move tokenizer to separate package

README.md

@@ -358,7 +358,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Odin Runes](https://github.com/leonid20000/OdinRunes)
 - [LLM-X](https://github.com/mrdjohnson/llm-x) (Progressive Web App)
 - [AnythingLLM (Docker + MacOs/Windows/Linux native app)](https://github.com/Mintplex-Labs/anything-llm)
-- [Screenpipe](https://github.com/mediar-ai/screenpipe) (24/7 screen & mic recording with AI-powered search, uses Ollama for local LLM features)
 - [Ollama Basic Chat: Uses HyperDiv Reactive UI](https://github.com/rapidarchitect/ollama_basic_chat)
 - [Ollama-chats RPG](https://github.com/drazdra/ollama-chats)
 - [IntelliBar](https://intellibar.app/) (AI-powered assistant for macOS)
@@ -466,7 +465,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Clueless](https://github.com/KashyapTan/clueless) (Open Source & Local Cluely: A desktop application LLM assistant to help you talk to anything on your screen using locally served Ollama models. Also undetectable to screenshare)
 - [ollama-co2](https://github.com/carbonatedWaterOrg/ollama-co2) (FastAPI web interface for monitoring and managing local and remote Ollama servers with real-time model monitoring and concurrent downloads)
 - [Hillnote](https://hillnote.com) (A Markdown-first workspace designed to supercharge your AI workflow. Create documents ready to integrate with Claude, ChatGPT, Gemini, Cursor, and more - all while keeping your work on your device.)
-- [Stakpak](https://github.com/stakpak/agent) (An open source, vendor neutral DevOps agent that works with any model, and any stack, for teams who just want to ship)
 
 ### Cloud
 

anthropic/anthropic.go

@@ -211,7 +211,6 @@ type MessageDelta struct {
 
 // DeltaUsage contains cumulative token usage
 type DeltaUsage struct {
-	InputTokens  int `json:"input_tokens"`
 	OutputTokens int `json:"output_tokens"`
 }
 
@@ -722,7 +721,6 @@ func (c *StreamConverter) Process(r api.ChatResponse) []StreamEvent {
 			})
 		}
 
-		c.inputTokens = r.Metrics.PromptEvalCount
 		c.outputTokens = r.Metrics.EvalCount
 		stopReason := mapStopReason(r.DoneReason, len(c.toolCallsSent) > 0)
 
@@ -734,7 +732,6 @@ func (c *StreamConverter) Process(r api.ChatResponse) []StreamEvent {
 					StopReason: stopReason,
 				},
 				Usage: DeltaUsage{
-					InputTokens:  c.inputTokens,
 					OutputTokens: c.outputTokens,
 				},
 			},

anthropic/anthropic_test.go

@@ -642,7 +642,7 @@ func TestStreamConverter_Basic(t *testing.T) {
 		},
 		Done:       true,
 		DoneReason: "stop",
-		Metrics:    api.Metrics{PromptEvalCount: 10, EvalCount: 5},
+		Metrics:    api.Metrics{EvalCount: 5},
 	}
 
 	events2 := conv.Process(resp2)
@@ -650,24 +650,6 @@ func TestStreamConverter_Basic(t *testing.T) {
 	// Should have content_block_delta, content_block_stop, message_delta, message_stop
 	hasStop := false
 	for _, e := range events2 {
-		if e.Event == "message_delta" {
-			if data, ok := e.Data.(MessageDeltaEvent); ok {
-				if data.Type != "message_delta" {
-					t.Errorf("unexpected data type: %+v", data)
-				}
-
-				if data.Delta.StopReason != "end_turn" {
-					t.Errorf("unexpected stop reason: %+v", data.Delta.StopReason)
-				}
-
-				if data.Usage.InputTokens != 10 || data.Usage.OutputTokens != 5 {
-					t.Errorf("unexpected usage: %+v", data.Usage)
-				}
-			} else {
-				t.Errorf("unexpected data: %+v", e.Data)
-			}
-		}
-
 		if e.Event == "message_stop" {
 			hasStop = true
 		}

cmd/cmd.go

@@ -29,7 +29,6 @@ import (
 	"github.com/containerd/console"
 	"github.com/mattn/go-runewidth"
 	"github.com/olekukonko/tablewriter"
-	"github.com/pkg/browser"
 	"github.com/spf13/cobra"
 	"golang.org/x/crypto/ssh"
 	"golang.org/x/sync/errgroup"
@@ -53,7 +52,7 @@ import (
 	"github.com/ollama/ollama/x/imagegen"
 )
 
-const ConnectInstructions = "If your browser did not open, navigate to:\n    %s\n\n"
+const ConnectInstructions = "To sign in, navigate to:\n    %s\n\n"
 
 // ensureThinkingSupport emits a warning if the model does not advertise thinking support
 func ensureThinkingSupport(ctx context.Context, client *api.Client, name string) {
@@ -664,7 +663,6 @@ func SigninHandler(cmd *cobra.Command, args []string) error {
 			fmt.Println()
 
 			if aErr.SigninURL != "" {
-				_ = browser.OpenURL(aErr.SigninURL)
 				fmt.Printf(ConnectInstructions, aErr.SigninURL)
 			}
 			return nil
@@ -1890,7 +1888,7 @@ func NewCLI() *cobra.Command {
 	serveCmd := &cobra.Command{
 		Use:     "serve",
 		Aliases: []string{"start"},
-		Short:   "Start Ollama",
+		Short:   "Start ollama",
 		Args:    cobra.ExactArgs(0),
 		RunE:    RunServer,
 	}

cmd/cmd_test.go

@@ -1553,7 +1553,7 @@ func TestShowInfoImageGen(t *testing.T) {
 		Details: api.ModelDetails{
 			Family:            "ZImagePipeline",
 			ParameterSize:     "10.3B",
-			QuantizationLevel: "Q8",
+			QuantizationLevel: "FP8",
 		},
 		Capabilities: []model.Capability{model.CapabilityImage},
 		Requires:     "0.14.0",
@@ -1565,7 +1565,7 @@ func TestShowInfoImageGen(t *testing.T) {
 	expect := "  Model\n" +
 		"    architecture    ZImagePipeline    \n" +
 		"    parameters      10.3B             \n" +
-		"    quantization    Q8                \n" +
+		"    quantization    FP8               \n" +
 		"    requires        0.14.0            \n" +
 		"\n" +
 		"  Capabilities\n" +

cmd/config/claude.go

@@ -6,8 +6,6 @@ import (
 	"os/exec"
 	"path/filepath"
 	"runtime"
-
-	"github.com/ollama/ollama/envconfig"
 )
 
 // Claude implements Runner for Claude Code integration
@@ -15,13 +13,11 @@ type Claude struct{}
 
 func (c *Claude) String() string { return "Claude Code" }
 
-func (c *Claude) args(model string, extra []string) []string {
-	var args []string
+func (c *Claude) args(model string) []string {
 	if model != "" {
-		args = append(args, "--model", model)
+		return []string{"--model", model}
 	}
-	args = append(args, extra...)
-	return args
+	return nil
 }
 
 func (c *Claude) findPath() (string, error) {
@@ -43,18 +39,18 @@ func (c *Claude) findPath() (string, error) {
 	return fallback, nil
 }
 
-func (c *Claude) Run(model string, args []string) error {
+func (c *Claude) Run(model string) error {
 	claudePath, err := c.findPath()
 	if err != nil {
 		return fmt.Errorf("claude is not installed, install from https://code.claude.com/docs/en/quickstart")
 	}
 
-	cmd := exec.Command(claudePath, c.args(model, args)...)
+	cmd := exec.Command(claudePath, c.args(model)...)
 	cmd.Stdin = os.Stdin
 	cmd.Stdout = os.Stdout
 	cmd.Stderr = os.Stderr
 	cmd.Env = append(os.Environ(),
-		"ANTHROPIC_BASE_URL="+envconfig.Host().String(),
+		"ANTHROPIC_BASE_URL=http://localhost:11434",
 		"ANTHROPIC_API_KEY=",
 		"ANTHROPIC_AUTH_TOKEN=ollama",
 	)

cmd/config/claude_test.go

@@ -84,21 +84,17 @@ func TestClaudeArgs(t *testing.T) {
 	tests := []struct {
 		name  string
 		model string
-		args  []string
 		want  []string
 	}{
-		{"with model", "llama3.2", nil, []string{"--model", "llama3.2"}},
-		{"empty model", "", nil, nil},
-		{"with model and verbose", "llama3.2", []string{"--verbose"}, []string{"--model", "llama3.2", "--verbose"}},
-		{"empty model with help", "", []string{"--help"}, []string{"--help"}},
-		{"with allowed tools", "llama3.2", []string{"--allowedTools", "Read,Write,Bash"}, []string{"--model", "llama3.2", "--allowedTools", "Read,Write,Bash"}},
+		{"with model", "llama3.2", []string{"--model", "llama3.2"}},
+		{"empty model", "", nil},
 	}
 
 	for _, tt := range tests {
 		t.Run(tt.name, func(t *testing.T) {
-			got := c.args(tt.model, tt.args)
+			got := c.args(tt.model)
 			if !slices.Equal(got, tt.want) {
-				t.Errorf("args(%q, %v) = %v, want %v", tt.model, tt.args, got, tt.want)
+				t.Errorf("args(%q) = %v, want %v", tt.model, got, tt.want)
 			}
 		})
 	}

cmd/config/codex.go

@@ -14,21 +14,20 @@ type Codex struct{}
 
 func (c *Codex) String() string { return "Codex" }
 
-func (c *Codex) args(model string, extra []string) []string {
+func (c *Codex) args(model string) []string {
 	args := []string{"--oss"}
 	if model != "" {
 		args = append(args, "-m", model)
 	}
-	args = append(args, extra...)
 	return args
 }
 
-func (c *Codex) Run(model string, args []string) error {
+func (c *Codex) Run(model string) error {
 	if err := checkCodexVersion(); err != nil {
 		return err
 	}
 
-	cmd := exec.Command("codex", c.args(model, args)...)
+	cmd := exec.Command("codex", c.args(model)...)
 	cmd.Stdin = os.Stdin
 	cmd.Stdout = os.Stdout
 	cmd.Stderr = os.Stderr

cmd/config/codex_test.go

@@ -11,20 +11,17 @@ func TestCodexArgs(t *testing.T) {
 	tests := []struct {
 		name  string
 		model string
-		args  []string
 		want  []string
 	}{
-		{"with model", "llama3.2", nil, []string{"--oss", "-m", "llama3.2"}},
-		{"empty model", "", nil, []string{"--oss"}},
-		{"with model and profile", "qwen3-coder", []string{"-p", "myprofile"}, []string{"--oss", "-m", "qwen3-coder", "-p", "myprofile"}},
-		{"with sandbox flag", "llama3.2", []string{"--sandbox", "workspace-write"}, []string{"--oss", "-m", "llama3.2", "--sandbox", "workspace-write"}},
+		{"with model", "llama3.2", []string{"--oss", "-m", "llama3.2"}},
+		{"empty model", "", []string{"--oss"}},
 	}
 
 	for _, tt := range tests {
 		t.Run(tt.name, func(t *testing.T) {
-			got := c.args(tt.model, tt.args)
+			got := c.args(tt.model)
 			if !slices.Equal(got, tt.want) {
-				t.Errorf("args(%q, %v) = %v, want %v", tt.model, tt.args, got, tt.want)
+				t.Errorf("args(%q) = %v, want %v", tt.model, got, tt.want)
 			}
 		})
 	}

cmd/config/config.go

@@ -6,7 +6,6 @@ import (
 	"encoding/json"
 	"errors"
 	"fmt"
-	"log/slog"
 	"os"
 	"path/filepath"
 	"strings"
@@ -21,14 +20,6 @@ type config struct {
 }
 
 func configPath() (string, error) {
-	home, err := os.UserHomeDir()
-	if err != nil {
-		return "", err
-	}
-	return filepath.Join(home, ".ollama", "config.json"), nil
-}
-
-func legacyConfigPath() (string, error) {
 	home, err := os.UserHomeDir()
 	if err != nil {
 		return "", err
@@ -36,46 +27,6 @@ func legacyConfigPath() (string, error) {
 	return filepath.Join(home, ".ollama", "config", "config.json"), nil
 }
 
-// migrateConfig moves the config from the legacy path to ~/.ollama/config.json
-func migrateConfig() (bool, error) {
-	oldPath, err := legacyConfigPath()
-	if err != nil {
-		return false, err
-	}
-
-	oldData, err := os.ReadFile(oldPath)
-	if err != nil {
-		if os.IsNotExist(err) {
-			return false, nil
-		}
-		return false, err
-	}
-
-	var js json.RawMessage
-	if err := json.Unmarshal(oldData, &js); err != nil {
-		slog.Warn("legacy config has invalid JSON, skipping migration", "path", oldPath, "error", err)
-		return false, nil
-	}
-
-	newPath, err := configPath()
-	if err != nil {
-		return false, err
-	}
-
-	if err := os.MkdirAll(filepath.Dir(newPath), 0o755); err != nil {
-		return false, err
-	}
-	if err := os.WriteFile(newPath, oldData, 0o644); err != nil {
-		return false, fmt.Errorf("write new config: %w", err)
-	}
-
-	_ = os.Remove(oldPath)
-	_ = os.Remove(filepath.Dir(oldPath)) // clean up empty directory
-
-	slog.Info("migrated config", "from", oldPath, "to", newPath)
-	return true, nil
-}
-
 func load() (*config, error) {
 	path, err := configPath()
 	if err != nil {
@@ -83,11 +34,6 @@ func load() (*config, error) {
 	}
 
 	data, err := os.ReadFile(path)
-	if err != nil && os.IsNotExist(err) {
-		if migrated, merr := migrateConfig(); merr == nil && migrated {
-			data, err = os.ReadFile(path)
-		}
-	}
 	if err != nil {
 		if os.IsNotExist(err) {
 			return &config{Integrations: make(map[string]*integration)}, nil

cmd/config/config_test.go

@@ -200,10 +200,12 @@ func TestLoadIntegration_CorruptedJSON(t *testing.T) {
 	tmpDir := t.TempDir()
 	setTestHome(t, tmpDir)
 
-	dir := filepath.Join(tmpDir, ".ollama")
+	// Create corrupted config.json file
+	dir := filepath.Join(tmpDir, ".ollama", "config")
 	os.MkdirAll(dir, 0o755)
 	os.WriteFile(filepath.Join(dir, "config.json"), []byte(`{corrupted json`), 0o644)
 
+	// Corrupted file is treated as empty, so loadIntegration returns not found
 	_, err := loadIntegration("test")
 	if err == nil {
 		t.Error("expected error for nonexistent integration in corrupted file")
@@ -265,7 +267,7 @@ func TestConfigPath(t *testing.T) {
 		t.Fatal(err)
 	}
 
-	expected := filepath.Join(tmpDir, ".ollama", "config.json")
+	expected := filepath.Join(tmpDir, ".ollama", "config", "config.json")
 	if path != expected {
 		t.Errorf("expected %s, got %s", expected, path)
 	}
@@ -320,183 +322,6 @@ func TestLoad(t *testing.T) {
 	})
 }
 
-func TestMigrateConfig(t *testing.T) {
-	t.Run("migrates legacy file to new location", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-
-		legacyDir := filepath.Join(tmpDir, ".ollama", "config")
-		os.MkdirAll(legacyDir, 0o755)
-		data := []byte(`{"integrations":{"claude":{"models":["llama3.2"]}}}`)
-		os.WriteFile(filepath.Join(legacyDir, "config.json"), data, 0o644)
-
-		migrated, err := migrateConfig()
-		if err != nil {
-			t.Fatal(err)
-		}
-		if !migrated {
-			t.Fatal("expected migration to occur")
-		}
-
-		newPath, _ := configPath()
-		got, err := os.ReadFile(newPath)
-		if err != nil {
-			t.Fatalf("new config not found: %v", err)
-		}
-		if string(got) != string(data) {
-			t.Errorf("content mismatch: got %s", got)
-		}
-
-		if _, err := os.Stat(filepath.Join(legacyDir, "config.json")); !os.IsNotExist(err) {
-			t.Error("legacy file should have been removed")
-		}
-
-		if _, err := os.Stat(legacyDir); !os.IsNotExist(err) {
-			t.Error("legacy directory should have been removed")
-		}
-	})
-
-	t.Run("no-op when no legacy file exists", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-
-		migrated, err := migrateConfig()
-		if err != nil {
-			t.Fatal(err)
-		}
-		if migrated {
-			t.Error("expected no migration")
-		}
-	})
-
-	t.Run("skips corrupt legacy file", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-
-		legacyDir := filepath.Join(tmpDir, ".ollama", "config")
-		os.MkdirAll(legacyDir, 0o755)
-		os.WriteFile(filepath.Join(legacyDir, "config.json"), []byte(`{corrupt`), 0o644)
-
-		migrated, err := migrateConfig()
-		if err != nil {
-			t.Fatal(err)
-		}
-		if migrated {
-			t.Error("should not migrate corrupt file")
-		}
-
-		if _, err := os.Stat(filepath.Join(legacyDir, "config.json")); os.IsNotExist(err) {
-			t.Error("corrupt legacy file should not have been deleted")
-		}
-	})
-
-	t.Run("new path takes precedence over legacy", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-
-		legacyDir := filepath.Join(tmpDir, ".ollama", "config")
-		os.MkdirAll(legacyDir, 0o755)
-		os.WriteFile(filepath.Join(legacyDir, "config.json"), []byte(`{"integrations":{"old":{"models":["old-model"]}}}`), 0o644)
-
-		newDir := filepath.Join(tmpDir, ".ollama")
-		os.WriteFile(filepath.Join(newDir, "config.json"), []byte(`{"integrations":{"new":{"models":["new-model"]}}}`), 0o644)
-
-		cfg, err := load()
-		if err != nil {
-			t.Fatal(err)
-		}
-		if _, ok := cfg.Integrations["new"]; !ok {
-			t.Error("expected new-path integration to be loaded")
-		}
-		if _, ok := cfg.Integrations["old"]; ok {
-			t.Error("legacy integration should not have been loaded")
-		}
-	})
-
-	t.Run("idempotent when called twice", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-
-		legacyDir := filepath.Join(tmpDir, ".ollama", "config")
-		os.MkdirAll(legacyDir, 0o755)
-		os.WriteFile(filepath.Join(legacyDir, "config.json"), []byte(`{"integrations":{}}`), 0o644)
-
-		if _, err := migrateConfig(); err != nil {
-			t.Fatal(err)
-		}
-
-		migrated, err := migrateConfig()
-		if err != nil {
-			t.Fatal(err)
-		}
-		if migrated {
-			t.Error("second migration should be a no-op")
-		}
-	})
-
-	t.Run("legacy directory preserved if not empty", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-
-		legacyDir := filepath.Join(tmpDir, ".ollama", "config")
-		os.MkdirAll(legacyDir, 0o755)
-		os.WriteFile(filepath.Join(legacyDir, "config.json"), []byte(`{"integrations":{}}`), 0o644)
-		os.WriteFile(filepath.Join(legacyDir, "other-file.txt"), []byte("keep me"), 0o644)
-
-		if _, err := migrateConfig(); err != nil {
-			t.Fatal(err)
-		}
-
-		if _, err := os.Stat(legacyDir); os.IsNotExist(err) {
-			t.Error("directory with other files should not have been removed")
-		}
-		if _, err := os.Stat(filepath.Join(legacyDir, "other-file.txt")); os.IsNotExist(err) {
-			t.Error("other files in legacy directory should be untouched")
-		}
-	})
-
-	t.Run("save writes to new path after migration", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-
-		legacyDir := filepath.Join(tmpDir, ".ollama", "config")
-		os.MkdirAll(legacyDir, 0o755)
-		os.WriteFile(filepath.Join(legacyDir, "config.json"), []byte(`{"integrations":{"claude":{"models":["llama3.2"]}}}`), 0o644)
-
-		// load triggers migration, then save should write to new path
-		if err := saveIntegration("codex", []string{"qwen2.5"}); err != nil {
-			t.Fatal(err)
-		}
-
-		newPath := filepath.Join(tmpDir, ".ollama", "config.json")
-		if _, err := os.Stat(newPath); os.IsNotExist(err) {
-			t.Error("save should write to new path")
-		}
-
-		// old path should not be recreated
-		if _, err := os.Stat(filepath.Join(legacyDir, "config.json")); !os.IsNotExist(err) {
-			t.Error("save should not recreate legacy path")
-		}
-	})
-
-	t.Run("load triggers migration transparently", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-
-		legacyDir := filepath.Join(tmpDir, ".ollama", "config")
-		os.MkdirAll(legacyDir, 0o755)
-		os.WriteFile(filepath.Join(legacyDir, "config.json"), []byte(`{"integrations":{"claude":{"models":["llama3.2"]}}}`), 0o644)
-
-		cfg, err := load()
-		if err != nil {
-			t.Fatal(err)
-		}
-		if cfg.Integrations["claude"] == nil || cfg.Integrations["claude"].Models[0] != "llama3.2" {
-			t.Error("migration via load() did not preserve data")
-		}
-	})
-}
-
 func TestSave(t *testing.T) {
 	tmpDir := t.TempDir()
 	setTestHome(t, tmpDir)

cmd/config/droid.go

@@ -7,8 +7,6 @@ import (
 	"os/exec"
 	"path/filepath"
 	"slices"
-
-	"github.com/ollama/ollama/envconfig"
 )
 
 // Droid implements Runner and Editor for Droid integration
@@ -39,7 +37,7 @@ type modelEntry struct {
 
 func (d *Droid) String() string { return "Droid" }
 
-func (d *Droid) Run(model string, args []string) error {
+func (d *Droid) Run(model string) error {
 	if _, err := exec.LookPath("droid"); err != nil {
 		return fmt.Errorf("droid is not installed, install from https://docs.factory.ai/cli/getting-started/quickstart")
 	}
@@ -53,7 +51,7 @@ func (d *Droid) Run(model string, args []string) error {
 		return fmt.Errorf("setup failed: %w", err)
 	}
 
-	cmd := exec.Command("droid", args...)
+	cmd := exec.Command("droid")
 	cmd.Stdin = os.Stdin
 	cmd.Stdout = os.Stdout
 	cmd.Stderr = os.Stderr
@@ -119,7 +117,7 @@ func (d *Droid) Edit(models []string) error {
 		newModels = append(newModels, modelEntry{
 			Model:           model,
 			DisplayName:     model,
-			BaseURL:         envconfig.Host().String() + "/v1",
+			BaseURL:         "http://localhost:11434/v1",
 			APIKey:          "ollama",
 			Provider:        "generic-chat-completion-api",
 			MaxOutputTokens: 64000,

cmd/config/droid_test.go

@@ -218,7 +218,7 @@ func TestDroidEdit(t *testing.T) {
 			}
 		}
 
-		if model["baseUrl"] != "http://127.0.0.1:11434/v1" {
+		if model["baseUrl"] != "http://localhost:11434/v1" {
 			t.Errorf("unexpected baseUrl: %s", model["baseUrl"])
 		}
 		if model["apiKey"] != "ollama" {
@@ -447,7 +447,7 @@ const testDroidSettingsFixture = `{
     {
       "model": "existing-ollama-model",
       "displayName": "existing-ollama-model",
-      "baseUrl": "http://127.0.0.1:11434/v1",
+      "baseUrl": "http://localhost:11434/v1",
       "apiKey": "ollama",
       "provider": "generic-chat-completion-api",
       "maxOutputTokens": 64000,

cmd/config/integrations.go

@@ -13,7 +13,6 @@ import (
 	"time"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/progress"
 	"github.com/spf13/cobra"
 )
 
