llm: update llama.cpp submodule to 7c26775

.github/workflows/release.yaml

@@ -304,11 +304,6 @@ 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-file: go.mod
@@ -442,7 +437,6 @@ jobs:
     env:
       OLLAMA_SKIP_IMAGE_BUILD: '1'
       PUSH: '1'
-      GH_TOKEN: ${{ github.token }}
     steps:
       - uses: actions/checkout@v4
       - name: Set Version
@@ -466,20 +460,15 @@ jobs:
           ls -lh dist/
           (cd dist; sha256sum * > sha256sum.txt)
           cat dist/sha256sum.txt
-      - name: Create or update Release
-        run: |
-          echo "Looking for existing release for ${{ env.RELEASE_VERSION }}"
-          OLD_TAG=$(gh release ls --json name,tagName | jq -r ".[] | select(.name == \"${{ env.RELEASE_VERSION }}\") | .tagName")
-          if [ -n "$OLD_TAG" ]; then
-            echo "Updating release ${{ env.RELEASE_VERSION }} to point to new tag ${GITHUB_REF_NAME}"
-            gh release edit ${OLD_TAG} --tag ${GITHUB_REF_NAME}
-          else
-            echo "Creating new release ${{ env.RELEASE_VERSION }} pointing to tag ${GITHUB_REF_NAME}"
-            gh release create ${GITHUB_REF_NAME} \
-              --title ${{ env.RELEASE_VERSION }} \
-              --draft \
-              --generate-notes \
-              --prerelease
-          fi
-          echo "Uploading artifacts for tag ${GITHUB_REF_NAME}"
-          gh release upload ${GITHUB_REF_NAME} dist/* --clobber
+      - uses: ncipollo/release-action@v1
+        with:
+          name: ${{ env.RELEASE_VERSION }}
+          allowUpdates: true
+          artifacts: 'dist/*'
+          draft: true
+          prerelease: true
+          omitBodyDuringUpdate: true
+          generateReleaseNotes: true
+          omitDraftDuringUpdate: true
+          omitPrereleaseDuringUpdate: true
+          replacesArtifacts: true

.github/workflows/test.yaml

@@ -58,7 +58,6 @@ jobs:
     runs-on: ${{ matrix.os }}
     env:
       GOARCH: ${{ matrix.arch }}
-      CGO_ENABLED: '1'
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-go@v5
@@ -80,7 +79,6 @@ jobs:
       - run: go generate -x ./...
         if: ${{ ! startsWith(matrix.os, 'windows-') }}
         name: 'Unix Go Generate'
-      - run: go build .
       - uses: actions/upload-artifact@v4
         with:
           name: ${{ matrix.os }}-${{ matrix.arch }}-libraries

Dockerfile

@@ -70,12 +70,12 @@ RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu_avx" sh gen_linux.sh
 FROM --platform=linux/amd64 cpu-builder-amd64 AS cpu_avx2-build-amd64
 RUN OLLAMA_SKIP_STATIC_GENERATE=1 OLLAMA_CPU_TARGET="cpu_avx2" sh gen_linux.sh
 
-FROM --platform=linux/arm64 rockylinux:8 AS cpu-builder-arm64
+FROM --platform=linux/arm64 centos:7 AS cpu-builder-arm64
 ARG CMAKE_VERSION
 ARG GOLANG_VERSION
 COPY ./scripts/rh_linux_deps.sh /
 RUN CMAKE_VERSION=${CMAKE_VERSION} GOLANG_VERSION=${GOLANG_VERSION} sh /rh_linux_deps.sh
-ENV PATH /opt/rh/gcc-toolset-10/root/usr/bin:$PATH
+ENV PATH /opt/rh/devtoolset-10/root/usr/bin:$PATH
 COPY --from=llm-code / /go/src/github.com/ollama/ollama/
 ARG OLLAMA_CUSTOM_CPU_DEFS
 ARG CGO_CFLAGS

README.md

@@ -53,8 +53,8 @@ Here are some example models that can be downloaded:
 | 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`            |
-| Gemma 2            | 27B        | 16GB  | `ollama run gemma2:27b`        |
+| Gemma              | 2B         | 1.4GB | `ollama run gemma:2b`          |
+| Gemma              | 7B         | 4.8GB | `ollama run gemma:7b`          |
 | Mistral            | 7B         | 4.1GB | `ollama run mistral`           |
 | Moondream 2        | 1.4B       | 829MB | `ollama run moondream`         |
 | Neural Chat        | 7B         | 4.1GB | `ollama run neural-chat`       |
@@ -182,12 +182,6 @@ $ 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
-```
-
 ### List models on your computer
 
 ```
@@ -292,7 +286,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Olpaka](https://github.com/Otacon/olpaka) (User-friendly Flutter Web App for Ollama)
 - [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)
 
 ### Terminal
 

api/types.go

@@ -159,18 +159,18 @@ type Options struct {
 
 // Runner options which must be set when the model is loaded into memory
 type Runner struct {
-	UseNUMA   bool  `json:"numa,omitempty"`
-	NumCtx    int   `json:"num_ctx,omitempty"`
-	NumBatch  int   `json:"num_batch,omitempty"`
-	NumGPU    int   `json:"num_gpu,omitempty"`
-	MainGPU   int   `json:"main_gpu,omitempty"`
-	LowVRAM   bool  `json:"low_vram,omitempty"`
-	F16KV     bool  `json:"f16_kv,omitempty"`
-	LogitsAll bool  `json:"logits_all,omitempty"`
-	VocabOnly bool  `json:"vocab_only,omitempty"`
-	UseMMap   *bool `json:"use_mmap,omitempty"`
-	UseMLock  bool  `json:"use_mlock,omitempty"`
-	NumThread int   `json:"num_thread,omitempty"`
+	UseNUMA   bool `json:"numa,omitempty"`
+	NumCtx    int  `json:"num_ctx,omitempty"`
+	NumBatch  int  `json:"num_batch,omitempty"`
+	NumGPU    int  `json:"num_gpu,omitempty"`
+	MainGPU   int  `json:"main_gpu,omitempty"`
+	LowVRAM   bool `json:"low_vram,omitempty"`
+	F16KV     bool `json:"f16_kv,omitempty"`
+	LogitsAll bool `json:"logits_all,omitempty"`
+	VocabOnly bool `json:"vocab_only,omitempty"`
+	UseMMap   bool `json:"use_mmap,omitempty"`
+	UseMLock  bool `json:"use_mlock,omitempty"`
+	NumThread int  `json:"num_thread,omitempty"`
 }
 
 // EmbeddingRequest is the request passed to [Client.Embeddings].
@@ -222,7 +222,6 @@ type ShowRequest struct {
 	Model    string `json:"model"`
 	System   string `json:"system"`
 	Template string `json:"template"`
-	Verbose  bool   `json:"verbose"`
 
 	Options map[string]interface{} `json:"options"`
 
@@ -232,16 +231,14 @@ type ShowRequest struct {
 
 // ShowResponse is the response returned from [Client.Show].
 type ShowResponse struct {
-	License       string         `json:"license,omitempty"`
-	Modelfile     string         `json:"modelfile,omitempty"`
-	Parameters    string         `json:"parameters,omitempty"`
-	Template      string         `json:"template,omitempty"`
-	System        string         `json:"system,omitempty"`
-	Details       ModelDetails   `json:"details,omitempty"`
-	Messages      []Message      `json:"messages,omitempty"`
-	ModelInfo     map[string]any `json:"model_info,omitempty"`
-	ProjectorInfo map[string]any `json:"projector_info,omitempty"`
-	ModifiedAt    time.Time      `json:"modified_at,omitempty"`
+	License    string       `json:"license,omitempty"`
+	Modelfile  string       `json:"modelfile,omitempty"`
+	Parameters string       `json:"parameters,omitempty"`
+	Template   string       `json:"template,omitempty"`
+	System     string       `json:"system,omitempty"`
+	Details    ModelDetails `json:"details,omitempty"`
+	Messages   []Message    `json:"messages,omitempty"`
+	ModifiedAt time.Time    `json:"modified_at,omitempty"`
 }
 
 // CopyRequest is the request passed to [Client.Copy].
@@ -314,13 +311,6 @@ type ProcessModelResponse struct {
 	SizeVRAM  int64        `json:"size_vram"`
 }
 
-type RetrieveModelResponse struct {
-	Id      string `json:"id"`
-	Object  string `json:"object"`
-	Created int64  `json:"created"`
-	OwnedBy string `json:"owned_by"`
-}
-
 type TokenResponse struct {
 	Token string `json:"token"`
 }
@@ -459,17 +449,6 @@ func (opts *Options) FromMap(m map[string]interface{}) error {
 					slice[i] = str
 				}
 				field.Set(reflect.ValueOf(slice))
-			case reflect.Pointer:
-				var b bool
-				if field.Type() == reflect.TypeOf(&b) {
-					val, ok := val.(bool)
-					if !ok {
-						return fmt.Errorf("option %q must be of type boolean", key)
-					}
-					field.Set(reflect.ValueOf(&val))
-				} else {
-					return fmt.Errorf("unknown type loading config params: %v %v", field.Kind(), field.Type())
-				}
 			default:
 				return fmt.Errorf("unknown type loading config params: %v", field.Kind())
 			}
@@ -512,7 +491,7 @@ func DefaultOptions() Options {
 			LowVRAM:   false,
 			F16KV:     true,
 			UseMLock:  false,
-			UseMMap:   nil,
+			UseMMap:   true,
 			UseNUMA:   false,
 		},
 	}
@@ -609,17 +588,6 @@ func FormatParams(params map[string][]string) (map[string]interface{}, error) {
 				case reflect.Slice:
 					// TODO: only string slices are supported right now
 					out[key] = vals
-				case reflect.Pointer:
-					var b bool
-					if field.Type() == reflect.TypeOf(&b) {
-						boolVal, err := strconv.ParseBool(vals[0])
-						if err != nil {
-							return nil, fmt.Errorf("invalid bool value %s", vals)
-						}
-						out[key] = &boolVal
-					} else {
-						return nil, fmt.Errorf("unknown type %s for %s", field.Kind(), key)
-					}
 				default:
 					return nil, fmt.Errorf("unknown type %s for %s", field.Kind(), key)
 				}

api/types_test.go

@@ -2,7 +2,6 @@ package api
 
 import (
 	"encoding/json"
-	"fmt"
 	"math"
 	"testing"
 	"time"
@@ -106,105 +105,3 @@ func TestDurationMarshalUnmarshal(t *testing.T) {
 		})
 	}
 }
-
-func TestUseMmapParsingFromJSON(t *testing.T) {
-	tr := true
-	fa := false
-	tests := []struct {
-		name string
-		req  string
-		exp  *bool
-	}{
-		{
-			name: "Undefined",
-			req:  `{ }`,
-			exp:  nil,
-		},
-		{
-			name: "True",
-			req:  `{ "use_mmap": true }`,
-			exp:  &tr,
-		},
-		{
-			name: "False",
-			req:  `{ "use_mmap": false }`,
-			exp:  &fa,
-		},
-	}
-
-	for _, test := range tests {
-		t.Run(test.name, func(t *testing.T) {
-			var oMap map[string]interface{}
-			err := json.Unmarshal([]byte(test.req), &oMap)
-			require.NoError(t, err)
-			opts := DefaultOptions()
-			err = opts.FromMap(oMap)
-			require.NoError(t, err)
-			assert.Equal(t, test.exp, opts.UseMMap)
-		})
-	}
-}
-
-func TestUseMmapFormatParams(t *testing.T) {
-	tr := true
-	fa := false
-	tests := []struct {
-		name string
-		req  map[string][]string
-		exp  *bool
-		err  error
-	}{
-		{
-			name: "True",
-			req: map[string][]string{
-				"use_mmap": {"true"},
-			},
-			exp: &tr,
-			err: nil,
-		},
-		{
-			name: "False",
-			req: map[string][]string{
-				"use_mmap": {"false"},
-			},
-			exp: &fa,
-			err: nil,
-		},
-		{
-			name: "Numeric True",
-			req: map[string][]string{
-				"use_mmap": {"1"},
-			},
-			exp: &tr,
-			err: nil,
-		},
-		{
-			name: "Numeric False",
-			req: map[string][]string{
-				"use_mmap": {"0"},
-			},
-			exp: &fa,
-			err: nil,
-		},
-		{
-			name: "invalid string",
-			req: map[string][]string{
-				"use_mmap": {"foo"},
-			},
-			exp: nil,
-			err: fmt.Errorf("invalid bool value [foo]"),
-		},
-	}
-
-	for _, test := range tests {
-		t.Run(test.name, func(t *testing.T) {
-			resp, err := FormatParams(test.req)
-			require.Equal(t, test.err, err)
-			respVal, ok := resp["use_mmap"]
-			if test.exp != nil {
-				assert.True(t, ok, "resp: %v", resp)
-				assert.Equal(t, *test.exp, *respVal.(*bool))
-			}
-		})
-	}
-}

app/lifecycle/logging.go

@@ -5,8 +5,6 @@ import (
 	"log/slog"
 	"os"
 	"path/filepath"
-	"strconv"
-	"strings"
 
 	"github.com/ollama/ollama/envconfig"
 )
@@ -26,7 +24,6 @@ func InitLogging() {
 		logFile = os.Stderr
 		// TODO - write one-line to the app.log file saying we're running in console mode to help avoid confusion
 	} else {
-		rotateLogs(AppLogFile)
 		logFile, err = os.OpenFile(AppLogFile, os.O_APPEND|os.O_WRONLY|os.O_CREATE, 0755)
 		if err != nil {
 			slog.Error(fmt.Sprintf("failed to create server log %v", err))
@@ -49,32 +46,3 @@ func InitLogging() {
 
 	slog.Info("ollama app started")
 }
-
-func rotateLogs(logFile string) {
-	if _, err := os.Stat(logFile); os.IsNotExist(err) {
-		return
-	}
-	index := strings.LastIndex(logFile, ".")
-	pre := logFile[:index]
-	post := "." + logFile[index+1:]
-	for i := LogRotationCount; i > 0; i-- {
-		older := pre + "-" + strconv.Itoa(i) + post
-		newer := pre + "-" + strconv.Itoa(i-1) + post
-		if i == 1 {
-			newer = pre + post
-		}
-		if _, err := os.Stat(newer); err == nil {
-			if _, err := os.Stat(older); err == nil {
-				err := os.Remove(older)
-				if err != nil {
-					slog.Warn("Failed to remove older log", "older", older, "error", err)
-					continue
-				}
-			}
-			err := os.Rename(newer, older)
-			if err != nil {
-				slog.Warn("Failed to rotate log", "older", older, "newer", newer, "error", err)
-			}
-		}
-	}
-}

app/lifecycle/logging_test.go

@@ -1,44 +0,0 @@
-package lifecycle
-
-import (
-	"os"
-	"path/filepath"
-	"strconv"
-	"testing"
-
-	"github.com/stretchr/testify/assert"
-	"github.com/stretchr/testify/require"
-)
-
-func TestRotateLogs(t *testing.T) {
-	logDir := t.TempDir()
-	logFile := filepath.Join(logDir, "testlog.log")
-
-	// No log exists
-	rotateLogs(logFile)
-
-	require.NoError(t, os.WriteFile(logFile, []byte("1"), 0644))
-	assert.FileExists(t, logFile)
-	// First rotation
-	rotateLogs(logFile)
-	assert.FileExists(t, filepath.Join(logDir, "testlog-1.log"))
-	assert.NoFileExists(t, filepath.Join(logDir, "testlog-2.log"))
-	assert.NoFileExists(t, logFile)
-
-	// Should be a no-op without a new log
-	rotateLogs(logFile)
-	assert.FileExists(t, filepath.Join(logDir, "testlog-1.log"))
-	assert.NoFileExists(t, filepath.Join(logDir, "testlog-2.log"))
-	assert.NoFileExists(t, logFile)
-
-	for i := 2; i <= LogRotationCount+1; i++ {
-		require.NoError(t, os.WriteFile(logFile, []byte(strconv.Itoa(i)), 0644))
-		assert.FileExists(t, logFile)
-		rotateLogs(logFile)
-		assert.NoFileExists(t, logFile)
-		for j := 1; j < i; j++ {
-			assert.FileExists(t, filepath.Join(logDir, "testlog-"+strconv.Itoa(j)+".log"))
-		}
-		assert.NoFileExists(t, filepath.Join(logDir, "testlog-"+strconv.Itoa(i+1)+".log"))
-	}
-}

app/lifecycle/paths.go

@@ -16,12 +16,11 @@ var (
 	AppDir     = "/opt/Ollama"
 	AppDataDir = "/opt/Ollama"
 	// TODO - should there be a distinct log dir?
-	UpdateStageDir   = "/tmp"
-	AppLogFile       = "/tmp/ollama_app.log"
-	ServerLogFile    = "/tmp/ollama.log"
-	UpgradeLogFile   = "/tmp/ollama_update.log"
-	Installer        = "OllamaSetup.exe"
-	LogRotationCount = 5
+	UpdateStageDir = "/tmp"
+	AppLogFile     = "/tmp/ollama_app.log"
+	ServerLogFile  = "/tmp/ollama.log"
+	UpgradeLogFile = "/tmp/ollama_update.log"
+	Installer      = "OllamaSetup.exe"
 )
 
 func init() {

app/lifecycle/server.go

@@ -54,7 +54,7 @@ func start(ctx context.Context, command string) (*exec.Cmd, error) {
 		return nil, fmt.Errorf("failed to spawn server stderr pipe: %w", err)
 	}
 
-	rotateLogs(ServerLogFile)
+	// TODO - rotation
 	logFile, err := os.OpenFile(ServerLogFile, os.O_APPEND|os.O_WRONLY|os.O_CREATE, 0755)
 	if err != nil {
 		return nil, fmt.Errorf("failed to create server log: %w", err)

app/ollama.iss

@@ -88,15 +88,10 @@ DialogFontSize=12
 [Files]
 Source: ".\app.exe"; DestDir: "{app}"; DestName: "{#MyAppExeName}" ; Flags: ignoreversion 64bit
 Source: "..\ollama.exe"; DestDir: "{app}"; Flags: ignoreversion 64bit
+Source: "..\dist\windows-{#ARCH}\*.dll"; DestDir: "{app}"; Flags: ignoreversion 64bit
 Source: "..\dist\windows-{#ARCH}\ollama_runners\*"; DestDir: "{app}\ollama_runners"; Flags: ignoreversion 64bit recursesubdirs
 Source: "..\dist\ollama_welcome.ps1"; DestDir: "{app}"; Flags: ignoreversion
 Source: ".\assets\app.ico"; DestDir: "{app}"; Flags: ignoreversion
-#if DirExists("..\dist\windows-amd64\cuda")
-  Source: "..\dist\windows-amd64\cuda\*"; DestDir: "{app}\cuda\"; Flags: ignoreversion recursesubdirs
-#endif
-#if DirExists("..\dist\windows-amd64\oneapi")
-  Source: "..\dist\windows-amd64\oneapi\*"; DestDir: "{app}\oneapi\"; Flags: ignoreversion recursesubdirs
-#endif
 #if DirExists("..\dist\windows-amd64\rocm")
   Source: "..\dist\windows-amd64\rocm\*"; DestDir: "{app}\rocm\"; Flags: ignoreversion recursesubdirs
 #endif

cmd/cmd.go

@@ -162,6 +162,9 @@ func tempZipFiles(path string) (string, error) {
 	}
 	defer tempfile.Close()
 
+	zipfile := zip.NewWriter(tempfile)
+	defer zipfile.Close()
+
 	detectContentType := func(path string) (string, error) {
 		f, err := os.Open(path)
 		if err != nil {
@@ -230,9 +233,6 @@ func tempZipFiles(path string) (string, error) {
 		files = append(files, tks...)
 	}
 
-	zipfile := zip.NewWriter(tempfile)
-	defer zipfile.Close()
-
 	for _, file := range files {
 		f, err := os.Open(file)
 		if err != nil {
@@ -287,12 +287,38 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string) (string, er
 }
 
 func RunHandler(cmd *cobra.Command, args []string) error {
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return err
+	}
+
+	name := args[0]
+
+	// check if the model exists on the server
+	show, err := client.Show(cmd.Context(), &api.ShowRequest{Name: name})
+	var statusError api.StatusError
+	switch {
+	case errors.As(err, &statusError) && statusError.StatusCode == http.StatusNotFound:
+		if err := PullHandler(cmd, []string{name}); err != nil {
+			return err
+		}
+
+		show, err = client.Show(cmd.Context(), &api.ShowRequest{Name: name})
+		if err != nil {
+			return err
+		}
+	case err != nil:
+		return err
+	}
+
 	interactive := true
 
 	opts := runOptions{
-		Model:    args[0],
-		WordWrap: os.Getenv("TERM") == "xterm-256color",
-		Options:  map[string]interface{}{},
+		Model:       args[0],
+		WordWrap:    os.Getenv("TERM") == "xterm-256color",
+		Options:     map[string]interface{}{},
+		MultiModal:  slices.Contains(show.Details.Families, "clip"),
+		ParentModel: show.Details.ParentModel,
 	}
 
 	format, err := cmd.Flags().GetString("format")
@@ -336,38 +362,11 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 	}
 	opts.WordWrap = !nowrap
 
-	// Fill out the rest of the options based on information about the
-	// model.
-	client, err := api.ClientFromEnvironment()
-	if err != nil {
-		return err
+	if !interactive {
+		return generate(cmd, opts)
 	}
 
-	name := args[0]
-	info, err := func() (*api.ShowResponse, error) {
-		showReq := &api.ShowRequest{Name: name}
-		info, err := client.Show(cmd.Context(), showReq)
-		var se api.StatusError
-		if errors.As(err, &se) && se.StatusCode == http.StatusNotFound {
-			if err := PullHandler(cmd, []string{name}); err != nil {
-				return nil, err
-			}
-			return client.Show(cmd.Context(), &api.ShowRequest{Name: name})
-		}
-		return info, err
-	}()
-	if err != nil {
-		return err
-	}
-
-	opts.MultiModal = slices.Contains(info.Details.Families, "clip")
-	opts.ParentModel = info.Details.ParentModel
-	opts.Messages = append(opts.Messages, info.Messages...)
-
-	if interactive {
-		return generateInteractive(cmd, opts)
-	}
-	return generate(cmd, opts)
+	return generateInteractive(cmd, opts)
 }
 
 func errFromUnknownKey(unknownKeyErr error) error {
@@ -580,6 +579,10 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
+	if len(args) != 1 {
+		return errors.New("missing model name")
+	}
+
 	license, errLicense := cmd.Flags().GetBool("license")
 	modelfile, errModelfile := cmd.Flags().GetBool("modelfile")
 	parameters, errParams := cmd.Flags().GetBool("parameters")
@@ -622,6 +625,8 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 
 	if flagsSet > 1 {
 		return errors.New("only one of '--license', '--modelfile', '--parameters', '--system', or '--template' can be specified")
+	} else if flagsSet == 0 {
+		return errors.New("one of '--license', '--modelfile', '--parameters', '--system', or '--template' must be specified")
 	}
 
 	req := api.ShowRequest{Name: args[0]}
@@ -630,141 +635,22 @@ func ShowHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	if flagsSet == 1 {
-		switch showType {
-		case "license":
-			fmt.Println(resp.License)
-		case "modelfile":
-			fmt.Println(resp.Modelfile)
-		case "parameters":
-			fmt.Println(resp.Parameters)
-		case "system":
-			fmt.Println(resp.System)
-		case "template":
-			fmt.Println(resp.Template)
-		}
-
-		return nil
+	switch showType {
+	case "license":
+		fmt.Println(resp.License)
+	case "modelfile":
+		fmt.Println(resp.Modelfile)
+	case "parameters":
+		fmt.Println(resp.Parameters)
+	case "system":
+		fmt.Println(resp.System)
+	case "template":
+		fmt.Println(resp.Template)
 	}
 
-	showInfo(resp)
-
 	return nil
 }
 
-func showInfo(resp *api.ShowResponse) {
-	arch := resp.ModelInfo["general.architecture"].(string)
-
-	modelData := [][]string{
-		{"arch", arch},
-		{"parameters", resp.Details.ParameterSize},
-		{"quantization", resp.Details.QuantizationLevel},
-		{"context length", fmt.Sprintf("%v", resp.ModelInfo[fmt.Sprintf("%s.context_length", arch)].(float64))},
-		{"embedding length", fmt.Sprintf("%v", resp.ModelInfo[fmt.Sprintf("%s.embedding_length", arch)].(float64))},
-	}
-
-	mainTableData := [][]string{
-		{"Model"},
-		{renderSubTable(modelData, false)},
-	}
-
-	if resp.ProjectorInfo != nil {
-		projectorData := [][]string{
-			{"arch", "clip"},
-			{"parameters", format.HumanNumber(uint64(resp.ProjectorInfo["general.parameter_count"].(float64)))},
-		}
-
-		if projectorType, ok := resp.ProjectorInfo["clip.projector_type"]; ok {
-			projectorData = append(projectorData, []string{"projector type", projectorType.(string)})
-		}
-
-		projectorData = append(projectorData,
-			[]string{"embedding length", fmt.Sprintf("%v", resp.ProjectorInfo["clip.vision.embedding_length"].(float64))},
-			[]string{"projection dimensionality", fmt.Sprintf("%v", resp.ProjectorInfo["clip.vision.projection_dim"].(float64))},
-		)
-
-		mainTableData = append(mainTableData,
-			[]string{"Projector"},
-			[]string{renderSubTable(projectorData, false)},
-		)
-	}
-
-	if resp.Parameters != "" {
-		mainTableData = append(mainTableData, []string{"Parameters"}, []string{formatParams(resp.Parameters)})
-	}
-
-	if resp.System != "" {
-		mainTableData = append(mainTableData, []string{"System"}, []string{renderSubTable(twoLines(resp.System), true)})
-	}
-
-	if resp.License != "" {
-		mainTableData = append(mainTableData, []string{"License"}, []string{renderSubTable(twoLines(resp.License), true)})
-	}
-
-	table := tablewriter.NewWriter(os.Stdout)
-	table.SetAutoWrapText(false)
-	table.SetBorder(false)
-	table.SetAlignment(tablewriter.ALIGN_LEFT)
-
-	for _, v := range mainTableData {
-		table.Append(v)
-	}
-
-	table.Render()
-}
-
-func renderSubTable(data [][]string, file bool) string {
-	var buf bytes.Buffer
-	table := tablewriter.NewWriter(&buf)
-	table.SetAutoWrapText(!file)
-	table.SetBorder(false)
-	table.SetNoWhiteSpace(true)
-	table.SetTablePadding("\t")
-	table.SetAlignment(tablewriter.ALIGN_LEFT)
-
-	for _, v := range data {
-		table.Append(v)
-	}
-
-	table.Render()
-
-	renderedTable := buf.String()
-	lines := strings.Split(renderedTable, "\n")
-	for i, line := range lines {
-		lines[i] = "\t" + line
-	}
-
-	return strings.Join(lines, "\n")
-}
-
-func twoLines(s string) [][]string {
-	lines := strings.Split(s, "\n")
-	res := [][]string{}
-
-	count := 0
-	for _, line := range lines {
-		line = strings.TrimSpace(line)
-		if line != "" {
-			count++
-			res = append(res, []string{line})
-			if count == 2 {
-				return res
-			}
-		}
-	}
-	return res
-}
-
-func formatParams(s string) string {
-	lines := strings.Split(s, "\n")
-	table := [][]string{}
-
-	for _, line := range lines {
-		table = append(table, strings.Fields(line))
-	}
-	return renderSubTable(table, false)
-}
-
 func CopyHandler(cmd *cobra.Command, args []string) error {
 	client, err := api.ClientFromEnvironment()
 	if err != nil {

cmd/interactive.go

@@ -31,40 +31,65 @@ const (
 )
 
 func loadModel(cmd *cobra.Command, opts *runOptions) error {
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return err
+	}
+
 	p := progress.NewProgress(os.Stderr)
 	defer p.StopAndClear()
 
 	spinner := progress.NewSpinner("")
 	p.Add("", spinner)
 
-	client, err := api.ClientFromEnvironment()
+	showReq := api.ShowRequest{Name: opts.Model}
+	showResp, err := client.Show(cmd.Context(), &showReq)
 	if err != nil {
 		return err
 	}
+	opts.MultiModal = slices.Contains(showResp.Details.Families, "clip")
+	opts.ParentModel = showResp.Details.ParentModel
 
-	chatReq := &api.ChatRequest{
-		Model:     opts.Model,
-		KeepAlive: opts.KeepAlive,
+	if len(showResp.Messages) > 0 {
+		opts.Messages = append(opts.Messages, showResp.Messages...)
 	}
 
-	return client.Chat(cmd.Context(), chatReq, func(resp api.ChatResponse) error {
+	chatReq := &api.ChatRequest{
+		Model:    opts.Model,
+		Messages: []api.Message{},
+	}
+
+	if opts.KeepAlive != nil {
+		chatReq.KeepAlive = opts.KeepAlive
+	}
+
+	err = client.Chat(cmd.Context(), chatReq, func(resp api.ChatResponse) error {
 		p.StopAndClear()
-		for _, msg := range opts.Messages {
-			switch msg.Role {
-			case "user":
-				fmt.Printf(">>> %s\n", msg.Content)
-			case "assistant":
-				state := &displayResponseState{}
-				displayResponse(msg.Content, opts.WordWrap, state)
-				fmt.Println()
-				fmt.Println()
+		if len(opts.Messages) > 0 {
+			for _, msg := range opts.Messages {
+				switch msg.Role {
+				case "user":
+					fmt.Printf(">>> %s\n", msg.Content)
+				case "assistant":
+					state := &displayResponseState{}
+					displayResponse(msg.Content, opts.WordWrap, state)
+					fmt.Println()
+					fmt.Println()
+				}
 			}
 		}
 		return nil
 	})
+	if err != nil {
+		return err
+	}
+
+	return nil
 }
 
 func generateInteractive(cmd *cobra.Command, opts runOptions) error {
+	opts.Messages = make([]api.Message, 0)
+
 	err := loadModel(cmd, &opts)
 	if err != nil {
 		return err
@@ -404,7 +429,15 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 
 				switch args[1] {
 				case "info":
-					showInfo(resp)
+					fmt.Println("Model details:")
+					if len(resp.Details.Families) > 0 {
+						fmt.Printf("Family              %s\n", strings.Join(resp.Details.Families, ", "))
+					} else if resp.Details.Family != "" {
+						fmt.Printf("Family              %s\n", resp.Details.Family)
+					}
+					fmt.Printf("Parameter Size      %s\n", resp.Details.ParameterSize)
+					fmt.Printf("Quantization Level  %s\n", resp.Details.QuantizationLevel)
+					fmt.Println("")
 				case "license":
 					if resp.License == "" {
 						fmt.Println("No license was specified for this model.")

convert/convert.go

@@ -1,134 +1,200 @@
 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"
 )
 
-type Parameters struct {
-	Architectures []string `json:"architectures"`
-	VocabSize     uint32   `json:"vocab_size"`
+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
 }
 
-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 ByteOrder interface {
+	binary.ByteOrder
+	binary.AppendByteOrder
 }
 
-func (Parameters) specialTypes() []string {
-	return []string{
-		"bos", "eos", "unk", "sep", "pad", "cls", "mask",
-	}
+type ModelArch interface {
+	GetTensors() error
+	LoadVocab() error
+	WriteGGUF(io.WriteSeeker) error
 }
 
-func (Parameters) writeFile(ws io.WriteSeeker, kv llm.KV, ts []*llm.Tensor) error {
-	return llm.WriteGGUF(ws, kv, ts)
+type ModelFormat interface {
+	GetLayerName(string) (string, error)
+	GetTensors(string, *Params) ([]llm.Tensor, error)
+	GetParams(string) (*Params, error)
+	GetModelArch(string, string, *Params) (ModelArch, error)
 }
 
-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
+type ModelData struct {
+	Path    string
+	Name    string
+	Params  *Params
+	Vocab   *Vocab
+	Tensors []llm.Tensor
+	Format  ModelFormat
 }
 
-func ConvertAdapter(d string, ws io.WriteSeeker) error {
-	c := &adapter{}
-
-	ts, err := parseNPZ(d)
+func GetModelFormat(dirname string) (ModelFormat, error) {
+	files, err := filepath.Glob(filepath.Join(dirname, "*"))
 	if err != nil {
-		return err
+		return nil, err
 	}
 
-	return c.writeFile(ws, c.KV(nil), c.Tensors(ts))
-}
-
-func Convert(d string, ws io.WriteSeeker) error {
-	f, err := os.Open(filepath.Join(d, "config.json"))
-	if err != nil {
-		return err
-	}
-	defer f.Close()
-
-	var p Parameters
-	if err := json.NewDecoder(f).Decode(&p); err != nil {
-		return err
-	}
-
-	if len(p.Architectures) < 1 {
-		return errors.New("unknown architecture")
-	}
-
-	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")
-	}
-
-	bts, err := os.ReadFile(filepath.Join(d, "config.json"))
-	if err != nil {
-		return err
-	}
-
-	if err := json.Unmarshal(bts, c); err != nil {
-		return err
-	}
-
-	t, err := parseTokenizer(d, c.specialTypes())
-	if err != nil {
-		return err
-	}
-
-	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)
+	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
 		}
 	}
 
-	ts, err := parseTensors(d)
+	return nil, fmt.Errorf("couldn't determine model format")
+}
+
+// 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"))
 	if err != nil {
-		return err
+		return nil, err
 	}
 
-	return c.writeFile(ws, c.KV(t), c.Tensors(ts))
+	// 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
+	}
+
+	v := &Vocab{
+		Tokens: make([]string, 0),
+		Scores: make([]float32, 0),
+		Types:  make([]int32, 0),
+	}
+
+	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))
+	}
+
+	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
+	}
+
+	slog.Info("reading user defined tokens")
+
+	var extraTokenData map[string]int
+	if err := json.Unmarshal(addIn, &extraTokenData); err != nil {
+		return nil, 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)
+		}
+	}
+
+	return v, nil
 }

convert/convert_adapter.go

@@ -1,56 +0,0 @@
-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

@@ -1,103 +0,0 @@
-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

@@ -1,176 +0,0 @@
-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

@@ -1,89 +0,0 @@
-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

@@ -1,34 +1,48 @@
+//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, d string) (*os.File, llm.KV, llm.Tensors) {
+func convertFull(t *testing.T, p string) (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 := Convert(d, f); err != nil {
+	if err := arch.WriteGGUF(f); err != nil {
 		t.Fatal(err)
 	}
 
@@ -36,200 +50,54 @@ func convertFull(t *testing.T, d string) (*os.File, llm.KV, llm.Tensors) {
 	if err != nil {
 		t.Fatal(err)
 	}
-	t.Cleanup(func() { r.Close() })
+	defer r.Close()
 
-	m, _, err := llm.DecodeGGML(r, math.MaxInt)
+	m, _, err := llm.DecodeGGML(r)
 	if err != nil {
 		t.Fatal(err)
 	}
 
-	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())
+	return m.KV(), m.Tensors()
 }
 
 func TestConvertFull(t *testing.T) {
-	cases := []string{
-		"Meta-Llama-3-8B-Instruct",
-		"Mistral-7B-Instruct-v0.2",
-		"Mixtral-8x7B-Instruct-v0.1",
-		"gemma-2b-it",
+	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},
 	}
 
-	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 {
+	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 {
 				t.Skipf("%s not found", 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)
-					}
+			kv, tensors := convertFull(t, p)
 
-					actual[k] = fmt.Sprintf("%x", sha256.Sum256(bts))
-				}
+			if kv.Architecture() != tt.arch {
+				t.Fatalf("expected llama, got %s", kv.Architecture())
 			}
 
-			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 kv.FileType().String() != "F16" {
+				t.Fatalf("expected F16, got %s", kv.FileType())
 			}
 
-			expectFile, err := os.Open(filepath.Join("testdata", fmt.Sprintf("%s.json", tt)))
-			if err != nil {
-				t.Fatal(err)
+			if len(tensors) != tt.tensors {
+				t.Fatalf("expected %d tensors, got %d", tt.tensors, len(tensors))
 			}
 
-			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)
-				}
+			layers := tensors.Layers()
+			if len(layers) != tt.layers {
+				t.Fatalf("expected %d layers, got %d", tt.layers, len(layers))
 			}
 		})
 	}
 }
-
-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

@@ -0,0 +1,102 @@
+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

@@ -0,0 +1,159 @@
+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

@@ -0,0 +1,79 @@
+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),
+	}
+
+	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

@@ -0,0 +1,87 @@
+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

@@ -1,74 +0,0 @@
-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

@@ -1,140 +0,0 @@
-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

@@ -1,140 +0,0 @@
-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

@@ -1,46 +0,0 @@
-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

@@ -0,0 +1,309 @@
+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

@@ -1,313 +0,0 @@
-{
-  "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",
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-}

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

@@ -1,313 +0,0 @@
-{
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-  "general.file_type": "1",
-  "general.quantization_version": "2",
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-}

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

@@ -1,348 +0,0 @@
-{
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-  "general.file_type": "1",
-  "general.quantization_version": "2",
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convert/testdata/gemma-2b-it.json

@@ -1,188 +0,0 @@
-{
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-  "blk.12.ffn_up.weight": "d22eacc612a7411953d948483c5fb201e11722955ee0754da866e7bec578ac6d",
-  "blk.13.attn_k.weight": "5864977e6b733ea942647d6feed5c76156c48c200649c22e4e11b9e5860e57f3",
-  "blk.13.attn_norm.weight": "87e053535144723db4145aa5402acc54331b7696752d852bb9fc542ff33f0fb5",
-  "blk.13.attn_output.weight": "078145f5ad83f8b14f97a869346f7fd1583b24d1e3edadaa95d3da4242973f8f",
-  "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

@@ -3,148 +3,19 @@ package convert
 import (
 	"cmp"
 	"crypto/sha256"
-	"encoding/hex"
 	"encoding/json"
-	"errors"
 	"fmt"
 	"log/slog"
 	"os"
-	"path/filepath"
 	"slices"
-)
 
-const (
-	_ int32 = iota
-	tokenTypeNormal
-	tokenTypeUnknown
-	tokenTypeControl
-	tokenTypeUserDefined
-	tokenTypeUnused
-	tokenTypeByte
+	"golang.org/x/exp/maps"
 )
 
 type Tokenizer struct {
-	*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"`
+	Version     string         `json:"version"`
+	AddedTokens []Token        `json:"added_tokens"`
+	Model       TokenizerModel `json:"model"`
 
 	PreTokenizer struct {
 		PreTokenizers []struct {
@@ -156,106 +27,80 @@ type tokenizer struct {
 	} `json:"pre_tokenizer"`
 }
 
-type token struct {
+type TokenizerModel struct {
+	Type   string         `json:"type"`
+	Vocab  map[string]int `json:"vocab"`
+	Merges []string       `json:"merges"`
+	Tokens []Token
+}
+
+type Token struct {
 	ID          int    `json:"id"`
 	Content     string `json:"content"`
 	Special     bool   `json:"special"`
 	UserDefined bool
 }
 
-type Vocabulary struct {
-	Model  string
-	Tokens []string
-	Scores []float32
-	Types  []int32
+func (t *Token) Type() int32 {
+	switch {
+	case t.Special:
+		return tokenTypeControl
+	case t.UserDefined:
+		return tokenTypeUserDefined
+	default:
+		return tokenTypeNormal
+	}
 }
 
-func parseVocabularyFromTokenizer(p string) (*Vocabulary, error) {
-	f, err := os.Open(filepath.Join(p, "tokenizer.json"))
+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)
 	if err != nil {
-		return nil, err
+		panic(err)
 	}
 	defer f.Close()
 
-	var t tokenizer
+	var t Tokenizer
 	if err := json.NewDecoder(f).Decode(&t); err != nil {
-		return nil, err
+		return "", nil, nil, err
 	}
 
-	var tokens []token
+	tokens = make([]Token, t.maxID()+1)
 	for k, v := range t.Model.Vocab {
-		tokens = append(tokens, token{
-			ID:      v,
-			Content: k,
-		})
+		tokens[v] = Token{ID: v, Content: k, Special: false, UserDefined: false}
 	}
 
-	for _, t := range t.AddedTokens {
-		t.UserDefined = true
-		tokens = append(tokens, t)
+	for _, v := range t.AddedTokens {
+		v.UserDefined = true
+		tokens[v.ID] = v
 	}
 
-	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)
+	sha256sum := sha256.New()
+	for _, pt := range t.PreTokenizer.PreTokenizers {
+		if pt.Type == "Split" && pt.Pattern.Regex != "" {
+			sha256sum.Write([]byte(pt.Pattern.Regex))
 		}
 	}
 
-	return &v, nil
-}
-
-func parseVocabulary(d string) (*Vocabulary, error) {
-	patterns := map[string]func(string) (*Vocabulary, error){
-		"tokenizer.model": parseSentencePiece,
-		"tokenizer.json":  parseVocabularyFromTokenizer,
+	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"
 	}
 
-	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")
+	return pre, tokens, t.Model.Merges, nil
 }

convert/tokenizer_spm.go

@@ -1,83 +0,0 @@
-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

@@ -0,0 +1,287 @@
+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

@@ -26,7 +26,7 @@ All durations are returned in nanoseconds.
 
 ### Streaming responses
 
-Certain endpoints stream responses as JSON objects. Streaming can be disabled by providing `{"stream": false}` for these endpoints.
+Certain endpoints stream responses as JSON objects and can optional return non-streamed responses.
 
 ## Generate a completion
 
@@ -777,12 +777,11 @@ A single JSON object will be returned.
 POST /api/show
 ```
 
