libwinpthread.dll.a" -File | Remove-Item -Force
+          Get-ChildItem -Path "C:\mingw64" -Recurse -Filter "libstdc++.dll.a" -File | Remove-Item -Force
       - uses: actions/setup-go@v5
         with:
-          go-version: "stable"
+          go-version-file: go.mod
           cache: true
       - run: go get
       - uses: actions/download-artifact@v4

.github/workflows/test.yaml

@@ -63,7 +63,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-go@v5
         with:
-          go-version: "stable"
+          go-version-file: go.mod
           cache: true
       - run: go get ./...
       - run: |
@@ -126,7 +126,7 @@ jobs:
     strategy:
       matrix:
         rocm-version:
-          - '6.1.2'
+          - '6.1.1'
     runs-on: linux
     container: rocm/dev-ubuntu-20.04:${{ matrix.rocm-version }}
     steps:
@@ -163,13 +163,13 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-go@v5
         with:
-          go-version: "stable"
+          go-version-file: go.mod
           cache: true
       - name: 'Install ROCm'
         run: |
           $ErrorActionPreference = "Stop"
           write-host "downloading AMD HIP Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-23.Q4-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
           write-host "Installing AMD HIP"
           Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
           write-host "Completed AMD HIP"
@@ -200,7 +200,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-go@v5
         with:
-          go-version: "stable"
+          go-version-file: go.mod
           cache: true
       - name: 'Install CUDA'
         run: |
@@ -255,7 +255,7 @@ jobs:
           submodules: recursive
       - uses: actions/setup-go@v5
         with:
-          go-version: "stable"
+          go-version-file: go.mod
           cache: false
       - run: |
           case ${{ matrix.arch }} in
@@ -297,7 +297,7 @@ jobs:
           submodules: recursive
       - uses: actions/setup-go@v5
         with:
-          go-version: "stable"
+          go-version-file: go.mod
           cache: true
       - run: |
           case ${{ matrix.arch }} in

Dockerfile

@@ -1,8 +1,8 @@
-ARG GOLANG_VERSION=1.22.5
+ARG GOLANG_VERSION=1.22.1
 ARG CMAKE_VERSION=3.22.1
 # this CUDA_VERSION corresponds with the one specified in docs/gpu.md
 ARG CUDA_VERSION=11.3.1
-ARG ROCM_VERSION=6.1.2
+ARG ROCM_VERSION=6.1.1
 
 # Copy the minimal context we need to run the generate scripts
 FROM scratch AS llm-code

README.md

@@ -35,10 +35,10 @@ The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `olla
 
 ## Quickstart
 
-To run and chat with [Llama 3.1](https://ollama.com/library/llama3.1):
+To run and chat with [Llama 3](https://ollama.com/library/llama3):
 
 ```
-ollama run llama3.1
+ollama run llama3
 ```
 
 ## Model library
@@ -49,9 +49,8 @@ Here are some example models that can be downloaded:
 
 | Model              | Parameters | Size  | Download                       |
 | ------------------ | ---------- | ----- | ------------------------------ |
-| Llama 3.1          | 8B         | 4.7GB | `ollama run llama3.1`          |
-| Llama 3.1          | 70B        | 40GB  | `ollama run llama3.1:70b`      |
-| Llama 3.1          | 405B       | 231GB | `ollama run llama3.1:405b`     |
+| Llama 3            | 8B         | 4.7GB | `ollama run llama3`            |
+| Llama 3            | 70B        | 40GB  | `ollama run llama3:70b`        |
 | Phi 3 Mini         | 3.8B       | 2.3GB | `ollama run phi3`              |
 | Phi 3 Medium       | 14B        | 7.9GB | `ollama run phi3:medium`       |
 | Gemma 2            | 9B         | 5.5GB | `ollama run gemma2`            |
@@ -65,8 +64,7 @@ Here are some example models that can be downloaded:
 | LLaVA              | 7B         | 4.5GB | `ollama run llava`             |
 | Solar              | 10.7B      | 6.1GB | `ollama run solar`             |
 
-> [!NOTE]
-> You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
+> Note: You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
 
 ## Customize a model
 
@@ -98,16 +96,16 @@ 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 `llama3.1` model:
+Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3` model:
 
 ```
-ollama pull llama3.1
+ollama pull llama3
 ```
 
 Create a `Modelfile`:
 
 ```
-FROM llama3.1
+FROM llama3
 
 # set the temperature to 1 [higher is more creative, lower is more coherent]
 PARAMETER temperature 1
@@ -142,7 +140,7 @@ ollama create mymodel -f ./Modelfile
 ### Pull a model
 
 ```
-ollama pull llama3.1
+ollama pull llama3
 ```
 
 > This command can also be used to update a local model. Only the diff will be pulled.
@@ -150,13 +148,13 @@ ollama pull llama3.1
 ### Remove a model
 
 ```
-ollama rm llama3.1
+ollama rm llama3
 ```
 
 ### Copy a model
 
 ```
-ollama cp llama3.1 my-model
+ollama cp llama3 my-model
 ```
 
 ### Multiline input
@@ -180,14 +178,14 @@ The image features a yellow smiley face, which is likely the central focus of th
 ### Pass the prompt as an argument
 
 ```
-$ ollama run llama3.1 "Summarize this file: $(cat README.md)"
+$ ollama run llama3 "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.
 ```
 
 ### Show model information
 
 ```
-ollama show llama3.1
+ollama show llama3
 ```
 
 ### List models on your computer
@@ -215,7 +213,7 @@ Next, start the server:
 Finally, in a separate shell, run a model:
 
 ```
-./ollama run llama3.1
+./ollama run llama3
 ```
 
 ## REST API
@@ -226,7 +224,7 @@ Ollama has a REST API for running and managing models.
 
 ```
 curl http://localhost:11434/api/generate -d '{
-  "model": "llama3.1",
+  "model": "llama3",
   "prompt":"Why is the sky blue?"
 }'
 ```
@@ -235,7 +233,7 @@ curl http://localhost:11434/api/generate -d '{
 
 ```
 curl http://localhost:11434/api/chat -d '{
-  "model": "llama3.1",
+  "model": "llama3",
   "messages": [
     { "role": "user", "content": "why is the sky blue?" }
   ]
@@ -295,10 +293,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [OllamaSpring](https://github.com/CrazyNeil/OllamaSpring) (Ollama Client for macOS)
 - [LLocal.in](https://github.com/kartikm7/llocal) (Easy to use Electron Desktop Client for Ollama)
 - [Ollama with Google Mesop](https://github.com/rapidarchitect/ollama_mesop/) (Mesop Chat Client implementation with Ollama)
-- [Kerlig AI](https://www.kerlig.com/) (AI writing assistant for macOS)
-- [AI Studio](https://github.com/MindWorkAI/AI-Studio)
-- [Sidellama](https://github.com/gyopak/sidellama) (browser-based LLM client)
-- [LLMStack](https://github.com/trypromptly/LLMStack) (No-code multi-agent framework to build LLM agents and workflows)
 
 ### Terminal
 
@@ -390,7 +384,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Llama Coder](https://github.com/ex3ndr/llama-coder) (Copilot alternative using Ollama)
 - [Ollama Copilot](https://github.com/bernardo-bruning/ollama-copilot) (Proxy that allows you to use ollama as a copilot like Github copilot)
 - [twinny](https://github.com/rjmacarthy/twinny) (Copilot and Copilot chat alternative using Ollama)
-- [Wingman-AI](https://github.com/RussellCanfield/wingman-ai) (Copilot code and chat alternative using Ollama and Hugging Face)
+- [Wingman-AI](https://github.com/RussellCanfield/wingman-ai) (Copilot code and chat alternative using Ollama and HuggingFace)
 - [Page Assist](https://github.com/n4ze3m/page-assist) (Chrome Extension)
 - [AI Telegram Bot](https://github.com/tusharhero/aitelegrambot) (Telegram bot using Ollama in backend)
 - [AI ST Completion](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (Sublime Text 4 AI assistant plugin with Ollama support)

api/client.go

@@ -347,16 +347,7 @@ func (c *Client) Heartbeat(ctx context.Context) error {
 	return nil
 }
 
-// Embed generates embeddings from a model.
-func (c *Client) Embed(ctx context.Context, req *EmbedRequest) (*EmbedResponse, error) {
-	var resp EmbedResponse
-	if err := c.do(ctx, http.MethodPost, "/api/embed", req, &resp); err != nil {
-		return nil, err
-	}
-	return &resp, nil
-}
-
-// Embeddings generates an embedding from a model.
+// Embeddings generates embeddings from a model.
 func (c *Client) Embeddings(ctx context.Context, req *EmbeddingRequest) (*EmbeddingResponse, error) {
 	var resp EmbeddingResponse
 	if err := c.do(ctx, http.MethodPost, "/api/embeddings", req, &resp); err != nil {

api/types.go

@@ -47,9 +47,6 @@ type GenerateRequest struct {
 	// Prompt is the textual prompt to send to the model.
 	Prompt string `json:"prompt"`
 
-	// Suffix is the text that comes after the inserted text.
-	Suffix string `json:"suffix"`
-
 	// System overrides the model's default system message/prompt.
 	System string `json:"system"`
 
@@ -100,85 +97,17 @@ type ChatRequest struct {
 	// followin the request.
 	KeepAlive *Duration `json:"keep_alive,omitempty"`
 
-	// Tools is an optional list of tools the model has access to.
-	Tools `json:"tools,omitempty"`
-
 	// Options lists model-specific options.
 	Options map[string]interface{} `json:"options"`
 }
 
-type Tools []Tool
-
-func (t Tools) String() string {
-	bts, _ := json.Marshal(t)
-	return string(bts)
-}
-
-func (t Tool) String() string {
-	bts, _ := json.Marshal(t)
-	return string(bts)
-}
-
 // Message is a single message in a chat sequence. The message contains the
 // role ("system", "user", or "assistant"), the content and an optional list
 // of images.
 type Message struct {
-	Role      string      `json:"role"`
-	Content   string      `json:"content"`
-	Images    []ImageData `json:"images,omitempty"`
-	ToolCalls []ToolCall  `json:"tool_calls,omitempty"`
-}
-
-func (m *Message) UnmarshalJSON(b []byte) error {
-	type Alias Message
-	var a Alias
-	if err := json.Unmarshal(b, &a); err != nil {
-		return err
-	}
-
-	*m = Message(a)
-	m.Role = strings.ToLower(m.Role)
-	return nil
-}
-
-type ToolCall struct {
-	Function ToolCallFunction `json:"function"`
-}
-
-type ToolCallFunction struct {
-	Name      string                    `json:"name"`
-	Arguments ToolCallFunctionArguments `json:"arguments"`
-}
-
-type ToolCallFunctionArguments map[string]any
-
-func (t *ToolCallFunctionArguments) String() string {
-	bts, _ := json.Marshal(t)
-	return string(bts)
-}
-
-type Tool struct {
-	Type     string       `json:"type"`
-	Function ToolFunction `json:"function"`
-}
-
-type ToolFunction struct {
-	Name        string `json:"name"`
-	Description string `json:"description"`
-	Parameters  struct {
-		Type       string   `json:"type"`
-		Required   []string `json:"required"`
-		Properties map[string]struct {
-			Type        string   `json:"type"`
-			Description string   `json:"description"`
-			Enum        []string `json:"enum,omitempty"`
-		} `json:"properties"`
-	} `json:"parameters"`
-}
-
-func (t *ToolFunction) String() string {
-	bts, _ := json.Marshal(t)
-	return string(bts)
+	Role    string      `json:"role"`
+	Content string      `json:"content"`
+	Images  []ImageData `json:"images,omitempty"`
 }
 
 // ChatResponse is the response returned by [Client.Chat]. Its fields are
@@ -214,7 +143,6 @@ type Options struct {
 	NumPredict       int      `json:"num_predict,omitempty"`
 	TopK             int      `json:"top_k,omitempty"`
 	TopP             float32  `json:"top_p,omitempty"`
-	MinP             float32  `json:"min_p,omitempty"`
 	TFSZ             float32  `json:"tfs_z,omitempty"`
 	TypicalP         float32  `json:"typical_p,omitempty"`
 	RepeatLastN      int      `json:"repeat_last_n,omitempty"`
@@ -245,30 +173,6 @@ type Runner struct {
 	NumThread int   `json:"num_thread,omitempty"`
 }
 
-// EmbedRequest is the request passed to [Client.Embed].
-type EmbedRequest struct {
-	// Model is the model name.
-	Model string `json:"model"`
-
-	// Input is the input to embed.
-	Input any `json:"input"`
-
-	// KeepAlive controls how long the model will stay loaded in memory following
-	// this request.
-	KeepAlive *Duration `json:"keep_alive,omitempty"`
-
-	Truncate *bool `json:"truncate,omitempty"`
-
-	// Options lists model-specific options.
-	Options map[string]interface{} `json:"options"`
-}
-
-// EmbedResponse is the response from [Client.Embed].
-type EmbedResponse struct {
-	Model      string      `json:"model"`
-	Embeddings [][]float32 `json:"embeddings"`
-}
-
 // EmbeddingRequest is the request passed to [Client.Embeddings].
 type EmbeddingRequest struct {
 	// Model is the model name.
@@ -315,10 +219,8 @@ type DeleteRequest struct {
 
 // ShowRequest is the request passed to [Client.Show].
 type ShowRequest struct {
-	Model  string `json:"model"`
-	System string `json:"system"`
-
-	// Template is deprecated
+	Model    string `json:"model"`
+	System   string `json:"system"`
 	Template string `json:"template"`
 	Verbose  bool   `json:"verbose"`
 

api/types_test.go

@@ -208,26 +208,3 @@ func TestUseMmapFormatParams(t *testing.T) {
 		})
 	}
 }
-
-func TestMessage_UnmarshalJSON(t *testing.T) {
-	tests := []struct {
-		input    string
-		expected string
-	}{
-		{`{"role": "USER", "content": "Hello!"}`, "user"},
-		{`{"role": "System", "content": "Initialization complete."}`, "system"},
-		{`{"role": "assistant", "content": "How can I help you?"}`, "assistant"},
-		{`{"role": "TOOl", "content": "Access granted."}`, "tool"},
-	}
-
-	for _, test := range tests {
-		var msg Message
-		if err := json.Unmarshal([]byte(test.input), &msg); err != nil {
-			t.Errorf("Unexpected error: %v", err)
-		}
-
-		if msg.Role != test.expected {
-			t.Errorf("role not lowercased: got %v, expected %v", msg.Role, test.expected)
-		}
-	}
-}

app/ollama.iss

@@ -127,10 +127,6 @@ Type: filesandordirs; Name: "{%USERPROFILE}\.ollama\models"
 Type: filesandordirs; Name: "{%USERPROFILE}\.ollama\history"
 ; NOTE: if the user has a custom OLLAMA_MODELS it will be preserved
 
-[InstallDelete]
-Type: filesandordirs; Name: "{%TEMP}\ollama*"
-Type: filesandordirs; Name: "{%LOCALAPPDATA}\Programs\Ollama"
-
 [Messages]
 WizardReady=Ollama Windows Preview
 ReadyLabel1=%nLet's get you up and running with your own large language models.
@@ -138,7 +134,7 @@ SetupAppRunningError=Another Ollama installer is running.%n%nPlease cancel or fi
 
 
 ;FinishedHeadingLabel=Run your first model
-;FinishedLabel=%nRun this command in a PowerShell or cmd terminal.%n%n%n    ollama run llama3.1
+;FinishedLabel=%nRun this command in a PowerShell or cmd terminal.%n%n%n    ollama run llama3
 ;ClickFinish=%n
 
 [Registry]

app/ollama_welcome.ps1

@@ -4,5 +4,5 @@ write-host "Welcome to Ollama!"
 write-host ""
 write-host "Run your first model:"
 write-host ""
-write-host "`tollama run llama3.1"
+write-host "`tollama run llama3"
 write-host ""

cmd/cmd.go

@@ -843,6 +843,7 @@ type runOptions struct {
 	WordWrap    bool
 	Format      string
 	System      string
+	Template    string
 	Images      []api.ImageData
 	Options     map[string]interface{}
 	MultiModal  bool
@@ -1036,6 +1037,7 @@ func generate(cmd *cobra.Command, opts runOptions) error {
 		Images:    opts.Images,
 		Format:    opts.Format,
 		System:    opts.System,
+		Template:  opts.Template,
 		Options:   opts.Options,
 		KeepAlive: opts.KeepAlive,
 	}
@@ -1341,10 +1343,10 @@ func NewCLI() *cobra.Command {
 				envVars["OLLAMA_NUM_PARALLEL"],
 				envVars["OLLAMA_NOPRUNE"],
 				envVars["OLLAMA_ORIGINS"],
-				envVars["OLLAMA_SCHED_SPREAD"],
 				envVars["OLLAMA_TMPDIR"],
 				envVars["OLLAMA_FLASH_ATTENTION"],
 				envVars["OLLAMA_LLM_LIBRARY"],
+				envVars["OLLAMA_MAX_VRAM"],
 			})
 		default:
 			appendEnvDocs(cmd, envs)

cmd/interactive.go

@@ -1,7 +1,6 @@
 package cmd
 
 import (
-	"cmp"
 	"errors"
 	"fmt"
 	"io"
@@ -10,14 +9,13 @@ import (
 	"path/filepath"
 	"regexp"
 	"slices"
+	"sort"
 	"strings"
 
 	"github.com/spf13/cobra"
-	"golang.org/x/exp/maps"
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
-	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/progress"
 	"github.com/ollama/ollama/readline"
 	"github.com/ollama/ollama/types/errtypes"
@@ -29,6 +27,7 @@ const (
 	MultilineNone MultilineState = iota
 	MultilinePrompt
 	MultilineSystem
+	MultilineTemplate
 )
 
 func loadModel(cmd *cobra.Command, opts *runOptions) error {
@@ -95,6 +94,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		fmt.Fprintln(os.Stderr, "Available Commands:")
 		fmt.Fprintln(os.Stderr, "  /set parameter ...     Set a parameter")
 		fmt.Fprintln(os.Stderr, "  /set system <string>   Set system message")
+		fmt.Fprintln(os.Stderr, "  /set template <string> Set prompt template")
 		fmt.Fprintln(os.Stderr, "  /set history           Enable history")
 		fmt.Fprintln(os.Stderr, "  /set nohistory         Disable history")
 		fmt.Fprintln(os.Stderr, "  /set wordwrap          Enable wordwrap")
@@ -140,7 +140,6 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		fmt.Fprintln(os.Stderr, "  /set parameter num_predict <int>      Max number of tokens to predict")
 		fmt.Fprintln(os.Stderr, "  /set parameter top_k <int>            Pick from top k num of tokens")
 		fmt.Fprintln(os.Stderr, "  /set parameter top_p <float>          Pick token based on sum of probabilities")
-		fmt.Fprintln(os.Stderr, "  /set parameter min_p <float>          Pick token based on top token probability * min_p")
 		fmt.Fprintln(os.Stderr, "  /set parameter num_ctx <int>          Set the context size")
 		fmt.Fprintln(os.Stderr, "  /set parameter temperature <float>    Set creativity level")
 		fmt.Fprintln(os.Stderr, "  /set parameter repeat_penalty <float> How strongly to penalize repetitions")
@@ -205,6 +204,10 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 				opts.Messages = append(opts.Messages, api.Message{Role: "system", Content: opts.System})
 				fmt.Println("Set system message.")
 				sb.Reset()
+			case MultilineTemplate:
+				opts.Template = sb.String()
+				fmt.Println("Set prompt template.")
+				sb.Reset()
 			}
 
 			multiline = MultilineNone
@@ -323,13 +326,17 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 					}
 					fmt.Printf("Set parameter '%s' to '%s'\n", args[2], strings.Join(params, ", "))
 					opts.Options[args[2]] = fp[args[2]]
-				case "system":
+				case "system", "template":
 					if len(args) < 3 {
 						usageSet()
 						continue
 					}
 
-					multiline = MultilineSystem
+					if args[1] == "system" {
+						multiline = MultilineSystem
+					} else if args[1] == "template" {
+						multiline = MultilineTemplate
+					}
 
 					line := strings.Join(args[2:], " ")
 					line, ok := strings.CutPrefix(line, `"""`)
@@ -349,17 +356,23 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 						continue
 					}
 
-					opts.System = sb.String() // for display in modelfile
-					newMessage := api.Message{Role: "system", Content: sb.String()}
-					// Check if the slice is not empty and the last message is from 'system'
-					if len(opts.Messages) > 0 && opts.Messages[len(opts.Messages)-1].Role == "system" {
-						// Replace the last message
-						opts.Messages[len(opts.Messages)-1] = newMessage
-					} else {
-						opts.Messages = append(opts.Messages, newMessage)
+					if args[1] == "system" {
+						opts.System = sb.String() // for display in modelfile
+						newMessage := api.Message{Role: "system", Content: sb.String()}
+						// Check if the slice is not empty and the last message is from 'system'
+						if len(opts.Messages) > 0 && opts.Messages[len(opts.Messages)-1].Role == "system" {
+							// Replace the last message
+							opts.Messages[len(opts.Messages)-1] = newMessage
+						} else {
+							opts.Messages = append(opts.Messages, newMessage)
+						}
+						fmt.Println("Set system message.")
+						sb.Reset()
+					} else if args[1] == "template" {
+						opts.Template = sb.String()
+						fmt.Println("Set prompt template.")
+						sb.Reset()
 					}
-					fmt.Println("Set system message.")
-					sb.Reset()
 
 					sb.Reset()
 					continue
@@ -378,9 +391,10 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 					return err
 				}
 				req := &api.ShowRequest{
-					Name:    opts.Model,
-					System:  opts.System,
-					Options: opts.Options,
+					Name:     opts.Model,
+					System:   opts.System,
+					Template: opts.Template,
+					Options:  opts.Options,
 				}
 				resp, err := client.Show(cmd.Context(), req)
 				if err != nil {
@@ -423,9 +437,12 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 						fmt.Println("No system message was specified for this model.")
 					}
 				case "template":
-					if resp.Template != "" {
+					switch {
+					case opts.Template != "":
+						fmt.Println(opts.Template + "\n")
+					case resp.Template != "":
 						fmt.Println(resp.Template)
-					} else {
+					default:
 						fmt.Println("No prompt template was specified for this model.")
 					}
 				default:
@@ -509,35 +526,35 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 }
 
 func buildModelfile(opts runOptions) string {
-	var f parser.File
-	f.Commands = append(f.Commands, parser.Command{Name: "model", Args: cmp.Or(opts.ParentModel, opts.Model)})
-
+	var mf strings.Builder
+	model := opts.ParentModel
+	if model == "" {
+		model = opts.Model
+	}
+	fmt.Fprintf(&mf, "FROM %s\n", model)
 	if opts.System != "" {
-		f.Commands = append(f.Commands, parser.Command{Name: "system", Args: opts.System})
+		fmt.Fprintf(&mf, "SYSTEM \"\"\"%s\"\"\"\n", opts.System)
 	}
 
-	keys := maps.Keys(opts.Options)
-	slices.Sort(keys)
+	if opts.Template != "" {
+		fmt.Fprintf(&mf, "TEMPLATE \"\"\"%s\"\"\"\n", opts.Template)
+	}
+
+	keys := make([]string, 0)
+	for k := range opts.Options {
+		keys = append(keys, k)
+	}
+	sort.Strings(keys)
 	for _, k := range keys {
-		v := opts.Options[k]
-		var cmds []parser.Command
-		switch t := v.(type) {
-		case []string:
-			for _, s := range t {
-				cmds = append(cmds, parser.Command{Name: k, Args: s})
-			}
-		default:
-			cmds = append(cmds, parser.Command{Name: k, Args: fmt.Sprintf("%v", t)})
-		}
-
-		f.Commands = append(f.Commands, cmds...)
+		fmt.Fprintf(&mf, "PARAMETER %s %v\n", k, opts.Options[k])
 	}
+	fmt.Fprintln(&mf)
 
 	for _, msg := range opts.Messages {
-		f.Commands = append(f.Commands, parser.Command{Name: "message", Args: fmt.Sprintf("%s: %s", msg.Role, msg.Content)})
+		fmt.Fprintf(&mf, "MESSAGE %s \"\"\"%s\"\"\"\n", msg.Role, msg.Content)
 	}
 
-	return f.String()
+	return mf.String()
 }
 
 func normalizeFilePath(fp string) string {

cmd/interactive_test.go

@@ -1,10 +1,12 @@
 package cmd
 
 import (
+	"bytes"
 	"testing"
+	"text/template"
 
-	"github.com/google/go-cmp/cmp"
 	"github.com/stretchr/testify/assert"
+	"github.com/stretchr/testify/require"
 
 	"github.com/ollama/ollama/api"
 )
@@ -55,53 +57,61 @@ d:\path with\spaces\seven.svg inbetween7 c:\users\jdoe\eight.png inbetween8
 
 func TestModelfileBuilder(t *testing.T) {
 	opts := runOptions{
-		Model:  "hork",
-		System: "You are part horse and part shark, but all hork. Do horklike things",
+		Model:    "hork",
+		System:   "You are part horse and part shark, but all hork. Do horklike things",
+		Template: "This is a template.",
 		Messages: []api.Message{
 			{Role: "user", Content: "Hey there hork!"},
 			{Role: "assistant", Content: "Yes it is true, I am half horse, half shark."},
 		},
-		Options: map[string]any{
-			"temperature":      0.9,
-			"seed":             42,
-			"penalize_newline": false,
-			"stop":             []string{"hi", "there"},
-		},
+		Options: map[string]interface{}{},
 	}
 
-	t.Run("model", func(t *testing.T) {
-		expect := `FROM hork
-SYSTEM You are part horse and part shark, but all hork. Do horklike things
+	opts.Options["temperature"] = 0.9
+	opts.Options["seed"] = 42
+	opts.Options["penalize_newline"] = false
+	opts.Options["stop"] = []string{"hi", "there"}
+
+	mf := buildModelfile(opts)
+	expectedModelfile := `FROM {{.Model}}
+SYSTEM """{{.System}}"""
+TEMPLATE """{{.Template}}"""
 PARAMETER penalize_newline false
 PARAMETER seed 42
-PARAMETER stop hi
-PARAMETER stop there
+PARAMETER stop [hi there]
 PARAMETER temperature 0.9
-MESSAGE user Hey there hork!
-MESSAGE assistant Yes it is true, I am half horse, half shark.
+
+MESSAGE user """Hey there hork!"""
+MESSAGE assistant """Yes it is true, I am half horse, half shark."""
 `
 
-		actual := buildModelfile(opts)
-		if diff := cmp.Diff(expect, actual); diff != "" {
-			t.Errorf("mismatch (-want +got):\n%s", diff)
-		}
-	})
+	tmpl, err := template.New("").Parse(expectedModelfile)
+	require.NoError(t, err)
 
-	t.Run("parent model", func(t *testing.T) {
-		opts.ParentModel = "horseshark"
-		expect := `FROM horseshark
-SYSTEM You are part horse and part shark, but all hork. Do horklike things
+	var buf bytes.Buffer
+	err = tmpl.Execute(&buf, opts)
+	require.NoError(t, err)
+	assert.Equal(t, buf.String(), mf)
+
+	opts.ParentModel = "horseshark"
+	mf = buildModelfile(opts)
+	expectedModelfile = `FROM {{.ParentModel}}
+SYSTEM """{{.System}}"""
+TEMPLATE """{{.Template}}"""
 PARAMETER penalize_newline false
 PARAMETER seed 42
-PARAMETER stop hi
-PARAMETER stop there
+PARAMETER stop [hi there]
 PARAMETER temperature 0.9
-MESSAGE user Hey there hork!
-MESSAGE assistant Yes it is true, I am half horse, half shark.
+
+MESSAGE user """Hey there hork!"""
+MESSAGE assistant """Yes it is true, I am half horse, half shark."""
 `
-		actual := buildModelfile(opts)
-		if diff := cmp.Diff(expect, actual); diff != "" {
-			t.Errorf("mismatch (-want +got):\n%s", diff)
-		}
-	})
+
+	tmpl, err = template.New("").Parse(expectedModelfile)
+	require.NoError(t, err)
+
+	var parentBuf bytes.Buffer
+	err = tmpl.Execute(&parentBuf, opts)
+	require.NoError(t, err)
+	assert.Equal(t, parentBuf.String(), mf)
 }

convert/convert.go

@@ -1,200 +1,134 @@
 package convert
 
 import (
-	"cmp"
-	"encoding/binary"
 	"encoding/json"
+	"errors"
 	"fmt"
 	"io"
 	"log/slog"
 	"os"
 	"path/filepath"
-	"slices"
-	"strings"
 
-	"google.golang.org/protobuf/proto"
-
-	"github.com/ollama/ollama/convert/sentencepiece"
 	"github.com/ollama/ollama/llm"
 )
 
-const (
-	_ int32 = iota
-	tokenTypeNormal
-	tokenTypeUnknown
-	tokenTypeControl
-	tokenTypeUserDefined
-	tokenTypeUnused
-	tokenTypeByte
-)
-
-type Params struct {
-	Architectures     []string `json:"architectures"`
-	VocabSize         int      `json:"vocab_size"`
-	HiddenSize        int      `json:"hidden_size"`       // n_embd
-	HiddenLayers      int      `json:"num_hidden_layers"` // n_layer
-	ContextSize       int      `json:"max_position_embeddings"`
-	IntermediateSize  int      `json:"intermediate_size"`
-	AttentionHeads    int      `json:"num_attention_heads"` // n_head
-	KeyValHeads       int      `json:"num_key_value_heads"`
-	NormEPS           float64  `json:"rms_norm_eps"`
-	BoSTokenID        int      `json:"bos_token_id"`
-	EoSTokenID        int      `json:"eos_token_id"`
-	HeadDimension     int      `json:"head_dim"`
-	PaddingTokenID    int      `json:"pad_token_id"`
-	RopeFrequencyBase float64  `json:"rope_theta"`
-
-	Experts     int `json:"num_local_experts"`
-	ExpertsUsed int `json:"num_experts_per_tok"`
-
-	PreTokenizer string
-
-	ByteOrder
+type Parameters struct {
+	Architectures []string `json:"architectures"`
+	VocabSize     uint32   `json:"vocab_size"`
 }
 
-type ByteOrder interface {
-	binary.ByteOrder
-	binary.AppendByteOrder
+func (Parameters) KV(t *Tokenizer) llm.KV {
+	kv := llm.KV{
+		"general.file_type":            uint32(1),
+		"general.quantization_version": uint32(2),
+		"tokenizer.ggml.pre":           t.Pre,
+		"tokenizer.ggml.model":         t.Vocabulary.Model,
+		"tokenizer.ggml.tokens":        t.Vocabulary.Tokens,
+		"tokenizer.ggml.scores":        t.Vocabulary.Scores,
+		"tokenizer.ggml.token_type":    t.Vocabulary.Types,
+	}
+
+	if t.Template != "" {
+		kv["tokenizer.chat_template"] = t.Template
+	}
+
+	for _, sv := range t.SpecialVocabulary {
+		kv[fmt.Sprintf("tokenizer.ggml.%s_token_id", sv.Key())] = uint32(sv.ID)
+		kv[fmt.Sprintf("tokenizer.ggml.add_%s_token", sv.Key())] = sv.AddToken
+	}
+
+	return kv
 }
 
-type ModelArch interface {
-	GetTensors() error
-	LoadVocab() error
-	WriteGGUF(io.WriteSeeker) error
+func (Parameters) specialTypes() []string {
+	return []string{
+		"bos", "eos", "unk", "sep", "pad", "cls", "mask",
+	}
 }
 
-type ModelFormat interface {
-	GetLayerName(string) (string, error)
-	GetTensors(string, *Params) ([]llm.Tensor, error)
-	GetParams(string) (*Params, error)
-	GetModelArch(string, string, *Params) (ModelArch, error)
+func (Parameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []*llm.Tensor) error {
+	return llm.WriteGGUF(ws, kv, ts)
 }
 
-type ModelData struct {
-	Path    string
-	Name    string
-	Params  *Params
-	Vocab   *Vocab
-	Tensors []llm.Tensor
-	Format  ModelFormat
+type Converter interface {
+	// KV maps parameters to LLM key-values
+	KV(*Tokenizer) llm.KV
+	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
+	Tensors([]Tensor) []*llm.Tensor
+
+	// tensorName returns the LLM tensor name for a specific input name
+	tensorName(string) string
+	// specialTypes returns any special token types the model uses
+	specialTypes() []string
+	writeFile(io.WriteSeeker, llm.KV, []*llm.Tensor) error
 }
 
-func GetModelFormat(dirname string) (ModelFormat, error) {
-	files, err := filepath.Glob(filepath.Join(dirname, "*"))
+func ConvertAdapter(d string, ws io.WriteSeeker) error {
+	c := &adapter{}
+
+	ts, err := parseNPZ(d)
 	if err != nil {
-		return nil, err
+		return err
 	}
 
-	for _, fn := range files {
-		if strings.HasSuffix(fn, ".safetensors") {
-			return &SafetensorFormat{}, nil
-		} else if strings.HasSuffix(fn, ".bin") || strings.HasSuffix(fn, ".pth") {
-			slog.Debug("model is torch")
-			return &TorchFormat{}, nil
-		}
-	}
-
-	return nil, fmt.Errorf("couldn't determine model format")
+	return c.writeFile(ws, c.KV(nil), c.Tensors(ts))
 }
 
-// Details on gguf's tokenizer can be found at:
-// https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#tokenizer
-type Vocab struct {
-	Tokens []string
-	Scores []float32
-	Types  []int32
-	Merges []string
-}
-
-func LoadSentencePieceTokens(dirpath string, params *Params) (*Vocab, error) {
-	slog.Info(fmt.Sprintf("reading vocab from %s", filepath.Join(dirpath, "tokenizer.model")))
-	in, err := os.ReadFile(filepath.Join(dirpath, "tokenizer.model"))
+func Convert(d string, ws io.WriteSeeker) error {
+	f, err := os.Open(filepath.Join(d, "config.json"))
 	if err != nil {
-		return nil, err
+		return err
+	}
+	defer f.Close()
+
+	var p Parameters
+	if err := json.NewDecoder(f).Decode(&p); err != nil {
+		return err
 	}
 
-	// To regenerate sentencepiece from the protobufs use:
-	// protoc -I=./ --go_out=./ sentencepiece_model.proto
-	modelProto := &sentencepiece.ModelProto{}
-	if err := proto.Unmarshal(in, modelProto); err != nil {
-		return nil, err
+	if len(p.Architectures) < 1 {
+		return errors.New("unknown architecture")
 	}
 
-	v := &Vocab{
-		Tokens: make([]string, 0),
-		Scores: make([]float32, 0),
-		Types:  make([]int32, 0),
+	var c Converter
+	switch p.Architectures[0] {
+	case "LlamaForCausalLM", "MistralForCausalLM":
+		c = &llama{}
+	case "MixtralForCausalLM":
+		c = &mixtral{}
+	case "GemmaForCausalLM":
+		c = &gemma{}
+	default:
+		return errors.New("unsupported architecture")
 	}
 
-	pieces := modelProto.GetPieces()
-	for _, p := range pieces {
-		v.Tokens = append(v.Tokens, p.GetPiece())
-		v.Scores = append(v.Scores, p.GetScore())
-		t := p.GetType()
-		switch t {
-		case sentencepiece.ModelProto_SentencePiece_UNKNOWN:
-		case sentencepiece.ModelProto_SentencePiece_CONTROL:
-		case sentencepiece.ModelProto_SentencePiece_UNUSED:
-		case sentencepiece.ModelProto_SentencePiece_BYTE:
-		default:
-			t = sentencepiece.ModelProto_SentencePiece_NORMAL
-		}
-		v.Types = append(v.Types, int32(t))
+	bts, err := os.ReadFile(filepath.Join(d, "config.json"))
+	if err != nil {
+		return err
 	}
 
-	slog.Info(fmt.Sprintf("vocab size: %d", len(v.Tokens)))
-
-	// add any additional tokens
-	addIn, err := os.ReadFile(filepath.Join(dirpath, "added_tokens.json"))
-	if os.IsNotExist(err) {
-		return v, nil
-	} else if err != nil {
-		return nil, err
+	if err := json.Unmarshal(bts, c); err != nil {
+		return err
 	}
 
-	slog.Info("reading user defined tokens")
-
-	var extraTokenData map[string]int
-	if err := json.Unmarshal(addIn, &extraTokenData); err != nil {
-		return nil, err
+	t, err := parseTokenizer(d, c.specialTypes())
+	if err != nil {
+		return err
 	}
 
-	type token struct {
-		key string
-		pos int
-	}
-
-	extraTokens := make([]token, 0)
-	for k, id := range extraTokenData {
-		extraTokens = append(extraTokens, token{k, id})
-	}
-
-	slices.SortFunc(extraTokens, func(a, b token) int {
-		return cmp.Compare(a.pos, b.pos)
-	})
-
-	numToks := len(v.Tokens)
-
-	for cnt, t := range extraTokens {
-		// the token id should match the specific index for the total number of tokens
-		if t.pos != cnt+numToks {
-			return nil, fmt.Errorf("token ID '%d' for '%s' doesn't match total token size", t.pos, t.key)
-		}
-		v.Tokens = append(v.Tokens, t.key)
-		v.Scores = append(v.Scores, -1000.0)
-		v.Types = append(v.Types, tokenTypeUserDefined)
-	}
-	slog.Info(fmt.Sprintf("vocab size w/ extra tokens: %d", len(v.Tokens)))
-
-	if params.VocabSize > len(v.Tokens) {
-		missingTokens := params.VocabSize - len(v.Tokens)
-		slog.Warn(fmt.Sprintf("vocab is missing %d tokens", missingTokens))
-		for cnt := range missingTokens {
-			v.Tokens = append(v.Tokens, fmt.Sprintf("<dummy%05d>", cnt+1))
-			v.Scores = append(v.Scores, -1)
-			v.Types = append(v.Types, tokenTypeUserDefined)
+	if vocabSize := int(p.VocabSize); vocabSize > len(t.Vocabulary.Tokens) {
+		slog.Warn("vocabulary is smaller than expected, padding with dummy tokens", "expect", p.VocabSize, "actual", len(t.Vocabulary.Tokens))
+		for i := range vocabSize - len(t.Vocabulary.Tokens) {
+			t.Vocabulary.Tokens = append(t.Vocabulary.Tokens, fmt.Sprintf("[PAD%d]", i))
+			t.Vocabulary.Scores = append(t.Vocabulary.Scores, -1)
+			t.Vocabulary.Types = append(t.Vocabulary.Types, tokenTypeUserDefined)
 		}
 	}
 
-	return v, nil
+	ts, err := parseTensors(d)
+	if err != nil {
+		return err
+	}
+
+	return c.writeFile(ws, c.KV(t), c.Tensors(ts))
 }

convert/convert_adapter.go

@@ -0,0 +1,56 @@
+package convert
+
+import (
+	"io"
+	"strings"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type adapter struct {
+	Parameters
+}
+
+var _ Converter = (*adapter)(nil)
+
+func (p *adapter) writeFile(ws io.WriteSeeker, kv llm.KV, ts []*llm.Tensor) error {
+	return llm.WriteGGLA(ws, kv, ts)
+}
+
+func (p *adapter) KV(t *Tokenizer) llm.KV {
+	// todo - need a way to pass these in
+	kv := llm.KV{
+		"r":     uint32(8),
+		"alpha": uint32(160),
+	}
+	return kv
+}
+
+func (p *adapter) Tensors(ts []Tensor) []*llm.Tensor {
+	var out []*llm.Tensor
+	for _, t := range ts {
+		name := p.tensorName(t.Name())
+
+		out = append(out, &llm.Tensor{
+			Name:     name,
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *adapter) tensorName(n string) string {
+	return strings.NewReplacer(
+		"model.layers", "blk",
+		"self_attn.q_proj", "attn_q.weight",
+		"self_attn.k_proj", "attn_k.weight",
+		"self_attn.v_proj", "attn_v.weight",
+		"self_attn.o_proj", "attn_output.weight",
+		"lora_a", "loraA",
+		"lora_b", "loraB",
+		".npy", "",
+	).Replace(n)
+}

convert/convert_gemma.go

@@ -0,0 +1,103 @@
+package convert
+
+import (
+	"strings"
+
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type gemma struct {
+	Parameters
+	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
+	HiddenSize            uint32  `json:"hidden_size"`
+	HiddenLayers          uint32  `json:"num_hidden_layers"`
+	IntermediateSize      uint32  `json:"intermediate_size"`
+	NumAttentionHeads     uint32  `json:"num_attention_heads"`
+	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
+	RMSNormEPS            float32 `json:"rms_norm_eps"`
+	HeadDim               uint32  `json:"head_dim"`
+}
+
+var _ Converter = (*gemma)(nil)
+
+func (p *gemma) KV(t *Tokenizer) llm.KV {
+	kv := p.Parameters.KV(t)
+	kv["general.architecture"] = "gemma"
+	kv["general.name"] = "gemma"
+	kv["gemma.context_length"] = p.MaxPositionEmbeddings
+	kv["gemma.embedding_length"] = p.HiddenSize
+	kv["gemma.block_count"] = p.HiddenLayers
+	kv["gemma.feed_forward_length"] = p.IntermediateSize
+	kv["gemma.attention.head_count"] = p.NumAttentionHeads
+	kv["gemma.attention.head_count_kv"] = p.NumKeyValueHeads
+	kv["gemma.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
+	kv["gemma.attention.key_length"] = p.HeadDim
+	kv["gemma.attention.value_length"] = p.HeadDim
+	kv["tokenizer.ggml.eot_token_id"] = uint32(107)
+	kv["tokenizer.ggml.middle_token_id"] = uint32(68)
+	kv["tokenizer.ggml.prefix_token_id"] = uint32(67)
+	kv["tokenizer.ggml.suffix_token_id"] = uint32(69)
+	return kv
+}
+
+func (p *gemma) Tensors(ts []Tensor) []*llm.Tensor {
+	var out []*llm.Tensor
+	for _, t := range ts {
+		name := p.tensorName(t.Name())
+		if strings.HasSuffix(name, "_norm.weight") {
+			t.SetRepacker(p.addOne)
+		}
+
+		out = append(out, &llm.Tensor{
+			Name:     name,
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *gemma) tensorName(n string) string {
+	return strings.NewReplacer(
+		"model.embed_tokens", "token_embd",
+		"model.norm", "output_norm",
+		"model.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.o_proj", "attn_output",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.down_proj", "ffn_down",
+		"mlp.up_proj", "ffn_up",
+		"post_attention_layernorm", "ffn_norm",
+		"block_sparse_moe.gate", "ffn_inp",
+	).Replace(n)
+}
+
+func (*gemma) addOne(_ string, data []float32, shape []uint64) ([]float32, error) {
+	n := tensor.New(tensor.WithShape(int(shape[0])), tensor.WithBacking(data))
+	ones := tensor.Ones(tensor.Float32, int(shape[0]))
+
+	n, err := n.Add(ones)
+	if err != nil {
+		return nil, err
+	}
+
+	ts, err := native.SelectF32(n, 0)
+	if err != nil {
+		return nil, err
+	}
+
+	var f32s []float32
+	for _, t := range ts {
+		f32s = append(f32s, t...)
+	}
+
+	return f32s, nil
+}

convert/convert_llama.go

@@ -0,0 +1,176 @@
+package convert
+
+import (
+	"cmp"
+	"fmt"
+	"strings"
+
+	"github.com/ollama/ollama/llm"
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+)
+
+type llama struct {
+	Parameters
+	NLayers               uint32  `json:"n_layers"`
+	NumHiddenLayers       uint32  `json:"num_hidden_layers"`
+	NLayer                uint32  `json:"n_layer"`
+	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
+	NCtx                  uint32  `json:"n_ctx"`
+	HiddenSize            uint32  `json:"hidden_size"`
+	NEmbd                 uint32  `json:"n_embd"`
+	IntermediateSize      uint32  `json:"intermediate_size"`
+	NInner                uint32  `json:"n_inner"`
+	NumAttentionHeads     uint32  `json:"num_attention_heads"`
+	NHead                 uint32  `json:"n_head"`
+	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
+	RopeTheta             float32 `json:"rope_theta"`
+	RopeScaling           struct {
+		Type   string  `json:"type"`
+		Factor float32 `json:"factor"`
+	} `json:"rope_scaling"`
+	RMSNormEPS       float32 `json:"rms_norm_eps"`
+	LayerNormEPS     float32 `json:"layer_norm_eps"`
+	LayerNormEpsilon float32 `json:"layer_norm_epsilon"`
+	NormEpsilon      float32 `json:"norm_epsilon"`
+}
+
+var _ Converter = (*llama)(nil)
+
+func (p *llama) KV(t *Tokenizer) llm.KV {
+	kv := p.Parameters.KV(t)
+	kv["general.architecture"] = "llama"
+	kv["general.name"] = "llama"
+	kv["llama.vocab_size"] = p.VocabSize
+
+	kv["llama.block_count"] = cmp.Or(p.NLayers, p.NumHiddenLayers, p.NLayer)
+
+	if contextLength := cmp.Or(p.MaxPositionEmbeddings, p.NCtx); contextLength > 0 {
+		kv["llama.context_length"] = contextLength
+	}
+
+	if embeddingLength := cmp.Or(p.HiddenSize, p.NEmbd); embeddingLength > 0 {
+		kv["llama.embedding_length"] = cmp.Or(p.HiddenSize, p.NEmbd)
+	}
+
+	if feedForwardLength := cmp.Or(p.IntermediateSize, p.NInner); feedForwardLength > 0 {
+		kv["llama.feed_forward_length"] = cmp.Or(p.IntermediateSize, p.NInner)
+	}
+
+	if headCount := cmp.Or(p.NumAttentionHeads, p.NHead); headCount > 0 {
+		kv["llama.attention.head_count"] = cmp.Or(p.NumAttentionHeads, p.NHead)
+		kv["llama.rope.dimension_count"] = p.HiddenSize / headCount
+	}
+
+	if p.RopeTheta > 0 {
+		kv["llama.rope.freq_base"] = p.RopeTheta
+	}
+
+	if p.RopeScaling.Type == "linear" {
+		kv["llama.rope.scaling.type"] = p.RopeScaling.Type
+		kv["llama.rope.scaling.factor"] = p.RopeScaling.Factor
+	}
+
+	if p.NumKeyValueHeads > 0 {
+		kv["llama.attention.head_count_kv"] = p.NumKeyValueHeads
+	}
+
+	if p.RMSNormEPS > 0 {
+		kv["llama.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
+	}
+
+	if layerNormEpsilon := cmp.Or(p.LayerNormEPS, p.LayerNormEpsilon, p.NormEpsilon); layerNormEpsilon > 0 {
+		kv["llama.attention.layer_norm_epsilon"] = layerNormEpsilon
+	}
+
+	if len(t.Merges) > 0 {
+		kv["tokenizer.ggml.merges"] = t.Merges
+	}
+
+	return kv
+}
+
+func (p *llama) Tensors(ts []Tensor) []*llm.Tensor {
+	var out []*llm.Tensor
+	for _, t := range ts {
+		name := p.tensorName(t.Name())
+		if strings.HasSuffix(name, "attn_q.weight") ||
+			strings.HasSuffix(name, "attn_k.weight") {
+			t.SetRepacker(p.repack)
+		}
+
+		out = append(out, &llm.Tensor{
+			Name:     name,
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *llama) tensorName(n string) string {
+	return strings.NewReplacer(
+		"lm_head", "output",
+		"model.embed_tokens", "token_embd",
+		"model.norm", "output_norm",
+		"model.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.o_proj", "attn_output",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.down_proj", "ffn_down",
+		"mlp.up_proj", "ffn_up",
+		"post_attention_layernorm", "ffn_norm",
+		// mixtral
+		"block_sparse_moe.gate", "ffn_gate_inp",
+	).Replace(n)
+}
+
+func (p *llama) repack(name string, data []float32, shape []uint64) ([]float32, error) {
+	var dims []int
+	for _, dim := range shape {
+		dims = append(dims, int(dim))
+	}
+
+	var heads uint32
+	if strings.HasSuffix(name, "q_proj.weight") {
+		heads = p.NumAttentionHeads
+	} else if strings.HasSuffix(name, "k_proj.weight") {
+		heads = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
+	} else {
+		return nil, fmt.Errorf("unknown tensor for repack: %s", name)
+	}
+
+	n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
+	if err := n.Reshape(append([]int{int(heads), 2, dims[0] / int(heads) / 2}, dims[1:]...)...); err != nil {
+		return nil, err
+	}
+
+	if err := n.T(0, 2, 1, 3); err != nil {
+		return nil, err
+	}
+
+	if err := n.Reshape(dims...); err != nil {
+		return nil, err
+	}
+
+	if err := n.Transpose(); err != nil {
+		return nil, err
+	}
+
+	ts, err := native.SelectF32(n, 1)
+	if err != nil {
+		return nil, err
+	}
+
+	var f32s []float32
+	for _, t := range ts {
+		f32s = append(f32s, t...)
+	}
+
+	return f32s, nil
+}

convert/convert_mixtral.go

@@ -0,0 +1,89 @@
+package convert
+
+import (
+	"fmt"
+	"io"
+	"slices"
+	"strings"
+
+	"github.com/ollama/ollama/llm"
+)
+
+type mixtral struct {
+	llama
+	NumLocalExperts    uint32 `json:"num_local_experts"`
+	NumExpertsPerToken uint32 `json:"num_experts_per_tok"`
+}
+
+var _ Converter = (*mixtral)(nil)
+
+func (p *mixtral) KV(t *Tokenizer) llm.KV {
+	kv := p.llama.KV(t)
+
+	if p.NumLocalExperts > 0 {
+		kv["llama.expert_count"] = p.NumLocalExperts
+	}
+
+	if p.NumExpertsPerToken > 0 {
+		kv["llama.expert_used_count"] = p.NumExpertsPerToken
+	}
+
+	return kv
+}
+
+func (p *mixtral) Tensors(ts []Tensor) []*llm.Tensor {
+	oldnew := []string{
+		"model.layers", "blk",
+		"w1", "ffn_gate_exps",
+		"w2", "ffn_down_exps",
+		"w3", "ffn_up_exps",
+	}
+
+	for i := range p.NumLocalExperts {
+		oldnew = append(oldnew, fmt.Sprintf(".block_sparse_moe.experts.%d.", i), ".")
+	}
+
+	// group experts of the same layer (model.layers.%d) and type (w[123]) into a single tensor
+	namer := strings.NewReplacer(oldnew...)
+	experts := make(map[string]experts)
+
+	// merge experts into a single tensor while removing them from ts
+	ts = slices.DeleteFunc(ts, func(t Tensor) bool {
+		if !strings.Contains(t.Name(), ".block_sparse_moe.experts.") {
+			return false
+		}
+
+		name := namer.Replace(t.Name())
+		experts[name] = append(experts[name], t)
+		return true
+	})
+
+	var out []*llm.Tensor
+	for n, e := range experts {
+		// TODO(mxyng): sanity check experts
+		out = append(out, &llm.Tensor{
+			Name:     n,
+			Kind:     e[0].Kind(),
+			Shape:    append([]uint64{uint64(len(e))}, e[0].Shape()...),
+			WriterTo: e,
+		})
+	}
+
+	return append(out, p.llama.Tensors(ts)...)
+}
+
+type experts []Tensor
+
+func (e experts) WriteTo(w io.Writer) (int64, error) {
+	// TODO(mxyng): experts _should_ be numerically sorted by expert but this should check
+	for _, t := range e {
+		// the canonical merged experts tensor stacks all experts along a new, 0 axis,
+		// e.g. `tensor.Stack(0, e[0], e[1:]...)`, which requires allocating temporary buffers
+		// this accomplishes the same thing by writing each expert tensor in sequence
+		if _, err := t.WriteTo(w); err != nil {
+			return 0, err
+		}
+	}
+
+	return 0, nil
+}

convert/convert_test.go

@@ -1,48 +1,34 @@
-//go:build slow
-
 package convert
 
 import (
+	"bytes"
+	"crypto/sha256"
+	"encoding/json"
+	"errors"
+	"flag"
+	"fmt"
+	"io"
+	"log/slog"
+	"math"
 	"os"
 	"path/filepath"
+	"slices"
 	"testing"
 