@@ -23,7 +22,7 @@ import (
 // Runner can run an integration with a model.
 
 type Runner interface {
-	Run(model string, args []string) error
+	Run(model string) error
 	// String returns the human-readable name of the integration
 	String() string
 }
@@ -42,29 +41,9 @@ type Editor interface {
 // integrations is the registry of available integrations.
 var integrations = map[string]Runner{
 	"claude":   &Claude{},
-	"clawdbot": &Openclaw{},
 	"codex":    &Codex{},
-	"moltbot":  &Openclaw{},
 	"droid":    &Droid{},
 	"opencode": &OpenCode{},
-	"openclaw": &Openclaw{},
-	"pi":       &Pi{},
-}
-
-// recommendedModels are shown when the user has no models or as suggestions.
-// Order matters: local models first, then cloud models.
-var recommendedModels = []selectItem{
-	{Name: "glm-4.7-flash", Description: "Recommended (requires ~25GB VRAM)"},
-	{Name: "qwen3:8b", Description: "Recommended (requires ~11GB VRAM)"},
-	{Name: "glm-4.7:cloud", Description: "Recommended"},
-	{Name: "kimi-k2.5:cloud", Description: "Recommended"},
-}
-
-// integrationAliases are hidden from the interactive selector but work as CLI arguments.
-var integrationAliases = map[string]bool{
-	"clawdbot": true,
-	"moltbot":  true,
-	"pi":       true,
 }
 
 func selectIntegration() (string, error) {
@@ -75,9 +54,6 @@ func selectIntegration() (string, error) {
 	names := slices.Sorted(maps.Keys(integrations))
 	var items []selectItem
 	for _, name := range names {
-		if integrationAliases[name] {
-			continue
-		}
 		r := integrations[name]
 		description := r.String()
 		if conn, err := loadIntegration(name); err == nil && len(conn.Models) > 0 {
@@ -106,25 +82,62 @@ func selectModels(ctx context.Context, name, current string) ([]string, error) {
 		return nil, err
 	}
 
-	var existing []modelInfo
-	for _, m := range models.Models {
-		existing = append(existing, modelInfo{Name: m.Name, Remote: m.RemoteModel != ""})
+	if len(models.Models) == 0 {
+		return nil, fmt.Errorf("no models available, run 'ollama pull <model>' first")
 	}
 
+	var items []selectItem
+	cloudModels := make(map[string]bool)
+	for _, m := range models.Models {
+		if m.RemoteModel != "" {
+			cloudModels[m.Name] = true
+		}
+		items = append(items, selectItem{Name: m.Name})
+	}
+
+	if len(items) == 0 {
+		return nil, fmt.Errorf("no local models available, run 'ollama pull <model>' first")
+	}
+
+	// Get previously configured models (saved config takes precedence)
 	var preChecked []string
 	if saved, err := loadIntegration(name); err == nil {
 		preChecked = saved.Models
 	} else if editor, ok := r.(Editor); ok {
 		preChecked = editor.Models()
 	}
-
-	items, preChecked, existingModels, cloudModels := buildModelList(existing, preChecked, current)
-
-	if len(items) == 0 {
-		return nil, fmt.Errorf("no models available")
+	checked := make(map[string]bool, len(preChecked))
+	for _, n := range preChecked {
+		checked[n] = true
 	}
 
+	// Resolve current to full name (e.g., "llama3.2" -> "llama3.2:latest")
+	for _, item := range items {
+		if item.Name == current || strings.HasPrefix(item.Name, current+":") {
+			current = item.Name
+			break
+		}
+	}
+
+	// If current model is configured, move to front of preChecked
+	if checked[current] {
+		preChecked = append([]string{current}, slices.DeleteFunc(preChecked, func(m string) bool { return m == current })...)
+	}
+
+	// Sort: checked first, then alphabetical
+	slices.SortFunc(items, func(a, b selectItem) int {
+		ac, bc := checked[a.Name], checked[b.Name]
+		if ac != bc {
+			if ac {
+				return -1
+			}
+			return 1
+		}
+		return strings.Compare(strings.ToLower(a.Name), strings.ToLower(b.Name))
+	})
+
 	var selected []string
+	// only editors support multi-model selection
 	if _, ok := r.(Editor); ok {
 		selected, err = multiSelectPrompt(fmt.Sprintf("Select models for %s:", r), items, preChecked)
 		if err != nil {
@@ -138,27 +151,7 @@ func selectModels(ctx context.Context, name, current string) ([]string, error) {
 		selected = []string{model}
 	}
 
-	var toPull []string
-	for _, m := range selected {
-		if !existingModels[m] {
-			toPull = append(toPull, m)
-		}
-	}
-	if len(toPull) > 0 {
-		msg := fmt.Sprintf("Download %s?", strings.Join(toPull, ", "))
-		if ok, err := confirmPrompt(msg); err != nil {
-			return nil, err
-		} else if !ok {
-			return nil, errCancelled
-		}
-		for _, m := range toPull {
-			fmt.Fprintf(os.Stderr, "\n")
-			if err := pullModel(ctx, client, m); err != nil {
-				return nil, fmt.Errorf("failed to pull %s: %w", m, err)
-			}
-		}
-	}
-
+	// if any model in selected is a cloud model, ensure signed in
 	var selectedCloudModels []string
 	for _, m := range selected {
 		if cloudModels[m] {
@@ -228,13 +221,13 @@ func selectModels(ctx context.Context, name, current string) ([]string, error) {
 	return selected, nil
 }
 
-func runIntegration(name, modelName string, args []string) error {
+func runIntegration(name, modelName string) error {
 	r, ok := integrations[name]
 	if !ok {
 		return fmt.Errorf("unknown integration: %s", name)
 	}
 	fmt.Fprintf(os.Stderr, "\nLaunching %s with %s...\n", r, modelName)
-	return r.Run(modelName, args)
+	return r.Run(modelName)
 }
 
 // LaunchCmd returns the cobra command for launching integrations.
@@ -243,7 +236,7 @@ func LaunchCmd(checkServerHeartbeat func(cmd *cobra.Command, args []string) erro
 	var configFlag bool
 
 	cmd := &cobra.Command{
-		Use:   "launch [INTEGRATION] [-- [EXTRA_ARGS...]]",
+		Use:   "launch [INTEGRATION]",
 		Short: "Launch an integration with Ollama",
 		Long: `Launch an integration configured with Ollama models.
 
@@ -252,43 +245,19 @@ Supported integrations:
   codex     Codex
   droid     Droid
   opencode  OpenCode
-  openclaw  OpenClaw (aliases: clawdbot, moltbot)
 
 Examples:
   ollama launch
   ollama launch claude
   ollama launch claude --model <model>
-  ollama launch droid --config (does not auto-launch)
-  ollama launch codex -- -p myprofile (pass extra args to integration)
-  ollama launch codex -- --sandbox workspace-write`,
-		Args:    cobra.ArbitraryArgs,
+  ollama launch droid --config (does not auto-launch)`,
+		Args:    cobra.MaximumNArgs(1),
 		PreRunE: checkServerHeartbeat,
 		RunE: func(cmd *cobra.Command, args []string) error {
-			// Extract integration name and args to pass through using -- separator
 			var name string
-			var passArgs []string
-			dashIdx := cmd.ArgsLenAtDash()
-
-			if dashIdx == -1 {
-				// No "--" separator: only allow 0 or 1 args (integration name)
-				if len(args) > 1 {
-					return fmt.Errorf("unexpected arguments: %v\nUse '--' to pass extra arguments to the integration", args[1:])
-				}
-				if len(args) == 1 {
-					name = args[0]
-				}
+			if len(args) > 0 {
+				name = args[0]
 			} else {
-				// "--" was used: args before it = integration name, args after = passthrough
-				if dashIdx > 1 {
-					return fmt.Errorf("expected at most 1 integration name before '--', got %d", dashIdx)
-				}
-				if dashIdx == 1 {
-					name = args[0]
-				}
-				passArgs = args[dashIdx:]
-			}
-
-			if name == "" {
 				var err error
 				name, err = selectIntegration()
 				if errors.Is(err, errCancelled) {
@@ -304,14 +273,16 @@ Examples:
 				return fmt.Errorf("unknown integration: %s", name)
 			}
 
+			// If launching without --model, use saved config if available
 			if !configFlag && modelFlag == "" {
 				if config, err := loadIntegration(name); err == nil && len(config.Models) > 0 {
-					return runIntegration(name, config.Models[0], passArgs)
+					return runIntegration(name, config.Models[0])
 				}
 			}
 
 			var models []string
 			if modelFlag != "" {
+				// When --model is specified, merge with existing models (new model becomes default)
 				models = []string{modelFlag}
 				if existing, err := loadIntegration(name); err == nil && len(existing.Models) > 0 {
 					for _, m := range existing.Models {
@@ -366,13 +337,13 @@ Examples:
 
 			if configFlag {
 				if launch, _ := confirmPrompt(fmt.Sprintf("\nLaunch %s now?", r)); launch {
-					return runIntegration(name, models[0], passArgs)
+					return runIntegration(name, models[0])
 				}
 				fmt.Fprintf(os.Stderr, "Run 'ollama launch %s' to start with %s\n", strings.ToLower(name), models[0])
 				return nil
 			}
 
-			return runIntegration(name, models[0], passArgs)
+			return runIntegration(name, models[0])
 		},
 	}
 
@@ -380,154 +351,3 @@ Examples:
 	cmd.Flags().BoolVar(&configFlag, "config", false, "Configure without launching")
 	return cmd
 }
-
-type modelInfo struct {
-	Name   string
-	Remote bool
-}
-
-// buildModelList merges existing models with recommendations, sorts them, and returns
-// the ordered items along with maps of existing and cloud model names.
-func buildModelList(existing []modelInfo, preChecked []string, current string) (items []selectItem, orderedChecked []string, existingModels, cloudModels map[string]bool) {
-	existingModels = make(map[string]bool)
-	cloudModels = make(map[string]bool)
-	recommended := make(map[string]bool)
-	var hasLocalModel, hasCloudModel bool
-
-	for _, rec := range recommendedModels {
-		recommended[rec.Name] = true
-	}
-
-	for _, m := range existing {
-		existingModels[m.Name] = true
-		if m.Remote {
-			cloudModels[m.Name] = true
-			hasCloudModel = true
-		} else {
-			hasLocalModel = true
-		}
-		displayName := strings.TrimSuffix(m.Name, ":latest")
-		existingModels[displayName] = true
-		item := selectItem{Name: displayName}
-		if recommended[displayName] {
-			item.Description = "recommended"
-		}
-		items = append(items, item)
-	}
-
-	for _, rec := range recommendedModels {
-		if existingModels[rec.Name] || existingModels[rec.Name+":latest"] {
-			continue
-		}
-		items = append(items, rec)
-		if isCloudModel(rec.Name) {
-			cloudModels[rec.Name] = true
-		}
-	}
-
-	checked := make(map[string]bool, len(preChecked))
-	for _, n := range preChecked {
-		checked[n] = true
-	}
-
-	// Resolve current to full name (e.g., "llama3.2" -> "llama3.2:latest")
-	for _, item := range items {
-		if item.Name == current || strings.HasPrefix(item.Name, current+":") {
-			current = item.Name
-			break
-		}
-	}
-
-	if checked[current] {
-		preChecked = append([]string{current}, slices.DeleteFunc(preChecked, func(m string) bool { return m == current })...)
-	}
-
-	// Non-existing models get "install?" suffix and are pushed to the bottom.
-	// When user has no models, preserve recommended order.
-	notInstalled := make(map[string]bool)
-	for i := range items {
-		if !existingModels[items[i].Name] {
-			notInstalled[items[i].Name] = true
-			if items[i].Description != "" {
-				items[i].Description += ", install?"
-			} else {
-				items[i].Description = "install?"
-			}
-		}
-	}
-
-	if hasLocalModel || hasCloudModel {
-		slices.SortStableFunc(items, func(a, b selectItem) int {
-			ac, bc := checked[a.Name], checked[b.Name]
-			aNew, bNew := notInstalled[a.Name], notInstalled[b.Name]
-
-			if ac != bc {
-				if ac {
-					return -1
-				}
-				return 1
-			}
-			if !ac && !bc && aNew != bNew {
-				if aNew {
-					return 1
-				}
-				return -1
-			}
-			return strings.Compare(strings.ToLower(a.Name), strings.ToLower(b.Name))
-		})
-	}
-
-	return items, preChecked, existingModels, cloudModels
-}
-
-func isCloudModel(name string) bool {
-	return strings.HasSuffix(name, ":cloud")
-}
-
-func pullModel(ctx context.Context, client *api.Client, model string) error {
-	p := progress.NewProgress(os.Stderr)
-	defer p.Stop()
-
-	bars := make(map[string]*progress.Bar)
-	var status string
-	var spinner *progress.Spinner
-
-	fn := func(resp api.ProgressResponse) error {
-		if resp.Digest != "" {
-			if resp.Completed == 0 {
-				return nil
-			}
-
-			if spinner != nil {
-				spinner.Stop()
-			}
-
-			bar, ok := bars[resp.Digest]
-			if !ok {
-				name, isDigest := strings.CutPrefix(resp.Digest, "sha256:")
-				name = strings.TrimSpace(name)
-				if isDigest {
-					name = name[:min(12, len(name))]
-				}
-				bar = progress.NewBar(fmt.Sprintf("pulling %s:", name), resp.Total, resp.Completed)
-				bars[resp.Digest] = bar
-				p.Add(resp.Digest, bar)
-			}
-
-			bar.Set(resp.Completed)
-		} else if status != resp.Status {
-			if spinner != nil {
-				spinner.Stop()
-			}
-
-			status = resp.Status
-			spinner = progress.NewSpinner(status)
-			p.Add(status, spinner)
-		}
-
-		return nil
-	}
-
-	request := api.PullRequest{Name: model}
-	return client.Pull(ctx, &request, fn)
-}

cmd/config/integrations_test.go

@@ -1,12 +1,10 @@
 package config
 
 import (
-	"fmt"
 	"slices"
 	"strings"
 	"testing"
 
-	"github.com/google/go-cmp/cmp"
 	"github.com/spf13/cobra"
 )
 
@@ -92,8 +90,8 @@ func TestLaunchCmd(t *testing.T) {
 	cmd := LaunchCmd(mockCheck)
 
 	t.Run("command structure", func(t *testing.T) {
-		if cmd.Use != "launch [INTEGRATION] [-- [EXTRA_ARGS...]]" {
-			t.Errorf("Use = %q, want %q", cmd.Use, "launch [INTEGRATION] [-- [EXTRA_ARGS...]]")
+		if cmd.Use != "launch [INTEGRATION]" {
+			t.Errorf("Use = %q, want %q", cmd.Use, "launch [INTEGRATION]")
 		}
 		if cmd.Short == "" {
 			t.Error("Short description should not be empty")
@@ -123,7 +121,7 @@ func TestLaunchCmd(t *testing.T) {
 }
 
 func TestRunIntegration_UnknownIntegration(t *testing.T) {
-	err := runIntegration("unknown-integration", "model", nil)
+	err := runIntegration("unknown-integration", "model")
 	if err == nil {
 		t.Error("expected error for unknown integration, got nil")
 	}
@@ -176,336 +174,15 @@ func TestLaunchCmd_NilHeartbeat(t *testing.T) {
 func TestAllIntegrations_HaveRequiredMethods(t *testing.T) {
 	for name, r := range integrations {
 		t.Run(name, func(t *testing.T) {
+			// Test String() doesn't panic and returns non-empty
 			displayName := r.String()
 			if displayName == "" {
 				t.Error("String() should not return empty")
 			}
-			var _ func(string, []string) error = r.Run
+
+			// Test Run() exists (we can't call it without actually running the command)
+			// Just verify the method is available
+			var _ func(string) error = r.Run
 		})
 	}
 }
-
-func TestParseArgs(t *testing.T) {
-	// Tests reflect cobra's ArgsLenAtDash() semantics:
-	// - cobra strips "--" from args
-	// - ArgsLenAtDash() returns the index where "--" was, or -1
-	tests := []struct {
-		name     string
-		args     []string // args as cobra delivers them (no "--")
-		dashIdx  int      // what ArgsLenAtDash() returns
-		wantName string
-		wantArgs []string
-		wantErr  bool
-	}{
-		{
-			name:     "no extra args, no dash",
-			args:     []string{"claude"},
-			dashIdx:  -1,
-			wantName: "claude",
-		},
-		{
-			name:     "with extra args after --",
-			args:     []string{"codex", "-p", "myprofile"},
-			dashIdx:  1,
-			wantName: "codex",
-			wantArgs: []string{"-p", "myprofile"},
-		},
-		{
-			name:     "extra args only after --",
-			args:     []string{"codex", "--sandbox", "workspace-write"},
-			dashIdx:  1,
-			wantName: "codex",
-			wantArgs: []string{"--sandbox", "workspace-write"},
-		},
-		{
-			name:     "-- at end with no args after",
-			args:     []string{"claude"},
-			dashIdx:  1,
-			wantName: "claude",
-		},
-		{
-			name:     "-- with no integration name",
-			args:     []string{"--verbose"},
-			dashIdx:  0,
-			wantName: "",
-			wantArgs: []string{"--verbose"},
-		},
-		{
-			name:    "multiple args before -- is error",
-			args:    []string{"claude", "codex", "--verbose"},
-			dashIdx: 2,
-			wantErr: true,
-		},
-		{
-			name:    "multiple args without -- is error",
-			args:    []string{"claude", "codex"},
-			dashIdx: -1,
-			wantErr: true,
-		},
-		{
-			name:     "no args, no dash",
-			args:     []string{},
-			dashIdx:  -1,
-			wantName: "",
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			// Simulate the parsing logic from LaunchCmd using dashIdx
-			var name string
-			var parsedArgs []string
-			var err error
-
-			dashIdx := tt.dashIdx
-			args := tt.args
-
-			if dashIdx == -1 {
-				if len(args) > 1 {
-					err = fmt.Errorf("unexpected arguments: %v", args[1:])
-				} else if len(args) == 1 {
-					name = args[0]
-				}
-			} else {
-				if dashIdx > 1 {
-					err = fmt.Errorf("expected at most 1 integration name before '--', got %d", dashIdx)
-				} else {
-					if dashIdx == 1 {
-						name = args[0]
-					}
-					parsedArgs = args[dashIdx:]
-				}
-			}
-
-			if tt.wantErr {
-				if err == nil {
-					t.Fatal("expected error, got nil")
-				}
-				return
-			}
-			if err != nil {
-				t.Fatalf("unexpected error: %v", err)
-			}
-			if name != tt.wantName {
-				t.Errorf("name = %q, want %q", name, tt.wantName)
-			}
-			if !slices.Equal(parsedArgs, tt.wantArgs) {
-				t.Errorf("args = %v, want %v", parsedArgs, tt.wantArgs)
-			}
-		})
-	}
-}
-
-func TestIsCloudModel(t *testing.T) {
-	tests := []struct {
-		name string
-		want bool
-	}{
-		{"glm-4.7:cloud", true},
-		{"kimi-k2.5:cloud", true},
-		{"glm-4.7-flash", false},
-		{"glm-4.7-flash:latest", false},
-		{"cloud-model", false},
-		{"model:cloudish", false},
-	}
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			if got := isCloudModel(tt.name); got != tt.want {
-				t.Errorf("isCloudModel(%q) = %v, want %v", tt.name, got, tt.want)
-			}
-		})
-	}
-}
-
-func names(items []selectItem) []string {
-	var out []string
-	for _, item := range items {
-		out = append(out, item.Name)
-	}
-	return out
-}
-
-func TestBuildModelList_NoExistingModels(t *testing.T) {
-	items, _, _, _ := buildModelList(nil, nil, "")
-
-	want := []string{"glm-4.7-flash", "qwen3:8b", "glm-4.7:cloud", "kimi-k2.5:cloud"}
-	if diff := cmp.Diff(want, names(items)); diff != "" {
-		t.Errorf("with no existing models, items should be recommended in order (-want +got):\n%s", diff)
-	}
-
-	for _, item := range items {
-		if !strings.HasSuffix(item.Description, "install?") {
-			t.Errorf("item %q should have description ending with 'install?', got %q", item.Name, item.Description)
-		}
-	}
-}
-
-func TestBuildModelList_OnlyLocalModels_CloudRecsAtBottom(t *testing.T) {
-	existing := []modelInfo{
-		{Name: "llama3.2:latest", Remote: false},
-		{Name: "qwen2.5:latest", Remote: false},
-	}
-
-	items, _, _, _ := buildModelList(existing, nil, "")
-	got := names(items)
-
-	want := []string{"llama3.2", "qwen2.5", "glm-4.7-flash", "glm-4.7:cloud", "kimi-k2.5:cloud", "qwen3:8b"}
-	if diff := cmp.Diff(want, got); diff != "" {
-		t.Errorf("cloud recs should be at bottom (-want +got):\n%s", diff)
-	}
-}
-
-func TestBuildModelList_BothCloudAndLocal_RegularSort(t *testing.T) {
-	existing := []modelInfo{
-		{Name: "llama3.2:latest", Remote: false},
-		{Name: "glm-4.7:cloud", Remote: true},
-	}
-
-	items, _, _, _ := buildModelList(existing, nil, "")
-	got := names(items)
-
-	want := []string{"glm-4.7:cloud", "llama3.2", "glm-4.7-flash", "kimi-k2.5:cloud", "qwen3:8b"}
-	if diff := cmp.Diff(want, got); diff != "" {
-		t.Errorf("mixed models should be alphabetical (-want +got):\n%s", diff)
-	}
-}
-
-func TestBuildModelList_PreCheckedFirst(t *testing.T) {
-	existing := []modelInfo{
-		{Name: "llama3.2:latest", Remote: false},
-		{Name: "glm-4.7:cloud", Remote: true},
-	}
-
-	items, _, _, _ := buildModelList(existing, []string{"llama3.2"}, "")
-	got := names(items)
-
-	if got[0] != "llama3.2" {
-		t.Errorf("pre-checked model should be first, got %v", got)
-	}
-}
-
-func TestBuildModelList_ExistingRecommendedMarked(t *testing.T) {
-	existing := []modelInfo{
-		{Name: "glm-4.7-flash", Remote: false},
-		{Name: "glm-4.7:cloud", Remote: true},
-	}
-
-	items, _, _, _ := buildModelList(existing, nil, "")
-
-	for _, item := range items {
-		switch item.Name {
-		case "glm-4.7-flash", "glm-4.7:cloud":
-			if strings.HasSuffix(item.Description, "install?") {
-				t.Errorf("installed recommended %q should not have 'install?' suffix, got %q", item.Name, item.Description)
-			}
-		case "kimi-k2.5:cloud", "qwen3:8b":
-			if !strings.HasSuffix(item.Description, "install?") {
-				t.Errorf("non-installed recommended %q should have 'install?' suffix, got %q", item.Name, item.Description)
-			}
-		}
-	}
-}
-
-func TestBuildModelList_ExistingCloudModelsNotPushedToBottom(t *testing.T) {
-	existing := []modelInfo{
-		{Name: "glm-4.7-flash", Remote: false},
-		{Name: "glm-4.7:cloud", Remote: true},
-	}
-
-	items, _, _, _ := buildModelList(existing, nil, "")
-	got := names(items)
-
-	// glm-4.7-flash and glm-4.7:cloud are installed so they sort normally;
-	// kimi-k2.5:cloud and qwen3:8b are not installed so they go to the bottom
-	want := []string{"glm-4.7-flash", "glm-4.7:cloud", "kimi-k2.5:cloud", "qwen3:8b"}
-	if diff := cmp.Diff(want, got); diff != "" {
-		t.Errorf("existing cloud models should sort normally (-want +got):\n%s", diff)
-	}
-}
-
-func TestBuildModelList_HasRecommendedCloudModel_OnlyNonInstalledAtBottom(t *testing.T) {
-	existing := []modelInfo{
-		{Name: "llama3.2:latest", Remote: false},
-		{Name: "kimi-k2.5:cloud", Remote: true},
-	}
-
-	items, _, _, _ := buildModelList(existing, nil, "")
-	got := names(items)
-
-	// kimi-k2.5:cloud is installed so it sorts normally;
-	// the rest of the recommendations are not installed so they go to the bottom
-	want := []string{"kimi-k2.5:cloud", "llama3.2", "glm-4.7-flash", "glm-4.7:cloud", "qwen3:8b"}
-	if diff := cmp.Diff(want, got); diff != "" {
-		t.Errorf("only non-installed models should be at bottom (-want +got):\n%s", diff)
-	}
-
-	for _, item := range items {
-		if !slices.Contains([]string{"kimi-k2.5:cloud", "llama3.2"}, item.Name) {
-			if !strings.HasSuffix(item.Description, "install?") {
-				t.Errorf("non-installed %q should have 'install?' suffix, got %q", item.Name, item.Description)
-			}
-		}
-	}
-}
-
-func TestBuildModelList_LatestTagStripped(t *testing.T) {
-	existing := []modelInfo{
-		{Name: "glm-4.7-flash:latest", Remote: false},
-		{Name: "llama3.2:latest", Remote: false},
-	}
-
-	items, _, existingModels, _ := buildModelList(existing, nil, "")
-	got := names(items)
-
-	// :latest should be stripped from display names
-	for _, name := range got {
-		if strings.HasSuffix(name, ":latest") {
-			t.Errorf("name %q should not have :latest suffix", name)
-		}
-	}
-
-	// glm-4.7-flash should not be duplicated (existing :latest matches the recommendation)
-	count := 0
-	for _, name := range got {
-		if name == "glm-4.7-flash" {
-			count++
-		}
-	}
-	if count != 1 {
-		t.Errorf("glm-4.7-flash should appear exactly once, got %d in %v", count, got)
-	}
-
-	// Stripped name should be in existingModels so it won't be pulled
-	if !existingModels["glm-4.7-flash"] {
-		t.Error("glm-4.7-flash should be in existingModels")
-	}
-}
-
-func TestBuildModelList_ReturnsExistingAndCloudMaps(t *testing.T) {
-	existing := []modelInfo{
-		{Name: "llama3.2:latest", Remote: false},
-		{Name: "glm-4.7:cloud", Remote: true},
-	}
-
-	_, _, existingModels, cloudModels := buildModelList(existing, nil, "")
-
-	if !existingModels["llama3.2"] {
-		t.Error("llama3.2 should be in existingModels")
-	}
-	if !existingModels["glm-4.7:cloud"] {
-		t.Error("glm-4.7:cloud should be in existingModels")
-	}
-	if existingModels["glm-4.7-flash"] {
-		t.Error("glm-4.7-flash should not be in existingModels (it's a recommendation)")
-	}
-
-	if !cloudModels["glm-4.7:cloud"] {
-		t.Error("glm-4.7:cloud should be in cloudModels")
-	}
-	if !cloudModels["kimi-k2.5:cloud"] {
-		t.Error("kimi-k2.5:cloud should be in cloudModels (recommended cloud)")
-	}
-	if cloudModels["llama3.2"] {
-		t.Error("llama3.2 should not be in cloudModels")
-	}
-}