-Show information about a model including details, modelfile, template, parameters, license, system prompt.
+Show information about a model including details, modelfile, template, parameters, license, and system prompt.
 
 ### Parameters
 
 - `name`: name of the model to show
-- `verbose`: (optional) if set to `true`, returns full data for verbose response fields
 
 ### Examples
 
@@ -799,40 +798,14 @@ curl http://localhost:11434/api/show -d '{
 ```json
 {
   "modelfile": "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this one, replace the FROM line with:\n# FROM llava:latest\n\nFROM /Users/matt/.ollama/models/blobs/sha256:200765e1283640ffbd013184bf496e261032fa75b99498a9613be4e94d63ad52\nTEMPLATE \"\"\"{{ .System }}\nUSER: {{ .Prompt }}\nASSISTANT: \"\"\"\nPARAMETER num_ctx 4096\nPARAMETER stop \"\u003c/s\u003e\"\nPARAMETER stop \"USER:\"\nPARAMETER stop \"ASSISTANT:\"",
-  "parameters": "num_keep                       24\nstop                           \"<|start_header_id|>\"\nstop                           \"<|end_header_id|>\"\nstop                           \"<|eot_id|>\"",
-  "template": "{{ if .System }}<|start_header_id|>system<|end_header_id|>\n\n{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>\n\n{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>\n\n{{ .Response }}<|eot_id|>",
+  "parameters": "num_ctx                        4096\nstop                           \u003c/s\u003e\nstop                           USER:\nstop                           ASSISTANT:",
+  "template": "{{ .System }}\nUSER: {{ .Prompt }}\nASSISTANT: ",
   "details": {
-    "parent_model": "",
     "format": "gguf",
     "family": "llama",
-    "families": [
-      "llama"
-    ],
-    "parameter_size": "8.0B",
+    "families": ["llama", "clip"],
+    "parameter_size": "7B",
     "quantization_level": "Q4_0"
-  },
-  "model_info": {
-    "general.architecture": "llama",
-    "general.file_type": 2,
-    "general.parameter_count": 8030261248,
-    "general.quantization_version": 2,
-    "llama.attention.head_count": 32,
-    "llama.attention.head_count_kv": 8,
-    "llama.attention.layer_norm_rms_epsilon": 0.00001,
-    "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,
-    "tokenizer.ggml.bos_token_id": 128000,
-    "tokenizer.ggml.eos_token_id": 128009,
-    "tokenizer.ggml.merges": [],            // populates if `verbose=true`
-    "tokenizer.ggml.model": "gpt2",
-    "tokenizer.ggml.pre": "llama-bpe",
-    "tokenizer.ggml.token_type": [],        // populates if `verbose=true`
-    "tokenizer.ggml.tokens": []             // populates if `verbose=true`
   }
 }
 ```

docs/development.md

@@ -104,7 +104,7 @@ like to use. For example, to compile an optimized binary for an Intel i9-9880H,
 you might use:
 
 ```
-OLLAMA_CUSTOM_CPU_DEFS="-DGGML_AVX=on -DGGML_AVX2=on -DGGML_F16C=on -DGGML_FMA=on" go generate ./...
+OLLAMA_CUSTOM_CPU_DEFS="-DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_F16C=on -DLLAMA_FMA=on" go generate ./...
 go build .
 ```
 

docs/faq.md

@@ -257,19 +257,3 @@ If you wish to override the `OLLAMA_KEEP_ALIVE` setting, use the `keep_alive` AP
 ## How do I manage the maximum number of requests the Ollama server can queue?
 
 If too many requests are sent to the server, it will respond with a 503 error indicating the server is overloaded.  You can adjust how many requests may be queue by setting `OLLAMA_MAX_QUEUE`.
-
-## How does Ollama handle concurrent requests?
-
-Ollama supports two levels of concurrent processing.  If your system has sufficient available memory (system memory when using CPU inference, or VRAM for GPU inference) then multiple models can be loaded at the same time.  For a given model, if there is sufficient available memory when the model is loaded, it is configured to allow parallel request processing.
-
-If there is insufficient available memory to load a new model request while one or more models are already loaded, all new requests will be queued until the new model can be loaded.  As prior models become idle, one or more will be unloaded to make room for the new model.  Queued requests will be processed in order.  When using GPU inference new models must be able to completely fit in VRAM to allow concurrent model loads.
-
-Parallel request processing for a given model results in increasing the context size by the number of parallel requests.  For example, a 2K context with 4 parallel requests will result in an 8K context and additional memory allocation.
-
-The following server settings may be used to adjust how Ollama handles concurrent requests on most platforms:
-
-- `OLLAMA_MAX_LOADED_MODELS` - The maximum number of models that can be loaded concurrently provided they fit in available memory.  The default is 3 * the number of GPUs or 3 for CPU inference.
-- `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 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

@@ -18,7 +18,7 @@ Check your compute compatibility to see if your card is supported:
 |                    | Quadro              | `RTX 8000` `RTX 6000` `RTX 5000` `RTX 4000`                                                                 |
 | 7.0                | NVIDIA              | `TITAN V` `V100` `Quadro GV100`                                                                             |
 | 6.1                | NVIDIA TITAN        | `TITAN Xp` `TITAN X`                                                                                        |
-|                    | GeForce GTX         | `GTX 1080 Ti` `GTX 1080` `GTX 1070 Ti` `GTX 1070` `GTX 1060` `GTX 1050 Ti` `GTX 1050`                       |
+|                    | GeForce GTX         | `GTX 1080 Ti` `GTX 1080` `GTX 1070 Ti` `GTX 1070` `GTX 1060` `GTX 1050`                                     |
 |                    | Quadro              | `P6000` `P5200` `P4200` `P3200` `P5000` `P4000` `P3000` `P2200` `P2000` `P1000` `P620` `P600` `P500` `P520` |
 |                    | Tesla               | `P40` `P4`                                                                                                  |
 | 6.0                | NVIDIA              | `Tesla P100` `Quadro GP100`                                                                                 |

docs/import.md

@@ -47,13 +47,19 @@ success
 
 ### Supported Quantizations
 
+<details>
+<summary>Legacy Quantization</summary>
+
 - `Q4_0`
 - `Q4_1`
 - `Q5_0`
 - `Q5_1`
 - `Q8_0`
 
-#### K-means Quantizations
+</details>
+
+<details>
+<summary>K-means Quantization</summary>`
 
 - `Q3_K_S`
 - `Q3_K_M`
@@ -64,6 +70,11 @@ success
 - `Q5_K_M`
 - `Q6_K`
 
+</details>
+
+> [!NOTE]
+> Activation-aware Weight Quantization (i.e. IQ) are not currently supported for automatic quantization however you can still import the quantized model into Ollama, see [Import GGUF](#import-gguf).
+
 ## Template Detection
 
 > [!NOTE]