 	"github.com/ollama/ollama/llm"
+	"golang.org/x/exp/maps"
 )
 
-func convertFull(t *testing.T, p string) (llm.KV, llm.Tensors) {
+func convertFull(t *testing.T, d string) (*os.File, llm.KV, llm.Tensors) {
 	t.Helper()
 
-	mf, err := GetModelFormat(p)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	params, err := mf.GetParams(p)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	arch, err := mf.GetModelArch("", p, params)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	if err := arch.LoadVocab(); err != nil {
-		t.Fatal(err)
-	}
-
-	if err := arch.GetTensors(); err != nil {
-		t.Fatal(err)
-	}
-
 	f, err := os.CreateTemp(t.TempDir(), "f16")
 	if err != nil {
 		t.Fatal(err)
 	}
 	defer f.Close()
 
-	if err := arch.WriteGGUF(f); err != nil {
+	if err := Convert(d, f); err != nil {
 		t.Fatal(err)
 	}
 
@@ -50,54 +36,200 @@ func convertFull(t *testing.T, p string) (llm.KV, llm.Tensors) {
 	if err != nil {
 		t.Fatal(err)
 	}
-	defer r.Close()
+	t.Cleanup(func() { r.Close() })
 
-	m, _, err := llm.DecodeGGML(r)
+	m, _, err := llm.DecodeGGML(r, math.MaxInt)
 	if err != nil {
 		t.Fatal(err)
 	}
 
-	return m.KV(), m.Tensors()
+	if _, err := r.Seek(0, io.SeekStart); err != nil {
+		t.Fatal(err)
+	}
+
+	return r, m.KV(), m.Tensors()
+}
+
+func TestMain(m *testing.M) {
+	var level slog.Level
+	flag.TextVar(&level, "level", slog.LevelInfo, "log level")
+	flag.Parse()
+	slog.SetLogLoggerLevel(level)
+	os.Exit(m.Run())
 }
 
 func TestConvertFull(t *testing.T) {
-	cases := []struct {
-		path    string
-		arch    string
-		tensors int
-		layers  int
-	}{
-		{"Meta-Llama-3-8B-Instruct", "llama", 291, 35},
-		{"Mistral-7B-Instruct-v0.2", "llama", 291, 35},
-		{"Mixtral-8x7B-Instruct-v0.1", "llama", 291, 35},
-		{"gemma-2b-it", "gemma", 164, 20},
+	cases := []string{
+		"Meta-Llama-3-8B-Instruct",
+		"Mistral-7B-Instruct-v0.2",
+		"Mixtral-8x7B-Instruct-v0.1",
+		"gemma-2b-it",
 	}
 
-	for _, tt := range cases {
-		t.Run(tt.path, func(t *testing.T) {
-			p := filepath.Join("testdata", tt.path)
-			if _, err := os.Stat(p); err != nil {
+	for i := range cases {
+		tt := cases[i]
+		t.Run(tt, func(t *testing.T) {
+			t.Parallel()
+
+			p := filepath.Join("testdata", tt)
+			if testing.Short() {
+				t.Skip("skipping in short mode")
+			} else if _, err := os.Stat(p); err != nil {
 				t.Skipf("%s not found", p)
 			}
 
-			kv, tensors := convertFull(t, p)
+			f, kv, tensors := convertFull(t, p)
+			actual := make(map[string]string)
+			for k, v := range kv {
+				if s, ok := v.(json.Marshaler); !ok {
+					actual[k] = fmt.Sprintf("%v", v)
+				} else {
+					bts, err := json.Marshal(s)
+					if err != nil {
+						t.Fatal(err)
+					}
 
-			if kv.Architecture() != tt.arch {
-				t.Fatalf("expected llama, got %s", kv.Architecture())
+					actual[k] = fmt.Sprintf("%x", sha256.Sum256(bts))
+				}
 			}
 
-			if kv.FileType().String() != "F16" {
-				t.Fatalf("expected F16, got %s", kv.FileType())
+			for _, tensor := range tensors.Items {
+				sha256sum := sha256.New()
+				sr := io.NewSectionReader(f, int64(tensors.Offset+tensor.Offset), int64(tensor.Size()))
+				if _, err := io.Copy(sha256sum, sr); err != nil {
+					t.Fatal(err)
+				}
+
+				actual[tensor.Name] = fmt.Sprintf("%x", sha256sum.Sum(nil))
 			}
 
-			if len(tensors) != tt.tensors {
-				t.Fatalf("expected %d tensors, got %d", tt.tensors, len(tensors))
+			expectFile, err := os.Open(filepath.Join("testdata", fmt.Sprintf("%s.json", tt)))
+			if err != nil {
+				t.Fatal(err)
 			}
 
-			layers := tensors.Layers()
-			if len(layers) != tt.layers {
-				t.Fatalf("expected %d layers, got %d", tt.layers, len(layers))
+			var expect map[string]string
+			if err := json.NewDecoder(expectFile).Decode(&expect); err != nil {
+				t.Fatal(err)
+			}
+
+			keys := maps.Keys(expect)
+			slices.Sort(keys)
+			for _, k := range keys {
+				if v, ok := actual[k]; !ok {
+					t.Errorf("missing %s", k)
+				} else if v != expect[k] {
+					t.Errorf("unexpected %s: want %s, got %s", k, expect[k], v)
+				}
 			}
 		})
 	}
 }
+
+func TestConvertNPZ(t *testing.T) {
+	cases := []string{
+		"adapters.npz",
+	}
+
+	for _, fn := range cases {
+		ts, err := parseNPZ(filepath.Join("testdata", fn))
+		if err != nil {
+			t.Fatal(err)
+		}
+		if len(ts) != 16*2*2 {
+			t.Errorf("got: %d want: %d total layers", len(ts), 16*2*2)
+		}
+
+		a := adapter{}
+
+		for _, m := range ts {
+			at := m.(adapterTensor)
+			if at.path != filepath.Join("testdata", fn) {
+				t.Errorf("got: %s want: %s", at.path, filepath.Join("testdata", fn))
+			}
+			if at.dtype != "F32" {
+				t.Errorf("got: %s but only F32s are currently supported", at.dtype)
+			}
+			if len(at.tensorBase.shape) != 2 {
+				t.Errorf("got: %d want: %d tensor shape", at.tensorBase.shape, 2)
+			}
+		}
+
+		var ws io.WriteSeeker = &memWriter{}
+		err = llm.WriteGGLA(ws, a.KV(nil), a.Tensors(ts))
+		if err != nil {
+			t.Fatal(err)
+		}
+
+		mw := ws.(*memWriter)
+		slog.Info(fmt.Sprintf("buffer len = %d", len(mw.buf)))
+		if len(mw.buf) == 0 {
+			t.Errorf("ggla layer not written correctly")
+		}
+		rs := bytes.NewReader(mw.buf)
+		ggml, _, err := llm.DecodeGGML(rs, len(mw.buf))
+		if err != nil {
+			t.Fatal(err)
+		}
+		if ggml == nil {
+			t.Fatalf("ggla didn't convert to ggml correctly")
+		}
+
+		kv := ggml.KV()
+		if kv == nil {
+			t.Fatalf("no lora KVs were set")
+		}
+
+		r, ok := kv["r"]
+		if !ok || r != uint32(8) {
+			t.Errorf("lora rank was not set correctly")
+		}
+
+		alpha, ok := kv["alpha"]
+		if !ok || alpha != uint32(160) {
+			t.Errorf("lora alpha was not set correctly")
+		}
+
+		gts := ggml.Tensors()
+		if len(ts) != len(gts.Items) {
+			t.Fatalf("got: %d want: %d tensors in ggla", len(gts.Items), len(ts))
+		}
+	}
+}
+
+type memWriter struct {
+	buf []byte
+	pos int
+}
+
+func (m *memWriter) Write(p []byte) (n int, err error) {
+	minCap := m.pos + len(p)
+	if minCap > cap(m.buf) {
+		buf2 := make([]byte, len(m.buf), minCap+len(p)) // add some extra
+		copy(buf2, m.buf)
+		m.buf = buf2
+	}
+	if minCap > len(m.buf) {
+		m.buf = m.buf[:minCap]
+	}
+	copy(m.buf[m.pos:], p)
+	m.pos += len(p)
+	return len(p), nil
+}
+
+func (m *memWriter) Seek(offset int64, whence int) (int64, error) {
+	newPos, offs := 0, int(offset)
+	switch whence {
+	case io.SeekStart:
+		newPos = offs
+	case io.SeekCurrent:
+		newPos = m.pos + offs
+	case io.SeekEnd:
+		newPos = len(m.buf) + offs
+	}
+	if newPos < 0 {
+		return 0, errors.New("negative result pos")
+	}
+	m.pos = newPos
+	return int64(newPos), nil
+}

convert/gemma.go

@@ -1,102 +0,0 @@
-package convert
-
-import (
-	"fmt"
-	"io"
-	"log/slog"
-	"strings"
-
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type GemmaModel struct {
-	ModelData
-}
-
-func addOnes(data []float32, vectorSize int) ([]float32, error) {
-	n := tensor.New(tensor.WithShape(vectorSize), tensor.WithBacking(data))
-	ones := tensor.Ones(tensor.Float32, vectorSize)
-
-	n, err := n.Add(ones)
-	if err != nil {
-		return nil, err
-	}
-
-	ts, err := native.SelectF32(n, 0)
-	if err != nil {
-		return nil, err
-	}
-
-	var f32s []float32
-	for _, t := range ts {
-		f32s = append(f32s, t...)
-	}
-
-	return f32s, nil
-}
-
-func (m *GemmaModel) GetTensors() error {
-	t, err := m.Format.GetTensors(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-
-	slog.Debug(fmt.Sprintf("Total tensors: %d", len(t)))
-	for _, l := range t {
-		if strings.HasSuffix(l.Name, "norm.weight") {
-			wt := l.WriterTo.(safetensorWriterTo)
-			wt.repacker = m.Repack
-			l.WriterTo = wt
-		}
-		m.Tensors = append(m.Tensors, l)
-	}
-
-	return nil
-}
-
-func (m *GemmaModel) LoadVocab() error {
-	v, err := LoadSentencePieceTokens(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-	m.Vocab = v
-	return nil
-}
-
-func (m *GemmaModel) Repack(_ string, data []float32, shape []uint64) ([]float32, error) {
-	return addOnes(data, int(shape[0]))
-}
-
-func (m *GemmaModel) WriteGGUF(ws io.WriteSeeker) error {
-	kv := llm.KV{
-		"general.architecture":                   "gemma",
-		"general.name":                           m.Name,
-		"gemma.context_length":                   uint32(m.Params.ContextSize),
-		"gemma.embedding_length":                 uint32(m.Params.HiddenSize),
-		"gemma.block_count":                      uint32(m.Params.HiddenLayers),
-		"gemma.feed_forward_length":              uint32(m.Params.IntermediateSize),
-		"gemma.attention.head_count":             uint32(m.Params.AttentionHeads),
-		"gemma.attention.head_count_kv":          uint32(m.Params.KeyValHeads),
-		"gemma.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
-		"gemma.attention.key_length":             uint32(m.Params.HeadDimension),
-		"gemma.attention.value_length":           uint32(m.Params.HeadDimension),
-		"general.file_type":                      uint32(1),
-		"tokenizer.ggml.model":                   "llama",
-
-		"tokenizer.ggml.tokens":     m.Vocab.Tokens,
-		"tokenizer.ggml.scores":     m.Vocab.Scores,
-		"tokenizer.ggml.token_type": m.Vocab.Types,
-
-		"tokenizer.ggml.bos_token_id":     uint32(m.Params.BoSTokenID),
-		"tokenizer.ggml.eos_token_id":     uint32(m.Params.EoSTokenID),
-		"tokenizer.ggml.padding_token_id": uint32(m.Params.PaddingTokenID),
-		"tokenizer.ggml.unknown_token_id": uint32(3),
-		"tokenizer.ggml.add_bos_token":    true,
-		"tokenizer.ggml.add_eos_token":    false,
-	}
-
-	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
-}

convert/llama.go

@@ -1,159 +0,0 @@
-package convert
-
-import (
-	"cmp"
-	"errors"
-	"fmt"
-	"io"
-	"os"
-	"path/filepath"
-	"regexp"
-	"strings"
-
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type LlamaModel struct {
-	ModelData
-}
-
-func (m *LlamaModel) GetTensors() error {
-	t, err := m.Format.GetTensors(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-
-	pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
-	re, err := regexp.Compile(pattern)
-	if err != nil {
-		return err
-	}
-
-	for _, l := range t {
-		matches := re.FindAllStringSubmatch(l.Name, -1)
-		if len(matches) > 0 {
-			switch m.Format.(type) {
-			case *TorchFormat:
-				wt := l.WriterTo.(torchWriterTo)
-				wt.repacker = m.Repack
-				l.WriterTo = wt
-			case *SafetensorFormat:
-				wt := l.WriterTo.(safetensorWriterTo)
-				wt.repacker = m.Repack
-				l.WriterTo = wt
-			}
-		}
-		m.Tensors = append(m.Tensors, l)
-	}
-
-	return nil
-}
-
-func (m *LlamaModel) LoadVocab() (err error) {
-	pre, ts, merges, err := parseTokens(filepath.Join(m.Path, "tokenizer.json"))
-	if errors.Is(err, os.ErrNotExist) {
-		return nil
-	} else if err != nil {
-		return err
-	}
-
-	m.Vocab = &Vocab{}
-	for _, t := range ts {
-		m.Vocab.Tokens = append(m.Vocab.Tokens, t.Content)
-		m.Vocab.Types = append(m.Vocab.Types, t.Type())
-	}
-
-	m.Vocab.Merges = merges
-	m.Params.PreTokenizer = pre
-	return nil
-}
-
-func (m *LlamaModel) WriteGGUF(ws io.WriteSeeker) error {
-	kv := llm.KV{
-		"general.architecture":                   "llama",
-		"general.name":                           m.Name,
-		"llama.vocab_size":                       uint32(len(m.Vocab.Tokens)),
-		"llama.context_length":                   uint32(m.Params.ContextSize),
-		"llama.embedding_length":                 uint32(m.Params.HiddenSize),
-		"llama.block_count":                      uint32(m.Params.HiddenLayers),
-		"llama.feed_forward_length":              uint32(m.Params.IntermediateSize),
-		"llama.rope.freq_base":                   float32(m.Params.RopeFrequencyBase),
-		"llama.rope.dimension_count":             uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
-		"llama.attention.head_count":             uint32(m.Params.AttentionHeads),
-		"llama.attention.head_count_kv":          uint32(m.Params.KeyValHeads),
-		"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
-		"general.file_type":                      uint32(1),
-		"tokenizer.ggml.model":                   "gpt2",
-
-		"tokenizer.ggml.pre":        m.Params.PreTokenizer,
-		"tokenizer.ggml.tokens":     m.Vocab.Tokens,
-		"tokenizer.ggml.token_type": m.Vocab.Types,
-
-		"tokenizer.ggml.bos_token_id":     uint32(m.Params.BoSTokenID),
-		"tokenizer.ggml.eos_token_id":     uint32(m.Params.EoSTokenID),
-		"tokenizer.ggml.unknown_token_id": uint32(0),
-	}
-
-	if len(m.Vocab.Merges) > 0 {
-		kv["tokenizer.ggml.merges"] = m.Vocab.Merges
-	} else {
-		kv["tokenizer.ggml.scores"] = m.Vocab.Scores
-	}
-
-	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
-}
-
-func (m *LlamaModel) Repack(name string, data []float32, shape []uint64) ([]float32, error) {
-	return llamaRepack(name, m.Params, data, shape)
-}
-
-func llamaRepack(name string, params *Params, data []float32, shape []uint64) ([]float32, error) {
-	var dims []int
-	for _, dim := range shape {
-		if dim != 0 {
-			dims = append(dims, int(dim))
-		}
-	}
-
-	var heads int
-	switch {
-	case strings.HasSuffix(name, "attn_q.weight"):
-		heads = params.AttentionHeads
-	case strings.HasSuffix(name, "attn_k.weight"):
-		heads = cmp.Or(params.KeyValHeads, params.AttentionHeads)
-	default:
-		return nil, fmt.Errorf("unknown tensor name: %s", name)
-	}
-
-	n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-	if err := n.Reshape(append([]int{heads, 2, dims[0] / heads / 2}, dims[1:]...)...); err != nil {
-		return nil, err
-	}
-
-	if err := n.T(0, 2, 1, 3); err != nil {
-		return nil, err
-	}
-
-	if err := n.Reshape(dims...); err != nil {
-		return nil, err
-	}
-
-	if err := n.Transpose(); err != nil {
-		return nil, err
-	}
-
-	ts, err := native.SelectF32(n, 1)
-	if err != nil {
-		return nil, err
-	}
-
-	var f32s []float32
-	for _, t := range ts {
-		f32s = append(f32s, t...)
-	}
-
-	return f32s, nil
-}

convert/mistral.go

@@ -1,84 +0,0 @@
-package convert
-
-import (
-	"io"
-	"regexp"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type MistralModel struct {
-	ModelData
-}
-
-func (m *MistralModel) GetTensors() error {
-	t, err := m.Format.GetTensors(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-
-	pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
-	re, err := regexp.Compile(pattern)
-	if err != nil {
-		return err
-	}
-
-	for _, l := range t {
-		matches := re.FindAllStringSubmatch(l.Name, -1)
-		if len(matches) > 0 {
-			wt := l.WriterTo.(safetensorWriterTo)
-			wt.repacker = m.Repack
-			l.WriterTo = wt
-		}
-		m.Tensors = append(m.Tensors, l)
-	}
-
-	return nil
-}
-
-func (m *MistralModel) LoadVocab() error {
-	v, err := LoadSentencePieceTokens(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-	m.Vocab = v
-	return nil
-}
-
-func (m *MistralModel) WriteGGUF(ws io.WriteSeeker) error {
-	kv := llm.KV{
-		"general.architecture":                   "llama",
-		"general.name":                           m.Name,
-		"llama.context_length":                   uint32(m.Params.ContextSize),
-		"llama.embedding_length":                 uint32(m.Params.HiddenSize),
-		"llama.block_count":                      uint32(m.Params.HiddenLayers),
-		"llama.feed_forward_length":              uint32(m.Params.IntermediateSize),
-		"llama.rope.dimension_count":             uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
-		"llama.attention.head_count":             uint32(m.Params.AttentionHeads),
-		"llama.attention.head_count_kv":          uint32(m.Params.KeyValHeads),
-		"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
-		"general.file_type":                      uint32(1),
-		"tokenizer.ggml.model":                   "llama",
-
-		"tokenizer.ggml.tokens":     m.Vocab.Tokens,
-		"tokenizer.ggml.scores":     m.Vocab.Scores,
-		"tokenizer.ggml.token_type": m.Vocab.Types,
-
-		"tokenizer.ggml.bos_token_id":     uint32(m.Params.BoSTokenID),
-		"tokenizer.ggml.eos_token_id":     uint32(m.Params.EoSTokenID),
-		"tokenizer.ggml.add_bos_token":    true,
-		"tokenizer.ggml.add_eos_token":    false,
-		"tokenizer.ggml.unknown_token_id": uint32(0),
-	}
-
-	if m.Params.HeadDimension > 0 {
-		kv["llama.attention.key_length"] = uint32(m.Params.HeadDimension)
-		kv["llama.attention.value_length"] = uint32(m.Params.HeadDimension)
-	}
-
-	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
-}
-
-func (m *MistralModel) Repack(name string, data []float32, shape []uint64) ([]float32, error) {
-	return llamaRepack(name, m.Params, data, shape)
-}

convert/mixtral.go

@@ -1,87 +0,0 @@
-package convert
-
-import (
-	"io"
-	"regexp"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type MixtralModel struct {
-	ModelData
-}
-
-func (m *MixtralModel) GetTensors() error {
-	t, err := m.Format.GetTensors(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-
-	pattern := `^blk\.[0-9]+\.attn_(?P<layer>q|k)\.weight$`
-	re, err := regexp.Compile(pattern)
-	if err != nil {
-		return err
-	}
-
-	for _, l := range t {
-		matches := re.FindAllStringSubmatch(l.Name, -1)
-		if len(matches) > 0 {
-			wt := l.WriterTo.(safetensorWriterTo)
-			wt.repacker = m.Repack
-			l.WriterTo = wt
-		}
-		m.Tensors = append(m.Tensors, l)
-	}
-
-	return nil
-}
-
-func (m *MixtralModel) LoadVocab() error {
-	v, err := LoadSentencePieceTokens(m.Path, m.Params)
-	if err != nil {
-		return err
-	}
-	m.Vocab = v
-	return nil
-}
-
-func (m *MixtralModel) WriteGGUF(ws io.WriteSeeker) error {
-	kv := llm.KV{
-		"general.architecture":          "llama",
-		"general.name":                  m.Name,
-		"llama.block_count":             uint32(m.Params.HiddenLayers),
-		"llama.context_length":          uint32(m.Params.ContextSize),
-		"llama.embedding_length":        uint32(m.Params.HiddenSize),
-		"llama.feed_forward_length":     uint32(m.Params.IntermediateSize),
-		"llama.attention.head_count":    uint32(m.Params.AttentionHeads),
-		"llama.attention.head_count_kv": uint32(m.Params.KeyValHeads),
-
-		"llama.rope.freq_base":                   float32(m.Params.RopeFrequencyBase),
-		"llama.attention.layer_norm_rms_epsilon": float32(m.Params.NormEPS),
-
-		"llama.expert_count":      uint32(m.Params.Experts),
-		"llama.expert_used_count": uint32(m.Params.ExpertsUsed),
-
-		"llama.vocab_size":           uint32(len(m.Vocab.Tokens)),
-		"llama.rope.dimension_count": uint32(m.Params.HiddenSize / m.Params.AttentionHeads),
-
-		"general.file_type":    uint32(1),
-		"tokenizer.ggml.model": "llama",
-
-		"tokenizer.ggml.tokens":     m.Vocab.Tokens,
-		"tokenizer.ggml.scores":     m.Vocab.Scores,
-		"tokenizer.ggml.token_type": m.Vocab.Types,
-
-		"tokenizer.ggml.bos_token_id":     uint32(m.Params.BoSTokenID),
-		"tokenizer.ggml.eos_token_id":     uint32(m.Params.EoSTokenID),
-		"tokenizer.ggml.unknown_token_id": uint32(0),
-		"tokenizer.ggml.add_bos_token":    true,
-		"tokenizer.ggml.add_eos_token":    false,
-	}
-
-	return llm.NewGGUFV3(m.Params.ByteOrder).Encode(ws, kv, m.Tensors)
-}
-
-func (m *MixtralModel) Repack(name string, data []float32, shape []uint64) ([]float32, error) {
-	return llamaRepack(name, m.Params, data, shape)
-}

convert/reader.go

@@ -0,0 +1,74 @@
+package convert
+
+import (
+	"errors"
+	"io"
+	"path/filepath"
+	"strings"
+)
+
+type Tensor interface {
+	Name() string
+	Shape() []uint64
+	Kind() uint32
+	SetRepacker(repacker)
+	WriteTo(io.Writer) (int64, error)
+}
+
+type tensorBase struct {
+	name  string
+	shape []uint64
+	repacker
+}
+
+func (t tensorBase) Name() string {
+	return t.name
+}
+
+func (t tensorBase) Shape() []uint64 {
+	return t.shape
+}
+
+func (t tensorBase) Kind() uint32 {
+	if strings.HasSuffix(t.name, ".block_sparse_moe.gate.weight") {
+		return 0
+	}
+
+	switch len(t.shape) {
+	case 0:
+		panic("invalid tensor shape")
+	case 1:
+		return 0
+	default:
+		return 1
+	}
+}
+
+func (t *tensorBase) SetRepacker(fn repacker) {
+	t.repacker = fn
+}
+
+type repacker func(string, []float32, []uint64) ([]float32, error)
+
+func parseTensors(d string) ([]Tensor, error) {
+	patterns := map[string]func(...string) ([]Tensor, error){
+		"model-*-of-*.safetensors": parseSafetensors,
+		"model.safetensors":        parseSafetensors,
+		"pytorch_model-*-of-*.bin": parseTorch,
+		"pytorch_model.bin":        parseTorch,
+		"consolidated.*.pth":       parseTorch,
+	}
+
+	for pattern, parseFn := range patterns {
+		matches, err := filepath.Glob(filepath.Join(d, pattern))
+		if err != nil {
+			return nil, err
+		}
+
+		if len(matches) > 0 {
+			return parseFn(matches...)
+		}
+	}
+
+	return nil, errors.New("unknown tensor format")
+}

convert/reader_npz.go

@@ -0,0 +1,140 @@
+package convert
+
+import (
+	"encoding/binary"
+	"fmt"
+	"io"
+	"log/slog"
+	"strings"
+
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+	"github.com/sbinet/npyio/npz"
+)
+
+type adapterTensor struct {
+	path  string
+	dtype string
+	*tensorBase
+}
+
+func DetectNPZ(fn string) (bool, error) {
+	f, err := npz.Open(fn)
+	if err != nil {
+		return false, err
+	}
+	defer f.Close()
+
+	if len(f.Keys()) > 0 && strings.HasSuffix(f.Keys()[0], ".npy") {
+		return true, nil
+	}
+
+	return false, nil
+}
+
+func parseNPZ(fn string) ([]Tensor, error) {
+	var ts []Tensor
+
+	f, err := npz.Open(fn)
+	if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+
+	for _, name := range f.Keys() {
+		slog.Info(fmt.Sprintf("reading layer '%s'", name))
+		h := f.Header(name)
+
+		shape := make([]uint64, 2)
+		for cnt, v := range h.Descr.Shape {
+			// llamacpp expects the loraB layer to be reversed
+			if strings.Contains(name, "lora_b") {
+				shape[len(shape)-cnt-1] = uint64(v)
+			} else {
+				shape[cnt] = uint64(v)
+			}
+		}
+
+		dtypeMap := map[string]string{
+			"<f2": "F16",
+			"<f4": "F32",
+		}
+		dtype, ok := dtypeMap[h.Descr.Type]
+		if !ok {
+			return nil, fmt.Errorf("Unknown type '%s' for '%s'", h.Descr.Type, name)
+		}
+
+		ts = append(ts, adapterTensor{
+			path:  fn,
+			dtype: dtype,
+			tensorBase: &tensorBase{
+				name:  name,
+				shape: shape,
+			},
+		})
+	}
+	return ts, nil
+}
+
+func (t adapterTensor) Kind() uint32 {
+	switch t.dtype {
+	case "F32":
+		return 0
+	case "F16":
+		return 1
+	}
+	return 0
+}
+
+func (t adapterTensor) WriteTo(w io.Writer) (int64, error) {
+	f, err := npz.Open(t.path)
+	if err != nil {
+		return 0, err
+	}
+	defer f.Close()
+
+	switch t.dtype {
+	case "F32":
+		var f32s []float32
+		err = f.Read(t.tensorBase.name, &f32s)
+		if err != nil {
+			return 0, err
+		}
+
+		// ggla expects the loraB to be transposed
+		if strings.Contains(t.tensorBase.name, "lora_b") {
+			f32s, err = transpose(f32s, t.tensorBase.shape)
+			if err != nil {
+				return 0, err
+			}
+		}
+
+		return 0, binary.Write(w, binary.LittleEndian, f32s)
+	}
+
+	return 0, fmt.Errorf("unknown data type: %s", t.dtype)
+}
+
+func transpose(f32s []float32, shape []uint64) ([]float32, error) {
+	if len(shape) != 2 {
+		return nil, fmt.Errorf("only 2 dimensions supported for transpose")
+	}
+
+	// the shape is already backward
+	n := tensor.New(tensor.WithShape(int(shape[1]), int(shape[0])), tensor.WithBacking(f32s))
+	if err := n.T(1, 0); err != nil {
+		return nil, err
+	}
+	if err := n.Transpose(); err != nil {
+		return nil, err
+	}
+	ts, err := native.SelectF32(n, 1)
+	if err != nil {
+		return nil, err
+	}
+	f32s = make([]float32, 0)
+	for _, t := range ts {
+		f32s = append(f32s, t...)
+	}
+	return f32s, nil
+}

convert/reader_safetensors.go

@@ -0,0 +1,140 @@
+package convert
+
+import (
+	"bytes"
+	"encoding/binary"
+	"encoding/json"
+	"fmt"
+	"io"
+	"os"
+	"slices"
+
+	"github.com/d4l3k/go-bfloat16"
+	"github.com/x448/float16"
+	"golang.org/x/exp/maps"
+)
+
+type safetensorMetadata struct {
+	Type    string   `json:"dtype"`
+	Shape   []uint64 `json:"shape"`
+	Offsets []int64  `json:"data_offsets"`
+}
+
+func parseSafetensors(ps ...string) ([]Tensor, error) {
+	var ts []Tensor
+	for _, p := range ps {
+		f, err := os.Open(p)
+		if err != nil {
+			return nil, err
+		}
+		defer f.Close()
+
+		var n int64
+		if err := binary.Read(f, binary.LittleEndian, &n); err != nil {
+			return nil, err
+		}
+
+		b := bytes.NewBuffer(make([]byte, 0, n))
+		if _, err = io.CopyN(b, f, n); err != nil {
+			return nil, err
+		}
+
+		var headers map[string]safetensorMetadata
+		if err := json.NewDecoder(b).Decode(&headers); err != nil {
+			return nil, err
+		}
+
+		keys := maps.Keys(headers)
+		slices.Sort(keys)
+
+		for _, key := range keys {
+			if value := headers[key]; value.Type != "" {
+				ts = append(ts, safetensor{
+					path:   p,
+					dtype:  value.Type,
+					offset: safetensorsPad(n, value.Offsets[0]),
+					size:   safetensorsPad(n, value.Offsets[1]) - safetensorsPad(n, value.Offsets[0]),
+					tensorBase: &tensorBase{
+						name:  key,
+						shape: value.Shape,
+					},
+				})
+			}
+		}
+	}
+
+	return ts, nil
+}
+
+func safetensorsPad(n, s int64) int64 {
+	return 8 + n + s
+}
+
+type safetensor struct {
+	path   string
+	dtype  string
+	offset int64
+	size   int64
+	*tensorBase
+}
+
+func (st safetensor) WriteTo(w io.Writer) (int64, error) {
+	f, err := os.Open(st.path)
+	if err != nil {
+		return 0, err
+	}
+	defer f.Close()
+
+	if _, err = f.Seek(st.offset, io.SeekStart); err != nil {
+		return 0, err
+	}
+
+	var f32s []float32
+	switch st.dtype {
+	case "F32":
+		f32s = make([]float32, st.size/4)
+		if err = binary.Read(f, binary.LittleEndian, f32s); err != nil {
+			return 0, err
+		}
+	case "F16":
+		u16s := make([]uint16, st.size/2)
+		if err = binary.Read(f, binary.LittleEndian, u16s); err != nil {
+			return 0, err
+		}
+
+		for _, b := range u16s {
+			f32s = append(f32s, float16.Frombits(b).Float32())
+		}
+
+	case "BF16":
+		u8s := make([]uint8, st.size)
+		if err = binary.Read(f, binary.LittleEndian, u8s); err != nil {
+			return 0, err
+		}
+
+		f32s = bfloat16.DecodeFloat32(u8s)
+	default:
+		return 0, fmt.Errorf("unknown data type: %s", st.dtype)
+	}
+
+	if st.repacker != nil {
+		f32s, err = st.repacker(st.Name(), f32s, st.Shape())
+		if err != nil {
+			return 0, err
+		}
+	}
+
+	switch st.Kind() {
+	case 0:
+		return 0, binary.Write(w, binary.LittleEndian, f32s)
+	case 1:
+		f16s := make([]uint16, len(f32s))
+		for i := range f32s {
+			f16s[i] = float16.Fromfloat32(f32s[i]).Bits()
+		}
+
+		return 0, binary.Write(w, binary.LittleEndian, f16s)
+	default:
+		return 0, fmt.Errorf("unknown storage type: %d", st.Kind())
+	}
+}

convert/reader_torch.go

@@ -0,0 +1,46 @@
+package convert
+
+import (
+	"io"
+
+	"github.com/nlpodyssey/gopickle/pytorch"
+	"github.com/nlpodyssey/gopickle/types"
+)
+
+func parseTorch(ps ...string) ([]Tensor, error) {
+	var ts []Tensor
+	for _, p := range ps {
+		pt, err := pytorch.Load(p)
+		if err != nil {
+			return nil, err
+		}
+
+		for _, k := range pt.(*types.Dict).Keys() {
+			t := pt.(*types.Dict).MustGet(k)
+
+			var shape []uint64
+			for dim := range t.(*pytorch.Tensor).Size {
+				shape = append(shape, uint64(dim))
+			}
+
+			ts = append(ts, torch{
+				storage: t.(*pytorch.Tensor).Source,
+				tensorBase: &tensorBase{
+					name:  k.(string),
+					shape: shape,
+				},
+			})
+		}
+	}
+
+	return ts, nil
+}
+
+type torch struct {
+	storage pytorch.StorageInterface
+	*tensorBase
+}
+
+func (pt torch) WriteTo(w io.Writer) (int64, error) {
+	return 0, nil
+}

convert/safetensors.go

@@ -1,309 +0,0 @@
-package convert
-
-import (
-	"bytes"
-	"encoding/binary"
-	"encoding/json"
-	"fmt"
-	"io"
-	"os"
-	"path/filepath"
-	"regexp"
-	"slices"
-	"strings"
-
-	"github.com/d4l3k/go-bfloat16"
-	"github.com/x448/float16"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type safetensorWriterTo struct {
-	t *llm.Tensor
-
-	params *Params
-	bo     ByteOrder
-
-	filename string
-	dtype    string
-
-	offset, size int64
-	repacker     func(string, []float32, []uint64) ([]float32, error)
-}
-
-type safetensorMetadata struct {
-	Type    string   `json:"dtype"`
-	Shape   []uint64 `json:"shape"`
-	Offsets []int64  `json:"data_offsets"`
-}
-
-type SafetensorFormat struct{}
-
-func (m *SafetensorFormat) GetTensors(dirpath string, params *Params) ([]llm.Tensor, error) {
-	var tensors []llm.Tensor
-	matches, err := filepath.Glob(filepath.Join(dirpath, "*.safetensors"))
-	if err != nil {
-		return nil, err
-	}
-
-	var offset uint64
-	for _, f := range matches {
-		var t []llm.Tensor
-		var err error
-		t, offset, err = m.readTensors(f, offset, params)
-		if err != nil {
-			return nil, err
-		}
-
-		tensors = append(tensors, t...)
-	}
-	return tensors, nil
-}
-
-func (m *SafetensorFormat) readTensors(fn string, offset uint64, params *Params) ([]llm.Tensor, uint64, error) {
-	f, err := os.Open(fn)
-	if err != nil {
-		return nil, 0, err
-	}
-	defer f.Close()
-
-	var n int64
-	if err := binary.Read(f, binary.LittleEndian, &n); err != nil {
-		return nil, 0, err
-	}
-
-	b := bytes.NewBuffer(make([]byte, 0, n))
-	if _, err = io.CopyN(b, f, n); err != nil {
-		return nil, 0, err
-	}
-
-	var headers map[string]safetensorMetadata
-	if err := json.NewDecoder(b).Decode(&headers); err != nil {
-		return nil, 0, err
-	}
-
-	var keys []string
-	for key := range headers {
-		if !strings.HasSuffix(key, "self_attn.rotary_embd.inv_freq") {
-			keys = append(keys, key)
-		}
-	}
-
-	slices.Sort(keys)
-
-	var tensors []llm.Tensor
-	for _, key := range keys {
-		value := headers[key]
-
-		var kind uint32
-		switch len(value.Shape) {
-		case 0:
-			// valuedata
-			continue
-		case 2:
-			kind = 1
-		}
-
-		name, err := m.GetLayerName(key)
-		if err != nil {
-			return nil, 0, err
-		}
-
-		shape := make([]uint64, len(value.Shape))
-		copy(shape, value.Shape)
-
-		pad := func(s int64) int64 {
-			return 8 + n + s
-		}
-
-		t := llm.Tensor{
-			Name:   name,
-			Kind:   kind,
-			Offset: offset,
-			Shape:  shape,
-		}
-
-		t.WriterTo = safetensorWriterTo{
-			t:        &t,
-			params:   params,
-			bo:       params.ByteOrder,
-			filename: fn,
-			dtype:    value.Type,
-			offset:   pad(value.Offsets[0]),
-			size:     pad(value.Offsets[1]) - pad(value.Offsets[0]),
-		}
-
-		offset += t.Size()
-		tensors = append(tensors, t)
-	}
-
-	return tensors, offset, nil
-}
-
-func (m *SafetensorFormat) GetParams(dirpath string) (*Params, error) {
-	f, err := os.Open(filepath.Join(dirpath, "config.json"))
-	if err != nil {
-		return nil, err
-	}
-	defer f.Close()
-
-	var params Params
-
-	if err := json.NewDecoder(f).Decode(&params); err != nil {
-		return nil, err
-	}
-
-	params.ByteOrder = binary.LittleEndian
-	return &params, nil
-}
-
-func (m *SafetensorFormat) GetLayerName(n string) (string, error) {
-	directMap := map[string]string{
-		"model.embed_tokens.weight": "token_embd.weight",
-		"lm_head.weight":            "output.weight",
-		"model.norm.weight":         "output_norm.weight",
-	}
-
-	tMap := map[string]string{
-		"model.layers.(\\d+).input_layernorm.weight":                    "blk.$1.attn_norm.weight",
-		"model.layers.(\\d+).mlp.down_proj.weight":                      "blk.$1.ffn_down.weight",
-		"model.layers.(\\d+).mlp.gate_proj.weight":                      "blk.$1.ffn_gate.weight",
-		"model.layers.(\\d+).mlp.up_proj.weight":                        "blk.$1.ffn_up.weight",
-		"model.layers.(\\d+).post_attention_layernorm.weight":           "blk.$1.ffn_norm.weight",
-		"model.layers.(\\d+).self_attn.k_proj.weight":                   "blk.$1.attn_k.weight",
-		"model.layers.(\\d+).self_attn.o_proj.weight":                   "blk.$1.attn_output.weight",
-		"model.layers.(\\d+).self_attn.q_proj.weight":                   "blk.$1.attn_q.weight",
-		"model.layers.(\\d+).self_attn.v_proj.weight":                   "blk.$1.attn_v.weight",
-		"model.layers.(\\d+).block_sparse_moe.gate.weight":              "blk.$1.ffn_gate_inp.weight",
-		"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w1.weight": "blk.$1.ffn_gate.$2.weight",
-		"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w2.weight": "blk.$1.ffn_down.$2.weight",
-		"model.layers.(\\d+).block_sparse_moe.experts.(\\d+).w3.weight": "blk.$1.ffn_up.$2.weight",
-	}
-
-	v, ok := directMap[n]
-	if ok {
-		return v, nil
-	}
-
-	// quick hack to rename the layers to gguf format
-	for k, v := range tMap {
-		re := regexp.MustCompile(k)
-		newName := re.ReplaceAllString(n, v)
-		if newName != n {
-			return newName, nil
-		}
-	}
-
-	return "", fmt.Errorf("couldn't find a layer name for '%s'", n)
-}
-
-func (r safetensorWriterTo) WriteTo(w io.Writer) (n int64, err error) {
-	f, err := os.Open(r.filename)
-	if err != nil {
-		return 0, err
-	}
-	defer f.Close()
-
-	if _, err = f.Seek(r.offset, io.SeekStart); err != nil {
-		return 0, err
-	}
-
-	var f32s []float32
-	switch r.dtype {
-	case "F32":
-		f32s = make([]float32, r.size/4)
-		if err = binary.Read(f, r.bo, f32s); err != nil {
-			return 0, err
-		}
-	case "F16":
-		u16s := make([]uint16, r.size/2)
-		if err = binary.Read(f, r.bo, u16s); err != nil {
-			return 0, err
-		}
-
-		for _, b := range u16s {
-			f32s = append(f32s, float16.Frombits(b).Float32())
-		}
-
-	case "BF16":
-		u8s := make([]uint8, r.size)
-		if err = binary.Read(f, r.bo, u8s); err != nil {
-			return 0, err
-		}
-
-		f32s = bfloat16.DecodeFloat32(u8s)
-	default:
-		return 0, fmt.Errorf("unknown data type: %s", r.dtype)
-	}
-
-	if r.repacker != nil {
-		f32s, err = r.repacker(r.t.Name, f32s, r.t.Shape)
-		if err != nil {
-			return 0, err
-		}
-	}
-
-	switch r.t.Kind {
-	case 0:
-		return 0, binary.Write(w, r.bo, f32s)
-	case 1:
-		f16s := make([]uint16, len(f32s))
-		for i := range f32s {
-			f16s[i] = float16.Fromfloat32(f32s[i]).Bits()
-		}
-
-		return 0, binary.Write(w, r.bo, f16s)
-	default:
-		return 0, fmt.Errorf("unknown storage type: %d", r.t.Kind)
-	}
-}
-
-func (m *SafetensorFormat) GetModelArch(name, dirPath string, params *Params) (ModelArch, error) {
-	switch len(params.Architectures) {
-	case 0:
-		return nil, fmt.Errorf("No architecture specified to convert")
-	case 1:
-		switch params.Architectures[0] {
-		case "LlamaForCausalLM":
-			return &LlamaModel{
-				ModelData{
-					Name:   name,
-					Path:   dirPath,
-					Params: params,
-					Format: m,
-				},
-			}, nil
-		case "MistralForCausalLM":
-			return &MistralModel{
-				ModelData{
-					Name:   name,
-					Path:   dirPath,
-					Params: params,
-					Format: m,
-				},
-			}, nil
-		case "MixtralForCausalLM":
-			return &MixtralModel{
-				ModelData{
-					Name:   name,
-					Path:   dirPath,
-					Params: params,
-					Format: m,
-				},
-			}, nil
-		case "GemmaForCausalLM":
-			return &GemmaModel{
-				ModelData{
-					Name:   name,
-					Path:   dirPath,
-					Params: params,
-					Format: m,
-				},
-			}, nil
-		default:
-			return nil, fmt.Errorf("Models based on '%s' are not yet supported", params.Architectures[0])
-		}
-	}
-
-	return nil, fmt.Errorf("Unknown error")
-}