cmd/config/openclaw.go

@@ -1,254 +0,0 @@
-package config
-
-import (
-	"bytes"
-	"encoding/json"
-	"fmt"
-	"io"
-	"os"
-	"os/exec"
-	"path/filepath"
-	"strings"
-
-	"github.com/ollama/ollama/envconfig"
-)
-
-type Openclaw struct{}
-
-func (c *Openclaw) String() string { return "OpenClaw" }
-
-const ansiGreen = "\033[32m"
-
-func (c *Openclaw) Run(model string, args []string) error {
-	bin := "openclaw"
-	if _, err := exec.LookPath(bin); err != nil {
-		bin = "clawdbot"
-		if _, err := exec.LookPath(bin); err != nil {
-			return fmt.Errorf("openclaw is not installed, install from https://docs.openclaw.ai")
-		}
-	}
-
-	models := []string{model}
-	if config, err := loadIntegration("openclaw"); err == nil && len(config.Models) > 0 {
-		models = config.Models
-	} else if config, err := loadIntegration("clawdbot"); err == nil && len(config.Models) > 0 {
-		models = config.Models
-	}
-	if err := c.Edit(models); err != nil {
-		return fmt.Errorf("setup failed: %w", err)
-	}
-
-	if !c.onboarded() {
-		// Onboarding not completed: run it (model already set via Edit)
-		// Use "ollama" as gateway token for simple local access
-		cmd := exec.Command(bin, "onboard",
-			"--auth-choice", "skip",
-			"--gateway-token", "ollama",
-		)
-		cmd.Stdin = os.Stdin
-		cmd.Stdout = os.Stdout
-		cmd.Stderr = os.Stderr
-		return cmd.Run()
-	}
-
-	// Onboarding completed: run gateway
-	cmd := exec.Command(bin, append([]string{"gateway"}, args...)...)
-	cmd.Stdin = os.Stdin
-
-	// Capture output to detect "already running" message
-	var outputBuf bytes.Buffer
-	cmd.Stdout = io.MultiWriter(os.Stdout, &outputBuf)
-	cmd.Stderr = io.MultiWriter(os.Stderr, &outputBuf)
-
-	err := cmd.Run()
-	if err != nil && strings.Contains(outputBuf.String(), "Gateway already running") {
-		fmt.Fprintf(os.Stderr, "%sOpenClaw has been configured with Ollama. Gateway is already running.%s\n", ansiGreen, ansiReset)
-		return nil
-	}
-	return err
-}
-
-// onboarded checks if OpenClaw onboarding wizard was completed
-// by looking for the wizard.lastRunAt marker in the config
-func (c *Openclaw) onboarded() bool {
-	home, err := os.UserHomeDir()
-	if err != nil {
-		return false
-	}
-
-	configPath := filepath.Join(home, ".openclaw", "openclaw.json")
-	legacyPath := filepath.Join(home, ".clawdbot", "clawdbot.json")
-
-	config := make(map[string]any)
-	if data, err := os.ReadFile(configPath); err == nil {
-		_ = json.Unmarshal(data, &config)
-	} else if data, err := os.ReadFile(legacyPath); err == nil {
-		_ = json.Unmarshal(data, &config)
-	} else {
-		return false
-	}
-
-	// Check for wizard.lastRunAt marker (set when onboarding completes)
-	wizard, _ := config["wizard"].(map[string]any)
-	if wizard == nil {
-		return false
-	}
-	lastRunAt, _ := wizard["lastRunAt"].(string)
-	return lastRunAt != ""
-}
-
-func (c *Openclaw) Paths() []string {
-	home, _ := os.UserHomeDir()
-	p := filepath.Join(home, ".openclaw", "openclaw.json")
-	if _, err := os.Stat(p); err == nil {
-		return []string{p}
-	}
-	legacy := filepath.Join(home, ".clawdbot", "clawdbot.json")
-	if _, err := os.Stat(legacy); err == nil {
-		return []string{legacy}
-	}
-	return nil
-}
-
-func (c *Openclaw) Edit(models []string) error {
-	if len(models) == 0 {
-		return nil
-	}
-
-	home, err := os.UserHomeDir()
-	if err != nil {
-		return err
-	}
-
-	configPath := filepath.Join(home, ".openclaw", "openclaw.json")
-	legacyPath := filepath.Join(home, ".clawdbot", "clawdbot.json")
-	if err := os.MkdirAll(filepath.Dir(configPath), 0o755); err != nil {
-		return err
-	}
-
-	// Read into map[string]any to preserve unknown fields
-	config := make(map[string]any)
-	if data, err := os.ReadFile(configPath); err == nil {
-		_ = json.Unmarshal(data, &config)
-	} else if data, err := os.ReadFile(legacyPath); err == nil {
-		_ = json.Unmarshal(data, &config)
-	}
-
-	// Navigate/create: models.providers.ollama (preserving other providers)
-	modelsSection, _ := config["models"].(map[string]any)
-	if modelsSection == nil {
-		modelsSection = make(map[string]any)
-	}
-	providers, _ := modelsSection["providers"].(map[string]any)
-	if providers == nil {
-		providers = make(map[string]any)
-	}
-	ollama, _ := providers["ollama"].(map[string]any)
-	if ollama == nil {
-		ollama = make(map[string]any)
-	}
-
-	ollama["baseUrl"] = envconfig.Host().String() + "/v1"
-	// needed to register provider
-	ollama["apiKey"] = "ollama-local"
-	// TODO(parthsareen): potentially move to responses
-	ollama["api"] = "openai-completions"
-
-	// Build map of existing models to preserve user customizations
-	existingModels, _ := ollama["models"].([]any)
-	existingByID := make(map[string]map[string]any)
-	for _, m := range existingModels {
-		if entry, ok := m.(map[string]any); ok {
-			if id, ok := entry["id"].(string); ok {
-				existingByID[id] = entry
-			}
-		}
-	}
-
-	var newModels []any
-	for _, model := range models {
-		entry := map[string]any{
-			"id":        model,
-			"name":      model,
-			"reasoning": false,
-			"input":     []any{"text"},
-			"cost": map[string]any{
-				"input":      0,
-				"output":     0,
-				"cacheRead":  0,
-				"cacheWrite": 0,
-			},
-			// TODO(parthsareen): get these values from API
-			"contextWindow": 131072,
-			"maxTokens":     16384,
-		}
-		// Merge existing fields (user customizations)
-		if existing, ok := existingByID[model]; ok {
-			for k, v := range existing {
-				if _, isNew := entry[k]; !isNew {
-					entry[k] = v
-				}
-			}
-		}
-		newModels = append(newModels, entry)
-	}
-	ollama["models"] = newModels
-
-	providers["ollama"] = ollama
-	modelsSection["providers"] = providers
-	config["models"] = modelsSection
-
-	// Update agents.defaults.model.primary (preserving other agent settings)
-	agents, _ := config["agents"].(map[string]any)
-	if agents == nil {
-		agents = make(map[string]any)
-	}
-	defaults, _ := agents["defaults"].(map[string]any)
-	if defaults == nil {
-		defaults = make(map[string]any)
-	}
-	modelConfig, _ := defaults["model"].(map[string]any)
-	if modelConfig == nil {
-		modelConfig = make(map[string]any)
-	}
-	modelConfig["primary"] = "ollama/" + models[0]
-	defaults["model"] = modelConfig
-	agents["defaults"] = defaults
-	config["agents"] = agents
-
-	data, err := json.MarshalIndent(config, "", "  ")
-	if err != nil {
-		return err
-	}
-	return writeWithBackup(configPath, data)
-}
-
-func (c *Openclaw) Models() []string {
-	home, err := os.UserHomeDir()
-	if err != nil {
-		return nil
-	}
-
-	config, err := readJSONFile(filepath.Join(home, ".openclaw", "openclaw.json"))
-	if err != nil {
-		config, err = readJSONFile(filepath.Join(home, ".clawdbot", "clawdbot.json"))
-		if err != nil {
-			return nil
-		}
-	}
-
-	modelsSection, _ := config["models"].(map[string]any)
-	providers, _ := modelsSection["providers"].(map[string]any)
-	ollama, _ := providers["ollama"].(map[string]any)
-	modelList, _ := ollama["models"].([]any)
-
-	var result []string
-	for _, m := range modelList {
-		if entry, ok := m.(map[string]any); ok {
-			if id, ok := entry["id"].(string); ok {
-				result = append(result, id)
-			}
-		}
-	}
-	return result
-}