docs/openai.md

@@ -65,7 +65,6 @@ curl http://localhost:11434/v1/chat/completions \
             }
         ]
     }'
-
 ```
 
 ## Endpoints
@@ -105,6 +104,7 @@ curl http://localhost:11434/v1/chat/completions \
 
 #### Notes
 
+- `finish_reason` will always be `stop`
 - `usage.prompt_tokens` will be 0 for completions where prompt evaluation is cached
 
 ## Models

docs/troubleshooting.md

@@ -22,7 +22,7 @@ docker logs <container-name>
 If manually running `ollama serve` in a terminal, the logs will be on that terminal.
 
 When you run Ollama on **Windows**, there are a few different locations. You can view them in the explorer window by hitting `<cmd>+R` and type in:
-- `explorer %LOCALAPPDATA%\Ollama` to view logs.  The most recent server logs will be in `server.log` and older logs will be in `server-#.log` 
+- `explorer %LOCALAPPDATA%\Ollama` to view logs
 - `explorer %LOCALAPPDATA%\Programs\Ollama` to browse the binaries (The installer adds this to your user PATH)
 - `explorer %HOMEPATH%\.ollama` to browse where models and configuration is stored
 - `explorer %TEMP%` where temporary executable files are stored in one or more `ollama*` directories
@@ -70,18 +70,14 @@ curl -fsSL https://ollama.com/install.sh | OLLAMA_VERSION="0.1.29" sh
 
 If your system is configured with the "noexec" flag where Ollama stores its temporary executable files, you can specify an alternate location by setting OLLAMA_TMPDIR to a location writable by the user ollama runs as. For example OLLAMA_TMPDIR=/usr/share/ollama/
 
-## NVIDIA GPU Discovery
+## Container fails to run on NVIDIA GPU
 
-When Ollama starts up, it takes inventory of the GPUs present in the system to determine compatibility and how much VRAM is available.  Sometimes this discovery can fail to find your GPUs.  In general, running the latest driver will yield the best results.
+Make sure you've set up the container runtime first as described in [docker.md](./docker.md)
 
-### Linux NVIDIA Troubleshooting
+Sometimes the container runtime can have difficulties initializing the GPU. When you check the server logs, this can show up as various error codes, such as "3" (not initialized), "46" (device unavailable), "100" (no device), "999" (unknown), or others. The following troubleshooting techniques may help resolve the problem
 
-If you are using a container to run Ollama, make sure you've set up the container runtime first as described in [docker.md](./docker.md)
-
-Sometimes the Ollama can have difficulties initializing the GPU. When you check the server logs, this can show up as various error codes, such as "3" (not initialized), "46" (device unavailable), "100" (no device), "999" (unknown), or others. The following troubleshooting techniques may help resolve the problem
-
-- If you are using a container, is the container runtime working?  Try `docker run --gpus all ubuntu nvidia-smi` - if this doesn't work, Ollama wont be able to see your NVIDIA GPU.
-- Is the uvm driver loaded? `sudo nvidia-modprobe -u`
+- Is the container runtime working?  Try `docker run --gpus all ubuntu nvidia-smi` - if this doesn't work, Ollama wont be able to see your NVIDIA GPU.
+- Is the uvm driver not loaded? `sudo nvidia-modprobe -u`
 - Try reloading the nvidia_uvm driver - `sudo rmmod nvidia_uvm` then `sudo modprobe nvidia_uvm`
 - Try rebooting
 - Make sure you're running the latest nvidia drivers
@@ -89,8 +85,3 @@ Sometimes the Ollama can have difficulties initializing the GPU. When you check
 If none of those resolve the problem, gather additional information and file an issue:
 - Set `CUDA_ERROR_LEVEL=50` and try again to get more diagnostic logs
 - Check dmesg for any errors `sudo dmesg | grep -i nvrm` and `sudo dmesg | grep -i nvidia`
-
-
-## Windows Terminal Errors
-
-Older versions of Windows 10 (e.g., 21H1) are known to have a bug where the standard terminal program does not display control characters correctly.  This can result in a long string of strings like `←[?25h←[?25l` being displayed, sometimes erroring with `The parameter is incorrect`  To resolve this problem, please update to Win 10 22H1 or newer.

docs/windows.md

@@ -19,7 +19,7 @@ Logs will often be helpful in diagnosing the problem (see
 
 ## System Requirements
 
-* Windows 10 22H2 or newer, Home or Pro
+* Windows 10 or newer, Home or Pro
 * 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
 
@@ -39,8 +39,8 @@ server.
 Ollama on Windows stores files in a few different locations.  You can view them in
 the explorer window by hitting `<cmd>+R` and type in:
 - `explorer %LOCALAPPDATA%\Ollama` contains logs, and downloaded updates
-    - *app.log* contains most resent logs from the GUI application
-    - *server.log* contains the most recent server logs
+    - *app.log* contains logs from the GUI application
+    - *server.log* contains the server logs
     - *upgrade.log* contains log output for upgrades
 - `explorer %LOCALAPPDATA%\Programs\Ollama` contains the binaries (The installer adds this to your user PATH)
 - `explorer %HOMEPATH%\.ollama` contains models and configuration

envconfig/config.go

@@ -4,14 +4,12 @@ import (
 	"errors"
 	"fmt"
 	"log/slog"
-	"math"
 	"net"
 	"os"
 	"path/filepath"
 	"runtime"
 	"strconv"
 	"strings"
-	"time"
 )
 
 type OllamaHost struct {
@@ -36,17 +34,17 @@ var (
 	// Set via OLLAMA_HOST in the environment
 	Host *OllamaHost
 	// Set via OLLAMA_KEEP_ALIVE in the environment
-	KeepAlive time.Duration
+	KeepAlive string
 	// Set via OLLAMA_LLM_LIBRARY in the environment
 	LLMLibrary string
 	// Set via OLLAMA_MAX_LOADED_MODELS in the environment
 	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_MAX_VRAM in the environment
+	MaxVRAM uint64
 	// Set via OLLAMA_NOHISTORY in the environment
 	NoHistory bool
 	// Set via OLLAMA_NOPRUNE in the environment
@@ -87,13 +85,13 @@ func AsMap() map[string]EnvVar {
 		"OLLAMA_HOST":              {"OLLAMA_HOST", Host, "IP Address for the ollama server (default 127.0.0.1:11434)"},
 		"OLLAMA_KEEP_ALIVE":        {"OLLAMA_KEEP_ALIVE", KeepAlive, "The duration that models stay loaded in memory (default \"5m\")"},
 		"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_LOADED_MODELS": {"OLLAMA_MAX_LOADED_MODELS", MaxRunners, "Maximum number of loaded models (default 1)"},
 		"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"},
-		"OLLAMA_NUM_PARALLEL":      {"OLLAMA_NUM_PARALLEL", NumParallel, "Maximum number of parallel requests"},
+		"OLLAMA_NUM_PARALLEL":      {"OLLAMA_NUM_PARALLEL", NumParallel, "Maximum number of parallel requests (default 1)"},
 		"OLLAMA_ORIGINS":           {"OLLAMA_ORIGINS", AllowOrigins, "A comma separated list of allowed origins"},
 		"OLLAMA_RUNNERS_DIR":       {"OLLAMA_RUNNERS_DIR", RunnersDir, "Location for runners"},
 		"OLLAMA_SCHED_SPREAD":      {"OLLAMA_SCHED_SPREAD", SchedSpread, "Always schedule model across all GPUs"},
@@ -131,10 +129,9 @@ func clean(key string) string {
 
 func init() {
 	// default values
-	NumParallel = 0 // Autoselect
-	MaxRunners = 0  // Autoselect
+	NumParallel = 1
+	MaxRunners = 1
 	MaxQueuedRequests = 512
-	KeepAlive = 5 * time.Minute
 
 	LoadConfig()
 }
@@ -208,8 +205,8 @@ func LoadConfig() {
 
 	if onp := clean("OLLAMA_NUM_PARALLEL"); onp != "" {
 		val, err := strconv.Atoi(onp)
-		if err != nil {
-			slog.Error("invalid setting, ignoring", "OLLAMA_NUM_PARALLEL", onp, "error", err)
+		if err != nil || val <= 0 {
+			slog.Error("invalid setting must be greater than zero", "OLLAMA_NUM_PARALLEL", onp, "error", err)
 		} else {
 			NumParallel = val
 		}
@@ -254,7 +251,7 @@ func LoadConfig() {
 	if maxRunners != "" {
 		m, err := strconv.Atoi(maxRunners)
 		if err != nil {
-			slog.Error("invalid setting, ignoring", "OLLAMA_MAX_LOADED_MODELS", maxRunners, "error", err)
+			slog.Error("invalid setting", "OLLAMA_MAX_LOADED_MODELS", maxRunners, "error", err)
 		} else {
 			MaxRunners = m
 		}
@@ -263,16 +260,13 @@ func LoadConfig() {
 	if onp := os.Getenv("OLLAMA_MAX_QUEUE"); onp != "" {
 		p, err := strconv.Atoi(onp)
 		if err != nil || p <= 0 {
-			slog.Error("invalid setting, ignoring", "OLLAMA_MAX_QUEUE", onp, "error", err)
+			slog.Error("invalid setting", "OLLAMA_MAX_QUEUE", onp, "error", err)
 		} else {
 			MaxQueuedRequests = p
 		}
 	}
 
-	ka := clean("OLLAMA_KEEP_ALIVE")
-	if ka != "" {
-		loadKeepAlive(ka)
-	}
+	KeepAlive = clean("OLLAMA_KEEP_ALIVE")
 
 	var err error
 	ModelsDir, err = getModelsDir()
@@ -350,24 +344,3 @@ func getOllamaHost() (*OllamaHost, error) {
 		Port:   port,
 	}, nil
 }
-
-func loadKeepAlive(ka string) {
-	v, err := strconv.Atoi(ka)
-	if err != nil {
-		d, err := time.ParseDuration(ka)
-		if err == nil {
-			if d < 0 {
-				KeepAlive = time.Duration(math.MaxInt64)
-			} else {
-				KeepAlive = d
-			}
-		}
-	} else {
-		d := time.Duration(v) * time.Second
-		if d < 0 {
-			KeepAlive = time.Duration(math.MaxInt64)
-		} else {
-			KeepAlive = d
-		}
-	}
-}

envconfig/config_test.go

@@ -2,10 +2,8 @@ package envconfig
 
 import (
 	"fmt"
-	"math"
 	"net"
 	"testing"
-	"time"
 
 	"github.com/stretchr/testify/assert"
 	"github.com/stretchr/testify/require"
@@ -25,21 +23,6 @@ func TestConfig(t *testing.T) {
 	t.Setenv("OLLAMA_FLASH_ATTENTION", "1")
 	LoadConfig()
 	require.True(t, FlashAttention)
-	t.Setenv("OLLAMA_KEEP_ALIVE", "")
-	LoadConfig()
-	require.Equal(t, 5*time.Minute, KeepAlive)
-	t.Setenv("OLLAMA_KEEP_ALIVE", "3")
-	LoadConfig()
-	require.Equal(t, 3*time.Second, KeepAlive)
-	t.Setenv("OLLAMA_KEEP_ALIVE", "1h")
-	LoadConfig()
-	require.Equal(t, 1*time.Hour, KeepAlive)
-	t.Setenv("OLLAMA_KEEP_ALIVE", "-1s")
-	LoadConfig()
-	require.Equal(t, time.Duration(math.MaxInt64), KeepAlive)
-	t.Setenv("OLLAMA_KEEP_ALIVE", "-1")
-	LoadConfig()
-	require.Equal(t, time.Duration(math.MaxInt64), KeepAlive)
 }
 
 func TestClientFromEnvironment(t *testing.T) {

go.mod

@@ -21,7 +21,6 @@ require (
 	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 (
@@ -72,7 +71,7 @@ require (
 	golang.org/x/net v0.25.0 // indirect
 	golang.org/x/sys v0.20.0
 	golang.org/x/term v0.20.0
-	golang.org/x/text v0.15.0
+	golang.org/x/text v0.15.0 // indirect
 	google.golang.org/protobuf v1.34.1
 	gopkg.in/yaml.v3 v3.0.1 // indirect
 )

go.sum

@@ -171,8 +171,6 @@ 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_windows.go

@@ -115,6 +115,8 @@ func AMDGetGPUInfo() []RocmGPUInfo {
 			continue
 		}
 
+		// TODO revisit this once ROCm v6 is available on windows.
+		// v5.7 only reports VRAM used by this process, so it's completely wrong and unusable
 		slog.Debug("amdgpu memory", "gpu", i, "total", format.HumanBytes2(totalMemory))
 		slog.Debug("amdgpu memory", "gpu", i, "available", format.HumanBytes2(freeMemory))
 		gpuInfo := RocmGPUInfo{
@@ -124,9 +126,6 @@ func AMDGetGPUInfo() []RocmGPUInfo {
 					TotalMemory: totalMemory,
 					FreeMemory:  freeMemory,
 				},
-				// Free memory reporting on Windows is not reliable until we bump to ROCm v6.2
-				UnreliableFreeMemory: true,
-
 				ID:             strconv.Itoa(i), // TODO this is probably wrong if we specify visible devices
 				DependencyPath: libDir,
 				MinimumMemory:  rocmMinimumMemory,

gpu/assets.go

@@ -77,27 +77,20 @@ func cleanupTmpDirs() {
 			continue
 		}
 		raw, err := os.ReadFile(filepath.Join(d, "ollama.pid"))
+		if err == nil {
+			pid, err := strconv.Atoi(string(raw))
+			if err == nil {
+				if proc, err := os.FindProcess(pid); err == nil && !errors.Is(proc.Signal(syscall.Signal(0)), os.ErrProcessDone) {
+					// Another running ollama, ignore this tmpdir
+					continue
+				}
+			}
+		} else {
+			slog.Debug("failed to open ollama.pid", "path", d, "error", err)
+		}
+		err = os.RemoveAll(d)
 		if err != nil {
-			slog.Warn("failed to read ollama.pid", "path", d, "error", err)
-			// No pid, ignore this tmpdir
-			continue
-		}
-
-		pid, err := strconv.Atoi(string(raw))
-		if err != nil {
-			slog.Warn("failed to parse pid", "path", d, "error", err)
-			continue
-		}
-
-		proc, err := os.FindProcess(pid)
-		if err == nil && !errors.Is(proc.Signal(syscall.Signal(0)), os.ErrProcessDone) {
-			slog.Warn("found running ollama", "pid", pid, "path", d)
-			// Another running ollama, ignore this tmpdir
-			continue
-		}
-
-		if err := os.Remove(d); err != nil {
-			slog.Warn("unable to cleanup stale tmpdir", "path", d, "error", err)
+			slog.Debug("unable to cleanup stale tmpdir", "path", d, "error", err)
 		}
 	}
 }

gpu/gpu.go

@@ -202,7 +202,7 @@ func GetGPUInfo() GpuInfoList {
 	}()
 
 	if !bootstrapped {
-		slog.Info("looking for compatible GPUs")
+		slog.Debug("Detecting GPUs")
 		needRefresh = false
 		cpuCapability = GetCPUCapability()
 		var memInfo C.mem_info_t
@@ -231,7 +231,7 @@ func GetGPUInfo() GpuInfoList {
 		// On windows we bundle the nvidia library one level above the runner dir
 		depPath := ""
 		if runtime.GOOS == "windows" && envconfig.RunnersDir != "" {
-			depPath = filepath.Join(filepath.Dir(envconfig.RunnersDir), "cuda")
+			depPath = filepath.Dir(envconfig.RunnersDir)
 		}
 
 		// Load ALL libraries
@@ -282,12 +282,6 @@ func GetGPUInfo() GpuInfoList {
 		// Intel
 		if envconfig.IntelGpu {
 			oHandles = initOneAPIHandles()
-			// On windows we bundle the oneapi library one level above the runner dir
-			depPath = ""
-			if runtime.GOOS == "windows" && envconfig.RunnersDir != "" {
-				depPath = filepath.Join(filepath.Dir(envconfig.RunnersDir), "oneapi")
-			}
-
 			for d := range oHandles.oneapi.num_drivers {
 				if oHandles.oneapi == nil {
 					// shouldn't happen
@@ -312,7 +306,7 @@ func GetGPUInfo() GpuInfoList {
 					gpuInfo.FreeMemory = uint64(memInfo.free)
 					gpuInfo.ID = C.GoString(&memInfo.gpu_id[0])
 					gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
-					gpuInfo.DependencyPath = depPath
+					// TODO dependency path?
 					oneapiGPUs = append(oneapiGPUs, gpuInfo)
 				}
 			}
@@ -320,9 +314,6 @@ func GetGPUInfo() GpuInfoList {
 
 		rocmGPUs = AMDGetGPUInfo()
 		bootstrapped = true
-		if len(cudaGPUs) == 0 && len(rocmGPUs) == 0 && len(oneapiGPUs) == 0 {
-			slog.Info("no compatible GPUs were discovered")
-		}
 	}
 
 	// For detected GPUs, load library if not loaded
@@ -517,23 +508,7 @@ func LoadNVCUDAMgmt(nvcudaLibPaths []string) (int, *C.nvcuda_handle_t, string) {
 		defer C.free(unsafe.Pointer(lib))
 		C.nvcuda_init(lib, &resp)
 		if resp.err != nil {
-			// Decide what log level based on the type of error message to help users understand why
-			msg := C.GoString(resp.err)
-			switch resp.cudaErr {
-			case C.CUDA_ERROR_INSUFFICIENT_DRIVER, C.CUDA_ERROR_SYSTEM_DRIVER_MISMATCH:
-				slog.Warn("version mismatch between driver and cuda driver library - reboot or upgrade may be required", "library", libPath, "error", msg)
-			case C.CUDA_ERROR_NO_DEVICE:
-				slog.Info("no nvidia devices detected", "library", libPath)
-			case C.CUDA_ERROR_UNKNOWN:
-				slog.Warn("unknown error initializing cuda driver library", "library", libPath, "error", msg)
-				slog.Warn("see https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md for more information")
-			default:
-				if strings.Contains(msg, "wrong ELF class") {
-					slog.Debug("skipping 32bit library", "library", libPath)
-				} else {
-					slog.Info("unable to load cuda driver library", "library", libPath, "error", msg)
-				}
-			}
+			slog.Debug("Unable to load nvcuda", "library", libPath, "error", C.GoString(resp.err))
 			C.free(unsafe.Pointer(resp.err))
 		} else {
 			return int(resp.num_devices), &resp.ch, libPath

gpu/gpu_darwin.go

@@ -56,7 +56,7 @@ func GetCPUInfo() GpuInfoList {
 func GetCPUMem() (memInfo, error) {
 	return memInfo{
 		TotalMemory: uint64(C.getPhysicalMemory()),
-		FreeMemory:  uint64(C.getFreeMemory()),
+		FreeMemory:  0,
 	}, nil
 }
 

gpu/gpu_info_cudart.c

@@ -40,7 +40,7 @@ void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
 
   for (i = 0; l[i].s != NULL; i++) {
     *l[i].p = LOAD_SYMBOL(resp->ch.handle, l[i].s);
-    if (!*(l[i].p)) {
+    if (!l[i].p) {
       char *msg = LOAD_ERR();
       LOG(resp->ch.verbose, "dlerr: %s\n", msg);
       UNLOAD_LIBRARY(resp->ch.handle);

gpu/gpu_info_darwin.h

@@ -2,4 +2,3 @@
 #include <stdint.h>
 uint64_t getRecommendedMaxVRAM();
 uint64_t getPhysicalMemory();
-uint64_t getFreeMemory();

gpu/gpu_info_darwin.m

@@ -1,5 +1,4 @@
-#import <Foundation/Foundation.h>
-#import <mach/mach.h>
+// go:build darwin
 #include "gpu_info_darwin.h"
 
 uint64_t getRecommendedMaxVRAM() {
@@ -9,27 +8,6 @@ uint64_t getRecommendedMaxVRAM() {
   return result;
 }
 
-// getPhysicalMemory returns the total physical memory in bytes
 uint64_t getPhysicalMemory() {
-  return [NSProcessInfo processInfo].physicalMemory;
-}
-
-// getFreeMemory returns the total free memory in bytes, including inactive
-// memory that can be reclaimed by the system.
-uint64_t getFreeMemory() {
-  mach_port_t host_port = mach_host_self();
-  mach_msg_type_number_t host_size = sizeof(vm_statistics64_data_t) / sizeof(integer_t);
-  vm_size_t pagesize;
-  vm_statistics64_data_t vm_stat;
-
-  host_page_size(host_port, &pagesize);
-  if (host_statistics64(host_port, HOST_VM_INFO64, (host_info64_t)&vm_stat, &host_size) != KERN_SUCCESS) {
-    return 0;
-  }
-
-  uint64_t free_memory = (uint64_t)vm_stat.free_count * pagesize;
-  free_memory += (uint64_t)vm_stat.speculative_count * pagesize;
-  free_memory += (uint64_t)vm_stat.inactive_count * pagesize;
-
-  return free_memory;
+  return [[NSProcessInfo processInfo] physicalMemory];
 }

gpu/gpu_info_nvcuda.c

@@ -7,7 +7,6 @@ void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
   CUresult ret;
   resp->err = NULL;
   resp->num_devices = 0;
-  resp->cudaErr = CUDA_SUCCESS;
   const int buflen = 256;
   char buf[buflen + 1];
   int i;
@@ -39,13 +38,12 @@ void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
             nvcuda_lib_path, msg);
     free(msg);
     resp->err = strdup(buf);
-    resp->cudaErr = -1;
     return;
   }
 
   for (i = 0; l[i].s != NULL; i++) {
     *l[i].p = LOAD_SYMBOL(resp->ch.handle, l[i].s);
-    if (!*(l[i].p)) {
+    if (!*l[i].p) {
       char *msg = LOAD_ERR();
       LOG(resp->ch.verbose, "dlerr: %s\n", msg);
       UNLOAD_LIBRARY(resp->ch.handle);
@@ -54,7 +52,6 @@ void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
               msg);
       free(msg);
       resp->err = strdup(buf);
-      resp->cudaErr = -1;
       return;
     }
   }
@@ -64,9 +61,12 @@ void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
     LOG(resp->ch.verbose, "cuInit err: %d\n", ret);
     UNLOAD_LIBRARY(resp->ch.handle);
     resp->ch.handle = NULL;
-    snprintf(buf, buflen, "cuda driver library init failure: %d", ret);
+    if (ret == CUDA_ERROR_INSUFFICIENT_DRIVER) {
+      resp->err = strdup("your nvidia driver is too old or missing.  If you have a CUDA GPU please upgrade to run ollama");
+      return;
+    }
+    snprintf(buf, buflen, "nvcuda init failure: %d", ret);
     resp->err = strdup(buf);
-    resp->cudaErr = ret;
     return;
   }
 
@@ -91,7 +91,6 @@ void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
     resp->ch.handle = NULL;
     snprintf(buf, buflen, "unable to get device count: %d", ret);
     resp->err = strdup(buf);
-    resp->cudaErr = ret;
     return;
   }
 }
@@ -107,13 +106,13 @@ void nvcuda_bootstrap(nvcuda_handle_t h, int i, mem_info_t *resp) {
   CUuuid uuid = {0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
 
   if (h.handle == NULL) {
-    resp->err = strdup("cuda driver library handle isn't initialized");
+    resp->err = strdup("nvcuda handle isn't initialized");
     return;
   }
 
   ret = (*h.cuDeviceGet)(&device, i);
   if (ret != CUDA_SUCCESS) {
-    snprintf(buf, buflen, "cuda driver library device failed to initialize");
+    snprintf(buf, buflen, "nvcuda device failed to initialize");
     resp->err = strdup(buf);
     return;
   }
@@ -169,14 +168,14 @@ void nvcuda_bootstrap(nvcuda_handle_t h, int i, mem_info_t *resp) {
   // To get memory we have to set (and release) a context
   ret = (*h.cuCtxCreate_v3)(&ctx, NULL, 0, 0, device);
   if (ret != CUDA_SUCCESS) {
-    snprintf(buf, buflen, "cuda driver library failed to get device context %d", ret);
+    snprintf(buf, buflen, "nvcuda failed to get device context %d", ret);
     resp->err = strdup(buf);
     return;
   }
 
   ret = (*h.cuMemGetInfo_v2)(&memInfo.free, &memInfo.total);
   if (ret != CUDA_SUCCESS) {
-    snprintf(buf, buflen, "cuda driver library device memory info lookup failure %d", ret);
+    snprintf(buf, buflen, "nvcuda device memory info lookup failure %d", ret);
     resp->err = strdup(buf);
     // Best effort on failure...
     (*h.cuCtxDestroy)(ctx);
@@ -194,7 +193,7 @@ void nvcuda_bootstrap(nvcuda_handle_t h, int i, mem_info_t *resp) {
 
   ret = (*h.cuCtxDestroy)(ctx);
   if (ret != CUDA_SUCCESS) {
-    LOG(1, "cuda driver library failed to release device context %d", ret);
+    LOG(1, "nvcuda failed to release device context %d", ret);
   }
 }
 
@@ -207,7 +206,7 @@ void nvcuda_get_free(nvcuda_handle_t h, int i, uint64_t *free, uint64_t *total)
 
   ret = (*h.cuDeviceGet)(&device, i);
   if (ret != CUDA_SUCCESS) {
-    LOG(1, "cuda driver library device failed to initialize");
+    LOG(1, "nvcuda device failed to initialize");
     return;
   }
 
@@ -215,13 +214,13 @@ void nvcuda_get_free(nvcuda_handle_t h, int i, uint64_t *free, uint64_t *total)
   // To get memory we have to set (and release) a context
   ret = (*h.cuCtxCreate_v3)(&ctx, NULL, 0, 0, device);
   if (ret != CUDA_SUCCESS) {
-    LOG(1, "cuda driver library failed to get device context %d", ret);
+    LOG(1, "nvcuda failed to get device context %d", ret);
     return;
   }
 
   ret = (*h.cuMemGetInfo_v2)(free, total);
   if (ret != CUDA_SUCCESS) {
-    LOG(1, "cuda driver library device memory info lookup failure %d", ret);
+    LOG(1, "nvcuda device memory info lookup failure %d", ret);
     // Best effort on failure...
     (*h.cuCtxDestroy)(ctx);
     return;
@@ -229,12 +228,12 @@ void nvcuda_get_free(nvcuda_handle_t h, int i, uint64_t *free, uint64_t *total)
 
   ret = (*h.cuCtxDestroy)(ctx);
   if (ret != CUDA_SUCCESS) {
-    LOG(1, "cuda driver library failed to release device context %d", ret);
+    LOG(1, "nvcuda failed to release device context %d", ret);
   }
 }
 
 void nvcuda_release(nvcuda_handle_t h) {
-  LOG(h.verbose, "releasing cuda driver library\n");
+  LOG(h.verbose, "releasing nvcuda library\n");
   UNLOAD_LIBRARY(h.handle);
   // TODO and other context release logic?
   h.handle = NULL;

gpu/gpu_info_nvcuda.h

@@ -7,12 +7,9 @@
 typedef enum cudaError_enum {
   CUDA_SUCCESS = 0,
   CUDA_ERROR_INVALID_VALUE = 1,
-  CUDA_ERROR_OUT_OF_MEMORY = 2,
+  CUDA_ERROR_MEMORY_ALLOCATION = 2,
   CUDA_ERROR_NOT_INITIALIZED = 3,
   CUDA_ERROR_INSUFFICIENT_DRIVER = 35,
-  CUDA_ERROR_NO_DEVICE = 100,
-  CUDA_ERROR_SYSTEM_DRIVER_MISMATCH = 803,
-  CUDA_ERROR_UNKNOWN = 999,
   // Other values omitted for now...
 } CUresult;
 
@@ -67,7 +64,6 @@ typedef struct nvcuda_init_resp {
   char *err;  // If err is non-null handle is invalid
   nvcuda_handle_t ch;
   int num_devices;
-  CUresult cudaErr;
 } nvcuda_init_resp_t;
 
 void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp);

gpu/gpu_info_nvml.c

@@ -42,7 +42,7 @@ void nvml_init(char *nvml_lib_path, nvml_init_resp_t *resp) {
     // LOG(resp->ch.verbose, "dlsym: %s\n", l[i].s);
 
     *l[i].p = LOAD_SYMBOL(resp->ch.handle, l[i].s);
-    if (!*(l[i].p)) {
+    if (!l[i].p) {
       resp->ch.handle = NULL;
       char *msg = LOAD_ERR();
       LOG(resp->ch.verbose, "dlerr: %s\n", msg);

gpu/gpu_info_oneapi.c

@@ -50,7 +50,7 @@ void oneapi_init(char *oneapi_lib_path, oneapi_init_resp_t *resp) {
     LOG(resp->oh.verbose, "dlsym: %s\n", l[i].s);
 
     *l[i].p = LOAD_SYMBOL(resp->oh.handle, l[i].s);
-    if (!*(l[i].p)) {
+    if (!l[i].p) {
       resp->oh.handle = NULL;
       char *msg = LOAD_ERR();