convert/testdata/Meta-Llama-3-8B-Instruct.json

@@ -0,0 +1,313 @@
+{
+  "general.architecture": "llama",
+  "general.file_type": "1",
+  "general.quantization_version": "2",
+  "llama.block_count": "32",
+  "llama.context_length": "8192",
+  "llama.embedding_length": "4096",
+  "llama.feed_forward_length": "14336",
+  "llama.rope.dimension_count": "128",
+  "llama.rope.freq_base": "500000",
+  "llama.vocab_size": "128256",
+  "llama.attention.head_count": "32",
+  "llama.attention.head_count_kv": "8",
+  "llama.attention.layer_norm_rms_epsilon": "1e-05",
+  "tokenizer.ggml.model": "gpt2",
+  "tokenizer.ggml.pre": "llama-bpe",
+  "tokenizer.ggml.bos_token_id": "128000",
+  "tokenizer.ggml.eos_token_id": "128009",
+  "tokenizer.ggml.merges": "d0cbac1fcc9dcf03724b8db5c9bfb593ae1cf68fb9bc72eb1d15274dcbbf618b",
+  "tokenizer.ggml.token_type": "d70a88809fd7da6f1f028622685cd64268a7a922c5d343c96f25b66327358978",
+  "tokenizer.ggml.tokens": "765b529dbcbc42dd202ce657341c63807b51f3b07e09898f6aa6196326865d5a",
+  "token_embd.weight": "b53102a11d9064bbd404833e3464b1b13e08ce73300b442312cccde2f19b2698",
+  "blk.0.attn_norm.weight": "7318df3cca9e8d153ff0a503026a1265e63d20b2a8c1dd7a2769585082b5d1ee",
+  "blk.0.ffn_down.weight": "b950806a1fc722c9fad7fd0b20c3c0a7fb50f14395e1e7663a590bfd62e20900",
+  "blk.0.ffn_gate.weight": "e73e580af6d4f08e060a74a3c25efdf5d3bed99e183d95a5a85ae859014839fd",
+  "blk.0.ffn_up.weight": "c8158af679ef99746da1befb67eebb19489e0bbe6ce7d97e13e348508244e516",
+  "blk.0.ffn_norm.weight": "7ec69c3c31e95e49a3359003b0033f6b9e85561a3e3fd83e7476661ecdd756bb",
+  "blk.0.attn_k.weight": "2732303257bac969b4964e0e32ec08b5a7f5c031bb02bf6ac4467b3ea0ebcf1e",
+  "blk.0.attn_output.weight": "ecda1d43b4ccc91cd5b366d7e7a275353990ac78561a07c83d9c77031aba12dc",
+  "blk.0.attn_q.weight": "569b1f5faf92b6f00910cf7effb2d5862f91038ce5c3b0019fc10e5d79fbd5e1",
+  "blk.0.attn_v.weight": "aa8416c5ef7e32fb54a1f20d6ac651656845d4af240564b397c39bd83e06e3b8",
+  "blk.1.attn_norm.weight": "03327e02862908c2a44b2f52decdb924bf4201f400b46f8037a9cb2e1d7a61ff",
+  "blk.1.ffn_down.weight": "5a83a87603f38c99f8e1e370a2d5f967bb45ac51d881a609304a7811027321e0",
+  "blk.1.ffn_gate.weight": "31da0572c79e655186c721c231376f85e56cdcc6257c28d08c8c5b40d5c22b40",
+  "blk.1.ffn_up.weight": "e0c811d64ca155c8de10a868e72015d43888834804614ee1aa2953129ffbc90f",
+  "blk.1.ffn_norm.weight": "5861f313d6137d6f0f904d423df47fffc6069e224ff746e1b637ac9c7f0af862",
+  "blk.1.attn_k.weight": "5fbbec0acca6457b9416ebdcd90e526885d0224537b7628f6be376a7f275313d",
+  "blk.1.attn_output.weight": "b237c9763fa3f75166a6f70b70f1566e77d0d89dfa164ed1b3137393e90575c3",
+  "blk.1.attn_q.weight": "c0a9cf4a98b4882b16f3eb2b49d933793dcc5357abb246fd3fe3134ed2b12e1c",
+  "blk.1.attn_v.weight": "96867111727200cac1af7865189dd41fd62b47584e5e5f33a91f1d34509cbd40",
+  "blk.2.attn_norm.weight": "f392f8a88ee3a95b1cc19c40dd4ef66317037b0faaa1800f610779e129ee0539",
+  "blk.2.ffn_down.weight": "73823eef46632aedcc8c1cb08a736b6aa97ca97842cd1fdfc5567d8dec459662",
+  "blk.2.ffn_gate.weight": "f4909ae19fc3848b00bb8b9050122e74f8e903b89e22937036f4cc9fea20a718",
+  "blk.2.ffn_up.weight": "16f4904a3d814ea68f00519724fc4943e48444a84c786bda39aa5efc298a7d84",
+  "blk.2.ffn_norm.weight": "e3ccdf56e75cb969f6f69c39caf6daf7c4e70e89e25df0f4d2e4bc60e159aafe",
+  "blk.2.attn_k.weight": "c3beb1e0a11bcf007ef0f0d8f6bdd3082d8b29090cd29597846b5d51e308a8e5",
+  "blk.2.attn_output.weight": "bb9f66c32cff51154fea92933c2cd62549236f8cb1a767f9ef28d3f99809b343",
+  "blk.2.attn_q.weight": "8eba394132eef2a05c5a92d62d2376000f7948448d7a2dc74e6b608203add20d",
+  "blk.2.attn_v.weight": "88f61f77c53567c617db3eef8f30621109a750e679f6784f7911739bd42c2f02",
+  "blk.3.attn_norm.weight": "7b996675b7ca75fa24107b3ebe0788653ede0f49ac83b8659d71ff54d591f81a",
+  "blk.3.ffn_down.weight": "2cb332bc05e4821962fdc9dcbcc7cc12630f32117711b687d18fb53c0bc4fbf4",
+  "blk.3.ffn_gate.weight": "340b387c7f208c8f0a6db904ef8d87c1e84b7d6ad57177abd32d86c8d18b760f",
+  "blk.3.ffn_up.weight": "07484433f8a7ee061c55aa0de2ecc009f769b0617c9c0ec096e9bb2946df9f0e",
+  "blk.3.ffn_norm.weight": "4f1a4ade36b393af341240bc894a2aab09cff7e4d56dc4658445deb107f9371b",
+  "blk.3.attn_k.weight": "483dcd96acb4528df84b9842970994630dbd82b8715ace394aa8b39fcf8d6291",
+  "blk.3.attn_output.weight": "beaff0810687923585642ee11d929cbf3b43dc6f87f30ddb552c222ab57bdbb3",
+  "blk.3.attn_q.weight": "0739355002f6fce520863add697e0ff25fc88215322dc3f993be7bb68dcce7e8",
+  "blk.3.attn_v.weight": "c216d17b6d90ee3e07f82598b8161fae34de2f392dbb0f745b682b578c324767",
+  "blk.4.attn_norm.weight": "91ab405bc4ba15bf63af233f266aa43aaab43789a9e6596e14a357c2ac7df217",
+  "blk.4.ffn_down.weight": "620f34ee75cdc73aecb8949af5fbb0d2437fd81422b6d8eb7acfc52addb9fc68",
+  "blk.4.ffn_gate.weight": "f6feec7bc9acadf35ec22532f8998d8e50f31afedabb19263590dcf8b9a92eee",
+  "blk.4.ffn_up.weight": "4a72af7cd28fd07b038f6cc4406678d120517280236ea85d9e76eff40ab2cc22",
+  "blk.4.ffn_norm.weight": "1805b37b44d5d682bdbd2fadeafb763ee001617d7870848cc487079ee34b21f9",
+  "blk.4.attn_k.weight": "a1e4f9d97cdf4c1b0d177cf00c4e32d1be30c1984a239b3c9bd73f8848888853",
+  "blk.4.attn_output.weight": "a1547e2497c423b0aff0eee71d9300d6fdf4e4986679418b6e637b69a9a6720b",
+  "blk.4.attn_q.weight": "0677483a9264ea6803d03d304d87a54632242cb516e8b76b6e3e8284c2f4de04",
+  "blk.4.attn_v.weight": "02691ba3af344fcc1969428ab0df811ac94aaa2fd91b0dc4ec1ac0a58806980d",
+  "blk.5.attn_norm.weight": "ba9c028335e5c895b87a5bd1448ca429248f9746ed97bdcb8679923206117156",
+  "blk.5.ffn_down.weight": "ccfdc9006acad1940a6bc05042a3947f1066acd671e0bb53b7684e9eea9ef5c9",
+  "blk.5.ffn_gate.weight": "623157679f1e742ccc3807c0b0153ddc8450104de75ec62f1370ec3807c09cf4",
+  "blk.5.ffn_up.weight": "05748804c65091f963729b58b085f58351891cac8a2861f5eae26b06aa60b2a0",
+  "blk.5.ffn_norm.weight": "84bae55af2efc8b8429f09056c8c04990c466dae31cb3f9356038b8957f1b406",
+  "blk.5.attn_k.weight": "8c766180c726b037d587fc52371de6e3307140c52409011609d1225624b6a3eb",
+  "blk.5.attn_output.weight": "490b582b3b1dc151ae55aee8b6743dad6c01fb49e43afefb6e68394b74be3d73",
+  "blk.5.attn_q.weight": "6f7b8ca4d9025ec836a44bbcca46be30c66b471a9fb62943ddff8288b3731409",
+  "blk.5.attn_v.weight": "9f70df3ba00c9e723214b3da83ff435a2163fff5915f75515c9664c05c866c27",
+  "blk.6.attn_norm.weight": "1a4a66613a682df6f061fc7c4d986f9f7e9175b62f0c42fc1ef31db536bd5942",
+  "blk.6.ffn_down.weight": "c56f25e4e49b443dbc82d88311ee63bc1f5002cc67e52f4787fd5f003aedeac1",
+  "blk.6.ffn_gate.weight": "31a5cf1aa9b831a81588d508550f51fc425f9517c43254d4ef7096d38029cf04",
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+}

convert/testdata/Mistral-7B-Instruct-v0.2.json

@@ -0,0 +1,313 @@
+{
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+  "general.file_type": "1",
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+}

convert/testdata/Mixtral-8x7B-Instruct-v0.1.json

@@ -0,0 +1,348 @@
+{
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+  "general.file_type": "1",
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+}

convert/testdata/gemma-2b-it.json

@@ -0,0 +1,188 @@
+{
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+  "blk.13.attn_q.weight": "3b8caf35504cbc4d1a7dd6e011a95760703b7f71e2218b030b1254f811362dd7",
+  "blk.13.attn_v.weight": "4fdf8365a603e043e5b40c4a21c84ac167f9be62794178f9d8a608dfe5653bf9",
+  "blk.13.ffn_down.weight": "a07d3abbfcacf48ba028df2cab895be32cc15022d23389a745286e79c1b1d1fd",
+  "blk.13.ffn_gate.weight": "1d2ab39666aa2909acc96787432a3ed13b19d25170f74665fadff9b17bbaffb1",
+  "blk.13.ffn_norm.weight": "4f2e809fda5f3eadf52578ee50e0ba36e53be91e55dce418c12dfe595f5f18e7",
+  "blk.13.ffn_up.weight": "8783d2720c2c37ca176a5801e0b3ef1f9cc9cf3ef1cd37af423aaf6b2a27e2bd",
+  "blk.14.attn_k.weight": "ce9428e2b55d43ae0c6690dbd56182f99adc427694ba8236b405cc8ea5035e86",
+  "blk.14.attn_norm.weight": "6abb35f9db8251d6ae954bda147c6ada2371b0574d11702e828f3c6ac99b7cc0",
+  "blk.14.attn_output.weight": "fe3880916d0ceb5bff672c88bbefb7060a545be609bf049beb2024b38221836d",
+  "blk.14.attn_q.weight": "7c8ad81be6f4a350931fd108b5f7c9e366e8c26ef62d1d85ffef5dca8fd893f8",
+  "blk.14.attn_v.weight": "e4bdedffacbebe38567a0734dfd67db90e911d9a9669fcde9a7c4ad8a0066c52",
+  "blk.14.ffn_down.weight": "ef6694dff1e05820aac0cd2b22f39ac7788b4967afc9250775575554c66aab2c",
+  "blk.14.ffn_gate.weight": "db63c4179e2db704bc505e2b4696e055b593e295a1b7c4c586fc793bdd5aab19",
+  "blk.14.ffn_norm.weight": "2796a62d832a9710148f95d533320492a33e712b2e5218659c548705bd11684d",
+  "blk.14.ffn_up.weight": "3f78c78d8c2d54df45f799d4ff902316628af296834afe4ceed63d4a324ff03e",
+  "blk.15.attn_k.weight": "6e810ee3859e07695645ee0c9a5efc7962668984a5f0a9325f47e462743b447c",
+  "blk.15.attn_norm.weight": "0956b576ae96db0b28cb09f761f801cfd9281432284664f0fe181c8d9c55d1ec",
+  "blk.15.attn_output.weight": "03a17f7e94208177aace5cc41b7f54670ba57873b7274ff6e23caf58cce110ca",
+  "blk.15.attn_q.weight": "b8edafe7d2216a6f8b4ae4905a906475490e6ea418f6e1d3cec563dbdc6fab91",
+  "blk.15.attn_v.weight": "f8ae8cae0f4cfa34a459824eba57350c3c248104ba5607e7d9dc7d7c39aaf4a6",
+  "blk.15.ffn_down.weight": "8d02eb439da852246d2ca67e9b7b6de0b090b80744355e64728a23e41926505b",
+  "blk.15.ffn_gate.weight": "ed5bf361c67db8731f186b775826f21c33bdb521111fd2d922539719a770239f",
+  "blk.15.ffn_norm.weight": "5942ca3c73209ac9a0c8bfd9b4aab7f7be7aee9aa12d9c35833493b44af76767",
+  "blk.15.ffn_up.weight": "f4bebf4ad99ec5f911327dec347be6c595814885309c7bc5647ce28c7f4d1cf5",
+  "blk.16.attn_k.weight": "756a534c19364448e0958b8948fe33891c6ccda0fbb4dfa2024e1f532a87804b",
+  "blk.16.attn_norm.weight": "386b7b9e4e6509f6af9c022d942b6c6c6cc136aeed8751ecb037c74d7c4bfb93",
+  "blk.16.attn_output.weight": "3ba1a766a25830b84d7c22178203635f9c5624caad290bc5e5d73da5d5e7a2ec",
+  "blk.16.attn_q.weight": "d39b0c91e1fda7685d50a0f7cc8d18c44b5bdc90a142c7fda0bc329cca1afa74",
+  "blk.16.attn_v.weight": "98b33fcb0ee3483cff1b06ecb44d7b7ffb4d34c268248e4d73dfdf82b2065b2f",
+  "blk.16.ffn_down.weight": "14006f5e4acb2f9416271ae562e299359cd2585739c7fc77ccbca54495563948",
+  "blk.16.ffn_gate.weight": "12f8abae2d301d8f88bedb6af98b1daecc7b0b8d05148594f931f30958d77aca",
+  "blk.16.ffn_norm.weight": "129a15a046ee96d06de288bd43c80f77a6b0fb3a159c7367154c6e4aaf362672",
+  "blk.16.ffn_up.weight": "b4a5911a45f3871ef1d4efb7dc7108645a564b70f818eccf45beebef2e844ee9",
+  "blk.17.attn_k.weight": "5e1bfcff0146ebdde3817b656952892eb671e14e75afc92fa53f84f8eecbec4c",
+  "blk.17.attn_norm.weight": "60bc988fab7c4b29ee9de599df41a8de00caa94fcd74677da011fac82f60f465",
+  "blk.17.attn_output.weight": "ba49b40d6a0b5685f749c24b0edbed3adc44dbe13b5d5e5fa1e56169fc746555",
+  "blk.17.attn_q.weight": "82bb415d24efcd14d03ace03f907bb70db6a204c76a0bdd1892e0fba165db87d",
+  "blk.17.attn_v.weight": "73dbe54beb91a899884e275ea81ffc5187a20cb7d5b68d5c299b783096999d94",
+  "blk.17.ffn_down.weight": "7c086166241e0664f8963fd1ca4ed74c737abfb2525ec20f8435821ff50158f3",
+  "blk.17.ffn_gate.weight": "51a32f78244d42a539f619c5ce661db9e6cf41636280a826d439b5444edcd28c",
+  "blk.17.ffn_norm.weight": "c4bb247fccd1ecc84875028af63dd20aaf5cbd17eb94a9bc36679c09285dccab",
+  "blk.17.ffn_up.weight": "b5886182790bc6fbadd63de9bc4ffee416f3b69a66280d197ab8c18edf769abf",
+  "output_norm.weight": "481f3097d0a20412e35b3a739b1b958487bcd41ff67744baa3c9acbddd2ee4d4"
+}

convert/tokenizer.go

@@ -3,19 +3,148 @@ package convert
 import (
 	"cmp"
 	"crypto/sha256"
+	"encoding/hex"
 	"encoding/json"
+	"errors"
 	"fmt"
 	"log/slog"
 	"os"
+	"path/filepath"
 	"slices"
+)
 
-	"golang.org/x/exp/maps"
+const (
+	_ int32 = iota
+	tokenTypeNormal
+	tokenTypeUnknown
+	tokenTypeControl
+	tokenTypeUserDefined
+	tokenTypeUnused
+	tokenTypeByte
 )
 
 type Tokenizer struct {
-	Version     string         `json:"version"`
-	AddedTokens []Token        `json:"added_tokens"`
-	Model       TokenizerModel `json:"model"`
+	*Vocabulary
+	SpecialVocabulary []*SpecialVocabulary
+	Merges            []string
+
+	Pre      string
+	Template string
+}
+
+func parseTokenizer(d string, specialTypes []string) (*Tokenizer, error) {
+	v, err := parseVocabulary(d)
+	if err != nil {
+		return nil, err
+	}
+
+	t := &Tokenizer{
+		Vocabulary: v,
+		Pre:        "default",
+	}
+
+	addedTokens := make(map[string]token)
+	if f, err := os.Open(filepath.Join(d, "tokenizer.json")); errors.Is(err, os.ErrNotExist) {
+	} else if err != nil {
+		return nil, err
+	} else {
+		defer f.Close()
+
+		var tt tokenizer
+		if err := json.NewDecoder(f).Decode(&tt); err != nil {
+			return nil, err
+		}
+
+		for _, t := range tt.AddedTokens {
+			addedTokens[t.Content] = t
+		}
+
+		t.Merges = tt.Model.Merges
+
+		sha256sum := sha256.New()
+		for _, pt := range tt.PreTokenizer.PreTokenizers {
+			switch pt.Type {
+			case "Split":
+				if pt.Pattern.Regex != "" {
+					sha256sum.Write([]byte(pt.Pattern.Regex))
+				}
+			}
+		}
+
+		switch digest := hex.EncodeToString(sha256sum.Sum(nil)); digest {
+		case "d98f9631be1e9607a9848c26c1f9eac1aa9fc21ac6ba82a2fc0741af9780a48f":
+			t.Pre = "llama-bpe"
+		case "03df5c5863ad70781dcfdef491ead25140f895fe8010964be0daefe27be32b02":
+			t.Pre = "deepseek-llm"
+		case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e":
+			t.Pre = "deepseek-coder"
+		case "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855":
+			// noop, empty pretokenizer
+		default:
+			slog.Warn("unknown pretokenizer, using default", "digest", digest)
+		}
+	}
+
+	if f, err := os.Open(filepath.Join(d, "tokenizer_config.json")); errors.Is(err, os.ErrNotExist) {
+	} else if err != nil {
+		return nil, err
+	} else {
+		defer f.Close()
+
+		var p map[string]json.RawMessage
+		if err := json.NewDecoder(f).Decode(&p); err != nil {
+			return nil, err
+		}
+
+		if template, ok := p["chat_template"]; ok {
+			if err := json.Unmarshal(template, &t.Template); err != nil {
+				return nil, err
+			}
+		}
+
+		for _, st := range specialTypes {
+			sv := SpecialVocabulary{Type: st}
+			if bts, ok := p[fmt.Sprintf("add_%s_token", st)]; ok {
+				if err := json.Unmarshal(bts, &sv.AddToken); err != nil {
+					return nil, err
+				}
+			}
+
+			if bts, ok := p[fmt.Sprintf("%s_token", st)]; ok {
+				var content string
+				if err := json.Unmarshal(bts, &content); err != nil {
+					var mm map[string]any
+					if err := json.Unmarshal(bts, &mm); err != nil {
+						continue
+					}
+
+					content, ok = mm["content"].(string)
+					if !ok {
+						continue
+					}
+				}
+
+				sv.Content = content
+			}
+
+			if id, ok := addedTokens[sv.Content]; ok {
+				sv.ID = id.ID
+				t.SpecialVocabulary = append(t.SpecialVocabulary, &sv)
+			}
+		}
+	}
+
+	return t, nil
+}
+
+type tokenizer struct {
+	Version     string  `json:"version"`
+	AddedTokens []token `json:"added_tokens"`
+	Model       struct {
+		Type   string         `json:"type"`
+		Vocab  map[string]int `json:"vocab"`
+		Merges []string       `json:"merges"`
+	} `json:"model"`
 
 	PreTokenizer struct {
 		PreTokenizers []struct {
@@ -27,80 +156,106 @@ type Tokenizer struct {
 	} `json:"pre_tokenizer"`
 }
 
-type TokenizerModel struct {
-	Type   string         `json:"type"`
-	Vocab  map[string]int `json:"vocab"`
-	Merges []string       `json:"merges"`
-	Tokens []Token
-}
-
-type Token struct {
+type token struct {
 	ID          int    `json:"id"`
 	Content     string `json:"content"`
 	Special     bool   `json:"special"`
 	UserDefined bool
 }
 
-func (t *Token) Type() int32 {
-	switch {
-	case t.Special:
-		return tokenTypeControl
-	case t.UserDefined:
-		return tokenTypeUserDefined
-	default:
-		return tokenTypeNormal
-	}
+type Vocabulary struct {
+	Model  string
+	Tokens []string
+	Scores []float32
+	Types  []int32
 }
 
-func (t *Tokenizer) maxID() int {
-	return max(
-		slices.Max(maps.Values(t.Model.Vocab)),
-		slices.MaxFunc(t.AddedTokens, func(a, b Token) int {
-			return cmp.Compare(a.ID, b.ID)
-		}).ID,
-	)
-}
-
-func parseTokens(dirpath string) (pre string, tokens []Token, merges []string, err error) {
-	f, err := os.Open(dirpath)
+func parseVocabularyFromTokenizer(p string) (*Vocabulary, error) {
+	f, err := os.Open(filepath.Join(p, "tokenizer.json"))
 	if err != nil {
-		panic(err)
+		return nil, err
 	}
 	defer f.Close()
 
-	var t Tokenizer
+	var t tokenizer
 	if err := json.NewDecoder(f).Decode(&t); err != nil {
-		return "", nil, nil, err
+		return nil, err
 	}
 
-	tokens = make([]Token, t.maxID()+1)
+	var tokens []token
 	for k, v := range t.Model.Vocab {
-		tokens[v] = Token{ID: v, Content: k, Special: false, UserDefined: false}
+		tokens = append(tokens, token{
+			ID:      v,
+			Content: k,
+		})
 	}
 
-	for _, v := range t.AddedTokens {
-		v.UserDefined = true
-		tokens[v.ID] = v
+	for _, t := range t.AddedTokens {
+		t.UserDefined = true
+		tokens = append(tokens, t)
 	}
 
-	sha256sum := sha256.New()
-	for _, pt := range t.PreTokenizer.PreTokenizers {
-		if pt.Type == "Split" && pt.Pattern.Regex != "" {
-			sha256sum.Write([]byte(pt.Pattern.Regex))
+	slices.SortFunc(tokens, func(i, j token) int {
+		return cmp.Compare(i.ID, j.ID)
+	})
+
+	v := Vocabulary{Model: "gpt2"}
+	for _, t := range tokens {
+		v.Tokens = append(v.Tokens, t.Content)
+		v.Scores = append(v.Scores, float32(t.ID))
+
+		switch {
+		case t.Special:
+			v.Types = append(v.Types, tokenTypeControl)
+		case t.UserDefined:
+			v.Types = append(v.Types, tokenTypeUserDefined)
+		default:
+			v.Types = append(v.Types, tokenTypeNormal)
 		}
 	}
 
-	switch digest := fmt.Sprintf("%x", sha256sum.Sum(nil)); digest {
-	case "d98f9631be1e9607a9848c26c1f9eac1aa9fc21ac6ba82a2fc0741af9780a48f":
-		pre = "llama-bpe"
-	case "03df5c5863ad70781dcfdef491ead25140f895fe8010964be0daefe27be32b02":
-		pre = "deepseek-llm"
-	case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e":
-		pre = "deepseek-coder"
-	default:
-		slog.Warn("unknown pretokenizer, using default", "digest", digest)
-		pre = "default"
+	return &v, nil
+}
+
+func parseVocabulary(d string) (*Vocabulary, error) {
+	patterns := map[string]func(string) (*Vocabulary, error){
+		"tokenizer.model": parseSentencePiece,
+		"tokenizer.json":  parseVocabularyFromTokenizer,
 	}
 
-	return pre, tokens, t.Model.Merges, nil
+	for pattern, parseFn := range patterns {
+		matches, err := filepath.Glob(filepath.Join(d, pattern))
+		if err != nil {
+			return nil, err
+		}
+
+		if len(matches) > 0 {
+			return parseFn(d)
+		}
+	}
+
+	return nil, errors.New("unknown tensor format")
+}
+
+type SpecialVocabulary struct {
+	Type     string
+	ID       int
+	Content  string
+	AddToken bool
+}
+
+func (sv SpecialVocabulary) Key() string {
+	switch t := sv.Type; t {
+	case "bos", "eos", "cls", "mask":
+		return t
+	case "unk":
+		return "unknown"
+	case "sep":
+		//nolint:misspell // this is an upstream typo
+		return "seperator"
+	case "pad":
+		return "padding"
+	}
+
+	panic("unknown special vocabulary type")
 }

convert/tokenizer_spm.go

@@ -0,0 +1,83 @@
+package convert
+
+import (
+	"cmp"
+	"encoding/json"
+	"errors"
+	"fmt"
+	"os"
+	"path/filepath"
+	"slices"
+
+	"google.golang.org/protobuf/proto"
+
+	"github.com/ollama/ollama/convert/sentencepiece"
+)
+
+func parseSentencePiece(d string) (*Vocabulary, error) {
+	bts, err := os.ReadFile(filepath.Join(d, "tokenizer.model"))
+	if err != nil {
+		return nil, err
+	}
+
+	var spm sentencepiece.ModelProto
+	if err := proto.Unmarshal(bts, &spm); err != nil {
+		return nil, err
+	}
+
+	v := Vocabulary{Model: "llama"}
+	for _, piece := range spm.GetPieces() {
+		v.Tokens = append(v.Tokens, piece.GetPiece())
+		v.Scores = append(v.Scores, piece.GetScore())
+
+		switch t := piece.GetType(); t {
+		case sentencepiece.ModelProto_SentencePiece_UNKNOWN,
+			sentencepiece.ModelProto_SentencePiece_CONTROL,
+			sentencepiece.ModelProto_SentencePiece_UNUSED,
+			sentencepiece.ModelProto_SentencePiece_BYTE:
+			v.Types = append(v.Types, int32(t))
+		default:
+			v.Types = append(v.Types, int32(sentencepiece.ModelProto_SentencePiece_NORMAL))
+		}
+	}
+
+	f, err := os.Open(filepath.Join(d, "added_tokens.json"))
+	if errors.Is(err, os.ErrNotExist) {
+		return &v, nil
+	} else if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+
+	var atm map[string]int
+	if err := json.NewDecoder(f).Decode(&atm); err != nil {
+		return nil, err
+	}
+
+	type t struct {
+		id      int
+		content string
+	}
+
+	var ts []t
+	for content, id := range atm {
+		ts = append(ts, t{id, content})
+	}
+
+	slices.SortFunc(ts, func(i, j t) int {
+		return cmp.Compare(i.id, j.id)
+	})
+
+	n := len(v.Tokens)
+	for i, t := range ts {
+		if t.id != i+n {
+			return nil, fmt.Errorf("invalid token id: %d", t.id)
+		}
+
+		v.Tokens = append(v.Tokens, t.content)
+		v.Scores = append(v.Scores, -1000.0)
+		v.Types = append(v.Types, tokenTypeUserDefined)
+	}
+
+	return &v, nil
+}

convert/torch.go

@@ -1,287 +0,0 @@
-package convert
-
-import (
-	"encoding/binary"
-	"encoding/json"
-	"fmt"
-	"io"
-	"log/slog"
-	"os"
-	"path/filepath"
-	"regexp"
-	"strings"
-
-	"github.com/nlpodyssey/gopickle/pytorch"
-	"github.com/nlpodyssey/gopickle/types"
-	"github.com/x448/float16"
-
-	"github.com/ollama/ollama/llm"
-)
-
-type torchWriterTo struct {
-	t *llm.Tensor
-
-	params *Params
-	bo     ByteOrder
-
-	storage  pytorch.StorageInterface
-	repacker func(string, []float32, []uint64) ([]float32, error)
-}
-
-type TorchFormat struct{}
-
-func (tf *TorchFormat) GetTensors(dirpath string, params *Params) ([]llm.Tensor, error) {
-	slog.Debug("getting torch tensors")
-
-	var files []string
-	if pt, _ := filepath.Glob(filepath.Join(dirpath, "consolidated*.pth")); len(pt) > 0 {
-		files = append(files, pt...)
-	} else if pt, _ := filepath.Glob(filepath.Join(dirpath, "pytorch_model*.pth")); len(pt) > 0 {
-		files = append(files, pt...)
-	}
-
-	var offset uint64
-	var tensors []llm.Tensor
-	for _, fn := range files {
-		m, err := pytorch.Load(fn)
-		if err != nil {
-			slog.Error(fmt.Sprintf("error unpickling: %q", err))
-			return []llm.Tensor{}, err
-		}
-
-		for _, k := range m.(*types.Dict).Keys() {
-			if strings.HasSuffix(k.(string), "self_attn.rotary_emb.inv_freq") {
-				continue
-			}
-
-			t, _ := m.(*types.Dict).Get(k)
-			tshape := t.(*pytorch.Tensor).Size
-
-			var size uint64
-			var kind uint32
-			switch len(tshape) {
-			case 0:
-				continue
-			case 1:
-				// convert to float32
-				kind = 0
-				size = uint64(tshape[0] * 4)
-			case 2:
-				// convert to float16
-				kind = 1
-				size = uint64(tshape[0] * tshape[1] * 2)
-			}
-
-			ggufName, err := tf.GetLayerName(k.(string))
-			if err != nil {
-				slog.Error(err.Error())
-				return nil, err
-			}
-			slog.Debug(fmt.Sprintf("'%35s': '%30s' %10d [%#v]", k.(string), ggufName, size, tshape))
-
-			shape := []uint64{0, 0, 0, 0}
-			for i := range tshape {
-				shape[i] = uint64(tshape[i])
-			}
-
-			tensor := llm.Tensor{
-				Name:   ggufName,
-				Kind:   kind,
-				Offset: offset, // calculate the offset
-				Shape:  shape,
-			}
-
-			tensor.WriterTo = torchWriterTo{
-				t:       &tensor,
-				params:  params,
-				bo:      params.ByteOrder,
-				storage: t.(*pytorch.Tensor).Source,
-			}
-
-			tensors = append(tensors, tensor)
-			offset += size
-		}
-	}
-
-	return tensors, nil
-}
-
-func getAltParams(dirpath string) (*Params, error) {
-	f, err := os.Open(filepath.Join(dirpath, "params.json"))
-	if err != nil {
-		slog.Error("no params.json")
-		return nil, err
-	}
-	defer f.Close()
-
-	type TorchParams struct {
-		HiddenSize     int     `json:"dim"`
-		AttentionHeads int     `json:"n_heads"`
-		KeyValHeads    int     `json:"n_kv_heads"`
-		HiddenLayers   int     `json:"n_layers"`
-		RopeTheta      float64 `json:"rope_theta"`
-		NormEPS        float64 `json:"norm_eps"`
-	}
-
-	var tparams TorchParams
-
-	d := json.NewDecoder(f)
-	err = d.Decode(&tparams)
-	if err != nil {
-		return nil, err
-	}
-
-	params := &Params{
-		Architectures:  []string{"LlamaForCausalLM"},
-		HiddenSize:     tparams.HiddenSize,
-		AttentionHeads: tparams.AttentionHeads,
-		KeyValHeads:    tparams.KeyValHeads,
-		HiddenLayers:   tparams.HiddenLayers,
-		NormEPS:        tparams.NormEPS,
-	}
-
-	switch {
-	case tparams.RopeTheta == 1000000:
-		// Codellama
-		params.ContextSize = 16384
-	case tparams.NormEPS == 1e-06:
-		// llama2
-		slog.Debug("Found llama2 - setting context size to 4096")
-		params.ContextSize = 4096
-	default:
-		params.ContextSize = 2048
-	}
-
-	params.ByteOrder = binary.LittleEndian
-	return params, nil
-}
-
-func (m *TorchFormat) GetParams(dirpath string) (*Params, error) {
-	f, err := os.Open(filepath.Join(dirpath, "config.json"))
-	if err != nil {
-		if os.IsNotExist(err) {
-			// try params.json instead
-			return getAltParams(dirpath)
-		} else {
-			return nil, err
-		}
-	}
-
-	var params Params
-	d := json.NewDecoder(f)
-	err = d.Decode(&params)
-	if err != nil {
-		return nil, err
-	}
-
-	params.ByteOrder = binary.LittleEndian
-	return &params, nil
-}
-
-func (m *TorchFormat) GetLayerName(n string) (string, error) {
-	directMap := map[string]string{
-		"tok_embeddings.weight":     "token_embd.weight",
-		"output.weight":             "output.weight",
-		"norm.weight":               "output_norm.weight",
-		"rope.freqs":                "rope_freqs.weight",
-		"model.embed_tokens.weight": "token_embd.weight",
-		"lm_head.weight":            "output.weight",
-		"model.norm.weight":         "output_norm.weight",
-	}
-
-	lMap := map[string]string{
-		"layers.(\\d+).attention_norm.weight":                 "blk.$1.attn_norm.weight",
-		"layers.(\\d+).attention_output_norm.weight":          "blk.$1.attn_norm.weight",
-		"layers.(\\d+).feed_forward.w2.weight":                "blk.$1.ffn_down.weight",
-		"layers.(\\d+).feed_forward.w1.weight":                "blk.$1.ffn_gate.weight",
-		"layers.(\\d+).feed_forward.w3.weight":                "blk.$1.ffn_up.weight",
-		"layers.(\\d+).ffn_norm.weight":                       "blk.$1.ffn_norm.weight",
-		"layers.(\\d+).attention.wk.weight":                   "blk.$1.attn_k.weight",
-		"layers.(\\d+).attention.wo.weight":                   "blk.$1.attn_output.weight",
-		"layers.(\\d+).attention.wq.weight":                   "blk.$1.attn_q.weight",
-		"layers.(\\d+).attention.wv.weight":                   "blk.$1.attn_v.weight",
-		"model.layers.(\\d+).input_layernorm.weight":          "blk.$1.attn_norm.weight",
-		"model.layers.(\\d+).mlp.down_proj.weight":            "blk.$1.ffn_down.weight",
-		"model.layers.(\\d+).mlp.gate_proj.weight":            "blk.$1.ffn_gate.weight",
-		"model.layers.(\\d+).mlp.up_proj.weight":              "blk.$1.ffn_up.weight",
-		"model.layers.(\\d+).post_attention_layernorm.weight": "blk.$1.ffn_norm.weight",
-		"model.layers.(\\d+).self_attn.k_proj.weight":         "blk.$1.attn_k.weight",
-		"model.layers.(\\d+).self_attn.o_proj.weight":         "blk.$1.attn_output.weight",
-		"model.layers.(\\d+).self_attn.q_proj.weight":         "blk.$1.attn_q.weight",
-		"model.layers.(\\d+).self_attn.v_proj.weight":         "blk.$1.attn_v.weight",
-	}
-
-	v, ok := directMap[n]
-	if ok {
-		return v, nil
-	}
-
-	// quick hack to rename the layers to gguf format
-	for k, v := range lMap {
-		re := regexp.MustCompile(k)
-		newName := re.ReplaceAllString(n, v)
-		if newName != n {
-			return newName, nil
-		}
-	}
-
-	return "", fmt.Errorf("couldn't find a layer name for '%s'", n)
-}
-
-func (r torchWriterTo) WriteTo(w io.Writer) (n int64, err error) {
-	var f32s []float32
-	switch s := r.storage.(type) {
-	case *pytorch.FloatStorage:
-		f32s = s.Data
-	case *pytorch.HalfStorage:
-		f32s = s.Data
-	case *pytorch.BFloat16Storage:
-		f32s = s.Data
-	default:
-		return 0, fmt.Errorf("unknown data type: %T", s)
-	}
-
-	if r.repacker != nil {
-		f32s, err = r.repacker(r.t.Name, f32s, r.t.Shape)
-		if err != nil {
-			return 0, err
-		}
-	}
-
-	switch r.t.Kind {
-	case 0:
-		return 0, binary.Write(w, r.bo, f32s)
-	case 1:
-		f16s := make([]uint16, len(f32s))
-		for i := range f32s {
-			f16s[i] = float16.Fromfloat32(f32s[i]).Bits()
-		}
-
-		return 0, binary.Write(w, r.bo, f16s)
-	default:
-		return 0, fmt.Errorf("unknown storage type: %d", r.t.Kind)
-	}
-}
-
-func (m *TorchFormat) GetModelArch(name, dirPath string, params *Params) (ModelArch, error) {
-	switch len(params.Architectures) {
-	case 0:
-		return nil, fmt.Errorf("No architecture specified to convert")
-	case 1:
-		switch params.Architectures[0] {
-		case "LlamaForCausalLM":
-			return &LlamaModel{
-				ModelData{
-					Name:   name,
-					Path:   dirPath,
-					Params: params,
-					Format: m,
-				},
-			}, nil
-		default:
-			return nil, fmt.Errorf("Models based on '%s' are not yet supported", params.Architectures[0])
-		}
-	}
-
-	return nil, fmt.Errorf("Unknown error")
-}

docs/api.md

@@ -40,7 +40,6 @@ 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
-- `suffix`: the text after the model response
 - `images`: (optional) a list of base64-encoded images (for multimodal models such as `llava`)
 
 Advanced parameters (optional):
@@ -58,8 +57,7 @@ Advanced parameters (optional):
 
 Enable JSON mode by setting the `format` parameter to `json`. This will structure the response as a valid JSON object. See the JSON mode [example](#request-json-mode) below.
 
-> [!IMPORTANT]
-> It's important to instruct the model to use JSON in the `prompt`. Otherwise, the model may generate large amounts whitespace.
+> Note: it's important to instruct the model to use JSON in the `prompt`. Otherwise, the model may generate large amounts whitespace.
 
 ### Examples
 
@@ -150,44 +148,8 @@ If `stream` is set to `false`, the response will be a single JSON object:
 }
 ```
 
-#### Request (with suffix)
-
-##### Request
-
-```shell
-curl http://localhost:11434/api/generate -d '{
-  "model": "codellama:code",
-  "prompt": "def compute_gcd(a, b):",
-  "suffix": "    return result",
-  "options": {
-    "temperature": 0
-  },
-  "stream": false
-}'
-```
-
-##### Response
-
-```json
-{
-  "model": "codellama:code",
-  "created_at": "2024-07-22T20:47:51.147561Z",
-  "response": "\n  if a == 0:\n    return b\n  else:\n    return compute_gcd(b % a, a)\n\ndef compute_lcm(a, b):\n  result = (a * b) / compute_gcd(a, b)\n",
-  "done": true,
-  "done_reason": "stop",
-  "context": [...],
-  "total_duration": 1162761250,
-  "load_duration": 6683708,
-  "prompt_eval_count": 17,
-  "prompt_eval_duration": 201222000,
-  "eval_count": 63,
-  "eval_duration": 953997000
-}
-```
-
 #### Request (JSON mode)
 
-> [!IMPORTANT]
 > When `format` is set to `json`, the output will always be a well-formed JSON object. It's important to also instruct the model to respond in JSON.
 
 ##### Request
@@ -336,7 +298,6 @@ curl http://localhost:11434/api/generate -d '{
     "num_predict": 100,
     "top_k": 20,
     "top_p": 0.9,
-    "min_p": 0.0,
     "tfs_z": 0.5,
     "typical_p": 0.7,
     "repeat_last_n": 33,
@@ -419,14 +380,12 @@ Generate the next message in a chat with a provided model. This is a streaming e
 
 - `model`: (required) the [model name](#model-names)
 - `messages`: the messages of the chat, this can be used to keep a chat memory
-- `tools`: tools for the model to use if supported. Requires `stream` to be set to `false`
 
 The `message` object has the following fields:
 
-- `role`: the role of the message, either `system`, `user`, `assistant`, or `tool`
+- `role`: the role of the message, either `system`, `user` or `assistant`
 - `content`: the content of the message
 - `images` (optional): a list of images to include in the message (for multimodal models such as `llava`)
-- `tool_calls` (optional): a list of tools the model wants to use
 
 Advanced parameters (optional):
 
@@ -587,7 +546,7 @@ Final response:
 
 ##### Request
 
-Send a chat message with images. The images should be provided as an array, with the individual images encoded in Base64.
+Send a chat message with a conversation history.
 
 ```shell
 curl http://localhost:11434/api/chat -d '{
@@ -663,79 +622,6 @@ curl http://localhost:11434/api/chat -d '{
 }
 ```
 
-#### Chat request (with tools)
-
-##### Request
-
-```
-curl http://localhost:11434/api/chat -d '{
-  "model": "mistral",
-  "messages": [
-    {
-      "role": "user",
-      "content": "What is the weather today in Paris?"
-    }
-  ],
-  "stream": false,
-  "tools": [
-    {
-      "type": "function",
-      "function": {
-        "name": "get_current_weather",
-        "description": "Get the current weather for a location",
-        "parameters": {
-          "type": "object",
-          "properties": {
-            "location": {
-              "type": "string",
-              "description": "The location to get the weather for, e.g. San Francisco, CA"
-            },
-            "format": {
-              "type": "string",
-              "description": "The format to return the weather in, e.g. 'celsius' or 'fahrenheit'",
-              "enum": ["celsius", "fahrenheit"]
-            }
-          },
-          "required": ["location", "format"]
-        }
-      }
-    }
-  ]
-}'
-```
-
-##### Response
-
-```json
-{
-  "model": "mistral:7b-instruct-v0.3-q4_K_M",
-  "created_at": "2024-07-22T20:33:28.123648Z",
-  "message": {
-    "role": "assistant",
-    "content": "",
-    "tool_calls": [
-      {
-        "function": {
-          "name": "get_current_weather",
-          "arguments": {
-            "format": "celsius",
-            "location": "Paris, FR"
-          }
-        }
-      }
-    ]
-  },
-  "done_reason": "stop",
-  "done": true,
-  "total_duration": 885095291,
-  "load_duration": 3753500,
-  "prompt_eval_count": 122,
-  "prompt_eval_duration": 328493000,
-  "eval_count": 33,
-  "eval_duration": 552222000
-}
-```
-
 ## Create a Model
 
 ```shell
@@ -1140,7 +1026,7 @@ If `stream` is set to `false`, then the response is a single JSON object:
 ## Generate Embeddings
 
 ```shell
-POST /api/embed
+POST /api/embeddings
 ```
 
 Generate embeddings from a model
@@ -1148,11 +1034,10 @@ Generate embeddings from a model
 ### Parameters
 
 - `model`: name of model to generate embeddings from
-- `input`: text or list of text to generate embeddings for
+- `prompt`: text to generate embeddings for
 
 Advanced parameters:
 
-- `truncate`: truncates the end of each input to fit within context length. Returns error if `false` and context length is exceeded. Defaults to `true`
 - `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
 - `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
 
@@ -1161,9 +1046,9 @@ Advanced parameters:
 #### Request
 
 ```shell
-curl http://localhost:11434/api/embed -d '{
+curl http://localhost:11434/api/embeddings -d '{
   "model": "all-minilm",
-  "input": "Why is the sky blue?"
+  "prompt": "Here is an article about llamas..."
 }'
 ```
 
@@ -1171,35 +1056,10 @@ curl http://localhost:11434/api/embed -d '{
 
 ```json
 {
-  "model": "all-minilm",
-  "embeddings": [[
-    0.010071029, -0.0017594862, 0.05007221, 0.04692972, 0.054916814,
-    0.008599704, 0.105441414, -0.025878139, 0.12958129, 0.031952348
-  ]]
-}
-```
-
-#### Request (Multiple input)
-
-```shell
-curl http://localhost:11434/api/embed -d '{
-  "model": "all-minilm",
-  "input": ["Why is the sky blue?", "Why is the grass green?"]
-}'
-```
-
-#### Response
-
-```json
-{
-  "model": "all-minilm",
-  "embeddings": [[
-    0.010071029, -0.0017594862, 0.05007221, 0.04692972, 0.054916814,
-    0.008599704, 0.105441414, -0.025878139, 0.12958129, 0.031952348
-  ],[
-    -0.0098027075, 0.06042469, 0.025257962, -0.006364387, 0.07272725,
-    0.017194884, 0.09032035, -0.051705178, 0.09951512, 0.09072481
-  ]]
+  "embedding": [
+    0.5670403838157654, 0.009260174818336964, 0.23178744316101074, -0.2916173040866852, -0.8924556970596313,
+    0.8785552978515625, -0.34576427936553955, 0.5742510557174683, -0.04222835972905159, -0.137906014919281
+  ]
 }
 ```
 
@@ -1246,45 +1106,3 @@ A single JSON object will be returned.
   ]
 }
 ```
-
-## Generate Embedding
-
-> Note: this endpoint has been superseded by `/api/embed`
-
-```shell
-POST /api/embeddings
-```
-
-Generate embeddings from a model
-
-### Parameters
-
-- `model`: name of model to generate embeddings from
-- `prompt`: text to generate embeddings for
-
-Advanced parameters:
-
-- `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
-- `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
-
-### Examples
-
-#### Request
-
-```shell
-curl http://localhost:11434/api/embeddings -d '{
-  "model": "all-minilm",
-  "prompt": "Here is an article about llamas..."
-}'
-```
-
-#### Response
-
-```json
-{
-  "embedding": [
-    0.5670403838157654, 0.009260174818336964, 0.23178744316101074, -0.2916173040866852, -0.8924556970596313,
-    0.8785552978515625, -0.34576427936553955, 0.5742510557174683, -0.04222835972905159, -0.137906014919281
-  ]
-}
-```

docs/docker.md

@@ -63,7 +63,7 @@ docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 114
 Now you can run a model:
 
 ```
-docker exec -it ollama ollama run llama3.1
+docker exec -it ollama ollama run llama3
 ```
 
 ### Try different models

docs/faq.md

@@ -227,7 +227,7 @@ curl http://localhost:11434/api/chat -d '{"model": "mistral"}'
 
 To preload a model using the CLI, use the command:
 ```shell
-ollama run llama3.1 ""
+ollama run llama3 ""
 ```
 
 ## How do I keep a model loaded in memory or make it unload immediately?
@@ -272,8 +272,4 @@ The following server settings may be used to adjust how Ollama handles concurren
 - `OLLAMA_NUM_PARALLEL` - The maximum number of parallel requests each model will process at the same time.  The default will auto-select either 4 or 1 based on available memory.
 - `OLLAMA_MAX_QUEUE` - The maximum number of requests Ollama will queue when busy before rejecting additional requests. The default is 512
 
-Note: Windows with Radeon GPUs currently default to 1 model maximum due to limitations in ROCm v5.7 for available VRAM reporting.  Once ROCm v6.2 is available, Windows Radeon will follow the defaults above.  You may enable concurrent model loads on Radeon on Windows, but ensure you don't load more models than will fit into your GPUs VRAM.
-
-## How does Ollama load models on multiple GPUs?
-
-Installing multiple GPUs of the same brand can be a great way to increase your available VRAM to load larger models.  When you load a new model, Ollama evaluates the required VRAM for the model against what is currently available.  If the model will entirely fit on any single GPU, Ollama will load the model on that GPU.  This typically provides the best performance as it reduces the amount of data transfering across the PCI bus during inference.  If the model does not fit entirely on one GPU, then it will be spread across all the available GPUs.
+Note: Windows with Radeon GPUs currently default to 1 model maximum due to limitations in ROCm v5.7 for available VRAM reporting.  Once ROCm v6 is available, Windows Radeon will follow the defaults above.  You may enable concurrent model loads on Radeon on Windows, but ensure you don't load more models than will fit into your GPUs VRAM.

docs/gpu.md

@@ -46,24 +46,13 @@ sudo modprobe nvidia_uvm`
 
 ## AMD Radeon
 Ollama supports the following AMD GPUs:
-
-### Linux Support
 | Family         | Cards and accelerators                                                                                                               |
 | -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
 | AMD Radeon RX  | `7900 XTX` `7900 XT` `7900 GRE` `7800 XT` `7700 XT` `7600 XT` `7600` `6950 XT` `6900 XTX` `6900XT` `6800 XT` `6800` `Vega 64` `Vega 56`    |
 | AMD Radeon PRO | `W7900` `W7800` `W7700` `W7600` `W7500` `W6900X` `W6800X Duo` `W6800X` `W6800` `V620` `V420` `V340` `V320` `Vega II Duo` `Vega II` `VII` `SSG` |
 | AMD Instinct   | `MI300X` `MI300A` `MI300` `MI250X` `MI250` `MI210` `MI200` `MI100` `MI60` `MI50`                                                               |
 
-### Windows Support
-With ROCm v6.1, the following GPUs are supported on Windows.
-
-| Family         | Cards and accelerators                                                                                                               |
-| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
-| AMD Radeon RX  | `7900 XTX` `7900 XT` `7900 GRE` `7800 XT` `7700 XT` `7600 XT` `7600` `6950 XT` `6900 XTX` `6900XT` `6800 XT` `6800`    |
-| AMD Radeon PRO | `W7900` `W7800` `W7700` `W7600` `W7500` `W6900X` `W6800X Duo` `W6800X` `W6800` `V620` |
-
-
-### Overrides on Linux
+### Overrides
 Ollama leverages the AMD ROCm library, which does not support all AMD GPUs. In
 some cases you can force the system to try to use a similar LLVM target that is
 close.  For example The Radeon RX 5400 is `gfx1034` (also known as 10.3.4)
@@ -74,7 +63,7 @@ would set `HSA_OVERRIDE_GFX_VERSION="10.3.0"` as an environment variable for the
 server.  If you have an unsupported AMD GPU you can experiment using the list of
 supported types below.
 
-At this time, the known supported GPU types on linux are the following LLVM Targets.
+At this time, the known supported GPU types are the following LLVM Targets.
 This table shows some example GPUs that map to these LLVM targets:
 | **LLVM Target** | **An Example GPU** |
 |-----------------|---------------------|

docs/modelfile.md

@@ -1,7 +1,6 @@
 # Ollama Model File
 
-> [!NOTE]
-> `Modelfile` syntax is in development
+> Note: `Modelfile` syntax is in development
 
 A model file is the blueprint to create and share models with Ollama.
 
@@ -141,7 +140,6 @@ PARAMETER <parameter> <parametervalue>
 | 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            |
-| min_p          | Alternative to the top_p, and aims to ensure a balance of quality and variety. The parameter *p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with *p*=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out. (Default: 0.0) | float      | min_p 0.05            |
 
 ### TEMPLATE
 

docs/openai.md

@@ -78,8 +78,8 @@ curl http://localhost:11434/v1/chat/completions \
 - [x] Streaming
 - [x] JSON mode
 - [x] Reproducible outputs
-- [x] Tools (streaming support coming soon)
 - [ ] Vision
+- [ ] Function calling
 - [ ] Logprobs
 
 #### Supported request fields
@@ -97,12 +97,16 @@ curl http://localhost:11434/v1/chat/completions \
 - [x] `temperature`
 - [x] `top_p`
 - [x] `max_tokens`
-- [x] `tools`
-- [ ] `tool_choice`
 - [ ] `logit_bias`
+- [ ] `tools`
+- [ ] `tool_choice`
 - [ ] `user`
 - [ ] `n`
 
+#### Notes
+
+- `usage.prompt_tokens` will be 0 for completions where prompt evaluation is cached
+
 ## Models
 
 Before using a model, pull it locally `ollama pull`:

docs/template.md

@@ -1,173 +0,0 @@
-# Template
-
-Ollama provides a powerful templating engine backed by Go's built-in templating engine to construct prompts for your large language model. This feature is a valuable tool to get the most out of your models.
-
-## Basic Template Structure
-
-A basic Go template consists of three main parts:
-
-* **Layout**: The overall structure of the template.
-* **Variables**: Placeholders for dynamic data that will be replaced with actual values when the template is rendered.
-* **Functions**: Custom functions or logic that can be used to manipulate the template's content.
-
-Here's an example of a simple chat template:
-
-```gotmpl
-{{- range .Messages }}
-{{ .Role }}: {{ .Content }}
-{{- end }}
-```
-
-In this example, we have:
-
-* A basic messages structure (layout)
-* Three variables: `Messages`, `Role`, and `Content` (variables)
-* A custom function (action) that iterates over an array of items (`range .Messages`) and displays each item
-
-## Adding templates to your model
-
-By default, models imported into Ollama have a default template of `{{ .Prompt }}`, i.e. user inputs are sent verbatim to the LLM. This is appropriate for text or code completion models but lacks essential markers for chat or instruction models.
-
-Omitting a template in these models puts the responsibility of correctly templating input onto the user. Adding a template allows users to easily get the best results from the model.
-
-To add templates in your model, you'll need to add a `TEMPLATE` command to the Modelfile. Here's an example using Meta's Llama 3.
-
-```dockerfile
-FROM llama3
-
-TEMPLATE """{{- if .System }}<|start_header_id|>system<|end_header_id|>
-
-{{ .System }}<|eot_id|>
-{{- end }}
-{{- range .Messages }}<|start_header_id|>{{ .Role }}<|end_header_id|>
-
-{{ .Content }}<|eot_id|>
-{{- end }}<|start_header_id|>assistant<|end_header_id|>
-
-"""
-```
-
-## Variables
-
-`System` (string): system prompt
-
-`Prompt` (string): user prompt
-
-`Response` (string): assistant response
-
-`Suffix` (string): text inserted after the assistant's response
-
-`Messages` (list): list of messages
-
-`Messages[].Role` (string): role which can be one of `system`, `user`, `assistant`, or `tool`
-
-`Messages[].Content` (string):  message content
-
-`Messages[].ToolCalls` (list): list of tools the model wants to call
-
-`Messages[].ToolCalls[].Function` (object): function to call
-
-`Messages[].ToolCalls[].Function.Name` (string): function name
-
-`Messages[].ToolCalls[].Function.Arguments` (map): mapping of argument name to argument value
-
-`Tools` (list): list of tools the model can access
-
-`Tools[].Type` (string): schema type. `type` is always `function`
-
-`Tools[].Function` (object): function definition
-
-`Tools[].Function.Name` (string): function name
-
-`Tools[].Function.Description` (string): function description
-
-`Tools[].Function.Parameters` (object): function parameters
-
-`Tools[].Function.Parameters.Type` (string): schema type. `type` is always `object`
-
-`Tools[].Function.Parameters.Required` (list): list of required properties
-
-`Tools[].Function.Parameters.Properties` (map): mapping of property name to property definition
-
-`Tools[].Function.Parameters.Properties[].Type` (string): property type
-
-`Tools[].Function.Parameters.Properties[].Description` (string): property description
-
-`Tools[].Function.Parameters.Properties[].Enum` (list): list of valid values
-
-## Tips and Best Practices
-
-Keep the following tips and best practices in mind when working with Go templates:
-
-* **Be mindful of dot**: Control flow structures like `range` and `with` changes the value `.`
-* **Out-of-scope variables**: Use `$.` to reference variables not currently in scope, starting from the root
-* **Whitespace control**: Use `-` to trim leading (`{{-`) and trailing (`-}}`) whitespace
-
-## Examples
-
-### Example Messages
-
-#### ChatML
-
-ChatML is a popular template format. It can be used for models such as Databrick's DBRX, Intel's Neural Chat, and Microsoft's Orca 2.
-
-```gotmpl
-{{- if .System }}<|im_start|>system
-{{ .System }}<|im_end|>
-{{ end }}
-{{- range .Messages }}<|im_start|>{{ .Role }}
-{{ .Content }}<|im_end|>
-{{ end }}<|im_start|>assistant
-{{ else }}
-{{ if .System }}<|im_start|>system
-{{ .System }}<|im_end|>
-```
-
-### Example Tools
-
-Tools support can be added to a model by adding a `{{ .Tools }}` node to the template. This feature is useful for models trained to call external tools and can a powerful tool for retrieving real-time data or performing complex tasks.
-
-#### Mistral
-
-Mistral v0.3 and Mixtral 8x22B supports tool calling.
-
-```gotmpl
-{{- range $index, $_ := .Messages }}
-{{- if eq .Role "user" }}
-{{- if and (le (len (slice $.Messages $index)) 2) $.Tools }}[AVAILABLE_TOOLS] {{ json $.Tools }}[/AVAILABLE_TOOLS]
-{{- end }}[INST] {{ if and (eq (len (slice $.Messages $index)) 1) $.System }}{{ $.System }}
-
-{{ end }}{{ .Content }}[/INST]
-{{- else if eq .Role "assistant" }}
-{{- if .Content }} {{ .Content }}</s>
-{{- else if .ToolCalls }}[TOOL_CALLS] [
-{{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ json .Function.Arguments }}}
-{{- end }}]</s>
-{{- end }}
-{{- else if eq .Role "tool" }}[TOOL_RESULTS] {"content": {{ .Content }}}[/TOOL_RESULTS]
-{{- end }}
-{{- end }}
-```
-
-### Example Fill-in-Middle
-
-Fill-in-middle support can be added to a model by adding a `{{ .Suffix }}` node to the template. This feature is useful for models that are trained to generate text in the middle of user input, such as code completion models.
-
-#### CodeLlama
-
-CodeLlama [7B](https://ollama.com/library/codellama:7b-code) and [13B](https://ollama.com/library/codellama:13b-code) code completion models support fill-in-middle.
-
-```gotmpl
-<PRE> {{ .Prompt }} <SUF>{{ .Suffix }} <MID>
-```
-
-> [!NOTE]
-> CodeLlama 34B and 70B code completion and all instruct and Python fine-tuned models do not support fill-in-middle.
-
-#### Codestral
-
-Codestral [22B](https://ollama.com/library/codestral:22b) supports fill-in-middle.
-
-```gotmpl
-[SUFFIX]{{ .Suffix }}[PREFIX] {{ .Prompt }}
-```

docs/tutorials/langchainjs.md

@@ -15,7 +15,7 @@ import { Ollama } from "@langchain/community/llms/ollama";
 
 const ollama = new Ollama({
   baseUrl: "http://localhost:11434",
-  model: "llama3.1",
+  model: "llama3",
 });
 
 const answer = await ollama.invoke(`why is the sky blue?`);
@@ -23,7 +23,7 @@ const answer = await ollama.invoke(`why is the sky blue?`);
 console.log(answer);
 ```
 