cmd/config/openclaw_test.go

@@ -1,878 +0,0 @@
-package config
-
-import (
-	"encoding/json"
-	"fmt"
-	"os"
-	"path/filepath"
-	"testing"
-)
-
-func TestOpenclawIntegration(t *testing.T) {
-	c := &Openclaw{}
-
-	t.Run("String", func(t *testing.T) {
-		if got := c.String(); got != "OpenClaw" {
-			t.Errorf("String() = %q, want %q", got, "OpenClaw")
-		}
-	})
-
-	t.Run("implements Runner", func(t *testing.T) {
-		var _ Runner = c
-	})
-
-	t.Run("implements Editor", func(t *testing.T) {
-		var _ Editor = c
-	})
-}
-
-func TestOpenclawEdit(t *testing.T) {
-	c := &Openclaw{}
-	tmpDir := t.TempDir()
-	setTestHome(t, tmpDir)
-
-	configDir := filepath.Join(tmpDir, ".openclaw")
-	configPath := filepath.Join(configDir, "openclaw.json")
-
-	cleanup := func() { os.RemoveAll(configDir) }
-
-	t.Run("fresh install", func(t *testing.T) {
-		cleanup()
-		if err := c.Edit([]string{"llama3.2"}); err != nil {
-			t.Fatal(err)
-		}
-		assertOpenclawModelExists(t, configPath, "llama3.2")
-		assertOpenclawPrimaryModel(t, configPath, "ollama/llama3.2")
-	})
-
-	t.Run("multiple models - first is primary", func(t *testing.T) {
-		cleanup()
-		if err := c.Edit([]string{"llama3.2", "mistral"}); err != nil {
-			t.Fatal(err)
-		}
-		assertOpenclawModelExists(t, configPath, "llama3.2")
-		assertOpenclawModelExists(t, configPath, "mistral")
-		assertOpenclawPrimaryModel(t, configPath, "ollama/llama3.2")
-	})
-
-	t.Run("preserve other providers", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(configPath, []byte(`{"models":{"providers":{"anthropic":{"apiKey":"xxx"}}}}`), 0o644)
-		if err := c.Edit([]string{"llama3.2"}); err != nil {
-			t.Fatal(err)
-		}
-		data, _ := os.ReadFile(configPath)
-		var cfg map[string]any
-		json.Unmarshal(data, &cfg)
-		models := cfg["models"].(map[string]any)
-		providers := models["providers"].(map[string]any)
-		if providers["anthropic"] == nil {
-			t.Error("anthropic provider was removed")
-		}
-	})
-
-	t.Run("preserve top-level keys", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(configPath, []byte(`{"theme":"dark","mcp":{"servers":{}}}`), 0o644)
-		if err := c.Edit([]string{"llama3.2"}); err != nil {
-			t.Fatal(err)
-		}
-		data, _ := os.ReadFile(configPath)
-		var cfg map[string]any
-		json.Unmarshal(data, &cfg)
-		if cfg["theme"] != "dark" {
-			t.Error("theme was removed")
-		}
-		if cfg["mcp"] == nil {
-			t.Error("mcp was removed")
-		}
-	})
-
-	t.Run("preserve user customizations on models", func(t *testing.T) {
-		cleanup()
-		c.Edit([]string{"llama3.2"})
-
-		// User adds custom field
-		data, _ := os.ReadFile(configPath)
-		var cfg map[string]any
-		json.Unmarshal(data, &cfg)
-		models := cfg["models"].(map[string]any)
-		providers := models["providers"].(map[string]any)
-		ollama := providers["ollama"].(map[string]any)
-		modelList := ollama["models"].([]any)
-		entry := modelList[0].(map[string]any)
-		entry["customField"] = "user-value"
-		configData, _ := json.MarshalIndent(cfg, "", "  ")
-		os.WriteFile(configPath, configData, 0o644)
-
-		// Re-run Edit
-		c.Edit([]string{"llama3.2"})
-
-		data, _ = os.ReadFile(configPath)
-		json.Unmarshal(data, &cfg)
-		models = cfg["models"].(map[string]any)
-		providers = models["providers"].(map[string]any)
-		ollama = providers["ollama"].(map[string]any)
-		modelList = ollama["models"].([]any)
-		entry = modelList[0].(map[string]any)
-		if entry["customField"] != "user-value" {
-			t.Error("custom field was lost")
-		}
-	})
-
-	t.Run("edit replaces models list", func(t *testing.T) {
-		cleanup()
-		c.Edit([]string{"llama3.2", "mistral"})
-		c.Edit([]string{"llama3.2"})
-
-		assertOpenclawModelExists(t, configPath, "llama3.2")
-		assertOpenclawModelNotExists(t, configPath, "mistral")
-	})
-
-	t.Run("empty models is no-op", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-		original := `{"existing":"data"}`
-		os.WriteFile(configPath, []byte(original), 0o644)
-
-		c.Edit([]string{})
-
-		data, _ := os.ReadFile(configPath)
-		if string(data) != original {
-			t.Error("empty models should not modify file")
-		}
-	})
-
-	t.Run("corrupted JSON treated as empty", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(configPath, []byte(`{corrupted`), 0o644)
-
-		if err := c.Edit([]string{"llama3.2"}); err != nil {
-			t.Fatal(err)
-		}
-
-		data, _ := os.ReadFile(configPath)
-		var cfg map[string]any
-		if err := json.Unmarshal(data, &cfg); err != nil {
-			t.Error("result should be valid JSON")
-		}
-	})
-
-	t.Run("wrong type models section", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(configPath, []byte(`{"models":"not a map"}`), 0o644)
-
-		if err := c.Edit([]string{"llama3.2"}); err != nil {
-			t.Fatal(err)
-		}
-		assertOpenclawModelExists(t, configPath, "llama3.2")
-	})
-}
-
-func TestOpenclawModels(t *testing.T) {
-	c := &Openclaw{}
-	tmpDir := t.TempDir()
-	setTestHome(t, tmpDir)
-
-	t.Run("no config returns nil", func(t *testing.T) {
-		if models := c.Models(); len(models) > 0 {
-			t.Errorf("expected nil/empty, got %v", models)
-		}
-	})
-
-	t.Run("returns all ollama models", func(t *testing.T) {
-		configDir := filepath.Join(tmpDir, ".openclaw")
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{
-			"models":{"providers":{"ollama":{"models":[
-				{"id":"llama3.2"},
-				{"id":"mistral"}
-			]}}}
-		}`), 0o644)
-
-		models := c.Models()
-		if len(models) != 2 {
-			t.Errorf("expected 2 models, got %v", models)
-		}
-	})
-}
-
-// Helper functions
-func assertOpenclawModelExists(t *testing.T, path, model string) {
-	t.Helper()
-	data, _ := os.ReadFile(path)
-	var cfg map[string]any
-	json.Unmarshal(data, &cfg)
-	models := cfg["models"].(map[string]any)
-	providers := models["providers"].(map[string]any)
-	ollama := providers["ollama"].(map[string]any)
-	modelList := ollama["models"].([]any)
-	for _, m := range modelList {
-		if entry, ok := m.(map[string]any); ok {
-			if entry["id"] == model {
-				return
-			}
-		}
-	}
-	t.Errorf("model %s not found", model)
-}
-
-func assertOpenclawModelNotExists(t *testing.T, path, model string) {
-	t.Helper()
-	data, _ := os.ReadFile(path)
-	var cfg map[string]any
-	json.Unmarshal(data, &cfg)
-	models, _ := cfg["models"].(map[string]any)
-	providers, _ := models["providers"].(map[string]any)
-	ollama, _ := providers["ollama"].(map[string]any)
-	modelList, _ := ollama["models"].([]any)
-	for _, m := range modelList {
-		if entry, ok := m.(map[string]any); ok {
-			if entry["id"] == model {
-				t.Errorf("model %s should not exist", model)
-			}
-		}
-	}
-}
-
-func assertOpenclawPrimaryModel(t *testing.T, path, expected string) {
-	t.Helper()
-	data, _ := os.ReadFile(path)
-	var cfg map[string]any
-	json.Unmarshal(data, &cfg)
-	agents := cfg["agents"].(map[string]any)
-	defaults := agents["defaults"].(map[string]any)
-	model := defaults["model"].(map[string]any)
-	if model["primary"] != expected {
-		t.Errorf("primary model = %v, want %v", model["primary"], expected)
-	}
-}
-
-func TestOpenclawPaths(t *testing.T) {
-	c := &Openclaw{}
-
-	t.Run("returns path when config exists", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		configDir := filepath.Join(tmpDir, ".openclaw")
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{}`), 0o644)
-
-		paths := c.Paths()
-		if len(paths) != 1 {
-			t.Errorf("expected 1 path, got %d", len(paths))
-		}
-	})
-
-	t.Run("returns nil when config missing", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		if paths := c.Paths(); paths != nil {
-			t.Errorf("expected nil, got %v", paths)
-		}
-	})
-}
-
-func TestOpenclawModelsEdgeCases(t *testing.T) {
-	c := &Openclaw{}
-	tmpDir := t.TempDir()
-	setTestHome(t, tmpDir)
-	configDir := filepath.Join(tmpDir, ".openclaw")
-	configPath := filepath.Join(configDir, "openclaw.json")
-	cleanup := func() { os.RemoveAll(configDir) }
-
-	t.Run("corrupted JSON returns nil", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(configPath, []byte(`{corrupted`), 0o644)
-		if models := c.Models(); models != nil {
-			t.Errorf("expected nil, got %v", models)
-		}
-	})
-
-	t.Run("wrong type at models level", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(configPath, []byte(`{"models":"string"}`), 0o644)
-		if models := c.Models(); models != nil {
-			t.Errorf("expected nil, got %v", models)
-		}
-	})
-
-	t.Run("wrong type at providers level", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(configPath, []byte(`{"models":{"providers":"string"}}`), 0o644)
-		if models := c.Models(); models != nil {
-			t.Errorf("expected nil, got %v", models)
-		}
-	})
-
-	t.Run("wrong type at ollama level", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(configPath, []byte(`{"models":{"providers":{"ollama":"string"}}}`), 0o644)
-		if models := c.Models(); models != nil {
-			t.Errorf("expected nil, got %v", models)
-		}
-	})
-
-	t.Run("model entry missing id", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(configPath, []byte(`{"models":{"providers":{"ollama":{"models":[{"name":"test"}]}}}}`), 0o644)
-		if len(c.Models()) != 0 {
-			t.Error("expected empty for missing id")
-		}
-	})
-
-	t.Run("model id is not string", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(configPath, []byte(`{"models":{"providers":{"ollama":{"models":[{"id":123}]}}}}`), 0o644)
-		if len(c.Models()) != 0 {
-			t.Error("expected empty for non-string id")
-		}
-	})
-}
-
-func TestOpenclawEditSchemaFields(t *testing.T) {
-	c := &Openclaw{}
-	tmpDir := t.TempDir()
-	setTestHome(t, tmpDir)
-	configPath := filepath.Join(tmpDir, ".openclaw", "openclaw.json")
-
-	if err := c.Edit([]string{"llama3.2"}); err != nil {
-		t.Fatal(err)
-	}
-
-	data, _ := os.ReadFile(configPath)
-	var cfg map[string]any
-	json.Unmarshal(data, &cfg)
-	models := cfg["models"].(map[string]any)
-	providers := models["providers"].(map[string]any)
-	ollama := providers["ollama"].(map[string]any)
-	modelList := ollama["models"].([]any)
-	entry := modelList[0].(map[string]any)
-
-	// Verify required schema fields
-	if entry["reasoning"] != false {
-		t.Error("reasoning should be false")
-	}
-	if entry["input"] == nil {
-		t.Error("input should be set")
-	}
-	if entry["contextWindow"] == nil {
-		t.Error("contextWindow should be set")
-	}
-	if entry["maxTokens"] == nil {
-		t.Error("maxTokens should be set")
-	}
-	cost := entry["cost"].(map[string]any)
-	if cost["cacheRead"] == nil {
-		t.Error("cost.cacheRead should be set")
-	}
-	if cost["cacheWrite"] == nil {
-		t.Error("cost.cacheWrite should be set")
-	}
-}
-
-func TestOpenclawEditModelNames(t *testing.T) {
-	c := &Openclaw{}
-	tmpDir := t.TempDir()
-	setTestHome(t, tmpDir)
-	configPath := filepath.Join(tmpDir, ".openclaw", "openclaw.json")
-	cleanup := func() { os.RemoveAll(filepath.Join(tmpDir, ".openclaw")) }
-
-	t.Run("model with colon tag", func(t *testing.T) {
-		cleanup()
-		if err := c.Edit([]string{"llama3.2:70b"}); err != nil {
-			t.Fatal(err)
-		}
-		assertOpenclawModelExists(t, configPath, "llama3.2:70b")
-		assertOpenclawPrimaryModel(t, configPath, "ollama/llama3.2:70b")
-	})
-
-	t.Run("model with slash", func(t *testing.T) {
-		cleanup()
-		if err := c.Edit([]string{"library/model:tag"}); err != nil {
-			t.Fatal(err)
-		}
-		assertOpenclawModelExists(t, configPath, "library/model:tag")
-		assertOpenclawPrimaryModel(t, configPath, "ollama/library/model:tag")
-	})
-
-	t.Run("model with hyphen", func(t *testing.T) {
-		cleanup()
-		if err := c.Edit([]string{"test-model"}); err != nil {
-			t.Fatal(err)
-		}
-		assertOpenclawModelExists(t, configPath, "test-model")
-	})
-}
-
-func TestOpenclawEditAgentsPreservation(t *testing.T) {
-	c := &Openclaw{}
-	tmpDir := t.TempDir()
-	setTestHome(t, tmpDir)
-	configDir := filepath.Join(tmpDir, ".openclaw")
-	configPath := filepath.Join(configDir, "openclaw.json")
-	cleanup := func() { os.RemoveAll(configDir) }
-
-	t.Run("preserve other agent defaults", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(configPath, []byte(`{"agents":{"defaults":{"model":{"primary":"old"},"temperature":0.7}}}`), 0o644)
-
-		c.Edit([]string{"llama3.2"})
-
-		data, _ := os.ReadFile(configPath)
-		var cfg map[string]any
-		json.Unmarshal(data, &cfg)
-		agents := cfg["agents"].(map[string]any)
-		defaults := agents["defaults"].(map[string]any)
-		if defaults["temperature"] != 0.7 {
-			t.Error("temperature setting was lost")
-		}
-	})
-
-	t.Run("preserve other agents besides defaults", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(configPath, []byte(`{"agents":{"defaults":{},"custom-agent":{"foo":"bar"}}}`), 0o644)
-
-		c.Edit([]string{"llama3.2"})
-
-		data, _ := os.ReadFile(configPath)
-		var cfg map[string]any
-		json.Unmarshal(data, &cfg)
-		agents := cfg["agents"].(map[string]any)
-		if agents["custom-agent"] == nil {
-			t.Error("custom-agent was lost")
-		}
-	})
-}
-
-const testOpenclawFixture = `{
-  "theme": "dark",
-  "mcp": {"servers": {"custom": {"enabled": true}}},
-  "models": {
-    "providers": {
-      "anthropic": {"apiKey": "xxx"},
-      "ollama": {
-        "baseUrl": "http://127.0.0.1:11434/v1",
-        "models": [{"id": "old-model", "customField": "preserved"}]
-      }
-    }
-  },
-  "agents": {
-    "defaults": {"model": {"primary": "old"}, "temperature": 0.7},
-    "custom-agent": {"foo": "bar"}
-  }
-}`
-
-func TestOpenclawEdit_RoundTrip(t *testing.T) {
-	c := &Openclaw{}
-	tmpDir := t.TempDir()
-	setTestHome(t, tmpDir)
-	configDir := filepath.Join(tmpDir, ".openclaw")
-	configPath := filepath.Join(configDir, "openclaw.json")
-
-	os.MkdirAll(configDir, 0o755)
-	os.WriteFile(configPath, []byte(testOpenclawFixture), 0o644)
-
-	if err := c.Edit([]string{"llama3.2", "mistral"}); err != nil {
-		t.Fatal(err)
-	}
-
-	data, _ := os.ReadFile(configPath)
-	var cfg map[string]any
-	json.Unmarshal(data, &cfg)
-
-	// Verify top-level preserved
-	if cfg["theme"] != "dark" {
-		t.Error("theme not preserved")
-	}
-	mcp := cfg["mcp"].(map[string]any)
-	servers := mcp["servers"].(map[string]any)
-	if servers["custom"] == nil {
-		t.Error("mcp.servers.custom not preserved")
-	}
-
-	// Verify other providers preserved
-	models := cfg["models"].(map[string]any)
-	providers := models["providers"].(map[string]any)
-	if providers["anthropic"] == nil {
-		t.Error("anthropic provider not preserved")
-	}
-
-	// Verify agents preserved
-	agents := cfg["agents"].(map[string]any)
-	if agents["custom-agent"] == nil {
-		t.Error("custom-agent not preserved")
-	}
-	defaults := agents["defaults"].(map[string]any)
-	if defaults["temperature"] != 0.7 {
-		t.Error("temperature not preserved")
-	}
-}
-
-func TestOpenclawEdit_Idempotent(t *testing.T) {
-	c := &Openclaw{}
-	tmpDir := t.TempDir()
-	setTestHome(t, tmpDir)
-	configDir := filepath.Join(tmpDir, ".openclaw")
-	configPath := filepath.Join(configDir, "openclaw.json")
-
-	os.MkdirAll(configDir, 0o755)
-	os.WriteFile(configPath, []byte(testOpenclawFixture), 0o644)
-
-	c.Edit([]string{"llama3.2", "mistral"})
-	firstData, _ := os.ReadFile(configPath)
-
-	c.Edit([]string{"llama3.2", "mistral"})
-	secondData, _ := os.ReadFile(configPath)
-
-	if string(firstData) != string(secondData) {
-		t.Error("repeated edits with same models produced different results")
-	}
-}
-
-func TestOpenclawEdit_MultipleConsecutiveEdits(t *testing.T) {
-	c := &Openclaw{}
-	tmpDir := t.TempDir()
-	setTestHome(t, tmpDir)
-	configDir := filepath.Join(tmpDir, ".openclaw")
-	configPath := filepath.Join(configDir, "openclaw.json")
-
-	os.MkdirAll(configDir, 0o755)
-	os.WriteFile(configPath, []byte(testOpenclawFixture), 0o644)
-
-	for i := range 10 {
-		models := []string{"model-a", "model-b"}
-		if i%2 == 0 {
-			models = []string{"model-x", "model-y", "model-z"}
-		}
-		if err := c.Edit(models); err != nil {
-			t.Fatalf("edit %d failed: %v", i, err)
-		}
-	}
-
-	data, _ := os.ReadFile(configPath)
-	var cfg map[string]any
-	if err := json.Unmarshal(data, &cfg); err != nil {
-		t.Fatalf("file is not valid JSON after multiple edits: %v", err)
-	}
-
-	if cfg["theme"] != "dark" {
-		t.Error("theme lost after multiple edits")
-	}
-}
-
-func TestOpenclawEdit_BackupCreated(t *testing.T) {
-	c := &Openclaw{}
-	tmpDir := t.TempDir()
-	setTestHome(t, tmpDir)
-	configDir := filepath.Join(tmpDir, ".openclaw")
-	configPath := filepath.Join(configDir, "openclaw.json")
-	backupDir := filepath.Join(os.TempDir(), "ollama-backups")
-
-	os.MkdirAll(configDir, 0o755)
-	uniqueMarker := fmt.Sprintf("test-marker-%d", os.Getpid())
-	original := fmt.Sprintf(`{"theme": "%s"}`, uniqueMarker)
-	os.WriteFile(configPath, []byte(original), 0o644)
-
-	if err := c.Edit([]string{"model-a"}); err != nil {
-		t.Fatal(err)
-	}
-
-	backups, _ := filepath.Glob(filepath.Join(backupDir, "openclaw.json.*"))
-	foundBackup := false
-	for _, backup := range backups {
-		data, _ := os.ReadFile(backup)
-		if string(data) == original {
-			foundBackup = true
-			break
-		}
-	}
-
-	if !foundBackup {
-		t.Error("backup with original content not found")
-	}
-}
-
-func TestOpenclawClawdbotAlias(t *testing.T) {
-	for _, alias := range []string{"clawdbot", "moltbot"} {
-		t.Run(alias+" alias resolves to Openclaw runner", func(t *testing.T) {
-			r, ok := integrations[alias]
-			if !ok {
-				t.Fatalf("%s not found in integrations", alias)
-			}
-			if _, ok := r.(*Openclaw); !ok {
-				t.Errorf("%s integration is %T, want *Openclaw", alias, r)
-			}
-		})
-
-		t.Run(alias+" is hidden from selector", func(t *testing.T) {
-			if !integrationAliases[alias] {
-				t.Errorf("%s should be in integrationAliases", alias)
-			}
-		})
-	}
-}
-
-func TestOpenclawLegacyPaths(t *testing.T) {
-	c := &Openclaw{}
-
-	t.Run("falls back to legacy clawdbot path", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		legacyDir := filepath.Join(tmpDir, ".clawdbot")
-		os.MkdirAll(legacyDir, 0o755)
-		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{}`), 0o644)
-
-		paths := c.Paths()
-		if len(paths) != 1 {
-			t.Fatalf("expected 1 path, got %d", len(paths))
-		}
-		if paths[0] != filepath.Join(legacyDir, "clawdbot.json") {
-			t.Errorf("expected legacy path, got %s", paths[0])
-		}
-	})
-
-	t.Run("prefers new path over legacy", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		newDir := filepath.Join(tmpDir, ".openclaw")
-		legacyDir := filepath.Join(tmpDir, ".clawdbot")
-		os.MkdirAll(newDir, 0o755)
-		os.MkdirAll(legacyDir, 0o755)
-		os.WriteFile(filepath.Join(newDir, "openclaw.json"), []byte(`{}`), 0o644)
-		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{}`), 0o644)
-
-		paths := c.Paths()
-		if len(paths) != 1 {
-			t.Fatalf("expected 1 path, got %d", len(paths))
-		}
-		if paths[0] != filepath.Join(newDir, "openclaw.json") {
-			t.Errorf("expected new path, got %s", paths[0])
-		}
-	})
-
-	t.Run("Models reads from legacy path", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		legacyDir := filepath.Join(tmpDir, ".clawdbot")
-		os.MkdirAll(legacyDir, 0o755)
-		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{
-			"models":{"providers":{"ollama":{"models":[{"id":"llama3.2"}]}}}
-		}`), 0o644)
-
-		models := c.Models()
-		if len(models) != 1 || models[0] != "llama3.2" {
-			t.Errorf("expected [llama3.2], got %v", models)
-		}
-	})
-
-	t.Run("Models prefers new path over legacy", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		newDir := filepath.Join(tmpDir, ".openclaw")
-		legacyDir := filepath.Join(tmpDir, ".clawdbot")
-		os.MkdirAll(newDir, 0o755)
-		os.MkdirAll(legacyDir, 0o755)
-		os.WriteFile(filepath.Join(newDir, "openclaw.json"), []byte(`{
-			"models":{"providers":{"ollama":{"models":[{"id":"new-model"}]}}}
-		}`), 0o644)
-		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{
-			"models":{"providers":{"ollama":{"models":[{"id":"legacy-model"}]}}}
-		}`), 0o644)
-
-		models := c.Models()
-		if len(models) != 1 || models[0] != "new-model" {
-			t.Errorf("expected [new-model], got %v", models)
-		}
-	})
-
-	t.Run("Edit reads new path over legacy when both exist", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		newDir := filepath.Join(tmpDir, ".openclaw")
-		legacyDir := filepath.Join(tmpDir, ".clawdbot")
-		os.MkdirAll(newDir, 0o755)
-		os.MkdirAll(legacyDir, 0o755)
-		os.WriteFile(filepath.Join(newDir, "openclaw.json"), []byte(`{"theme":"new"}`), 0o644)
-		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{"theme":"legacy"}`), 0o644)
-
-		if err := c.Edit([]string{"llama3.2"}); err != nil {
-			t.Fatal(err)
-		}
-
-		data, _ := os.ReadFile(filepath.Join(newDir, "openclaw.json"))
-		var cfg map[string]any
-		json.Unmarshal(data, &cfg)
-		if cfg["theme"] != "new" {
-			t.Errorf("expected theme from new config, got %v", cfg["theme"])
-		}
-	})
-
-	t.Run("Edit migrates from legacy config", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		legacyDir := filepath.Join(tmpDir, ".clawdbot")
-		os.MkdirAll(legacyDir, 0o755)
-		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{"theme":"dark"}`), 0o644)
-
-		if err := c.Edit([]string{"llama3.2"}); err != nil {
-			t.Fatal(err)
-		}
-
-		// Should write to new path
-		newPath := filepath.Join(tmpDir, ".openclaw", "openclaw.json")
-		data, err := os.ReadFile(newPath)
-		if err != nil {
-			t.Fatal("expected new config file to be created")
-		}
-		var cfg map[string]any
-		json.Unmarshal(data, &cfg)
-		if cfg["theme"] != "dark" {
-			t.Error("legacy theme setting was not migrated")
-		}
-	})
-}
-
-func TestOpenclawEdit_CreatesDirectoryIfMissing(t *testing.T) {
-	c := &Openclaw{}
-	tmpDir := t.TempDir()
-	setTestHome(t, tmpDir)
-	configDir := filepath.Join(tmpDir, ".openclaw")
-
-	if _, err := os.Stat(configDir); !os.IsNotExist(err) {
-		t.Fatal("directory should not exist before test")
-	}
-
-	if err := c.Edit([]string{"model-a"}); err != nil {
-		t.Fatal(err)
-	}
-
-	if _, err := os.Stat(configDir); os.IsNotExist(err) {
-		t.Fatal("directory was not created")
-	}
-}
-
-func TestOpenclawOnboarded(t *testing.T) {
-	c := &Openclaw{}
-
-	t.Run("returns false when no config exists", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		if c.onboarded() {
-			t.Error("expected false when no config exists")
-		}
-	})
-
-	t.Run("returns false when config exists but no wizard section", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		configDir := filepath.Join(tmpDir, ".openclaw")
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{"theme":"dark"}`), 0o644)
-
-		if c.onboarded() {
-			t.Error("expected false when no wizard section")
-		}
-	})
-
-	t.Run("returns false when wizard section exists but no lastRunAt", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		configDir := filepath.Join(tmpDir, ".openclaw")
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{"wizard":{}}`), 0o644)
-
-		if c.onboarded() {
-			t.Error("expected false when wizard.lastRunAt is missing")
-		}
-	})
-
-	t.Run("returns false when wizard.lastRunAt is empty string", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		configDir := filepath.Join(tmpDir, ".openclaw")
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{"wizard":{"lastRunAt":""}}`), 0o644)
-
-		if c.onboarded() {
-			t.Error("expected false when wizard.lastRunAt is empty")
-		}
-	})
-
-	t.Run("returns true when wizard.lastRunAt is set", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		configDir := filepath.Join(tmpDir, ".openclaw")
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{"wizard":{"lastRunAt":"2024-01-01T00:00:00Z"}}`), 0o644)
-
-		if !c.onboarded() {
-			t.Error("expected true when wizard.lastRunAt is set")
-		}
-	})
-
-	t.Run("checks legacy clawdbot path", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		legacyDir := filepath.Join(tmpDir, ".clawdbot")
-		os.MkdirAll(legacyDir, 0o755)
-		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{"wizard":{"lastRunAt":"2024-01-01T00:00:00Z"}}`), 0o644)
-
-		if !c.onboarded() {
-			t.Error("expected true when legacy config has wizard.lastRunAt")
-		}
-	})
-
-	t.Run("prefers new path over legacy", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		newDir := filepath.Join(tmpDir, ".openclaw")
-		legacyDir := filepath.Join(tmpDir, ".clawdbot")
-		os.MkdirAll(newDir, 0o755)
-		os.MkdirAll(legacyDir, 0o755)
-		// New path has no wizard marker
-		os.WriteFile(filepath.Join(newDir, "openclaw.json"), []byte(`{}`), 0o644)
-		// Legacy has wizard marker
-		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{"wizard":{"lastRunAt":"2024-01-01T00:00:00Z"}}`), 0o644)
-
-		if c.onboarded() {
-			t.Error("expected false - should prefer new path which has no wizard marker")
-		}
-	})
-
-	t.Run("handles corrupted JSON gracefully", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		configDir := filepath.Join(tmpDir, ".openclaw")
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{corrupted`), 0o644)
-
-		if c.onboarded() {
-			t.Error("expected false for corrupted JSON")
-		}
-	})
-
-	t.Run("handles wrong type for wizard section", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-		configDir := filepath.Join(tmpDir, ".openclaw")
-		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{"wizard":"not a map"}`), 0o644)
-
-		if c.onboarded() {
-			t.Error("expected false when wizard is wrong type")
-		}
-	})
-}

cmd/config/opencode.go

@@ -9,8 +9,6 @@ import (
 	"path/filepath"
 	"slices"
 	"strings"
-
-	"github.com/ollama/ollama/envconfig"
 )
 
 // OpenCode implements Runner and Editor for OpenCode integration
@@ -18,7 +16,7 @@ type OpenCode struct{}
 
 func (o *OpenCode) String() string { return "OpenCode" }
 
-func (o *OpenCode) Run(model string, args []string) error {
+func (o *OpenCode) Run(model string) error {
 	if _, err := exec.LookPath("opencode"); err != nil {
 		return fmt.Errorf("opencode is not installed, install from https://opencode.ai")
 	}
@@ -32,7 +30,7 @@ func (o *OpenCode) Run(model string, args []string) error {
 		return fmt.Errorf("setup failed: %w", err)
 	}
 