       LOG(resp->oh.verbose, "dlerr: %s\n", msg);
@@ -98,7 +98,7 @@ void oneapi_init(char *oneapi_lib_path, oneapi_init_resp_t *resp) {
   }
 
   for (d = 0; d < resp->oh.num_drivers; d++) {
-    LOG(resp->oh.verbose, "calling zesDeviceGet count %d: %p\n", d, resp->oh.drivers[d]);
+    LOG(resp->oh.verbose, "calling zesDeviceGet %d\n", resp->oh.drivers[d]);
     ret = (*resp->oh.zesDeviceGet)(resp->oh.drivers[d],
                                    &resp->oh.num_devices[d], NULL);
     if (ret != ZE_RESULT_SUCCESS) {

gpu/types.go

@@ -29,11 +29,6 @@ type GpuInfo struct {
 	// Extra environment variables specific to the GPU as list of [key,value]
 	EnvWorkarounds [][2]string `json:"envs,omitempty"`
 
-	// Set to true if we can NOT reliably discover FreeMemory.  A value of true indicates
-	// the FreeMemory is best effort, and may over or under report actual memory usage
-	// False indicates FreeMemory can generally be trusted on this GPU
-	UnreliableFreeMemory bool
-
 	// GPU information
 	ID      string `json:"gpu_id"`  // string to use for selection of this specific GPU
 	Name    string `json:"name"`    // user friendly name if available

llm/ext_server/CMakeLists.txt

@@ -1,13 +1,14 @@
-set(TARGET ollama_llama_server)
-option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
-include_directories(${CMAKE_CURRENT_SOURCE_DIR})
-add_executable(${TARGET} server.cpp utils.hpp json.hpp httplib.h)
-install(TARGETS ${TARGET} RUNTIME)
-target_compile_definitions(${TARGET} PRIVATE
-    SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
-)
-target_link_libraries(${TARGET} PRIVATE ggml llama common llava ${CMAKE_THREAD_LIBS_INIT})
-if (WIN32)
-    TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
-endif()
+
+set(TARGET ollama_llama_server)
+option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
+include_directories(${CMAKE_CURRENT_SOURCE_DIR})
+add_executable(${TARGET} server.cpp utils.hpp json.hpp httplib.h)
+install(TARGETS ${TARGET} RUNTIME)
+target_compile_definitions(${TARGET} PRIVATE
+    SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
+)
+target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
+if (WIN32)
+    TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
+endif()
 target_compile_features(${TARGET} PRIVATE cxx_std_11)

llm/ext_server/server.cpp

@@ -56,6 +56,7 @@ struct server_params {
     std::string hostname = "127.0.0.1";
     std::vector<std::string> api_keys;
     std::string public_path = "examples/server/public";
+    std::string chat_template = "";
     int32_t port = 8080;
     int32_t read_timeout = 600;
     int32_t write_timeout = 600;
@@ -426,6 +427,16 @@ struct llama_server_context
         return true;
     }
 
+    void validate_model_chat_template(server_params & sparams) {
+        llama_chat_message chat[] = {{"user", "test"}};
+        std::vector<char> buf(1);
+        int res = llama_chat_apply_template(model, nullptr, chat, 1, true, buf.data(), buf.size());
+        if (res < 0) {
+            LOG_ERROR("The chat template comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {});
+            sparams.chat_template = "chatml";
+        }
+    }
+
     void initialize() {
         // create slots
         all_slots_are_idle = true;
@@ -1382,50 +1393,12 @@ struct llama_server_context
         }
     }
 
-    std::string common_prefix(const std::string& str1, const std::string& str2) {
-        auto mismatch_pair = std::mismatch(str1.begin(), str1.end(), str2.begin());
-        return std::string(str1.begin(), mismatch_pair.first);
-    }
-
-    // Find the slot that has the greatest common prefix
-    server_slot *prefix_slot(const json &prompt) {
-        if (!prompt.is_string()) {
-            return nullptr;
-        }
-
-        std::string prompt_str = prompt.get<std::string>();
-        server_slot *slot = nullptr;
-        size_t longest = 0;
-
-        for (server_slot &s : slots) {
-            if (s.available() && s.prompt.is_string()) {
-                std::string s_prompt = s.prompt.get<std::string>();
-                std::string prefix = common_prefix(s_prompt, prompt_str);
-
-                if (prefix.size() > longest) {
-                    slot = &s;
-                    longest = prefix.size();
-                }
-            }
-        }
-
-        if (!slot) {
-            return get_slot(-1);
-        }
-
-        LOG_DEBUG("slot with common prefix found", {{
-            "slot_id", slot->id,
-            "characters", longest
-        }});
-        return slot;
-    }
-
     void process_single_task(task_server& task)
     {
         switch (task.type)
         {
             case TASK_TYPE_COMPLETION: {
-                server_slot *slot = prefix_slot(task.data["prompt"]);
+                server_slot *slot = get_slot(json_value(task.data, "slot_id", -1));
                 if (slot == nullptr)
                 {
                     // if no slot is available, we defer this task for processing later
@@ -1692,23 +1665,22 @@ struct llama_server_context
                     if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx)
                     {
                         const int n_left = slot.n_ctx - slot.params.n_keep;
-                        const int n_shift = n_left / 2;
-                        const int n_erase = slot.n_prompt_tokens - slot.params.n_keep - n_shift;
+                        const int n_block_size = n_left / 2;
+                        const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;
 
                         std::vector<llama_token> new_tokens(
                             prompt_tokens.begin(),
                             prompt_tokens.begin() + slot.params.n_keep);
                         new_tokens.insert(
                             new_tokens.end(),
-                            prompt_tokens.begin() + slot.params.n_keep + n_erase,
+                            prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size,
                             prompt_tokens.end());
 
-                        LOG_INFO("input truncated", {
-                            {"n_ctx",        slot.n_ctx},
-                            {"n_keep",       slot.params.n_keep},
-                            {"n_left",       n_left},
-                            {"n_shift",      n_shift},
-                            {"n_erase",      n_erase},
+                        LOG_VERBOSE("input truncated", {
+                            {"n_ctx",      slot.n_ctx},
+                            {"n_keep",     slot.params.n_keep},
+                            {"n_left",     n_left},
+                            {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
                         });
                         slot.truncated = true;
                         prompt_tokens = new_tokens;
@@ -1743,7 +1715,7 @@ struct llama_server_context
                             slot.n_past -= 1;
                         }
 
-                        slot.n_prompt_tokens_processed = slot.n_prompt_tokens;
+                        slot.n_prompt_tokens_processed = slot.n_prompt_tokens - slot.n_past;
 
                         if (slot.ga_n != 1)
                         {
@@ -2563,6 +2535,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, g
                 invalid_param = true;
                 break;
             }
+            sparams.chat_template = argv[i];
         }
         else if (arg == "--override-kv")
         {
@@ -3035,6 +3008,11 @@ int main(int argc, char **argv) {
     }
     const auto model_meta = llama.model_meta();
 
+    if (sparams.chat_template.empty()) { // custom chat template is not supplied
+        // check if the template comes with the model is supported by us
+        llama.validate_model_chat_template(sparams);
+    }
+
     // Middleware for API key validation
     auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool {
         // If API key is not set, skip validation

llm/generate/gen_darwin.sh

@@ -18,16 +18,16 @@ sign() {
     fi
 }
 
-COMMON_DARWIN_DEFS="-DBUILD_SHARED_LIBS=off -DCMAKE_OSX_DEPLOYMENT_TARGET=11.3 -DLLAMA_METAL_MACOSX_VERSION_MIN=11.3 -DCMAKE_SYSTEM_NAME=Darwin -DGGML_METAL_EMBED_LIBRARY=on -DGGML_OPENMP=off"
+COMMON_DARWIN_DEFS="-DCMAKE_OSX_DEPLOYMENT_TARGET=11.3 -DLLAMA_METAL_MACOSX_VERSION_MIN=11.3 -DCMAKE_SYSTEM_NAME=Darwin -DLLAMA_METAL_EMBED_LIBRARY=on"
 
 case "${GOARCH}" in
 "amd64")
-    COMMON_CPU_DEFS="${COMMON_DARWIN_DEFS} -DCMAKE_SYSTEM_PROCESSOR=${ARCH} -DCMAKE_OSX_ARCHITECTURES=${ARCH} -DGGML_METAL=off -DGGML_NATIVE=off"
+    COMMON_CPU_DEFS="${COMMON_DARWIN_DEFS} -DCMAKE_SYSTEM_PROCESSOR=${ARCH} -DCMAKE_OSX_ARCHITECTURES=${ARCH} -DLLAMA_METAL=off -DLLAMA_NATIVE=off"
 
     # Static build for linking into the Go binary
     init_vars
     CMAKE_TARGETS="--target llama --target ggml"
-    CMAKE_DEFS="${COMMON_CPU_DEFS} -DGGML_BLAS=off -DGGML_ACCELERATE=off -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off ${CMAKE_DEFS}"
+    CMAKE_DEFS="${COMMON_CPU_DEFS} -DBUILD_SHARED_LIBS=off -DLLAMA_ACCELERATE=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
     BUILD_DIR="../build/darwin/${ARCH}_static"
     echo "Building static library"
     build
@@ -37,7 +37,7 @@ case "${GOARCH}" in
         # CPU first for the default library, set up as lowest common denominator for maximum compatibility (including Rosetta)
         #
         init_vars
-        CMAKE_DEFS="${COMMON_CPU_DEFS} -DGGML_ACCELERATE=off -DGGML_BLAS=off -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off ${CMAKE_DEFS}"
+        CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_ACCELERATE=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
         BUILD_DIR="../build/darwin/${ARCH}/cpu"
         echo "Building LCD CPU"
         build
@@ -49,7 +49,7 @@ case "${GOARCH}" in
         # Approximately 400% faster than LCD on same CPU
         #
         init_vars
-        CMAKE_DEFS="${COMMON_CPU_DEFS} -DGGML_ACCELERATE=off -DGGML_BLAS=off -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off ${CMAKE_DEFS}"
+        CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_ACCELERATE=off -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
         BUILD_DIR="../build/darwin/${ARCH}/cpu_avx"
         echo "Building AVX CPU"
         build
@@ -61,7 +61,7 @@ case "${GOARCH}" in
         # Approximately 10% faster than AVX on same CPU
         #
         init_vars
-        CMAKE_DEFS="${COMMON_CPU_DEFS} -DGGML_ACCELERATE=on -DGGML_BLAS=off -DGGML_AVX=on -DGGML_AVX2=on -DGGML_AVX512=off -DGGML_FMA=on -DGGML_F16C=on ${CMAKE_DEFS}"
+        CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_ACCELERATE=on -DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_AVX512=off -DLLAMA_FMA=on -DLLAMA_F16C=on ${CMAKE_DEFS}"
         BUILD_DIR="../build/darwin/${ARCH}/cpu_avx2"
         echo "Building AVX2 CPU"
         EXTRA_LIBS="${EXTRA_LIBS} -framework Accelerate -framework Foundation"
@@ -75,14 +75,14 @@ case "${GOARCH}" in
     # Static build for linking into the Go binary
     init_vars
     CMAKE_TARGETS="--target llama --target ggml"
-    CMAKE_DEFS="${COMMON_DARWIN_DEFS} -DCMAKE_OSX_DEPLOYMENT_TARGET=11.3 -DCMAKE_SYSTEM_NAME=Darwin -DCMAKE_SYSTEM_PROCESSOR=${ARCH} -DCMAKE_OSX_ARCHITECTURES=${ARCH} ${CMAKE_DEFS}"
+    CMAKE_DEFS="-DCMAKE_OSX_DEPLOYMENT_TARGET=11.3 -DCMAKE_SYSTEM_NAME=Darwin -DBUILD_SHARED_LIBS=off -DCMAKE_SYSTEM_PROCESSOR=${ARCH} -DCMAKE_OSX_ARCHITECTURES=${ARCH} -DLLAMA_METAL=off -DLLAMA_ACCELERATE=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
     BUILD_DIR="../build/darwin/${ARCH}_static"
     echo "Building static library"
     build
 
     if [ -z "$OLLAMA_SKIP_METAL_GENERATE" ]; then
         init_vars
-        CMAKE_DEFS="${COMMON_DARWIN_DEFS} -DCMAKE_SYSTEM_PROCESSOR=${ARCH} -DCMAKE_OSX_ARCHITECTURES=${ARCH} ${CMAKE_DEFS}"
+        CMAKE_DEFS="${COMMON_DARWIN_DEFS} -DLLAMA_ACCELERATE=on -DCMAKE_SYSTEM_PROCESSOR=${ARCH} -DCMAKE_OSX_ARCHITECTURES=${ARCH} -DLLAMA_METAL=on ${CMAKE_DEFS}"
         BUILD_DIR="../build/darwin/${ARCH}/metal"
         EXTRA_LIBS="${EXTRA_LIBS} -framework Accelerate -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders"
         build

llm/generate/gen_linux.sh

@@ -51,7 +51,7 @@ if [ -z "${CUDACXX}" ]; then
         export CUDACXX=$(command -v nvcc)
     fi
 fi
-COMMON_CMAKE_DEFS="-DBUILD_SHARED_LIBS=off -DCMAKE_POSITION_INDEPENDENT_CODE=on -DGGML_NATIVE=off -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_OPENMP=off"
+COMMON_CMAKE_DEFS="-DCMAKE_POSITION_INDEPENDENT_CODE=on -DLLAMA_NATIVE=off -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off"
 source $(dirname $0)/gen_common.sh
 init_vars
 git_module_setup
@@ -64,7 +64,7 @@ if [ -z "${OLLAMA_SKIP_STATIC_GENERATE}" -o "${OLLAMA_CPU_TARGET}" = "static" ];
     # Static build for linking into the Go binary
     init_vars
     CMAKE_TARGETS="--target llama --target ggml"
-    CMAKE_DEFS="-DBUILD_SHARED_LIBS=off -DGGML_NATIVE=off -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_OPENMP=off ${CMAKE_DEFS}"
+    CMAKE_DEFS="-DBUILD_SHARED_LIBS=off -DLLAMA_NATIVE=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
     BUILD_DIR="../build/linux/${ARCH}_static"
     echo "Building static library"
     build
@@ -77,29 +77,29 @@ if [ -z "${OLLAMA_SKIP_CPU_GENERATE}" ]; then
     if [ -n "${OLLAMA_CUSTOM_CPU_DEFS}" ]; then
         init_vars
         echo "OLLAMA_CUSTOM_CPU_DEFS=\"${OLLAMA_CUSTOM_CPU_DEFS}\""
-        CMAKE_DEFS="${OLLAMA_CUSTOM_CPU_DEFS} -DBUILD_SHARED_LIBS=off -DCMAKE_POSITION_INDEPENDENT_CODE=on ${CMAKE_DEFS}"
+        CMAKE_DEFS="${OLLAMA_CUSTOM_CPU_DEFS} -DCMAKE_POSITION_INDEPENDENT_CODE=on ${CMAKE_DEFS}"
         BUILD_DIR="../build/linux/${ARCH}/cpu"
         echo "Building custom CPU"
         build
         compress
     else
         # Darwin Rosetta x86 emulation does NOT support AVX, AVX2, AVX512
-        # -DGGML_AVX -- 2011 Intel Sandy Bridge & AMD Bulldozer
-        # -DGGML_F16C -- 2012 Intel Ivy Bridge & AMD 2011 Bulldozer (No significant improvement over just AVX)
-        # -DGGML_AVX2 -- 2013 Intel Haswell & 2015 AMD Excavator / 2017 AMD Zen
-        # -DGGML_FMA (FMA3) -- 2013 Intel Haswell & 2012 AMD Piledriver
+        # -DLLAMA_AVX -- 2011 Intel Sandy Bridge & AMD Bulldozer
+        # -DLLAMA_F16C -- 2012 Intel Ivy Bridge & AMD 2011 Bulldozer (No significant improvement over just AVX)
+        # -DLLAMA_AVX2 -- 2013 Intel Haswell & 2015 AMD Excavator / 2017 AMD Zen
+        # -DLLAMA_FMA (FMA3) -- 2013 Intel Haswell & 2012 AMD Piledriver
         # Note: the following seem to yield slower results than AVX2 - ymmv
-        # -DGGML_AVX512 -- 2017 Intel Skylake and High End DeskTop (HEDT)
-        # -DGGML_AVX512_VBMI -- 2018 Intel Cannon Lake
-        # -DGGML_AVX512_VNNI -- 2021 Intel Alder Lake
+        # -DLLAMA_AVX512 -- 2017 Intel Skylake and High End DeskTop (HEDT)
+        # -DLLAMA_AVX512_VBMI -- 2018 Intel Cannon Lake
+        # -DLLAMA_AVX512_VNNI -- 2021 Intel Alder Lake
 
-        COMMON_CPU_DEFS="-DBUILD_SHARED_LIBS=off -DCMAKE_POSITION_INDEPENDENT_CODE=on -DGGML_NATIVE=off -DGGML_OPENMP=off"
+        COMMON_CPU_DEFS="-DCMAKE_POSITION_INDEPENDENT_CODE=on -DLLAMA_NATIVE=off"
         if [ -z "${OLLAMA_CPU_TARGET}" -o "${OLLAMA_CPU_TARGET}" = "cpu" ]; then
             #
             # CPU first for the default library, set up as lowest common denominator for maximum compatibility (including Rosetta)
             #
             init_vars
-            CMAKE_DEFS="${COMMON_CPU_DEFS} -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off ${CMAKE_DEFS}"
+            CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
             BUILD_DIR="../build/linux/${ARCH}/cpu"
             echo "Building LCD CPU"
             build
@@ -116,7 +116,7 @@ if [ -z "${OLLAMA_SKIP_CPU_GENERATE}" ]; then
                 # Approximately 400% faster than LCD on same CPU
                 #
                 init_vars
-                CMAKE_DEFS="${COMMON_CPU_DEFS} -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off ${CMAKE_DEFS}"
+                CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_AVX=on -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
                 BUILD_DIR="../build/linux/${ARCH}/cpu_avx"
                 echo "Building AVX CPU"
                 build
@@ -129,7 +129,7 @@ if [ -z "${OLLAMA_SKIP_CPU_GENERATE}" ]; then
                 # Approximately 10% faster than AVX on same CPU
                 #
                 init_vars
-                CMAKE_DEFS="${COMMON_CPU_DEFS} -DGGML_AVX=on -DGGML_AVX2=on -DGGML_AVX512=off -DGGML_FMA=on -DGGML_F16C=on ${CMAKE_DEFS}"
+                CMAKE_DEFS="${COMMON_CPU_DEFS} -DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_AVX512=off -DLLAMA_FMA=on -DLLAMA_F16C=on ${CMAKE_DEFS}"
                 BUILD_DIR="../build/linux/${ARCH}/cpu_avx2"
                 echo "Building AVX2 CPU"
                 build
@@ -170,15 +170,15 @@ if [ -z "${OLLAMA_SKIP_CUDA_GENERATE}" -a -d "${CUDA_LIB_DIR}" ]; then
         #
         # CUDA compute < 6.0 lacks proper FP16 support on ARM.
         # Disabling has minimal performance effect while maintaining compatibility.
-        ARM64_DEFS="-DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_CUDA_F16=off"
+        ARM64_DEFS="-DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_CUDA_F16=off"
     fi
     # Users building from source can tune the exact flags we pass to cmake for configuring llama.cpp
     if [ -n "${OLLAMA_CUSTOM_CUDA_DEFS}" ]; then
         echo "OLLAMA_CUSTOM_CUDA_DEFS=\"${OLLAMA_CUSTOM_CUDA_DEFS}\""
-        CMAKE_CUDA_DEFS="-DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES} ${OLLAMA_CUSTOM_CUDA_DEFS}"
+        CMAKE_CUDA_DEFS="-DLLAMA_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES} ${OLLAMA_CUSTOM_CUDA_DEFS}"
         echo "Building custom CUDA GPU"
     else
-        CMAKE_CUDA_DEFS="-DGGML_CUDA=on -DCMAKE_CUDA_FLAGS=-t8 -DGGML_CUDA_FORCE_MMQ=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES} -DCMAKE_LIBRARY_PATH=/usr/local/cuda/compat"
+        CMAKE_CUDA_DEFS="-DLLAMA_CUDA=on -DLLAMA_CUDA_FORCE_MMQ=on -DCMAKE_CUDA_ARCHITECTURES=${CMAKE_CUDA_ARCHITECTURES}"
     fi
     CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} ${ARM64_DEFS} ${CMAKE_CUDA_DEFS}"
     BUILD_DIR="../build/linux/${ARCH}/cuda${CUDA_VARIANT}"
@@ -216,7 +216,7 @@ if [ -z "${OLLAMA_SKIP_ONEAPI_GENERATE}" -a -d "${ONEAPI_ROOT}" ]; then
     init_vars
     source ${ONEAPI_ROOT}/setvars.sh --force # set up environment variables for oneAPI
     CC=icx
-    CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL=ON -DGGML_SYCL_F16=OFF"
+    CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL=ON -DLLAMA_SYCL_F16=OFF"
     BUILD_DIR="../build/linux/${ARCH}/oneapi"
     EXTRA_LIBS="-fsycl -Wl,-rpath,${ONEAPI_ROOT}/compiler/latest/lib,-rpath,${ONEAPI_ROOT}/mkl/latest/lib,-rpath,${ONEAPI_ROOT}/tbb/latest/lib,-rpath,${ONEAPI_ROOT}/compiler/latest/opt/oclfpga/linux64/lib -lOpenCL -lmkl_core -lmkl_sycl_blas -lmkl_intel_ilp64 -lmkl_tbb_thread -ltbb"
     DEBUG_FLAGS="" # icx compiles with -O0 if we pass -g, so we must remove it
@@ -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} ${CMAKE_DEFS} -DGGML_HIPBLAS=on -DCMAKE_C_COMPILER=$ROCM_PATH/llvm/bin/clang -DCMAKE_CXX_COMPILER=$ROCM_PATH/llvm/bin/clang++ -DAMDGPU_TARGETS=$(amdGPUs) -DGPU_TARGETS=$(amdGPUs)"
+    CMAKE_DEFS="${COMMON_CMAKE_DEFS} ${CMAKE_DEFS} -DLLAMA_HIPBLAS=on -DCMAKE_C_COMPILER=$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

@@ -1,7 +1,5 @@
 #!powershell
 
-$ErrorActionPreference = "Stop"
-
 function amdGPUs {
     if ($env:AMDGPU_TARGETS) {
         return $env:AMDGPU_TARGETS
@@ -39,8 +37,7 @@ function init_vars {
     }
     $script:cmakeDefs = @(
         "-DBUILD_SHARED_LIBS=on",
-        "-DGGML_NATIVE=off",
-        "-DGGML_OPENMP=off"
+        "-DLLAMA_NATIVE=off"
         )
     $script:commonCpuDefs = @("-DCMAKE_POSITION_INDEPENDENT_CODE=on")
     $script:ARCH = $Env:PROCESSOR_ARCHITECTURE.ToLower()
@@ -86,9 +83,9 @@ function init_vars {
 function git_module_setup {
     # TODO add flags to skip the init/patch logic to make it easier to mod llama.cpp code in-repo
     & git submodule init
-    if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
+    if ($LASTEXITCODE -ne 0) { throw($LASTEXITCODE)}
     & git submodule update --force "${script:llamacppDir}"
-    if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
+    if ($LASTEXITCODE -ne 0) { throw($LASTEXITCODE)}
 }
 
 function apply_patches {
@@ -122,7 +119,7 @@ function build {
     write-host "generating config with: cmake -S ${script:llamacppDir} -B $script:buildDir $script:cmakeDefs"
     & cmake --version
     & cmake -S "${script:llamacppDir}" -B $script:buildDir $script:cmakeDefs
-    if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
+    if ($LASTEXITCODE -ne 0) { throw($LASTEXITCODE)}
     if ($cmakeDefs -contains "-G") {
         $extra=@("-j8")
     } else {
@@ -130,7 +127,7 @@ function build {
     }
     write-host "building with: cmake --build $script:buildDir --config $script:config $($script:cmakeTargets | ForEach-Object { `"--target`", $_ }) $extra"
     & cmake --build $script:buildDir --config $script:config ($script:cmakeTargets | ForEach-Object { "--target", $_ }) $extra
-    if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
+    if ($LASTEXITCODE -ne 0) { write-host "cmake build exit status $LASTEXITCODE"; throw($LASTEXITCODE)}
     # Rearrange output to be consistent between different generators
     if ($null -ne ${script:config} -And (test-path -path "${script:buildDir}/bin/${script:config}" ) ) {
         mv -force "${script:buildDir}/bin/${script:config}/*" "${script:buildDir}/bin/"
@@ -144,7 +141,7 @@ function sign {
         foreach ($file in @(get-childitem "${script:buildDir}/bin/*.exe") + @(get-childitem "${script:buildDir}/bin/*.dll")){
             & "${script:SignTool}" sign /v /fd sha256 /t http://timestamp.digicert.com /f "${script:OLLAMA_CERT}" `
                 /csp "Google Cloud KMS Provider" /kc "${env:KEY_CONTAINER}" $file
-            if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
+            if ($LASTEXITCODE -ne 0) { throw($LASTEXITCODE)}
         }
     }
 }
@@ -182,9 +179,9 @@ function cleanup {
 }
 
 
-# -DGGML_AVX -- 2011 Intel Sandy Bridge & AMD Bulldozer
-# -DGGML_AVX2 -- 2013 Intel Haswell & 2015 AMD Excavator / 2017 AMD Zen
-# -DGGML_FMA (FMA3) -- 2013 Intel Haswell & 2012 AMD Piledriver
+# -DLLAMA_AVX -- 2011 Intel Sandy Bridge & AMD Bulldozer
+# -DLLAMA_AVX2 -- 2013 Intel Haswell & 2015 AMD Excavator / 2017 AMD Zen
+# -DLLAMA_FMA (FMA3) -- 2013 Intel Haswell & 2012 AMD Piledriver
 
 
 function build_static() {
@@ -204,13 +201,12 @@ function build_static() {
             "-DCMAKE_C_COMPILER=gcc.exe",
             "-DCMAKE_CXX_COMPILER=g++.exe",
             "-DBUILD_SHARED_LIBS=off",
-            "-DGGML_NATIVE=off",
-            "-DGGML_AVX=off",
-            "-DGGML_AVX2=off",
-            "-DGGML_AVX512=off",
-            "-DGGML_F16C=off",
-            "-DGGML_FMA=off",
-            "-DGGML_OPENMP=off")
+            "-DLLAMA_NATIVE=off",
+            "-DLLAMA_AVX=off",
+            "-DLLAMA_AVX2=off",
+            "-DLLAMA_AVX512=off",
+            "-DLLAMA_F16C=off",
+            "-DLLAMA_FMA=off")
         $script:buildDir="../build/windows/${script:ARCH}_static"
         write-host "Building static library"
         build
@@ -220,11 +216,17 @@ function build_static() {
     }
 }
 
-function build_cpu($gen_arch) {
+function build_cpu() {
+    if ($script:ARCH -eq "arm64") {
+        $gen_arch = "ARM64"
+    } else { # amd64
+        $gen_arch = "x64"
+    }
+
     if ((-not "${env:OLLAMA_SKIP_CPU_GENERATE}" ) -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "cpu"))) {
         # remaining llama.cpp builds use MSVC 
         init_vars