-That will get us the same thing as if we ran `ollama run llama3.1 "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.
+That will get us the same thing as if we ran `ollama run llama3 "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

docs/windows.md

@@ -23,8 +23,6 @@ Logs will often be helpful in diagnosing the problem (see
 * NVIDIA 452.39 or newer Drivers if you have an NVIDIA card
 * AMD Radeon Driver https://www.amd.com/en/support if you have a Radeon card
 
-Ollama uses unicode characters for progress indication, which may render as unknown squares in some older terminal fonts in Windows 10. If you see this, try changing your terminal font settings.
-
 ## API Access
 
 Here's a quick example showing API access from `powershell`

envconfig/config.go

@@ -43,6 +43,8 @@ var (
 	MaxRunners int
 	// Set via OLLAMA_MAX_QUEUE in the environment
 	MaxQueuedRequests int
+	// Set via OLLAMA_MAX_VRAM in the environment
+	MaxVRAM uint64
 	// Set via OLLAMA_MODELS in the environment
 	ModelsDir string
 	// Set via OLLAMA_NOHISTORY in the environment
@@ -87,6 +89,7 @@ func AsMap() map[string]EnvVar {
 		"OLLAMA_LLM_LIBRARY":       {"OLLAMA_LLM_LIBRARY", LLMLibrary, "Set LLM library to bypass autodetection"},
 		"OLLAMA_MAX_LOADED_MODELS": {"OLLAMA_MAX_LOADED_MODELS", MaxRunners, "Maximum number of loaded models per GPU"},
 		"OLLAMA_MAX_QUEUE":         {"OLLAMA_MAX_QUEUE", MaxQueuedRequests, "Maximum number of queued requests"},
+		"OLLAMA_MAX_VRAM":          {"OLLAMA_MAX_VRAM", MaxVRAM, "Maximum VRAM"},
 		"OLLAMA_MODELS":            {"OLLAMA_MODELS", ModelsDir, "The path to the models directory"},
 		"OLLAMA_NOHISTORY":         {"OLLAMA_NOHISTORY", NoHistory, "Do not preserve readline history"},
 		"OLLAMA_NOPRUNE":           {"OLLAMA_NOPRUNE", NoPrune, "Do not prune model blobs on startup"},
@@ -191,6 +194,16 @@ func LoadConfig() {
 
 	TmpDir = clean("OLLAMA_TMPDIR")
 
+	userLimit := clean("OLLAMA_MAX_VRAM")
+	if userLimit != "" {
+		avail, err := strconv.ParseUint(userLimit, 10, 64)
+		if err != nil {
+			slog.Error("invalid setting, ignoring", "OLLAMA_MAX_VRAM", userLimit, "error", err)
+		} else {
+			MaxVRAM = avail
+		}
+	}
+
 	LLMLibrary = clean("OLLAMA_LLM_LIBRARY")
 
 	if onp := clean("OLLAMA_NUM_PARALLEL"); onp != "" {

go.mod

@@ -18,10 +18,10 @@ require (
 require (
 	github.com/agnivade/levenshtein v1.1.1
 	github.com/d4l3k/go-bfloat16 v0.0.0-20211005043715-690c3bdd05f1
-	github.com/google/go-cmp v0.6.0
 	github.com/mattn/go-runewidth v0.0.14
 	github.com/nlpodyssey/gopickle v0.3.0
 	github.com/pdevine/tensor v0.0.0-20240510204454-f88f4562727c
+	github.com/sbinet/npyio v0.9.0
 )
 
 require (

go.sum

@@ -171,6 +171,8 @@ github.com/rogpeppe/go-internal v1.8.0 h1:FCbCCtXNOY3UtUuHUYaghJg4y7Fd14rXifAYUA
 github.com/rogpeppe/go-internal v1.8.0/go.mod h1:WmiCO8CzOY8rg0OYDC4/i/2WRWAB6poM+XZ2dLUbcbE=
 github.com/russross/blackfriday/v2 v2.1.0/go.mod h1:+Rmxgy9KzJVeS9/2gXHxylqXiyQDYRxCVz55jmeOWTM=
 github.com/ruudk/golang-pdf417 v0.0.0-20181029194003-1af4ab5afa58/go.mod h1:6lfFZQK844Gfx8o5WFuvpxWRwnSoipWe/p622j1v06w=
+github.com/sbinet/npyio v0.9.0 h1:A7h8OyYsOsc+NPRtynRMSf70xSgATZNpamNp8nQ8Tjc=
+github.com/sbinet/npyio v0.9.0/go.mod h1:vgjQEMRTS9aMS9GdXhr+5jounCmGqjDO2JI+IpSokns=
 github.com/spf13/cobra v1.7.0 h1:hyqWnYt1ZQShIddO5kBpj3vu05/++x6tJ6dg8EC572I=
 github.com/spf13/cobra v1.7.0/go.mod h1:uLxZILRyS/50WlhOIKD7W6V5bgeIt+4sICxh6uRMrb0=
 github.com/spf13/pflag v1.0.5 h1:iy+VFUOCP1a+8yFto/drg2CJ5u0yRoB7fZw3DKv/JXA=

gpu/amd_common.go

@@ -49,17 +49,9 @@ func rocmGetVisibleDevicesEnv(gpuInfo []GpuInfo) (string, string) {
 }
 
 func commonAMDValidateLibDir() (string, error) {
-	// Favor our bundled version
-
-	// Installer payload location if we're running the installed binary
-	exe, err := os.Executable()
-	if err == nil {
-		rocmTargetDir := filepath.Join(filepath.Dir(exe), "rocm")
-		if rocmLibUsable(rocmTargetDir) {
-			slog.Debug("detected ROCM next to ollama executable " + rocmTargetDir)
-			return rocmTargetDir, nil
-		}
-	}
+	// We try to favor system paths first, so that we can wire up the subprocess to use
+	// the system version.  Only use our bundled version if the system version doesn't work
+	// This gives users a more recovery options if versions have subtle problems at runtime
 
 	// Prefer explicit HIP env var
 	hipPath := os.Getenv("HIP_PATH")
@@ -95,5 +87,14 @@ func commonAMDValidateLibDir() (string, error) {
 		}
 	}
 
+	// Installer payload location if we're running the installed binary
+	exe, err := os.Executable()
+	if err == nil {
+		rocmTargetDir := filepath.Join(filepath.Dir(exe), "rocm")
+		if rocmLibUsable(rocmTargetDir) {
+			slog.Debug("detected ROCM next to ollama executable " + rocmTargetDir)
+			return rocmTargetDir, nil
+		}
+	}
 	return "", fmt.Errorf("no suitable rocm found, falling back to CPU")
 }

gpu/amd_hip_windows.go

@@ -33,10 +33,9 @@ type HipLib struct {
 }
 
 func NewHipLib() (*HipLib, error) {
-	// At runtime we depend on v6, so discover GPUs with the same library for a consistent set of GPUs
-	h, err := windows.LoadLibrary("amdhip64_6.dll")
+	h, err := windows.LoadLibrary("amdhip64.dll")
 	if err != nil {
-		return nil, fmt.Errorf("unable to load amdhip64_6.dll, please make sure to upgrade to the latest amd driver: %w", err)
+		return nil, fmt.Errorf("unable to load amdhip64.dll: %w", err)
 	}
 	hl := &HipLib{}
 	hl.dll = h
@@ -85,8 +84,9 @@ func (hl *HipLib) AMDDriverVersion() (driverMajor, driverMinor int, err error) {
 	}
 
 	slog.Debug("hipDriverGetVersion", "version", version)
-	driverMajor = version / 10000000
-	driverMinor = (version - (driverMajor * 10000000)) / 100000
+	// TODO - this isn't actually right, but the docs claim hipDriverGetVersion isn't accurate anyway...
+	driverMajor = version / 1000
+	driverMinor = (version - (driverMajor * 1000)) / 10
 
 	return driverMajor, driverMinor, nil
 }

gpu/amd_linux.go

@@ -10,7 +10,6 @@ import (
 	"path/filepath"
 	"regexp"
 	"slices"
-	"sort"
 	"strconv"
 	"strings"
 
@@ -83,20 +82,6 @@ func AMDGetGPUInfo() []RocmGPUInfo {
 	// The amdgpu driver always exposes the host CPU(s) first, but we have to skip them and subtract
 	// from the other IDs to get alignment with the HIP libraries expectations (zero is the first GPU, not the CPU)
 	matches, _ := filepath.Glob(GPUPropertiesFileGlob)
-	sort.Slice(matches, func(i, j int) bool {
-		// /sys/class/kfd/kfd/topology/nodes/<number>/properties
-		a, err := strconv.ParseInt(filepath.Base(filepath.Dir(matches[i])), 10, 64)
-		if err != nil {
-			slog.Debug("parse err", "error", err, "match", matches[i])
-			return false
-		}
-		b, err := strconv.ParseInt(filepath.Base(filepath.Dir(matches[j])), 10, 64)
-		if err != nil {
-			slog.Debug("parse err", "error", err, "match", matches[i])
-			return false
-		}
-		return a < b
-	})
 	cpuCount := 0
 	for _, match := range matches {
 		slog.Debug("evaluating amdgpu node " + match)

gpu/amd_windows.go

@@ -22,8 +22,8 @@ const (
 
 var (
 	// Used to validate if the given ROCm lib is usable
-	ROCmLibGlobs          = []string{"hipblas.dll", "rocblas"}                 // This is not sufficient to discern v5 vs v6
-	RocmStandardLocations = []string{"C:\\Program Files\\AMD\\ROCm\\6.1\\bin"} // TODO glob?
+	ROCmLibGlobs          = []string{"hipblas.dll", "rocblas"}                 // TODO - probably include more coverage of files here...
+	RocmStandardLocations = []string{"C:\\Program Files\\AMD\\ROCm\\5.7\\bin"} // TODO glob?
 )
 
 func AMDGetGPUInfo() []RocmGPUInfo {
@@ -35,11 +35,12 @@ func AMDGetGPUInfo() []RocmGPUInfo {
 	}
 	defer hl.Release()
 
-	driverMajor, driverMinor, err := hl.AMDDriverVersion()
-	if err != nil {
-		// For now this is benign, but we may eventually need to fail compatibility checks
-		slog.Debug("error looking up amd driver version", "error", err)
-	}
+	// TODO - this reports incorrect version information, so omitting for now
+	// driverMajor, driverMinor, err := hl.AMDDriverVersion()
+	// if err != nil {
+	// 	// For now this is benign, but we may eventually need to fail compatibility checks
+	// 	slog.Debug("error looking up amd driver version", "error", err)
+	// }
 
 	// Note: the HIP library automatically handles subsetting to any HIP_VISIBLE_DEVICES the user specified
 	count := hl.HipGetDeviceCount()
@@ -92,8 +93,7 @@ func AMDGetGPUInfo() []RocmGPUInfo {
 			continue
 		}
 		if gfxOverride == "" {
-			// Strip off Target Features when comparing
-			if !slices.Contains[[]string, string](supported, strings.Split(gfx, ":")[0]) {
+			if !slices.Contains[[]string, string](supported, gfx) {
 				slog.Warn("amdgpu is not supported", "gpu", i, "gpu_type", gfx, "library", libDir, "supported_types", supported)
 				// TODO - consider discrete markdown just for ROCM troubleshooting?
 				slog.Warn("See https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md for HSA_OVERRIDE_GFX_VERSION usage")
@@ -132,8 +132,10 @@ func AMDGetGPUInfo() []RocmGPUInfo {
 				MinimumMemory:  rocmMinimumMemory,
 				Name:           name,
 				Compute:        gfx,
-				DriverMajor:    driverMajor,
-				DriverMinor:    driverMinor,
+
+				// TODO - this information isn't accurate on windows, so don't report it until we find the right way to retrieve
+				// DriverMajor:    driverMajor,
+				// DriverMinor:    driverMinor,
 			},
 			index: i,
 		}

gpu/gpu.go

@@ -274,28 +274,6 @@ func GetGPUInfo() GpuInfoList {
 				gpuInfo.DriverMajor = driverMajor
 				gpuInfo.DriverMinor = driverMinor
 
-				// query the management library as well so we can record any skew between the two
-				// which represents overhead on the GPU we must set aside on subsequent updates
-				if cHandles.nvml != nil {
-					C.nvml_get_free(*cHandles.nvml, C.int(gpuInfo.index), &memInfo.free, &memInfo.total, &memInfo.used)
-					if memInfo.err != nil {
-						slog.Warn("error looking up nvidia GPU memory", "error", C.GoString(memInfo.err))
-						C.free(unsafe.Pointer(memInfo.err))
-					} else {
-						if memInfo.free != 0 && uint64(memInfo.free) > gpuInfo.FreeMemory {
-							gpuInfo.OSOverhead = uint64(memInfo.free) - gpuInfo.FreeMemory
-							slog.Info("detected OS VRAM overhead",
-								"id", gpuInfo.ID,
-								"library", gpuInfo.Library,
-								"compute", gpuInfo.Compute,
-								"driver", fmt.Sprintf("%d.%d", gpuInfo.DriverMajor, gpuInfo.DriverMinor),
-								"name", gpuInfo.Name,
-								"overhead", format.HumanBytes2(gpuInfo.OSOverhead),
-							)
-						}
-					}
-				}
-
 				// TODO potentially sort on our own algorithm instead of what the underlying GPU library does...
 				cudaGPUs = append(cudaGPUs, gpuInfo)
 			}
@@ -360,17 +338,14 @@ func GetGPUInfo() GpuInfoList {
 					"before",
 					"total", format.HumanBytes2(cpus[0].TotalMemory),
 					"free", format.HumanBytes2(cpus[0].FreeMemory),
-					"free_swap", format.HumanBytes2(cpus[0].FreeSwap),
 				),
 				slog.Group(
 					"now",
 					"total", format.HumanBytes2(mem.TotalMemory),
 					"free", format.HumanBytes2(mem.FreeMemory),
-					"free_swap", format.HumanBytes2(mem.FreeSwap),
 				),
 			)
 			cpus[0].FreeMemory = mem.FreeMemory
-			cpus[0].FreeSwap = mem.FreeSwap
 		}
 
 		var memInfo C.mem_info_t
@@ -399,14 +374,9 @@ func GetGPUInfo() GpuInfoList {
 				slog.Warn("error looking up nvidia GPU memory")
 				continue
 			}
-			if cHandles.nvml != nil && gpu.OSOverhead > 0 {
-				// When using the management library update based on recorded overhead
-				memInfo.free -= C.uint64_t(gpu.OSOverhead)
-			}
 			slog.Debug("updating cuda memory data",
 				"gpu", gpu.ID,
 				"name", gpu.Name,
-				"overhead", format.HumanBytes2(gpu.OSOverhead),
 				slog.Group(
 					"before",
 					"total", format.HumanBytes2(gpu.TotalMemory),

gpu/gpu_darwin.go

@@ -57,7 +57,6 @@ func GetCPUMem() (memInfo, error) {
 	return memInfo{
 		TotalMemory: uint64(C.getPhysicalMemory()),
 		FreeMemory:  uint64(C.getFreeMemory()),
-		// FreeSwap omitted as Darwin uses dynamic paging
 	}, nil
 }
 

gpu/gpu_linux.go

@@ -50,7 +50,7 @@ var OneapiMgmtName = "libze_intel_gpu.so"
 
 func GetCPUMem() (memInfo, error) {
 	var mem memInfo
-	var total, available, free, buffers, cached, freeSwap uint64
+	var total, available, free, buffers, cached uint64
 	f, err := os.Open("/proc/meminfo")
 	if err != nil {
 		return mem, err
@@ -70,21 +70,20 @@ func GetCPUMem() (memInfo, error) {
 			_, err = fmt.Sscanf(line, "Buffers:%d", &buffers)
 		case strings.HasPrefix(line, "Cached:"):
 			_, err = fmt.Sscanf(line, "Cached:%d", &cached)
-		case strings.HasPrefix(line, "SwapFree:"):
-			_, err = fmt.Sscanf(line, "SwapFree:%d", &freeSwap)
 		default:
 			continue
 		}
 		if err != nil {
 			return mem, err
 		}
+
+		if total > 0 && available > 0 {
+			mem.TotalMemory = total * format.KibiByte
+			mem.FreeMemory = available * format.KibiByte
+			return mem, nil
+		}
 	}
 	mem.TotalMemory = total * format.KibiByte
-	mem.FreeSwap = freeSwap * format.KibiByte
-	if available > 0 {
-		mem.FreeMemory = available * format.KibiByte
-	} else {
-		mem.FreeMemory = (free + buffers + cached) * format.KibiByte
-	}
+	mem.FreeMemory = (free + buffers + cached) * format.KibiByte
 	return mem, nil
 }

gpu/gpu_windows.go

@@ -51,5 +51,5 @@ func GetCPUMem() (memInfo, error) {
 	if r1 == 0 {
 		return memInfo{}, fmt.Errorf("GlobalMemoryStatusEx failed: %w", err)
 	}
-	return memInfo{TotalMemory: memStatus.TotalPhys, FreeMemory: memStatus.AvailPhys, FreeSwap: memStatus.AvailPageFile}, nil
+	return memInfo{TotalMemory: memStatus.TotalPhys, FreeMemory: memStatus.AvailPhys}, nil
 }

gpu/types.go

@@ -10,7 +10,6 @@ import (
 type memInfo struct {
 	TotalMemory uint64 `json:"total_memory,omitempty"`
 	FreeMemory  uint64 `json:"free_memory,omitempty"`
-	FreeSwap    uint64 `json:"free_swap,omitempty"`
 }
 
 // Beginning of an `ollama info` command
@@ -53,8 +52,7 @@ type CPUInfo struct {
 
 type CudaGPUInfo struct {
 	GpuInfo
-	OSOverhead uint64 // Memory overhead between the driver library and management library
-	index      int    //nolint:unused,nolintlint
+	index int //nolint:unused,nolintlint
 }
 type CudaGPUInfoList []CudaGPUInfo
 

integration/concurrency_test.go

@@ -69,7 +69,7 @@ func TestIntegrationConcurrentPredictOrcaMini(t *testing.T) {
 	reqLimit := len(req)
 	iterLimit := 5
 
-	vram := os.Getenv("OLLAMA_MAX_VRAM") // TODO - discover actual VRAM
+	vram := os.Getenv("OLLAMA_MAX_VRAM")
 	if vram != "" {
 		max, err := strconv.ParseUint(vram, 10, 64)
 		require.NoError(t, err)
@@ -106,7 +106,7 @@ func TestIntegrationConcurrentPredictOrcaMini(t *testing.T) {
 
 // Stress the system if we know how much VRAM it has, and attempt to load more models than will fit
 func TestMultiModelStress(t *testing.T) {
-	vram := os.Getenv("OLLAMA_MAX_VRAM") // TODO - discover actual VRAM
+	vram := os.Getenv("OLLAMA_MAX_VRAM")
 	if vram == "" {
 		t.Skip("OLLAMA_MAX_VRAM not specified, can't pick the right models for the stress test")
 	}

integration/context_test.go

@@ -12,7 +12,7 @@ import (
 
 func TestContextExhaustion(t *testing.T) {
 	// Longer needed for small footprint GPUs
-	ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
+	ctx, cancel := context.WithTimeout(context.Background(), 6*time.Minute)
 	defer cancel()
 	// Set up the test data
 	req := api.GenerateRequest{
@@ -25,10 +25,5 @@ func TestContextExhaustion(t *testing.T) {
 			"num_ctx":     128,
 		},
 	}
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
-	if err := PullIfMissing(ctx, client, req.Model); err != nil {
-		t.Fatalf("PullIfMissing failed: %v", err)
-	}
-	DoGenerate(ctx, t, client, req, []string{"once", "upon", "lived"}, 120*time.Second, 10*time.Second)
+	GenerateTestHelper(ctx, t, req, []string{"once", "upon", "lived"})
 }

integration/embed_test.go

@@ -1,201 +0,0 @@
-//go:build integration
-
-package integration
-
-import (
-	"context"
-	"math"
-	"testing"
-	"time"
-
-	"github.com/ollama/ollama/api"
-)
-
-func floatsEqual32(a, b float32) bool {
-	return math.Abs(float64(a-b)) <= 1e-4
-}
-
-func floatsEqual64(a, b float64) bool {
-	return math.Abs(a-b) <= 1e-4
-}
-
-func TestAllMiniLMEmbeddings(t *testing.T) {
-	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
-	defer cancel()
-
-	req := api.EmbeddingRequest{
-		Model:  "all-minilm",
-		Prompt: "why is the sky blue?",
-	}
-
-	res, err := embeddingTestHelper(ctx, t, req)
-
-	if err != nil {
-		t.Fatalf("error: %v", err)
-	}
-
-	if len(res.Embedding) != 384 {
-		t.Fatalf("expected 384 floats, got %d", len(res.Embedding))
-	}
-
-	if !floatsEqual64(res.Embedding[0], 0.06642947345972061) {
-		t.Fatalf("expected 0.06642947345972061, got %.16f", res.Embedding[0])
-	}
-}
-
-func TestAllMiniLMEmbed(t *testing.T) {
-	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
-	defer cancel()
-
-	req := api.EmbedRequest{
-		Model: "all-minilm",
-		Input: "why is the sky blue?",
-	}
-
-	res, err := embedTestHelper(ctx, t, req)
-
-	if err != nil {
-		t.Fatalf("error: %v", err)
-	}
-
-	if len(res.Embeddings) != 1 {
-		t.Fatalf("expected 1 embedding, got %d", len(res.Embeddings))
-	}
-
-	if len(res.Embeddings[0]) != 384 {
-		t.Fatalf("expected 384 floats, got %d", len(res.Embeddings[0]))
-	}
-
-	if !floatsEqual32(res.Embeddings[0][0], 0.010071031) {
-		t.Fatalf("expected 0.010071031, got %.8f", res.Embeddings[0][0])
-	}
-}
-
-func TestAllMiniLMBatchEmbed(t *testing.T) {
-	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
-	defer cancel()
-
-	req := api.EmbedRequest{
-		Model: "all-minilm",
-		Input: []string{"why is the sky blue?", "why is the grass green?"},
-	}
-
-	res, err := embedTestHelper(ctx, t, req)
-
-	if err != nil {
-		t.Fatalf("error: %v", err)
-	}
-
-	if len(res.Embeddings) != 2 {
-		t.Fatalf("expected 2 embeddings, got %d", len(res.Embeddings))
-	}
-
-	if len(res.Embeddings[0]) != 384 {
-		t.Fatalf("expected 384 floats, got %d", len(res.Embeddings[0]))
-	}
-
-	if !floatsEqual32(res.Embeddings[0][0], 0.010071031) || !floatsEqual32(res.Embeddings[1][0], -0.009802706) {
-		t.Fatalf("expected 0.010071031 and -0.009802706, got %.8f and %.8f", res.Embeddings[0][0], res.Embeddings[1][0])
-	}
-}
-
-func TestAllMiniLMEmbedTruncate(t *testing.T) {
-	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
-	defer cancel()
-
-	truncTrue, truncFalse := true, false
-
-	type testReq struct {
-		Name    string
-		Request api.EmbedRequest
-	}
-
-	reqs := []testReq{
-		{
-			Name: "Target Truncation",
-			Request: api.EmbedRequest{
-				Model: "all-minilm",
-				Input: "why",
-			},
-		},
-		{
-			Name: "Default Truncate",
-			Request: api.EmbedRequest{
-				Model:   "all-minilm",
-				Input:   "why is the sky blue?",
-				Options: map[string]any{"num_ctx": 1},
-			},
-		},
-		{
-			Name: "Explicit Truncate",
-			Request: api.EmbedRequest{
-				Model:    "all-minilm",
-				Input:    "why is the sky blue?",
-				Truncate: &truncTrue,
-				Options:  map[string]any{"num_ctx": 1},
-			},
-		},
-	}
-
-	res := make(map[string]*api.EmbedResponse)
-
-	for _, req := range reqs {
-		response, err := embedTestHelper(ctx, t, req.Request)
-		if err != nil {
-			t.Fatalf("error: %v", err)
-		}
-		res[req.Name] = response
-	}
-
-	if res["Target Truncation"].Embeddings[0][0] != res["Default Truncate"].Embeddings[0][0] {
-		t.Fatal("expected default request to truncate correctly")
-	}
-
-	if res["Default Truncate"].Embeddings[0][0] != res["Explicit Truncate"].Embeddings[0][0] {
-		t.Fatal("expected default request and truncate true request to be the same")
-	}
-
-	// check that truncate set to false returns an error if context length is exceeded
-	_, err := embedTestHelper(ctx, t, api.EmbedRequest{
-		Model:    "all-minilm",
-		Input:    "why is the sky blue?",
-		Truncate: &truncFalse,
-		Options:  map[string]any{"num_ctx": 1},
-	})
-
-	if err == nil {
-		t.Fatal("expected error, got nil")
-	}
-}
-
-func embeddingTestHelper(ctx context.Context, t *testing.T, req api.EmbeddingRequest) (*api.EmbeddingResponse, error) {
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
-	if err := PullIfMissing(ctx, client, req.Model); err != nil {
-		t.Fatalf("failed to pull model %s: %v", req.Model, err)
-	}
-
-	response, err := client.Embeddings(ctx, &req)
-
-	if err != nil {
-		return nil, err
-	}
-
-	return response, nil
-}
-
-func embedTestHelper(ctx context.Context, t *testing.T, req api.EmbedRequest) (*api.EmbedResponse, error) {
-	client, _, cleanup := InitServerConnection(ctx, t)
-	defer cleanup()
-	if err := PullIfMissing(ctx, client, req.Model); err != nil {
-		t.Fatalf("failed to pull model %s: %v", req.Model, err)
-	}
-
-	response, err := client.Embed(ctx, &req)
-
-	if err != nil {
-		return nil, err
-	}
-
-	return response, nil
-}

llm/ext_server/server.cpp

@@ -41,7 +41,6 @@
 
 #if defined(_WIN32)
 #include <windows.h>
-#include <errhandlingapi.h>
 #endif
 
 #include <cstddef>
@@ -2438,6 +2437,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, g
             params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
             params.use_mmap = false;
         }
+        else if (arg == "--lora-base")
+        {
+            if (++i >= argc)
+            {
+                invalid_param = true;
+                break;
+            }
+            params.lora_base = argv[i];
+        }
         else if (arg == "-v" || arg == "--verbose")
         {
             server_verbose = true;
@@ -2729,9 +2737,6 @@ int wmain(int argc, wchar_t **wargv) {
     for (int i = 0; i < argc; ++i) {
         argv[i] = wchar_to_char(wargv[i]);
     }
-
-    // Adjust error mode to avoid error dialog after we start.
-    SetErrorMode(SEM_FAILCRITICALERRORS);
 #else
 int main(int argc, char **argv) {
 #endif
@@ -3183,33 +3188,26 @@ int main(int argc, char **argv) {
                     prompt = "";
                 }
 
-                if (prompt.size() == 1) {
-                    prompt = prompt[0];
+                json image_data;
+                if (body.count("image_data") != 0) {
+                    image_data = body["image_data"];
+                }
+                else
+                {
+                    image_data = "";
                 }
 
                 // create and queue the task
-                json responses;
-                {
-                    const int id_task = llama.queue_tasks.get_new_id();
-                    llama.queue_results.add_waiting_task_id(id_task);
-                    llama.request_completion(id_task, {{"prompt", prompt}}, true, -1);
+                const int task_id = llama.queue_tasks.get_new_id();
+                llama.queue_results.add_waiting_task_id(task_id);
+                llama.request_completion(task_id, { {"prompt", prompt}, { "n_predict", 0}, {"image_data", image_data} }, true, -1);
 
-                    // get the result
-                    task_result result = llama.queue_results.recv(id_task);
-                    llama.queue_results.remove_waiting_task_id(id_task);
-                    if (result.error) {
-                        return res.set_content(result.result_json.dump(), "application/json; charset=utf-8");
-                    }
+                // get the result
+                task_result result = llama.queue_results.recv(task_id);
+                llama.queue_results.remove_waiting_task_id(task_id);
 
-                    responses = result.result_json.value("results", std::vector<json>{result.result_json});
-                    json embeddings = json::array();
-                    for (auto & elem : responses) {
-                        embeddings.push_back(elem.at("embedding"));
-                    }
-                    // send the result
-                    json embedding_res = json{{"embedding", embeddings}};
-                    return res.set_content(embedding_res.dump(), "application/json; charset=utf-8");
-                }
+                // send the result
+                return res.set_content(result.result_json.dump(), "application/json; charset=utf-8");
             });
 
     // GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!?

llm/generate/gen_linux.sh

@@ -178,7 +178,7 @@ if [ -z "${OLLAMA_SKIP_CUDA_GENERATE}" -a -d "${CUDA_LIB_DIR}" ]; then
         CMAKE_CUDA_DEFS="-DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES} ${OLLAMA_CUSTOM_CUDA_DEFS}"
         echo "Building custom CUDA GPU"
     else
-        CMAKE_CUDA_DEFS="-DGGML_CUDA=on -DCMAKE_CUDA_FLAGS=-t8 -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES}"
+        CMAKE_CUDA_DEFS="-DGGML_CUDA=on -DCMAKE_CUDA_FLAGS=-t8 -DGGML_CUDA_FORCE_MMQ=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES} -DCMAKE_LIBRARY_PATH=/usr/local/cuda/compat"
     fi
     CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} ${ARM64_DEFS} ${CMAKE_CUDA_DEFS}"
     BUILD_DIR="../build/linux/${ARCH}/cuda${CUDA_VARIANT}"
@@ -254,7 +254,7 @@ if [ -z "${OLLAMA_SKIP_ROCM_GENERATE}" -a -d "${ROCM_PATH}" ]; then
         ROCM_VARIANT=_v$(ls ${ROCM_PATH}/lib/librocblas.so.*.*.????? | cut -f5 -d. || true)
     fi
     init_vars
-    CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} -DGGML_HIPBLAS=on -DLLAMA_CUDA_NO_PEER_COPY=on -DCMAKE_C_COMPILER=$ROCM_PATH/llvm/bin/clang -DCMAKE_CXX_COMPILER=$ROCM_PATH/llvm/bin/clang++ -DAMDGPU_TARGETS=$(amdGPUs) -DGPU_TARGETS=$(amdGPUs)"
+    CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} -DGGML_HIPBLAS=on -DCMAKE_C_COMPILER=$ROCM_PATH/llvm/bin/clang -DCMAKE_CXX_COMPILER=$ROCM_PATH/llvm/bin/clang++ -DAMDGPU_TARGETS=$(amdGPUs) -DGPU_TARGETS=$(amdGPUs)"
     # Users building from source can tune the exact flags we pass to cmake for configuring llama.cpp
     if [ -n "${OLLAMA_CUSTOM_ROCM_DEFS}" ]; then
         echo "OLLAMA_CUSTOM_ROCM_DEFS=\"${OLLAMA_CUSTOM_ROCM_DEFS}\""

llm/generate/gen_windows.ps1

@@ -6,9 +6,18 @@ function amdGPUs {
     if ($env:AMDGPU_TARGETS) {
         return $env:AMDGPU_TARGETS
     }
-    # Current supported rocblas list from ROCm v6.1.2 on windows
-    # https://rocm.docs.amd.com/projects/install-on-windows/en/latest/reference/system-requirements.html#windows-supported-gpus
+    # TODO - load from some common data file for linux + windows build consistency
     $GPU_LIST = @(
+        "gfx900"
+        "gfx906:xnack-"
+        "gfx908:xnack-"
+        "gfx90a:xnack+"
+        "gfx90a:xnack-"
+        "gfx940"
+        "gfx941"
+        "gfx942"
+        "gfx1010"
+        "gfx1012"
         "gfx1030"
         "gfx1100"
         "gfx1101"
@@ -357,7 +366,6 @@ function build_rocm() {
             "-DCMAKE_C_COMPILER=clang.exe",
             "-DCMAKE_CXX_COMPILER=clang++.exe",
             "-DGGML_HIPBLAS=on",
-            "-DLLAMA_CUDA_NO_PEER_COPY=on",
             "-DHIP_PLATFORM=amd",
             "-DGGML_AVX=on",
             "-DGGML_AVX2=off",
@@ -386,6 +394,7 @@ function build_rocm() {
         sign
         install
 
+        # Assumes v5.7, may need adjustments for v6
         rm -ea 0 -recurse -force -path "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\"
         md "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\rocblas\library\" -ea 0 > $null
         cp "${env:HIP_PATH}\bin\hipblas.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\rocm\"

llm/ggla.go

@@ -1,9 +1,12 @@
 package llm
 
 import (
+	"bytes"
 	"encoding/binary"
 	"errors"
+	"fmt"
 	"io"
+	"log/slog"
 	"slices"
 )
 
@@ -16,6 +19,7 @@ func (c *containerGGLA) Name() string {
 }
 
 func (c *containerGGLA) Decode(rs io.ReadSeeker) (model, error) {
+	slog.Info("decoding ggla")
 	if err := binary.Read(rs, binary.LittleEndian, &c.version); err != nil {
 		return nil, err
 	}
@@ -36,6 +40,8 @@ type ggla struct {
 
 	kv      KV
 	tensors []*Tensor
+
+	tensorOffset uint64
 }
 
 func newGGLA(container *containerGGLA) *ggla {
@@ -50,10 +56,13 @@ func (llm *ggla) KV() KV {
 }
 
 func (llm *ggla) Tensors() Tensors {
-	return llm.tensors
+	return Tensors{
+		Items:  llm.tensors,
+		Offset: llm.tensorOffset,
+	}
 }
 
-func (llm *ggla) decode(rs io.ReadSeeker) (retErr error) {
+func (llm *ggla) decode(rs io.ReadSeeker) error {
 	var r uint32
 	if err := binary.Read(rs, binary.LittleEndian, &r); err != nil {
 		return err
@@ -66,21 +75,22 @@ func (llm *ggla) decode(rs io.ReadSeeker) (retErr error) {
 	}
 	llm.kv["alpha"] = alpha
 
+	offset, err := rs.Seek(0, io.SeekCurrent)
+	if err != nil {
+		return err
+	}
+
+	llm.tensorOffset = uint64(offset)
+
 	for {
 		var dims uint32
 		if err := binary.Read(rs, binary.LittleEndian, &dims); err != nil {
 			if errors.Is(err, io.EOF) {
-				return nil
+				break
 			}
 			return err
 		}
 
-		defer func() {
-			if errors.Is(retErr, io.EOF) {
-				retErr = io.ErrUnexpectedEOF
-			}
-		}()
-
 		var namesize uint32
 		if err := binary.Read(rs, binary.LittleEndian, &namesize); err != nil {
 			return err
@@ -111,13 +121,14 @@ func (llm *ggla) decode(rs io.ReadSeeker) (retErr error) {
 		}
 
 		t.Name = string(name)
+		slog.Info(fmt.Sprintf("%s: [%d, %d] k=%d", t.Name, t.Shape[0], t.Shape[1], t.Kind))
 
 		offset, err := rs.Seek(0, io.SeekCurrent)
 		if err != nil {
 			return err
 		}
 
-		if _, err := rs.Seek((offset+31)&-32-offset, io.SeekCurrent); err != nil {
+		if _, err := rs.Seek((offset+31)&-32, io.SeekStart); err != nil {
 			return err
 		}
 
@@ -134,4 +145,87 @@ func (llm *ggla) decode(rs io.ReadSeeker) (retErr error) {
 
 		llm.tensors = append(llm.tensors, &t)
 	}
+	return nil
+}
+
+func WriteGGLA(ws io.WriteSeeker, kv KV, ts []*Tensor) error {
+	slog.Debug("writing ggla")
+	if err := binary.Write(ws, binary.LittleEndian, []byte("algg")); err != nil {
+		return err
+	}
+
+	if err := binary.Write(ws, binary.LittleEndian, uint32(1)); err != nil {
+		return err
+	}
+
+	var r uint32
+	var alpha uint32
+	var ok bool
+
+	if r, ok = kv["r"].(uint32); !ok {
+		r = 8
+	}
+
+	if err := binary.Write(ws, binary.LittleEndian, r); err != nil {
+		return err
+	}
+
+	if alpha, ok = kv["alpha"].(uint32); !ok {
+		alpha = 16
+	}
+
+	if err := binary.Write(ws, binary.LittleEndian, alpha); err != nil {
+		return err
+	}
+
+	for _, t := range ts {
+		dims := 0
+		for cnt := range len(t.Shape) {
+			if t.Shape[cnt] > 0 {
+				dims++
+			}
+		}
+
+		if err := binary.Write(ws, binary.LittleEndian, uint32(dims)); err != nil {
+			return err
+		}
+
+		if err := binary.Write(ws, binary.LittleEndian, uint32(len(t.Name))); err != nil {
+			return err
+		}
+
+		if err := binary.Write(ws, binary.LittleEndian, t.Kind); err != nil {
+			return err
+		}
+
+		for cnt := range dims {
+			if err := binary.Write(ws, binary.LittleEndian, uint32(t.Shape[dims-1-cnt])); err != nil {
+				return err
+			}
+		}
+
+		if err := binary.Write(ws, binary.LittleEndian, []byte(t.Name)); err != nil {
+			return err
+		}
+
+		offset, err := ws.Seek(0, io.SeekCurrent)
+		if err != nil {
+			return err
+		}
+
+		var alignment int32 = 32
+		pad := gglaPadding(int32(offset), alignment)
+		if err := binary.Write(ws, binary.LittleEndian, bytes.Repeat([]byte{0}, int(pad))); err != nil {
+			return err
+		}
+
+		if _, err := t.WriteTo(ws); err != nil {
+			return err
+		}
+	}
+	return nil
+}
+
+func gglaPadding(offset, align int32) int32 {
+	return (align - offset%align) % align
 }

llm/ggml.go

@@ -112,11 +112,14 @@ func (kv KV) ChatTemplate() string {
 	return s
 }
 
-type Tensors []*Tensor
+type Tensors struct {
+	Items  []*Tensor
+	Offset uint64
+}
 
 func (ts Tensors) Layers() map[string]Layer {
 	layers := make(map[string]Layer)
-	for _, t := range ts {
+	for _, t := range ts.Items {
 		parts := strings.Split(t.Name, ".")
 		if parts[0] == "blk" {
 			// join first and second part, e.g. blk.%d
@@ -424,32 +427,6 @@ func (llm GGML) GraphSize(context, batch uint64) (partialOffload, fullOffload ui
 			4*batch*(3*embedding+vocab)+embedding*vocab*105/128,
 			4*batch*(2*embedding+1+2*embeddingHeadsK*headsKV+context+context*headsKV)+4*embeddingHeadsK*context*headsKV+embedding*embeddingHeadsK*headsKV*9/16,
 		)
-	case "chatglm":
-		fullOffload = 4 * batch * (embedding + vocab)
-		partialOffload = 4*batch*(embedding+vocab) + embedding*vocab*105/128
-		if qkvBias, ok := layers["blk.0"]["attn_qkv.bias"]; ok {
-			fullOffload = max(
-				fullOffload,
-				4*batch*(2+
-					2*embedding+
-					context+
-					context*heads+
-					embeddingHeadsK*heads+
-					qkvBias.Shape[0]),
-			)
-
-			partialOffload = max(
-				partialOffload,
-				4*batch*(1+
-					2*embedding+
-					embeddingHeadsK*heads+
-					context+
-					context*heads)+
-					4*embeddingHeadsK*context+
-					4*context*embeddingHeadsK+
-					4*qkvBias.Shape[0],
-			)
-		}
 	}
 
 	return

llm/gguf.go

@@ -2,11 +2,16 @@ package llm
 
 import (
 	"bytes"
+	"cmp"
 	"encoding/binary"
 	"encoding/json"
 	"fmt"
 	"io"
+	"log/slog"
+	"slices"
 	"strings"
+
+	"golang.org/x/exp/maps"
 )
 
 type containerGGUF struct {
@@ -89,6 +94,7 @@ type gguf struct {
 	tensors []*Tensor
 
 	parameters uint64
+	tensorOffset uint64
 
 	scratch [16 << 10]byte
 }
@@ -100,16 +106,15 @@ func newGGUF(container *containerGGUF) *gguf {
 	}
 }
 
-func NewGGUFV3(bo binary.ByteOrder) *gguf {
-	return newGGUF(&containerGGUF{ByteOrder: bo, Version: 3})
-}
-
 func (llm *gguf) KV() KV {
 	return llm.kv
 }
 
 func (llm *gguf) Tensors() Tensors {
-	return llm.tensors
+	return Tensors{
+		Items:  llm.tensors,
+		Offset: llm.tensorOffset,
+	}
 }
 
 func (llm *gguf) numTensor() uint64 {
@@ -199,7 +204,7 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
 			return fmt.Errorf("failed to read tensor dimensions: %w", err)
 		}
 
-		shape := [4]uint64{1, 1, 1, 1}
+		shape := make([]uint64, dims)
 		for i := 0; uint32(i) < dims; i++ {
 			shape[i], err = readGGUF[uint64](llm, rs)
 			if err != nil {
@@ -236,13 +241,21 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
 		alignment = 32
 	}
 
+	offset, err := rs.Seek(0, io.SeekCurrent)
+	if err != nil {
+		return err
+	}
+
+	padding := ggufPadding(offset, int64(alignment))
+	llm.tensorOffset = uint64(offset + padding)
+
 	for _, tensor := range llm.tensors {
 		offset, err := rs.Seek(0, io.SeekCurrent)
 		if err != nil {
 			return fmt.Errorf("failed to get current offset: %w", err)
 		}
 
-		padding := llm.padding(offset, int64(alignment))
+		padding := ggufPadding(offset, int64(alignment))
 		if _, err := rs.Seek(padding, io.SeekCurrent); err != nil {
 			return fmt.Errorf("failed to seek to init padding: %w", err)
 		}
@@ -261,12 +274,12 @@ func readGGUF[T any](llm *gguf, r io.Reader) (T, error) {
 	return t, err
 }
 
-func writeGGUF[V any](llm *gguf, w io.Writer, t uint32, v V) error {
-	if err := binary.Write(w, llm.ByteOrder, t); err != nil {
+func writeGGUF[V any](w io.Writer, t uint32, v V) error {
+	if err := binary.Write(w, binary.LittleEndian, t); err != nil {
 		return err
 	}
 
-	return binary.Write(w, llm.ByteOrder, v)
+	return binary.Write(w, binary.LittleEndian, v)
 }
 
 func readGGUFV1String(llm *gguf, r io.Reader) (string, error) {
@@ -330,12 +343,12 @@ func readGGUFString(llm *gguf, r io.Reader) (string, error) {
 	return string(buf), nil
 }
 
-func writeGGUFString(llm *gguf, w io.Writer, s string) error {
-	if err := binary.Write(w, llm.ByteOrder, ggufTypeString); err != nil {
+func writeGGUFString(w io.Writer, s string) error {
+	if err := binary.Write(w, binary.LittleEndian, ggufTypeString); err != nil {
 		return err
 	}
 
-	if err := binary.Write(w, llm.ByteOrder, uint64(len(s))); err != nil {
+	if err := binary.Write(w, binary.LittleEndian, uint64(len(s))); err != nil {
 		return err
 	}
 
@@ -476,21 +489,21 @@ func readGGUFArray(llm *gguf, r io.Reader) (*array, error) {
 	return a, nil
 }
 
-func writeGGUFArray[S ~[]E, E any](llm *gguf, w io.Writer, t uint32, s S) error {
-	if err := binary.Write(w, llm.ByteOrder, ggufTypeArray); err != nil {
+func writeGGUFArray[S ~[]E, E any](w io.Writer, t uint32, s S) error {
+	if err := binary.Write(w, binary.LittleEndian, ggufTypeArray); err != nil {
 		return err
 	}
 