-	cmd := exec.Command("opencode", args...)
+	cmd := exec.Command("opencode")
 	cmd.Stdin = os.Stdin
 	cmd.Stdout = os.Stdout
 	cmd.Stderr = os.Stderr
@@ -90,7 +88,7 @@ func (o *OpenCode) Edit(modelList []string) error {
 			"npm":  "@ai-sdk/openai-compatible",
 			"name": "Ollama (local)",
 			"options": map[string]any{
-				"baseURL": envconfig.Host().String() + "/v1",
+				"baseURL": "http://localhost:11434/v1",
 			},
 		}
 	}

cmd/config/pi.go

@@ -1,196 +0,0 @@
-package config
-
-import (
-	"encoding/json"
-	"fmt"
-	"os"
-	"os/exec"
-	"path/filepath"
-	"slices"
-
-	"github.com/ollama/ollama/envconfig"
-)
-
-// Pi implements Runner and Editor for Pi (Pi Coding Agent) integration
-type Pi struct{}
-
-func (p *Pi) String() string { return "Pi" }
-
-func (p *Pi) Run(model string, args []string) error {
-	if _, err := exec.LookPath("pi"); err != nil {
-		return fmt.Errorf("pi is not installed, install with: npm install -g @mariozechner/pi-coding-agent")
-	}
-
-	// Call Edit() to ensure config is up-to-date before launch
-	models := []string{model}
-	if config, err := loadIntegration("pi"); err == nil && len(config.Models) > 0 {
-		models = config.Models
-	}
-	if err := p.Edit(models); err != nil {
-		return fmt.Errorf("setup failed: %w", err)
-	}
-
-	cmd := exec.Command("pi", args...)
-	cmd.Stdin = os.Stdin
-	cmd.Stdout = os.Stdout
-	cmd.Stderr = os.Stderr
-	return cmd.Run()
-}
-
-func (p *Pi) Paths() []string {
-	home, err := os.UserHomeDir()
-	if err != nil {
-		return nil
-	}
-
-	var paths []string
-	modelsPath := filepath.Join(home, ".pi", "agent", "models.json")
-	if _, err := os.Stat(modelsPath); err == nil {
-		paths = append(paths, modelsPath)
-	}
-	settingsPath := filepath.Join(home, ".pi", "agent", "settings.json")
-	if _, err := os.Stat(settingsPath); err == nil {
-		paths = append(paths, settingsPath)
-	}
-	return paths
-}
-
-func (p *Pi) Edit(models []string) error {
-	if len(models) == 0 {
-		return nil
-	}
-
-	home, err := os.UserHomeDir()
-	if err != nil {
-		return err
-	}
-
-	configPath := filepath.Join(home, ".pi", "agent", "models.json")
-	if err := os.MkdirAll(filepath.Dir(configPath), 0o755); err != nil {
-		return err
-	}
-
-	config := make(map[string]any)
-	if data, err := os.ReadFile(configPath); err == nil {
-		_ = json.Unmarshal(data, &config)
-	}
-
-	providers, ok := config["providers"].(map[string]any)
-	if !ok {
-		providers = make(map[string]any)
-	}
-
-	ollama, ok := providers["ollama"].(map[string]any)
-	if !ok {
-		ollama = map[string]any{
-			"baseUrl": envconfig.Host().String() + "/v1",
-			"api":     "openai-completions",
-			"apiKey":  "ollama",
-		}
-	}
-
-	existingModels, ok := ollama["models"].([]any)
-	if !ok {
-		existingModels = make([]any, 0)
-	}
-
-	// Build set of selected models to track which need to be added
-	selectedSet := make(map[string]bool, len(models))
-	for _, m := range models {
-		selectedSet[m] = true
-	}
-
-	// Build new models list:
-	// 1. Keep user-managed models (no _launch marker) - untouched
-	// 2. Keep ollama-managed models (_launch marker) that are still selected
-	// 3. Add new ollama-managed models
-	var newModels []any
-	for _, m := range existingModels {
-		if modelObj, ok := m.(map[string]any); ok {
-			if id, ok := modelObj["id"].(string); ok {
-				// User-managed model (no _launch marker) - always preserve
-				if !isPiOllamaModel(modelObj) {
-					newModels = append(newModels, m)
-				} else if selectedSet[id] {
-					// Ollama-managed and still selected - keep it
-					newModels = append(newModels, m)
-					selectedSet[id] = false
-				}
-			}
-		}
-	}
-
-	// Add newly selected models that weren't already in the list
-	for _, model := range models {
-		if selectedSet[model] {
-			newModels = append(newModels, map[string]any{
-				"id":      model,
-				"_launch": true,
-			})
-		}
-	}
-
-	ollama["models"] = newModels
-	providers["ollama"] = ollama
-	config["providers"] = providers
-
-	configData, err := json.MarshalIndent(config, "", "  ")
-	if err != nil {
-		return err
-	}
-	if err := writeWithBackup(configPath, configData); err != nil {
-		return err
-	}
-
-	// Update settings.json with default provider and model
-	settingsPath := filepath.Join(home, ".pi", "agent", "settings.json")
-	settings := make(map[string]any)
-	if data, err := os.ReadFile(settingsPath); err == nil {
-		_ = json.Unmarshal(data, &settings)
-	}
-
-	settings["defaultProvider"] = "ollama"
-	settings["defaultModel"] = models[0]
-
-	settingsData, err := json.MarshalIndent(settings, "", "  ")
-	if err != nil {
-		return err
-	}
-	return writeWithBackup(settingsPath, settingsData)
-}
-
-func (p *Pi) Models() []string {
-	home, err := os.UserHomeDir()
-	if err != nil {
-		return nil
-	}
-
-	configPath := filepath.Join(home, ".pi", "agent", "models.json")
-	config, err := readJSONFile(configPath)
-	if err != nil {
-		return nil
-	}
-
-	providers, _ := config["providers"].(map[string]any)
-	ollama, _ := providers["ollama"].(map[string]any)
-	models, _ := ollama["models"].([]any)
-
-	var result []string
-	for _, m := range models {
-		if modelObj, ok := m.(map[string]any); ok {
-			if id, ok := modelObj["id"].(string); ok {
-				result = append(result, id)
-			}
-		}
-	}
-	slices.Sort(result)
-	return result
-}
-
-// isPiOllamaModel reports whether a model config entry is managed by ollama launch
-func isPiOllamaModel(cfg map[string]any) bool {
-	if v, ok := cfg["_launch"].(bool); ok && v {
-		return true
-	}
-	return false
-}

cmd/config/pi_test.go

@@ -1,609 +0,0 @@
-package config
-
-import (
-	"encoding/json"
-	"os"
-	"path/filepath"
-	"testing"
-)
-
-func TestPiIntegration(t *testing.T) {
-	pi := &Pi{}
-
-	t.Run("String", func(t *testing.T) {
-		if got := pi.String(); got != "Pi" {
-			t.Errorf("String() = %q, want %q", got, "Pi")
-		}
-	})
-
-	t.Run("implements Runner", func(t *testing.T) {
-		var _ Runner = pi
-	})
-
-	t.Run("implements Editor", func(t *testing.T) {
-		var _ Editor = pi
-	})
-}
-
-func TestPiPaths(t *testing.T) {
-	pi := &Pi{}
-
-	t.Run("returns empty when no config exists", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-
-		paths := pi.Paths()
-		if len(paths) != 0 {
-			t.Errorf("Paths() = %v, want empty", paths)
-		}
-	})
-
-	t.Run("returns path when config exists", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-
-		configDir := filepath.Join(tmpDir, ".pi", "agent")
-		if err := os.MkdirAll(configDir, 0o755); err != nil {
-			t.Fatal(err)
-		}
-		configPath := filepath.Join(configDir, "models.json")
-		if err := os.WriteFile(configPath, []byte("{}"), 0o644); err != nil {
-			t.Fatal(err)
-		}
-
-		paths := pi.Paths()
-		if len(paths) != 1 || paths[0] != configPath {
-			t.Errorf("Paths() = %v, want [%s]", paths, configPath)
-		}
-	})
-}
-
-func TestPiEdit(t *testing.T) {
-	pi := &Pi{}
-	tmpDir := t.TempDir()
-	setTestHome(t, tmpDir)
-
-	configDir := filepath.Join(tmpDir, ".pi", "agent")
-	configPath := filepath.Join(configDir, "models.json")
-
-	cleanup := func() {
-		os.RemoveAll(configDir)
-	}
-
-	readConfig := func() map[string]any {
-		data, _ := os.ReadFile(configPath)
-		var cfg map[string]any
-		json.Unmarshal(data, &cfg)
-		return cfg
-	}
-
-	t.Run("returns nil for empty models", func(t *testing.T) {
-		if err := pi.Edit([]string{}); err != nil {
-			t.Errorf("Edit([]) error = %v, want nil", err)
-		}
-	})
-
-	t.Run("creates config with models", func(t *testing.T) {
-		cleanup()
-
-		models := []string{"llama3.2", "qwen3:8b"}
-		if err := pi.Edit(models); err != nil {
-			t.Fatalf("Edit() error = %v", err)
-		}
-
-		cfg := readConfig()
-
-		providers, ok := cfg["providers"].(map[string]any)
-		if !ok {
-			t.Error("Config missing providers")
-		}
-
-		ollama, ok := providers["ollama"].(map[string]any)
-		if !ok {
-			t.Error("Providers missing ollama")
-		}
-
-		modelsArray, ok := ollama["models"].([]any)
-		if !ok || len(modelsArray) != 2 {
-			t.Errorf("Expected 2 models, got %v", modelsArray)
-		}
-
-		if ollama["baseUrl"] == nil {
-			t.Error("Missing baseUrl")
-		}
-		if ollama["api"] != "openai-completions" {
-			t.Errorf("Expected api=openai-completions, got %v", ollama["api"])
-		}
-		if ollama["apiKey"] != "ollama" {
-			t.Errorf("Expected apiKey=ollama, got %v", ollama["apiKey"])
-		}
-	})
-
-	t.Run("updates existing config preserving ollama provider settings", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-
-		existingConfig := `{
-			"providers": {
-				"ollama": {
-					"baseUrl": "http://custom:8080/v1",
-					"api": "custom-api",
-					"apiKey": "custom-key",
-					"models": [
-						{"id": "old-model", "_launch": true}
-					]
-				}
-			}
-		}`
-		if err := os.WriteFile(configPath, []byte(existingConfig), 0o644); err != nil {
-			t.Fatal(err)
-		}
-
-		models := []string{"new-model"}
-		if err := pi.Edit(models); err != nil {
-			t.Fatalf("Edit() error = %v", err)
-		}
-
-		cfg := readConfig()
-		providers := cfg["providers"].(map[string]any)
-		ollama := providers["ollama"].(map[string]any)
-
-		if ollama["baseUrl"] != "http://custom:8080/v1" {
-			t.Errorf("Custom baseUrl not preserved, got %v", ollama["baseUrl"])
-		}
-		if ollama["api"] != "custom-api" {
-			t.Errorf("Custom api not preserved, got %v", ollama["api"])
-		}
-		if ollama["apiKey"] != "custom-key" {
-			t.Errorf("Custom apiKey not preserved, got %v", ollama["apiKey"])
-		}
-
-		modelsArray := ollama["models"].([]any)
-		if len(modelsArray) != 1 {
-			t.Errorf("Expected 1 model after update, got %d", len(modelsArray))
-		} else {
-			modelEntry := modelsArray[0].(map[string]any)
-			if modelEntry["id"] != "new-model" {
-				t.Errorf("Expected new-model, got %v", modelEntry["id"])
-			}
-			// Verify _launch marker is present
-			if modelEntry["_launch"] != true {
-				t.Errorf("Expected _launch marker to be true")
-			}
-		}
-	})
-
-	t.Run("replaces old models with new ones", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-
-		// Old models must have _launch marker to be managed by us
-		existingConfig := `{
-			"providers": {
-				"ollama": {
-					"baseUrl": "http://localhost:11434/v1",
-					"api": "openai-completions",
-					"apiKey": "ollama",
-					"models": [
-						{"id": "old-model-1", "_launch": true},
-						{"id": "old-model-2", "_launch": true}
-					]
-				}
-			}
-		}`
-		if err := os.WriteFile(configPath, []byte(existingConfig), 0o644); err != nil {
-			t.Fatal(err)
-		}
-
-		newModels := []string{"new-model-1", "new-model-2"}
-		if err := pi.Edit(newModels); err != nil {
-			t.Fatalf("Edit() error = %v", err)
-		}
-
-		cfg := readConfig()
-		providers := cfg["providers"].(map[string]any)
-		ollama := providers["ollama"].(map[string]any)
-		modelsArray := ollama["models"].([]any)
-
-		if len(modelsArray) != 2 {
-			t.Errorf("Expected 2 models, got %d", len(modelsArray))
-		}
-
-		modelIDs := make(map[string]bool)
-		for _, m := range modelsArray {
-			modelObj := m.(map[string]any)
-			id := modelObj["id"].(string)
-			modelIDs[id] = true
-		}
-
-		if !modelIDs["new-model-1"] || !modelIDs["new-model-2"] {
-			t.Errorf("Expected new models, got %v", modelIDs)
-		}
-		if modelIDs["old-model-1"] || modelIDs["old-model-2"] {
-			t.Errorf("Old models should have been removed, got %v", modelIDs)
-		}
-	})
-
-	t.Run("handles partial overlap in model list", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-
-		// Models must have _launch marker to be managed
-		existingConfig := `{
-			"providers": {
-				"ollama": {
-					"baseUrl": "http://localhost:11434/v1",
-					"api": "openai-completions",
-					"apiKey": "ollama",
-					"models": [
-						{"id": "keep-model", "_launch": true},
-						{"id": "remove-model", "_launch": true}
-					]
-				}
-			}
-		}`
-		if err := os.WriteFile(configPath, []byte(existingConfig), 0o644); err != nil {
-			t.Fatal(err)
-		}
-
-		newModels := []string{"keep-model", "add-model"}
-		if err := pi.Edit(newModels); err != nil {
-			t.Fatalf("Edit() error = %v", err)
-		}
-
-		cfg := readConfig()
-		providers := cfg["providers"].(map[string]any)
-		ollama := providers["ollama"].(map[string]any)
-		modelsArray := ollama["models"].([]any)
-
-		if len(modelsArray) != 2 {
-			t.Errorf("Expected 2 models, got %d", len(modelsArray))
-		}
-
-		modelIDs := make(map[string]bool)
-		for _, m := range modelsArray {
-			modelObj := m.(map[string]any)
-			id := modelObj["id"].(string)
-			modelIDs[id] = true
-		}
-
-		if !modelIDs["keep-model"] || !modelIDs["add-model"] {
-			t.Errorf("Expected keep-model and add-model, got %v", modelIDs)
-		}
-		if modelIDs["remove-model"] {
-			t.Errorf("remove-model should have been removed")
-		}
-	})
-
-	t.Run("handles corrupt config gracefully", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-
-		if err := os.WriteFile(configPath, []byte("{invalid json}"), 0o644); err != nil {
-			t.Fatal(err)
-		}
-
-		models := []string{"test-model"}
-		if err := pi.Edit(models); err != nil {
-			t.Fatalf("Edit() should not fail with corrupt config, got %v", err)
-		}
-
-		data, err := os.ReadFile(configPath)
-		if err != nil {
-			t.Fatalf("Failed to read config: %v", err)
-		}
-
-		var cfg map[string]any
-		if err := json.Unmarshal(data, &cfg); err != nil {
-			t.Fatalf("Config should be valid after Edit, got parse error: %v", err)
-		}
-
-		providers := cfg["providers"].(map[string]any)
-		ollama := providers["ollama"].(map[string]any)
-		modelsArray := ollama["models"].([]any)
-
-		if len(modelsArray) != 1 {
-			t.Errorf("Expected 1 model, got %d", len(modelsArray))
-		}
-	})
-
-	// CRITICAL SAFETY TEST: verifies we don't stomp on user configs
-	t.Run("preserves user-managed models without _launch marker", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-
-		// User has manually configured models in ollama provider (no _launch marker)
-		existingConfig := `{
-			"providers": {
-				"ollama": {
-					"baseUrl": "http://localhost:11434/v1",
-					"api": "openai-completions",
-					"apiKey": "ollama",
-					"models": [
-						{"id": "user-model-1"},
-						{"id": "user-model-2", "customField": "preserved"},
-						{"id": "ollama-managed", "_launch": true}
-					]
-				}
-			}
-		}`
-		if err := os.WriteFile(configPath, []byte(existingConfig), 0o644); err != nil {
-			t.Fatal(err)
-		}
-
-		// Add a new ollama-managed model
-		newModels := []string{"new-ollama-model"}
-		if err := pi.Edit(newModels); err != nil {
-			t.Fatalf("Edit() error = %v", err)
-		}
-
-		cfg := readConfig()
-		providers := cfg["providers"].(map[string]any)
-		ollama := providers["ollama"].(map[string]any)
-		modelsArray := ollama["models"].([]any)
-
-		// Should have: new-ollama-model (managed) + 2 user models (preserved)
-		if len(modelsArray) != 3 {
-			t.Errorf("Expected 3 models (1 new managed + 2 preserved user models), got %d", len(modelsArray))
-		}
-
-		modelIDs := make(map[string]map[string]any)
-		for _, m := range modelsArray {
-			modelObj := m.(map[string]any)
-			id := modelObj["id"].(string)
-			modelIDs[id] = modelObj
-		}
-
-		// Verify new model has _launch marker
-		if m, ok := modelIDs["new-ollama-model"]; !ok {
-			t.Errorf("new-ollama-model should be present")
-		} else if m["_launch"] != true {
-			t.Errorf("new-ollama-model should have _launch marker")
-		}
-
-		// Verify user models are preserved
-		if _, ok := modelIDs["user-model-1"]; !ok {
-			t.Errorf("user-model-1 should be preserved")
-		}
-		if _, ok := modelIDs["user-model-2"]; !ok {
-			t.Errorf("user-model-2 should be preserved")
-		} else if modelIDs["user-model-2"]["customField"] != "preserved" {
-			t.Errorf("user-model-2 customField should be preserved")
-		}
-
-		// Verify old ollama-managed model is removed (not in new list)
-		if _, ok := modelIDs["ollama-managed"]; ok {
-			t.Errorf("ollama-managed should be removed (old ollama model not in new selection)")
-		}
-	})
-
-	t.Run("updates settings.json with default provider and model", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-
-		// Create existing settings with other fields
-		settingsPath := filepath.Join(configDir, "settings.json")
-		existingSettings := `{
-			"theme": "dark",
-			"customSetting": "value",
-			"defaultProvider": "anthropic",
-			"defaultModel": "claude-3"
-		}`
-		if err := os.WriteFile(settingsPath, []byte(existingSettings), 0o644); err != nil {
-			t.Fatal(err)
-		}
-
-		models := []string{"llama3.2"}
-		if err := pi.Edit(models); err != nil {
-			t.Fatalf("Edit() error = %v", err)
-		}
-
-		data, err := os.ReadFile(settingsPath)
-		if err != nil {
-			t.Fatalf("Failed to read settings: %v", err)
-		}
-
-		var settings map[string]any
-		if err := json.Unmarshal(data, &settings); err != nil {
-			t.Fatalf("Failed to parse settings: %v", err)
-		}
-
-		// Verify defaultProvider is set to ollama
-		if settings["defaultProvider"] != "ollama" {
-			t.Errorf("defaultProvider = %v, want ollama", settings["defaultProvider"])
-		}
-
-		// Verify defaultModel is set to first model
-		if settings["defaultModel"] != "llama3.2" {
-			t.Errorf("defaultModel = %v, want llama3.2", settings["defaultModel"])
-		}
-
-		// Verify other fields are preserved
-		if settings["theme"] != "dark" {
-			t.Errorf("theme = %v, want dark (preserved)", settings["theme"])
-		}
-		if settings["customSetting"] != "value" {
-			t.Errorf("customSetting = %v, want value (preserved)", settings["customSetting"])
-		}
-	})
-
-	t.Run("creates settings.json if it does not exist", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-
-		models := []string{"qwen3:8b"}
-		if err := pi.Edit(models); err != nil {
-			t.Fatalf("Edit() error = %v", err)
-		}
-
-		settingsPath := filepath.Join(configDir, "settings.json")
-		data, err := os.ReadFile(settingsPath)
-		if err != nil {
-			t.Fatalf("settings.json should be created: %v", err)
-		}
-
-		var settings map[string]any
-		if err := json.Unmarshal(data, &settings); err != nil {
-			t.Fatalf("Failed to parse settings: %v", err)
-		}
-
-		if settings["defaultProvider"] != "ollama" {
-			t.Errorf("defaultProvider = %v, want ollama", settings["defaultProvider"])
-		}
-		if settings["defaultModel"] != "qwen3:8b" {
-			t.Errorf("defaultModel = %v, want qwen3:8b", settings["defaultModel"])
-		}
-	})
-
-	t.Run("handles corrupt settings.json gracefully", func(t *testing.T) {
-		cleanup()
-		os.MkdirAll(configDir, 0o755)
-
-		// Create corrupt settings
-		settingsPath := filepath.Join(configDir, "settings.json")
-		if err := os.WriteFile(settingsPath, []byte("{invalid"), 0o644); err != nil {
-			t.Fatal(err)
-		}
-
-		models := []string{"test-model"}
-		if err := pi.Edit(models); err != nil {
-			t.Fatalf("Edit() should not fail with corrupt settings, got %v", err)
-		}
-
-		data, err := os.ReadFile(settingsPath)
-		if err != nil {
-			t.Fatalf("Failed to read settings: %v", err)
-		}
-
-		var settings map[string]any
-		if err := json.Unmarshal(data, &settings); err != nil {
-			t.Fatalf("settings.json should be valid after Edit, got parse error: %v", err)
-		}
-
-		if settings["defaultProvider"] != "ollama" {
-			t.Errorf("defaultProvider = %v, want ollama", settings["defaultProvider"])
-		}
-		if settings["defaultModel"] != "test-model" {
-			t.Errorf("defaultModel = %v, want test-model", settings["defaultModel"])
-		}
-	})
-}
-
-func TestPiModels(t *testing.T) {
-	pi := &Pi{}
-
-	t.Run("returns nil when no config exists", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-
-		models := pi.Models()
-		if models != nil {
-			t.Errorf("Models() = %v, want nil", models)
-		}
-	})
-
-	t.Run("returns models from config", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-
-		configDir := filepath.Join(tmpDir, ".pi", "agent")
-		if err := os.MkdirAll(configDir, 0o755); err != nil {
-			t.Fatal(err)
-		}
-		config := `{
-			"providers": {
-				"ollama": {
-					"models": [
-						{"id": "llama3.2"},
-						{"id": "qwen3:8b"}
-					]
-				}
-			}
-		}`
-		configPath := filepath.Join(configDir, "models.json")
-		if err := os.WriteFile(configPath, []byte(config), 0o644); err != nil {
-			t.Fatal(err)
-		}
-
-		models := pi.Models()
-		if len(models) != 2 {
-			t.Errorf("Models() returned %d models, want 2", len(models))
-		}
-		if models[0] != "llama3.2" || models[1] != "qwen3:8b" {
-			t.Errorf("Models() = %v, want [llama3.2 qwen3:8b] (sorted)", models)
-		}
-	})
-
-	t.Run("returns sorted models", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-
-		configDir := filepath.Join(tmpDir, ".pi", "agent")
-		if err := os.MkdirAll(configDir, 0o755); err != nil {
-			t.Fatal(err)
-		}
-		config := `{
-			"providers": {
-				"ollama": {
-					"models": [
-						{"id": "z-model"},
-						{"id": "a-model"},
-						{"id": "m-model"}
-					]
-				}
-			}
-		}`
-		configPath := filepath.Join(configDir, "models.json")
-		if err := os.WriteFile(configPath, []byte(config), 0o644); err != nil {
-			t.Fatal(err)
-		}
-
-		models := pi.Models()
-		if models[0] != "a-model" || models[1] != "m-model" || models[2] != "z-model" {
-			t.Errorf("Models() = %v, want [a-model m-model z-model] (sorted)", models)
-		}
-	})
-
-	t.Run("returns nil when models array is missing", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-
-		configDir := filepath.Join(tmpDir, ".pi", "agent")
-		if err := os.MkdirAll(configDir, 0o755); err != nil {
-			t.Fatal(err)
-		}
-		config := `{
-			"providers": {
-				"ollama": {}
-			}
-		}`
-		configPath := filepath.Join(configDir, "models.json")
-		if err := os.WriteFile(configPath, []byte(config), 0o644); err != nil {
-			t.Fatal(err)
-		}
-
-		models := pi.Models()
-		if models != nil {
-			t.Errorf("Models() = %v, want nil when models array is missing", models)
-		}
-	})
-
-	t.Run("handles corrupt config gracefully", func(t *testing.T) {
-		tmpDir := t.TempDir()
-		setTestHome(t, tmpDir)
-
-		configDir := filepath.Join(tmpDir, ".pi", "agent")
-		if err := os.MkdirAll(configDir, 0o755); err != nil {
-			t.Fatal(err)
-		}
-		configPath := filepath.Join(configDir, "models.json")
-		if err := os.WriteFile(configPath, []byte("{invalid json}"), 0o644); err != nil {
-			t.Fatal(err)
-		}
-
-		models := pi.Models()
-		if models != nil {
-			t.Errorf("Models() = %v, want nil for corrupt config", models)
-		}
-	})
-}

cmd/config/selector.go

@@ -275,11 +275,7 @@ func parseInput(r io.Reader) (inputEvent, byte, error) {
 func renderSelect(w io.Writer, prompt string, s *selectState) int {
 	filtered := s.filtered()
 
-	if s.filter == "" {
-		fmt.Fprintf(w, "%s %sType to filter...%s\r\n", prompt, ansiGray, ansiReset)
-	} else {
-		fmt.Fprintf(w, "%s %s\r\n", prompt, s.filter)
-	}
+	fmt.Fprintf(w, "%s %s\r\n", prompt, s.filter)
 	lineCount := 1
 
 	if len(filtered) == 0 {
@@ -318,11 +314,7 @@ func renderSelect(w io.Writer, prompt string, s *selectState) int {
 func renderMultiSelect(w io.Writer, prompt string, s *multiSelectState) int {
 	filtered := s.filtered()
 