-        $script:cmakeDefs = $script:commonCpuDefs + @("-A", $gen_arch, "-DGGML_AVX=off", "-DGGML_AVX2=off", "-DGGML_AVX512=off", "-DGGML_FMA=off", "-DGGML_F16C=off") + $script:cmakeDefs
+        $script:cmakeDefs = $script:commonCpuDefs + @("-A", $gen_arch, "-DLLAMA_AVX=off", "-DLLAMA_AVX2=off", "-DLLAMA_AVX512=off", "-DLLAMA_FMA=off", "-DLLAMA_F16C=off") + $script:cmakeDefs
         $script:buildDir="../build/windows/${script:ARCH}/cpu"
         $script:distDir="$script:DIST_BASE\cpu"
         write-host "Building LCD CPU"
@@ -239,7 +241,7 @@ function build_cpu($gen_arch) {
 function build_cpu_avx() {
     if ((-not "${env:OLLAMA_SKIP_CPU_GENERATE}" ) -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "cpu_avx"))) {
         init_vars
-        $script:cmakeDefs = $script:commonCpuDefs + @("-A", "x64", "-DGGML_AVX=on", "-DGGML_AVX2=off", "-DGGML_AVX512=off", "-DGGML_FMA=off", "-DGGML_F16C=off") + $script:cmakeDefs
+        $script:cmakeDefs = $script:commonCpuDefs + @("-A", "x64", "-DLLAMA_AVX=on", "-DLLAMA_AVX2=off", "-DLLAMA_AVX512=off", "-DLLAMA_FMA=off", "-DLLAMA_F16C=off") + $script:cmakeDefs
         $script:buildDir="../build/windows/${script:ARCH}/cpu_avx"
         $script:distDir="$script:DIST_BASE\cpu_avx"
         write-host "Building AVX CPU"
@@ -254,7 +256,7 @@ function build_cpu_avx() {
 function build_cpu_avx2() {
     if ((-not "${env:OLLAMA_SKIP_CPU_GENERATE}" ) -and ((-not "${env:OLLAMA_CPU_TARGET}") -or ("${env:OLLAMA_CPU_TARGET}" -eq "cpu_avx2"))) {
         init_vars
-        $script:cmakeDefs = $script:commonCpuDefs + @("-A", "x64", "-DGGML_AVX=on", "-DGGML_AVX2=on", "-DGGML_AVX512=off", "-DGGML_FMA=on", "-DGGML_F16C=on") + $script:cmakeDefs
+        $script:cmakeDefs = $script:commonCpuDefs + @("-A", "x64", "-DLLAMA_AVX=on", "-DLLAMA_AVX2=on", "-DLLAMA_AVX512=off", "-DLLAMA_FMA=on", "-DLLAMA_F16C=on") + $script:cmakeDefs
         $script:buildDir="../build/windows/${script:ARCH}/cpu_avx2"
         $script:distDir="$script:DIST_BASE\cpu_avx2"
         write-host "Building AVX2 CPU"
@@ -279,11 +281,11 @@ function build_cuda() {
         $script:distDir="$script:DIST_BASE\cuda$script:CUDA_VARIANT"
         $script:cmakeDefs += @(
             "-A", "x64",
-            "-DGGML_CUDA=ON",
-            "-DGGML_AVX=on",
-            "-DGGML_AVX2=off",
+            "-DLLAMA_CUDA=ON",
+            "-DLLAMA_AVX=on",
+            "-DLLAMA_AVX2=off",
             "-DCUDAToolkit_INCLUDE_DIR=$script:CUDA_INCLUDE_DIR",
-            "-DCMAKE_CUDA_FLAGS=-t8",
+            "-DCMAKE_CUDA_FLAGS=-t8"
             "-DCMAKE_CUDA_ARCHITECTURES=${script:CMAKE_CUDA_ARCHITECTURES}"
             )
         if ($null -ne $env:OLLAMA_CUSTOM_CUDA_DEFS) {
@@ -295,12 +297,10 @@ function build_cuda() {
         sign
         install
 
-        rm -ea 0 -recurse -force -path "${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\"
-        md "${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\" -ea 0 > $null
-        write-host "copying CUDA dependencies to ${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\"
-        cp "${script:CUDA_LIB_DIR}\cudart64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\"
-        cp "${script:CUDA_LIB_DIR}\cublas64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\"
-        cp "${script:CUDA_LIB_DIR}\cublasLt64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\cuda\"
+        write-host "copying CUDA dependencies to ${script:SRC_DIR}\dist\windows-${script:ARCH}\"
+        cp "${script:CUDA_LIB_DIR}\cudart64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\"
+        cp "${script:CUDA_LIB_DIR}\cublas64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\"
+        cp "${script:CUDA_LIB_DIR}\cublasLt64_*.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\"
     } else {
         write-host "Skipping CUDA generation step"
     }
@@ -319,7 +319,7 @@ function build_oneapi() {
     $script:distDir ="$script:DIST_BASE\oneapi$script:ONEAPI_VARIANT"
     $script:cmakeDefs += @(
       "-G", "MinGW Makefiles",
-      "-DGGML_SYCL=ON",
+      "-DLLAMA_SYCL=ON",
       "-DCMAKE_C_COMPILER=icx",
       "-DCMAKE_CXX_COMPILER=icx",
       "-DCMAKE_BUILD_TYPE=Release"
@@ -334,18 +334,16 @@ function build_oneapi() {
     sign
     install
 
-    rm -ea 0 -recurse -force -path "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
-    md "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\" -ea 0 > $null
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\libirngmd.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\libmmd.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_level_zero.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_unified_runtime.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_win_proxy_loader.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\svml_dispmd.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
-    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\sycl7.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
-    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_core.2.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
-    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_sycl_blas.4.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
-    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_tbb_thread.2.dll" "${script:SRC_DIR}\dist\windows-${script:ARCH}\oneapi\"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\libirngmd.dll" "${script:distDir}"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\libmmd.dll" "${script:distDir}"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_level_zero.dll" "${script:distDir}"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_unified_runtime.dll" "${script:distDir}"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\pi_win_proxy_loader.dll" "${script:distDir}"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\svml_dispmd.dll" "${script:distDir}"
+    cp "${env:ONEAPI_ROOT}\compiler\latest\bin\sycl7.dll" "${script:distDir}"
+    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_core.2.dll" "${script:distDir}"
+    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_sycl_blas.4.dll" "${script:distDir}"
+    cp "${env:ONEAPI_ROOT}\mkl\latest\bin\mkl_tbb_thread.2.dll" "${script:distDir}"
   } else {
     Write-Host "Skipping oneAPI generation step"
   }
@@ -365,10 +363,10 @@ function build_rocm() {
             "-G", "Ninja", 
             "-DCMAKE_C_COMPILER=clang.exe",
             "-DCMAKE_CXX_COMPILER=clang++.exe",
-            "-DGGML_HIPBLAS=on",
+            "-DLLAMA_HIPBLAS=on",
             "-DHIP_PLATFORM=amd",
-            "-DGGML_AVX=on",
-            "-DGGML_AVX2=off",
+            "-DLLAMA_AVX=on",
+            "-DLLAMA_AVX2=off",
             "-DCMAKE_POSITION_INDEPENDENT_CODE=on",
             "-DAMDGPU_TARGETS=$(amdGPUs)",
             "-DGPU_TARGETS=$(amdGPUs)"
@@ -410,16 +408,29 @@ init_vars
 if ($($args.count) -eq 0) {
     git_module_setup
     apply_patches
-    build_static
-    if ($script:ARCH -eq "arm64") {
-        build_cpu("ARM64")
-    } else { # amd64
-        build_cpu("x64")
-        build_cpu_avx
-        build_cpu_avx2
-        build_cuda
-        build_oneapi
-        build_rocm
+
+    $tasks = @("build_static", "build_cpu")
+    $jobs = @()
+    if ($script:ARCH -ne "arm64") {
+        $tasks += $("build_cpu_avx", "build_cpu_avx2", "build_cuda", "build_oneapi", "build_rocm")
+    }
+    foreach ($t in $tasks) {
+        $jobs += @(Start-ThreadJob -ThrottleLimit 12 -FilePath .\gen_windows.ps1 -ArgumentList $t -Name $t)
+    }
+    get-job
+    foreach ($job in $jobs) {
+        write-host "----" $job.Name output follows
+        receive-job -wait -job $job
+        write-host "----" $job.Name $job.State
+        write-host ""
+        if ($job.State -contains 'Failed') {
+            cleanup
+            write-host "Terminating remaining jobs (this takes a while, you can ^C)"
+            # TODO find some way to kill the spawned cmake processes faster
+            remove-job -force -job $jobs
+            exit(-1)
+        }
+        get-job
     }
 
     cleanup

llm/ggla.go

@@ -1,12 +1,9 @@
 package llm
 
 import (
-	"bytes"
 	"encoding/binary"
 	"errors"
-	"fmt"
 	"io"
-	"log/slog"
 	"slices"
 )
 
@@ -19,7 +16,6 @@ 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
 	}
@@ -40,8 +36,6 @@ type ggla struct {
 
 	kv      KV
 	tensors []*Tensor
-
-	tensorOffset uint64
 }
 
 func newGGLA(container *containerGGLA) *ggla {
@@ -56,10 +50,7 @@ func (llm *ggla) KV() KV {
 }
 
 func (llm *ggla) Tensors() Tensors {
-	return Tensors{
-		Items:  llm.tensors,
-		Offset: llm.tensorOffset,
-	}
+	return llm.tensors
 }
 
 func (llm *ggla) decode(rs io.ReadSeeker) error {
@@ -75,19 +66,9 @@ func (llm *ggla) decode(rs io.ReadSeeker) 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) {
-				break
-			}
 			return err
 		}
 
@@ -121,7 +102,6 @@ func (llm *ggla) decode(rs io.ReadSeeker) 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 {
@@ -145,87 +125,4 @@ func (llm *ggla) decode(rs io.ReadSeeker) 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

@@ -6,8 +6,6 @@ import (
 	"fmt"
 	"io"
 	"strings"
-
-	"github.com/ollama/ollama/util/bufioutil"
 )
 
 type GGML struct {
@@ -71,30 +69,6 @@ func (kv KV) HeadCountKV() uint64 {
 	return 1
 }
 
-func (kv KV) EmbeddingHeadCount() uint64 {
-	if heads := kv.HeadCount(); heads > 0 {
-		return kv.EmbeddingLength() / kv.HeadCount()
-	}
-
-	return 0
-}
-
-func (kv KV) EmbeddingHeadCountK() uint64 {
-	if k := kv.u64(fmt.Sprintf("%s.attention.key_length", kv.Architecture())); k > 0 {
-		return k
-	}
-
-	return kv.EmbeddingHeadCount()
-}
-
-func (kv KV) EmbeddingHeadCountV() uint64 {
-	if v := kv.u64(fmt.Sprintf("%s.attention.value_length", kv.Architecture())); v > 0 {
-		return v
-	}
-
-	return kv.EmbeddingHeadCount()
-}
-
 func (kv KV) GQA() uint64 {
 	return kv.HeadCount() / kv.HeadCountKV()
 }
@@ -112,14 +86,11 @@ func (kv KV) ChatTemplate() string {
 	return s
 }
 
-type Tensors struct {
-	Items  []*Tensor
-	Offset uint64
-}
+type Tensors []*Tensor
 
 func (ts Tensors) Layers() map[string]Layer {
 	layers := make(map[string]Layer)
-	for _, t := range ts.Items {
+	for _, t := range ts {
 		parts := strings.Split(t.Name, ".")
 		if parts[0] == "blk" {
 			// join first and second part, e.g. blk.%d
@@ -283,18 +254,7 @@ func DetectGGMLType(b []byte) string {
 	}
 }
 
-// DecodeGGML decodes a GGML model from the given reader.
-//
-// It collects array values for arrays with a size less than or equal to
-// maxArraySize. If maxArraySize is 0, the default value of 1024 is used. If
-// the maxArraySize is negative, all arrays are collected.
-func DecodeGGML(rs io.ReadSeeker, maxArraySize int) (*GGML, int64, error) {
-	if maxArraySize == 0 {
-		maxArraySize = 1024
-	}
-
-	rs = bufioutil.NewBufferedSeeker(rs, 32<<10)
-
+func DecodeGGML(rs io.ReadSeeker) (*GGML, int64, error) {
 	var magic uint32
 	if err := binary.Read(rs, binary.LittleEndian, &magic); err != nil {
 		return nil, 0, err
@@ -307,15 +267,17 @@ func DecodeGGML(rs io.ReadSeeker, maxArraySize int) (*GGML, int64, error) {
 	case FILE_MAGIC_GGLA:
 		c = &containerGGLA{}
 	case FILE_MAGIC_GGUF_LE:
-		c = &containerGGUF{ByteOrder: binary.LittleEndian, maxArraySize: maxArraySize}
+		c = &containerGGUF{ByteOrder: binary.LittleEndian}
 	case FILE_MAGIC_GGUF_BE:
-		c = &containerGGUF{ByteOrder: binary.BigEndian, maxArraySize: maxArraySize}
+		c = &containerGGUF{ByteOrder: binary.BigEndian}
 	default:
 		return nil, 0, errors.New("invalid file magic")
 	}
 
 	model, err := c.Decode(rs)
-	if err != nil {
+	if errors.Is(err, io.EOF) {
+		// noop
+	} else if err != nil {
 		return nil, 0, err
 	}
 
@@ -335,10 +297,7 @@ func (llm GGML) GraphSize(context, batch uint64) (partialOffload, fullOffload ui
 	embedding := llm.KV().EmbeddingLength()
 	heads := llm.KV().HeadCount()
 	headsKV := llm.KV().HeadCountKV()
-	vocab := uint64(llm.KV()["tokenizer.ggml.tokens"].(*array).size)
-
-	embeddingHeads := llm.KV().EmbeddingHeadCount()
-	embeddingHeadsK := llm.KV().EmbeddingHeadCountK()
+	vocab := uint64(len(llm.KV()["tokenizer.ggml.tokens"].([]any)))
 
 	layers := llm.Tensors().Layers()
 
@@ -349,7 +308,7 @@ func (llm GGML) GraphSize(context, batch uint64) (partialOffload, fullOffload ui
 		partialOffload = 4 * batch * embedding
 		partialOffload += max(
 			// 4*batch*(4+6*embedding+context*(2*heads)+llm.KV().GQA()),
-			4*batch*(1+embedding+max(context, embedding))+embedding*embedding*9/16+4*context*(batch*heads+embeddingHeads*headsKV),
+			4*batch*(1+embedding+max(context, embedding))+embedding*embedding*9/16+4*context*(batch*heads+embedding/heads*headsKV),
 			4*batch*(embedding+vocab)+embedding*vocab*105/128,
 		)
 
@@ -357,30 +316,21 @@ func (llm GGML) GraphSize(context, batch uint64) (partialOffload, fullOffload ui
 			// mixtral 8x22b
 			ff := uint64(llm.KV()["llama.feed_forward_length"].(uint32))
 			partialOffload = max(
-				3*ffnGateExpsWeight.Size()+4*batch*(2*ff+headsKV+embedding+context+embeddingHeads*headsKV),
-				4*(context*batch*heads+context*embeddingHeads*headsKV+batch*1024+embeddingHeads*headsKV*batch),
+				3*ffnGateExpsWeight.Size()+4*batch*(2*ff+headsKV+embedding+context+embedding/heads*headsKV),
+				4*(context*batch*heads+context*embedding/heads*headsKV+batch*1024+embedding/heads*headsKV*batch),
 			)
 		} else if ffnGateWeight, ok := layers["blk.0"]["ffn_gate.0.weight"]; ok {
 			// mixtral 8x7b
 			ffnGateWeight1 := ffnGateWeight.Shape[1]
 			fullOffload = 4 * batch * (2 + 3*embedding + context*(1+heads) + 2*headsKV + ffnGateWeight1)
 			partialOffload = max(
-				4*batch*(3+embeddingHeads*headsKV+embedding+context*(1+heads)+ffnGateWeight1)+(embedding*embedding+3*embedding*headsKV*ffnGateWeight1)*9/16,
+				4*batch*(3+embedding/heads*headsKV+embedding+context*(1+heads)+ffnGateWeight1)+(embedding*embedding+3*embedding*headsKV*ffnGateWeight1)*9/16,
 				4*batch*(1+2*embedding+context*(1+heads))+embedding*(6*context*headsKV/heads+embedding*9/16),
 			)
 		}
-	case "gemma", "gemma2":
-		fullOffload = max(
-			4*batch*(embedding+vocab),
-			4*batch*(2+context+context*heads+2*embedding+2*embeddingHeadsK*heads),
-		)
-
-		partialOffload = max(
-			4*embedding*batch+embedding*vocab*105/128+4*vocab*batch,
-			4*batch*(2*embedding+1+2*embeddingHeadsK*heads+context+context*heads)+
-				4*embeddingHeadsK*context*8+
-				embedding*embeddingHeadsK*heads*9/16,
-		)
+	case "gemma":
+		fullOffload = 4 * batch * (embedding + vocab)
+		partialOffload = 4*batch*(2*embedding+vocab+1) + embedding*vocab*105/128
 	case "command-r":
 		fullOffload = max(
 			4*batch*(embedding+vocab),
@@ -417,16 +367,6 @@ func (llm GGML) GraphSize(context, batch uint64) (partialOffload, fullOffload ui
 			4*batch*(vocab+2*embedding),
 			fullOffload,
 		)
-	case "deepseek2":
-		fullOffload = max(
-			4*batch*(3*embedding+vocab),
-			4*batch*(3*embedding+2+context*(1+headsKV)+2*embeddingHeadsK*headsKV),
-		)
-
-		partialOffload = max(
-			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,
-		)
 	}
 
 	return

llm/ggml_test.go

@@ -1 +0,0 @@
-package llm

llm/gguf.go

@@ -2,16 +2,12 @@ package llm
 
 import (
 	"bytes"
-	"cmp"
 	"encoding/binary"
-	"encoding/json"
 	"fmt"
 	"io"
-	"log/slog"
-	"slices"
 	"strings"
 
-	"golang.org/x/exp/maps"
+	"log/slog"
 )
 
 type containerGGUF struct {
@@ -33,12 +29,6 @@ type containerGGUF struct {
 		NumTensor uint64
 		NumKV     uint64
 	}
-
-	maxArraySize int
-}
-
-func (c *containerGGUF) canCollectArray(size int) bool {
-	return c.maxArraySize < 0 || size <= c.maxArraySize
 }
 
 func (c *containerGGUF) Name() string {
@@ -64,6 +54,7 @@ func (c *containerGGUF) Decode(rs io.ReadSeeker) (model, error) {
 	}
 
 	model := newGGUF(c)
+	slog.Debug(fmt.Sprintf("model = %#v", model))
 	if err := model.Decode(rs); err != nil {
 		return nil, err
 	}
@@ -94,9 +85,6 @@ type gguf struct {
 	tensors []*Tensor
 
 	parameters uint64
-	tensorOffset uint64
-
-	scratch [16 << 10]byte
 }
 
 func newGGUF(container *containerGGUF) *gguf {
@@ -106,15 +94,16 @@ 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 Tensors{
-		Items:  llm.tensors,
-		Offset: llm.tensorOffset,
-	}
+	return llm.tensors
 }
 
 func (llm *gguf) numTensor() uint64 {
@@ -192,34 +181,34 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
 	}
 
 	// decode tensors
-	for range llm.numTensor() {
+	for i := 0; uint64(i) < llm.numTensor(); i++ {
 		name, err := readGGUFString(llm, rs)
 		if err != nil {
-			return fmt.Errorf("failed to read tensor name: %w", err)
+			return err
 		}
 
 		// dims is the number of dimensions in the tensor
 		dims, err := readGGUF[uint32](llm, rs)
 		if err != nil {
-			return fmt.Errorf("failed to read tensor dimensions: %w", err)
+			return err
 		}
 
-		shape := make([]uint64, dims)
+		shape := [4]uint64{1, 1, 1, 1}
 		for i := 0; uint32(i) < dims; i++ {
 			shape[i], err = readGGUF[uint64](llm, rs)
 			if err != nil {
-				return fmt.Errorf("failed to read tensor shape: %w", err)
+				return err
 			}
 		}
 
 		kind, err := readGGUF[uint32](llm, rs)
 		if err != nil {
-			return fmt.Errorf("failed to read tensor kind: %w", err)
+			return err
 		}
 
 		offset, err := readGGUF[uint64](llm, rs)
 		if err != nil {
-			return fmt.Errorf("failed to read tensor offset: %w", err)
+			return err
 		}
 
 		tensor := Tensor{
@@ -246,22 +235,19 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
 		return err
 	}
 
-	padding := ggufPadding(offset, int64(alignment))
-	llm.tensorOffset = uint64(offset + padding)
+	padding := llm.padding(offset, int64(alignment))
+	if _, err := rs.Seek(padding, io.SeekCurrent); err != nil {
+		return err
+	}
 
 	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 := ggufPadding(offset, int64(alignment))
-		if _, err := rs.Seek(padding, io.SeekCurrent); err != nil {
-			return fmt.Errorf("failed to seek to init padding: %w", err)
-		}
-
 		if _, err := rs.Seek(int64(tensor.Size()), io.SeekCurrent); err != nil {
-			return fmt.Errorf("failed to seek to tensor: %w", err)
+			return err
+		}
+
+		padding := llm.padding(int64(tensor.Size()), int64(alignment))
+		if _, err := rs.Seek(padding, io.SeekCurrent); err != nil {
+			return err
 		}
 	}
 
@@ -274,12 +260,12 @@ func readGGUF[T any](llm *gguf, r io.Reader) (T, error) {
 	return t, err
 }
 
-func writeGGUF[V any](w io.Writer, t uint32, v V) error {
-	if err := binary.Write(w, binary.LittleEndian, t); err != nil {
+func writeGGUF[V any](llm *gguf, w io.Writer, t uint32, v V) error {
+	if err := binary.Write(w, llm.ByteOrder, t); err != nil {
 		return err
 	}
 
-	return binary.Write(w, binary.LittleEndian, v)
+	return binary.Write(w, llm.ByteOrder, v)
 }
 
 func readGGUFV1String(llm *gguf, r io.Reader) (string, error) {
@@ -299,56 +285,30 @@ func readGGUFV1String(llm *gguf, r io.Reader) (string, error) {
 	return b.String(), nil
 }
 
-func discardGGUFString(llm *gguf, r io.Reader) error {
-	buf := llm.scratch[:8]
-	_, err := io.ReadFull(r, buf)
-	if err != nil {
-		return err
-	}
-
-	size := int(llm.ByteOrder.Uint64(buf))
-	for size > 0 {
-		n, err := r.Read(llm.scratch[:min(size, cap(llm.scratch))])
-		if err != nil {
-			return err
-		}
-		size -= n
-	}
-	return nil
-}
-
 func readGGUFString(llm *gguf, r io.Reader) (string, error) {
 	if llm.Version == 1 {
 		return readGGUFV1String(llm, r)
 	}
 
-	buf := llm.scratch[:8]
-	_, err := io.ReadFull(r, buf)
-	if err != nil {
+	var length uint64
+	if err := binary.Read(r, llm.ByteOrder, &length); err != nil {
 		return "", err
 	}
 
-	length := int(llm.ByteOrder.Uint64(buf))
-	if length > len(llm.scratch) {
-		buf = make([]byte, length)
-	} else {
-		buf = llm.scratch[:length]
-	}
-	clear(buf)
-
-	_, err = io.ReadFull(r, buf)
-	if err != nil {
+	var b bytes.Buffer
+	if _, err := io.CopyN(&b, r, int64(length)); err != nil {
 		return "", err
 	}
-	return string(buf), nil
+
+	return b.String(), nil
 }
 
-func writeGGUFString(w io.Writer, s string) error {
-	if err := binary.Write(w, binary.LittleEndian, ggufTypeString); err != nil {
+func writeGGUFString(llm *gguf, w io.Writer, s string) error {
+	if err := binary.Write(w, llm.ByteOrder, ggufTypeString); err != nil {
 		return err
 	}
 
-	if err := binary.Write(w, binary.LittleEndian, uint64(len(s))); err != nil {
+	if err := binary.Write(w, llm.ByteOrder, uint64(len(s))); err != nil {
 		return err
 	}
 
@@ -356,16 +316,7 @@ func writeGGUFString(w io.Writer, s string) error {
 	return err
 }
 
-type array struct {
-	size   int
-	values []any
-}
-
-func (a *array) MarshalJSON() ([]byte, error) {
-	return json.Marshal(a.values)
-}
-
-func readGGUFV1Array(llm *gguf, r io.Reader) (*array, error) {
+func readGGUFV1Array(llm *gguf, r io.Reader) (a []any, err error) {
 	t, err := readGGUF[uint32](llm, r)
 	if err != nil {
 		return nil, err
@@ -376,12 +327,7 @@ func readGGUFV1Array(llm *gguf, r io.Reader) (*array, error) {
 		return nil, err
 	}
 
-	a := &array{size: int(n)}
-	if llm.canCollectArray(int(n)) {
-		a.values = make([]any, 0, int(n))
-	}
-
-	for i := range n {
+	for i := 0; uint32(i) < n; i++ {
 		var e any
 		switch t {
 		case ggufTypeUint8:
@@ -415,15 +361,13 @@ func readGGUFV1Array(llm *gguf, r io.Reader) (*array, error) {
 			return nil, err
 		}
 
-		if a.values != nil {
-			a.values[i] = e
-		}
+		a = append(a, e)
 	}
 
-	return a, nil
+	return
 }
 
-func readGGUFArray(llm *gguf, r io.Reader) (*array, error) {
+func readGGUFArray(llm *gguf, r io.Reader) (a []any, err error) {
 	if llm.Version == 1 {
 		return readGGUFV1Array(llm, r)
 	}
@@ -438,12 +382,7 @@ func readGGUFArray(llm *gguf, r io.Reader) (*array, error) {
 		return nil, err
 	}
 
-	a := &array{size: int(n)}
-	if llm.canCollectArray(int(n)) {
-		a.values = make([]any, int(n))
-	}
-
-	for i := range n {
+	for i := 0; uint64(i) < n; i++ {
 		var e any
 		switch t {
 		case ggufTypeUint8:
@@ -469,11 +408,7 @@ func readGGUFArray(llm *gguf, r io.Reader) (*array, error) {
 		case ggufTypeBool:
 			e, err = readGGUF[bool](llm, r)
 		case ggufTypeString:
-			if a.values != nil {
-				e, err = readGGUFString(llm, r)
-			} else {
-				err = discardGGUFString(llm, r)
-			}
+			e, err = readGGUFString(llm, r)
 		default:
 			return nil, fmt.Errorf("invalid array type: %d", t)
 		}
@@ -481,29 +416,27 @@ func readGGUFArray(llm *gguf, r io.Reader) (*array, error) {
 			return nil, err
 		}
 
-		if a.values != nil {
-			a.values[i] = e
-		}
+		a = append(a, e)
 	}
 
-	return a, nil
+	return
 }
 
-func writeGGUFArray[S ~[]E, E any](w io.Writer, t uint32, s S) error {
-	if err := binary.Write(w, binary.LittleEndian, ggufTypeArray); err != 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 {
 		return err
 	}
 
-	if err := binary.Write(w, binary.LittleEndian, t); err != nil {
+	if err := binary.Write(w, llm.ByteOrder, t); err != nil {
 		return err
 	}
 
-	if err := binary.Write(w, binary.LittleEndian, uint64(len(s))); err != nil {
+	if err := binary.Write(w, llm.ByteOrder, uint64(len(s))); err != nil {
 		return err
 	}
 
 	for _, e := range s {
-		if err := binary.Write(w, binary.LittleEndian, e); err != nil {
+		if err := binary.Write(w, llm.ByteOrder, e); err != nil {
 			return err
 		}
 	}
@@ -511,55 +444,193 @@ func writeGGUFArray[S ~[]E, E any](w io.Writer, t uint32, s S) error {
 	return nil
 }
 
-func WriteGGUF(ws io.WriteSeeker, kv KV, ts []*Tensor) error {
-	if err := binary.Write(ws, binary.LittleEndian, []byte("GGUF")); err != 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",
+	},
+}
+
+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 {
 		return err
 	}
 
-	if err := binary.Write(ws, binary.LittleEndian, uint32(3)); err != nil {
+	if err := binary.Write(ws, llm.ByteOrder, llm.Version); err != nil {
 		return err
 	}
 
-	if err := binary.Write(ws, binary.LittleEndian, uint64(len(ts))); err != nil {
+	if err := binary.Write(ws, llm.ByteOrder, llm.numTensor()); err != nil {
 		return err