-	if err := binary.Write(w, llm.ByteOrder, t); err != nil {
+	if err := binary.Write(w, binary.LittleEndian, t); err != nil {
 		return err
 	}
 
-	if err := binary.Write(w, llm.ByteOrder, uint64(len(s))); err != nil {
+	if err := binary.Write(w, binary.LittleEndian, uint64(len(s))); err != nil {
 		return err
 	}
 
 	for _, e := range s {
-		if err := binary.Write(w, llm.ByteOrder, e); err != nil {
+		if err := binary.Write(w, binary.LittleEndian, e); err != nil {
 			return err
 		}
 	}
@@ -498,194 +511,55 @@ func writeGGUFArray[S ~[]E, E any](llm *gguf, w io.Writer, t uint32, s S) error
 	return nil
 }
 
-var ggufKVOrder = map[string][]string{
-	"llama": {
-		"general.architecture",
-		"general.name",
-		"llama.vocab_size",
-		"llama.context_length",
-		"llama.embedding_length",
-		"llama.block_count",
-		"llama.feed_forward_length",
-		"llama.attention.head_count",
-		"llama.attention.head_count_kv",
-		"llama.attention.layer_norm_rms_epsilon",
-		"llama.rope.freq_base",
-		"llama.rope.dimension_count",
-		"llama.expert_count",
-		"llama.expert_used_count",
-		"gemma.context_length",
-		"gemma.embedding_length",
-		"gemma.block_count",
-		"gemma.feed_forward_length",
-		"gemma.attention.head_count",
-		"gemma.attention.head_count_kv",
-		"gemma.attention.layer_norm_rms_epsilon",
-		"gemma.attention.key_length",
-		"gemma.attention.value_length",
-		"general.file_type",
-		"tokenizer.ggml.pre",
-		"tokenizer.ggml.model",
-		"tokenizer.ggml.tokens",
-		"tokenizer.ggml.scores",
-		"tokenizer.ggml.merges",
-		"tokenizer.ggml.token_type",
-		"tokenizer.ggml.bos_token_id",
-		"tokenizer.ggml.eos_token_id",
-		"tokenizer.ggml.unknown_token_id",
-		"tokenizer.ggml.padding_token_id",
-		"tokenizer.ggml.add_bos_token",
-		"tokenizer.ggml.add_eos_token",
-		"tokenizer.chat_template",
-		"bert.pooling_type",
-	},
-}
-
-func (llm *gguf) Encode(ws io.WriteSeeker, kv KV, tensors []Tensor) error {
-	switch llm.Version {
-	case 3:
-		llm.V3.NumTensor = uint64(len(tensors))
-		llm.V3.NumKV = uint64(len(kv))
-	default:
-		return fmt.Errorf("not implemented: ggufv%d", llm.Version)
-	}
-
-	if err := binary.Write(ws, llm.ByteOrder, []byte("GGUF")); err != nil {
+func WriteGGUF(ws io.WriteSeeker, kv KV, ts []*Tensor) error {
+	if err := binary.Write(ws, binary.LittleEndian, []byte("GGUF")); err != nil {
 		return err
 	}
 
-	if err := binary.Write(ws, llm.ByteOrder, llm.Version); err != nil {
+	if err := binary.Write(ws, binary.LittleEndian, uint32(3)); err != nil {
 		return err
 	}
 
-	if err := binary.Write(ws, llm.ByteOrder, llm.numTensor()); err != nil {
+	if err := binary.Write(ws, binary.LittleEndian, uint64(len(ts))); err != nil {
 		return err
 	}
 
-	if err := binary.Write(ws, llm.ByteOrder, llm.numKV()); err != nil {
+	if err := binary.Write(ws, binary.LittleEndian, uint64(len(kv))); err != nil {
 		return err
 	}
 
-	kvCheck := make(map[string]bool)
-	for k := range kv {
-		kvCheck[k] = false
-	}
+	keys := maps.Keys(kv)
+	slices.Sort(keys)
 
-	for _, k := range ggufKVOrder["llama"] {
-		v, ok := kv[k]
-		if !ok {
-			continue
-		}
-		kvCheck[k] = true
-
-		if err := binary.Write(ws, llm.ByteOrder, uint64(len(k))); err != nil {
-			return err
-		}
-
-		if err := binary.Write(ws, llm.ByteOrder, []byte(k)); err != nil {
-			return err
-		}
-
-		var err error
-		switch v := v.(type) {
-		case uint32:
-			err = writeGGUF(llm, ws, ggufTypeUint32, v)
-		case float32:
-			err = writeGGUF(llm, ws, ggufTypeFloat32, v)
-		case bool:
-			err = writeGGUF(llm, ws, ggufTypeBool, v)
-		case string:
-			err = writeGGUFString(llm, ws, v)
-		case []int32:
-			err = writeGGUFArray(llm, ws, ggufTypeInt32, v)
-		case []uint32:
-			err = writeGGUFArray(llm, ws, ggufTypeUint32, v)
-		case []float32:
-			err = writeGGUFArray(llm, ws, ggufTypeFloat32, v)
-		case []string:
-			if err := binary.Write(ws, llm.ByteOrder, ggufTypeArray); err != nil {
-				return err
-			}
-
-			if err := binary.Write(ws, llm.ByteOrder, ggufTypeString); err != nil {
-				return err
-			}
-
-			if err := binary.Write(ws, llm.ByteOrder, uint64(len(v))); err != nil {
-				return err
-			}
-
-			for _, e := range v {
-				if err := binary.Write(ws, llm.ByteOrder, uint64(len(e))); err != nil {
-					return err
-				}
-
-				if err := binary.Write(ws, llm.ByteOrder, []byte(e)); err != nil {
-					return err
-				}
-			}
-		default:
-			return fmt.Errorf("improper type for '%s'", k)
-		}
-		if err != nil {
+	for _, key := range keys {
+		if err := ggufWriteKV(ws, key, kv[key]); err != nil {
 			return err
 		}
 	}
 
-	for k, v := range kvCheck {
-		if !v {
-			return fmt.Errorf("Didn't know how to write kv %s", k)
+	slices.SortFunc(ts, func(a, b *Tensor) int {
+		var i, j int
+		if n, err := fmt.Sscanf(a.Name, "blk.%d", &i); err != nil || n != 1 {
+			return cmp.Compare(a.Name, b.Name)
+		} else if n, err := fmt.Sscanf(b.Name, "blk.%d", &j); err != nil || n != 1 {
+			return cmp.Compare(a.Name, b.Name)
 		}
-	}
 
-	for _, tensor := range tensors {
-		if err := binary.Write(ws, llm.ByteOrder, uint64(len(tensor.Name))); err != nil {
-			return err
-		}
-
-		if err := binary.Write(ws, llm.ByteOrder, []byte(tensor.Name)); err != nil {
-			return err
-		}
-
-		var dims int
-		for cnt := range len(tensor.Shape) {
-			if tensor.Shape[cnt] > 0 {
-				dims++
-			}
-		}
-
-		if err := binary.Write(ws, llm.ByteOrder, uint32(dims)); err != nil {
-			return err
-		}
-
-		for i := range dims {
-			if err := binary.Write(ws, llm.ByteOrder, tensor.Shape[dims-1-i]); err != nil {
-				return err
-			}
-		}
-
-		if err := binary.Write(ws, llm.ByteOrder, tensor.Kind); err != nil {
-			return err
-		}
-
-		if err := binary.Write(ws, llm.ByteOrder, tensor.Offset); err != nil {
+		return cmp.Compare(i, j)
+	})
+
+	var s uint64
+	for _, t := range ts {
+		t.Offset = s
+		if err := ggufWriteTensorInfo(ws, t); err != nil {
 			return err
 		}
+		s += t.Size()
 	}
 
 	var alignment int64 = 32
-	for _, tensor := range tensors {
-		offset, err := ws.Seek(0, io.SeekCurrent)
-		if err != nil {
-			return err
-		}
-
-		padding := llm.padding(offset, alignment)
-		if err := binary.Write(ws, llm.ByteOrder, bytes.Repeat([]byte{0}, int(padding))); err != nil {
-			return err
-		}
-
-		if _, err := tensor.WriteTo(ws); err != nil {
+	for _, t := range ts {
+		if err := ggufWriteTensor(ws, t, alignment); err != nil {
 			return err
 		}
 	}
@@ -693,6 +567,103 @@ func (llm *gguf) Encode(ws io.WriteSeeker, kv KV, tensors []Tensor) error {
 	return nil
 }
 
-func (gguf) padding(offset, align int64) int64 {
+func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
+	slog.Debug(k, "type", fmt.Sprintf("%T", v))
+	if err := binary.Write(ws, binary.LittleEndian, uint64(len(k))); err != nil {
+		return err
+	}
+
+	if err := binary.Write(ws, binary.LittleEndian, []byte(k)); err != nil {
+		return err
+	}
+
+	var err error
+	switch v := v.(type) {
+	case uint32:
+		err = writeGGUF(ws, ggufTypeUint32, v)
+	case float32:
+		err = writeGGUF(ws, ggufTypeFloat32, v)
+	case bool:
+		err = writeGGUF(ws, ggufTypeBool, v)
+	case string:
+		err = writeGGUFString(ws, v)
+	case []int32:
+		err = writeGGUFArray(ws, ggufTypeInt32, v)
+	case []uint32:
+		err = writeGGUFArray(ws, ggufTypeUint32, v)
+	case []float32:
+		err = writeGGUFArray(ws, ggufTypeFloat32, v)
+	case []string:
+		if err := binary.Write(ws, binary.LittleEndian, ggufTypeArray); err != nil {
+			return err
+		}
+
+		if err := binary.Write(ws, binary.LittleEndian, ggufTypeString); err != nil {
+			return err
+		}
+
+		if err := binary.Write(ws, binary.LittleEndian, uint64(len(v))); err != nil {
+			return err
+		}
+
+		for _, e := range v {
+			if err := binary.Write(ws, binary.LittleEndian, uint64(len(e))); err != nil {
+				return err
+			}
+
+			if err := binary.Write(ws, binary.LittleEndian, []byte(e)); err != nil {
+				return err
+			}
+		}
+	default:
+		return fmt.Errorf("improper type for '%s'", k)
+	}
+
+	return err
+}
+
+func ggufWriteTensorInfo(ws io.WriteSeeker, t *Tensor) error {
+	if err := binary.Write(ws, binary.LittleEndian, uint64(len(t.Name))); err != nil {
+		return err
+	}
+
+	if err := binary.Write(ws, binary.LittleEndian, []byte(t.Name)); err != nil {
+		return err
+	}
+
+	if err := binary.Write(ws, binary.LittleEndian, uint32(len(t.Shape))); err != nil {
+		return err
+	}
+
+	for i := range len(t.Shape) {
+		if err := binary.Write(ws, binary.LittleEndian, t.Shape[len(t.Shape)-i-1]); err != nil {
+			return err
+		}
+	}
+
+	if err := binary.Write(ws, binary.LittleEndian, t.Kind); err != nil {
+		return err
+	}
+
+	return binary.Write(ws, binary.LittleEndian, t.Offset)
+}
+
+func ggufWriteTensor(ws io.WriteSeeker, t *Tensor, alignment int64) error {
+	slog.Debug(t.Name, "kind", t.Kind, "shape", t.Shape, "offset", t.Offset)
+
+	offset, err := ws.Seek(0, io.SeekCurrent)
+	if err != nil {
+		return err
+	}
+
+	if err := binary.Write(ws, binary.LittleEndian, bytes.Repeat([]byte{0}, int(ggufPadding(offset, alignment)))); err != nil {
+		return err
+	}
+
+	_, err = t.WriteTo(ws)
+	return err
+}
+
+func ggufPadding(offset, align int64) int64 {
 	return (align - offset%align) % align
 }

llm/llm.go

@@ -4,8 +4,8 @@ package llm
 // #cgo LDFLAGS: -lllama -lggml -lstdc++ -lpthread
 // #cgo darwin,arm64 LDFLAGS: -L${SRCDIR}/build/darwin/arm64_static -L${SRCDIR}/build/darwin/arm64_static/src -L${SRCDIR}/build/darwin/arm64_static/ggml/src -framework Accelerate -framework Metal
 // #cgo darwin,amd64 LDFLAGS: -L${SRCDIR}/build/darwin/x86_64_static -L${SRCDIR}/build/darwin/x86_64_static/src -L${SRCDIR}/build/darwin/x86_64_static/ggml/src
-// #cgo windows,amd64 LDFLAGS: -static-libstdc++ -static-libgcc -static -L${SRCDIR}/build/windows/amd64_static -L${SRCDIR}/build/windows/amd64_static/src -L${SRCDIR}/build/windows/amd64_static/ggml/src
-// #cgo windows,arm64 LDFLAGS: -static-libstdc++ -static-libgcc -static -L${SRCDIR}/build/windows/arm64_static -L${SRCDIR}/build/windows/arm64_static/src -L${SRCDIR}/build/windows/arm64_static/ggml/src
+// #cgo windows,amd64 LDFLAGS: -L${SRCDIR}/build/windows/amd64_static -L${SRCDIR}/build/windows/amd64_static/src -L${SRCDIR}/build/windows/amd64_static/ggml/src
+// #cgo windows,arm64 LDFLAGS: -L${SRCDIR}/build/windows/arm64_static -L${SRCDIR}/build/windows/arm64_static/src -L${SRCDIR}/build/windows/arm64_static/ggml/src
 // #cgo linux,amd64 LDFLAGS: -L${SRCDIR}/build/linux/x86_64_static -L${SRCDIR}/build/linux/x86_64_static/src -L${SRCDIR}/build/linux/x86_64_static/ggml/src
 // #cgo linux,arm64 LDFLAGS: -L${SRCDIR}/build/linux/arm64_static -L${SRCDIR}/build/linux/arm64_static/src -L${SRCDIR}/build/linux/arm64_static/ggml/src
 // #include <stdlib.h>
@@ -33,7 +33,7 @@ func Quantize(infile, outfile string, ftype fileType) error {
 	params.ftype = ftype.Value()
 
 	if rc := C.llama_model_quantize(cinfile, coutfile, &params); rc != 0 {
-		return fmt.Errorf("failed to quantize model. This model architecture may not be supported, or you may need to upgrade Ollama to the latest version")
+		return fmt.Errorf("llama_model_quantize: %d", rc)
 	}
 
 	return nil

llm/llm_darwin_amd64.go

@@ -2,10 +2,7 @@ package llm
 
 import (
 	"embed"
-	"syscall"
 )
 
 //go:embed build/darwin/x86_64/*/bin/*
 var libEmbed embed.FS
-
-var LlamaServerSysProcAttr = &syscall.SysProcAttr{}

llm/llm_darwin_arm64.go

@@ -2,10 +2,7 @@ package llm
 
 import (
 	"embed"
-	"syscall"
 )
 
 //go:embed build/darwin/arm64/*/bin/*
 var libEmbed embed.FS
-
-var LlamaServerSysProcAttr = &syscall.SysProcAttr{}

llm/llm_linux.go

@@ -1,11 +1,6 @@
 package llm
 
-import (
-	"embed"
-	"syscall"
-)
+import "embed"
 
 //go:embed build/linux/*/*/bin/*
 var libEmbed embed.FS
-
-var LlamaServerSysProcAttr = &syscall.SysProcAttr{}

llm/llm_windows.go

@@ -1,20 +1,6 @@
 package llm
 
-import (
-	"embed"
-	"syscall"
-)
+import "embed"
 
 // unused on windows
 var libEmbed embed.FS
-
-const CREATE_DEFAULT_ERROR_MODE = 0x04000000
-
-var LlamaServerSysProcAttr = &syscall.SysProcAttr{
-	// Wire up the default error handling logic If for some reason a DLL is
-	// missing in the path this will pop up a GUI Dialog explaining the fault so
-	// the user can either fix their PATH, or report a bug. Without this
-	// setting, the process exits immediately with a generic exit status but no
-	// way to (easily) figure out what the actual missing DLL was.
-	CreationFlags: CREATE_DEFAULT_ERROR_MODE,
-}

llm/memory_test.go

@@ -2,7 +2,6 @@ package llm
 
 import (
 	"bytes"
-	"encoding/binary"
 	"fmt"
 	"os"
 	"testing"
@@ -20,10 +19,9 @@ func TestEstimateGPULayers(t *testing.T) {
 	f, err := os.CreateTemp(t.TempDir(), modelName)
 	require.NoError(t, err)
 	defer f.Close()
-	gguf := NewGGUFV3(binary.LittleEndian)
 	inputLayerCount := 5
 
-	tensors := []Tensor{
+	tensors := []*Tensor{
 		{Name: "blk.0.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
 		{Name: "blk.1.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
 		{Name: "blk.2.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
@@ -32,7 +30,7 @@ func TestEstimateGPULayers(t *testing.T) {
 		{Name: "output.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
 	}
 	assert.Len(t, tensors, inputLayerCount+1)
-	err = gguf.Encode(f, KV{
+	err = WriteGGUF(f, KV{
 		"general.architecture":          "llama",
 		"general.name":                  "name",
 		"llama.context_length":          uint32(32),

llm/patches/05-default-pretokenizer.diff

@@ -1,8 +1,8 @@
 diff --git a/src/llama.cpp b/src/llama.cpp
-index a207451f..2ddf431d 100644
+index 2b9ace28..172640e2 100644
 --- a/src/llama.cpp
 +++ b/src/llama.cpp
-@@ -5347,16 +5347,7 @@ static void llm_load_vocab(
+@@ -5357,16 +5357,7 @@ static void llm_load_vocab(
          if (vocab.type == LLAMA_VOCAB_TYPE_BPE) {
              vocab.tokenizer_add_space_prefix = false;
              vocab.tokenizer_clean_spaces = true;
@@ -20,9 +20,9 @@ index a207451f..2ddf431d 100644
                  vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
              } else if (
                      tokenizer_pre == "llama3"   ||
-@@ -5443,7 +5434,8 @@ static void llm_load_vocab(
-                 tokenizer_pre == "codeshell") {
-                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CODESHELL;
+@@ -5439,7 +5430,8 @@ static void llm_load_vocab(
+                 tokenizer_pre == "jais") {
+                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_JAIS;
              } else {
 -                throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
 +                LLAMA_LOG_WARN("%s: missing or unrecognized pre-tokenizer type, using: 'default'\n", __func__);

llm/patches/06-qwen2.diff

@@ -0,0 +1,13 @@
+diff --git a/src/llama.cpp b/src/llama.cpp
+index 40d2ec2c..f34eb79a 100644
+--- a/src/llama.cpp
++++ b/src/llama.cpp
+@@ -6943,7 +6943,7 @@ static struct ggml_tensor * llm_build_kqv(
+         struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
+         cb(kq, "kq", il);
+ 
+-        if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX) {
++        if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_QWEN2) {
+             // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
+             // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
+             ggml_mul_mat_set_prec(kq, GGML_PREC_F32);

llm/patches/09-lora.diff

@@ -1,358 +0,0 @@
-diff --git a/common/common.cpp b/common/common.cpp
-index dbb724fb..c26fe6ee 100644
---- a/common/common.cpp
-+++ b/common/common.cpp
-@@ -2087,14 +2087,27 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
-     for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
-         const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]);
-         float lora_scale = std::get<1>(params.lora_adapter[i]);
-+
-+        // try to load as gguf
-         auto adapter = llama_lora_adapter_init(model, lora_adapter.c_str());
-         if (adapter == nullptr) {
--            fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
--            llama_free(lctx);
--            llama_free_model(model);
--            return std::make_tuple(nullptr, nullptr);
-+            fprintf(stderr, "%s: error: failed to apply lora adapter, trying ggla\n", __func__);
-+
-+            // if that fails, try loading as ggla for compatibility
-+            int err = llama_model_apply_lora_from_file(model,
-+                                                    lora_adapter.c_str(),
-+                                                    lora_scale,
-+                                                    nullptr,
-+                                                    params.n_threads);
-+            if (err != 0) {
-+                fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
-+                llama_free(lctx);
-+                llama_free_model(model);
-+                return std::make_tuple(nullptr, nullptr);
-+            }
-+        } else {
-+            llama_lora_adapter_set(lctx, adapter, lora_scale);
-         }
--        llama_lora_adapter_set(lctx, adapter, lora_scale);
-     }
- 
-     if (params.ignore_eos) {
-diff --git a/include/llama.h b/include/llama.h
-index 93fd77ca..b0fb37a6 100644
---- a/include/llama.h
-+++ b/include/llama.h
-@@ -1160,6 +1160,20 @@ extern "C" {
- 
-     LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx);
- 
-+    // Apply a LoRA adapter to a loaded model
-+    // path_base_model is the path to a higher quality model to use as a base for
-+    // the layers modified by the adapter. Can be NULL to use the current loaded model.
-+    // The model needs to be reloaded before applying a new adapter, otherwise the adapter
-+    // will be applied on top of the previous one
-+    // Returns 0 on success
-+    LLAMA_API int32_t llama_model_apply_lora_from_file(
-+            const struct llama_model * model,
-+                            const char * path_lora,
-+                                float   scale,
-+                            const char * path_base_model,
-+                                int32_t   n_threads);
-+
-+
- #ifdef __cplusplus
- }
- #endif
-diff --git a/src/llama.cpp b/src/llama.cpp
-index 80a0dd0f..9d7b0e17 100644
---- a/src/llama.cpp
-+++ b/src/llama.cpp
-@@ -21880,3 +21880,290 @@ static void llama_log_callback_default(ggml_log_level level, const char * text,
-     fputs(text, stderr);
-     fflush(stderr);
- }
-+
-+static int llama_apply_lora_from_file_internal(
-+    const struct llama_model & model, const char * path_lora, float scale, const char * path_base_model, int n_threads
-+) {
-+    LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
-+
-+    const int64_t t_start_lora_us = ggml_time_us();
-+
-+    llama_file fin(path_lora, "rb");
-+
-+    // verify magic and version
-+    {
-+        uint32_t magic = fin.read_u32();
-+        if (magic != LLAMA_FILE_MAGIC_GGLA) {
-+            LLAMA_LOG_ERROR("%s: bad file magic\n", __func__);
-+            return 1;
-+        }
-+
-+        uint32_t format_version = fin.read_u32();
-+        if (format_version != 1) {
-+            LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
-+            return 1;
-+        }
-+    }
-+
-+    int32_t lora_r = fin.read_u32();
-+    int32_t lora_alpha = fin.read_u32();
-+    float scaling = scale * (float)lora_alpha / (float)lora_r;
-+
-+    LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
-+
-+    // load base model
-+    std::unique_ptr<llama_model_loader> ml;
-+    if (path_base_model) {
-+        LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
-+        ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*check_tensors*/ false, /*kv_overrides*/ nullptr));
-+        ml->init_mappings(/*prefetch*/ false); // no prefetching
-+    }
-+
-+    struct tensor_meta {
-+        std::string name;
-+        ggml_type type;
-+        int32_t ne[2];
-+        size_t offset;
-+    };
-+    std::map<std::string, tensor_meta> tensor_meta_map;
-+
-+    // load all tensor meta
-+    while (true) {
-+        if (fin.tell() == fin.size) {
-+            // eof
-+            break;
-+        }
-+
-+        int32_t n_dims;
-+        int32_t name_len;
-+        int32_t ftype;
-+
-+        fin.read_raw(&n_dims, sizeof(n_dims));
-+        fin.read_raw(&name_len, sizeof(name_len));
-+        fin.read_raw(&ftype, sizeof(ftype));
-+
-+        if (n_dims != 1 && n_dims != 2) {
-+            LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
-+            return 1;
-+        }
-+
-+        int32_t ne[2] = { 1, 1 };
-+        for (int i = 0; i < n_dims; ++i) {
-+            fin.read_raw(&ne[i], sizeof(ne[i]));
-+        }
-+
-+        std::string name;
-+        {
-+            GGML_ASSERT(name_len < GGML_MAX_NAME);
-+            char buf[GGML_MAX_NAME];
-+            fin.read_raw(buf, name_len);
-+            name = std::string(buf, name_len);
-+        }
-+
-+        // check for lora suffix
-+        std::string lora_suffix;
-+        if (name.length() > 6) {
-+            lora_suffix = name.substr(name.length() - 6);
-+        }
-+        if (lora_suffix != ".loraA" && lora_suffix != ".loraB") {
-+            LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
-+            return 1;
-+        }
-+
-+        // tensor type
-+        ggml_type wtype;
-+        switch (ftype) {
-+            case 0: wtype = GGML_TYPE_F32;  break;
-+            case 1: wtype = GGML_TYPE_F16;  break;
-+            default:
-+                    {
-+                        LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n",
-+                                __func__, ftype);
-+                        return 1;
-+                    }
-+        }
-+
-+        // data offset
-+        size_t offset = fin.tell();
-+        offset = (offset + 31) & -32;
-+
-+        // skip tensor data
-+        fin.seek(offset + ggml_row_size(wtype, ne[0]) * ne[1], SEEK_SET);
-+
-+        tensor_meta_map.emplace(name, tensor_meta{ name, wtype, { ne[0], ne[1] }, offset });
-+    }
-+
-+    bool warned = false;
-+    int n_tensors = 0;
-+
-+    // apply
-+    ggml_backend_t backend_cpu = ggml_backend_cpu_init();
-+    if (backend_cpu == nullptr) {
-+        LLAMA_LOG_ERROR("%s: error: failed to initialize cpu backend\n", __func__);
-+        return 1;
-+    }
-+    ggml_backend_cpu_set_n_threads(backend_cpu, n_threads);
-+
-+    std::vector<no_init<uint8_t>> read_buf;
-+    for (const auto & it : model.tensors_by_name) {
-+        const std::string & base_name = it.first;
-+        ggml_tensor * model_t = it.second;
-+
-+        if (tensor_meta_map.find(base_name + ".loraA") == tensor_meta_map.end() ||
-+            tensor_meta_map.find(base_name + ".loraB") == tensor_meta_map.end()) {
-+            continue;
-+        }
-+
-+        tensor_meta & metaA = tensor_meta_map.at(base_name + ".loraA");
-+        tensor_meta & metaB = tensor_meta_map.at(base_name + ".loraB");
-+
-+        ggml_init_params lora_init_params = {
-+            /* .mem_size   */ ggml_tensor_overhead()*128 + ggml_graph_overhead(),
-+            /* .mem_buffer */ nullptr,
-+            /* .no_alloc   */ true,
-+        };
-+        ggml_context * lora_ctx = ggml_init(lora_init_params);
-+        if (lora_ctx == nullptr) {
-+            LLAMA_LOG_ERROR("%s: error: failed to initialize lora context\n", __func__);
-+            ggml_backend_free(backend_cpu);
-+            return 1;
-+        }
-+
-+        // create tensors
-+        ggml_tensor * loraA = ggml_new_tensor_2d(lora_ctx, metaA.type, metaA.ne[0], metaA.ne[1]);
-+        ggml_tensor * loraB = ggml_new_tensor_2d(lora_ctx, metaB.type, metaB.ne[0], metaB.ne[1]);
-+        ggml_set_name(loraA, metaA.name.c_str());
-+        ggml_set_name(loraB, metaB.name.c_str());
-+
-+        ggml_tensor * base_t;
-+        if (ml) {
-+            if (!ml->get_tensor_meta(base_name.c_str())) {
-+                LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
-+                return 1;
-+            }
-+            base_t = ggml_dup_tensor(lora_ctx, ml->get_tensor_meta(base_name.c_str()));
-+        } else {
-+            base_t = ggml_dup_tensor(lora_ctx, model_t);
-+        }
-+        ggml_set_name(base_t, base_name.c_str());
-+
-+        // allocate in backend buffer
-+        ggml_backend_buffer_t lora_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
-+        if (lora_buf == nullptr) {
-+            LLAMA_LOG_ERROR("%s: error: failed to allocate lora tensors\n", __func__);
-+            return 1;
-+        }
-+
-+        // load tensor data
-+        auto load_tensor = [&read_buf, &fin](const tensor_meta & tensor_meta, ggml_tensor * tensor) {
-+            read_buf.resize(ggml_nbytes(tensor));
-+            fin.seek(tensor_meta.offset, SEEK_SET);
-+            fin.read_raw(read_buf.data(), ggml_nbytes(tensor));
-+            ggml_backend_tensor_set(tensor, read_buf.data(), 0, read_buf.size());
-+        };
-+        load_tensor(metaA, loraA);
-+        load_tensor(metaB, loraB);
-+
-+        // load base model tensor data
-+        if (ml) {
-+            ml->load_data_for(base_t);
-+        } else {
-+            ggml_backend_tensor_copy(model_t, base_t);
-+        }
-+
-+        if (ggml_is_quantized(base_t->type) && !warned) {
-+            LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, "
-+                            "use a f16 or f32 base model with --lora-base\n", __func__);
-+            warned = true;
-+        }
-+
-+        if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
-+            LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
-+                            " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
-+            ggml_free(lora_ctx);
-+            ggml_backend_buffer_free(lora_buf);
-+            ggml_backend_free(backend_cpu);
-+            return 1;
-+        }
-+
-+        auto build_lora_graph = [&]() {
-+            // w = w + BA*s
-+            ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
-+            ggml_set_name(BA, "BA");
-+
-+            if (scaling != 1.0f) {
-+                BA = ggml_scale(lora_ctx, BA, scaling);
-+                ggml_set_name(BA, "BA_scaled");
-+            }
-+
-+            ggml_tensor * r;
-+            r = ggml_add_inplace(lora_ctx, base_t, BA);
-+            ggml_set_name(r, "r_add");
-+
-+            if (base_t->type != model_t->type) {
-+                // convert the result to the model type
-+                r = ggml_cast(lora_ctx, r, model_t->type);
-+                ggml_set_name(r, "r_cast");
-+            }
-+
-+            return r;
-+        };
-+
-+        ggml_cgraph * gf = ggml_new_graph(lora_ctx);
-+        ggml_tensor * r = build_lora_graph();
-+        ggml_build_forward_expand(gf, r);
-+
-+        ggml_backend_buffer_t graph_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
-+        if (graph_buf == nullptr) {
-+            LLAMA_LOG_ERROR("%s: error: failed to allocate graph tensors\n", __func__);
-+            ggml_free(lora_ctx);
-+            ggml_backend_buffer_free(lora_buf);
-+            ggml_backend_free(backend_cpu);
-+            return 1;
-+        }
-+
-+        ggml_backend_graph_compute(backend_cpu, gf);
-+
-+        ggml_backend_tensor_set(model_t, r->data, 0, ggml_nbytes(r));
-+
-+#if 0
-+        // TODO: use scheduler with fallback to CPU for less copies between CPU and GPU
-+        //ggml_backend_sched_t sched = ggml_backend_sched_new(backends.data(), backends.size(), GGML_DEFAULT_GRAPH_SIZE);
-+
-+        // sched compute
-+        ggml_build_forward_expand(gf, build_graph());
-+        ggml_backend_sched_init_measure(sched, gf);
-+
-+        // create the graph again, since the previous one was destroyed by the measure
-+        ggml_graph_clear(gf);
-+        ggml_build_forward_expand(gf, build_graph());
-+        ggml_backend_sched_graph_compute(sched, gf);
-+        ggml_backend_sched_free(sched);
-+#endif
-+
-+        ggml_backend_buffer_free(lora_buf);
-+        ggml_backend_buffer_free(graph_buf);
-+        ggml_free(lora_ctx);
-+
-+        n_tensors++;
-+        if (n_tensors % 4 == 0) {
-+            LLAMA_LOG_INFO(".");
-+        }
-+    }
-+
-+    ggml_backend_free(backend_cpu);
-+
-+    const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
-+    LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);
-+
-+    return 0;
-+}
-+
-+int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
-+    try {
-+        return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads);
-+    } catch (const std::exception & err) {
-+        LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
-+        return 1;
-+    }
-+}
-\ No newline at end of file

llm/patches/10-lora.diff

@@ -0,0 +1,78 @@
+diff --git a/CMakeLists.txt b/CMakeLists.txt
+index 4f6cd687..b8c6896b 100644
+--- a/CMakeLists.txt
++++ b/CMakeLists.txt
+@@ -189,3 +189,4 @@ if (LLAMA_BUILD_EXAMPLES)
+     add_subdirectory(examples)
+     add_subdirectory(pocs)
+ endif()
++add_subdirectory(../ext_server ext_server) # ollama
+diff --git a/src/llama.cpp b/src/llama.cpp
+index 2b9ace28..b0151571 100644
+--- a/src/llama.cpp
++++ b/src/llama.cpp
+@@ -18609,6 +18609,20 @@ static int llama_apply_lora_from_file_internal(
+             return 1;
+         }
+ 
++        // show tensor data
++        auto show_tensor = [](std::string name, ggml_tensor *t) {
++            LLAMA_LOG_INFO("%s\n", name.c_str());
++
++            for(int i=0; i<3; i++) {
++                for(int j=0; j<3; j++) {
++                    float v = ggml_get_f32_nd(t, i, j, 0, 0);
++                    LLAMA_LOG_INFO("%.8f ", v);
++                }
++                LLAMA_LOG_INFO(" ...\n");
++            }
++            LLAMA_LOG_INFO(" ...\n");
++        };
++
+         // load tensor data
+         auto load_tensor = [&read_buf, &fin](const tensor_meta & tensor_meta, ggml_tensor * tensor) {
+             read_buf.resize(ggml_nbytes(tensor));
+@@ -18619,6 +18633,9 @@ static int llama_apply_lora_from_file_internal(
+         load_tensor(metaA, loraA);
+         load_tensor(metaB, loraB);
+ 
++        show_tensor(base_name + ".loraA", loraA);
++        show_tensor(base_name + ".loraB", loraB);
++
+         // load base model tensor data
+         if (ml) {
+             ml->load_data_for(base_t);
+@@ -18633,8 +18650,10 @@ static int llama_apply_lora_from_file_internal(
+         }
+ 
+         if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
+-            LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
+-                            " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
++            LLAMA_LOG_ERROR("%s: incompatible tensors: base [%lld, %lld] loraA [%lld, %lld] loraB [%lld, %lld]\n", __func__,
++                            base_t->ne[0], base_t->ne[1],
++                            loraA->ne[0], loraA->ne[1],
++                            loraB->ne[0], loraB->ne[1]);
+             ggml_free(lora_ctx);
+             ggml_backend_buffer_free(lora_buf);
+             ggml_backend_free(backend_cpu);
+@@ -18643,14 +18662,18 @@ static int llama_apply_lora_from_file_internal(
+ 
+         auto build_lora_graph = [&]() {
+             // w = w + BA*s
+-            ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
++            ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraB, loraA);
+             ggml_set_name(BA, "BA");
+ 
+             if (scaling != 1.0f) {
+-                BA = ggml_scale(lora_ctx, BA, scaling);
++                //BA = ggml_scale(lora_ctx, BA, scaling);
++                BA = ggml_scale(lora_ctx, BA, 20.0);
+                 ggml_set_name(BA, "BA_scaled");
+             }
+ 
++            // transpose matrix before we add
++            BA = ggml_cont(lora_ctx, ggml_transpose(lora_ctx, BA));
++
+             ggml_tensor * r;
+             r = ggml_add_inplace(lora_ctx, base_t, BA);
+             ggml_set_name(r, "r_add");

llm/server.go

@@ -33,7 +33,7 @@ type LlamaServer interface {
 	Ping(ctx context.Context) error
 	WaitUntilRunning(ctx context.Context) error
 	Completion(ctx context.Context, req CompletionRequest, fn func(CompletionResponse)) error
-	Embed(ctx context.Context, input []string) ([][]float32, error)
+	Embedding(ctx context.Context, prompt string) ([]float64, error)
 	Tokenize(ctx context.Context, content string) ([]int, error)
 	Detokenize(ctx context.Context, tokens []int) (string, error)
 	Close() error
@@ -88,7 +88,6 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 	var estimate MemoryEstimate
 	var systemTotalMemory uint64
 	var systemFreeMemory uint64
-	var systemSwapFreeMemory uint64
 
 	systemMemInfo, err := gpu.GetCPUMem()
 	if err != nil {
@@ -96,8 +95,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 	} else {
 		systemTotalMemory = systemMemInfo.TotalMemory
 		systemFreeMemory = systemMemInfo.FreeMemory
-		systemSwapFreeMemory = systemMemInfo.FreeSwap
-		slog.Debug("system memory", "total", format.HumanBytes2(systemTotalMemory), "free", format.HumanBytes2(systemFreeMemory), "free_swap", format.HumanBytes2(systemSwapFreeMemory))
+		slog.Debug("system memory", "total", format.HumanBytes2(systemTotalMemory), "free", systemFreeMemory)
 	}
 
 	// If the user wants zero GPU layers, reset the gpu list to be CPU/system ram info
@@ -124,16 +122,6 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		}
 	}
 
-	// On linux, over-allocating CPU memory will almost always result in an error
-	if runtime.GOOS == "linux" {
-		systemMemoryRequired := estimate.TotalSize - estimate.VRAMSize
-		available := systemFreeMemory + systemSwapFreeMemory
-		if systemMemoryRequired > available {
-			slog.Warn("model request too large for system", "requested", format.HumanBytes2(systemMemoryRequired), "available", available, "total", format.HumanBytes2(systemTotalMemory), "free", format.HumanBytes2(systemFreeMemory), "swap", format.HumanBytes2(systemSwapFreeMemory))
-			return nil, fmt.Errorf("model requires more system memory (%s) than is available (%s)", format.HumanBytes2(systemMemoryRequired), format.HumanBytes2(available))
-		}
-	}
-
 	estimate.log()
 
 	// Loop through potential servers
@@ -266,6 +254,10 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		params = append(params, "--tensor-split", estimate.TensorSplit)
 	}
 
+	if estimate.TensorSplit != "" {
+		params = append(params, "--tensor-split", estimate.TensorSplit)
+	}
+
 	for i := range len(servers) {
 		dir := availableServers[servers[i]]
 		if dir == "" {
@@ -346,7 +338,6 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		s.cmd.Env = os.Environ()
 		s.cmd.Stdout = os.Stdout
 		s.cmd.Stderr = s.status
-		s.cmd.SysProcAttr = LlamaServerSysProcAttr
 
 		envWorkarounds := [][2]string{}
 		for _, gpu := range gpus {
@@ -386,10 +377,8 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 			filteredEnv := []string{}
 			for _, ev := range s.cmd.Env {
 				if strings.HasPrefix(ev, "CUDA_") ||
-					strings.HasPrefix(ev, "ROCR_") ||
 					strings.HasPrefix(ev, "ROCM_") ||
 					strings.HasPrefix(ev, "HIP_") ||
-					strings.HasPrefix(ev, "GPU_") ||
 					strings.HasPrefix(ev, "HSA_") ||
 					strings.HasPrefix(ev, "GGML_") ||
 					strings.HasPrefix(ev, "PATH=") ||
@@ -418,17 +407,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 
 		// reap subprocess when it exits
 		go func() {
-			err := s.cmd.Wait()
-			// Favor a more detailed message over the process exit status
-			if err != nil && s.status != nil && s.status.LastErrMsg != "" {
-				slog.Debug("llama runner terminated", "error", err)
-				if strings.Contains(s.status.LastErrMsg, "unknown model") {
-					s.status.LastErrMsg = "this model is not supported by your version of Ollama. You may need to upgrade"
-				}
-				s.done <- fmt.Errorf(s.status.LastErrMsg)
-			} else {
-				s.done <- err
-			}
+			s.done <- s.cmd.Wait()
 		}()
 
 		return s, nil
@@ -591,7 +570,14 @@ func (s *llmServer) WaitUntilRunning(ctx context.Context) error {
 			slog.Warn("client connection closed before server finished loading, aborting load")
 			return fmt.Errorf("timed out waiting for llama runner to start: %w", ctx.Err())
 		case err := <-s.done:
-			return fmt.Errorf("llama runner process has terminated: %w", err)
+			msg := ""
+			if s.status != nil && s.status.LastErrMsg != "" {
+				msg = s.status.LastErrMsg
+			}
+			if strings.Contains(msg, "unknown model") {
+				return fmt.Errorf("this model is not supported by your version of Ollama. You may need to upgrade")
+			}
+			return fmt.Errorf("llama runner process has terminated: %v %s", err, msg)
 		default:
 		}
 		if time.Now().After(stallTimer) {
@@ -693,7 +679,7 @@ type CompletionRequest struct {
 	Prompt  string
 	Format  string
 	Images  []ImageData
-	Options *api.Options
+	Options api.Options
 }
 
 type CompletionResponse struct {
@@ -727,7 +713,6 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 		"temperature":       req.Options.Temperature,
 		"top_k":             req.Options.TopK,
 		"top_p":             req.Options.TopP,
-		"min_p":             req.Options.MinP,
 		"tfs_z":             req.Options.TFSZ,
 		"typical_p":         req.Options.TypicalP,
 		"repeat_last_n":     req.Options.RepeatLastN,
@@ -874,15 +859,15 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 	return nil
 }
 
-type EmbedRequest struct {
-	Content []string `json:"content"`
+type EmbeddingRequest struct {
+	Content string `json:"content"`
 }
 
-type EmbedResponse struct {
-	Embedding [][]float32 `json:"embedding"`
+type EmbeddingResponse struct {
+	Embedding []float64 `json:"embedding"`
 }
 
-func (s *llmServer) Embed(ctx context.Context, input []string) ([][]float32, error) {
+func (s *llmServer) Embedding(ctx context.Context, prompt string) ([]float64, error) {
 	if err := s.sem.Acquire(ctx, 1); err != nil {
 		slog.Error("Failed to acquire semaphore", "error", err)
 		return nil, err
@@ -897,7 +882,7 @@ func (s *llmServer) Embed(ctx context.Context, input []string) ([][]float32, err
 		return nil, fmt.Errorf("unexpected server status: %s", status.ToString())
 	}
 
-	data, err := json.Marshal(EmbedRequest{Content: input})
+	data, err := json.Marshal(TokenizeRequest{Content: prompt})
 	if err != nil {
 		return nil, fmt.Errorf("error marshaling embed data: %w", err)
 	}
@@ -924,7 +909,7 @@ func (s *llmServer) Embed(ctx context.Context, input []string) ([][]float32, err
 		return nil, fmt.Errorf("%s", body)
 	}
 
-	var embedding EmbedResponse
+	var embedding EmbeddingResponse
 	if err := json.Unmarshal(body, &embedding); err != nil {
 		return nil, fmt.Errorf("unmarshal tokenize response: %w", err)
 	}

macapp/src/app.tsx

@@ -19,7 +19,7 @@ export default function () {
   const [step, setStep] = useState<Step>(Step.WELCOME)
   const [commandCopied, setCommandCopied] = useState<boolean>(false)
 
-  const command = 'ollama run llama3.1'
+  const command = 'ollama run llama3'
 
   return (
     <div className='drag'>

openai/openai.go

@@ -3,14 +3,11 @@ package openai
 
 import (
 	"bytes"
-	"encoding/base64"
 	"encoding/json"
 	"fmt"
 	"io"
-	"log/slog"
 	"math/rand"
 	"net/http"
-	"strings"
 	"time"
 
 	"github.com/gin-gonic/gin"
@@ -30,9 +27,8 @@ type ErrorResponse struct {
 }
 
 type Message struct {
-	Role      string     `json:"role"`
-	Content   any        `json:"content"`
-	ToolCalls []ToolCall `json:"tool_calls,omitempty"`
+	Role    string `json:"role"`
+	Content string `json:"content"`
 }
 
 type Choice struct {
@@ -63,11 +59,6 @@ type ResponseFormat struct {
 	Type string `json:"type"`
 }
 
-type EmbedRequest struct {
-	Input any    `json:"input"`
-	Model string `json:"model"`
-}
-
 type ChatCompletionRequest struct {
 	Model            string          `json:"model"`
 	Messages         []Message       `json:"messages"`
@@ -80,7 +71,6 @@ type ChatCompletionRequest struct {
 	PresencePenalty  *float64        `json:"presence_penalty_penalty"`
 	TopP             *float64        `json:"top_p"`
 	ResponseFormat   *ResponseFormat `json:"response_format"`
-	Tools            []api.Tool      `json:"tools"`
 }
 
 type ChatCompletion struct {
@@ -114,7 +104,6 @@ type CompletionRequest struct {
 	Stream           bool     `json:"stream"`
 	Temperature      *float32 `json:"temperature"`
 	TopP             float32  `json:"top_p"`
-	Suffix           string   `json:"suffix"`
 }
 
 type Completion struct {
@@ -136,15 +125,6 @@ type CompletionChunk struct {
 	SystemFingerprint string                `json:"system_fingerprint"`
 }
 
-type ToolCall struct {
-	ID       string `json:"id"`
-	Type     string `json:"type"`
-	Function struct {
-		Name      string `json:"name"`
-		Arguments string `json:"arguments"`
-	} `json:"function"`
-}
-
 type Model struct {
 	Id      string `json:"id"`
 	Object  string `json:"object"`
@@ -152,23 +132,11 @@ type Model struct {
 	OwnedBy string `json:"owned_by"`
 }
 
-type Embedding struct {
-	Object    string    `json:"object"`
-	Embedding []float32 `json:"embedding"`
-	Index     int       `json:"index"`
-}
-
 type ListCompletion struct {
 	Object string  `json:"object"`
 	Data   []Model `json:"data"`
 }
 
-type EmbeddingList struct {
-	Object string      `json:"object"`
-	Data   []Embedding `json:"data"`
-	Model  string      `json:"model"`
-}
-
 func NewError(code int, message string) ErrorResponse {
 	var etype string
 	switch code {
@@ -183,36 +151,7 @@ func NewError(code int, message string) ErrorResponse {
 	return ErrorResponse{Error{Type: etype, Message: message}}
 }
 
-func toolCallId() string {
-	const letterBytes = "abcdefghijklmnopqrstuvwxyz0123456789"
-	b := make([]byte, 8)
-	for i := range b {
-		b[i] = letterBytes[rand.Intn(len(letterBytes))]
-	}
-	return "call_" + strings.ToLower(string(b))
-}
-
-func parseToolCalls(respToolCalls []api.ToolCall) []ToolCall {
-	toolCalls := make([]ToolCall, len(respToolCalls))
-	for i, tc := range respToolCalls {
-		toolCalls[i].ID = toolCallId()
-		toolCalls[i].Type = "function"
-		toolCalls[i].Function.Name = tc.Function.Name
-
-		args, err := json.Marshal(tc.Function.Arguments)
-		if err != nil {
-			slog.Error("could not marshall function arguments to json", "error", err)
-			continue
-		}
-
-		toolCalls[i].Function.Arguments = string(args)
-	}
-	return toolCalls
-}
-
 func toChatCompletion(id string, r api.ChatResponse) ChatCompletion {
-	toolCalls := parseToolCalls(r.Message.ToolCalls)
-
 	return ChatCompletion{
 		Id:                id,
 		Object:            "chat.completion",
@@ -221,7 +160,7 @@ func toChatCompletion(id string, r api.ChatResponse) ChatCompletion {
 		SystemFingerprint: "fp_ollama",
 		Choices: []Choice{{
 			Index:   0,
-			Message: Message{Role: r.Message.Role, Content: r.Message.Content, ToolCalls: toolCalls},
+			Message: Message{Role: r.Message.Role, Content: r.Message.Content},
 			FinishReason: func(reason string) *string {
 				if len(reason) > 0 {
 					return &reason
@@ -230,6 +169,7 @@ func toChatCompletion(id string, r api.ChatResponse) ChatCompletion {
 			}(r.DoneReason),
 		}},
 		Usage: Usage{
+			// TODO: ollama returns 0 for prompt eval if the prompt was cached, but openai returns the actual count
 			PromptTokens:     r.PromptEvalCount,
 			CompletionTokens: r.EvalCount,
 			TotalTokens:      r.PromptEvalCount + r.EvalCount,
@@ -238,8 +178,6 @@ func toChatCompletion(id string, r api.ChatResponse) ChatCompletion {
 }
 
 func toChunk(id string, r api.ChatResponse) ChatCompletionChunk {
-	toolCalls := parseToolCalls(r.Message.ToolCalls)
-
 	return ChatCompletionChunk{
 		Id:                id,
 		Object:            "chat.completion.chunk",
@@ -248,7 +186,7 @@ func toChunk(id string, r api.ChatResponse) ChatCompletionChunk {
 		SystemFingerprint: "fp_ollama",
 		Choices: []ChunkChoice{{
 			Index: 0,
-			Delta: Message{Role: "assistant", Content: r.Message.Content, ToolCalls: toolCalls},
+			Delta: Message{Role: "assistant", Content: r.Message.Content},
 			FinishReason: func(reason string) *string {
 				if len(reason) > 0 {
 					return &reason
@@ -277,6 +215,7 @@ func toCompletion(id string, r api.GenerateResponse) Completion {
 			}(r.DoneReason),
 		}},
 		Usage: Usage{
+			// TODO: ollama returns 0 for prompt eval if the prompt was cached, but openai returns the actual count
 			PromptTokens:     r.PromptEvalCount,
 			CompletionTokens: r.EvalCount,
 			TotalTokens:      r.PromptEvalCount + r.EvalCount,
@@ -321,27 +260,6 @@ func toListCompletion(r api.ListResponse) ListCompletion {
 	}
 }
 