-	if s.filter == "" {
-		fmt.Fprintf(w, "%s %sType to filter...%s\r\n", prompt, ansiGray, ansiReset)
-	} else {
-		fmt.Fprintf(w, "%s %s\r\n", prompt, s.filter)
-	}
+	fmt.Fprintf(w, "%s %s\r\n", prompt, s.filter)
 	lineCount := 1
 
 	if len(filtered) == 0 {
@@ -353,15 +345,10 @@ func renderMultiSelect(w io.Writer, prompt string, s *multiSelectState) int {
 				suffix = " " + ansiGray + "(default)" + ansiReset
 			}
 
-			desc := ""
-			if item.Description != "" {
-				desc = " " + ansiGray + "- " + item.Description + ansiReset
-			}
-
 			if idx == s.highlighted && !s.focusOnButton {
-				fmt.Fprintf(w, "  %s%s %s %s%s%s%s\r\n", ansiBold, prefix, checkbox, item.Name, ansiReset, desc, suffix)
+				fmt.Fprintf(w, "  %s%s %s %s%s%s\r\n", ansiBold, prefix, checkbox, item.Name, ansiReset, suffix)
 			} else {
-				fmt.Fprintf(w, "  %s %s %s%s%s\r\n", prefix, checkbox, item.Name, desc, suffix)
+				fmt.Fprintf(w, "  %s %s %s%s\r\n", prefix, checkbox, item.Name, suffix)
 			}
 			lineCount++
 		}

convert/convert.go

@@ -313,12 +313,8 @@ func LoadModelMetadata(fsys fs.FS) (ModelKV, *Tokenizer, error) {
 		conv = &deepseek2Model{}
 	case "Glm4MoeLiteForCausalLM":
 		conv = &glm4MoeLiteModel{}
-	case "GlmOcrForConditionalGeneration":
-		conv = &glmOcrModel{}
 	case "Lfm2ForCausalLM":
 		conv = &lfm2Model{}
-	case "Qwen3NextForCausalLM":
-		conv = &qwen3NextModel{}
 	default:
 		return nil, nil, fmt.Errorf("unsupported architecture %q", p.Architectures[0])
 	}

convert/convert_glmocr.go

@@ -1,455 +0,0 @@
-package convert
-
-import (
-	"cmp"
-	"encoding/json"
-	"io/fs"
-	"log/slog"
-	"regexp"
-	"strconv"
-	"strings"
-
-	"github.com/ollama/ollama/fs/ggml"
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-)
-
-// normalToNeoXRepacker creates a repacker that permutes Q/K weights from interleaved (LLaMA)
-// to NeoX ordering for compatibility with GGML's M-RoPE kernel.
-//
-// For weights: reshape [out, in] -> [n_heads, head_dim, in], permute rotary dims, reshape back
-// For biases: reshape [out] -> [n_heads, head_dim], permute rotary dims, reshape back
-func normalToNeoXRepacker(nHeads, headDim int, partialRotaryFactor float32) func(string, []float32, []uint64) ([]float32, error) {
-	return func(_ string, data []float32, shape []uint64) ([]float32, error) {
-		rotaryDim := int(float32(headDim) * partialRotaryFactor)
-		if rotaryDim%2 != 0 {
-			rotaryDim = (rotaryDim / 2) * 2 // Round down to even
-		}
-
-		// Handle 1D (bias) or 2D (weight) tensors
-		is1D := len(shape) == 1
-		var inFeatures int
-		if is1D {
-			inFeatures = 1
-		} else {
-			inFeatures = int(shape[1])
-		}
-		outFeatures := int(shape[0])
-		nEffectiveHeads := outFeatures / headDim
-
-		if nEffectiveHeads != nHeads {
-			slog.Warn("normalToNeoX: unexpected head count", "effective", nEffectiveHeads, "expected", nHeads)
-		}
-
-		// Reshape to [n_heads, head_dim, in_features]
-		reshaped := make([]float32, len(data))
-		copy(reshaped, data)
-
-		// Permute the rotary dimensions: even indices first, then odd
-		// For each head, reorder [0,1,2,3,4,5...] to [0,2,4...,1,3,5...]
-		result := make([]float32, len(data))
-		halfRotary := rotaryDim / 2
-
-		for h := range nEffectiveHeads {
-			for f := range inFeatures {
-				for i := range halfRotary {
-					// Even dim (0, 2, 4, ...) -> position i
-					srcIdx := h*headDim*inFeatures + (2*i)*inFeatures + f
-					dstIdx := h*headDim*inFeatures + i*inFeatures + f
-					result[dstIdx] = reshaped[srcIdx]
-
-					// Odd dim (1, 3, 5, ...) -> position halfRotary + i
-					srcIdx = h*headDim*inFeatures + (2*i+1)*inFeatures + f
-					dstIdx = h*headDim*inFeatures + (halfRotary+i)*inFeatures + f
-					result[dstIdx] = reshaped[srcIdx]
-				}
-
-				// Non-rotary part: copy as-is
-				for i := rotaryDim; i < headDim; i++ {
-					srcIdx := h*headDim*inFeatures + i*inFeatures + f
-					result[srcIdx] = reshaped[srcIdx]
-				}
-			}
-		}
-
-		return result, nil
-	}
-}
-
-type glmOcrModel struct {
-	ModelParameters
-
-	TextConfig struct {
-		HiddenSize          uint32  `json:"hidden_size"`
-		IntermediateSize    uint32  `json:"intermediate_size"`
-		NumHiddenLayers     uint32  `json:"num_hidden_layers"`
-		NumAttentionHeads   uint32  `json:"num_attention_heads"`
-		NumKeyValueHeads    uint32  `json:"num_key_value_heads"`
-		HeadDim             uint32  `json:"head_dim"`
-		MaxPositionEmbed    uint32  `json:"max_position_embeddings"`
-		RMSNormEps          float32 `json:"rms_norm_eps"`
-		PartialRotaryFactor float32 `json:"partial_rotary_factor"`
-		RopeParameters      struct {
-			RopeType            string  `json:"rope_type"`
-			MRopeSection        []int32 `json:"mrope_section"`
-			RopeTheta           float32 `json:"rope_theta"`
-			PartialRotaryFactor float32 `json:"partial_rotary_factor"`
-		} `json:"rope_parameters"`
-	} `json:"text_config"`
-
-	VisionConfig struct {
-		HiddenSize        uint32  `json:"hidden_size"`
-		IntermediateSize  uint32  `json:"intermediate_size"`
-		Depth             uint32  `json:"depth"`
-		NumHeads          uint32  `json:"num_heads"`
-		ImageSize         uint32  `json:"image_size"`
-		PatchSize         uint32  `json:"patch_size"`
-		OutHiddenSize     uint32  `json:"out_hidden_size"`
-		RMSNormEps        float32 `json:"rms_norm_eps"`
-		SpatialMergeSize  uint32  `json:"spatial_merge_size"`
-		TemporalPatchSize uint32  `json:"temporal_patch_size"`
-	} `json:"vision_config"`
-
-	ImageStartTokenID uint32 `json:"image_start_token_id"`
-	ImageEndTokenID   uint32 `json:"image_end_token_id"`
-	VideoStartTokenID uint32 `json:"video_start_token_id"`
-	VideoEndTokenID   uint32 `json:"video_end_token_id"`
-	ImageTokenID      uint32 `json:"image_token_id"`
-	VideoTokenID      uint32 `json:"video_token_id"`
-
-	// Preprocessor config (preprocessor_config.json)
-	Preprocessor struct {
-		Size struct {
-			ShortestEdge uint32 `json:"shortest_edge"`
-			LongestEdge  uint32 `json:"longest_edge"`
-		} `json:"size"`
-		PatchSize         uint32    `json:"patch_size"`
-		TemporalPatchSize uint32    `json:"temporal_patch_size"`
-		MergeSize         uint32    `json:"merge_size"`
-		ImageMean         []float32 `json:"image_mean"`
-		ImageStd          []float32 `json:"image_std"`
-	} `json:"-"`
-}
-
-var _ ModelConverter = (*glmOcrModel)(nil)
-
-func (m *glmOcrModel) parseMore(fsys fs.FS) error {
-	bts, err := fs.ReadFile(fsys, "preprocessor_config.json")
-	if err != nil {
-		return err
-	}
-
-	return json.Unmarshal(bts, &m.Preprocessor)
-}
-
-func (m *glmOcrModel) KV(t *Tokenizer) KV {
-	kv := m.ModelParameters.KV(t)
-	kv["general.architecture"] = "glmocr"
-
-	// Text model parameters
-	kv["glmocr.block_count"] = cmp.Or(m.TextConfig.NumHiddenLayers, 16)
-	kv["glmocr.embedding_length"] = cmp.Or(m.TextConfig.HiddenSize, 1536)
-	kv["glmocr.attention.head_count"] = cmp.Or(m.TextConfig.NumAttentionHeads, 16)
-	kv["glmocr.attention.head_count_kv"] = cmp.Or(m.TextConfig.NumKeyValueHeads, 8)
-	headDim := cmp.Or(m.TextConfig.HeadDim, m.TextConfig.HiddenSize/m.TextConfig.NumAttentionHeads)
-	kv["glmocr.attention.key_length"] = headDim
-	kv["glmocr.attention.value_length"] = headDim
-	kv["glmocr.feed_forward_length"] = cmp.Or(m.TextConfig.IntermediateSize, 4608)
-	kv["glmocr.attention.layer_norm_rms_epsilon"] = cmp.Or(m.TextConfig.RMSNormEps, 1e-5)
-	kv["glmocr.context_length"] = cmp.Or(m.TextConfig.MaxPositionEmbed, 131072)
-	kv["glmocr.rope.freq_base"] = cmp.Or(m.TextConfig.RopeParameters.RopeTheta, float32(10000))
-	kv["glmocr.rope.partial_rotary_factor"] = cmp.Or(m.TextConfig.RopeParameters.PartialRotaryFactor, m.TextConfig.PartialRotaryFactor, float32(1.0))
-	if len(m.TextConfig.RopeParameters.MRopeSection) > 0 {
-		kv["glmocr.rope.mrope_section"] = m.TextConfig.RopeParameters.MRopeSection
-	}
-
-	// Vision model parameters
-	kv["glmocr.vision.block_count"] = cmp.Or(m.VisionConfig.Depth, 24)
-	kv["glmocr.vision.embedding_length"] = cmp.Or(m.VisionConfig.HiddenSize, 1024)
-	kv["glmocr.vision.attention.head_count"] = cmp.Or(m.VisionConfig.NumHeads, 16)
-	kv["glmocr.vision.image_size"] = cmp.Or(m.VisionConfig.ImageSize, 336)
-	kv["glmocr.vision.patch_size"] = cmp.Or(m.VisionConfig.PatchSize, m.Preprocessor.PatchSize, 14)
-	kv["glmocr.vision.spatial_merge_size"] = cmp.Or(m.VisionConfig.SpatialMergeSize, m.Preprocessor.MergeSize, 2)
-	kv["glmocr.vision.temporal_patch_size"] = cmp.Or(m.VisionConfig.TemporalPatchSize, m.Preprocessor.TemporalPatchSize, 2)
-	kv["glmocr.vision.out_hidden_size"] = cmp.Or(m.VisionConfig.OutHiddenSize, 1536)
-	kv["glmocr.vision.intermediate_size"] = cmp.Or(m.VisionConfig.IntermediateSize, 4096)
-	kv["glmocr.vision.attention.layer_norm_rms_epsilon"] = cmp.Or(m.VisionConfig.RMSNormEps, 1e-5)
-
-	// Preprocessor-derived image settings (min/max pixels and normalization)
-	// Note: fs.Config.keyValue() auto-prepends architecture prefix, so use full key
-	if m.Preprocessor.Size.ShortestEdge > 0 {
-		kv["glmocr.vision.min_pixels"] = m.Preprocessor.Size.ShortestEdge
-	}
-	if m.Preprocessor.Size.LongestEdge > 0 {
-		kv["glmocr.vision.max_pixels"] = m.Preprocessor.Size.LongestEdge
-	}
-	if len(m.Preprocessor.ImageMean) == 3 {
-		kv["glmocr.vision.image_mean"] = m.Preprocessor.ImageMean
-	}
-	if len(m.Preprocessor.ImageStd) == 3 {
-		kv["glmocr.vision.image_std"] = m.Preprocessor.ImageStd
-	}
-
-	// Special tokens
-	kv["glmocr.image_token_id"] = m.ImageTokenID
-	kv["glmocr.image_start_token_id"] = m.ImageStartTokenID
-	kv["glmocr.image_end_token_id"] = m.ImageEndTokenID
-	kv["glmocr.video_token_id"] = m.VideoTokenID
-	kv["glmocr.video_start_token_id"] = m.VideoStartTokenID
-	kv["glmocr.video_end_token_id"] = m.VideoEndTokenID
-
-	return kv
-}
-
-func (m *glmOcrModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
-
-	// Skip layers >= num_hidden_layers (Multi-Token Prediction layers not needed for basic inference)
-	numLayers := int(cmp.Or(m.TextConfig.NumHiddenLayers, 16))
-	skipLayer := func(name string) bool {
-		// Tensor names are already replaced to "blk.N.xxx" format
-		re := regexp.MustCompile(`^blk\.(\d+)`)
-		matches := re.FindStringSubmatch(name)
-		if matches == nil {
-			return false
-		}
-		blkNum, err := strconv.Atoi(matches[1])
-		if err != nil {
-			return false
-		}
-		return blkNum >= numLayers
-	}
-
-	for _, t := range ts {
-		name := t.Name()
-
-		// Skip next-n prediction layers (layers >= num_hidden_layers)
-		if skipLayer(name) {
-			continue
-		}
-
-		// Split ffn_gate_up into separate gate and up projections
-		if strings.Contains(name, "ffn_gate_up") {
-			for t := range splitDim(t, 0,
-				split{Replacer: strings.NewReplacer("ffn_gate_up", "ffn_gate")},
-				split{Replacer: strings.NewReplacer("ffn_gate_up", "ffn_up")},
-			) {
-				out = append(out, t)
-			}
-			continue
-		}
-
-		if strings.HasSuffix(name, "patch_embd.weight") {
-			shape := t.Shape()
-			if len(shape) == 5 && shape[2] == 2 {
-				newShape := []uint64{shape[0], shape[1], shape[3], shape[4]}
-
-				t0 := t.Clone()
-				t0.SetRepacker(func(_ string, data []float32, shape []uint64) ([]float32, error) {
-					dims := make([]int, len(shape))
-					for i := range shape {
-						dims[i] = int(shape[i])
-					}
-					var tt tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-					tt, err := tt.Slice(nil, nil, tensor.S(0, 1), nil, nil)
-					if err != nil {
-						return nil, err
-					}
-					tt = tensor.Materialize(tt)
-					newDims := []int{int(shape[0]), int(shape[1]), int(shape[3]), int(shape[4])}
-					if err := tt.Reshape(newDims...); err != nil {
-						return nil, err
-					}
-					if err := tt.Reshape(tt.Shape().TotalSize()); err != nil {
-						return nil, err
-					}
-					return native.VectorF32(tt.(*tensor.Dense))
-				})
-				out = append(out, &ggml.Tensor{
-					Name:     strings.Replace(name, "patch_embd.weight", "patch_embd_0.weight", 1),
-					Kind:     t.Kind(),
-					Shape:    newShape,
-					WriterTo: t0,
-				})
-
-				t1 := t.Clone()
-				t1.SetRepacker(func(_ string, data []float32, shape []uint64) ([]float32, error) {
-					dims := make([]int, len(shape))
-					for i := range shape {
-						dims[i] = int(shape[i])
-					}
-					var tt tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-					tt, err := tt.Slice(nil, nil, tensor.S(1, 2), nil, nil)
-					if err != nil {
-						return nil, err
-					}
-					tt = tensor.Materialize(tt)
-					newDims := []int{int(shape[0]), int(shape[1]), int(shape[3]), int(shape[4])}
-					if err := tt.Reshape(newDims...); err != nil {
-						return nil, err
-					}
-					if err := tt.Reshape(tt.Shape().TotalSize()); err != nil {
-						return nil, err
-					}
-					return native.VectorF32(tt.(*tensor.Dense))
-				})
-				out = append(out, &ggml.Tensor{
-					Name:     strings.Replace(name, "patch_embd.weight", "patch_embd_1.weight", 1),
-					Kind:     t.Kind(),
-					Shape:    newShape,
-					WriterTo: t1,
-				})
-
-				continue
-			}
-
-			if len(shape) == 4 {
-				out = append(out, &ggml.Tensor{
-					Name:     strings.Replace(name, "patch_embd.weight", "patch_embd_0.weight", 1),
-					Kind:     t.Kind(),
-					Shape:    t.Shape(),
-					WriterTo: t,
-				})
-				continue
-			}
-
-			slog.Warn("glmocr: patch_embed weight has unexpected shape - not splitting", "shape", shape)
-			// Fall through to default handling
-		}
-
-		// Handle pre-split patch embedding weights
-		// Pattern 1: v.patch_embd.0.weight, v.patch_embd.1.weight -> patch_embd_0.weight, patch_embd_1.weight
-		// Pattern 2: v.patch_embd.weight.0, v.patch_embd.weight.1 -> patch_embd_0.weight, patch_embd_1.weight
-		if strings.Contains(name, "patch_embd.0.") {
-			out = append(out, &ggml.Tensor{
-				Name:     strings.Replace(name, "patch_embd.0.", "patch_embd_0.", 1),
-				Kind:     t.Kind(),
-				Shape:    t.Shape(),
-				WriterTo: t,
-			})
-			continue
-		}
-		if strings.Contains(name, "patch_embd.1.") {
-			out = append(out, &ggml.Tensor{
-				Name:     strings.Replace(name, "patch_embd.1.", "patch_embd_1.", 1),
-				Kind:     t.Kind(),
-				Shape:    t.Shape(),
-				WriterTo: t,
-			})
-			continue
-		}
-		// Handle .weight.0 and .weight.1 suffix patterns
-		if strings.HasSuffix(name, "patch_embd.weight.0") {
-			out = append(out, &ggml.Tensor{
-				Name:     strings.Replace(name, "patch_embd.weight.0", "patch_embd_0.weight", 1),
-				Kind:     t.Kind(),
-				Shape:    t.Shape(),
-				WriterTo: t,
-			})
-			continue
-		}
-		if strings.HasSuffix(name, "patch_embd.weight.1") {
-			out = append(out, &ggml.Tensor{
-				Name:     strings.Replace(name, "patch_embd.weight.1", "patch_embd_1.weight", 1),
-				Kind:     t.Kind(),
-				Shape:    t.Shape(),
-				WriterTo: t,
-			})
-			continue
-		}
-
-		// Permute Q/K weights for M-RoPE compatibility (interleaved -> NeoX ordering)
-		// GGML's M-RoPE kernel uses NeoX-style rotation, but GLM-OCR uses interleaved (LLaMA-style)
-		// We permute at conversion time so the weights work correctly with GGML's kernel
-		// This aligns Q/K rotary dimensions with GGML's NeoX-style rotation
-		if len(m.TextConfig.RopeParameters.MRopeSection) > 0 &&
-			strings.Contains(name, "blk.") && (strings.Contains(name, "attn_q.") || strings.Contains(name, "attn_k.")) {
-			// Get config values for permutation
-			nHeads := int(cmp.Or(m.TextConfig.NumAttentionHeads, 16))
-			nKVHeads := int(cmp.Or(m.TextConfig.NumKeyValueHeads, 8))
-			hiddenSize := int(cmp.Or(m.TextConfig.HiddenSize, 1536))
-			headDim := int(cmp.Or(m.TextConfig.HeadDim, uint32(hiddenSize/nHeads)))
-			partialRotaryFactor := cmp.Or(m.TextConfig.PartialRotaryFactor, m.TextConfig.RopeParameters.PartialRotaryFactor, float32(1.0))
-
-			// Use appropriate head count: nHeads for Q, nKVHeads for K
-			effectiveHeads := nHeads
-			if strings.Contains(name, "attn_k.") {
-				effectiveHeads = nKVHeads
-			}
-
-			permutedT := t.Clone()
-			permutedT.SetRepacker(normalToNeoXRepacker(effectiveHeads, headDim, partialRotaryFactor))
-			out = append(out, &ggml.Tensor{
-				Name:     name,
-				Kind:     t.Kind(),
-				Shape:    t.Shape(),
-				WriterTo: permutedT,
-			})
-			continue
-		}
-
-		out = append(out, &ggml.Tensor{
-			Name:     name,
-			Kind:     t.Kind(),
-			Shape:    t.Shape(),
-			WriterTo: t,
-		})
-	}
-
-	return out
-}
-
-func (m *glmOcrModel) Replacements() []string {
-	return []string{
-		// Vision encoder
-		"model.visual.patch_embed.proj_1", "v.patch_embd_1", // Second temporal split
-		"model.visual.patch_embed.proj", "v.patch_embd",
-		"model.visual.blocks", "v.blk",
-		"model.visual.post_layernorm", "v.post_ln",
-		"model.visual.downsample", "mm.patch_merger",
-
-		// Vision attention
-		"attn.qkv", "attn_qkv",
-		"attn.proj", "attn_out",
-		"attn.q_norm", "attn_q_norm",
-		"attn.k_norm", "attn_k_norm",
-
-		// Vision norms
-		"norm1", "ln1",
-		"norm2", "ln2",
-
-		// Vision MLP
-		"mlp.gate_proj", "ffn_gate",
-		"mlp.up_proj", "ffn_up",
-		"mlp.down_proj", "ffn_down",
-
-		// Merger (multimodal projector)
-		"model.visual.merger.proj", "mm.model.fc",
-		"model.visual.merger.post_projection_norm", "mm.post_norm",
-		"model.visual.merger.gate_proj", "mm.gate",
-		"model.visual.merger.up_proj", "mm.up",
-		"model.visual.merger.down_proj", "mm.down",
-
-		// Language model
-		"model.language_model.embed_tokens", "token_embd",
-		"model.language_model.layers", "blk",
-		"model.language_model.norm", "output_norm",
-		"lm_head", "output",
-
-		// Language model attention
-		"self_attn.q_proj", "attn_q",
-		"self_attn.k_proj", "attn_k",
-		"self_attn.v_proj", "attn_v",
-		"self_attn.o_proj", "attn_out",
-
-		// Language model norms
-		"input_layernorm", "attn_norm",
-		"post_attention_layernorm", "ffn_norm",
-		"post_self_attn_layernorm", "post_attn_norm",
-		"post_mlp_layernorm", "post_ffn_norm",
-
-		// Language model MLP (remove mlp. prefix so ffn_* names work)
-		"mlp.gate_up_proj", "ffn_gate_up",
-		"mlp.down_proj", "ffn_down",
-	}
-}