 	}
 
-	if err := binary.Write(ws, binary.LittleEndian, uint64(len(kv))); err != nil {
+	if err := binary.Write(ws, llm.ByteOrder, llm.numKV()); err != nil {
 		return err
 	}
 
-	keys := maps.Keys(kv)
-	slices.Sort(keys)
+	kvCheck := make(map[string]bool)
+	for k := range kv {
+		kvCheck[k] = false
+	}
 
-	for _, key := range keys {
-		if err := ggufWriteKV(ws, key, kv[key]); err != nil {
+	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 {
 			return err
 		}
 	}
 
-	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 k, v := range kvCheck {
+		if !v {
+			return fmt.Errorf("Didn't know how to write kv %s", k)
 		}
+	}
 
-		return cmp.Compare(i, j)
-	})
-
-	var s uint64
-	for _, t := range ts {
-		t.Offset = s
-		if err := ggufWriteTensorInfo(ws, t); err != nil {
+	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 err
 		}
-		s += t.Size()
 	}
 
 	var alignment int64 = 32
-	for _, t := range ts {
-		if err := ggufWriteTensor(ws, t, alignment); err != nil {
+	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 {
 			return err
 		}
 	}
@@ -567,103 +638,6 @@ func WriteGGUF(ws io.WriteSeeker, kv KV, ts []*Tensor) error {
 	return nil
 }
 
-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 {
+func (gguf) padding(offset, align int64) int64 {
 	return (align - offset%align) % align
 }

llm/llm.go

@@ -1,13 +1,12 @@
 package llm
 
-// #cgo CFLAGS: -Illama.cpp -Illama.cpp/include -Illama.cpp/ggml/include
-// #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: -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
+// #cgo CFLAGS: -Illama.cpp
+// #cgo darwin,arm64 LDFLAGS: ${SRCDIR}/build/darwin/arm64_static/libllama.a -lstdc++
+// #cgo darwin,amd64 LDFLAGS: ${SRCDIR}/build/darwin/x86_64_static/libllama.a -lstdc++
+// #cgo windows,amd64 LDFLAGS: ${SRCDIR}/build/windows/amd64_static/libllama.a -static -lstdc++
+// #cgo windows,arm64 LDFLAGS: ${SRCDIR}/build/windows/arm64_static/libllama.a -static -lstdc++
+// #cgo linux,amd64 LDFLAGS: ${SRCDIR}/build/linux/x86_64_static/libllama.a -lstdc++
+// #cgo linux,arm64 LDFLAGS: ${SRCDIR}/build/linux/arm64_static/libllama.a -lstdc++
 // #include <stdlib.h>
 // #include "llama.h"
 import "C"

llm/memory.go

@@ -1,7 +1,6 @@
 package llm
 
 import (
-	"fmt"
 	"log/slog"
 	"strconv"
 	"strings"
@@ -50,18 +49,6 @@ type MemoryEstimate struct {
 
 	// For multi-GPU scenarios, this is the size in bytes per GPU
 	GPUSizes []uint64
-
-	// internal fields for logging purposes
-	inferenceLibrary    string
-	layersRequested     int
-	layersModel         int
-	availableList       []string
-	kv                  uint64
-	allocationsList     []string
-	memoryWeights       uint64
-	memoryLayerOutput   uint64
-	graphFullOffload    uint64
-	graphPartialOffload uint64
 }
 
 // Given a model and one or more GPU targets, predict how many layers and bytes we can load, and the total size
@@ -115,8 +102,8 @@ func EstimateGPULayers(gpus []gpu.GpuInfo, ggml *GGML, projectors []string, opts
 		slog.Warn("model missing blk.0 layer size")
 	}
 
-	// fp16 k,v = sizeof(float16) * n_ctx * n_layer * (n_embd_head_k + n_embd_head_v) * n_head_kv
-	var kv uint64 = 2 * uint64(opts.NumCtx) * ggml.KV().BlockCount() * (ggml.KV().EmbeddingHeadCountK() + ggml.KV().EmbeddingHeadCountV()) * ggml.KV().HeadCountKV()
+	// fp16 k,v = (1 (k) + 1 (v)) * sizeof(float16) * n_ctx * n_layer * n_embd / n_head * n_head_kv
+	var kv uint64 = 2 * 2 * uint64(opts.NumCtx) * ggml.KV().BlockCount() * ggml.KV().EmbeddingLength() / ggml.KV().HeadCount() * ggml.KV().HeadCountKV()
 
 	// KV is proportional to the number of layers
 	layerSize += kv / ggml.KV().BlockCount()
@@ -180,11 +167,6 @@ func EstimateGPULayers(gpus []gpu.GpuInfo, ggml *GGML, projectors []string, opts
 
 	// For all the layers, find where they can fit on the GPU(s)
 	for i := range int(ggml.KV().BlockCount()) {
-		// Some models have inconsistent layer sizes
-		if blk, ok := layers[fmt.Sprintf("blk.%d", i)]; ok {
-			layerSize = blk.size()
-			layerSize += kv / ggml.KV().BlockCount()
-		}
 		memoryWeights += layerSize
 
 		if opts.NumGPU >= 0 && layerCount >= opts.NumGPU {
@@ -270,86 +252,78 @@ func EstimateGPULayers(gpus []gpu.GpuInfo, ggml *GGML, projectors []string, opts
 		allocationsList = append(allocationsList, format.HumanBytes2(a))
 	}
 
-	estimate := MemoryEstimate{
-		TotalSize: memoryRequiredTotal,
-		Layers:    0,
-		Graph:     0,
-		VRAMSize:  0,
-		GPUSizes:  []uint64{},
-
-		inferenceLibrary:    gpus[0].Library,
-		layersRequested:     opts.NumGPU,
-		layersModel:         int(ggml.KV().BlockCount()) + 1,
-		availableList:       availableList,
-		kv:                  kv,
-		allocationsList:     allocationsList,
-		memoryWeights:       memoryWeights,
-		memoryLayerOutput:   memoryLayerOutput,
-		graphFullOffload:    graphFullOffload,
-		graphPartialOffload: graphPartialOffload,
-	}
-
-	if gpus[0].Library == "cpu" {
-		return estimate
-	}
-	if layerCount == 0 {
-		slog.Debug("insufficient VRAM to load any model layers")
-		return estimate
-	}
-	estimate.Layers = layerCount
-	estimate.Graph = graphOffload
-	estimate.VRAMSize = memoryRequiredPartial
-	estimate.TotalSize = memoryRequiredTotal
-	estimate.TensorSplit = tensorSplit
-	estimate.GPUSizes = gpuAllocations
-	return estimate
-}
-
-func (m MemoryEstimate) log() {
 	slog.Info(
-		"offload to "+m.inferenceLibrary,
+		"offload to gpu",
 		slog.Group(
 			"layers",
 			// requested number of layers to offload
-			"requested", m.layersRequested,
+			"requested", opts.NumGPU,
 			// The number of layers the model has (including output)
-			"model", m.layersModel,
+			"model", int(ggml.KV().BlockCount())+1,
 			// estimated number of layers that can be offloaded
-			"offload", m.Layers,
-			// multi-gpu split for tensors
-			"split", m.TensorSplit,
+			"offload", layerCount,
+			// multi-gpu split for tesnors
+			"split", tensorSplit,
 		),
 		slog.Group(
 			"memory",
 			// memory available by GPU for offloading
-			"available", m.availableList,
+			"available", availableList,
 			slog.Group(
 				"required",
 				// memory required for full offloading
-				"full", format.HumanBytes2(m.TotalSize),
+				"full", format.HumanBytes2(memoryRequiredTotal),
 				// memory required to offload layers.estimate layers
-				"partial", format.HumanBytes2(m.VRAMSize),
+				"partial", format.HumanBytes2(memoryRequiredPartial),
 				// memory of KV cache
-				"kv", format.HumanBytes2(m.kv),
+				"kv", format.HumanBytes2(kv),
 				// Allocations across the GPUs
-				"allocations", m.allocationsList,
+				"allocations", allocationsList,
 			),
 			slog.Group(
 				"weights",
 				// memory of the weights
-				"total", format.HumanBytes2(m.memoryWeights),
+				"total", format.HumanBytes2(memoryWeights),
 				// memory of repeating layers
-				"repeating", format.HumanBytes2(m.memoryWeights-m.memoryLayerOutput),
+				"repeating", format.HumanBytes2(memoryWeights-memoryLayerOutput),
 				// memory of non-repeating layers
-				"nonrepeating", format.HumanBytes2(m.memoryLayerOutput),
+				"nonrepeating", format.HumanBytes2(memoryLayerOutput),
 			),
 			slog.Group(
 				"graph",
 				// memory of graph when fully offloaded
-				"full", format.HumanBytes2(m.graphFullOffload),
+				"full", format.HumanBytes2(graphFullOffload),
 				// memory of graph when not fully offloaded
-				"partial", format.HumanBytes2(m.graphPartialOffload),
+				"partial", format.HumanBytes2(graphPartialOffload),
 			),
 		),
 	)
+	if gpus[0].Library == "cpu" {
+		return MemoryEstimate{
+			Layers:    0,
+			Graph:     0,
+			VRAMSize:  0,
+			TotalSize: memoryRequiredTotal,
+			GPUSizes:  []uint64{},
+		}
+	}
+	if layerCount == 0 {
+		slog.Debug("insufficient VRAM to load any model layers")
+		return MemoryEstimate{
+			Layers:    0,
+			Graph:     0,
+			VRAMSize:  0,
+			TotalSize: memoryRequiredTotal,
+			GPUSizes:  []uint64{},
+		}
+	}
+
+	return MemoryEstimate{
+		Layers:      layerCount,
+		Graph:       graphOffload,
+		VRAMSize:    memoryRequiredPartial,
+		TotalSize:   memoryRequiredTotal,
+		TensorSplit: tensorSplit,
+		GPUSizes:    gpuAllocations,
+	}
 }

llm/memory_test.go

@@ -2,6 +2,7 @@ package llm
 
 import (
 	"bytes"
+	"encoding/binary"
 	"fmt"
 	"os"
 	"testing"
@@ -19,18 +20,18 @@ 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{
-		{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))},
-		{Name: "blk.3.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
-		{Name: "blk.4.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
-		{Name: "output.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
+	tensors := []Tensor{
+		{Name: "blk.0.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: &bytes.Reader{}},
+		{Name: "blk.1.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: &bytes.Reader{}},
+		{Name: "blk.2.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: &bytes.Reader{}},
+		{Name: "blk.3.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: &bytes.Reader{}},
+		{Name: "blk.4.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: &bytes.Reader{}},
+		{Name: "output.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: &bytes.Reader{}},
 	}
 	assert.Len(t, tensors, inputLayerCount+1)
-	err = WriteGGUF(f, KV{
+	err = gguf.Encode(f, KV{
 		"general.architecture":          "llama",
 		"general.name":                  "name",
 		"llama.context_length":          uint32(32),
@@ -44,10 +45,8 @@ func TestEstimateGPULayers(t *testing.T) {
 	}, tensors)
 	require.NoError(t, err)
 
-	ggml, err := LoadModel(f.Name(), 0)
-	if err != nil {
-		t.Fatal(err)
-	}
+	ggml, err := LoadModel(f.Name())
+	require.NoError(t, err)
 
 	// Simple CPU scenario
 	gpus := []gpu.GpuInfo{

llm/patches/01-load-progress.diff

@@ -1,8 +1,8 @@
 diff --git a/common/common.cpp b/common/common.cpp
-index 2c05a4d4..927f0e3d 100644
+index ba1ecf0e..cead57cc 100644
 --- a/common/common.cpp
 +++ b/common/common.cpp
-@@ -2093,6 +2093,8 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
+@@ -1836,6 +1836,8 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
      mparams.use_mmap        = params.use_mmap;
      mparams.use_mlock       = params.use_mlock;
      mparams.check_tensors   = params.check_tensors;
@@ -12,20 +12,20 @@ index 2c05a4d4..927f0e3d 100644
          mparams.kv_overrides = NULL;
      } else {
 diff --git a/common/common.h b/common/common.h
-index 65c0ef81..ebca2c77 100644
+index d80344f2..71e84834 100644
 --- a/common/common.h
 +++ b/common/common.h
-@@ -184,6 +184,13 @@ struct gpt_params {
+@@ -174,6 +174,13 @@ struct gpt_params {
+     // multimodal models (see examples/llava)
      std::string mmproj = "";        // path to multimodal projector
      std::vector<std::string> image; // path to image file(s)
- 
++
 +    // Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
 +    // If the provided progress_callback returns true, model loading continues.
 +    // If it returns false, model loading is immediately aborted.
 +    llama_progress_callback progress_callback = NULL;
 +    // context pointer passed to the progress callback
 +    void * progress_callback_user_data;
-+
-     // embedding
-     bool embedding         = false; // get only sentence embedding
-     int32_t embd_normalize = 2;     // normalisation for embendings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
+ };
+ 
+ void gpt_params_handle_model_default(gpt_params & params);

llm/patches/03-load_exception.diff

@@ -1,8 +1,17 @@
-diff --git a/src/llama.cpp b/src/llama.cpp
-index 73f52435..58a00fb1 100644
---- a/src/llama.cpp
-+++ b/src/llama.cpp
-@@ -7241,7 +7241,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
+From 544a2d2e646d39e878d87dfbb3398a356bc560ab Mon Sep 17 00:00:00 2001
+From: Michael Yang <mxyng@pm.me>
+Date: Thu, 23 May 2024 11:18:45 -0700
+Subject: [PATCH] throw exception on load errors
+
+---
+ llama.cpp | 25 ++++++++++++++++---------
+ 1 file changed, 16 insertions(+), 9 deletions(-)
+
+diff --git a/llama.cpp b/llama.cpp
+index 15c66077..8ba90b6a 100644
+--- a/llama.cpp
++++ b/llama.cpp
+@@ -6346,7 +6346,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
          }
      } catch (const std::exception & err) {
          LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
@@ -11,7 +20,7 @@ index 73f52435..58a00fb1 100644
      }
  
      return 0;
-@@ -17564,16 +17564,23 @@ struct llama_model * llama_load_model_from_file(
+@@ -15600,16 +15600,23 @@ struct llama_model * llama_load_model_from_file(
          }
          model->rpc_servers.push_back(servers);
      }
@@ -43,3 +52,6 @@ index 73f52435..58a00fb1 100644
      }
  
      return model;
+-- 
+2.45.1
+

llm/patches/04-metal.diff

@@ -1,7 +1,7 @@
-diff --git a/ggml/src/ggml-metal.m b/ggml/src/ggml-metal.m
+diff --git a/ggml-metal.m b/ggml-metal.m
 index 0207b787..b5e9884b 100644
---- a/ggml/src/ggml-metal.m
-+++ b/ggml/src/ggml-metal.m
+--- a/ggml-metal.m
++++ b/ggml-metal.m
 @@ -1396,27 +1396,23 @@ static enum ggml_status ggml_metal_graph_compute(
                          // to the matrix-vector kernel
                          int ne11_mm_min = 1;

llm/patches/05-default-pretokenizer.diff

@@ -1,11 +1,11 @@
-diff --git a/src/llama.cpp b/src/llama.cpp
-index 2b9ace28..172640e2 100644
---- a/src/llama.cpp
-+++ b/src/llama.cpp
-@@ -5357,16 +5357,7 @@ static void llm_load_vocab(
+diff --git a/llama.cpp b/llama.cpp
+index 40d2ec2c..74f3ee9c 100644
+--- a/llama.cpp
++++ b/llama.cpp
+@@ -4642,16 +4642,7 @@ static void llm_load_vocab(
+ 
+         // for now, only BPE models have pre-tokenizers
          if (vocab.type == LLAMA_VOCAB_TYPE_BPE) {
-             vocab.tokenizer_add_space_prefix = false;
-             vocab.tokenizer_clean_spaces = true;
 -            if (tokenizer_pre.empty()) {
 -                LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__);
 -                LLAMA_LOG_WARN("%s:                                             \n", __func__);
@@ -15,18 +15,18 @@ index 2b9ace28..172640e2 100644
 -                LLAMA_LOG_WARN("%s: ************************************        \n", __func__);
 -                LLAMA_LOG_WARN("%s:                                             \n", __func__);
 -                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
--            } else if (tokenizer_pre == "default") {
-+            if (tokenizer_pre == "default") {
+-            } else if (
++            if (
+                     tokenizer_pre == "default") {
                  vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
              } else if (
-                     tokenizer_pre == "llama3"   ||
-@@ -5439,7 +5430,8 @@ static void llm_load_vocab(
-                 tokenizer_pre == "jais") {
-                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_JAIS;
+@@ -4703,7 +4694,8 @@ static void llm_load_vocab(
+                 tokenizer_pre == "smaug-bpe") {
+                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMAUG;
              } 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__);
 +                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
              }
-         } else if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
+         } else {
              vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;

llm/patches/06-qwen2.diff

@@ -1,7 +1,7 @@
-diff --git a/src/llama.cpp b/src/llama.cpp
+diff --git a/llama.cpp b/llama.cpp
 index 40d2ec2c..f34eb79a 100644
---- a/src/llama.cpp
-+++ b/src/llama.cpp
+--- a/llama.cpp
++++ b/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);

llm/patches/07-embeddings.diff

@@ -1,45 +0,0 @@
-diff --git a/src/llama.cpp b/src/llama.cpp
-index 1fe2b9f7..a43312a7 100644
---- a/src/llama.cpp
-+++ b/src/llama.cpp
-@@ -13689,7 +13689,7 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
-     const auto n_embd  = hparams.n_embd;
- 
-     // TODO: use a per-batch flag for logits presence instead
--    const bool has_logits = !cparams.embeddings;
-+    const bool has_logits =  cparams.causal_attn;
-     const bool has_embd   =  lctx.is_encoding || (cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE));
- 
-     const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
-@@ -13959,17 +13959,25 @@ static int llama_decode_internal(
-             // no output
-             res  = nullptr;
-             embd = nullptr;
--        } else if (cparams.embeddings) {
--            res = nullptr; // do not extract logits for embedding case
--            embd = gf->nodes[gf->n_nodes - 1];
--            if (strcmp(embd->name, "result_embd_pooled") != 0) {
--                embd = gf->nodes[gf->n_nodes - 2];
-+        }
-+
-+        if (cparams.embeddings) {
-+            for (int i = gf->n_nodes - 1; i >= 0; --i) {
-+                embd = gf->nodes[i];
-+                if (strcmp(embd->name, "result_embd_pooled") == 0) {
-+                    break;
-+                }
-             }
-             GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0 && "missing embeddings tensor");
--        } else {
-+         } else {
-             embd = nullptr; // do not extract embeddings when not needed
-             GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor");
-         }
-+
-+        if (!cparams.causal_attn) {
-+            res = nullptr; // do not extract logits when not needed
-+        }
-+
-         // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
- 
-         ggml_backend_sched_alloc_graph(lctx.sched, gf);

llm/patches/08-clip-unicode.diff

@@ -1,42 +0,0 @@
-diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
-index 95fbe3d0..5a02a6ec 100644
---- a/examples/llava/clip.cpp
-+++ b/examples/llava/clip.cpp
-@@ -32,6 +33,14 @@
- #include <cinttypes>
- #include <limits>
- 
-+#if defined(_WIN32)
-+#define WIN32_LEAN_AND_MEAN
-+#ifndef NOMINMAX
-+    #define NOMINMAX
-+#endif
-+#include <windows.h>
-+#endif
-+
- //#define CLIP_DEBUG_FUNCTIONS
- 
- // RGB uint8 image
-@@ -1055,7 +1064,22 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
-             return nullptr;
-         }
- 
-+#ifdef _WIN32
-+        int wlen = MultiByteToWideChar(CP_UTF8, 0, fname, -1, NULL, 0);
-+        if (!wlen) {
-+            return NULL;
-+        }
-+        wchar_t * wbuf = (wchar_t *) malloc(wlen * sizeof(wchar_t));
-+        wlen = MultiByteToWideChar(CP_UTF8, 0, fname, -1, wbuf, wlen);
-+        if (!wlen) {
-+            free(wbuf);
-+            return NULL;
-+        }
-+        auto fin = std::ifstream(wbuf, std::ios::binary);
-+        free(wbuf);
-+#else
-         auto fin = std::ifstream(fname, std::ios::binary);
-+#endif
-         if (!fin) {
-             LOG_TEE("cannot open model file for loading tensors\n");
-             clip_free(new_clip);

llm/patches/09-pooling.diff

@@ -1,60 +0,0 @@
-diff --git a/src/llama.cpp b/src/llama.cpp
-index 721b8f4e..cfe7ac40 100644
---- a/src/llama.cpp
-+++ b/src/llama.cpp
-@@ -8420,14 +8420,14 @@ struct llm_build_context {
-     }
- 
-     struct ggml_tensor * build_inp_mean() {
--        lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
-+        lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, cparams.n_seq_max);
-         cb(lctx.inp_mean, "inp_mean", -1);
-         ggml_set_input(lctx.inp_mean);
-         return lctx.inp_mean;
-     }
- 
-     struct ggml_tensor * build_inp_cls() {
--        lctx.inp_cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
-+        lctx.inp_cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, cparams.n_seq_max);
-         cb(lctx.inp_cls, "inp_cls", -1);
-         ggml_set_input(lctx.inp_cls);
-         return lctx.inp_cls;
-@@ -13847,19 +13847,16 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
-         GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
- 
-         float * data = (float *) lctx.inp_mean->data;
--        memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean));
-+        memset(lctx.inp_mean->data, 0, n_tokens * cparams.n_seq_max * ggml_element_size(lctx.inp_mean));
- 
-         std::vector<uint64_t> sum(n_tokens, 0);
-         for (int i = 0; i < n_tokens; ++i) {
-             const llama_seq_id seq_id = batch.seq_id[i][0];
--
--            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN");
--
-             sum[seq_id] += 1;
-         }
- 
--        std::vector<float> div(n_tokens, 0.0f);
--        for (int i = 0; i < n_tokens; ++i) {
-+        std::vector<float> div(cparams.n_seq_max, 0.0f);
-+        for (uint32_t i = 0; i < cparams.n_seq_max; ++i) {
-             const uint64_t s = sum[i];
-             if (s > 0) {
-                 div[i] = 1.0f/float(s);
-@@ -13879,14 +13876,11 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
-         GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
- 
-         uint32_t * data = (uint32_t *) lctx.inp_cls->data;
--        memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
-+        memset(lctx.inp_cls->data, 0, cparams.n_seq_max * ggml_element_size(lctx.inp_cls));
- 
-         for (int i = 0; i < n_tokens; ++i) {
-             const llama_seq_id seq_id = batch.seq_id[i][0];
-             const llama_pos    pos    = batch.pos[i];
--
--            GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS");
--
-             if (pos == 0) {
-                 data[seq_id] = i;
-             }

llm/patches/10-lora.diff

@@ -1,78 +0,0 @@
-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/payload.go

@@ -38,7 +38,7 @@ func Init() error {
 	}
 
 	var variants []string
-	for v := range getAvailableServers() {
+	for v := range availableServers() {
 		variants = append(variants, v)
 	}
 	slog.Info(fmt.Sprintf("Dynamic LLM libraries %v", variants))
@@ -50,7 +50,7 @@ func Init() error {
 // binary names may contain an optional variant separated by '_'
 // For example, "ollama_rocm_v6" and "ollama_rocm_v5" or "ollama_cpu" and "ollama_cpu_avx2"
 // Any library without a variant is the lowest common denominator
-func getAvailableServers() map[string]string {
+func availableServers() map[string]string {
 	payloadsDir, err := gpu.PayloadsDir()
 	if err != nil {
 		slog.Error("payload lookup error", "error", err)
@@ -58,7 +58,7 @@ func getAvailableServers() map[string]string {
 	}
 
 	// glob payloadsDir for files that start with ollama_
-	pattern := filepath.Join(payloadsDir, "*", "ollama_*")
+	pattern := filepath.Join(payloadsDir, "*")
 
 	files, err := filepath.Glob(pattern)
 	if err != nil {
@@ -69,7 +69,7 @@ func getAvailableServers() map[string]string {
 	servers := make(map[string]string)
 	for _, file := range files {
 		slog.Debug("availableServers : found", "file", file)
-		servers[filepath.Base(filepath.Dir(file))] = filepath.Dir(file)
+		servers[filepath.Base(file)] = file
 	}
 
 	return servers
@@ -80,7 +80,7 @@ func getAvailableServers() map[string]string {
 // TODO - switch to metadata based mapping
 func serversForGpu(info gpu.GpuInfo) []string {
 	// glob workDir for files that start with ollama_
-	availableServers := getAvailableServers()
+	availableServers := availableServers()
 	requested := info.Library
 	if info.Variant != gpu.CPUCapabilityNone {
 		requested += "_" + info.Variant.String()
@@ -115,29 +115,27 @@ func serversForGpu(info gpu.GpuInfo) []string {
 		servers = append(servers, alt...)
 	}
 
-	if !(runtime.GOOS == "darwin" && runtime.GOARCH == "arm64") {
-		// Load up the best CPU variant if not primary requested
-		if info.Library != "cpu" {
-			variant := gpu.GetCPUCapability()
-			// If no variant, then we fall back to default
-			// If we have a variant, try that if we find an exact match
-			// Attempting to run the wrong CPU instructions will panic the
-			// process
-			if variant != gpu.CPUCapabilityNone {
-				for cmp := range availableServers {
-					if cmp == "cpu_"+variant.String() {
-						servers = append(servers, cmp)
-						break
-					}
+	// Load up the best CPU variant if not primary requested