-func toEmbeddingList(model string, r api.EmbedResponse) EmbeddingList {
-	if r.Embeddings != nil {
-		var data []Embedding
-		for i, e := range r.Embeddings {
-			data = append(data, Embedding{
-				Object:    "embedding",
-				Embedding: e,
-				Index:     i,
-			})
-		}
-
-		return EmbeddingList{
-			Object: "list",
-			Data:   data,
-			Model:  model,
-		}
-	}
-
-	return EmbeddingList{}
-}
-
 func toModel(r api.ShowResponse, m string) Model {
 	return Model{
 		Id:      m,
@@ -351,77 +269,10 @@ func toModel(r api.ShowResponse, m string) Model {
 	}
 }
 
-func fromChatRequest(r ChatCompletionRequest) (*api.ChatRequest, error) {
+func fromChatRequest(r ChatCompletionRequest) api.ChatRequest {
 	var messages []api.Message
 	for _, msg := range r.Messages {
-		switch content := msg.Content.(type) {
-		case string:
-			messages = append(messages, api.Message{Role: msg.Role, Content: content})
-		case []any:
-			for _, c := range content {
-				data, ok := c.(map[string]any)
-				if !ok {
-					return nil, fmt.Errorf("invalid message format")
-				}
-				switch data["type"] {
-				case "text":
-					text, ok := data["text"].(string)
-					if !ok {
-						return nil, fmt.Errorf("invalid message format")
-					}
-					messages = append(messages, api.Message{Role: msg.Role, Content: text})
-				case "image_url":
-					var url string
-					if urlMap, ok := data["image_url"].(map[string]any); ok {
-						if url, ok = urlMap["url"].(string); !ok {
-							return nil, fmt.Errorf("invalid message format")
-						}
-					} else {
-						if url, ok = data["image_url"].(string); !ok {
-							return nil, fmt.Errorf("invalid message format")
-						}
-					}
-
-					types := []string{"jpeg", "jpg", "png"}
-					valid := false
-					for _, t := range types {
-						prefix := "data:image/" + t + ";base64,"
-						if strings.HasPrefix(url, prefix) {
-							url = strings.TrimPrefix(url, prefix)
-							valid = true
-							break
-						}
-					}
-
-					if !valid {
-						return nil, fmt.Errorf("invalid image input")
-					}
-
-					img, err := base64.StdEncoding.DecodeString(url)
-					if err != nil {
-						return nil, fmt.Errorf("invalid message format")
-					}
-
-					messages = append(messages, api.Message{Role: msg.Role, Images: []api.ImageData{img}})
-				default:
-					return nil, fmt.Errorf("invalid message format")
-				}
-			}
-		default:
-			if msg.ToolCalls == nil {
-				return nil, fmt.Errorf("invalid message content type: %T", content)
-			}
-
-			toolCalls := make([]api.ToolCall, len(msg.ToolCalls))
-			for i, tc := range msg.ToolCalls {
-				toolCalls[i].Function.Name = tc.Function.Name
-				err := json.Unmarshal([]byte(tc.Function.Arguments), &toolCalls[i].Function.Arguments)
-				if err != nil {
-					return nil, fmt.Errorf("invalid tool call arguments")
-				}
-			}
-			messages = append(messages, api.Message{Role: msg.Role, ToolCalls: toolCalls})
-		}
+		messages = append(messages, api.Message{Role: msg.Role, Content: msg.Content})
 	}
 
 	options := make(map[string]interface{})
@@ -472,14 +323,13 @@ func fromChatRequest(r ChatCompletionRequest) (*api.ChatRequest, error) {
 		format = "json"
 	}
 
-	return &api.ChatRequest{
+	return api.ChatRequest{
 		Model:    r.Model,
 		Messages: messages,
 		Format:   format,
 		Options:  options,
 		Stream:   &r.Stream,
-		Tools:    r.Tools,
-	}, nil
+	}
 }
 
 func fromCompleteRequest(r CompletionRequest) (api.GenerateRequest, error) {
@@ -488,16 +338,12 @@ func fromCompleteRequest(r CompletionRequest) (api.GenerateRequest, error) {
 	switch stop := r.Stop.(type) {
 	case string:
 		options["stop"] = []string{stop}
-	case []any:
-		var stops []string
-		for _, s := range stop {
-			if str, ok := s.(string); ok {
-				stops = append(stops, str)
-			} else {
-				return api.GenerateRequest{}, fmt.Errorf("invalid type for 'stop' field: %T", s)
-			}
+	case []string:
+		options["stop"] = stop
+	default:
+		if r.Stop != nil {
+			return api.GenerateRequest{}, fmt.Errorf("invalid type for 'stop' field: %T", r.Stop)
 		}
-		options["stop"] = stops
 	}
 
 	if r.MaxTokens != nil {
@@ -529,7 +375,6 @@ func fromCompleteRequest(r CompletionRequest) (api.GenerateRequest, error) {
 		Prompt:  r.Prompt,
 		Options: options,
 		Stream:  &r.Stream,
-		Suffix:  r.Suffix,
 	}, nil
 }
 
@@ -558,11 +403,6 @@ type RetrieveWriter struct {
 	model string
 }
 
-type EmbedWriter struct {
-	BaseWriter
-	model string
-}
-
 func (w *BaseWriter) writeError(code int, data []byte) (int, error) {
 	var serr api.StatusError
 	err := json.Unmarshal(data, &serr)
@@ -728,33 +568,6 @@ func (w *RetrieveWriter) Write(data []byte) (int, error) {
 	return w.writeResponse(data)
 }
 
-func (w *EmbedWriter) writeResponse(data []byte) (int, error) {
-	var embedResponse api.EmbedResponse
-	err := json.Unmarshal(data, &embedResponse)
-
-	if err != nil {
-		return 0, err
-	}
-
-	w.ResponseWriter.Header().Set("Content-Type", "application/json")
-	err = json.NewEncoder(w.ResponseWriter).Encode(toEmbeddingList(w.model, embedResponse))
-
-	if err != nil {
-		return 0, err
-	}
-
-	return len(data), nil
-}
-
-func (w *EmbedWriter) Write(data []byte) (int, error) {
-	code := w.ResponseWriter.Status()
-	if code != http.StatusOK {
-		return w.writeError(code, data)
-	}
-
-	return w.writeResponse(data)
-}
-
 func ListMiddleware() gin.HandlerFunc {
 	return func(c *gin.Context) {
 		w := &ListWriter{
@@ -818,47 +631,6 @@ func CompletionsMiddleware() gin.HandlerFunc {
 			id:         fmt.Sprintf("cmpl-%d", rand.Intn(999)),
 		}
 
-		c.Writer = w
-		c.Next()
-	}
-}
-
-func EmbeddingsMiddleware() gin.HandlerFunc {
-	return func(c *gin.Context) {
-		var req EmbedRequest
-		err := c.ShouldBindJSON(&req)
-		if err != nil {
-			c.AbortWithStatusJSON(http.StatusBadRequest, NewError(http.StatusBadRequest, err.Error()))
-			return
-		}
-
-		if req.Input == "" {
-			req.Input = []string{""}
-		}
-
-		if req.Input == nil {
-			c.AbortWithStatusJSON(http.StatusBadRequest, NewError(http.StatusBadRequest, "invalid input"))
-			return
-		}
-
-		if v, ok := req.Input.([]any); ok && len(v) == 0 {
-			c.AbortWithStatusJSON(http.StatusBadRequest, NewError(http.StatusBadRequest, "invalid input"))
-			return
-		}
-
-		var b bytes.Buffer
-		if err := json.NewEncoder(&b).Encode(api.EmbedRequest{Model: req.Model, Input: req.Input}); err != nil {
-			c.AbortWithStatusJSON(http.StatusInternalServerError, NewError(http.StatusInternalServerError, err.Error()))
-			return
-		}
-
-		c.Request.Body = io.NopCloser(&b)
-
-		w := &EmbedWriter{
-			BaseWriter: BaseWriter{ResponseWriter: c.Writer},
-			model:      req.Model,
-		}
-
 		c.Writer = w
 
 		c.Next()
@@ -880,14 +652,7 @@ func ChatMiddleware() gin.HandlerFunc {
 		}
 
 		var b bytes.Buffer
-
-		chatReq, err := fromChatRequest(req)
-		if err != nil {
-			c.AbortWithStatusJSON(http.StatusBadRequest, NewError(http.StatusBadRequest, err.Error()))
-			return
-		}
-
-		if err := json.NewEncoder(&b).Encode(chatReq); err != nil {
+		if err := json.NewEncoder(&b).Encode(fromChatRequest(req)); err != nil {
 			c.AbortWithStatusJSON(http.StatusInternalServerError, NewError(http.StatusInternalServerError, err.Error()))
 			return
 		}

openai/openai_test.go

@@ -2,8 +2,8 @@ package openai
 
 import (
 	"bytes"
-	"encoding/base64"
 	"encoding/json"
+	"fmt"
 	"io"
 	"net/http"
 	"net/http/httptest"
@@ -16,387 +16,7 @@ import (
 	"github.com/stretchr/testify/assert"
 )
 
-const prefix = `data:image/jpeg;base64,`
-const image = `iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNk+A8AAQUBAScY42YAAAAASUVORK5CYII=`
-const imageURL = prefix + image
-
-func prepareRequest(req *http.Request, body any) {
-	bodyBytes, _ := json.Marshal(body)
-	req.Body = io.NopCloser(bytes.NewReader(bodyBytes))
-	req.Header.Set("Content-Type", "application/json")
-}
-
-func captureRequestMiddleware(capturedRequest any) gin.HandlerFunc {
-	return func(c *gin.Context) {
-		bodyBytes, _ := io.ReadAll(c.Request.Body)
-		c.Request.Body = io.NopCloser(bytes.NewReader(bodyBytes))
-		err := json.Unmarshal(bodyBytes, capturedRequest)
-		if err != nil {
-			c.AbortWithStatusJSON(http.StatusInternalServerError, "failed to unmarshal request")
-		}
-		c.Next()
-	}
-}
-
-func TestChatMiddleware(t *testing.T) {
-	type testCase struct {
-		Name     string
-		Setup    func(t *testing.T, req *http.Request)
-		Expected func(t *testing.T, req *api.ChatRequest, resp *httptest.ResponseRecorder)
-	}
-
-	var capturedRequest *api.ChatRequest
-
-	testCases := []testCase{
-		{
-			Name: "chat handler",
-			Setup: func(t *testing.T, req *http.Request) {
-				body := ChatCompletionRequest{
-					Model:    "test-model",
-					Messages: []Message{{Role: "user", Content: "Hello"}},
-				}
-				prepareRequest(req, body)
-			},
-			Expected: func(t *testing.T, req *api.ChatRequest, resp *httptest.ResponseRecorder) {
-				if resp.Code != http.StatusOK {
-					t.Fatalf("expected 200, got %d", resp.Code)
-				}
-
-				if req.Messages[0].Role != "user" {
-					t.Fatalf("expected 'user', got %s", req.Messages[0].Role)
-				}
-
-				if req.Messages[0].Content != "Hello" {
-					t.Fatalf("expected 'Hello', got %s", req.Messages[0].Content)
-				}
-			},
-		},
-		{
-			Name: "chat handler with image content",
-			Setup: func(t *testing.T, req *http.Request) {
-				body := ChatCompletionRequest{
-					Model: "test-model",
-					Messages: []Message{
-						{
-							Role: "user", Content: []map[string]any{
-								{"type": "text", "text": "Hello"},
-								{"type": "image_url", "image_url": map[string]string{"url": imageURL}},
-							},
-						},
-					},
-				}
-				prepareRequest(req, body)
-			},
-			Expected: func(t *testing.T, req *api.ChatRequest, resp *httptest.ResponseRecorder) {
-				if resp.Code != http.StatusOK {
-					t.Fatalf("expected 200, got %d", resp.Code)
-				}
-
-				if req.Messages[0].Role != "user" {
-					t.Fatalf("expected 'user', got %s", req.Messages[0].Role)
-				}
-
-				if req.Messages[0].Content != "Hello" {
-					t.Fatalf("expected 'Hello', got %s", req.Messages[0].Content)
-				}
-
-				img, _ := base64.StdEncoding.DecodeString(imageURL[len(prefix):])
-
-				if req.Messages[1].Role != "user" {
-					t.Fatalf("expected 'user', got %s", req.Messages[1].Role)
-				}
-
-				if !bytes.Equal(req.Messages[1].Images[0], img) {
-					t.Fatalf("expected image encoding, got %s", req.Messages[1].Images[0])
-				}
-			},
-		},
-		{
-			Name: "chat handler with tools",
-			Setup: func(t *testing.T, req *http.Request) {
-				body := ChatCompletionRequest{
-					Model: "test-model",
-					Messages: []Message{
-						{Role: "user", Content: "What's the weather like in Paris Today?"},
-						{Role: "assistant", ToolCalls: []ToolCall{{
-							ID:   "id",
-							Type: "function",
-							Function: struct {
-								Name      string `json:"name"`
-								Arguments string `json:"arguments"`
-							}{
-								Name:      "get_current_weather",
-								Arguments: "{\"location\": \"Paris, France\", \"format\": \"celsius\"}",
-							},
-						}}},
-					},
-				}
-				prepareRequest(req, body)
-			},
-			Expected: func(t *testing.T, req *api.ChatRequest, resp *httptest.ResponseRecorder) {
-				if resp.Code != 200 {
-					t.Fatalf("expected 200, got %d", resp.Code)
-				}
-
-				if req.Messages[0].Content != "What's the weather like in Paris Today?" {
-					t.Fatalf("expected What's the weather like in Paris Today?, got %s", req.Messages[0].Content)
-				}
-
-				if req.Messages[1].ToolCalls[0].Function.Arguments["location"] != "Paris, France" {
-					t.Fatalf("expected 'Paris, France', got %v", req.Messages[1].ToolCalls[0].Function.Arguments["location"])
-				}
-
-				if req.Messages[1].ToolCalls[0].Function.Arguments["format"] != "celsius" {
-					t.Fatalf("expected celsius, got %v", req.Messages[1].ToolCalls[0].Function.Arguments["format"])
-				}
-			},
-		},
-		{
-			Name: "chat handler error forwarding",
-			Setup: func(t *testing.T, req *http.Request) {
-				body := ChatCompletionRequest{
-					Model:    "test-model",
-					Messages: []Message{{Role: "user", Content: 2}},
-				}
-				prepareRequest(req, body)
-			},
-			Expected: func(t *testing.T, req *api.ChatRequest, resp *httptest.ResponseRecorder) {
-				if resp.Code != http.StatusBadRequest {
-					t.Fatalf("expected 400, got %d", resp.Code)
-				}
-
-				if !strings.Contains(resp.Body.String(), "invalid message content type") {
-					t.Fatalf("error was not forwarded")
-				}
-			},
-		},
-	}
-
-	endpoint := func(c *gin.Context) {
-		c.Status(http.StatusOK)
-	}
-
-	gin.SetMode(gin.TestMode)
-	router := gin.New()
-	router.Use(ChatMiddleware(), captureRequestMiddleware(&capturedRequest))
-	router.Handle(http.MethodPost, "/api/chat", endpoint)
-
-	for _, tc := range testCases {
-		t.Run(tc.Name, func(t *testing.T) {
-			req, _ := http.NewRequest(http.MethodPost, "/api/chat", nil)
-
-			tc.Setup(t, req)
-
-			resp := httptest.NewRecorder()
-			router.ServeHTTP(resp, req)
-
-			tc.Expected(t, capturedRequest, resp)
-
-			capturedRequest = nil
-		})
-	}
-}
-
-func TestCompletionsMiddleware(t *testing.T) {
-	type testCase struct {
-		Name     string
-		Setup    func(t *testing.T, req *http.Request)
-		Expected func(t *testing.T, req *api.GenerateRequest, resp *httptest.ResponseRecorder)
-	}
-
-	var capturedRequest *api.GenerateRequest
-
-	testCases := []testCase{
-		{
-			Name: "completions handler",
-			Setup: func(t *testing.T, req *http.Request) {
-				temp := float32(0.8)
-				body := CompletionRequest{
-					Model:       "test-model",
-					Prompt:      "Hello",
-					Temperature: &temp,
-					Stop:        []string{"\n", "stop"},
-					Suffix:      "suffix",
-				}
-				prepareRequest(req, body)
-			},
-			Expected: func(t *testing.T, req *api.GenerateRequest, resp *httptest.ResponseRecorder) {
-				if req.Prompt != "Hello" {
-					t.Fatalf("expected 'Hello', got %s", req.Prompt)
-				}
-
-				if req.Options["temperature"] != 1.6 {
-					t.Fatalf("expected 1.6, got %f", req.Options["temperature"])
-				}
-
-				stopTokens, ok := req.Options["stop"].([]any)
-
-				if !ok {
-					t.Fatalf("expected stop tokens to be a list")
-				}
-
-				if stopTokens[0] != "\n" || stopTokens[1] != "stop" {
-					t.Fatalf("expected ['\\n', 'stop'], got %v", stopTokens)
-				}
-
-				if req.Suffix != "suffix" {
-					t.Fatalf("expected 'suffix', got %s", req.Suffix)
-				}
-			},
-		},
-		{
-			Name: "completions handler error forwarding",
-			Setup: func(t *testing.T, req *http.Request) {
-				body := CompletionRequest{
-					Model:       "test-model",
-					Prompt:      "Hello",
-					Temperature: nil,
-					Stop:        []int{1, 2},
-					Suffix:      "suffix",
-				}
-				prepareRequest(req, body)
-			},
-			Expected: func(t *testing.T, req *api.GenerateRequest, resp *httptest.ResponseRecorder) {
-				if resp.Code != http.StatusBadRequest {
-					t.Fatalf("expected 400, got %d", resp.Code)
-				}
-
-				if !strings.Contains(resp.Body.String(), "invalid type for 'stop' field") {
-					t.Fatalf("error was not forwarded")
-				}
-			},
-		},
-	}
-
-	endpoint := func(c *gin.Context) {
-		c.Status(http.StatusOK)
-	}
-
-	gin.SetMode(gin.TestMode)
-	router := gin.New()
-	router.Use(CompletionsMiddleware(), captureRequestMiddleware(&capturedRequest))
-	router.Handle(http.MethodPost, "/api/generate", endpoint)
-
-	for _, tc := range testCases {
-		t.Run(tc.Name, func(t *testing.T) {
-			req, _ := http.NewRequest(http.MethodPost, "/api/generate", nil)
-
-			tc.Setup(t, req)
-
-			resp := httptest.NewRecorder()
-			router.ServeHTTP(resp, req)
-
-			tc.Expected(t, capturedRequest, resp)
-
-			capturedRequest = nil
-		})
-	}
-}
-
-func TestEmbeddingsMiddleware(t *testing.T) {
-	type testCase struct {
-		Name     string
-		Setup    func(t *testing.T, req *http.Request)
-		Expected func(t *testing.T, req *api.EmbedRequest, resp *httptest.ResponseRecorder)
-	}
-
-	var capturedRequest *api.EmbedRequest
-
-	testCases := []testCase{
-		{
-			Name: "embed handler single input",
-			Setup: func(t *testing.T, req *http.Request) {
-				body := EmbedRequest{
-					Input: "Hello",
-					Model: "test-model",
-				}
-				prepareRequest(req, body)
-			},
-			Expected: func(t *testing.T, req *api.EmbedRequest, resp *httptest.ResponseRecorder) {
-				if req.Input != "Hello" {
-					t.Fatalf("expected 'Hello', got %s", req.Input)
-				}
-
-				if req.Model != "test-model" {
-					t.Fatalf("expected 'test-model', got %s", req.Model)
-				}
-			},
-		},
-		{
-			Name: "embed handler batch input",
-			Setup: func(t *testing.T, req *http.Request) {
-				body := EmbedRequest{
-					Input: []string{"Hello", "World"},
-					Model: "test-model",
-				}
-				prepareRequest(req, body)
-			},
-			Expected: func(t *testing.T, req *api.EmbedRequest, resp *httptest.ResponseRecorder) {
-				input, ok := req.Input.([]any)
-
-				if !ok {
-					t.Fatalf("expected input to be a list")
-				}
-
-				if input[0].(string) != "Hello" {
-					t.Fatalf("expected 'Hello', got %s", input[0])
-				}
-
-				if input[1].(string) != "World" {
-					t.Fatalf("expected 'World', got %s", input[1])
-				}
-
-				if req.Model != "test-model" {
-					t.Fatalf("expected 'test-model', got %s", req.Model)
-				}
-			},
-		},
-		{
-			Name: "embed handler error forwarding",
-			Setup: func(t *testing.T, req *http.Request) {
-				body := EmbedRequest{
-					Model: "test-model",
-				}
-				prepareRequest(req, body)
-			},
-			Expected: func(t *testing.T, req *api.EmbedRequest, resp *httptest.ResponseRecorder) {
-				if resp.Code != http.StatusBadRequest {
-					t.Fatalf("expected 400, got %d", resp.Code)
-				}
-
-				if !strings.Contains(resp.Body.String(), "invalid input") {
-					t.Fatalf("error was not forwarded")
-				}
-			},
-		},
-	}
-
-	endpoint := func(c *gin.Context) {
-		c.Status(http.StatusOK)
-	}
-
-	gin.SetMode(gin.TestMode)
-	router := gin.New()
-	router.Use(EmbeddingsMiddleware(), captureRequestMiddleware(&capturedRequest))
-	router.Handle(http.MethodPost, "/api/embed", endpoint)
-
-	for _, tc := range testCases {
-		t.Run(tc.Name, func(t *testing.T) {
-			req, _ := http.NewRequest(http.MethodPost, "/api/embed", nil)
-
-			tc.Setup(t, req)
-
-			resp := httptest.NewRecorder()
-			router.ServeHTTP(resp, req)
-
-			tc.Expected(t, capturedRequest, resp)
-
-			capturedRequest = nil
-		})
-	}
-}
-
-func TestMiddlewareResponses(t *testing.T) {
+func TestMiddleware(t *testing.T) {
 	type testCase struct {
 		Name     string
 		Method   string
@@ -409,6 +29,188 @@ func TestMiddlewareResponses(t *testing.T) {
 	}
 
 	testCases := []testCase{
+		{
+			Name:     "chat handler",
+			Method:   http.MethodPost,
+			Path:     "/api/chat",
+			TestPath: "/api/chat",
+			Handler:  ChatMiddleware,
+			Endpoint: func(c *gin.Context) {
+				var chatReq api.ChatRequest
+				if err := c.ShouldBindJSON(&chatReq); err != nil {
+					c.JSON(http.StatusBadRequest, gin.H{"error": "invalid request"})
+					return
+				}
+
+				userMessage := chatReq.Messages[0].Content
+				var assistantMessage string
+
+				switch userMessage {
+				case "Hello":
+					assistantMessage = "Hello!"
+				default:
+					assistantMessage = "I'm not sure how to respond to that."
+				}
+
+				c.JSON(http.StatusOK, api.ChatResponse{
+					Message: api.Message{
+						Role:    "assistant",
+						Content: assistantMessage,
+					},
+				})
+			},
+			Setup: func(t *testing.T, req *http.Request) {
+				body := ChatCompletionRequest{
+					Model:    "test-model",
+					Messages: []Message{{Role: "user", Content: "Hello"}},
+				}
+
+				bodyBytes, _ := json.Marshal(body)
+
+				req.Body = io.NopCloser(bytes.NewReader(bodyBytes))
+				req.Header.Set("Content-Type", "application/json")
+			},
+			Expected: func(t *testing.T, resp *httptest.ResponseRecorder) {
+				assert.Equal(t, http.StatusOK, resp.Code)
+
+				var chatResp ChatCompletion
+				if err := json.NewDecoder(resp.Body).Decode(&chatResp); err != nil {
+					t.Fatal(err)
+				}
+
+				if chatResp.Object != "chat.completion" {
+					t.Fatalf("expected chat.completion, got %s", chatResp.Object)
+				}
+
+				if chatResp.Choices[0].Message.Content != "Hello!" {
+					t.Fatalf("expected Hello!, got %s", chatResp.Choices[0].Message.Content)
+				}
+			},
+		},
+		{
+			Name:     "completions handler",
+			Method:   http.MethodPost,
+			Path:     "/api/generate",
+			TestPath: "/api/generate",
+			Handler:  CompletionsMiddleware,
+			Endpoint: func(c *gin.Context) {
+				c.JSON(http.StatusOK, api.GenerateResponse{
+					Response: "Hello!",
+				})
+			},
+			Setup: func(t *testing.T, req *http.Request) {
+				body := CompletionRequest{
+					Model:  "test-model",
+					Prompt: "Hello",
+				}
+
+				bodyBytes, _ := json.Marshal(body)
+
+				req.Body = io.NopCloser(bytes.NewReader(bodyBytes))
+				req.Header.Set("Content-Type", "application/json")
+			},
+			Expected: func(t *testing.T, resp *httptest.ResponseRecorder) {
+				assert.Equal(t, http.StatusOK, resp.Code)
+				var completionResp Completion
+				if err := json.NewDecoder(resp.Body).Decode(&completionResp); err != nil {
+					t.Fatal(err)
+				}
+
+				if completionResp.Object != "text_completion" {
+					t.Fatalf("expected text_completion, got %s", completionResp.Object)
+				}
+
+				if completionResp.Choices[0].Text != "Hello!" {
+					t.Fatalf("expected Hello!, got %s", completionResp.Choices[0].Text)
+				}
+			},
+		},
+		{
+			Name:     "completions handler with params",
+			Method:   http.MethodPost,
+			Path:     "/api/generate",
+			TestPath: "/api/generate",
+			Handler:  CompletionsMiddleware,
+			Endpoint: func(c *gin.Context) {
+				var generateReq api.GenerateRequest
+				if err := c.ShouldBindJSON(&generateReq); err != nil {
+					c.JSON(http.StatusBadRequest, gin.H{"error": "invalid request"})
+					return
+				}
+
+				temperature := generateReq.Options["temperature"].(float64)
+				var assistantMessage string
+
+				switch temperature {
+				case 1.6:
+					assistantMessage = "Received temperature of 1.6"
+				default:
+					assistantMessage = fmt.Sprintf("Received temperature of %f", temperature)
+				}
+
+				c.JSON(http.StatusOK, api.GenerateResponse{
+					Response: assistantMessage,
+				})
+			},
+			Setup: func(t *testing.T, req *http.Request) {
+				temp := float32(0.8)
+				body := CompletionRequest{
+					Model:       "test-model",
+					Prompt:      "Hello",
+					Temperature: &temp,
+				}
+
+				bodyBytes, _ := json.Marshal(body)
+
+				req.Body = io.NopCloser(bytes.NewReader(bodyBytes))
+				req.Header.Set("Content-Type", "application/json")
+			},
+			Expected: func(t *testing.T, resp *httptest.ResponseRecorder) {
+				assert.Equal(t, http.StatusOK, resp.Code)
+				var completionResp Completion
+				if err := json.NewDecoder(resp.Body).Decode(&completionResp); err != nil {
+					t.Fatal(err)
+				}
+
+				if completionResp.Object != "text_completion" {
+					t.Fatalf("expected text_completion, got %s", completionResp.Object)
+				}
+
+				if completionResp.Choices[0].Text != "Received temperature of 1.6" {
+					t.Fatalf("expected Received temperature of 1.6, got %s", completionResp.Choices[0].Text)
+				}
+			},
+		},
+		{
+			Name:     "completions handler with error",
+			Method:   http.MethodPost,
+			Path:     "/api/generate",
+			TestPath: "/api/generate",
+			Handler:  CompletionsMiddleware,
+			Endpoint: func(c *gin.Context) {
+				c.JSON(http.StatusBadRequest, gin.H{"error": "invalid request"})
+			},
+			Setup: func(t *testing.T, req *http.Request) {
+				body := CompletionRequest{
+					Model:  "test-model",
+					Prompt: "Hello",
+				}
+
+				bodyBytes, _ := json.Marshal(body)
+
+				req.Body = io.NopCloser(bytes.NewReader(bodyBytes))
+				req.Header.Set("Content-Type", "application/json")
+			},
+			Expected: func(t *testing.T, resp *httptest.ResponseRecorder) {
+				if resp.Code != http.StatusBadRequest {
+					t.Fatalf("expected 400, got %d", resp.Code)
+				}
+
+				if !strings.Contains(resp.Body.String(), `"invalid request"`) {
+					t.Fatalf("error was not forwarded")
+				}
+			},
+		},
 		{
 			Name:     "list handler",
 			Method:   http.MethodGet,
@@ -425,6 +227,8 @@ func TestMiddlewareResponses(t *testing.T) {
 				})
 			},
 			Expected: func(t *testing.T, resp *httptest.ResponseRecorder) {
+				assert.Equal(t, http.StatusOK, resp.Code)
+
 				var listResp ListCompletion
 				if err := json.NewDecoder(resp.Body).Decode(&listResp); err != nil {
 					t.Fatal(err)
@@ -488,8 +292,6 @@ func TestMiddlewareResponses(t *testing.T) {
 			resp := httptest.NewRecorder()
 			router.ServeHTTP(resp, req)
 
-			assert.Equal(t, http.StatusOK, resp.Code)
-
 			tc.Expected(t, resp)
 		})
 	}

parser/parser_test.go

@@ -451,7 +451,6 @@ func TestParseFileParameters(t *testing.T) {
 		"num_predict 1":                {"num_predict", "1"},
 		"top_k 1":                      {"top_k", "1"},
 		"top_p 1.0":                    {"top_p", "1.0"},
-		"min_p 0.05":                   {"min_p", "0.05"},
 		"tfs_z 1.0":                    {"tfs_z", "1.0"},
 		"typical_p 1.0":                {"typical_p", "1.0"},
 		"repeat_last_n 1":              {"repeat_last_n", "1"},

scripts/build_windows.ps1

@@ -107,12 +107,9 @@ function gatherDependencies() {
 
     # TODO - this varies based on host build system and MSVC version - drive from dumpbin output
     # currently works for Win11 + MSVC 2019 + Cuda V11
-    cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\msvcp140*.dll" "${script:DEPS_DIR}\ollama_runners\"
+    cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\msvcp140.dll" "${script:DEPS_DIR}\ollama_runners\"
     cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140.dll" "${script:DEPS_DIR}\ollama_runners\"
     cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140_1.dll" "${script:DEPS_DIR}\ollama_runners\"
-    foreach ($part in $("runtime", "stdio", "filesystem", "math", "convert", "heap", "string", "time", "locale", "environment")) {
-        cp "$env:VCToolsRedistDir\..\..\..\Tools\Llvm\x64\bin\api-ms-win-crt-${part}*.dll" "${script:DEPS_DIR}\ollama_runners\"
-    }
 
 
     cp "${script:SRC_DIR}\app\ollama_welcome.ps1" "${script:SRC_DIR}\dist\"

scripts/install.sh

@@ -198,29 +198,19 @@ if check_gpu lspci amdgpu || check_gpu lshw amdgpu; then
     exit 0
 fi
 
-CUDA_REPO_ERR_MSG="NVIDIA GPU detected, but your OS and Architecture are not supported by NVIDIA.  Please install the CUDA driver manually https://docs.nvidia.com/cuda/cuda-installation-guide-linux/"
 # ref: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#rhel-7-centos-7
 # ref: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#rhel-8-rocky-8
 # ref: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#rhel-9-rocky-9
 # ref: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#fedora
 install_cuda_driver_yum() {
     status 'Installing NVIDIA repository...'
-    
     case $PACKAGE_MANAGER in
         yum)
             $SUDO $PACKAGE_MANAGER -y install yum-utils
-            if curl -I --silent --fail --location "https://developer.download.nvidia.com/compute/cuda/repos/$1$2/$(uname -m)/cuda-$1$2.repo" >/dev/null ; then
-                $SUDO $PACKAGE_MANAGER-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$1$2/$(uname -m)/cuda-$1$2.repo
-            else
-                error $CUDA_REPO_ERR_MSG
-            fi
+            $SUDO $PACKAGE_MANAGER-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$1$2/$(uname -m)/cuda-$1$2.repo
             ;;
         dnf)
-            if curl -I --silent --fail --location "https://developer.download.nvidia.com/compute/cuda/repos/$1$2/$(uname -m)/cuda-$1$2.repo" >/dev/null ; then
-                $SUDO $PACKAGE_MANAGER config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$1$2/$(uname -m)/cuda-$1$2.repo
-            else
-                error $CUDA_REPO_ERR_MSG
-            fi
+            $SUDO $PACKAGE_MANAGER config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/$1$2/$(uname -m)/cuda-$1$2.repo
             ;;
     esac
 
@@ -245,11 +235,7 @@ install_cuda_driver_yum() {
 # ref: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#debian
 install_cuda_driver_apt() {
     status 'Installing NVIDIA repository...'
-    if curl -I --silent --fail --location "https://developer.download.nvidia.com/compute/cuda/repos/$1$2/$(uname -m)/cuda-keyring_1.1-1_all.deb" >/dev/null ; then
-        curl -fsSL -o $TEMP_DIR/cuda-keyring.deb https://developer.download.nvidia.com/compute/cuda/repos/$1$2/$(uname -m)/cuda-keyring_1.1-1_all.deb
-    else
-        error $CUDA_REPO_ERR_MSG
-    fi
+    curl -fsSL -o $TEMP_DIR/cuda-keyring.deb https://developer.download.nvidia.com/compute/cuda/repos/$1$2/$(uname -m)/cuda-keyring_1.1-1_all.deb
 
     case $1 in
         debian)

server/auth.go

@@ -67,7 +67,7 @@ func getAuthorizationToken(ctx context.Context, challenge registryChallenge) (st
 
 	headers.Add("Authorization", signature)
 
-	response, err := makeRequest(ctx, http.MethodGet, redirectURL, headers, nil, &registryOptions{})
+	response, err := makeRequest(ctx, http.MethodGet, redirectURL, headers, nil, nil)
 	if err != nil {
 		return "", err
 	}

server/download.go

@@ -8,7 +8,6 @@ import (
 	"io"
 	"log/slog"
 	"math"
-	"math/rand/v2"
 	"net/http"
 	"net/url"
 	"os"
@@ -44,19 +43,17 @@ type blobDownload struct {
 
 	context.CancelFunc
 
-	done       chan struct{}
+	done       bool
 	err        error
 	references atomic.Int32
 }
 
 type blobDownloadPart struct {
-	N         int
-	Offset    int64
-	Size      int64
-	Completed atomic.Int64
-
-	lastUpdatedMu sync.Mutex
-	lastUpdated   time.Time
+	N           int
+	Offset      int64
+	Size        int64
+	Completed   int64
+	lastUpdated time.Time
 
 	*blobDownload `json:"-"`
 }
@@ -74,7 +71,7 @@ func (p *blobDownloadPart) Name() string {
 }
 
 func (p *blobDownloadPart) StartsAt() int64 {
-	return p.Offset + p.Completed.Load()
+	return p.Offset + p.Completed
 }
 
 func (p *blobDownloadPart) StopsAt() int64 {
@@ -84,9 +81,7 @@ func (p *blobDownloadPart) StopsAt() int64 {
 func (p *blobDownloadPart) Write(b []byte) (n int, err error) {
 	n = len(b)
 	p.blobDownload.Completed.Add(int64(n))
-	p.lastUpdatedMu.Lock()
 	p.lastUpdated = time.Now()
-	p.lastUpdatedMu.Unlock()
 	return n, nil
 }
 
@@ -96,8 +91,6 @@ func (b *blobDownload) Prepare(ctx context.Context, requestURL *url.URL, opts *r
 		return err
 	}
 
-	b.done = make(chan struct{})
-
 	for _, partFilePath := range partFilePaths {
 		part, err := b.readPart(partFilePath)
 		if err != nil {
@@ -105,7 +98,7 @@ func (b *blobDownload) Prepare(ctx context.Context, requestURL *url.URL, opts *r
 		}
 
 		b.Total += part.Size
-		b.Completed.Add(part.Completed.Load())
+		b.Completed.Add(part.Completed)
 		b.Parts = append(b.Parts, part)
 	}
 
@@ -145,36 +138,9 @@ func (b *blobDownload) Prepare(ctx context.Context, requestURL *url.URL, opts *r
 }
 
 func (b *blobDownload) Run(ctx context.Context, requestURL *url.URL, opts *registryOptions) {
-	defer close(b.done)
 	b.err = b.run(ctx, requestURL, opts)
 }
 
-func newBackoff(maxBackoff time.Duration) func(ctx context.Context) error {
-	var n int
-	return func(ctx context.Context) error {
-		if ctx.Err() != nil {
-			return ctx.Err()
-		}
-
-		n++
-
-		// n^2 backoff timer is a little smoother than the
-		// common choice of 2^n.
-		d := min(time.Duration(n*n)*10*time.Millisecond, maxBackoff)
-		// Randomize the delay between 0.5-1.5 x msec, in order
-		// to prevent accidental "thundering herd" problems.
-		d = time.Duration(float64(d) * (rand.Float64() + 0.5))
-		t := time.NewTimer(d)
-		defer t.Stop()
-		select {
-		case <-ctx.Done():
-			return ctx.Err()
-		case <-t.C:
-			return nil
-		}
-	}
-}
-
 func (b *blobDownload) run(ctx context.Context, requestURL *url.URL, opts *registryOptions) error {
 	defer blobDownloadManager.Delete(b.Digest)
 	ctx, b.CancelFunc = context.WithCancel(ctx)
@@ -187,57 +153,11 @@ func (b *blobDownload) run(ctx context.Context, requestURL *url.URL, opts *regis
 
 	_ = file.Truncate(b.Total)
 
-	directURL, err := func() (*url.URL, error) {
-		ctx, cancel := context.WithTimeout(ctx, 30*time.Second)
-		defer cancel()
-
-		backoff := newBackoff(10 * time.Second)
-		for {
-			// shallow clone opts to be used in the closure
-			// without affecting the outer opts.
-			newOpts := new(registryOptions)
-			*newOpts = *opts
-
-			newOpts.CheckRedirect = func(req *http.Request, via []*http.Request) error {
-				if len(via) > 10 {
-					return errors.New("maxium redirects exceeded (10) for directURL")
-				}
-
-				// if the hostname is the same, allow the redirect
-				if req.URL.Hostname() == requestURL.Hostname() {
-					return nil
-				}
-
-				// stop at the first redirect that is not
-				// the same hostname as the original
-				// request.
-				return http.ErrUseLastResponse
-			}
-
-			resp, err := makeRequestWithRetry(ctx, http.MethodGet, requestURL, nil, nil, newOpts)
-			if err != nil {
-				slog.Warn("failed to get direct URL; backing off and retrying", "err", err)
-				if err := backoff(ctx); err != nil {
-					return nil, err
-				}
-				continue
-			}
-			defer resp.Body.Close()
-			if resp.StatusCode != http.StatusTemporaryRedirect {
-				return nil, fmt.Errorf("unexpected status code %d", resp.StatusCode)
-			}
-			return resp.Location()
-		}
-	}()
-	if err != nil {
-		return err
-	}
-
 	g, inner := errgroup.WithContext(ctx)
 	g.SetLimit(numDownloadParts)
 	for i := range b.Parts {
 		part := b.Parts[i]
-		if part.Completed.Load() == part.Size {
+		if part.Completed == part.Size {
 			continue
 		}
 
@@ -245,7 +165,7 @@ func (b *blobDownload) run(ctx context.Context, requestURL *url.URL, opts *regis
 			var err error
 			for try := 0; try < maxRetries; try++ {
 				w := io.NewOffsetWriter(file, part.StartsAt())
-				err = b.downloadChunk(inner, directURL, w, part)
+				err = b.downloadChunk(inner, requestURL, w, part, opts)
 				switch {
 				case errors.Is(err, context.Canceled), errors.Is(err, syscall.ENOSPC):
 					// return immediately if the context is canceled or the device is out of space
@@ -286,31 +206,29 @@ func (b *blobDownload) run(ctx context.Context, requestURL *url.URL, opts *regis
 		return err
 	}
 
+	b.done = true
 	return nil
 }
 
-func (b *blobDownload) downloadChunk(ctx context.Context, requestURL *url.URL, w io.Writer, part *blobDownloadPart) error {
+func (b *blobDownload) downloadChunk(ctx context.Context, requestURL *url.URL, w io.Writer, part *blobDownloadPart, opts *registryOptions) error {
 	g, ctx := errgroup.WithContext(ctx)
 	g.Go(func() error {
-		req, err := http.NewRequestWithContext(ctx, http.MethodGet, requestURL.String(), nil)
-		if err != nil {
-			return err
-		}
-		req.Header.Set("Range", fmt.Sprintf("bytes=%d-%d", part.StartsAt(), part.StopsAt()-1))
-		resp, err := http.DefaultClient.Do(req)
+		headers := make(http.Header)
+		headers.Set("Range", fmt.Sprintf("bytes=%d-%d", part.StartsAt(), part.StopsAt()-1))
+		resp, err := makeRequestWithRetry(ctx, http.MethodGet, requestURL, headers, nil, opts)
 		if err != nil {
 			return err
 		}
 		defer resp.Body.Close()
 
-		n, err := io.CopyN(w, io.TeeReader(resp.Body, part), part.Size-part.Completed.Load())
+		n, err := io.CopyN(w, io.TeeReader(resp.Body, part), part.Size-part.Completed)
 		if err != nil && !errors.Is(err, context.Canceled) && !errors.Is(err, io.ErrUnexpectedEOF) {
 			// rollback progress
 			b.Completed.Add(-n)
 			return err
 		}
 
-		part.Completed.Add(n)
+		part.Completed += n
 		if err := b.writePart(part.Name(), part); err != nil {
 			return err
 		}
@@ -324,21 +242,15 @@ func (b *blobDownload) downloadChunk(ctx context.Context, requestURL *url.URL, w
 		for {
 			select {
 			case <-ticker.C:
-				if part.Completed.Load() >= part.Size {
+				if part.Completed >= part.Size {
 					return nil
 				}
 
-				part.lastUpdatedMu.Lock()
-				lastUpdated := part.lastUpdated
-				part.lastUpdatedMu.Unlock()
-
-				if !lastUpdated.IsZero() && time.Since(lastUpdated) > 5*time.Second {
+				if !part.lastUpdated.IsZero() && time.Since(part.lastUpdated) > 5*time.Second {
 					const msg = "%s part %d stalled; retrying. If this persists, press ctrl-c to exit, then 'ollama pull' to find a faster connection."
 					slog.Info(fmt.Sprintf(msg, b.Digest[7:19], part.N))
 					// reset last updated
-					part.lastUpdatedMu.Lock()
 					part.lastUpdated = time.Time{}
-					part.lastUpdatedMu.Unlock()
 					return errPartStalled
 				}
 			case <-ctx.Done():
@@ -403,8 +315,6 @@ func (b *blobDownload) Wait(ctx context.Context, fn func(api.ProgressResponse))
 	ticker := time.NewTicker(60 * time.Millisecond)
 	for {
 		select {
-		case <-b.done:
-			return b.err
 		case <-ticker.C:
 			fn(api.ProgressResponse{
 				Status:    fmt.Sprintf("pulling %s", b.Digest[7:19]),
@@ -412,6 +322,10 @@ func (b *blobDownload) Wait(ctx context.Context, fn func(api.ProgressResponse))
 				Total:     b.Total,
 				Completed: b.Completed.Load(),
 			})
+
+			if b.done || b.err != nil {
+				return b.err
+			}
 		case <-ctx.Done():
 			return ctx.Err()
 		}

server/images.go

@@ -34,28 +34,15 @@ import (
 	"github.com/ollama/ollama/version"
 )
 
-var (
-	errCapabilities         = errors.New("does not support")
-	errCapabilityCompletion = errors.New("completion")
-	errCapabilityTools      = errors.New("tools")
-	errCapabilityInsert     = errors.New("insert")
-)
-
 type Capability string
 
-const (
-	CapabilityCompletion = Capability("completion")
-	CapabilityTools      = Capability("tools")
-	CapabilityInsert     = Capability("insert")
-)
+const CapabilityCompletion = Capability("completion")
 
 type registryOptions struct {
 	Insecure bool
 	Username string
 	Password string
 	Token    string
-
-	CheckRedirect func(req *http.Request, via []*http.Request) error
 }
 
 type Model struct {
@@ -75,10 +62,7 @@ type Model struct {
 	Template *template.Template
 }
 
-// CheckCapabilities checks if the model has the specified capabilities returning an error describing
-// any missing or unknown capabilities
-func (m *Model) CheckCapabilities(caps ...Capability) error {
-	var errs []error
+func (m *Model) Has(caps ...Capability) bool {
 	for _, cap := range caps {
 		switch cap {
 		case CapabilityCompletion:
@@ -97,28 +81,15 @@ func (m *Model) CheckCapabilities(caps ...Capability) error {
 			}
 
 			if _, ok := ggml.KV()[fmt.Sprintf("%s.pooling_type", ggml.KV().Architecture())]; ok {
-				errs = append(errs, errCapabilityCompletion)
-			}
-		case CapabilityTools:
-			if !slices.Contains(m.Template.Vars(), "tools") {
-				errs = append(errs, errCapabilityTools)
-			}
-		case CapabilityInsert:
-			vars := m.Template.Vars()
-			if !slices.Contains(vars, "suffix") {
-				errs = append(errs, errCapabilityInsert)
+				return false
 			}
 		default:
 			slog.Error("unknown capability", "capability", cap)
-			return fmt.Errorf("unknown capability: %s", cap)
+			return false
 		}
 	}
 
-	if err := errors.Join(errs...); err != nil {
-		return fmt.Errorf("%w %w", errCapabilities, errors.Join(errs...))
-	}
-
-	return nil
+	return true
 }
 
 func (m *Model) String() string {
@@ -494,12 +465,6 @@ func CreateModel(ctx context.Context, name model.Name, modelFileDir, quantizatio
 				layers = append(layers, baseLayer.Layer)
 			}
 		case "license", "template", "system":
-			if c.Name == "template" {
-				if _, err := template.Parse(c.Args); err != nil {
-					return fmt.Errorf("%w: %s", errBadTemplate, err)
-				}
-			}
-
 			if c.Name != "license" {
 				// replace
 				layers = slices.DeleteFunc(layers, func(layer *Layer) bool {
@@ -1133,9 +1098,7 @@ func makeRequest(ctx context.Context, method string, requestURL *url.URL, header
 		req.ContentLength = contentLength
 	}
 
-	resp, err := (&http.Client{
-		CheckRedirect: regOpts.CheckRedirect,
-	}).Do(req)
+	resp, err := http.DefaultClient.Do(req)
 	if err != nil {
 		return nil, err
 	}

server/model.go

@@ -4,7 +4,6 @@ import (
 	"archive/zip"
 	"bytes"
 	"context"
-	"encoding/json"
 	"errors"
 	"fmt"
 	"io"
@@ -12,9 +11,6 @@ import (
 	"net/http"
 	"os"
 	"path/filepath"
-	"slices"
-	"strings"
-	"text/template/parse"
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/convert"
@@ -133,38 +129,24 @@ func extractFromZipFile(p string, file *os.File, fn func(api.ProgressResponse))
 }
 
 func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(api.ProgressResponse)) (layers []*layerGGML, err error) {
+	layerType := "application/vnd.ollama.image.model"
+	convertAdapter, err := convert.DetectNPZ(file.Name())
+	if err != nil {
+		return nil, err
+	}
+
 	tempDir, err := os.MkdirTemp(filepath.Dir(file.Name()), "")
 	if err != nil {
 		return nil, err
 	}
 	defer os.RemoveAll(tempDir)
 
-	if err := extractFromZipFile(tempDir, file, fn); err != nil {
-		return nil, err
-	}
-
-	mf, err := convert.GetModelFormat(tempDir)
-	if err != nil {
-		return nil, err
-	}
-
-	params, err := mf.GetParams(tempDir)
-	if err != nil {
-		return nil, err
-	}
-
-	mArch, err := mf.GetModelArch("", tempDir, params)
-	if err != nil {
-		return nil, err
-	}
-
-	fn(api.ProgressResponse{Status: "processing tensors"})
-	if err := mArch.GetTensors(); err != nil {
-		return nil, err
-	}
-
-	if err := mArch.LoadVocab(); err != nil {
-		return nil, err
+	if !convertAdapter {
+		if err := extractFromZipFile(tempDir, file, fn); err != nil {
+			return nil, err
+		}
+	} else {
+		layerType = "application/vnd.ollama.image.adapter"
 	}
 
 	fn(api.ProgressResponse{Status: "converting model"})
@@ -178,15 +160,22 @@ func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(a
 	defer temp.Close()
 	defer os.Remove(temp.Name())
 
-	if err = mArch.WriteGGUF(temp); err != nil {
-		return nil, err
+	if convertAdapter {
+		slog.Info("convert adapter")
+		if err := convert.ConvertAdapter(file.Name(), temp); err != nil {
+			return nil, err
+		}
+	} else {
+		if err := convert.Convert(tempDir, temp); err != nil {
+			return nil, err
+		}
 	}
 
 	if _, err := temp.Seek(0, io.SeekStart); err != nil {
 		return nil, err
 	}
 
-	layer, err := NewLayer(temp, "application/vnd.ollama.image.model")
+	layer, err := NewLayer(temp, layerType)
 	if err != nil {
 		return nil, err
 	}
@@ -205,7 +194,11 @@ func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(a
 	layers = append(layers, &layerGGML{layer, ggml})
 
 	intermediateBlobs[digest] = layer.Digest
-	return detectChatTemplate(layers)
+	if !convertAdapter {
+		return detectChatTemplate(layers)
+	}
+
+	return layers, nil
 }
 
 func parseFromFile(ctx context.Context, file *os.File, digest string, fn func(api.ProgressResponse)) (layers []*layerGGML, err error) {
@@ -263,27 +256,13 @@ func detectChatTemplate(layers []*layerGGML) ([]*layerGGML, error) {
 			if t, err := template.Named(s); err != nil {
 				slog.Debug("template detection", "error", err)
 			} else {
-				layer, err := NewLayer(t.Reader(), "application/vnd.ollama.image.template")
+				tmpl, err := NewLayer(t.Reader(), "application/vnd.ollama.image.template")
 				if err != nil {
 					return nil, err
 				}
 