convert/convert_qwen3next.go

@@ -1,512 +0,0 @@
-package convert
-
-import (
-	"fmt"
-	"io/fs"
-	"math"
-	"slices"
-	"strings"
-
-	"github.com/pdevine/tensor"
-	"github.com/pdevine/tensor/native"
-
-	"github.com/ollama/ollama/fs/ggml"
-)
-
-type qwen3NextModel struct {
-	ModelParameters
-	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
-	HiddenSize            uint32  `json:"hidden_size"`
-	NumHiddenLayers       uint32  `json:"num_hidden_layers"`
-	IntermediateSize      uint32  `json:"intermediate_size"`
-	NumAttentionHeads     uint32  `json:"num_attention_heads"`
-	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
-	HeadDim               uint32  `json:"head_dim"`
-	RopeTheta             float32 `json:"rope_theta"`
-	RMSNormEPS            float32 `json:"rms_norm_eps"`
-
-	// MoE config
-	NumExperts             uint32 `json:"num_experts"`
-	NumExpertsPerToken     uint32 `json:"num_experts_per_tok"`
-	NormTopkProb           bool   `json:"norm_topk_prob"`
-	MoEIntermediateSize    uint32 `json:"moe_intermediate_size"`
-	SharedExpertIntermSize uint32 `json:"shared_expert_intermediate_size"`
-
-	// Hybrid attention config
-	FullAttentionInterval uint32 `json:"full_attention_interval"`
-
-	// Linear attention (Gated Delta Net) config
-	LinearConvKernelDim uint32 `json:"linear_conv_kernel_dim"`
-	LinearKeyHeadDim    uint32 `json:"linear_key_head_dim"`
-	LinearNumKeyHeads   uint32 `json:"linear_num_key_heads"`
-	LinearNumValueHeads uint32 `json:"linear_num_value_heads"`
-	LinearValueHeadDim  uint32 `json:"linear_value_head_dim"`
-
-	// RoPE config
-	PartialRotaryFactor float32 `json:"partial_rotary_factor"`
-	RopeScaling         struct {
-		Type   string     `json:"type"`
-		Factor ropeFactor `json:"factor"`
-	} `json:"rope_scaling"`
-}
-
-var _ ModelConverter = (*qwen3NextModel)(nil)
-
-func (q *qwen3NextModel) parseMore(_ fs.FS) error {
-	if q.NumHiddenLayers == 0 {
-		return fmt.Errorf("qwen3next: num_hidden_layers must be set")
-	}
-	if q.NumAttentionHeads == 0 {
-		return fmt.Errorf("qwen3next: num_attention_heads must be set")
-	}
-	if q.NumKeyValueHeads == 0 {
-		return fmt.Errorf("qwen3next: num_key_value_heads must be set")
-	}
-	if q.HeadDim == 0 {
-		return fmt.Errorf("qwen3next: head_dim must be set")
-	}
-	if q.RopeTheta == 0 {
-		return fmt.Errorf("qwen3next: rope_theta must be set")
-	}
-	if q.PartialRotaryFactor <= 0 || q.PartialRotaryFactor > 1 {
-		return fmt.Errorf("qwen3next: partial_rotary_factor must be in (0,1], got %v", q.PartialRotaryFactor)
-	}
-	if q.LinearNumKeyHeads == 0 || q.LinearNumValueHeads == 0 || q.LinearKeyHeadDim == 0 || q.LinearValueHeadDim == 0 {
-		return fmt.Errorf("qwen3next: linear attention config must be set (linear_num_key_heads, linear_num_value_heads, linear_key_head_dim, linear_value_head_dim)")
-	}
-	if q.FullAttentionInterval == 0 {
-		return fmt.Errorf("qwen3next: full_attention_interval must be set")
-	}
-	if q.FullAttentionInterval > q.NumHiddenLayers {
-		return fmt.Errorf("qwen3next: full_attention_interval (%d) exceeds num_hidden_layers (%d)", q.FullAttentionInterval, q.NumHiddenLayers)
-	}
-
-	hasFull := false
-	for i := range q.NumHiddenLayers {
-		if (i+1)%q.FullAttentionInterval == 0 {
-			hasFull = true
-			break
-		}
-	}
-	if !hasFull {
-		return fmt.Errorf("qwen3next: head_count_kv would be all zeros (full_attention_interval=%d, num_hidden_layers=%d)", q.FullAttentionInterval, q.NumHiddenLayers)
-	}
-
-	return nil
-}
-
-func (q *qwen3NextModel) KV(t *Tokenizer) KV {
-	kv := q.ModelParameters.KV(t)
-	kv["general.architecture"] = "qwen3next"
-	kv["tokenizer.ggml.pre"] = "qwen2"
-	kv["block_count"] = q.NumHiddenLayers
-	kv["context_length"] = q.MaxPositionEmbeddings
-	kv["embedding_length"] = q.HiddenSize
-	kv["feed_forward_length"] = q.IntermediateSize
-	kv["attention.head_count"] = q.NumAttentionHeads
-	headDim := q.HeadDim
-	if headDim == 0 && q.NumAttentionHeads > 0 {
-		headDim = q.HiddenSize / q.NumAttentionHeads
-	}
-	kv["attention.key_length"] = headDim
-	kv["attention.value_length"] = headDim
-	kv["attention.layer_norm_rms_epsilon"] = q.RMSNormEPS
-	kv["rope.freq_base"] = q.RopeTheta
-
-	// RoPE dimension count (partial rotary)
-	// partial_rotary_factor = 0.25 means only 25% of head_dim uses RoPE
-	partialRotary := q.PartialRotaryFactor
-	if partialRotary > 0 && partialRotary <= 1 {
-		kv["rope.dimension_count"] = uint32(float32(headDim) * partialRotary)
-	}
-
-	// MoE config
-	if q.NumExperts > 0 {
-		kv["expert_count"] = q.NumExperts
-		kv["expert_used_count"] = q.NumExpertsPerToken
-		kv["norm_top_k_prob"] = q.NormTopkProb
-		if q.MoEIntermediateSize > 0 {
-			kv["expert_feed_forward_length"] = q.MoEIntermediateSize
-		}
-		if q.SharedExpertIntermSize > 0 {
-			kv["expert_shared_feed_forward_length"] = q.SharedExpertIntermSize
-		}
-	}
-
-	// SSM/Linear attention config
-	// d_inner = linear_value_head_dim * linear_num_value_heads
-	dInner := q.LinearValueHeadDim * q.LinearNumValueHeads
-	kv["ssm.inner_size"] = dInner
-	kv["ssm.state_size"] = q.LinearKeyHeadDim        // head_k_dim
-	kv["ssm.group_count"] = q.LinearNumKeyHeads      // num_k_heads
-	kv["ssm.time_step_rank"] = q.LinearNumValueHeads // num_v_heads
-	kv["ssm.conv_kernel"] = q.LinearConvKernelDim
-	interval := q.FullAttentionInterval
-	kv["full_attention_interval"] = interval
-
-	// Build per-layer KV head count array to identify layer types
-	// 0 = recurrent (linear attention), non-zero = full attention
-	kvHeadCounts := make([]uint32, q.NumHiddenLayers)
-	for i := range q.NumHiddenLayers {
-		// Full attention every full_attention_interval layers (starting at interval-1)
-		if interval > 0 && (i+1)%interval == 0 {
-			kvHeadCounts[i] = q.NumKeyValueHeads
-		}
-		// else stays 0 (recurrent layer)
-	}
-	kv["attention.head_count_kv"] = kvHeadCounts
-
-	// RoPE scaling
-	if q.RopeScaling.Type != "" {
-		kv["rope.scaling.type"] = q.RopeScaling.Type
-		kv["rope.scaling.factor"] = q.RopeScaling.Factor
-	}
-
-	return kv
-}
-
-func (q *qwen3NextModel) Tensors(ts []Tensor) []*ggml.Tensor {
-	var out []*ggml.Tensor
-
-	// Create merges for expert tensors - stack individual experts into batched tensors
-	merges := make([]merge, q.NumHiddenLayers*3)
-	for i := range q.NumHiddenLayers {
-		merges[i*3+0] = merge{
-			fmt.Sprintf("blk.%d.mlp.experts.*.gate_proj.weight", i),
-			fmt.Sprintf("blk.%d.ffn_gate_exps.weight", i),
-		}
-		merges[i*3+1] = merge{
-			fmt.Sprintf("blk.%d.mlp.experts.*.up_proj.weight", i),
-			fmt.Sprintf("blk.%d.ffn_up_exps.weight", i),
-		}
-		merges[i*3+2] = merge{
-			fmt.Sprintf("blk.%d.mlp.experts.*.down_proj.weight", i),
-			fmt.Sprintf("blk.%d.ffn_down_exps.weight", i),
-		}
-	}
-
-	// Merge expert tensors
-	merged, remaining := mergeTensors(ts, merges...)
-	out = append(out, merged...)
-
-	// Process remaining tensors
-	for _, t := range remaining {
-		name := t.Name()
-		shape := t.Shape()
-
-		// Split linear_attn.in_proj_qkvz (ssm_in) into attn_qkv + attn_gate when possible
-		if strings.HasSuffix(name, ".ssm_in.weight") {
-			if qkv, gate, ok := q.splitQKVZTensor(t); ok {
-				out = append(out, qkv, gate)
-				continue
-			}
-			panic(fmt.Sprintf("qwen3next: failed to split %s into attn_qkv/attn_gate (shape=%v)", name, shape))
-		}
-
-		switch {
-		// Add 1 to norm weights (except ssm_norm which is linear_attn.norm)
-		// This matches the Python converter behavior for qwen3next
-		case strings.HasSuffix(name, "_norm.weight") && !strings.HasSuffix(name, ".ssm_norm.weight"):
-			t.SetRepacker(q.addOne)
-			out = append(out, &ggml.Tensor{
-				Name:     name,
-				Kind:     t.Kind(),
-				Shape:    slices.Clone(shape),
-				WriterTo: t,
-			})
-
-		// Handle linear attention A_log -> ssm_a (negate and exp)
-		// Note: name has already been transformed by Replacements at this point
-		case strings.HasSuffix(name, ".ssm_a"):
-			t.SetRepacker(func(_ string, data []float32, shape []uint64) ([]float32, error) {
-				// Compute -exp(A_log)
-				result := make([]float32, len(data))
-				for i, v := range data {
-					// -exp(v)
-					result[i] = -float32(math.Exp(float64(v)))
-				}
-				return result, nil
-			})
-			out = append(out, &ggml.Tensor{
-				Name:     name,
-				Kind:     t.Kind(),
-				Shape:    slices.Clone(shape),
-				WriterTo: t,
-			})
-
-		// Squeeze conv1d weights: [1, D, K] or [D, 1, K] -> [D, K]
-		case strings.HasSuffix(name, ".ssm_conv1d.weight"):
-			newShape := slices.Clone(shape)
-			if len(shape) == 3 {
-				if shape[0] == 1 {
-					// [1, D, K] -> [D, K]
-					newShape = []uint64{shape[1], shape[2]}
-				} else if shape[1] == 1 {
-					// [D, 1, K] -> [D, K]
-					newShape = []uint64{shape[0], shape[2]}
-				}
-			}
-			out = append(out, &ggml.Tensor{
-				Name:     name,
-				Kind:     t.Kind(),
-				Shape:    newShape,
-				WriterTo: t,
-			})
-		// Squeeze shared expert gate: [D, 1] or [1, D] -> [D]
-		case strings.HasSuffix(name, ".ffn_gate_inp_shexp.weight"):
-			newShape := slices.Clone(shape)
-			if len(shape) == 2 {
-				if shape[0] == 1 && shape[1] > 1 {
-					newShape = []uint64{shape[1]}
-				} else if shape[1] == 1 && shape[0] > 1 {
-					newShape = []uint64{shape[0]}
-				}
-			}
-			out = append(out, &ggml.Tensor{
-				Name:     name,
-				Kind:     t.Kind(),
-				Shape:    newShape,
-				WriterTo: t,
-			})
-
-		default:
-			out = append(out, &ggml.Tensor{
-				Name:     name,
-				Kind:     t.Kind(),
-				Shape:    slices.Clone(shape),
-				WriterTo: t,
-			})
-		}
-	}
-
-	return out
-}
-
-type qkvzSplitSpec struct {
-	hidden    int
-	headKDim  int
-	headVDim  int
-	numKHeads int
-	numVHeads int
-	qkvzDim   int
-	qkvOut    int
-	gateOut   int
-}
-
-func (q *qwen3NextModel) qkvzSpec(shape []uint64) (qkvzSplitSpec, bool) {
-	if len(shape) != 2 {
-		return qkvzSplitSpec{}, false
-	}
-
-	numKHeads := int(q.LinearNumKeyHeads)
-	numVHeads := int(q.LinearNumValueHeads)
-	headKDim := int(q.LinearKeyHeadDim)
-	headVDim := int(q.LinearValueHeadDim)
-	if numKHeads == 0 || numVHeads == 0 || headKDim == 0 || headVDim == 0 {
-		return qkvzSplitSpec{}, false
-	}
-	if numVHeads%numKHeads != 0 {
-		return qkvzSplitSpec{}, false
-	}
-
-	hidden := int(shape[1])
-	vPerHead := headVDim * (numVHeads / numKHeads)
-	qkvzDim := 2*headKDim + 2*vPerHead
-	expectedOut := qkvzDim * numKHeads
-	if int(shape[0]) != expectedOut {
-		return qkvzSplitSpec{}, false
-	}
-
-	return qkvzSplitSpec{
-		hidden:    hidden,
-		headKDim:  headKDim,
-		headVDim:  headVDim,
-		numKHeads: numKHeads,
-		numVHeads: numVHeads,
-		qkvzDim:   qkvzDim,
-		qkvOut:    2*headKDim*numKHeads + headVDim*numVHeads,
-		gateOut:   headVDim * numVHeads,
-	}, true
-}
-
-func (q *qwen3NextModel) splitQKVZTensor(t Tensor) (*ggml.Tensor, *ggml.Tensor, bool) {
-	spec, ok := q.qkvzSpec(t.Shape())
-	if !ok {
-		return nil, nil, false
-	}
-
-	qkvTensor := t.Clone()
-	qkvTensor.SetRepacker(q.repackQKVZ(spec, false))
-
-	gateTensor := t.Clone()
-	gateTensor.SetRepacker(q.repackQKVZ(spec, true))
-
-	qkvName := strings.Replace(t.Name(), "ssm_in", "attn_qkv", 1)
-	gateName := strings.Replace(t.Name(), "ssm_in", "attn_gate", 1)
-
-	return &ggml.Tensor{
-			Name:     qkvName,
-			Kind:     t.Kind(),
-			Shape:    []uint64{uint64(spec.qkvOut), uint64(spec.hidden)},
-			WriterTo: qkvTensor,
-		}, &ggml.Tensor{
-			Name:     gateName,
-			Kind:     t.Kind(),
-			Shape:    []uint64{uint64(spec.gateOut), uint64(spec.hidden)},
-			WriterTo: gateTensor,
-		}, true
-}
-
-func (q *qwen3NextModel) repackQKVZ(spec qkvzSplitSpec, extractGate bool) Repacker {
-	vPerHead := spec.headVDim * (spec.numVHeads / spec.numKHeads)
-
-	return func(_ string, data []float32, shape []uint64) ([]float32, error) {
-		dims := make([]int, len(shape))
-		for i := range shape {
-			dims[i] = int(shape[i])
-		}
-
-		var tt tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
-		var err error
-
-		// Convert to [hidden, out_features] layout for slicing
-		tt, err = tensor.Transpose(tt, 1, 0)
-		if err != nil {
-			return nil, err
-		}
-		tt = tensor.Materialize(tt)
-
-		if err := tt.Reshape(spec.hidden, spec.numKHeads, spec.qkvzDim); err != nil {
-			return nil, err
-		}
-
-		offset := 0
-		qSlice, err := tt.Slice(nil, nil, tensor.S(offset, offset+spec.headKDim))
-		if err != nil {
-			return nil, err
-		}
-		offset += spec.headKDim
-		kSlice, err := tt.Slice(nil, nil, tensor.S(offset, offset+spec.headKDim))
-		if err != nil {
-			return nil, err
-		}
-		offset += spec.headKDim
-		vSlice, err := tt.Slice(nil, nil, tensor.S(offset, offset+vPerHead))
-		if err != nil {
-			return nil, err
-		}
-		offset += vPerHead
-		zSlice, err := tt.Slice(nil, nil, tensor.S(offset, offset+vPerHead))
-		if err != nil {
-			return nil, err
-		}
-
-		qMat := tensor.Materialize(qSlice).(*tensor.Dense)
-		kMat := tensor.Materialize(kSlice).(*tensor.Dense)
-		vMat := tensor.Materialize(vSlice).(*tensor.Dense)
-		zMat := tensor.Materialize(zSlice).(*tensor.Dense)
-
-		if err := qMat.Reshape(spec.hidden, spec.numKHeads*spec.headKDim); err != nil {
-			return nil, err
-		}
-		if err := kMat.Reshape(spec.hidden, spec.numKHeads*spec.headKDim); err != nil {
-			return nil, err
-		}
-		if err := vMat.Reshape(spec.hidden, spec.numKHeads*vPerHead); err != nil {
-			return nil, err
-		}
-		if err := zMat.Reshape(spec.hidden, spec.numKHeads*vPerHead); err != nil {
-			return nil, err
-		}
-
-		var out tensor.Tensor
-		if extractGate {
-			out = zMat
-		} else {
-			out, err = tensor.Concat(1, qMat, kMat, vMat)
-			if err != nil {
-				return nil, err
-			}
-		}
-
-		out = tensor.Materialize(out)
-		out, err = tensor.Transpose(out, 1, 0)
-		if err != nil {
-			return nil, err
-		}
-		out = tensor.Materialize(out)
-
-		if err := out.Reshape(out.Shape().TotalSize()); err != nil {
-			return nil, err
-		}
-
-		return native.VectorF32(out.(*tensor.Dense))
-	}
-}
-
-// addOne adds 1.0 to all elements in the tensor (for norm weights)
-func (*qwen3NextModel) 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
-}
-
-func (q *qwen3NextModel) Replacements() []string {
-	return []string{
-		// Embeddings and output
-		"lm_head", "output",
-		"model.embed_tokens", "token_embd",
-		"model.norm", "output_norm",
-		"model.layers", "blk",
-
-		// Layer norms
-		"input_layernorm", "attn_norm",
-		"post_attention_layernorm", "post_attention_norm",
-
-		// Full attention (self_attn)
-		"self_attn.q_proj", "attn_q",
-		"self_attn.q_norm", "attn_q_norm",
-		"self_attn.k_proj", "attn_k",
-		"self_attn.k_norm", "attn_k_norm",
-		"self_attn.v_proj", "attn_v",
-		"self_attn.o_proj", "attn_output",
-
-		// Linear attention (Gated Delta Net)
-		"linear_attn.in_proj_qkvz", "ssm_in",
-		"linear_attn.in_proj_ba", "ssm_ba",
-		"linear_attn.conv1d", "ssm_conv1d",
-		"linear_attn.dt_bias", "ssm_dt",
-		"linear_attn.dt_proj", "ssm_dt",
-		"linear_attn.A_log", "ssm_a",
-		"linear_attn.norm", "ssm_norm",
-		"linear_attn.out_proj", "ssm_out",
-
-		// MoE (experts are stacked via mergeTensors, not replaced here)
-		"mlp.gate.weight", "ffn_gate_inp.weight",
-		"mlp.shared_expert.down_proj", "ffn_down_shexp",
-		"mlp.shared_expert.gate_proj", "ffn_gate_shexp",
-		"mlp.shared_expert.up_proj", "ffn_up_shexp",
-		"mlp.shared_expert_gate", "ffn_gate_inp_shexp",
-
-		// Dense FFN (if any layers use it)
-		"mlp.down_proj", "ffn_down",
-		"mlp.gate_proj", "ffn_gate",
-		"mlp.up_proj", "ffn_up",
-	}
-}

convert/reader.go

@@ -41,7 +41,6 @@ func (t tensorBase) Kind() uint32 {
 	if strings.HasSuffix(t.name, ".ffn_gate_inp.weight") ||
 		strings.HasSuffix(t.name, ".bias") ||
 		strings.HasSuffix(t.name, ".shortconv.conv.weight") ||
-		strings.HasSuffix(t.name, ".ssm_conv1d.weight") || // SSM conv kernel must be F32 for Metal
 		t.name == "token_types.weight" ||
 		t.name == "v.positional_embedding_vlm" ||
 		t.name == "v.tile_position_embd.weight" ||

convert/reader_safetensors.go

@@ -99,8 +99,6 @@ func (st safetensor) Kind() uint32 {
 	if st.dtype == "BF16" &&
 		!strings.HasPrefix(st.name, "v.") &&
 		!strings.HasPrefix(st.name, "s.") &&
-		!strings.HasPrefix(st.name, "mm.") &&
-		!strings.Contains(st.name, "ffn_gate_inp_shexp.weight") &&
 		kind != tensorKindFP32 {
 		kind = tensorKindBF16
 	}

docs/docs.json

@@ -71,10 +71,6 @@
     {
       "source": "/api",
       "destination": "/api/introduction"
-    },
-    {
-      "source": "/integrations/clawdbot",
-      "destination": "/integrations/openclaw"
     }
   ],
   "navigation": {
@@ -107,7 +103,6 @@
             "pages": [
               "/integrations/claude-code",
               "/integrations/cline",
-              "/integrations/openclaw",
               "/integrations/codex",
               "/integrations/droid",
               "/integrations/goose",

docs/gpu.mdx

@@ -10,7 +10,6 @@ Check your compute compatibility to see if your card is supported:
 
 | Compute Capability | Family              | Cards                                                                                                                          |
 | ------------------ | ------------------- | ------------------------------------------------------------------------------------------------------------------------------ |
-| 12.1               | NVIDIA              | `GB10 (DGX Spark)`                                                                                                             |
 | 12.0               | GeForce RTX 50xx    | `RTX 5060` `RTX 5060 Ti` `RTX 5070` `RTX 5070 Ti` `RTX 5080` `RTX 5090`                                                        |
 |                    | NVIDIA Professional | `RTX PRO 4000 Blackwell` `RTX PRO 4500 Blackwell` `RTX PRO 5000 Blackwell` `RTX PRO 6000 Blackwell`                            |
 | 9.0                | NVIDIA              | `H200` `H100`                                                                                                                  |
@@ -164,4 +163,4 @@ To select specific Vulkan GPU(s), you can set the environment variable
 `GGML_VK_VISIBLE_DEVICES` to one or more numeric IDs on the Ollama server as
 described in the [FAQ](faq#how-do-i-configure-ollama-server). If you
 encounter any problems with Vulkan based GPUs, you can disable all Vulkan GPUs
-by setting `GGML_VK_VISIBLE_DEVICES=-1` 
+by setting `GGML_VK_VISIBLE_DEVICES=-1` 

docs/import.mdx

@@ -134,12 +134,22 @@ success
 
 ### Supported Quantizations
 
+- `q4_0`
+- `q4_1`
+- `q5_0`
+- `q5_1`
 - `q8_0`
 
 #### K-means Quantizations
 
+- `q3_K_S`
+- `q3_K_M`
+- `q3_K_L`
 - `q4_K_S`
 - `q4_K_M`
+- `q5_K_S`
+- `q5_K_M`
+- `q6_K`
 