+	if info.Library != "cpu" {
+		variant := gpu.GetCPUCapability()
+		// If no variant, then we fall back to default
+		// If we have a variant, try that if we find an exact match
+		// Attempting to run the wrong CPU instructions will panic the
+		// process
+		if variant != gpu.CPUCapabilityNone {
+			for cmp := range availableServers {
+				if cmp == "cpu_"+variant.String() {
+					servers = append(servers, cmp)
+					break
 				}
-			} else {
-				servers = append(servers, "cpu")
 			}
+		} else {
+			servers = append(servers, "cpu")
 		}
+	}
 
-		if len(servers) == 0 {
-			servers = []string{"cpu"}
-		}
+	if len(servers) == 0 {
+		servers = []string{"cpu"}
 	}
 
 	return servers
@@ -149,7 +147,7 @@ func serverForCpu() string {
 		return "metal"
 	}
 	variant := gpu.GetCPUCapability()
-	availableServers := getAvailableServers()
+	availableServers := availableServers()
 	if variant != gpu.CPUCapabilityNone {
 		for cmp := range availableServers {
 			if cmp == "cpu_"+variant.String() {

llm/server.go

@@ -60,12 +60,7 @@ type llmServer struct {
 	sem *semaphore.Weighted
 }
 
-// LoadModel will load a model from disk. The model must be in the GGML format.
-//
-// It collects array values for arrays with a size less than or equal to
-// maxArraySize. If maxArraySize is 0, the default value of 1024 is used. If
-// the maxArraySize is negative, all arrays are collected.
-func LoadModel(model string, maxArraySize int) (*GGML, error) {
+func LoadModel(model string) (*GGML, error) {
 	if _, err := os.Stat(model); err != nil {
 		return nil, err
 	}
@@ -76,27 +71,17 @@ func LoadModel(model string, maxArraySize int) (*GGML, error) {
 	}
 	defer f.Close()
 
-	ggml, _, err := DecodeGGML(f, maxArraySize)
+	ggml, _, err := DecodeGGML(f)
 	return ggml, err
 }
 
 // NewLlamaServer will run a server for the given GPUs
 // The gpu list must be a single family.
-func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, projectors []string, opts api.Options, numParallel int) (LlamaServer, error) {
+func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, projectors []string, opts api.Options) (LlamaServer, error) {
 	var err error
 	var cpuRunner string
 	var estimate MemoryEstimate
-	var systemTotalMemory uint64
-	var systemFreeMemory uint64
-
-	systemMemInfo, err := gpu.GetCPUMem()
-	if err != nil {
-		slog.Error("failed to lookup system memory", "error", err)
-	} else {
-		systemTotalMemory = systemMemInfo.TotalMemory
-		systemFreeMemory = systemMemInfo.FreeMemory
-		slog.Debug("system memory", "total", format.HumanBytes2(systemTotalMemory), "free", systemFreeMemory)
-	}
+	var systemMemory uint64
 
 	// If the user wants zero GPU layers, reset the gpu list to be CPU/system ram info
 	if opts.NumGPU == 0 {
@@ -106,10 +91,19 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		cpuRunner = serverForCpu()
 		estimate = EstimateGPULayers(gpus, ggml, projectors, opts)
 	} else {
+		if gpus[0].Library == "metal" {
+			memInfo, err := gpu.GetCPUMem()
+			if err != nil {
+				slog.Error("failed to lookup system memory", "error", err)
+			} else {
+				systemMemory = memInfo.TotalMemory
+				slog.Debug("system memory", "total", format.HumanBytes2(systemMemory))
+			}
+		}
 		estimate = EstimateGPULayers(gpus, ggml, projectors, opts)
 
 		switch {
-		case gpus[0].Library == "metal" && estimate.VRAMSize > systemTotalMemory:
+		case gpus[0].Library == "metal" && estimate.VRAMSize > systemMemory:
 			// disable partial offloading when model is greater than total system memory as this
 			// can lead to locking up the system
 			opts.NumGPU = 0
@@ -122,8 +116,6 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		}
 	}
 
-	estimate.log()
-
 	// Loop through potential servers
 	finalErr := errors.New("no suitable llama servers found")
 
@@ -131,20 +123,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		return nil, errors.New("ollama supports only one lora adapter, but multiple were provided")
 	}
 
-	availableServers := getAvailableServers()
-	if len(availableServers) == 0 {
-		if runtime.GOOS != "windows" {
-			slog.Warn("llama server binary disappeared, reinitializing payloads")
-			err = Init()
-			if err != nil {
-				slog.Warn("failed to reinitialize payloads", "error", err)
-				return nil, err
-			}
-			availableServers = getAvailableServers()
-		} else {
-			return nil, finalErr
-		}
-	}
+	availableServers := availableServers()
 	var servers []string
 	if cpuRunner != "" {
 		servers = []string{cpuRunner}
@@ -221,8 +200,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		if g.Library == "metal" &&
 			uint64(opts.NumGPU) > 0 &&
 			uint64(opts.NumGPU) < ggml.KV().BlockCount()+1 {
-			opts.UseMMap = new(bool)
-			*opts.UseMMap = false
+			opts.UseMMap = false
 		}
 	}
 
@@ -230,13 +208,7 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		params = append(params, "--flash-attn")
 	}
 
-	// Windows CUDA should not use mmap for best performance
-	// Linux  with a model larger than free space, mmap leads to thrashing
-	// For CPU loads we want the memory to be allocated, not FS cache
-	if (runtime.GOOS == "windows" && gpus[0].Library == "cuda" && opts.UseMMap == nil) ||
-		(runtime.GOOS == "linux" && systemFreeMemory < estimate.TotalSize && opts.UseMMap == nil) ||
-		(gpus[0].Library == "cpu" && opts.UseMMap == nil) ||
-		(opts.UseMMap != nil && !*opts.UseMMap) {
+	if !opts.UseMMap {
 		params = append(params, "--no-mmap")
 	}
 
@@ -248,6 +220,15 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		params = append(params, "--numa")
 	}
 
+	numParallel := envconfig.NumParallel
+
+	// TODO (jmorganca): multimodal models don't support parallel yet
+	// see https://github.com/ollama/ollama/issues/4165
+	if len(projectors) > 0 {
+		numParallel = 1
+		slog.Warn("multimodal models don't support parallel requests yet")
+	}
+
 	params = append(params, "--parallel", fmt.Sprintf("%d", numParallel))
 
 	if estimate.TensorSplit != "" {
@@ -290,8 +271,8 @@ func NewLlamaServer(gpus gpu.GpuInfoList, model string, ggml *GGML, adapters, pr
 		if runtime.GOOS == "windows" {
 			pathEnv = "PATH"
 		}
-		// prepend the server directory to LD_LIBRARY_PATH/PATH and the parent dir for common dependencies
-		libraryPaths := []string{dir, filepath.Dir(dir)}
+		// prepend the server directory to LD_LIBRARY_PATH/PATH
+		libraryPaths := []string{dir}
 
 		if libraryPath, ok := os.LookupEnv(pathEnv); ok {
 			// Append our runner directory to the path
@@ -424,7 +405,7 @@ func projectorMemoryRequirements(filename string) uint64 {
 	}
 	defer file.Close()
 
-	ggml, _, err := DecodeGGML(file, 0)
+	ggml, _, err := DecodeGGML(file)
 	if err != nil {
 		return 0
 	}
@@ -574,9 +555,6 @@ func (s *llmServer) WaitUntilRunning(ctx context.Context) error {
 			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:
 		}
@@ -699,9 +677,10 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 	}
 	defer s.sem.Release(1)
 
-	// put an upper limit on num_predict to avoid the model running on forever
+	// only allow maximum 10 "context shifts" to avoid infinite generation
 	if req.Options.NumPredict < 0 || req.Options.NumPredict > 10*s.options.NumCtx {
 		req.Options.NumPredict = 10 * s.options.NumCtx
+		slog.Debug("setting token limit to 10x num_ctx", "num_ctx", s.options.NumCtx, "num_predict", req.Options.NumPredict)
 	}
 
 	request := map[string]any{

llm/status.go

@@ -25,7 +25,6 @@ var errorPrefixes = []string{
 	"CUDA error",
 	"cudaMalloc failed",
 	"\"ERR\"",
-	"error loading model",
 }
 
 func (w *StatusWriter) Write(b []byte) (int, error) {

openai/openai.go

@@ -12,7 +12,6 @@ import (
 
 	"github.com/gin-gonic/gin"
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/types/model"
 )
 
 type Error struct {
@@ -43,12 +42,6 @@ type ChunkChoice struct {
 	FinishReason *string `json:"finish_reason"`
 }
 
-type CompleteChunkChoice struct {
-	Text         string  `json:"text"`
-	Index        int     `json:"index"`
-	FinishReason *string `json:"finish_reason"`
-}
-
 type Usage struct {
 	PromptTokens     int `json:"prompt_tokens"`
 	CompletionTokens int `json:"completion_tokens"`
@@ -92,51 +85,6 @@ type ChatCompletionChunk struct {
 	Choices           []ChunkChoice `json:"choices"`
 }
 
-// TODO (https://github.com/ollama/ollama/issues/5259): support []string, []int and [][]int
-type CompletionRequest struct {
-	Model            string   `json:"model"`
-	Prompt           string   `json:"prompt"`
-	FrequencyPenalty float32  `json:"frequency_penalty"`
-	MaxTokens        *int     `json:"max_tokens"`
-	PresencePenalty  float32  `json:"presence_penalty"`
-	Seed             *int     `json:"seed"`
-	Stop             any      `json:"stop"`
-	Stream           bool     `json:"stream"`
-	Temperature      *float32 `json:"temperature"`
-	TopP             float32  `json:"top_p"`
-}
-
-type Completion struct {
-	Id                string                `json:"id"`
-	Object            string                `json:"object"`
-	Created           int64                 `json:"created"`
-	Model             string                `json:"model"`
-	SystemFingerprint string                `json:"system_fingerprint"`
-	Choices           []CompleteChunkChoice `json:"choices"`
-	Usage             Usage                 `json:"usage,omitempty"`
-}
-
-type CompletionChunk struct {
-	Id                string                `json:"id"`
-	Object            string                `json:"object"`
-	Created           int64                 `json:"created"`
-	Choices           []CompleteChunkChoice `json:"choices"`
-	Model             string                `json:"model"`
-	SystemFingerprint string                `json:"system_fingerprint"`
-}
-
-type Model struct {
-	Id      string `json:"id"`
-	Object  string `json:"object"`
-	Created int64  `json:"created"`
-	OwnedBy string `json:"owned_by"`
-}
-
-type ListCompletion struct {
-	Object string  `json:"object"`
-	Data   []Model `json:"data"`
-}
-
 func NewError(code int, message string) ErrorResponse {
 	var etype string
 	switch code {
@@ -197,79 +145,7 @@ func toChunk(id string, r api.ChatResponse) ChatCompletionChunk {
 	}
 }
 
-func toCompletion(id string, r api.GenerateResponse) Completion {
-	return Completion{
-		Id:                id,
-		Object:            "text_completion",
-		Created:           r.CreatedAt.Unix(),
-		Model:             r.Model,
-		SystemFingerprint: "fp_ollama",
-		Choices: []CompleteChunkChoice{{
-			Text:  r.Response,
-			Index: 0,
-			FinishReason: func(reason string) *string {
-				if len(reason) > 0 {
-					return &reason
-				}
-				return nil
-			}(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,
-		},
-	}
-}
-
-func toCompleteChunk(id string, r api.GenerateResponse) CompletionChunk {
-	return CompletionChunk{
-		Id:                id,
-		Object:            "text_completion",
-		Created:           time.Now().Unix(),
-		Model:             r.Model,
-		SystemFingerprint: "fp_ollama",
-		Choices: []CompleteChunkChoice{{
-			Text:  r.Response,
-			Index: 0,
-			FinishReason: func(reason string) *string {
-				if len(reason) > 0 {
-					return &reason
-				}
-				return nil
-			}(r.DoneReason),
-		}},
-	}
-}
-
-func toListCompletion(r api.ListResponse) ListCompletion {
-	var data []Model
-	for _, m := range r.Models {
-		data = append(data, Model{
-			Id:      m.Name,
-			Object:  "model",
-			Created: m.ModifiedAt.Unix(),
-			OwnedBy: model.ParseName(m.Name).Namespace,
-		})
-	}
-
-	return ListCompletion{
-		Object: "list",
-		Data:   data,
-	}
-}
-
-func toModel(r api.ShowResponse, m string) Model {
-	return Model{
-		Id:      m,
-		Object:  "model",
-		Created: r.ModifiedAt.Unix(),
-		OwnedBy: model.ParseName(m).Namespace,
-	}
-}
-
-func fromChatRequest(r ChatCompletionRequest) api.ChatRequest {
+func fromRequest(r ChatCompletionRequest) api.ChatRequest {
 	var messages []api.Message
 	for _, msg := range r.Messages {
 		messages = append(messages, api.Message{Role: msg.Role, Content: msg.Content})
@@ -280,7 +156,7 @@ func fromChatRequest(r ChatCompletionRequest) api.ChatRequest {
 	switch stop := r.Stop.(type) {
 	case string:
 		options["stop"] = []string{stop}
-	case []any:
+	case []interface{}:
 		var stops []string
 		for _, s := range stop {
 			if str, ok := s.(string); ok {
@@ -332,78 +208,13 @@ func fromChatRequest(r ChatCompletionRequest) api.ChatRequest {
 	}
 }
 
-func fromCompleteRequest(r CompletionRequest) (api.GenerateRequest, error) {
-	options := make(map[string]any)
-
-	switch stop := r.Stop.(type) {
-	case string:
-		options["stop"] = []string{stop}
-	case []string:
-		options["stop"] = stop
-	default:
-		if r.Stop != nil {
-			return api.GenerateRequest{}, fmt.Errorf("invalid type for 'stop' field: %T", r.Stop)
-		}
-	}
-
-	if r.MaxTokens != nil {
-		options["num_predict"] = *r.MaxTokens
-	}
-
-	if r.Temperature != nil {
-		options["temperature"] = *r.Temperature * 2.0
-	} else {
-		options["temperature"] = 1.0
-	}
-
-	if r.Seed != nil {
-		options["seed"] = *r.Seed
-	}
-
-	options["frequency_penalty"] = r.FrequencyPenalty * 2.0
-
-	options["presence_penalty"] = r.PresencePenalty * 2.0
-
-	if r.TopP != 0.0 {
-		options["top_p"] = r.TopP
-	} else {
-		options["top_p"] = 1.0
-	}
-
-	return api.GenerateRequest{
-		Model:   r.Model,
-		Prompt:  r.Prompt,
-		Options: options,
-		Stream:  &r.Stream,
-	}, nil
-}
-
-type BaseWriter struct {
+type writer struct {
+	stream bool
+	id     string
 	gin.ResponseWriter
 }
 
-type ChatWriter struct {
-	stream bool
-	id     string
-	BaseWriter
-}
-
-type CompleteWriter struct {
-	stream bool
-	id     string
-	BaseWriter
-}
-
-type ListWriter struct {
-	BaseWriter
-}
-
-type RetrieveWriter struct {
-	BaseWriter
-	model string
-}
-
-func (w *BaseWriter) writeError(code int, data []byte) (int, error) {
+func (w *writer) writeError(code int, data []byte) (int, error) {
 	var serr api.StatusError
 	err := json.Unmarshal(data, &serr)
 	if err != nil {
@@ -419,7 +230,7 @@ func (w *BaseWriter) writeError(code int, data []byte) (int, error) {
 	return len(data), nil
 }
 
-func (w *ChatWriter) writeResponse(data []byte) (int, error) {
+func (w *writer) writeResponse(data []byte) (int, error) {
 	var chatResponse api.ChatResponse
 	err := json.Unmarshal(data, &chatResponse)
 	if err != nil {
@@ -459,7 +270,7 @@ func (w *ChatWriter) writeResponse(data []byte) (int, error) {
 	return len(data), nil
 }
 
-func (w *ChatWriter) Write(data []byte) (int, error) {
+func (w *writer) Write(data []byte) (int, error) {
 	code := w.ResponseWriter.Status()
 	if code != http.StatusOK {
 		return w.writeError(code, data)
@@ -468,176 +279,7 @@ func (w *ChatWriter) Write(data []byte) (int, error) {
 	return w.writeResponse(data)
 }
 
-func (w *CompleteWriter) writeResponse(data []byte) (int, error) {
-	var generateResponse api.GenerateResponse
-	err := json.Unmarshal(data, &generateResponse)
-	if err != nil {
-		return 0, err
-	}
-
-	// completion chunk
-	if w.stream {
-		d, err := json.Marshal(toCompleteChunk(w.id, generateResponse))
-		if err != nil {
-			return 0, err
-		}
-
-		w.ResponseWriter.Header().Set("Content-Type", "text/event-stream")
-		_, err = w.ResponseWriter.Write([]byte(fmt.Sprintf("data: %s\n\n", d)))
-		if err != nil {
-			return 0, err
-		}
-
-		if generateResponse.Done {
-			_, err = w.ResponseWriter.Write([]byte("data: [DONE]\n\n"))
-			if err != nil {
-				return 0, err
-			}
-		}
-
-		return len(data), nil
-	}
-
-	// completion
-	w.ResponseWriter.Header().Set("Content-Type", "application/json")
-	err = json.NewEncoder(w.ResponseWriter).Encode(toCompletion(w.id, generateResponse))
-	if err != nil {
-		return 0, err
-	}
-
-	return len(data), nil
-}
-
-func (w *CompleteWriter) Write(data []byte) (int, error) {
-	code := w.ResponseWriter.Status()
-	if code != http.StatusOK {
-		return w.writeError(code, data)
-	}
-
-	return w.writeResponse(data)
-}
-
-func (w *ListWriter) writeResponse(data []byte) (int, error) {
-	var listResponse api.ListResponse
-	err := json.Unmarshal(data, &listResponse)
-	if err != nil {
-		return 0, err
-	}
-
-	w.ResponseWriter.Header().Set("Content-Type", "application/json")
-	err = json.NewEncoder(w.ResponseWriter).Encode(toListCompletion(listResponse))
-	if err != nil {
-		return 0, err
-	}
-
-	return len(data), nil
-}
-
-func (w *ListWriter) Write(data []byte) (int, error) {
-	code := w.ResponseWriter.Status()
-	if code != http.StatusOK {
-		return w.writeError(code, data)
-	}
-
-	return w.writeResponse(data)
-}
-
-func (w *RetrieveWriter) writeResponse(data []byte) (int, error) {
-	var showResponse api.ShowResponse
-	err := json.Unmarshal(data, &showResponse)
-	if err != nil {
-		return 0, err
-	}
-
-	// retrieve completion
-	w.ResponseWriter.Header().Set("Content-Type", "application/json")
-	err = json.NewEncoder(w.ResponseWriter).Encode(toModel(showResponse, w.model))
-	if err != nil {
-		return 0, err
-	}
-
-	return len(data), nil
-}
-
-func (w *RetrieveWriter) 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{
-			BaseWriter: BaseWriter{ResponseWriter: c.Writer},
-		}
-
-		c.Writer = w
-
-		c.Next()
-	}
-}
-
-func RetrieveMiddleware() gin.HandlerFunc {
-	return func(c *gin.Context) {
-		var b bytes.Buffer
-		if err := json.NewEncoder(&b).Encode(api.ShowRequest{Name: c.Param("model")}); err != nil {
-			c.AbortWithStatusJSON(http.StatusInternalServerError, NewError(http.StatusInternalServerError, err.Error()))
-			return
-		}
-
-		c.Request.Body = io.NopCloser(&b)
-
-		// response writer
-		w := &RetrieveWriter{
-			BaseWriter: BaseWriter{ResponseWriter: c.Writer},
-			model:      c.Param("model"),
-		}
-
-		c.Writer = w
-
-		c.Next()
-	}
-}
-
-func CompletionsMiddleware() gin.HandlerFunc {
-	return func(c *gin.Context) {
-		var req CompletionRequest
-		err := c.ShouldBindJSON(&req)
-		if err != nil {
-			c.AbortWithStatusJSON(http.StatusBadRequest, NewError(http.StatusBadRequest, err.Error()))
-			return
-		}
-
-		var b bytes.Buffer
-		genReq, err := fromCompleteRequest(req)
-		if err != nil {
-			c.AbortWithStatusJSON(http.StatusBadRequest, NewError(http.StatusBadRequest, err.Error()))
-			return
-		}
-
-		if err := json.NewEncoder(&b).Encode(genReq); err != nil {
-			c.AbortWithStatusJSON(http.StatusInternalServerError, NewError(http.StatusInternalServerError, err.Error()))
-			return
-		}
-
-		c.Request.Body = io.NopCloser(&b)
-
-		w := &CompleteWriter{
-			BaseWriter: BaseWriter{ResponseWriter: c.Writer},
-			stream:     req.Stream,
-			id:         fmt.Sprintf("cmpl-%d", rand.Intn(999)),
-		}
-
-		c.Writer = w
-
-		c.Next()
-	}
-}
-
-func ChatMiddleware() gin.HandlerFunc {
+func Middleware() gin.HandlerFunc {
 	return func(c *gin.Context) {
 		var req ChatCompletionRequest
 		err := c.ShouldBindJSON(&req)
@@ -652,17 +294,17 @@ func ChatMiddleware() gin.HandlerFunc {
 		}
 
 		var b bytes.Buffer
-		if err := json.NewEncoder(&b).Encode(fromChatRequest(req)); err != nil {
+		if err := json.NewEncoder(&b).Encode(fromRequest(req)); err != nil {
 			c.AbortWithStatusJSON(http.StatusInternalServerError, NewError(http.StatusInternalServerError, err.Error()))
 			return
 		}
 
 		c.Request.Body = io.NopCloser(&b)
 
-		w := &ChatWriter{
-			BaseWriter: BaseWriter{ResponseWriter: c.Writer},
-			stream:     req.Stream,
-			id:         fmt.Sprintf("chatcmpl-%d", rand.Intn(999)),
+		w := &writer{
+			ResponseWriter: c.Writer,
+			stream:         req.Stream,
+			id:             fmt.Sprintf("chatcmpl-%d", rand.Intn(999)),
 		}
 
 		c.Writer = w

openai/openai_test.go

@@ -1,298 +0,0 @@
-package openai
-
-import (
-	"bytes"
-	"encoding/json"
-	"fmt"
-	"io"
-	"net/http"
-	"net/http/httptest"
-	"strings"
-	"testing"
-	"time"
-
-	"github.com/gin-gonic/gin"
-	"github.com/ollama/ollama/api"
-	"github.com/stretchr/testify/assert"
-)
-
-func TestMiddleware(t *testing.T) {
-	type testCase struct {
-		Name     string
-		Method   string
-		Path     string
-		TestPath string
-		Handler  func() gin.HandlerFunc
-		Endpoint func(c *gin.Context)
-		Setup    func(t *testing.T, req *http.Request)
-		Expected func(t *testing.T, resp *httptest.ResponseRecorder)
-	}
-
-	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,
-			Path:     "/api/tags",
-			TestPath: "/api/tags",
-			Handler:  ListMiddleware,
-			Endpoint: func(c *gin.Context) {
-				c.JSON(http.StatusOK, api.ListResponse{
-					Models: []api.ListModelResponse{
-						{
-							Name: "Test Model",
-						},
-					},
-				})
-			},
-			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)
-				}
-
-				if listResp.Object != "list" {
-					t.Fatalf("expected list, got %s", listResp.Object)
-				}
-
-				if len(listResp.Data) != 1 {
-					t.Fatalf("expected 1, got %d", len(listResp.Data))
-				}
-
-				if listResp.Data[0].Id != "Test Model" {
-					t.Fatalf("expected Test Model, got %s", listResp.Data[0].Id)
-				}
-			},
-		},
-		{
-			Name:     "retrieve model",
-			Method:   http.MethodGet,
-			Path:     "/api/show/:model",
-			TestPath: "/api/show/test-model",
-			Handler:  RetrieveMiddleware,
-			Endpoint: func(c *gin.Context) {
-				c.JSON(http.StatusOK, api.ShowResponse{
-					ModifiedAt: time.Date(2024, 6, 17, 13, 45, 0, 0, time.UTC),
-				})
-			},
-			Expected: func(t *testing.T, resp *httptest.ResponseRecorder) {
-				var retrieveResp Model
-				if err := json.NewDecoder(resp.Body).Decode(&retrieveResp); err != nil {
-					t.Fatal(err)
-				}
-
-				if retrieveResp.Object != "model" {
-					t.Fatalf("Expected object to be model, got %s", retrieveResp.Object)
-				}
-
-				if retrieveResp.Id != "test-model" {
-					t.Fatalf("Expected id to be test-model, got %s", retrieveResp.Id)
-				}
-			},
-		},
-	}
-
-	gin.SetMode(gin.TestMode)
-	router := gin.New()
-
-	for _, tc := range testCases {
-		t.Run(tc.Name, func(t *testing.T) {
-			router = gin.New()
-			router.Use(tc.Handler())
-			router.Handle(tc.Method, tc.Path, tc.Endpoint)
-			req, _ := http.NewRequest(tc.Method, tc.TestPath, nil)
-
-			if tc.Setup != nil {
-				tc.Setup(t, req)
-			}
-
-			resp := httptest.NewRecorder()
-			router.ServeHTTP(resp, req)
-
-			tc.Expected(t, resp)
-		})
-	}
-}

parser/parser.go

@@ -124,7 +124,7 @@ func ParseFile(r io.Reader) (*File, error) {
 			case stateComment, stateNil:
 				// pass
 			case stateValue:
-				s, ok := unquote(strings.TrimSpace(b.String()))
+				s, ok := unquote(b.String())
 				if !ok || isSpace(r) {
 					if _, err := b.WriteRune(r); err != nil {
 						return nil, err
@@ -158,7 +158,7 @@ func ParseFile(r io.Reader) (*File, error) {
 	case stateComment, stateNil:
 		// pass; nothing to flush
 	case stateValue:
-		s, ok := unquote(strings.TrimSpace(b.String()))
+		s, ok := unquote(b.String())
 		if !ok {
 			return nil, io.ErrUnexpectedEOF
 		}

parser/parser_test.go

@@ -22,13 +22,7 @@ ADAPTER adapter1
 LICENSE MIT
 PARAMETER param1 value1
 PARAMETER param2 value2
-TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
-
-{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
-
-{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
-
-{{ .Response }}<|eot_id|>"""    
+TEMPLATE template1
 `
 
 	reader := strings.NewReader(input)
@@ -42,40 +36,7 @@ TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
 		{Name: "license", Args: "MIT"},
 		{Name: "param1", Args: "value1"},
 		{Name: "param2", Args: "value2"},
-		{Name: "template", Args: "{{ if .System }}<|start_header_id|>system<|end_header_id|>\n\n{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>\n\n{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>\n\n{{ .Response }}<|eot_id|>"},
-	}
-
-	assert.Equal(t, expectedCommands, modelfile.Commands)
-}
-
-func TestParseFileTrimSpace(t *testing.T) {
-	input := `
-FROM "     model 1"
-ADAPTER      adapter3
-LICENSE "MIT       "
-PARAMETER param1        value1
-PARAMETER param2    value2
-TEMPLATE """   {{ if .System }}<|start_header_id|>system<|end_header_id|>
-
-{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>
-
-{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>