-				layer.status = fmt.Sprintf("using autodetected template %s", t.Name)
-				layers = append(layers, &layerGGML{layer, nil})
-
-				if t.Parameters != nil {
-					var b bytes.Buffer
-					if err := json.NewEncoder(&b).Encode(t.Parameters); err != nil {
-						return nil, err
-					}
-
-					layer, err := NewLayer(&b, "application/vnd.ollama.image.params")
-					if err != nil {
-						return nil, err
-					}
-
-					layers = append(layers, &layerGGML{layer, nil})
-				}
+				tmpl.status = fmt.Sprintf("using autodetected template %s", t.Name)
+				layers = append(layers, &layerGGML{tmpl, nil})
 			}
 		}
 	}
@@ -307,113 +286,3 @@ func detectContentType(r io.Reader) (string, error) {
 
 	return "unknown", nil
 }
-
-// parseToolCalls attempts to parse a JSON string into a slice of ToolCalls.
-// mxyng: this only really works if the input contains tool calls in some JSON format
-func (m *Model) parseToolCalls(s string) ([]api.ToolCall, bool) {
-	// create a subtree from the node that ranges over .ToolCalls
-	tmpl := m.Template.Subtree(func(n parse.Node) bool {
-		if t, ok := n.(*parse.RangeNode); ok {
-			return slices.Contains(template.Identifiers(t.Pipe), "ToolCalls")
-		}
-
-		return false
-	})
-
-	if tmpl == nil {
-		return nil, false
-	}
-
-	var b bytes.Buffer
-	if err := tmpl.Execute(&b, map[string][]api.ToolCall{
-		"ToolCalls": {
-			{
-				Function: api.ToolCallFunction{
-					Name: "@@name@@",
-					Arguments: api.ToolCallFunctionArguments{
-						"@@argument@@": 1,
-					},
-				},
-			},
-		},
-	}); err != nil {
-		return nil, false
-	}
-
-	var kv map[string]any
-	// execute the subtree with placeholders to identify the keys
-	// trim any commands that might exist in the template
-	if err := json.Unmarshal(bytes.TrimSuffix(b.Bytes(), []byte(",")), &kv); err != nil {
-		return nil, false
-	}
-
-	// find the keys that correspond to the name and arguments fields
-	var name, arguments string
-	for k, v := range kv {
-		switch v.(type) {
-		case string:
-			name = k
-		case map[string]any:
-			arguments = k
-		}
-	}
-
-	if name == "" || arguments == "" {
-		return nil, false
-	}
-
-	var objs []map[string]any
-	for offset := 0; offset < len(s); {
-		var obj map[string]any
-		decoder := json.NewDecoder(strings.NewReader(s[offset:]))
-		if err := decoder.Decode(&obj); errors.Is(err, io.EOF) || errors.Is(err, io.ErrUnexpectedEOF) {
-			break
-		} else if syntax := &(json.SyntaxError{}); errors.As(err, &syntax) {
-			// skip over any syntax errors
-			offset += int(syntax.Offset)
-		} else if unmarshalType := &(json.UnmarshalTypeError{}); errors.As(err, &unmarshalType) {
-			// skip over any unmarshalable types
-			offset += int(unmarshalType.Offset)
-		} else if err != nil {
-			slog.Error("parseToolCalls", "error", err)
-			return nil, false
-		} else {
-			offset += int(decoder.InputOffset())
-
-			// collect all nested objects
-			var collect func(any) []map[string]any
-			collect = func(obj any) (all []map[string]any) {
-				switch o := obj.(type) {
-				case map[string]any:
-					all = append(all, o)
-					for _, v := range o {
-						all = append(all, collect(v)...)
-					}
-				case []any:
-					for _, v := range o {
-						all = append(all, collect(v)...)
-					}
-				}
-
-				return all
-			}
-			objs = append(objs, collect(obj)...)
-		}
-	}
-
-	var toolCalls []api.ToolCall
-	for _, kv := range objs {
-		n, nok := kv[name].(string)
-		a, aok := kv[arguments].(map[string]any)
-		if nok && aok {
-			toolCalls = append(toolCalls, api.ToolCall{
-				Function: api.ToolCallFunction{
-					Name:      n,
-					Arguments: a,
-				},
-			})
-		}
-	}
-
-	return toolCalls, len(toolCalls) > 0
-}

server/model_test.go

@@ -3,9 +3,7 @@ package server
 import (
 	"archive/zip"
 	"bytes"
-	"encoding/json"
 	"errors"
-	"fmt"
 	"io"
 	"os"
 	"path/filepath"
@@ -13,9 +11,7 @@ import (
 	"strings"
 	"testing"
 
-	"github.com/google/go-cmp/cmp"
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/template"
 )
 
 func createZipFile(t *testing.T, name string) *os.File {
@@ -114,123 +110,3 @@ func TestExtractFromZipFile(t *testing.T) {
 		})
 	}
 }
-
-func readFile(t *testing.T, base, name string) *bytes.Buffer {
-	t.Helper()
-
-	bts, err := os.ReadFile(filepath.Join(base, name))
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	return bytes.NewBuffer(bts)
-}
-
-func TestExecuteWithTools(t *testing.T) {
-	p := filepath.Join("testdata", "tools")
-	cases := []struct {
-		model  string
-		output string
-		ok     bool
-	}{
-		{"mistral", `[TOOL_CALLS]  [{"name": "get_current_weather", "arguments": {"format":"fahrenheit","location":"San Francisco, CA"}},{"name": "get_current_weather", "arguments": {"format":"celsius","location":"Toronto, Canada"}}]`, true},
-		{"mistral", `[TOOL_CALLS]  [{"name": "get_current_weather", "arguments": {"format":"fahrenheit","location":"San Francisco, CA"}},{"name": "get_current_weather", "arguments": {"format":"celsius","location":"Toronto, Canada"}}]
-
-The temperature in San Francisco, CA is 70°F and in Toronto, Canada is 20°C.`, true},
-		{"mistral", `I'm not aware of that information. However, I can suggest searching for the weather using the "get_current_weather" function:
-
-		[{"name": "get_current_weather", "arguments": {"format":"fahrenheit","location":"San Francisco, CA"}},{"name": "get_current_weather", "arguments": {"format":"celsius","location":"Toronto, Canada"}}]`, true},
-		{"mistral", " The weather in San Francisco, CA is 70°F and in Toronto, Canada is 20°C.", false},
-		{"command-r-plus", "Action: ```json" + `
-[
-    {
-        "tool_name": "get_current_weather",
-        "parameters": {
-            "format": "fahrenheit",
-            "location": "San Francisco, CA"
-        }
-    },
-    {
-        "tool_name": "get_current_weather",
-        "parameters": {
-            "format": "celsius",
-            "location": "Toronto, Canada"
-        }
-    }
-]
-` + "```", true},
-		{"command-r-plus", " The weather in San Francisco, CA is 70°F and in Toronto, Canada is 20°C.", false},
-		{"firefunction", ` functools[{"name": "get_current_weather", "arguments": {"format":"fahrenheit","location":"San Francisco, CA"}},{"name": "get_current_weather", "arguments": {"format":"celsius","location":"Toronto, Canada"}}]`, true},
-		{"firefunction", " The weather in San Francisco, CA is 70°F and in Toronto, Canada is 20°C.", false},
-		{"llama3-groq-tool-use", `<tool_call>
-{"name": "get_current_weather", "arguments": {"format":"fahrenheit","location":"San Francisco, CA"}}
-{"name": "get_current_weather", "arguments": {"format":"celsius","location":"Toronto, Canada"}}
-</tool_call>`, true},
-		{"xlam", `{"tool_calls": [{"name": "get_current_weather", "arguments": {"format":"fahrenheit","location":"San Francisco, CA"}},{"name": "get_current_weather", "arguments": {"format":"celsius","location":"Toronto, Canada"}}]}`, true},
-	}
-
-	var tools []api.Tool
-	if err := json.Unmarshal(readFile(t, p, "tools.json").Bytes(), &tools); err != nil {
-		t.Fatal(err)
-	}
-
-	var messages []api.Message
-	if err := json.Unmarshal(readFile(t, p, "messages.json").Bytes(), &messages); err != nil {
-		t.Fatal(err)
-	}
-
-	calls := []api.ToolCall{
-		{
-			Function: api.ToolCallFunction{
-				Name: "get_current_weather",
-				Arguments: api.ToolCallFunctionArguments{
-					"format":   "fahrenheit",
-					"location": "San Francisco, CA",
-				},
-			},
-		},
-		{
-			Function: api.ToolCallFunction{
-				Name: "get_current_weather",
-				Arguments: api.ToolCallFunctionArguments{
-					"format":   "celsius",
-					"location": "Toronto, Canada",
-				},
-			},
-		},
-	}
-
-	for _, tt := range cases {
-		t.Run(tt.model, func(t *testing.T) {
-			tmpl, err := template.Parse(readFile(t, p, fmt.Sprintf("%s.gotmpl", tt.model)).String())
-			if err != nil {
-				t.Fatal(err)
-			}
-
-			t.Run("template", func(t *testing.T) {
-				var actual bytes.Buffer
-				if err := tmpl.Execute(&actual, template.Values{Tools: tools, Messages: messages}); err != nil {
-					t.Fatal(err)
-				}
-
-				if diff := cmp.Diff(actual.String(), readFile(t, p, fmt.Sprintf("%s.out", tt.model)).String()); diff != "" {
-					t.Errorf("mismatch (-got +want):\n%s", diff)
-				}
-			})
-
-			t.Run("parse", func(t *testing.T) {
-				m := &Model{Template: tmpl}
-				actual, ok := m.parseToolCalls(tt.output)
-				if ok != tt.ok {
-					t.Fatalf("expected %t, got %t", tt.ok, ok)
-				}
-
-				if tt.ok {
-					if diff := cmp.Diff(actual, calls); diff != "" {
-						t.Errorf("mismatch (-got +want):\n%s", diff)
-					}
-				}
-			})
-		})
-	}
-}

server/prompt.go

@@ -1,74 +1,217 @@
 package server
 
 import (
-	"bytes"
-	"context"
+	"fmt"
 	"log/slog"
+	"strings"
+
+	"text/template/parse"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/llm"
 	"github.com/ollama/ollama/template"
 )
 
-type tokenizeFunc func(context.Context, string) ([]int, error)
-
-// chatPrompt accepts a list of messages and returns the prompt and images that should be used for the next chat turn.
-// chatPrompt truncates any messages that exceed the context window of the model, making sure to always include 1) the
-// latest message and 2) system messages
-func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.Options, msgs []api.Message, tools []api.Tool) (prompt string, images []llm.ImageData, _ error) {
-	var system []api.Message
-	// always include the last message
-	n := len(msgs) - 1
-	// in reverse, find all messages that fit into context window
-	for i := n - 1; i >= 0; i-- {
-		system = make([]api.Message, 0)
-		for j := range i {
-			if msgs[j].Role == "system" {
-				system = append(system, msgs[j])
+// isResponseNode checks if the node contains .Response
+func isResponseNode(node *parse.ActionNode) bool {
+	for _, cmd := range node.Pipe.Cmds {
+		for _, arg := range cmd.Args {
+			if fieldNode, ok := arg.(*parse.FieldNode); ok && len(fieldNode.Ident) > 0 {
+				if fieldNode.Ident[0] == "Response" {
+					return true
+				}
 			}
 		}
-
-		var b bytes.Buffer
-		if err := m.Template.Execute(&b, template.Values{Messages: append(system, msgs[i:]...), Tools: tools}); err != nil {
-			return "", nil, err
-		}
-
-		s, err := tokenize(ctx, b.String())
-		if err != nil {
-			return "", nil, err
-		}
-
-		c := len(s)
-		if m.ProjectorPaths != nil {
-			for _, m := range msgs[i:] {
-				// images are represented as 768 sized embeddings
-				// TODO: get embedding length from project metadata
-				c += 768 * len(m.Images)
-			}
-		}
-
-		if c > opts.NumCtx {
-			slog.Debug("truncating input messages which exceed context length", "truncated", len(msgs[i:]))
-			break
-		} else {
-			n = i
-		}
 	}
-
-	// truncate any messages that do not fit into the context window
-	var b bytes.Buffer
-	if err := m.Template.Execute(&b, template.Values{Messages: append(system, msgs[n:]...), Tools: tools}); err != nil {
-		return "", nil, err
-	}
-
-	for _, m := range msgs[n:] {
-		for _, i := range m.Images {
-			images = append(images, llm.ImageData{
-				ID:   len(images),
-				Data: i,
-			})
-		}
-	}
-
-	return b.String(), images, nil
+	return false
+}
+
+// formatTemplateForResponse formats the template AST to:
+// 1. remove all nodes after the first .Response (if generate=true)
+// 2. add a .Response node to the end if it doesn't exist
+// TODO(jmorganca): this should recursively cut the template before the first .Response
+func formatTemplateForResponse(tmpl *template.Template, generate bool) {
+	var found bool
+	for i, node := range tmpl.Tree.Root.Nodes {
+		if actionNode, ok := node.(*parse.ActionNode); ok {
+			if isResponseNode(actionNode) {
+				found = true
+				if generate {
+					tmpl.Tree.Root.Nodes = tmpl.Tree.Root.Nodes[:i+1]
+					break
+				}
+			}
+		}
+	}
+
+	if !found {
+		// add the response node if it doesn't exist
+		responseFieldNode := &parse.FieldNode{NodeType: parse.NodeField, Ident: []string{"Response"}}
+		responsePipeNode := &parse.PipeNode{NodeType: parse.NodePipe, Cmds: []*parse.CommandNode{{NodeType: parse.NodeCommand, Args: []parse.Node{responseFieldNode}}}}
+		responseActionNode := &parse.ActionNode{NodeType: parse.NodeAction, Pipe: responsePipeNode}
+		tmpl.Tree.Root.Nodes = append(tmpl.Tree.Root.Nodes, responseActionNode)
+	}
+}
+
+// Prompt renders a prompt from a template. If generate is set to true,
+// the response and parts of the template following it are not rendered
+func Prompt(tmpl *template.Template, system, prompt, response string, generate bool) (string, error) {
+	formatTemplateForResponse(tmpl, generate)
+
+	vars := map[string]any{
+		"System":   system,
+		"Prompt":   prompt,
+		"Response": response,
+	}
+
+	var sb strings.Builder
+	if err := tmpl.Execute(&sb, vars); err != nil {
+		return "", err
+	}
+
+	return sb.String(), nil
+}
+
+func countTokens(tmpl *template.Template, system string, prompt string, response string, encode func(string) ([]int, error)) (int, error) {
+	rendered, err := Prompt(tmpl, system, prompt, response, false)
+	if err != nil {
+		return 0, err
+	}
+
+	tokens, err := encode(rendered)
+	if err != nil {
+		slog.Error("failed to encode prompt", "err", err)
+		return 0, err
+	}
+
+	return len(tokens), err
+}
+
+// ChatPrompt builds up a prompt from a series of messages, truncating based on context window size
+func ChatPrompt(tmpl *template.Template, messages []api.Message, window int, encode func(string) ([]int, error)) (string, error) {
+	type prompt struct {
+		System   string
+		Prompt   string
+		Response string
+
+		images []int
+		tokens int
+	}
+
+	var p prompt
+
+	// iterate through messages to build up {system,user,response} prompts
+	var imgId int
+	var prompts []prompt
+	for _, msg := range messages {
+		switch strings.ToLower(msg.Role) {
+		case "system":
+			if p.System != "" || p.Prompt != "" || p.Response != "" {
+				prompts = append(prompts, p)
+				p = prompt{}
+			}
+
+			p.System = msg.Content
+		case "user":
+			if p.Prompt != "" || p.Response != "" {
+				prompts = append(prompts, p)
+				p = prompt{}
+			}
+
+			var sb strings.Builder
+			for range msg.Images {
+				fmt.Fprintf(&sb, "[img-%d] ", imgId)
+				p.images = append(p.images, imgId)
+				imgId += 1
+			}
+
+			sb.WriteString(msg.Content)
+			p.Prompt = sb.String()
+		case "assistant":
+			if p.Response != "" {
+				prompts = append(prompts, p)
+				p = prompt{}
+			}
+
+			p.Response = msg.Content
+		default:
+			return "", fmt.Errorf("invalid role: %s, role must be one of [system, user, assistant]", msg.Role)
+		}
+	}
+
+	// add final prompt
+	if p.System != "" || p.Prompt != "" || p.Response != "" {
+		prompts = append(prompts, p)
+	}
+
+	// calculate token lengths for each prompt, estimating 768 tokens per images
+	for i, p := range prompts {
+		tokens, err := countTokens(tmpl, p.System, p.Prompt, p.Response, encode)
+		if err != nil {
+			return "", err
+		}
+
+		prompts[i].tokens = tokens + len(prompts[i].images)*768
+	}
+
+	// truncate images and prompts starting from the beginning of the list
+	// until either one prompt remains or the total tokens fits the context window
+	// TODO (jmorganca): this doesn't account for the context window room required for the response
+	for {
+		var required int
+		for _, p := range prompts {
+			required += p.tokens
+		}
+
+		required += 1 // for bos token
+
+		if required <= window {
+			slog.Debug("prompt now fits in context window", "required", required, "window", window)
+			break
+		}
+
+		prompt := &prompts[0]
+
+		if len(prompt.images) > 1 {
+			img := prompt.images[0]
+			slog.Debug("prompt longer than context window, removing image", "id", img, "required", required, "window", window)
+			prompt.images = prompt.images[1:]
+			prompt.Prompt = strings.Replace(prompt.Prompt, fmt.Sprintf(" [img-%d]", img), "", 1)
+			prompt.tokens -= 768
+			continue
+		}
+
+		if len(prompts) > 1 {
+			slog.Debug("required tokens longer than context window, removing first prompt", "prompt", prompts[0].tokens, "required", required, "window", window)
+			system := prompt.System
+			prompts = prompts[1:]
+
+			if system != "" && prompts[0].System == "" {
+				prompts[0].System = system
+
+				tokens, err := countTokens(tmpl, prompts[0].System, prompts[0].Prompt, prompts[0].Response, encode)
+				if err != nil {
+					return "", err
+				}
+
+				prompts[0].tokens = tokens + len(prompts[0].images)*768
+			}
+
+			continue
+		}
+
+		// stop truncating if there's only one prompt left
+		break
+	}
+
+	var sb strings.Builder
+	for i, p := range prompts {
+		// last prompt should leave the response unrendered (for completion)
+		rendered, err := Prompt(tmpl, p.System, p.Prompt, p.Response, i == len(prompts)-1)
+		if err != nil {
+			return "", err
+		}
+		sb.WriteString(rendered)
+	}
+
+	return sb.String(), nil
 }

server/prompt_test.go

@@ -1,209 +1,215 @@
 package server
 
 import (
-	"bytes"
-	"context"
+	"strings"
 	"testing"
 
-	"github.com/google/go-cmp/cmp"
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/template"
 )
 
-func TestChatPrompt(t *testing.T) {
-	type expect struct {
-		prompt string
-		images [][]byte
-	}
-
-	cases := []struct {
-		name  string
-		limit int
-		msgs  []api.Message
-		expect
+func TestPrompt(t *testing.T) {
+	tests := []struct {
+		name     string
+		template string
+		system   string
+		prompt   string
+		response string
+		generate bool
+		want     string
 	}{
 		{
-			name:  "messages",
-			limit: 64,
-			msgs: []api.Message{
-				{Role: "user", Content: "You're a test, Harry!"},
-				{Role: "assistant", Content: "I-I'm a what?"},
-				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager."},
-			},
-			expect: expect{
-				prompt: "You're a test, Harry! I-I'm a what? A test. And a thumping good one at that, I'd wager. ",
-			},
+			name:     "simple prompt",
+			template: "[INST] {{ .System }} {{ .Prompt }} [/INST]",
+			system:   "You are a Wizard.",
+			prompt:   "What are the potion ingredients?",
+			want:     "[INST] You are a Wizard. What are the potion ingredients? [/INST]",
 		},
 		{
-			name:  "truncate messages",
-			limit: 1,
-			msgs: []api.Message{
-				{Role: "user", Content: "You're a test, Harry!"},
-				{Role: "assistant", Content: "I-I'm a what?"},
-				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager."},
-			},
-			expect: expect{
-				prompt: "A test. And a thumping good one at that, I'd wager. ",
-			},
+			name:     "implicit response",
+			template: "[INST] {{ .System }} {{ .Prompt }} [/INST]",
+			system:   "You are a Wizard.",
+			prompt:   "What are the potion ingredients?",
+			response: "I don't know.",
+			want:     "[INST] You are a Wizard. What are the potion ingredients? [/INST]I don't know.",
 		},
 		{
-			name:  "truncate messages with image",
-			limit: 64,
-			msgs: []api.Message{
-				{Role: "user", Content: "You're a test, Harry!"},
-				{Role: "assistant", Content: "I-I'm a what?"},
-				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager.", Images: []api.ImageData{[]byte("something")}},
-			},
-			expect: expect{
-				prompt: "[img-0] A test. And a thumping good one at that, I'd wager. ",
-				images: [][]byte{
-					[]byte("something"),
-				},
-			},
+			name:     "response",
+			template: "[INST] {{ .System }} {{ .Prompt }} [/INST] {{ .Response }}",
+			system:   "You are a Wizard.",
+			prompt:   "What are the potion ingredients?",
+			response: "I don't know.",
+			want:     "[INST] You are a Wizard. What are the potion ingredients? [/INST] I don't know.",
 		},
 		{
-			name:  "truncate messages with images",
-			limit: 64,
-			msgs: []api.Message{
-				{Role: "user", Content: "You're a test, Harry!", Images: []api.ImageData{[]byte("something")}},
-				{Role: "assistant", Content: "I-I'm a what?"},
-				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager.", Images: []api.ImageData{[]byte("somethingelse")}},
-			},
-			expect: expect{
-				prompt: "[img-0] A test. And a thumping good one at that, I'd wager. ",
-				images: [][]byte{
-					[]byte("somethingelse"),
-				},
-			},
+			name:     "cut",
+			template: "<system>{{ .System }}</system><user>{{ .Prompt }}</user><assistant>{{ .Response }}</assistant>",
+			system:   "You are a Wizard.",
+			prompt:   "What are the potion ingredients?",
+			response: "I don't know.",
+			generate: true,
+			want:     "<system>You are a Wizard.</system><user>What are the potion ingredients?</user><assistant>I don't know.",
 		},
 		{
-			name:  "messages with images",
-			limit: 2048,
-			msgs: []api.Message{
-				{Role: "user", Content: "You're a test, Harry!", Images: []api.ImageData{[]byte("something")}},
-				{Role: "assistant", Content: "I-I'm a what?"},
-				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager.", Images: []api.ImageData{[]byte("somethingelse")}},
-			},
-			expect: expect{
-				prompt: "[img-0] You're a test, Harry! I-I'm a what? [img-1] A test. And a thumping good one at that, I'd wager. ",
-				images: [][]byte{
-					[]byte("something"),
-					[]byte("somethingelse"),
-				},
-			},
-		},
-		{
-			name:  "message with image tag",
-			limit: 2048,
-			msgs: []api.Message{
-				{Role: "user", Content: "You're a test, Harry! [img]", Images: []api.ImageData{[]byte("something")}},
-				{Role: "assistant", Content: "I-I'm a what?"},
-				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager.", Images: []api.ImageData{[]byte("somethingelse")}},
-			},
-			expect: expect{
-				prompt: "You're a test, Harry! [img-0] I-I'm a what? [img-1] A test. And a thumping good one at that, I'd wager. ",
-				images: [][]byte{
-					[]byte("something"),
-					[]byte("somethingelse"),
-				},
-			},
-		},
-		{
-			name:  "messages with interleaved images",
-			limit: 2048,
-			msgs: []api.Message{
-				{Role: "user", Content: "You're a test, Harry!"},
-				{Role: "user", Images: []api.ImageData{[]byte("something")}},
-				{Role: "user", Images: []api.ImageData{[]byte("somethingelse")}},
-				{Role: "assistant", Content: "I-I'm a what?"},
-				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager."},
-			},
-			expect: expect{
-				prompt: "You're a test, Harry!\n\n[img-0]\n\n[img-1] I-I'm a what? A test. And a thumping good one at that, I'd wager. ",
-				images: [][]byte{
-					[]byte("something"),
-					[]byte("somethingelse"),
-				},
-			},
-		},
-		{
-			name:  "truncate message with interleaved images",
-			limit: 1024,
-			msgs: []api.Message{
-				{Role: "user", Content: "You're a test, Harry!"},
-				{Role: "user", Images: []api.ImageData{[]byte("something")}},
-				{Role: "user", Images: []api.ImageData{[]byte("somethingelse")}},
-				{Role: "assistant", Content: "I-I'm a what?"},
-				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager."},
-			},
-			expect: expect{
-				prompt: "[img-0] I-I'm a what? A test. And a thumping good one at that, I'd wager. ",
-				images: [][]byte{
-					[]byte("somethingelse"),
-				},
-			},
-		},
-		{
-			name:  "message with system prompt",
-			limit: 2048,
-			msgs: []api.Message{
-				{Role: "system", Content: "You are the Test Who Lived."},
-				{Role: "user", Content: "You're a test, Harry!"},
-				{Role: "assistant", Content: "I-I'm a what?"},
-				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager."},
-			},
-			expect: expect{
-				prompt: "You are the Test Who Lived. You're a test, Harry! I-I'm a what? A test. And a thumping good one at that, I'd wager. ",
-			},
-		},
-		{
-			name:  "out of order system",
-			limit: 2048,
-			msgs: []api.Message{
-				{Role: "user", Content: "You're a test, Harry!"},
-				{Role: "assistant", Content: "I-I'm a what?"},
-				{Role: "system", Content: "You are the Test Who Lived."},
-				{Role: "user", Content: "A test. And a thumping good one at that, I'd wager."},
-			},
-			expect: expect{
-				prompt: "You're a test, Harry! I-I'm a what? You are the Test Who Lived. A test. And a thumping good one at that, I'd wager. ",
-			},
+			name:     "nocut",
+			template: "<system>{{ .System }}</system><user>{{ .Prompt }}</user><assistant>{{ .Response }}</assistant>",
+			system:   "You are a Wizard.",
+			prompt:   "What are the potion ingredients?",
+			response: "I don't know.",
+			want:     "<system>You are a Wizard.</system><user>What are the potion ingredients?</user><assistant>I don't know.</assistant>",
 		},
 	}
 
-	tmpl, err := template.Parse(`
-{{- if .System }}{{ .System }} {{ end }}
-{{- if .Prompt }}{{ .Prompt }} {{ end }}
-{{- if .Response }}{{ .Response }} {{ end }}`)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	for _, tt := range cases {
-		t.Run(tt.name, func(t *testing.T) {
-			model := Model{Template: tmpl, ProjectorPaths: []string{"vision"}}
-			opts := api.Options{Runner: api.Runner{NumCtx: tt.limit}}
-			prompt, images, err := chatPrompt(context.TODO(), &model, mockRunner{}.Tokenize, &opts, tt.msgs, nil)
+	for _, tc := range tests {
+		t.Run(tc.name, func(t *testing.T) {
+			tmpl, err := template.Parse(tc.template)
 			if err != nil {
 				t.Fatal(err)
 			}
 
-			if diff := cmp.Diff(prompt, tt.prompt); diff != "" {
-				t.Errorf("mismatch (-got +want):\n%s", diff)
+			got, err := Prompt(tmpl, tc.system, tc.prompt, tc.response, tc.generate)
+			if err != nil {
+				t.Errorf("error = %v", err)
 			}
 
-			if len(images) != len(tt.images) {
-				t.Fatalf("expected %d images, got %d", len(tt.images), len(images))
-			}
-
-			for i := range images {
-				if images[i].ID != i {
-					t.Errorf("expected ID %d, got %d", i, images[i].ID)
-				}
-
-				if !bytes.Equal(images[i].Data, tt.images[i]) {
-					t.Errorf("expected %q, got %q", tt.images[i], images[i])
-				}
+			if got != tc.want {
+				t.Errorf("got = %v, want %v", got, tc.want)
+			}
+		})
+	}
+}
+
+func TestChatPrompt(t *testing.T) {
+	tests := []struct {
+		name     string
+		template string
+		messages []api.Message
+		window   int
+		want     string
+	}{
+		{
+			name:     "simple prompt",
+			template: "[INST] {{ .Prompt }} [/INST]",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello"},
+			},
+			window: 1024,
+			want:   "[INST] Hello [/INST]",
+		},
+		{
+			name:     "with system message",
+			template: "[INST] {{ if .System }}<<SYS>>{{ .System }}<</SYS>> {{ end }}{{ .Prompt }} [/INST]",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a Wizard."},
+				{Role: "user", Content: "Hello"},
+			},
+			window: 1024,
+			want:   "[INST] <<SYS>>You are a Wizard.<</SYS>> Hello [/INST]",
+		},
+		{
+			name:     "with response",
+			template: "[INST] {{ if .System }}<<SYS>>{{ .System }}<</SYS>> {{ end }}{{ .Prompt }} [/INST] {{ .Response }}",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a Wizard."},
+				{Role: "user", Content: "Hello"},
+				{Role: "assistant", Content: "I am?"},
+			},
+			window: 1024,
+			want:   "[INST] <<SYS>>You are a Wizard.<</SYS>> Hello [/INST] I am?",
+		},
+		{
+			name:     "with implicit response",
+			template: "[INST] {{ if .System }}<<SYS>>{{ .System }}<</SYS>> {{ end }}{{ .Prompt }} [/INST]",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a Wizard."},
+				{Role: "user", Content: "Hello"},
+				{Role: "assistant", Content: "I am?"},
+			},
+			window: 1024,
+			want:   "[INST] <<SYS>>You are a Wizard.<</SYS>> Hello [/INST]I am?",
+		},
+		{
+			name:     "with conversation",
+			template: "[INST] {{ if .System }}<<SYS>>{{ .System }}<</SYS>> {{ end }}{{ .Prompt }} [/INST] {{ .Response }} ",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a Wizard."},
+				{Role: "user", Content: "What are the potion ingredients?"},
+				{Role: "assistant", Content: "sugar"},
+				{Role: "user", Content: "Anything else?"},
+			},
+			window: 1024,
+			want:   "[INST] <<SYS>>You are a Wizard.<</SYS>> What are the potion ingredients? [/INST] sugar [INST] Anything else? [/INST] ",
+		},
+		{
+			name:     "with truncation",
+			template: "{{ .System }} {{ .Prompt }} {{ .Response }} ",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a Wizard."},
+				{Role: "user", Content: "Hello"},
+				{Role: "assistant", Content: "I am?"},
+				{Role: "user", Content: "Why is the sky blue?"},
+				{Role: "assistant", Content: "The sky is blue from rayleigh scattering"},
+			},
+			window: 10,
+			want:   "You are a Wizard. Why is the sky blue? The sky is blue from rayleigh scattering",
+		},
+		{
+			name:     "images",
+			template: "{{ .System }} {{ .Prompt }}",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a Wizard."},
+				{Role: "user", Content: "Hello", Images: []api.ImageData{[]byte("base64")}},
+			},
+			window: 1024,
+			want:   "You are a Wizard. [img-0] Hello",
+		},
+		{
+			name:     "images truncated",
+			template: "{{ .System }} {{ .Prompt }}",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a Wizard."},
+				{Role: "user", Content: "Hello", Images: []api.ImageData{[]byte("img1"), []byte("img2")}},
+			},
+			window: 1024,
+			want:   "You are a Wizard. [img-0] [img-1] Hello",
+		},
+		{
+			name:     "empty list",
+			template: "{{ .System }} {{ .Prompt }}",
+			messages: []api.Message{},
+			window:   1024,
+			want:     "",
+		},
+		{
+			name:     "empty prompt",
+			template: "[INST] {{ if .System }}<<SYS>>{{ .System }}<</SYS>> {{ end }}{{ .Prompt }} [/INST] {{ .Response }} ",
+			messages: []api.Message{
+				{Role: "user", Content: ""},
+			},
+			window: 1024,
+			want:   "",
+		},
+	}
+
+	encode := func(s string) ([]int, error) {
+		words := strings.Fields(s)
+		return make([]int, len(words)), nil
+	}
+
+	for _, tc := range tests {
+		t.Run(tc.name, func(t *testing.T) {
+			tmpl, err := template.Parse(tc.template)
+			if err != nil {
+				t.Fatal(err)
+			}
+
+			got, err := ChatPrompt(tmpl, tc.messages, tc.window, encode)
+			if err != nil {
+				t.Errorf("error = %v", err)
+			}
+
+			if got != tc.want {
+				t.Errorf("got: %q, want: %q", got, tc.want)
 			}
 		})
 	}

server/routes.go

@@ -1,15 +1,14 @@
 package server
 
 import (
-	"bytes"
 	"cmp"
 	"context"
 	"encoding/json"
 	"errors"
 	"fmt"
 	"io"
+	"io/fs"
 	"log/slog"
-	"math"
 	"net"
 	"net/http"
 	"net/netip"
@@ -55,9 +54,6 @@ func init() {
 	gin.SetMode(mode)
 }
 
-var errRequired = errors.New("is required")
-var errBadTemplate = errors.New("template error")
-
 func modelOptions(model *Model, requestOpts map[string]interface{}) (api.Options, error) {
 	opts := api.DefaultOptions()
 	if err := opts.FromMap(model.Options); err != nil {
@@ -71,220 +67,250 @@ func modelOptions(model *Model, requestOpts map[string]interface{}) (api.Options
 	return opts, nil
 }
 
-// scheduleRunner schedules a runner after validating inputs such as capabilities and model options.
-// It returns the allocated runner, model instance, and consolidated options if successful and error otherwise.
-func (s *Server) scheduleRunner(ctx context.Context, name string, caps []Capability, requestOpts map[string]any, keepAlive *api.Duration) (llm.LlamaServer, *Model, *api.Options, error) {
-	if name == "" {
-		return nil, nil, nil, fmt.Errorf("model %w", errRequired)
-	}
-
-	model, err := GetModel(name)
-	if err != nil {
-		return nil, nil, nil, err
-	}
-
-	if err := model.CheckCapabilities(caps...); err != nil {
-		return nil, nil, nil, fmt.Errorf("%s %w", name, err)
-	}
-
-	opts, err := modelOptions(model, requestOpts)
-	if err != nil {
-		return nil, nil, nil, err
-	}
-
-	runnerCh, errCh := s.sched.GetRunner(ctx, model, opts, keepAlive)
-	var runner *runnerRef
-	select {
-	case runner = <-runnerCh:
-	case err = <-errCh:
-		return nil, nil, nil, err
-	}
-
-	return runner.llama, model, &opts, nil
+func isSupportedImageType(image []byte) bool {
+	contentType := http.DetectContentType(image)
+	allowedTypes := []string{"image/jpeg", "image/jpg", "image/png"}
+	return slices.Contains(allowedTypes, contentType)
 }
 
 func (s *Server) GenerateHandler(c *gin.Context) {
 	checkpointStart := time.Now()
 	var req api.GenerateRequest
-	if err := c.ShouldBindJSON(&req); errors.Is(err, io.EOF) {
+	err := c.ShouldBindJSON(&req)
+
+	switch {
+	case errors.Is(err, io.EOF):
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "missing request body"})
 		return
-	} else if err != nil {
+	case err != nil:
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
 		return
 	}
 
-	if req.Format != "" && req.Format != "json" {
-		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "format must be empty or \"json\""})
+	// validate the request
+	switch {
+	case req.Model == "":
+		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "model is required"})
 		return
-	} else if req.Raw && (req.Template != "" || req.System != "" || len(req.Context) > 0) {
+	case len(req.Format) > 0 && req.Format != "json":
+		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "format must be json"})
+		return
+	case req.Raw && (req.Template != "" || req.System != "" || len(req.Context) > 0):
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "raw mode does not support template, system, or context"})
 		return
 	}
 
-	caps := []Capability{CapabilityCompletion}
-	if req.Suffix != "" {
-		caps = append(caps, CapabilityInsert)
+	for _, img := range req.Images {
+		if !isSupportedImageType(img) {
+			c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "unsupported image format"})
+			return
+		}
 	}
 
-	r, m, opts, err := s.scheduleRunner(c.Request.Context(), req.Model, caps, req.Options, req.KeepAlive)
-	if errors.Is(err, errCapabilityCompletion) {
-		c.JSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("%q does not support generate", req.Model)})
-		return
-	} else if err != nil {
-		handleScheduleError(c, req.Model, err)
+	model, err := GetModel(req.Model)
+	if err != nil {
+		var pErr *fs.PathError
+		if errors.As(err, &pErr) {
+			c.JSON(http.StatusNotFound, gin.H{"error": fmt.Sprintf("model '%s' not found, try pulling it first", req.Model)})
+			return
+		}
+		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 		return
 	}
 
-	checkpointLoaded := time.Now()
+	if !model.Has(CapabilityCompletion) {
+		c.JSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("%s does not support generate", req.Model)})
+		return
+	}
 
-	if req.Prompt == "" {
+	opts, err := modelOptions(model, req.Options)
+	if err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+		return
+	}
+
+	rCh, eCh := s.sched.GetRunner(c.Request.Context(), model, opts, req.KeepAlive)
+	var runner *runnerRef
+	select {
+	case runner = <-rCh:
+	case err = <-eCh:
+		handleErrorResponse(c, err)
+		return
+	}
+
+	// an empty request loads the model
+	// note: for a short while template was used in lieu
+	// of `raw` mode so we need to check for it too
+	if req.Prompt == "" && req.Template == "" && req.System == "" {
 		c.JSON(http.StatusOK, api.GenerateResponse{
-			Model:      req.Model,
 			CreatedAt:  time.Now().UTC(),
+			Model:      req.Model,
 			Done:       true,
 			DoneReason: "load",
 		})
 		return
 	}
 
-	images := make([]llm.ImageData, len(req.Images))
-	for i := range req.Images {
-		images[i] = llm.ImageData{ID: i, Data: req.Images[i]}
+	tmpl, err := template.Parse(req.Template)
+	if err != nil {
+		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+		return
 	}
 
-	prompt := req.Prompt
-	if !req.Raw {
-		tmpl := m.Template
-		if req.Template != "" {
-			tmpl, err = template.Parse(req.Template)
-			if err != nil {
-				c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
-				return
-			}
+	checkpointLoaded := time.Now()
+
+	var prompt string
+	switch {
+	case req.Raw:
+		prompt = req.Prompt
+	case req.Prompt != "":
+		if req.Template == "" {
+			tmpl = model.Template
 		}
 
-		var b bytes.Buffer
-		if req.Context != nil {
-			s, err := r.Detokenize(c.Request.Context(), req.Context)
-			if err != nil {
-				c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
-				return
-			}
-
-			b.WriteString(s)
+		if req.System == "" {
+			req.System = model.System
 		}
 
-		var values template.Values
-		if req.Suffix != "" {
-			values.Prompt = prompt
-			values.Suffix = req.Suffix
-		} else {
-			var msgs []api.Message
-			if req.System != "" {
-				msgs = append(msgs, api.Message{Role: "system", Content: req.System})
-			} else if m.System != "" {
-				msgs = append(msgs, api.Message{Role: "system", Content: m.System})
-			}
+		slog.Debug("generate handler", "prompt", req.Prompt)
+		slog.Debug("generate handler", "template", req.Template)
+		slog.Debug("generate handler", "system", req.System)
 
-			for _, i := range images {
-				msgs = append(msgs, api.Message{Role: "user", Content: fmt.Sprintf("[img-%d]", i.ID)})
-			}
-
-			values.Messages = append(msgs, api.Message{Role: "user", Content: req.Prompt})
+		var sb strings.Builder
+		for i := range req.Images {
+			fmt.Fprintf(&sb, "[img-%d] ", i)
 		}
 
-		if err := tmpl.Execute(&b, values); err != nil {
+		sb.WriteString(req.Prompt)
+
+		p, err := Prompt(tmpl, req.System, sb.String(), "", true)
+		if err != nil {
 			c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 			return
 		}
 
-		prompt = b.String()
+		sb.Reset()
+		if req.Context != nil {
+			prev, err := runner.llama.Detokenize(c.Request.Context(), req.Context)
+			if err != nil {
+				c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+				return
+			}
+
+			sb.WriteString(prev)
+		}
+
+		sb.WriteString(p)
+
+		prompt = sb.String()
 	}
 
-	slog.Debug("generate request", "prompt", prompt, "images", images)
+	slog.Debug("generate handler", "prompt", prompt)
 
 	ch := make(chan any)
+	var generated strings.Builder
 	go func() {
-		// TODO (jmorganca): avoid building the response twice both here and below
-		var sb strings.Builder
 		defer close(ch)
-		if err := r.Completion(c.Request.Context(), llm.CompletionRequest{
-			Prompt:  prompt,
-			Images:  images,
-			Format:  req.Format,
-			Options: opts,
-		}, func(cr llm.CompletionResponse) {
-			res := api.GenerateResponse{
+
+		fn := func(r llm.CompletionResponse) {
+			// Build up the full response
+			if _, err := generated.WriteString(r.Content); err != nil {
+				ch <- gin.H{"error": err.Error()}
+				return
+			}
+
+			resp := api.GenerateResponse{
 				Model:      req.Model,
 				CreatedAt:  time.Now().UTC(),
-				Response:   cr.Content,
-				Done:       cr.Done,
-				DoneReason: cr.DoneReason,
+				Done:       r.Done,
+				Response:   r.Content,
+				DoneReason: r.DoneReason,
 				Metrics: api.Metrics{
-					PromptEvalCount:    cr.PromptEvalCount,
-					PromptEvalDuration: cr.PromptEvalDuration,
-					EvalCount:          cr.EvalCount,
-					EvalDuration:       cr.EvalDuration,
+					PromptEvalCount:    r.PromptEvalCount,
+					PromptEvalDuration: r.PromptEvalDuration,
+					EvalCount:          r.EvalCount,
+					EvalDuration:       r.EvalDuration,
 				},
 			}
 
-			if _, err := sb.WriteString(cr.Content); err != nil {
-				ch <- gin.H{"error": err.Error()}
-			}
-
-			if cr.Done {
-				res.TotalDuration = time.Since(checkpointStart)
-				res.LoadDuration = checkpointLoaded.Sub(checkpointStart)
+			if r.Done {
+				resp.TotalDuration = time.Since(checkpointStart)
+				resp.LoadDuration = checkpointLoaded.Sub(checkpointStart)
 
 				if !req.Raw {
-					tokens, err := r.Tokenize(c.Request.Context(), prompt+sb.String())
+					p, err := Prompt(tmpl, req.System, req.Prompt, generated.String(), false)
+					if err != nil {
+						c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+						return
+					}
+
+					// TODO (jmorganca): encode() should not strip special tokens
+					tokens, err := runner.llama.Tokenize(c.Request.Context(), p)
 					if err != nil {
 						ch <- gin.H{"error": err.Error()}
 						return
 					}
-					res.Context = append(req.Context, tokens...)
+
+					resp.Context = append(req.Context, tokens...)
 				}
 			}
 
-			ch <- res
-		}); err != nil {
+			ch <- resp
+		}
+
+		var images []llm.ImageData
+		for i := range req.Images {
+			images = append(images, llm.ImageData{
+				ID:   i,
+				Data: req.Images[i],
+			})
+		}
+
+		// Start prediction
+		req := llm.CompletionRequest{
+			Prompt:  prompt,
+			Format:  req.Format,
+			Images:  images,
+			Options: opts,
+		}
+		if err := runner.llama.Completion(c.Request.Context(), req, fn); err != nil {
 			ch <- gin.H{"error": err.Error()}
 		}
 	}()
 
 	if req.Stream != nil && !*req.Stream {
-		var r api.GenerateResponse
+		// Accumulate responses into the final response
+		var final api.GenerateResponse
 		var sb strings.Builder
-		for rr := range ch {
-			switch t := rr.(type) {
+		for resp := range ch {
+			switch r := resp.(type) {
 			case api.GenerateResponse:
-				sb.WriteString(t.Response)
-				r = t
+				sb.WriteString(r.Response)
+				final = r
 			case gin.H:
-				msg, ok := t["error"].(string)
-				if !ok {
-					msg = "unexpected error format in response"
+				if errorMsg, ok := r["error"].(string); ok {
+					c.JSON(http.StatusInternalServerError, gin.H{"error": errorMsg})
+					return
+				} else {
+					c.JSON(http.StatusInternalServerError, gin.H{"error": "unexpected error format in response"})
+					return
 				}
-
-				c.JSON(http.StatusInternalServerError, gin.H{"error": msg})
-				return
 			default:
-				c.JSON(http.StatusInternalServerError, gin.H{"error": "unexpected response"})
+				c.JSON(http.StatusInternalServerError, gin.H{"error": "unexpected error"})
 				return
 			}
 		}
 
-		r.Response = sb.String()
-		c.JSON(http.StatusOK, r)
+		final.Response = sb.String()
+		c.JSON(http.StatusOK, final)
 		return
 	}
 
 	streamResponse(c, ch)
 }
 
-func (s *Server) EmbedHandler(c *gin.Context) {
-	var req api.EmbedRequest
+func (s *Server) EmbeddingsHandler(c *gin.Context) {
+	var req api.EmbeddingRequest
 	err := c.ShouldBindJSON(&req)
 	switch {
 	case errors.Is(err, io.EOF):
@@ -295,122 +321,34 @@ func (s *Server) EmbedHandler(c *gin.Context) {
 		return
 	}
 
-	truncate := true
-
-	if req.Truncate != nil && !*req.Truncate {
-		truncate = false
-	}
-
-	var input []string
-
-	switch i := req.Input.(type) {
-	case string:
-		if len(i) > 0 {
-			input = append(input, i)
-		}
-	case []any:
-		for _, v := range i {
-			if _, ok := v.(string); !ok {
-				c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "invalid input type"})
-				return
-			}
-			input = append(input, v.(string))
-		}
-	default:
-		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "invalid input type"})
+	if req.Model == "" {
+		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "model is required"})
 		return
 	}
 
-	if len(input) == 0 {
-		c.JSON(http.StatusOK, api.EmbedResponse{Model: req.Model, Embeddings: [][]float32{}})
-		return
-	}
-
-	r, m, opts, err := s.scheduleRunner(c.Request.Context(), req.Model, []Capability{}, req.Options, req.KeepAlive)
+	model, err := GetModel(req.Model)
 	if err != nil {
-		handleScheduleError(c, req.Model, err)
+		var pErr *fs.PathError
+		if errors.As(err, &pErr) {
+			c.JSON(http.StatusNotFound, gin.H{"error": fmt.Sprintf("model '%s' not found, try pulling it first", req.Model)})
+			return
+		}
+		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 		return
 	}
 
-	kvData, err := getKVData(m.ModelPath, false)
+	opts, err := modelOptions(model, req.Options)
 	if err != nil {
 		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 		return
 	}
 
-	for i, s := range input {
-		tokens, err := r.Tokenize(c.Request.Context(), s)
-		if err != nil {
-			c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
-			return
-		}
-
-		ctxLen := min(opts.NumCtx, int(kvData.ContextLength()))
-		if len(tokens) > ctxLen {
-			if !truncate {
-				c.JSON(http.StatusBadRequest, gin.H{"error": "input length exceeds maximum context length"})
-				return
-			}
-
-			tokens = tokens[:ctxLen]
-			s, err = r.Detokenize(c.Request.Context(), tokens)
-			if err != nil {
-				c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
-				return
-			}
-		}
-
-		input[i] = s
-	}
-	embeddings, err := r.Embed(c.Request.Context(), input)
-
-	if err != nil {
-		slog.Error("embedding generation failed", "error", err)
-		c.JSON(http.StatusInternalServerError, gin.H{"error": "failed to generate embedding"})
-		return
-	}
-
-	for i, e := range embeddings {
-		embeddings[i] = normalize(e)
-	}
-
-	resp := api.EmbedResponse{
-		Model:      req.Model,
-		Embeddings: embeddings,
-	}
-	c.JSON(http.StatusOK, resp)
-}
-
-func normalize(vec []float32) []float32 {
-	var sum float32
-	for _, v := range vec {
-		sum += v * v
-	}
-
-	norm := float32(0.0)
-	if sum > 0 {
-		norm = float32(1.0 / math.Sqrt(float64(sum)))
-	}
-
-	for i := range vec {
-		vec[i] *= norm
-	}
-	return vec
-}
-
-func (s *Server) EmbeddingsHandler(c *gin.Context) {
-	var req api.EmbeddingRequest
-	if err := c.ShouldBindJSON(&req); errors.Is(err, io.EOF) {
-		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "missing request body"})
-		return
-	} else if err != nil {
-		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
-		return
-	}
-
-	r, _, _, err := s.scheduleRunner(c.Request.Context(), req.Model, []Capability{}, req.Options, req.KeepAlive)
-	if err != nil {
-		handleScheduleError(c, req.Model, err)
+	rCh, eCh := s.sched.GetRunner(c.Request.Context(), model, opts, req.KeepAlive)
+	var runner *runnerRef
+	select {
+	case runner = <-rCh:
+	case err = <-eCh:
+		handleErrorResponse(c, err)
 		return
 	}
 
@@ -420,20 +358,13 @@ func (s *Server) EmbeddingsHandler(c *gin.Context) {
 		return
 	}
 
-	embeddings, err := r.Embed(c.Request.Context(), []string{req.Prompt})
-
+	embedding, err := runner.llama.Embedding(c.Request.Context(), req.Prompt)
 	if err != nil {
 		slog.Info(fmt.Sprintf("embedding generation failed: %v", err))
 		c.JSON(http.StatusInternalServerError, gin.H{"error": "failed to generate embedding"})
 		return
 	}
 
-	embedding := make([]float64, len(embeddings[0]))
-
-	for i, v := range embeddings[0] {
-		embedding[i] = float64(v)
-	}
-
 	resp := api.EmbeddingResponse{
 		Embedding: embedding,
 	}
@@ -609,9 +540,7 @@ func (s *Server) CreateModelHandler(c *gin.Context) {
 		defer cancel()
 
 		quantization := cmp.Or(r.Quantize, r.Quantization)
-		if err := CreateModel(ctx, name, filepath.Dir(r.Path), strings.ToUpper(quantization), f, fn); errors.Is(err, errBadTemplate) {
-			ch <- gin.H{"error": err.Error(), "status": http.StatusBadRequest}
-		} else if err != nil {
+		if err := CreateModel(ctx, name, filepath.Dir(r.Path), strings.ToUpper(quantization), f, fn); err != nil {
 			ch <- gin.H{"error": err.Error()}
 		}
 	}()
@@ -713,9 +642,16 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 		m.System = req.System
 	}
 
-	msgs := make([]api.Message, len(m.Messages))
-	for i, msg := range m.Messages {
-		msgs[i] = api.Message{Role: msg.Role, Content: msg.Content}
+	if req.Template != "" {
+		m.Template, err = template.Parse(req.Template)
+		if err != nil {
+			return nil, err
+		}
+	}
+
+	msgs := make([]api.Message, 0)
+	for _, msg := range m.Messages {
+		msgs = append(msgs, api.Message{Role: msg.Role, Content: msg.Content})
 	}
 
 	n := model.ParseName(req.Model)
@@ -1058,7 +994,6 @@ func (s *Server) GenerateRoutes() http.Handler {
 	r.POST("/api/pull", s.PullModelHandler)
 	r.POST("/api/generate", s.GenerateHandler)
 	r.POST("/api/chat", s.ChatHandler)
-	r.POST("/api/embed", s.EmbedHandler)
 	r.POST("/api/embeddings", s.EmbeddingsHandler)
 	r.POST("/api/create", s.CreateModelHandler)
 	r.POST("/api/push", s.PushModelHandler)
@@ -1072,7 +1007,6 @@ func (s *Server) GenerateRoutes() http.Handler {
 	// Compatibility endpoints
 	r.POST("/v1/chat/completions", openai.ChatMiddleware(), s.ChatHandler)
 	r.POST("/v1/completions", openai.CompletionsMiddleware(), s.GenerateHandler)
-	r.POST("/v1/embeddings", openai.EmbeddingsMiddleware(), s.EmbedHandler)
 	r.GET("/v1/models", openai.ListMiddleware(), s.ListModelsHandler)
 	r.GET("/v1/models/:model", openai.RetrieveMiddleware(), s.ShowModelHandler)
 
@@ -1199,15 +1133,11 @@ func waitForStream(c *gin.Context, ch chan interface{}) {
 				return
 			}
 		case gin.H:
-			status, ok := r["status"].(int)
-			if !ok {
-				status = http.StatusInternalServerError
-			}
 			if errorMsg, ok := r["error"].(string); ok {
-				c.JSON(status, gin.H{"error": errorMsg})
+				c.JSON(http.StatusInternalServerError, gin.H{"error": errorMsg})
 				return
 			} else {
-				c.JSON(status, gin.H{"error": "unexpected error format in progress response"})
+				c.JSON(http.StatusInternalServerError, gin.H{"error": "unexpected error format in progress response"})
 				return
 			}
 		default:
@@ -1284,67 +1214,132 @@ func (s *Server) ProcessHandler(c *gin.Context) {
 	c.JSON(http.StatusOK, api.ProcessResponse{Models: models})
 }
 
+// ChatPrompt builds up a prompt from a series of messages for the currently `loaded` model
+func chatPrompt(ctx context.Context, runner *runnerRef, template *template.Template, messages []api.Message, numCtx int) (string, error) {
+	encode := func(s string) ([]int, error) {
+		return runner.llama.Tokenize(ctx, s)
+	}
+
+	prompt, err := ChatPrompt(template, messages, numCtx, encode)
+	if err != nil {
+		return "", err
+	}
+
+	return prompt, nil
+}
+
 func (s *Server) ChatHandler(c *gin.Context) {
 	checkpointStart := time.Now()
 
 	var req api.ChatRequest
-	if err := c.ShouldBindJSON(&req); errors.Is(err, io.EOF) {
+	err := c.ShouldBindJSON(&req)
+	switch {
+	case errors.Is(err, io.EOF):
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "missing request body"})
 		return
-	} else if err != nil {
+	case err != nil:
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
 		return
 	}
 
-	caps := []Capability{CapabilityCompletion}
-	if len(req.Tools) > 0 {
-		caps = append(caps, CapabilityTools)
-	}
-
-	r, m, opts, err := s.scheduleRunner(c.Request.Context(), req.Model, caps, req.Options, req.KeepAlive)
-	if errors.Is(err, errCapabilityCompletion) {
-		c.JSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("%q does not support chat", req.Model)})
+	// validate the request
+	switch {
+	case req.Model == "":
+		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "model is required"})
 		return
-	} else if err != nil {
-		handleScheduleError(c, req.Model, err)
+	case len(req.Format) > 0 && req.Format != "json":
+		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "format must be json"})
 		return
 	}
 
-	checkpointLoaded := time.Now()
-
-	if len(req.Messages) == 0 {
-		c.JSON(http.StatusOK, api.ChatResponse{
-			Model:      req.Model,
-			CreatedAt:  time.Now().UTC(),
-			Message:    api.Message{Role: "assistant"},
-			Done:       true,
-			DoneReason: "load",
-		})
+	model, err := GetModel(req.Model)
+	if err != nil {
+		var pErr *fs.PathError
+		if errors.As(err, &pErr) {
+			c.JSON(http.StatusNotFound, gin.H{"error": fmt.Sprintf("model '%s' not found, try pulling it first", req.Model)})
+			return
+		}
+		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 		return
 	}
 
-	if req.Messages[0].Role != "system" && m.System != "" {
-		req.Messages = append([]api.Message{{Role: "system", Content: m.System}}, req.Messages...)
+	if !model.Has(CapabilityCompletion) {
+		c.JSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("%s does not support chat", req.Model)})
+		return
 	}
 
-	prompt, images, err := chatPrompt(c.Request.Context(), m, r.Tokenize, opts, req.Messages, req.Tools)
+	opts, err := modelOptions(model, req.Options)
 	if err != nil {
 		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 		return
 	}
 
-	slog.Debug("chat request", "images", len(images), "prompt", prompt)
+	rCh, eCh := s.sched.GetRunner(c.Request.Context(), model, opts, req.KeepAlive)
+	var runner *runnerRef
+	select {
+	case runner = <-rCh:
+	case err = <-eCh:
+		handleErrorResponse(c, err)
+		return
+	}
+
+	checkpointLoaded := time.Now()
+
+	// if the first message is not a system message, then add the model's default system message
+	if len(req.Messages) > 0 && req.Messages[0].Role != "system" {
+		req.Messages = append([]api.Message{
+			{
+				Role:    "system",
+				Content: model.System,
+			},
+		}, req.Messages...)
+	}
+
+	prompt, err := chatPrompt(c.Request.Context(), runner, model.Template, req.Messages, opts.NumCtx)
+	if err != nil {
+		c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
+		return
+	}
+
+	// an empty request loads the model
+	if len(req.Messages) == 0 || prompt == "" {
+		resp := api.ChatResponse{
+			CreatedAt:  time.Now().UTC(),
+			Model:      req.Model,
+			Done:       true,
+			DoneReason: "load",
+			Message:    api.Message{Role: "assistant"},
+		}
+		c.JSON(http.StatusOK, resp)
+		return
+	}
+
+	// only send images that are in the prompt
+	var i int
+	var images []llm.ImageData
+	for _, m := range req.Messages {
+		for _, img := range m.Images {
+			if !isSupportedImageType(img) {
+				c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "unsupported image format"})
+				return
+			}
+
+			if strings.Contains(prompt, fmt.Sprintf("[img-%d]", i)) {
+				images = append(images, llm.ImageData{Data: img, ID: i})
+			}
+			i += 1
+		}
+	}
+
+	slog.Debug("chat handler", "prompt", prompt, "images", len(images))
 
 	ch := make(chan any)
+
 	go func() {
 		defer close(ch)
-		if err := r.Completion(c.Request.Context(), llm.CompletionRequest{
-			Prompt:  prompt,
-			Images:  images,
-			Format:  req.Format,
-			Options: opts,
-		}, func(r llm.CompletionResponse) {
-			res := api.ChatResponse{
+
+		fn := func(r llm.CompletionResponse) {
+			resp := api.ChatResponse{
 				Model:      req.Model,
 				CreatedAt:  time.Now().UTC(),
 				Message:    api.Message{Role: "assistant", Content: r.Content},
@@ -1359,85 +1354,62 @@ func (s *Server) ChatHandler(c *gin.Context) {
 			}
 
 			if r.Done {
-				res.TotalDuration = time.Since(checkpointStart)
-				res.LoadDuration = checkpointLoaded.Sub(checkpointStart)
+				resp.TotalDuration = time.Since(checkpointStart)
+				resp.LoadDuration = checkpointLoaded.Sub(checkpointStart)
 			}
 
-			ch <- res
-		}); err != nil {
+			ch <- resp
+		}
+
+		if err := runner.llama.Completion(c.Request.Context(), llm.CompletionRequest{
+			Prompt:  prompt,
+			Format:  req.Format,
+			Images:  images,
+			Options: opts,
+		}, fn); err != nil {
 			ch <- gin.H{"error": err.Error()}
 		}
 	}()
 
-	if (req.Stream != nil && !*req.Stream) || ((req.Stream == nil || *req.Stream) && len(req.Tools) > 0) {
-		var resp api.ChatResponse
+	if req.Stream != nil && !*req.Stream {
+		// Accumulate responses into the final response
+		var final api.ChatResponse
 		var sb strings.Builder
-		for rr := range ch {
-			switch t := rr.(type) {
+		for resp := range ch {
+			switch r := resp.(type) {
 			case api.ChatResponse:
-				sb.WriteString(t.Message.Content)
-				resp = t
+				sb.WriteString(r.Message.Content)
+				final = r
 			case gin.H:
-				msg, ok := t["error"].(string)
-				if !ok {
-					msg = "unexpected error format in response"
+				if errorMsg, ok := r["error"].(string); ok {
+					c.JSON(http.StatusInternalServerError, gin.H{"error": errorMsg})
+					return
+				} else {
+					c.JSON(http.StatusInternalServerError, gin.H{"error": "unexpected error format in response"})
+					return
 				}
-
-				c.JSON(http.StatusInternalServerError, gin.H{"error": msg})
-				return
 			default:
-				c.JSON(http.StatusInternalServerError, gin.H{"error": "unexpected response"})
+				c.JSON(http.StatusInternalServerError, gin.H{"error": "unexpected error"})
 				return
 			}
 		}
 
-		resp.Message.Content = sb.String()
-
-		if len(req.Tools) > 0 {
-			if toolCalls, ok := m.parseToolCalls(sb.String()); ok {
-				resp.Message.ToolCalls = toolCalls
-				resp.Message.Content = ""
-			}
-		}
-
-		if (req.Stream == nil || *req.Stream) && len(resp.Message.ToolCalls) > 0 {
-			toolCh := make(chan any)
-			go func() {
-				defer close(toolCh)
-				toolCalls := resp.Message.ToolCalls
-				for _, toolCall := range toolCalls {
-					toolCh <- api.ChatResponse{
-						Model:     resp.Model,
-						CreatedAt: resp.CreatedAt,
-						Message:   api.Message{Role: "assistant", ToolCalls: []api.ToolCall{toolCall}},
-					}
-				}
-				resp.Message.ToolCalls = nil
-				resp.DoneReason = "tool_calls"
-				toolCh <- resp
-			}()
-			streamResponse(c, toolCh)
-			return
-		}
-
-		c.JSON(http.StatusOK, resp)
+		final.Message = api.Message{Role: "assistant", Content: sb.String()}
+		c.JSON(http.StatusOK, final)
 		return
 	}
 
 	streamResponse(c, ch)
 }
 
-func handleScheduleError(c *gin.Context, name string, err error) {
-	switch {
-	case errors.Is(err, errCapabilities), errors.Is(err, errRequired):
-		c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
-	case errors.Is(err, context.Canceled):
+func handleErrorResponse(c *gin.Context, err error) {
+	if errors.Is(err, context.Canceled) {
 		c.JSON(499, gin.H{"error": "request canceled"})
-	case errors.Is(err, ErrMaxQueue):
-		c.JSON(http.StatusServiceUnavailable, gin.H{"error": err.Error()})
-	case errors.Is(err, os.ErrNotExist):
-		c.JSON(http.StatusNotFound, gin.H{"error": fmt.Sprintf("model %q not found, try pulling it first", name)})
-	default:
-		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+		return
 	}
+	if errors.Is(err, ErrMaxQueue) {
+		c.JSON(http.StatusServiceUnavailable, gin.H{"error": err.Error()})
+		return
+	}
+	c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 }

server/routes_create_test.go

@@ -2,7 +2,6 @@ package server
 
 import (
 	"bytes"
-	"encoding/binary"
 	"encoding/json"
 	"fmt"
 	"io"
@@ -21,7 +20,7 @@ import (
 
 var stream bool = false
 
-func createBinFile(t *testing.T, kv map[string]any, ti []llm.Tensor) string {
+func createBinFile(t *testing.T, kv map[string]any, ti []*llm.Tensor) string {
 	t.Helper()
 
 	f, err := os.CreateTemp(t.TempDir(), "")
@@ -30,7 +29,7 @@ func createBinFile(t *testing.T, kv map[string]any, ti []llm.Tensor) string {
 	}
 	defer f.Close()
 
-	if err := llm.NewGGUFV3(binary.LittleEndian).Encode(f, kv, ti); err != nil {
+	if err := llm.WriteGGUF(f, kv, ti); err != nil {
 		t.Fatal(err)
 	}
 
@@ -85,8 +84,6 @@ func checkFileExists(t *testing.T, p string, expect []string) {
 }
 
 func TestCreateFromBin(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
 	p := t.TempDir()
 	t.Setenv("OLLAMA_MODELS", p)
 	envconfig.LoadConfig()
@@ -113,8 +110,6 @@ func TestCreateFromBin(t *testing.T) {
 }
 
 func TestCreateFromModel(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
 	p := t.TempDir()
 	t.Setenv("OLLAMA_MODELS", p)
 	envconfig.LoadConfig()
@@ -156,8 +151,6 @@ func TestCreateFromModel(t *testing.T) {
 }
 
 func TestCreateRemovesLayers(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
 	p := t.TempDir()
 	t.Setenv("OLLAMA_MODELS", p)
 	envconfig.LoadConfig()
@@ -205,8 +198,6 @@ func TestCreateRemovesLayers(t *testing.T) {
 }
 
 func TestCreateUnsetsSystem(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
 	p := t.TempDir()
 	t.Setenv("OLLAMA_MODELS", p)
 	envconfig.LoadConfig()
@@ -263,8 +254,6 @@ func TestCreateUnsetsSystem(t *testing.T) {
 }
 
 func TestCreateMergeParameters(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
 	p := t.TempDir()
 	t.Setenv("OLLAMA_MODELS", p)
 	envconfig.LoadConfig()
@@ -368,8 +357,6 @@ func TestCreateMergeParameters(t *testing.T) {
 }
 
 func TestCreateReplacesMessages(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
 	p := t.TempDir()
 	t.Setenv("OLLAMA_MODELS", p)
 	envconfig.LoadConfig()
@@ -446,8 +433,6 @@ func TestCreateReplacesMessages(t *testing.T) {
 }
 
 func TestCreateTemplateSystem(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
 	p := t.TempDir()
 	t.Setenv("OLLAMA_MODELS", p)
 	envconfig.LoadConfig()
@@ -491,47 +476,9 @@ func TestCreateTemplateSystem(t *testing.T) {
 	if string(system) != "Say bye!" {
 		t.Errorf("expected \"Say bye!\", actual %s", system)
 	}
-
-	t.Run("incomplete template", func(t *testing.T) {
-		w := createRequest(t, s.CreateModelHandler, api.CreateRequest{
-			Name:      "test",
-			Modelfile: fmt.Sprintf("FROM %s\nTEMPLATE {{ .Prompt", createBinFile(t, nil, nil)),
-			Stream:    &stream,
-		})
-	
-		if w.Code != http.StatusBadRequest {
-			t.Fatalf("expected status code 400, actual %d", w.Code)
-		}
-	})
-
-	t.Run("template with unclosed if", func(t *testing.T) {
-		w := createRequest(t, s.CreateModelHandler, api.CreateRequest{
-			Name:      "test",
-			Modelfile: fmt.Sprintf("FROM %s\nTEMPLATE {{ if .Prompt }}", createBinFile(t, nil, nil)),
-			Stream:    &stream,
-		})
-	
-		if w.Code != http.StatusBadRequest {
-			t.Fatalf("expected status code 400, actual %d", w.Code)
-		}
-	})
-
-	t.Run("template with undefined function", func(t *testing.T) {
-		w := createRequest(t, s.CreateModelHandler, api.CreateRequest{
-			Name:      "test",
-			Modelfile: fmt.Sprintf("FROM %s\nTEMPLATE {{  Prompt }}", createBinFile(t, nil, nil)),
-			Stream:    &stream,
-		})
-	
-		if w.Code != http.StatusBadRequest {
-			t.Fatalf("expected status code 400, actual %d", w.Code)
-		}
-	})
 }
 
 func TestCreateLicenses(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
 	p := t.TempDir()
 	t.Setenv("OLLAMA_MODELS", p)
 	envconfig.LoadConfig()
@@ -578,8 +525,6 @@ func TestCreateLicenses(t *testing.T) {
 }
 
 func TestCreateDetectTemplate(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
 	p := t.TempDir()
 	t.Setenv("OLLAMA_MODELS", p)
 	envconfig.LoadConfig()
@@ -599,10 +544,9 @@ func TestCreateDetectTemplate(t *testing.T) {
 		}
 
 		checkFileExists(t, filepath.Join(p, "blobs", "*"), []string{
-			filepath.Join(p, "blobs", "sha256-0d79f567714c62c048378f2107fb332dabee0135d080c302d884317da9433cc5"),
+			filepath.Join(p, "blobs", "sha256-2f8e594e6f34b1b4d36a246628eeb3365ce442303d656f1fcc69e821722acea0"),
+			filepath.Join(p, "blobs", "sha256-542b217f179c7825eeb5bca3c77d2b75ed05bafbd3451d9188891a60a85337c6"),
 			filepath.Join(p, "blobs", "sha256-553c4a3f747b3d22a4946875f1cc8ed011c2930d83f864a0c7265f9ec0a20413"),
-			filepath.Join(p, "blobs", "sha256-c608dc615584cd20d9d830363dabf8a4783ae5d34245c3d8c115edb3bc7b28e4"),
-			filepath.Join(p, "blobs", "sha256-ea34c57ba5b78b740aafe2aeb74dc6507fc3ad14170b64c26a04fb9e36c88d75"),
 		})
 	})
 

server/routes_delete_test.go

@@ -8,15 +8,12 @@ import (
 	"path/filepath"
 	"testing"
 
-	"github.com/gin-gonic/gin"
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/types/model"
 )
 
 func TestDelete(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
 	p := t.TempDir()
 	t.Setenv("OLLAMA_MODELS", p)
 	envconfig.LoadConfig()
@@ -80,8 +77,6 @@ func TestDelete(t *testing.T) {
 }
 
 func TestDeleteDuplicateLayers(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
 	p := t.TempDir()
 	t.Setenv("OLLAMA_MODELS", p)
 	var s Server

server/routes_generate_test.go

@@ -1,714 +0,0 @@
-package server
-
-import (
-	"bytes"
-	"context"
-	"encoding/json"
-	"fmt"
-	"io"
-	"net/http"
-	"strings"
-	"testing"
-	"time"
-
-	"github.com/gin-gonic/gin"
-	"github.com/google/go-cmp/cmp"
-
-	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/gpu"
-	"github.com/ollama/ollama/llm"
-)
-
-type mockRunner struct {
-	llm.LlamaServer
-
-	// CompletionRequest is only valid until the next call to Completion
-	llm.CompletionRequest
-	llm.CompletionResponse
-}
-
-func (m *mockRunner) Completion(_ context.Context, r llm.CompletionRequest, fn func(r llm.CompletionResponse)) error {
-	m.CompletionRequest = r
-	fn(m.CompletionResponse)
-	return nil
-}
-
-func (mockRunner) Tokenize(_ context.Context, s string) (tokens []int, err error) {
-	for range strings.Fields(s) {
-		tokens = append(tokens, len(tokens))
-	}
-
-	return
-}
-
-func newMockServer(mock *mockRunner) func(gpu.GpuInfoList, string, *llm.GGML, []string, []string, api.Options, int) (llm.LlamaServer, error) {
-	return func(gpus gpu.GpuInfoList, model string, ggml *llm.GGML, projectors, system []string, opts api.Options, numParallel int) (llm.LlamaServer, error) {
-		return mock, nil
-	}
-}
-
-func TestGenerateChat(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
-	mock := mockRunner{
-		CompletionResponse: llm.CompletionResponse{
-			Done:               true,
-			DoneReason:         "stop",
-			PromptEvalCount:    1,
-			PromptEvalDuration: 1,
-			EvalCount:          1,
-			EvalDuration:       1,
-		},
-	}
-
-	s := Server{
-		sched: &Scheduler{
-			pendingReqCh:  make(chan *LlmRequest, 1),
-			finishedReqCh: make(chan *LlmRequest, 1),
-			expiredCh:     make(chan *runnerRef, 1),
-			unloadedCh:    make(chan any, 1),
-			loaded:        make(map[string]*runnerRef),
-			newServerFn:   newMockServer(&mock),
-			getGpuFn:      gpu.GetGPUInfo,
-			getCpuFn:      gpu.GetCPUInfo,
-			reschedDelay:  250 * time.Millisecond,
-			loadFn: func(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList, numParallel int) {
-				// add small delay to simulate loading
-				time.Sleep(time.Millisecond)
-				req.successCh <- &runnerRef{
-					llama: &mock,
-				}
-			},
-		},
-	}
-
-	go s.sched.Run(context.TODO())
-
-	w := createRequest(t, s.CreateModelHandler, api.CreateRequest{
-		Model: "test",
-		Modelfile: fmt.Sprintf(`FROM %s
-		TEMPLATE """
-{{- if .System }}System: {{ .System }} {{ end }}
-{{- if .Prompt }}User: {{ .Prompt }} {{ end }}
-{{- if .Response }}Assistant: {{ .Response }} {{ end }}"""
-`, createBinFile(t, llm.KV{
-			"general.architecture":          "llama",
-			"llama.block_count":             uint32(1),
-			"llama.context_length":          uint32(8192),
-			"llama.embedding_length":        uint32(4096),
-			"llama.attention.head_count":    uint32(32),
-			"llama.attention.head_count_kv": uint32(8),
-			"tokenizer.ggml.tokens":         []string{""},
-			"tokenizer.ggml.scores":         []float32{0},
-			"tokenizer.ggml.token_type":     []int32{0},
-		}, []llm.Tensor{
-			{Name: "token_embd.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.attn_norm.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.ffn_down.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.ffn_gate.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.ffn_up.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.ffn_norm.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.attn_k.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.attn_output.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.attn_q.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.attn_v.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "output.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-		})),
-		Stream: &stream,
-	})
-
-	if w.Code != http.StatusOK {
-		t.Fatalf("expected status 200, got %d", w.Code)
-	}
-
-	t.Run("missing body", func(t *testing.T) {
-		w := createRequest(t, s.ChatHandler, nil)
-		if w.Code != http.StatusBadRequest {
-			t.Errorf("expected status 400, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(w.Body.String(), `{"error":"model is required"}`); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-	})
-
-	t.Run("missing model", func(t *testing.T) {
-		w := createRequest(t, s.ChatHandler, api.ChatRequest{})
-		if w.Code != http.StatusBadRequest {
-			t.Errorf("expected status 400, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(w.Body.String(), `{"error":"model is required"}`); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-	})
-
-	t.Run("missing capabilities chat", func(t *testing.T) {
-		w := createRequest(t, s.CreateModelHandler, api.CreateRequest{
-			Model: "bert",
-			Modelfile: fmt.Sprintf("FROM %s", createBinFile(t, llm.KV{
-				"general.architecture": "bert",
-				"bert.pooling_type":    uint32(0),
-			}, []llm.Tensor{})),
-			Stream: &stream,
-		})
-
-		if w.Code != http.StatusOK {
-			t.Fatalf("expected status 200, got %d", w.Code)
-		}
-
-		w = createRequest(t, s.ChatHandler, api.ChatRequest{
-			Model: "bert",
-		})
-
-		if w.Code != http.StatusBadRequest {
-			t.Errorf("expected status 400, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(w.Body.String(), `{"error":"\"bert\" does not support chat"}`); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-	})
-
-	t.Run("load model", func(t *testing.T) {
-		w := createRequest(t, s.ChatHandler, api.ChatRequest{
-			Model: "test",
-		})
-
-		if w.Code != http.StatusOK {
-			t.Errorf("expected status 200, got %d", w.Code)
-		}
-
-		var actual api.ChatResponse
-		if err := json.NewDecoder(w.Body).Decode(&actual); err != nil {
-			t.Fatal(err)
-		}
-
-		if actual.Model != "test" {
-			t.Errorf("expected model test, got %s", actual.Model)
-		}
-
-		if !actual.Done {
-			t.Errorf("expected done true, got false")
-		}
-
-		if actual.DoneReason != "load" {
-			t.Errorf("expected done reason load, got %s", actual.DoneReason)
-		}
-	})
-
-	checkChatResponse := func(t *testing.T, body io.Reader, model, content string) {
-		t.Helper()
-
-		var actual api.ChatResponse
-		if err := json.NewDecoder(body).Decode(&actual); err != nil {
-			t.Fatal(err)
-		}
-
-		if actual.Model != model {
-			t.Errorf("expected model test, got %s", actual.Model)
-		}
-
-		if !actual.Done {
-			t.Errorf("expected done false, got true")
-		}
-
-		if actual.DoneReason != "stop" {
-			t.Errorf("expected done reason stop, got %s", actual.DoneReason)
-		}
-
-		if diff := cmp.Diff(actual.Message, api.Message{
-			Role:    "assistant",
-			Content: content,
-		}); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-
-		if actual.PromptEvalCount == 0 {
-			t.Errorf("expected prompt eval count > 0, got 0")
-		}
-
-		if actual.PromptEvalDuration == 0 {
-			t.Errorf("expected prompt eval duration > 0, got 0")
-		}
-
-		if actual.EvalCount == 0 {
-			t.Errorf("expected eval count > 0, got 0")
-		}
-
-		if actual.EvalDuration == 0 {
-			t.Errorf("expected eval duration > 0, got 0")
-		}
-
-		if actual.LoadDuration == 0 {
-			t.Errorf("expected load duration > 0, got 0")
-		}
-
-		if actual.TotalDuration == 0 {
-			t.Errorf("expected total duration > 0, got 0")
-		}
-	}
-
-	mock.CompletionResponse.Content = "Hi!"
-	t.Run("messages", func(t *testing.T) {
-		w := createRequest(t, s.ChatHandler, api.ChatRequest{
-			Model: "test",
-			Messages: []api.Message{
-				{Role: "user", Content: "Hello!"},
-			},
-			Stream: &stream,
-		})
-
-		if w.Code != http.StatusOK {
-			t.Errorf("expected status 200, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(mock.CompletionRequest.Prompt, "User: Hello! "); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-
-		checkChatResponse(t, w.Body, "test", "Hi!")
-	})
-
-	w = createRequest(t, s.CreateModelHandler, api.CreateRequest{
-		Model:     "test-system",
-		Modelfile: "FROM test\nSYSTEM You are a helpful assistant.",
-	})
-
-	if w.Code != http.StatusOK {
-		t.Fatalf("expected status 200, got %d", w.Code)
-	}
-
-	t.Run("messages with model system", func(t *testing.T) {
-		w := createRequest(t, s.ChatHandler, api.ChatRequest{
-			Model: "test-system",
-			Messages: []api.Message{
-				{Role: "user", Content: "Hello!"},
-			},
-			Stream: &stream,
-		})
-
-		if w.Code != http.StatusOK {
-			t.Errorf("expected status 200, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(mock.CompletionRequest.Prompt, "System: You are a helpful assistant. User: Hello! "); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-
-		checkChatResponse(t, w.Body, "test-system", "Hi!")
-	})
-
-	mock.CompletionResponse.Content = "Abra kadabra!"
-	t.Run("messages with system", func(t *testing.T) {
-		w := createRequest(t, s.ChatHandler, api.ChatRequest{
-			Model: "test-system",
-			Messages: []api.Message{
-				{Role: "system", Content: "You can perform magic tricks."},
-				{Role: "user", Content: "Hello!"},
-			},
-			Stream: &stream,
-		})
-
-		if w.Code != http.StatusOK {
-			t.Errorf("expected status 200, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(mock.CompletionRequest.Prompt, "System: You can perform magic tricks. User: Hello! "); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-
-		checkChatResponse(t, w.Body, "test-system", "Abra kadabra!")
-	})
-
-	t.Run("messages with interleaved system", func(t *testing.T) {
-		w := createRequest(t, s.ChatHandler, api.ChatRequest{
-			Model: "test-system",
-			Messages: []api.Message{
-				{Role: "user", Content: "Hello!"},
-				{Role: "assistant", Content: "I can help you with that."},
-				{Role: "system", Content: "You can perform magic tricks."},
-				{Role: "user", Content: "Help me write tests."},
-			},
-			Stream: &stream,
-		})
-
-		if w.Code != http.StatusOK {
-			t.Errorf("expected status 200, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(mock.CompletionRequest.Prompt, "System: You are a helpful assistant. User: Hello! Assistant: I can help you with that. System: You can perform magic tricks. User: Help me write tests. "); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-
-		checkChatResponse(t, w.Body, "test-system", "Abra kadabra!")
-	})
-}
-
-func TestGenerate(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
-	mock := mockRunner{
-		CompletionResponse: llm.CompletionResponse{
-			Done:               true,
-			DoneReason:         "stop",
-			PromptEvalCount:    1,
-			PromptEvalDuration: 1,
-			EvalCount:          1,
-			EvalDuration:       1,
-		},
-	}
-
-	s := Server{
-		sched: &Scheduler{
-			pendingReqCh:  make(chan *LlmRequest, 1),
-			finishedReqCh: make(chan *LlmRequest, 1),
-			expiredCh:     make(chan *runnerRef, 1),
-			unloadedCh:    make(chan any, 1),
-			loaded:        make(map[string]*runnerRef),
-			newServerFn:   newMockServer(&mock),
-			getGpuFn:      gpu.GetGPUInfo,
-			getCpuFn:      gpu.GetCPUInfo,
-			reschedDelay:  250 * time.Millisecond,
-			loadFn: func(req *LlmRequest, ggml *llm.GGML, gpus gpu.GpuInfoList, numParallel int) {
-				// add small delay to simulate loading
-				time.Sleep(time.Millisecond)
-				req.successCh <- &runnerRef{
-					llama: &mock,
-				}
-			},
-		},
-	}
-
-	go s.sched.Run(context.TODO())
-
-	w := createRequest(t, s.CreateModelHandler, api.CreateRequest{
-		Model: "test",
-		Modelfile: fmt.Sprintf(`FROM %s
-		TEMPLATE """
-{{- if .System }}System: {{ .System }} {{ end }}
-{{- if .Prompt }}User: {{ .Prompt }} {{ end }}
-{{- if .Response }}Assistant: {{ .Response }} {{ end }}"""
-`, createBinFile(t, llm.KV{
-			"general.architecture":          "llama",
-			"llama.block_count":             uint32(1),
-			"llama.context_length":          uint32(8192),
-			"llama.embedding_length":        uint32(4096),
-			"llama.attention.head_count":    uint32(32),
-			"llama.attention.head_count_kv": uint32(8),
-			"tokenizer.ggml.tokens":         []string{""},
-			"tokenizer.ggml.scores":         []float32{0},
-			"tokenizer.ggml.token_type":     []int32{0},
-		}, []llm.Tensor{
-			{Name: "token_embd.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.attn_norm.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.ffn_down.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.ffn_gate.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.ffn_up.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.ffn_norm.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.attn_k.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.attn_output.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.attn_q.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "blk.0.attn_v.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-			{Name: "output.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
-		})),
-		Stream: &stream,
-	})
-
-	if w.Code != http.StatusOK {
-		t.Fatalf("expected status 200, got %d", w.Code)
-	}
-
-	t.Run("missing body", func(t *testing.T) {
-		w := createRequest(t, s.GenerateHandler, nil)
-		if w.Code != http.StatusBadRequest {
-			t.Errorf("expected status 400, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(w.Body.String(), `{"error":"model is required"}`); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-	})
-
-	t.Run("missing model", func(t *testing.T) {
-		w := createRequest(t, s.GenerateHandler, api.GenerateRequest{})
-		if w.Code != http.StatusBadRequest {
-			t.Errorf("expected status 400, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(w.Body.String(), `{"error":"model is required"}`); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-	})
-
-	t.Run("missing capabilities generate", func(t *testing.T) {
-		w := createRequest(t, s.CreateModelHandler, api.CreateRequest{
-			Model: "bert",
-			Modelfile: fmt.Sprintf("FROM %s", createBinFile(t, llm.KV{
-				"general.architecture": "bert",
-				"bert.pooling_type":    uint32(0),
-			}, []llm.Tensor{})),
-			Stream: &stream,
-		})
-
-		if w.Code != http.StatusOK {
-			t.Fatalf("expected status 200, got %d", w.Code)
-		}
-
-		w = createRequest(t, s.GenerateHandler, api.GenerateRequest{
-			Model: "bert",
-		})
-
-		if w.Code != http.StatusBadRequest {
-			t.Errorf("expected status 400, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(w.Body.String(), `{"error":"\"bert\" does not support generate"}`); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-	})
-
-	t.Run("missing capabilities suffix", func(t *testing.T) {
-		w := createRequest(t, s.GenerateHandler, api.GenerateRequest{
-			Model:  "test",
-			Prompt: "def add(",
-			Suffix: "    return c",
-		})
-
-		if w.Code != http.StatusBadRequest {
-			t.Errorf("expected status 400, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(w.Body.String(), `{"error":"test does not support insert"}`); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-	})
-
-	t.Run("load model", func(t *testing.T) {
-		w := createRequest(t, s.GenerateHandler, api.GenerateRequest{
-			Model: "test",
-		})
-
-		if w.Code != http.StatusOK {
-			t.Errorf("expected status 200, got %d", w.Code)
-		}
-
-		var actual api.GenerateResponse
-		if err := json.NewDecoder(w.Body).Decode(&actual); err != nil {
-			t.Fatal(err)
-		}
-
-		if actual.Model != "test" {
-			t.Errorf("expected model test, got %s", actual.Model)
-		}
-
-		if !actual.Done {
-			t.Errorf("expected done true, got false")
-		}
-
-		if actual.DoneReason != "load" {
-			t.Errorf("expected done reason load, got %s", actual.DoneReason)
-		}
-	})
-
-	checkGenerateResponse := func(t *testing.T, body io.Reader, model, content string) {
-		t.Helper()
-
-		var actual api.GenerateResponse
-		if err := json.NewDecoder(body).Decode(&actual); err != nil {
-			t.Fatal(err)
-		}
-
-		if actual.Model != model {
-			t.Errorf("expected model test, got %s", actual.Model)
-		}
-
-		if !actual.Done {
-			t.Errorf("expected done false, got true")
-		}
-
-		if actual.DoneReason != "stop" {
-			t.Errorf("expected done reason stop, got %s", actual.DoneReason)
-		}
-
-		if actual.Response != content {
-			t.Errorf("expected response %s, got %s", content, actual.Response)
-		}
-
-		if actual.Context == nil {
-			t.Errorf("expected context not nil")
-		}
-
-		if actual.PromptEvalCount == 0 {
-			t.Errorf("expected prompt eval count > 0, got 0")
-		}
-
-		if actual.PromptEvalDuration == 0 {
-			t.Errorf("expected prompt eval duration > 0, got 0")
-		}
-
-		if actual.EvalCount == 0 {
-			t.Errorf("expected eval count > 0, got 0")
-		}
-
-		if actual.EvalDuration == 0 {
-			t.Errorf("expected eval duration > 0, got 0")
-		}
-
-		if actual.LoadDuration == 0 {
-			t.Errorf("expected load duration > 0, got 0")
-		}
-
-		if actual.TotalDuration == 0 {
-			t.Errorf("expected total duration > 0, got 0")
-		}
-	}
-
-	mock.CompletionResponse.Content = "Hi!"
-	t.Run("prompt", func(t *testing.T) {
-		w := createRequest(t, s.GenerateHandler, api.GenerateRequest{
-			Model:  "test",
-			Prompt: "Hello!",
-			Stream: &stream,
-		})
-
-		if w.Code != http.StatusOK {
-			t.Errorf("expected status 200, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(mock.CompletionRequest.Prompt, "User: Hello! "); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-
-		checkGenerateResponse(t, w.Body, "test", "Hi!")
-	})
-
-	w = createRequest(t, s.CreateModelHandler, api.CreateRequest{
-		Model:     "test-system",
-		Modelfile: "FROM test\nSYSTEM You are a helpful assistant.",
-	})
-
-	if w.Code != http.StatusOK {
-		t.Fatalf("expected status 200, got %d", w.Code)
-	}
-
-	t.Run("prompt with model system", func(t *testing.T) {
-		w := createRequest(t, s.GenerateHandler, api.GenerateRequest{
-			Model:  "test-system",
-			Prompt: "Hello!",
-			Stream: &stream,
-		})
-
-		if w.Code != http.StatusOK {
-			t.Errorf("expected status 200, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(mock.CompletionRequest.Prompt, "System: You are a helpful assistant. User: Hello! "); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-
-		checkGenerateResponse(t, w.Body, "test-system", "Hi!")
-	})
-
-	mock.CompletionResponse.Content = "Abra kadabra!"
-	t.Run("prompt with system", func(t *testing.T) {
-		w := createRequest(t, s.GenerateHandler, api.GenerateRequest{
-			Model:  "test-system",
-			Prompt: "Hello!",
-			System: "You can perform magic tricks.",
-			Stream: &stream,
-		})
-
-		if w.Code != http.StatusOK {
-			t.Errorf("expected status 200, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(mock.CompletionRequest.Prompt, "System: You can perform magic tricks. User: Hello! "); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-
-		checkGenerateResponse(t, w.Body, "test-system", "Abra kadabra!")
-	})
-
-	t.Run("prompt with template", func(t *testing.T) {
-		w := createRequest(t, s.GenerateHandler, api.GenerateRequest{
-			Model:  "test-system",
-			Prompt: "Help me write tests.",
-			System: "You can perform magic tricks.",
-			Template: `{{- if .System }}{{ .System }} {{ end }}
-{{- if .Prompt }}### USER {{ .Prompt }} {{ end }}
-{{- if .Response }}### ASSISTANT {{ .Response }} {{ end }}`,
-			Stream: &stream,
-		})
-
-		if w.Code != http.StatusOK {
-			t.Errorf("expected status 200, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(mock.CompletionRequest.Prompt, "You can perform magic tricks. ### USER Help me write tests. "); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-
-		checkGenerateResponse(t, w.Body, "test-system", "Abra kadabra!")
-	})
-
-	w = createRequest(t, s.CreateModelHandler, api.CreateRequest{
-		Model: "test-suffix",
-		Modelfile: `FROM test
-TEMPLATE """{{- if .Suffix }}<PRE> {{ .Prompt }} <SUF>{{ .Suffix }} <MID>
-{{- else }}{{ .Prompt }}
-{{- end }}"""`,
-	})
-
-	if w.Code != http.StatusOK {
-		t.Fatalf("expected status 200, got %d", w.Code)
-	}
-
-	t.Run("prompt with suffix", func(t *testing.T) {
-		w := createRequest(t, s.GenerateHandler, api.GenerateRequest{
-			Model:  "test-suffix",
-			Prompt: "def add(",
-			Suffix: "    return c",
-		})
-
-		if w.Code != http.StatusOK {
-			t.Errorf("expected status 200, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(mock.CompletionRequest.Prompt, "<PRE> def add( <SUF>    return c <MID>"); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-	})
-
-	t.Run("prompt without suffix", func(t *testing.T) {
-		w := createRequest(t, s.GenerateHandler, api.GenerateRequest{
-			Model:  "test-suffix",
-			Prompt: "def add(",
-		})
-
-		if w.Code != http.StatusOK {
-			t.Errorf("expected status 200, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(mock.CompletionRequest.Prompt, "def add("); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-	})
-
-	t.Run("raw", func(t *testing.T) {
-		w := createRequest(t, s.GenerateHandler, api.GenerateRequest{
-			Model:  "test-system",
-			Prompt: "Help me write tests.",
-			Raw:    true,
-			Stream: &stream,
-		})
-
-		if w.Code != http.StatusOK {
-			t.Errorf("expected status 200, got %d", w.Code)
-		}
-
-		if diff := cmp.Diff(mock.CompletionRequest.Prompt, "Help me write tests."); diff != "" {
-			t.Errorf("mismatch (-got +want):\n%s", diff)
-		}
-	})
-}

server/routes_list_test.go

@@ -7,14 +7,11 @@ import (
 	"slices"
 	"testing"
 
-	"github.com/gin-gonic/gin"
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
 )
 
 func TestList(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
 	t.Setenv("OLLAMA_MODELS", t.TempDir())
 	envconfig.LoadConfig()
 

server/routes_test.go

@@ -7,7 +7,6 @@ import (
 	"encoding/json"
 	"fmt"
 	"io"
-	"math"
 	"net/http"
 	"net/http/httptest"
 	"os"
@@ -273,77 +272,6 @@ func Test_Routes(t *testing.T) {
 				assert.Equal(t, "library", retrieveResp.OwnedBy)
 			},
 		},
-		{
-			Name:   "Embed Handler Empty Input",
-			Method: http.MethodPost,
-			Path:   "/api/embed",
-			Setup: func(t *testing.T, req *http.Request) {
-				embedReq := api.EmbedRequest{
-					Model: "t-bone",
-					Input: "",
-				}
-				jsonData, err := json.Marshal(embedReq)
-				require.NoError(t, err)
-				req.Body = io.NopCloser(bytes.NewReader(jsonData))
-			},
-			Expected: func(t *testing.T, resp *http.Response) {
-				contentType := resp.Header.Get("Content-Type")
-				if contentType != "application/json; charset=utf-8" {
-					t.Fatalf("expected content type application/json; charset=utf-8, got %s", contentType)
-				}
-				body, err := io.ReadAll(resp.Body)
-				if err != nil {
-					t.Fatal(err)
-				}
-
-				var embedResp api.EmbedResponse
-				err = json.Unmarshal(body, &embedResp)
-				if err != nil {
-					t.Fatal(err)
-				}
-
-				if embedResp.Model != "t-bone" {
-					t.Fatalf("expected model t-bone, got %s", embedResp.Model)
-				}
-
-				if embedResp.Embeddings == nil {
-					t.Fatalf("expected embeddings to not be nil, got %v", embedResp.Embeddings)
-				}
-
-				if len(embedResp.Embeddings) != 0 {
-					t.Fatalf("expected embeddings to be empty, got %v", embedResp.Embeddings)
-				}
-			},
-		},
-		{
-			Name:   "Embed Handler Invalid Input",
-			Method: http.MethodPost,
-			Path:   "/api/embed",
-			Setup: func(t *testing.T, req *http.Request) {
-				embedReq := api.EmbedRequest{
-					Model: "t-bone",
-					Input: 2,
-				}
-				jsonData, err := json.Marshal(embedReq)
-				require.NoError(t, err)
-				req.Body = io.NopCloser(bytes.NewReader(jsonData))
-			},
-			Expected: func(t *testing.T, resp *http.Response) {
-				contentType := resp.Header.Get("Content-Type")
-				if contentType != "application/json; charset=utf-8" {
-					t.Fatalf("expected content type application/json; charset=utf-8, got %s", contentType)
-				}
-				_, err := io.ReadAll(resp.Body)
-
-				if err != nil {
-					t.Fatal(err)
-				}
-
-				if resp.StatusCode != http.StatusBadRequest {
-					t.Fatalf("expected status code 400, got %d", resp.StatusCode)
-				}
-			},
-		},
 	}
 
 	t.Setenv("OLLAMA_MODELS", t.TempDir())
@@ -492,38 +420,3 @@ func TestShow(t *testing.T) {
 		t.Fatal("Expected projector architecture to be 'clip', but got", resp.ProjectorInfo["general.architecture"])
 	}
 }
-
-func TestNormalize(t *testing.T) {
-	type testCase struct {
-		input []float32
-	}
-
-	testCases := []testCase{
-		{input: []float32{1}},
-		{input: []float32{0, 1, 2, 3}},
-		{input: []float32{0.1, 0.2, 0.3}},
-		{input: []float32{-0.1, 0.2, 0.3, -0.4}},
-		{input: []float32{0, 0, 0}},
-	}
-
-	isNormalized := func(vec []float32) (res bool) {
-		sum := 0.0
-		for _, v := range vec {
-			sum += float64(v * v)
-		}
-		if math.Abs(sum-1) > 1e-6 {
-			return sum == 0
-		} else {
-			return true
-		}
-	}
-
-	for _, tc := range testCases {
-		t.Run("", func(t *testing.T) {
-			normalized := normalize(tc.input)
-			if !isNormalized(normalized) {
-				t.Errorf("Vector %v is not normalized", tc.input)
-			}
-		})
-	}
-}

server/sched.go

@@ -133,8 +133,17 @@ func (s *Scheduler) processPending(ctx context.Context) {
 				numParallel = 1
 				slog.Warn("multimodal models don't support parallel requests yet")
 			}
+			// Keep NumCtx and numParallel in sync
+			if numParallel > 1 {
+				pending.opts.NumCtx = pending.origNumCtx * numParallel
+			}
 
 			for {
+				cpus := s.getCpuFn()
+				var systemMem gpu.GpuInfo
+				if len(cpus) > 0 {
+					systemMem = cpus[0]
+				}
 				var runnerToExpire *runnerRef
 				s.loadedMu.Lock()
 				runner := s.loaded[pending.model.ModelPath]
@@ -188,15 +197,46 @@ func (s *Scheduler) processPending(ctx context.Context) {
 						break
 					}
 
+					estimate := llm.EstimateGPULayers(gpus, ggml, pending.model.ProjectorPaths, pending.opts)
+					maxSize := systemMem.FreeMemory
+
+					// Add available GPU memory to the total pool
+					// macOS hardware has unified memory so don't double count
+					if runtime.GOOS != "darwin" {
+						for _, gpu := range gpus {
+							if gpu.Library == "cpu" {
+								continue
+							}
+							if loadedCount == 0 {
+								// If no other models are loaded, set the limit based on what's available
+								maxSize += gpu.FreeMemory
+							} else {
+								// Other models could be unloaded, favor total memory for limit
+								maxSize += gpu.TotalMemory
+							}
+						}
+					}
+
+					// Block attempting to load a model larger than system memory + GPU memory
+					if estimate.TotalSize > maxSize {
+						slog.Warn("model request too large for system", "requested", format.HumanBytes2(estimate.TotalSize), "system", format.HumanBytes2(maxSize))
+
+						// Linux will crash if over-allocating memory - return an error to the user.
+						// TODO (jmorganca): add reasonable upper limits for darwin and windows as well
+						if runtime.GOOS == "linux" {
+							pending.errCh <- fmt.Errorf("requested model (%s) is too large for this system (%s)", format.HumanBytes2(estimate.TotalSize), format.HumanBytes2(maxSize))
+							break
+						}
+					}
+
 					// Evaluate if the model will fit in the available system memory, or if we should unload a model first
 					if len(gpus) == 1 && gpus[0].Library == "cpu" {
 						// simplifying assumption of defaultParallel when in CPU mode
 						if numParallel <= 0 {
 							numParallel = defaultParallel
+							pending.opts.NumCtx = pending.origNumCtx * numParallel
 						}
 
-						pending.opts.NumCtx = pending.origNumCtx * numParallel
-
 						if loadedCount == 0 {
 							slog.Debug("cpu mode with first model, loading")
 							s.loadFn(pending, ggml, gpus, numParallel)