-
-{{ .Response }}<|eot_id|>   """    
-`
-
-	reader := strings.NewReader(input)
-
-	modelfile, err := ParseFile(reader)
-	require.NoError(t, err)
-
-	expectedCommands := []Command{
-		{Name: "model", Args: "     model 1"},
-		{Name: "adapter", Args: "adapter3"},
-		{Name: "license", Args: "MIT       "},
-		{Name: "param1", Args: "value1"},
-		{Name: "param2", Args: "value2"},
-		{Name: "template", Args: "   {{ if .System }}<|start_header_id|>system<|end_header_id|>\n\n{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>\n\n{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>\n\n{{ .Response }}<|eot_id|>   "},
+		{Name: "template", Args: "template1"},
 	}
 
 	assert.Equal(t, expectedCommands, modelfile.Commands)
@@ -87,26 +48,6 @@ func TestParseFileFrom(t *testing.T) {
 		expected []Command
 		err      error
 	}{
-		{
-			"FROM \"FOO  BAR  \"",
-			[]Command{{Name: "model", Args: "FOO  BAR  "}},
-			nil,
-		},
-		{
-			"FROM \"FOO BAR\"\nPARAMETER param1 value1",
-			[]Command{{Name: "model", Args: "FOO BAR"}, {Name: "param1", Args: "value1"}},
-			nil,
-		},
-		{
-			"FROM     FOOO BAR    ",
-			[]Command{{Name: "model", Args: "FOOO BAR"}},
-			nil,
-		},
-		{
-			"FROM /what/is/the path ",
-			[]Command{{Name: "model", Args: "/what/is/the path"}},
-			nil,
-		},
 		{
 			"FROM foo",
 			[]Command{{Name: "model", Args: "foo"}},
@@ -145,11 +86,6 @@ func TestParseFileFrom(t *testing.T) {
 			[]Command{{Name: "param1", Args: "value1"}, {Name: "model", Args: "foo"}},
 			nil,
 		},
-		{
-			"PARAMETER what the \nFROM lemons make lemonade ",
-			[]Command{{Name: "what", Args: "the"}, {Name: "model", Args: "lemons make lemonade"}},
-			nil,
-		},
 	}
 
 	for _, c := range cases {
@@ -463,7 +399,7 @@ func TestParseFileParameters(t *testing.T) {
 		"mirostat_eta 1.0":             {"mirostat_eta", "1.0"},
 		"penalize_newline true":        {"penalize_newline", "true"},
 		"stop ### User:":               {"stop", "### User:"},
-		"stop ### User: ":              {"stop", "### User:"},
+		"stop ### User: ":              {"stop", "### User: "},
 		"stop \"### User:\"":           {"stop", "### User:"},
 		"stop \"### User: \"":          {"stop", "### User: "},
 		"stop \"\"\"### User:\"\"\"":   {"stop", "### User:"},

scripts/build_windows.ps1

@@ -103,19 +103,19 @@ function buildApp() {
 function gatherDependencies() {
     write-host "Gathering runtime dependencies"
     cd "${script:SRC_DIR}"
-    md "${script:DEPS_DIR}\ollama_runners" -ea 0 > $null
+    md "${script:DEPS_DIR}" -ea 0 > $null
 
     # 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 "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140.dll" "${script:DEPS_DIR}\ollama_runners\"
-    cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140_1.dll" "${script:DEPS_DIR}\ollama_runners\"
+    cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\msvcp140.dll" "${script:DEPS_DIR}\"
+    cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140.dll" "${script:DEPS_DIR}\"
+    cp "${env:VCToolsRedistDir}\x64\Microsoft.VC*.CRT\vcruntime140_1.dll" "${script:DEPS_DIR}\"
 
 
     cp "${script:SRC_DIR}\app\ollama_welcome.ps1" "${script:SRC_DIR}\dist\"
     if ("${env:KEY_CONTAINER}") {
         write-host "about to sign"
-        foreach ($file in (get-childitem "${script:DEPS_DIR}\cuda\cu*.dll") + @("${script:SRC_DIR}\dist\ollama_welcome.ps1")){
+        foreach ($file in (get-childitem "${script:DEPS_DIR}/cu*.dll") + @("${script:SRC_DIR}\dist\ollama_welcome.ps1")){
             write-host "signing $file"
             & "${script:SignTool}" sign /v /fd sha256 /t http://timestamp.digicert.com /f "${script:OLLAMA_CERT}" `
                 /csp "Google Cloud KMS Provider" /kc ${env:KEY_CONTAINER} $file

scripts/install.sh

@@ -279,7 +279,7 @@ if ! check_gpu nvidia-smi || [ -z "$(nvidia-smi | grep -o "CUDA Version: [0-9]*\
     case $OS_NAME in
         centos|rhel) install_cuda_driver_yum 'rhel' $(echo $OS_VERSION | cut -d '.' -f 1) ;;
         rocky) install_cuda_driver_yum 'rhel' $(echo $OS_VERSION | cut -c1) ;;
-        fedora) [ $OS_VERSION -lt '39' ] && install_cuda_driver_yum $OS_NAME $OS_VERSION || install_cuda_driver_yum $OS_NAME '39';;
+        fedora) [ $OS_VERSION -lt '37' ] && install_cuda_driver_yum $OS_NAME $OS_VERSION || install_cuda_driver_yum $OS_NAME '37';;
         amzn) install_cuda_driver_yum 'fedora' '37' ;;
         debian) install_cuda_driver_apt $OS_NAME $OS_VERSION ;;
         ubuntu) install_cuda_driver_apt $OS_NAME $(echo $OS_VERSION | sed 's/\.//') ;;

scripts/rh_linux_deps.sh

@@ -6,21 +6,10 @@ set -ex
 MACHINE=$(uname -m)
 
 if grep -i "centos" /etc/system-release >/dev/null; then
-    # As of 7/1/2024 mirrorlist.centos.org has been taken offline, so adjust accordingly
-    sed -i s/mirror.centos.org/vault.centos.org/g /etc/yum.repos.d/*.repo
-    sed -i s/^#.*baseurl=http/baseurl=http/g /etc/yum.repos.d/*.repo
-    sed -i s/^mirrorlist=http/#mirrorlist=http/g /etc/yum.repos.d/*.repo
-
     # Centos 7 derivatives have too old of a git version to run our generate script
     # uninstall and ignore failures
     yum remove -y git
     yum -y install epel-release centos-release-scl
-
-    # The release packages reinstate the mirrors, undo that again
-    sed -i s/mirror.centos.org/vault.centos.org/g /etc/yum.repos.d/*.repo
-    sed -i s/^#.*baseurl=http/baseurl=http/g /etc/yum.repos.d/*.repo
-    sed -i s/^mirrorlist=http/#mirrorlist=http/g /etc/yum.repos.d/*.repo
-
     yum -y install dnf
     if [ "${MACHINE}" = "x86_64" ]; then
         yum -y install https://repo.ius.io/ius-release-el7.rpm

server/images.go

@@ -28,16 +28,11 @@ import (
 	"github.com/ollama/ollama/format"
 	"github.com/ollama/ollama/llm"
 	"github.com/ollama/ollama/parser"
-	"github.com/ollama/ollama/template"
 	"github.com/ollama/ollama/types/errtypes"
 	"github.com/ollama/ollama/types/model"
 	"github.com/ollama/ollama/version"
 )
 
-type Capability string
-
-const CapabilityCompletion = Capability("completion")
-
 type registryOptions struct {
 	Insecure bool
 	Username string
@@ -53,43 +48,16 @@ type Model struct {
 	ParentModel    string
 	AdapterPaths   []string
 	ProjectorPaths []string
+	Template       string
 	System         string
 	License        []string
 	Digest         string
 	Options        map[string]interface{}
 	Messages       []Message
-
-	Template *template.Template
 }
 
-func (m *Model) Has(caps ...Capability) bool {
-	for _, cap := range caps {
-		switch cap {
-		case CapabilityCompletion:
-			f, err := os.Open(m.ModelPath)
-			if err != nil {
-				slog.Error("couldn't open model file", "error", err)
-				continue
-			}
-			defer f.Close()
-
-			// TODO(mxyng): decode the GGML into model to avoid doing this multiple times
-			ggml, _, err := llm.DecodeGGML(f, 0)
-			if err != nil {
-				slog.Error("couldn't decode ggml", "error", err)
-				continue
-			}
-
-			if _, ok := ggml.KV()[fmt.Sprintf("%s.pooling_type", ggml.KV().Architecture())]; ok {
-				return false
-			}
-		default:
-			slog.Error("unknown capability", "capability", cap)
-			return false
-		}
-	}
-
-	return true
+func (m *Model) IsEmbedding() bool {
+	return slices.Contains(m.Config.ModelFamilies, "bert") || slices.Contains(m.Config.ModelFamilies, "nomic-bert")
 }
 
 func (m *Model) String() string {
@@ -114,10 +82,10 @@ func (m *Model) String() string {
 		})
 	}
 
-	if m.Template != nil {
+	if m.Template != "" {
 		modelfile.Commands = append(modelfile.Commands, parser.Command{
 			Name: "template",
-			Args: m.Template.String(),
+			Args: m.Template,
 		})
 	}
 
@@ -167,6 +135,13 @@ type Message struct {
 	Content string `json:"content"`
 }
 
+type ManifestV2 struct {
+	SchemaVersion int      `json:"schemaVersion"`
+	MediaType     string   `json:"mediaType"`
+	Config        *Layer   `json:"config"`
+	Layers        []*Layer `json:"layers"`
+}
+
 type ConfigV2 struct {
 	ModelFormat   string   `json:"model_format"`
 	ModelFamily   string   `json:"model_family"`
@@ -185,7 +160,7 @@ type RootFS struct {
 	DiffIDs []string `json:"diff_ids"`
 }
 
-func GetManifest(mp ModelPath) (*Manifest, string, error) {
+func GetManifest(mp ModelPath) (*ManifestV2, string, error) {
 	fp, err := mp.GetManifestPath()
 	if err != nil {
 		return nil, "", err
@@ -195,7 +170,7 @@ func GetManifest(mp ModelPath) (*Manifest, string, error) {
 		return nil, "", err
 	}
 
-	var manifest *Manifest
+	var manifest *ManifestV2
 
 	bts, err := os.ReadFile(fp)
 	if err != nil {
@@ -223,7 +198,8 @@ func GetModel(name string) (*Model, error) {
 		Name:      mp.GetFullTagname(),
 		ShortName: mp.GetShortTagname(),
 		Digest:    digest,
-		Template:  template.DefaultTemplate,
+		Template:  "{{ .Prompt }}",
+		License:   []string{},
 	}
 
 	filename, err := GetBlobsPath(manifest.Config.Digest)
@@ -259,17 +235,13 @@ func GetModel(name string) (*Model, error) {
 			model.AdapterPaths = append(model.AdapterPaths, filename)
 		case "application/vnd.ollama.image.projector":
 			model.ProjectorPaths = append(model.ProjectorPaths, filename)
-		case "application/vnd.ollama.image.prompt",
-			"application/vnd.ollama.image.template":
+		case "application/vnd.ollama.image.template":
 			bts, err := os.ReadFile(filename)
 			if err != nil {
 				return nil, err
 			}
 
-			model.Template, err = template.Parse(string(bts))
-			if err != nil {
-				return nil, err
-			}
+			model.Template = string(bts)
 		case "application/vnd.ollama.image.system":
 			bts, err := os.ReadFile(filename)
 			if err != nil {
@@ -277,6 +249,13 @@ func GetModel(name string) (*Model, error) {
 			}
 
 			model.System = string(bts)
+		case "application/vnd.ollama.image.prompt":
+			bts, err := os.ReadFile(filename)
+			if err != nil {
+				return nil, err
+			}
+
+			model.Template = string(bts)
 		case "application/vnd.ollama.image.params":
 			params, err := os.Open(filename)
 			if err != nil {
@@ -435,22 +414,17 @@ func CreateModel(ctx context.Context, name model.Name, modelFileDir, quantizatio
 							return err
 						}
 
-						layer, err := NewLayer(temp, baseLayer.MediaType)
+						layers, err := parseFromFile(ctx, temp, "", fn)
 						if err != nil {
 							return err
 						}
 
-						if _, err := temp.Seek(0, io.SeekStart); err != nil {
-							return err
+						if len(layers) != 1 {
+							return errors.New("quantization failed")
 						}
 
-						ggml, _, err := llm.DecodeGGML(temp, 0)
-						if err != nil {
-							return err
-						}
-
-						baseLayer.Layer = layer
-						baseLayer.GGML = ggml
+						baseLayer.Layer = layers[0].Layer
+						baseLayer.GGML = layers[0].GGML
 					}
 				}
 
@@ -843,7 +817,7 @@ func PushModel(ctx context.Context, name string, regOpts *registryOptions, fn fu
 func PullModel(ctx context.Context, name string, regOpts *registryOptions, fn func(api.ProgressResponse)) error {
 	mp := ParseModelPath(name)
 
-	var manifest *Manifest
+	var manifest *ManifestV2
 	var err error
 	var noprune string
 
@@ -950,7 +924,7 @@ func PullModel(ctx context.Context, name string, regOpts *registryOptions, fn fu
 	return nil
 }
 
-func pullModelManifest(ctx context.Context, mp ModelPath, regOpts *registryOptions) (*Manifest, error) {
+func pullModelManifest(ctx context.Context, mp ModelPath, regOpts *registryOptions) (*ManifestV2, error) {
 	requestURL := mp.BaseURL().JoinPath("v2", mp.GetNamespaceRepository(), "manifests", mp.Tag)
 
 	headers := make(http.Header)
@@ -961,7 +935,7 @@ func pullModelManifest(ctx context.Context, mp ModelPath, regOpts *registryOptio
 	}
 	defer resp.Body.Close()
 
-	var m *Manifest
+	var m *ManifestV2
 	if err := json.NewDecoder(resp.Body).Decode(&m); err != nil {
 		return nil, err
 	}

server/manifest.go

@@ -14,10 +14,7 @@ import (
 )
 
 type Manifest struct {
-	SchemaVersion int      `json:"schemaVersion"`
-	MediaType     string   `json:"mediaType"`
-	Config        *Layer   `json:"config"`
-	Layers        []*Layer `json:"layers"`
+	ManifestV2
 
 	filepath string
 	fi       os.FileInfo
@@ -69,7 +66,7 @@ func ParseNamedManifest(n model.Name) (*Manifest, error) {
 
 	p := filepath.Join(manifests, n.Filepath())
 
-	var m Manifest
+	var m ManifestV2
 	f, err := os.Open(p)
 	if err != nil {
 		return nil, err
@@ -86,11 +83,12 @@ func ParseNamedManifest(n model.Name) (*Manifest, error) {
 		return nil, err
 	}
 
-	m.filepath = p
-	m.fi = fi
-	m.digest = fmt.Sprintf("%x", sha256sum.Sum(nil))
-
-	return &m, nil
+	return &Manifest{
+		ManifestV2: m,
+		filepath:   p,
+		fi:         fi,
+		digest:     fmt.Sprintf("%x", sha256sum.Sum(nil)),
+	}, nil
 }
 
 func WriteManifest(name model.Name, config *Layer, layers []*Layer) error {
@@ -110,7 +108,7 @@ func WriteManifest(name model.Name, config *Layer, layers []*Layer) error {
 	}
 	defer f.Close()
 
-	m := Manifest{
+	m := ManifestV2{
 		SchemaVersion: 2,
 		MediaType:     "application/vnd.docker.distribution.manifest.v2+json",
 		Config:        config,

server/manifest_test.go

@@ -25,7 +25,7 @@ func createManifest(t *testing.T, path, name string) {
 	}
 	defer f.Close()
 
-	if err := json.NewEncoder(f).Encode(Manifest{}); err != nil {
+	if err := json.NewEncoder(f).Encode(ManifestV2{}); err != nil {
 		t.Fatal(err)
 	}
 }

server/model.go

@@ -15,7 +15,7 @@ import (
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/convert"
 	"github.com/ollama/ollama/llm"
-	"github.com/ollama/ollama/template"
+	"github.com/ollama/ollama/templates"
 	"github.com/ollama/ollama/types/model"
 )
 
@@ -63,7 +63,7 @@ func parseFromModel(ctx context.Context, name model.Name, fn func(api.ProgressRe
 			}
 			defer blob.Close()
 
-			ggml, _, err := llm.DecodeGGML(blob, 0)
+			ggml, _, err := llm.DecodeGGML(blob)
 			if err != nil {
 				return nil, err
 			}
@@ -77,105 +77,95 @@ func parseFromModel(ctx context.Context, name model.Name, fn func(api.ProgressRe
 	return layers, nil
 }
 
-func extractFromZipFile(p string, file *os.File, fn func(api.ProgressResponse)) error {
+func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(api.ProgressResponse)) (layers []*layerGGML, err error) {
 	stat, err := file.Stat()
 	if err != nil {
-		return err
+		return nil, err
 	}
 
 	r, err := zip.NewReader(file, stat.Size())
 	if err != nil {
-		return err
+		return nil, err
 	}
 
+	tempdir, err := os.MkdirTemp(filepath.Dir(file.Name()), "")
+	if err != nil {
+		return nil, err
+	}
+	defer os.RemoveAll(tempdir)
+
 	fn(api.ProgressResponse{Status: "unpacking model metadata"})
 	for _, f := range r.File {
-		if !filepath.IsLocal(f.Name) {
-			return fmt.Errorf("%w: %s", zip.ErrInsecurePath, f.Name)
-		}
-
-		n := filepath.Join(p, f.Name)
-		if err := os.MkdirAll(filepath.Dir(n), 0o750); err != nil {
-			return err
-		}
-
 		// TODO(mxyng): this should not write out all files to disk
-		outfile, err := os.Create(n)
+		outfile, err := os.Create(filepath.Join(tempdir, f.Name))
 		if err != nil {
-			return err
+			return nil, err
 		}
 		defer outfile.Close()
 
 		infile, err := f.Open()
 		if err != nil {
-			return err
+			return nil, err
 		}
 		defer infile.Close()
 
 		if _, err = io.Copy(outfile, infile); err != nil {
-			return err
+			return nil, err
 		}
 
 		if err := outfile.Close(); err != nil {
-			return err
+			return nil, err
 		}
 
 		if err := infile.Close(); err != nil {
-			return err
-		}
-	}
-
-	return nil
-}
-
-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 !convertAdapter {
-		if err := extractFromZipFile(tempDir, file, fn); err != nil {
 			return nil, err
 		}
-	} else {
-		layerType = "application/vnd.ollama.image.adapter"
+	}
+
+	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
 	}
 
 	fn(api.ProgressResponse{Status: "converting model"})
 
 	// TODO(mxyng): this should write directly into a layer
 	// e.g. NewLayer(arch.Reader(), "application/vnd.ollama.image.model")
-	temp, err := os.CreateTemp(tempDir, "fp16")
+	temp, err := os.CreateTemp(tempdir, "fp16")
 	if err != nil {
 		return nil, err
 	}
 	defer temp.Close()
 	defer os.Remove(temp.Name())
 
-	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 = mArch.WriteGGUF(temp); err != nil {
+		return nil, err
 	}
 
 	if _, err := temp.Seek(0, io.SeekStart); err != nil {
 		return nil, err
 	}
 
-	layer, err := NewLayer(temp, layerType)
+	layer, err := NewLayer(temp, "application/vnd.ollama.image.model")
 	if err != nil {
 		return nil, err
 	}
@@ -186,7 +176,7 @@ func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(a
 	}
 	defer bin.Close()
 
-	ggml, _, err := llm.DecodeGGML(bin, 0)
+	ggml, _, err := llm.DecodeGGML(bin)
 	if err != nil {
 		return nil, err
 	}
@@ -194,11 +184,7 @@ func parseFromZipFile(_ context.Context, file *os.File, digest string, fn func(a
 	layers = append(layers, &layerGGML{layer, ggml})
 
 	intermediateBlobs[digest] = layer.Digest
-	if !convertAdapter {
-		return detectChatTemplate(layers)
-	}
-
-	return layers, nil
+	return detectChatTemplate(layers)
 }
 
 func parseFromFile(ctx context.Context, file *os.File, digest string, fn func(api.ProgressResponse)) (layers []*layerGGML, err error) {
@@ -224,7 +210,7 @@ func parseFromFile(ctx context.Context, file *os.File, digest string, fn func(ap
 
 	var offset int64
 	for offset < stat.Size() {
-		ggml, n, err := llm.DecodeGGML(file, 0)
+		ggml, n, err := llm.DecodeGGML(file)
 		if errors.Is(err, io.EOF) {
 			break
 		} else if err != nil {
@@ -253,7 +239,7 @@ func parseFromFile(ctx context.Context, file *os.File, digest string, fn func(ap
 func detectChatTemplate(layers []*layerGGML) ([]*layerGGML, error) {
 	for _, layer := range layers {
 		if s := layer.GGML.KV().ChatTemplate(); s != "" {
-			if t, err := template.Named(s); err != nil {
+			if t, err := templates.NamedTemplate(s); err != nil {
 				slog.Debug("template detection", "error", err)
 			} else {
 				tmpl, err := NewLayer(t.Reader(), "application/vnd.ollama.image.template")

server/model_test.go

@@ -1,112 +0,0 @@
-package server
-
-import (
-	"archive/zip"
-	"bytes"
-	"errors"
-	"io"
-	"os"
-	"path/filepath"
-	"slices"
-	"strings"
-	"testing"
-
-	"github.com/ollama/ollama/api"
-)
-
-func createZipFile(t *testing.T, name string) *os.File {
-	t.Helper()
-
-	f, err := os.CreateTemp(t.TempDir(), "")
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	zf := zip.NewWriter(f)
-	defer zf.Close()
-
-	zh, err := zf.CreateHeader(&zip.FileHeader{Name: name})
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	if _, err := io.Copy(zh, bytes.NewReader([]byte(""))); err != nil {
-		t.Fatal(err)
-	}
-
-	return f
-}
-
-func TestExtractFromZipFile(t *testing.T) {
-	cases := []struct {
-		name   string
-		expect []string
-		err    error
-	}{
-		{
-			name:   "good",
-			expect: []string{"good"},
-		},
-		{
-			name:   strings.Join([]string{"path", "..", "to", "good"}, string(os.PathSeparator)),
-			expect: []string{filepath.Join("to", "good")},
-		},
-		{
-			name:   strings.Join([]string{"path", "..", "to", "..", "good"}, string(os.PathSeparator)),
-			expect: []string{"good"},
-		},
-		{
-			name:   strings.Join([]string{"path", "to", "..", "..", "good"}, string(os.PathSeparator)),
-			expect: []string{"good"},
-		},
-		{
-			name: strings.Join([]string{"..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "..", "bad"}, string(os.PathSeparator)),
-			err:  zip.ErrInsecurePath,
-		},
-		{
-			name: strings.Join([]string{"path", "..", "..", "to", "bad"}, string(os.PathSeparator)),
-			err:  zip.ErrInsecurePath,
-		},
-	}
-
-	for _, tt := range cases {
-		t.Run(tt.name, func(t *testing.T) {
-			f := createZipFile(t, tt.name)
-			defer f.Close()
-
-			tempDir := t.TempDir()
-			if err := extractFromZipFile(tempDir, f, func(api.ProgressResponse) {}); !errors.Is(err, tt.err) {
-				t.Fatal(err)
-			}
-
-			var matches []string
-			if err := filepath.Walk(tempDir, func(p string, fi os.FileInfo, err error) error {
-				if err != nil {
-					return err
-				}
-
-				if !fi.IsDir() {
-					matches = append(matches, p)
-				}
-
-				return nil
-			}); err != nil {
-				t.Fatal(err)
-			}
-
-			var actual []string
-			for _, match := range matches {
-				rel, err := filepath.Rel(tempDir, match)
-				if err != nil {
-					t.Error(err)
-				}
-
-				actual = append(actual, rel)
-			}
-
-			if !slices.Equal(actual, tt.expect) {
-				t.Fatalf("expected %d files, got %d", len(tt.expect), len(matches))
-			}
-		})
-	}
-}

server/modelpath.go

@@ -103,9 +103,18 @@ func (mp ModelPath) GetShortTagname() string {
 	return fmt.Sprintf("%s/%s/%s:%s", mp.Registry, mp.Namespace, mp.Repository, mp.Tag)
 }
 
+// modelsDir returns the value of the OLLAMA_MODELS environment variable or the user's home directory if OLLAMA_MODELS is not set.
+// The models directory is where Ollama stores its model files and manifests.
+func modelsDir() (string, error) {
+	return envconfig.ModelsDir, nil
+}
+
 // GetManifestPath returns the path to the manifest file for the given model path, it is up to the caller to create the directory if it does not exist.
 func (mp ModelPath) GetManifestPath() (string, error) {
-	dir := envconfig.ModelsDir
+	dir, err := modelsDir()
+	if err != nil {
+		return "", err
+	}
 
 	return filepath.Join(dir, "manifests", mp.Registry, mp.Namespace, mp.Repository, mp.Tag), nil
 }
@@ -118,7 +127,10 @@ func (mp ModelPath) BaseURL() *url.URL {
 }
 
 func GetManifestPath() (string, error) {
-	dir := envconfig.ModelsDir
+	dir, err := modelsDir()
+	if err != nil {
+		return "", err
+	}
 
 	path := filepath.Join(dir, "manifests")
 	if err := os.MkdirAll(path, 0o755); err != nil {
@@ -129,7 +141,10 @@ func GetManifestPath() (string, error) {
 }
 
 func GetBlobsPath(digest string) (string, error) {
-	dir := envconfig.ModelsDir
+	dir, err := modelsDir()
+	if err != nil {
+		return "", err
+	}
 
 	// only accept actual sha256 digests
 	pattern := "^sha256[:-][0-9a-fA-F]{64}$"

server/prompt.go

@@ -4,11 +4,10 @@ import (
 	"fmt"
 	"log/slog"
 	"strings"
-
+	"text/template"
 	"text/template/parse"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/template"
 )
 
 // isResponseNode checks if the node contains .Response
@@ -54,8 +53,13 @@ func formatTemplateForResponse(tmpl *template.Template, generate bool) {
 
 // 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)
+func Prompt(tmpl, system, prompt, response string, generate bool) (string, error) {
+	parsed, err := template.New("").Option("missingkey=zero").Parse(tmpl)
+	if err != nil {
+		return "", err
+	}
+
+	formatTemplateForResponse(parsed, generate)
 
 	vars := map[string]any{
 		"System":   system,
@@ -64,14 +68,14 @@ func Prompt(tmpl *template.Template, system, prompt, response string, generate b
 	}
 
 	var sb strings.Builder
-	if err := tmpl.Execute(&sb, vars); err != nil {
+	if err := parsed.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) {
+func countTokens(tmpl string, 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
@@ -87,7 +91,7 @@ func countTokens(tmpl *template.Template, system string, prompt string, response
 }
 
 // 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) {
+func ChatPrompt(tmpl string, messages []api.Message, window int, encode func(string) ([]int, error)) (string, error) {
 	type prompt struct {
 		System   string
 		Prompt   string

server/prompt_test.go

@@ -5,7 +5,6 @@ import (
 	"testing"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/template"
 )
 
 func TestPrompt(t *testing.T) {
@@ -62,12 +61,7 @@ func TestPrompt(t *testing.T) {
 
 	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 := Prompt(tmpl, tc.system, tc.prompt, tc.response, tc.generate)
+			got, err := Prompt(tc.template, tc.system, tc.prompt, tc.response, tc.generate)
 			if err != nil {
 				t.Errorf("error = %v", err)
 			}
@@ -198,12 +192,7 @@ func TestChatPrompt(t *testing.T) {
 
 	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)
+			got, err := ChatPrompt(tc.template, tc.messages, tc.window, encode)
 			if err != nil {
 				t.Errorf("error = %v", err)
 			}