 ## Sharing your model on ollama.com
 

docs/integrations/openclaw.mdx

@@ -1,50 +0,0 @@
----
-title: OpenClaw
----
-
-OpenClaw is a personal AI assistant that runs on your own devices. It bridges messaging services (WhatsApp, Telegram, Slack, Discord, iMessage, and more) to AI coding agents through a centralized gateway.
-
-## Install
-
-Install [OpenClaw](https://openclaw.ai/) 
-
-```bash
-npm install -g openclaw@latest
-```
-
-Then run the onboarding wizard:
-
-```bash
-openclaw onboard --install-daemon
-```
-
-<Note>OpenClaw requires a larger context window. It is recommended to use a context window of at least 64k tokens. See [Context length](/context-length) for more information.</Note>
-
-## Usage with Ollama
-
-### Quick setup
-
-```bash
-ollama launch openclaw
-```
-
-<Note>Previously known as Clawdbot. `ollama launch clawdbot` still works as an alias.</Note>
-
-This configures OpenClaw to use Ollama and starts the gateway.
-If the gateway is already running, no changes need to be made as the gateway will auto-reload the changes. 
-
-
-To configure without launching:
-
-```shell
-ollama launch openclaw --config
-```
-
-## Recommended Models
-
-- `qwen3-coder`
-- `glm-4.7`
-- `gpt-oss:20b`
-- `gpt-oss:120b`
-
-Cloud models are also available at [ollama.com/search?c=cloud](https://ollama.com/search?c=cloud).

docs/integrations/opencode.mdx

@@ -9,7 +9,7 @@ OpenCode is an open-source AI coding assistant that runs in your terminal.
 Install the [OpenCode CLI](https://opencode.ai):
 
 ```bash
-curl -fsSL https://opencode.ai/install | bash
+curl -fsSL https://opencode.ai/install.sh | bash
 ```
 
 <Note>OpenCode requires a larger context window. It is recommended to use a context window of at least 64k tokens. See [Context length](/context-length) for more information.</Note>

envconfig/config.go

@@ -201,7 +201,7 @@ var (
 	// Enable the new Ollama engine
 	NewEngine = Bool("OLLAMA_NEW_ENGINE")
 	// ContextLength sets the default context length
-	ContextLength = Uint("OLLAMA_CONTEXT_LENGTH", 0)
+	ContextLength = Uint("OLLAMA_CONTEXT_LENGTH", 4096)
 	// Auth enables authentication between the Ollama client and server
 	UseAuth = Bool("OLLAMA_AUTH")
 	// Enable Vulkan backend
@@ -290,7 +290,7 @@ func AsMap() map[string]EnvVar {
 		"OLLAMA_ORIGINS":           {"OLLAMA_ORIGINS", AllowedOrigins(), "A comma separated list of allowed origins"},
 		"OLLAMA_SCHED_SPREAD":      {"OLLAMA_SCHED_SPREAD", SchedSpread(), "Always schedule model across all GPUs"},
 		"OLLAMA_MULTIUSER_CACHE":   {"OLLAMA_MULTIUSER_CACHE", MultiUserCache(), "Optimize prompt caching for multi-user scenarios"},
-		"OLLAMA_CONTEXT_LENGTH":    {"OLLAMA_CONTEXT_LENGTH", ContextLength(), "Context length to use unless otherwise specified (default: 4k/32k/256k based on VRAM)"},
+		"OLLAMA_CONTEXT_LENGTH":    {"OLLAMA_CONTEXT_LENGTH", ContextLength(), "Context length to use unless otherwise specified (default: 4096)"},
 		"OLLAMA_NEW_ENGINE":        {"OLLAMA_NEW_ENGINE", NewEngine(), "Enable the new Ollama engine"},
 		"OLLAMA_REMOTES":           {"OLLAMA_REMOTES", Remotes(), "Allowed hosts for remote models (default \"ollama.com\")"},
 

envconfig/config_test.go

@@ -282,7 +282,7 @@ func TestVar(t *testing.T) {
 
 func TestContextLength(t *testing.T) {
 	cases := map[string]uint{
-		"":     0,
+		"":     4096,
 		"2048": 2048,
 	}
 

fs/ggml/ggml.go

@@ -268,10 +268,8 @@ func (kv KV) OllamaEngineRequired() bool {
 		"olmo3",
 		"qwen25vl",
 		"qwen3", "qwen3moe",
-		"qwen3next",
 		"qwen3vl", "qwen3vlmoe",
 		"glm4moelite",
-		"glmocr",
 		"lfm2",
 	}, kv.Architecture())
 }
@@ -861,13 +859,11 @@ func (f GGML) FlashAttention() bool {
 		"bert",
 		"gemma3",
 		"glm4moelite",
-		"glmocr",
 		"gptoss", "gpt-oss",
 		"lfm2",
 		"mistral3",
 		"olmo3",
 		"qwen3", "qwen3moe",
-		"qwen3next",
 		"qwen3vl", "qwen3vlmoe",
 	}, f.KV().String("general.architecture"))
 }

go.mod

@@ -27,7 +27,6 @@ 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/pkg/browser v0.0.0-20240102092130-5ac0b6a4141c
 	github.com/tkrajina/typescriptify-golang-structs v0.2.0
 	github.com/wk8/go-ordered-map/v2 v2.1.8
 	golang.org/x/image v0.22.0

go.sum

@@ -174,8 +174,6 @@ github.com/phpdave11/gofpdf v1.4.2/go.mod h1:zpO6xFn9yxo3YLyMvW8HcKWVdbNqgIfOOp2
 github.com/phpdave11/gofpdi v1.0.12/go.mod h1:vBmVV0Do6hSBHC8uKUQ71JGW+ZGQq74llk/7bXwjDoI=
 github.com/pierrec/lz4/v4 v4.1.8 h1:ieHkV+i2BRzngO4Wd/3HGowuZStgq6QkPsD1eolNAO4=
 github.com/pierrec/lz4/v4 v4.1.8/go.mod h1:gZWDp/Ze/IJXGXf23ltt2EXimqmTUXEy0GFuRQyBid4=
-github.com/pkg/browser v0.0.0-20240102092130-5ac0b6a4141c h1:+mdjkGKdHQG3305AYmdv1U2eRNDiU2ErMBj1gwrq8eQ=
-github.com/pkg/browser v0.0.0-20240102092130-5ac0b6a4141c/go.mod h1:7rwL4CYBLnjLxUqIJNnCWiEdr3bn6IUYi15bNlnbCCU=
 github.com/pkg/errors v0.8.1/go.mod h1:bwawxfHBFNV+L2hUp1rHADufV3IMtnDRdf1r5NINEl0=
 github.com/pkg/errors v0.9.1 h1:FEBLx1zS214owpjy7qsBeixbURkuhQAwrK5UwLGTwt4=
 github.com/pkg/errors v0.9.1/go.mod h1:bwawxfHBFNV+L2hUp1rHADufV3IMtnDRdf1r5NINEl0=
@@ -306,7 +304,6 @@ golang.org/x/sys v0.0.0-20210330210617-4fbd30eecc44/go.mod h1:h1NjWce9XRLGQEsW7w
 golang.org/x/sys v0.0.0-20210423082822-04245dca01da/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
 golang.org/x/sys v0.0.0-20210510120138-977fb7262007/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.0.0-20210630005230-0f9fa26af87c/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
-golang.org/x/sys v0.1.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.5.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.37.0 h1:fdNQudmxPjkdUTPnLn5mdQv7Zwvbvpaxqs831goi9kQ=

kvcache/cache.go

@@ -75,10 +75,3 @@ type Cache interface {
 	// removed by calling Remove(seq, 0, math.MaxInt32)
 	Remove(seq int, beginIndex, endIndex int32) error
 }
-
-// CheckpointCache optionally supports restoring recurrent state to a prior
-// position to avoid full prompt reprocessing when a prefix mismatch occurs.
-// The returned position is the number of tokens that can be kept (prefix length).
-type CheckpointCache interface {
-	PrepareRestore(seq int, targetPos int32) (int32, bool)
-}

llama/patches/0033-ggml-metal-solve_tri.patch

@@ -1,276 +0,0 @@
-From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
-From: Jeffrey Morgan <jmorganca@gmail.com>
-Date: Tue, 3 Feb 2026 12:00:00 -0800
-Subject: [PATCH] ggml: metal solve_tri
-
----
- ggml/src/ggml-metal/ggml-metal-device.cpp | 20 +++++++
- ggml/src/ggml-metal/ggml-metal-device.h   |  1 +
- ggml/src/ggml-metal/ggml-metal-device.m   | 11 ++++
- ggml/src/ggml-metal/ggml-metal-impl.h     | 21 ++++++++
- ggml/src/ggml-metal/ggml-metal-ops.cpp    | 63 +++++++++++++++++++++++
- ggml/src/ggml-metal/ggml-metal-ops.h      |  1 +
- ggml/src/ggml-metal/ggml-metal.metal      | 60 +++++++++++++++++++++
- 7 files changed, 177 insertions(+)
-
-diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp
-index 680904d13..83385c9ef 100644
---- a/ggml/src/ggml-metal/ggml-metal-device.cpp
-+++ b/ggml/src/ggml-metal/ggml-metal-device.cpp
-@@ -1370,6 +1370,26 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_l2_norm(ggml_met
-     return res;
- }
- 
-+ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_solve_tri(ggml_metal_library_t lib, const ggml_tensor * op) {
-+    assert(op->op == GGML_OP_SOLVE_TRI);
-+
-+    GGML_ASSERT(ggml_is_contiguous(op->src[0]));
-+    GGML_ASSERT(ggml_is_contiguous(op->src[1]));
-+
-+    char base[256];
-+    char name[256];
-+
-+    snprintf(base, 256, "kernel_solve_tri_f32");
-+    snprintf(name, 256, "%s", base);
-+
-+    ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
-+    if (!res.pipeline) {
-+        res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
-+    }
-+
-+    return res;
-+}
-+
- ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_group_norm(ggml_metal_library_t lib, const ggml_tensor * op) {
-     assert(op->op == GGML_OP_GROUP_NORM);
- 
-diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h
-index 0a8b9211a..8a9d17460 100644
---- a/ggml/src/ggml-metal/ggml-metal-device.h
-+++ b/ggml/src/ggml-metal/ggml-metal-device.h
-@@ -133,6 +133,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k
- struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k_merge       (ggml_metal_library_t lib, const struct ggml_tensor * op);
- struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin               (ggml_metal_library_t lib, enum ggml_op op, int32_t n_fuse, bool row);
- struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_l2_norm           (ggml_metal_library_t lib, const struct ggml_tensor * op);
-+struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_solve_tri         (ggml_metal_library_t lib, const struct ggml_tensor * op);
- struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_group_norm        (ggml_metal_library_t lib, const struct ggml_tensor * op);
- struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_norm              (ggml_metal_library_t lib, const struct ggml_tensor * op, int32_t n_fuse);
- struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_rope              (ggml_metal_library_t lib, const struct ggml_tensor * op);
-diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m
-index 7b5ee968c..4e5acfbe5 100644
---- a/ggml/src/ggml-metal/ggml-metal-device.m
-+++ b/ggml/src/ggml-metal/ggml-metal-device.m
-@@ -1023,6 +1023,17 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
-             return has_simdgroup_reduction && ggml_is_contiguous_rows(op->src[0]);
-         case GGML_OP_L2_NORM:
-             return has_simdgroup_reduction && (op->ne[0] % 4 == 0 && ggml_is_contiguous_1(op->src[0]));
-+        case GGML_OP_SOLVE_TRI:
-+            return ggml_is_contiguous(op->src[0]) &&
-+                ggml_is_contiguous(op->src[1]) &&
-+                op->src[0]->type == GGML_TYPE_F32 &&
-+                op->src[1]->type == GGML_TYPE_F32 &&
-+                op->type == GGML_TYPE_F32;
-+        case GGML_OP_COUNT_EQUAL:
-+            return has_simdgroup_reduction &&
-+                op->src[0]->type == GGML_TYPE_I32 &&
-+                op->src[1]->type == GGML_TYPE_I32 &&
-+                op->type == GGML_TYPE_I64;
-         case GGML_OP_ARGMAX:
-             return has_simdgroup_reduction;
-         case GGML_OP_NORM:
-diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h
-index 8944b07e9..cfdea9c07 100644
---- a/ggml/src/ggml-metal/ggml-metal-impl.h
-+++ b/ggml/src/ggml-metal/ggml-metal-impl.h
-@@ -500,6 +500,27 @@ typedef struct {
-     float    eps;
- } ggml_metal_kargs_l2_norm;
- 
-+typedef struct {
-+    int32_t  ne00;
-+    int32_t  ne01;
-+    int32_t  ne02;
-+    int32_t  ne03;
-+    uint64_t nb00;
-+    uint64_t nb01;
-+    uint64_t nb02;
-+    uint64_t nb03;
-+    int32_t  ne10;
-+    int32_t  ne11;
-+    uint64_t nb10;
-+    uint64_t nb11;
-+    uint64_t nb12;
-+    uint64_t nb13;
-+    uint64_t nb0;
-+    uint64_t nb1;
-+    uint64_t nb2;
-+    uint64_t nb3;
-+} ggml_metal_kargs_solve_tri;
-+
- typedef struct {
-     int64_t  ne00;
-     int64_t  ne01;
-diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp
-index 80864f303..4ac135603 100644
---- a/ggml/src/ggml-metal/ggml-metal-ops.cpp
-+++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp
-@@ -357,6 +357,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
-             {
-                 n_fuse = ggml_metal_op_l2_norm(ctx, idx);
-             } break;
-+        case GGML_OP_SOLVE_TRI:
-+            {
-+                n_fuse = ggml_metal_op_solve_tri(ctx, idx);
-+            } break;
-         case GGML_OP_GROUP_NORM:
-             {
-                 n_fuse = ggml_metal_op_group_norm(ctx, idx);
-@@ -2931,6 +2935,65 @@ int ggml_metal_op_l2_norm(ggml_metal_op_t ctx, int idx) {
-     return 1;
- }
- 
-+int ggml_metal_op_solve_tri(ggml_metal_op_t ctx, int idx) {
-+    ggml_tensor * op = ctx->node(idx);
-+
-+    ggml_metal_library_t lib = ctx->lib;
-+    ggml_metal_encoder_t enc = ctx->enc;
-+
-+    GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
-+    GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
-+    GGML_TENSOR_LOCALS( int32_t, ne1, op->src[1], ne);
-+    GGML_TENSOR_LOCALS(uint64_t, nb1, op->src[1], nb);
-+    GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
-+    GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
-+
-+    ggml_metal_kargs_solve_tri args = {
-+        /*.ne00 =*/ ne00,
-+        /*.ne01 =*/ ne01,
-+        /*.ne02 =*/ ne02,
-+        /*.ne03 =*/ ne03,
-+        /*.nb00 =*/ nb00,
-+        /*.nb01 =*/ nb01,
-+        /*.nb02 =*/ nb02,
-+        /*.nb03 =*/ nb03,
-+        /*.ne10 =*/ ne10,
-+        /*.ne11 =*/ ne11,
-+        /*.nb10 =*/ nb10,
-+        /*.nb11 =*/ nb11,
-+        /*.nb12 =*/ nb12,
-+        /*.nb13 =*/ nb13,
-+        /*.nb0  =*/ nb0,
-+        /*.nb1  =*/ nb1,
-+        /*.nb2  =*/ nb2,
-+        /*.nb3  =*/ nb3,
-+    };
-+
-+    auto pipeline = ggml_metal_library_get_pipeline_solve_tri(lib, op);
-+
-+    const int64_t ncols = ne10;
-+    const int64_t n_batches = (int64_t)ne02 * ne03;
-+    const int64_t nr = n_batches * ncols;
-+
-+    int nth = 64;
-+    nth = std::min(nth, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
-+    if (nth < 1) {
-+        nth = 1;
-+    }
-+
-+    const int64_t n_tg = (nr + nth - 1) / nth;
-+
-+    ggml_metal_encoder_set_pipeline(enc, pipeline);
-+    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-+    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-+    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[1]), 2);
-+    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         3);
-+
-+    ggml_metal_encoder_dispatch_threadgroups(enc, n_tg, 1, 1, nth, 1, 1);
-+
-+    return 1;
-+}
-+
- int ggml_metal_op_group_norm(ggml_metal_op_t ctx, int idx) {
-     ggml_tensor * op = ctx->node(idx);
- 
-diff --git a/ggml/src/ggml-metal/ggml-metal-ops.h b/ggml/src/ggml-metal/ggml-metal-ops.h
-index 902b54452..a475183d3 100644
---- a/ggml/src/ggml-metal/ggml-metal-ops.h
-+++ b/ggml/src/ggml-metal/ggml-metal-ops.h
-@@ -68,6 +68,7 @@ int ggml_metal_op_add_id            (ggml_metal_op_t ctx, int idx);
- int ggml_metal_op_flash_attn_ext    (ggml_metal_op_t ctx, int idx);
- int ggml_metal_op_bin               (ggml_metal_op_t ctx, int idx);
- int ggml_metal_op_l2_norm           (ggml_metal_op_t ctx, int idx);
-+int ggml_metal_op_solve_tri         (ggml_metal_op_t ctx, int idx);
- int ggml_metal_op_group_norm        (ggml_metal_op_t ctx, int idx);
- int ggml_metal_op_norm              (ggml_metal_op_t ctx, int idx);
- int ggml_metal_op_rope              (ggml_metal_op_t ctx, int idx);
-diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
-index d33c16079..c37447a10 100644
---- a/ggml/src/ggml-metal/ggml-metal.metal
-+++ b/ggml/src/ggml-metal/ggml-metal.metal
-@@ -3012,6 +3012,66 @@ kernel void kernel_l2_norm_f32(
-     }
- }
- 
-+kernel void kernel_solve_tri_f32(
-+        constant ggml_metal_kargs_solve_tri & args,
-+        device const char * src0,
-+        device const char * src1,
-+        device       char * dst,
-+        uint   tgpig[[threadgroup_position_in_grid]],
-+        ushort tpitg[[thread_position_in_threadgroup]],
-+        ushort   ntg[[threads_per_threadgroup]]) {
-+    const uint64_t ncols = (uint64_t) args.ne10;
-+    const uint64_t n_batches = (uint64_t) args.ne02 * (uint64_t) args.ne03;
-+    const uint64_t nr = n_batches * ncols;
-+
-+    const uint64_t gid = (uint64_t) tgpig * (uint64_t) ntg + (uint64_t) tpitg;
-+    if (gid >= nr) {
-+        return;
-+    }
-+
-+    const uint64_t i03 = gid / ((uint64_t) args.ne02 * ncols);
-+    const uint64_t rem = gid - i03 * (uint64_t) args.ne02 * ncols;
-+    const uint64_t i02 = rem / ncols;
-+    const uint64_t i01 = rem - i02 * ncols;
-+
-+    const uint64_t sa0 = args.nb00 / sizeof(float);
-+    const uint64_t sa1 = args.nb01 / sizeof(float);
-+    const uint64_t sa2 = args.nb02 / sizeof(float);
-+    const uint64_t sa3 = args.nb03 / sizeof(float);
-+
-+    const uint64_t sb0 = args.nb10 / sizeof(float);
-+    const uint64_t sb1 = args.nb11 / sizeof(float);
-+    const uint64_t sb2 = args.nb12 / sizeof(float);
-+    const uint64_t sb3 = args.nb13 / sizeof(float);
-+
-+    const uint64_t sx0 = args.nb0 / sizeof(float);
-+    const uint64_t sx1 = args.nb1 / sizeof(float);
-+    const uint64_t sx2 = args.nb2 / sizeof(float);
-+    const uint64_t sx3 = args.nb3 / sizeof(float);
-+
-+    device const float * A = (device const float *) src0;
-+    device const float * B = (device const float *) src1;
-+    device       float * X = (device       float *) dst;
-+
-+    const uint64_t A_base = i02 * sa2 + i03 * sa3;
-+    const uint64_t B_base = i02 * sb2 + i03 * sb3;
-+    const uint64_t X_base = i02 * sx2 + i03 * sx3;
-+
-+    const uint64_t n = (uint64_t) args.ne11;
-+
-+    for (uint64_t i00 = 0; i00 < n; ++i00) {
-+        float sum = 0.0f;
-+        for (uint64_t t = 0; t < i00; ++t) {
-+            sum += A[A_base + i00 * sa1 + t * sa0] *
-+                X[X_base + t * sx1 + i01 * sx0];
-+        }
-+
-+        const float diag = A[A_base + i00 * sa1 + i00 * sa0];
-+        X[X_base + i00 * sx1 + i01 * sx0] =
-+            (B[B_base + i00 * sb1 + i01 * sb0] - sum) / diag;
-+    }
-+}
-+
- kernel void kernel_group_norm_f32(
-         constant ggml_metal_kargs_group_norm & args,
-         device const float * src0,

llm/server.go

@@ -34,6 +34,7 @@ import (
 	"github.com/ollama/ollama/logutil"
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type filteredEnv []string
@@ -80,7 +81,6 @@ type LlamaServer interface {
 	GetPort() int
 	GetDeviceInfos(ctx context.Context) []ml.DeviceInfo
 	HasExited() bool
-	ContextLength() int
 }
 
 // llmServer is an instance of a runner hosting a single model
@@ -116,7 +116,7 @@ type llamaServer struct {
 type ollamaServer struct {
 	llmServer
 
-	textProcessor model.TextProcessor // textProcessor handles text encoding/decoding
+	tokenizer tokenizer.Tokenizer // textProcessor handles text encoding/decoding
 }
 
 // LoadModel will load a model from disk. The model must be in the GGML format.
@@ -142,11 +142,11 @@ func LoadModel(model string, maxArraySize int) (*ggml.GGML, error) {
 // NewLlamaServer will run a server for the given GPUs
 func NewLlamaServer(systemInfo ml.SystemInfo, gpus []ml.DeviceInfo, modelPath string, f *ggml.GGML, adapters, projectors []string, opts api.Options, numParallel int) (LlamaServer, error) {
 	var llamaModel *llama.Model
-	var textProcessor model.TextProcessor
+	var tokenizer tokenizer.Tokenizer
 	var err error
 	if envconfig.NewEngine() || f.KV().OllamaEngineRequired() {
 		if len(projectors) == 0 {
-			textProcessor, err = model.NewTextProcessor(modelPath)
+			tokenizer, err = model.NewTextProcessor(modelPath)
 		} else {
 			err = errors.New("split vision models aren't supported")
 		}
@@ -155,7 +155,7 @@ func NewLlamaServer(systemInfo ml.SystemInfo, gpus []ml.DeviceInfo, modelPath st
 			slog.Debug("model not yet supported by Ollama engine, switching to compatibility mode", "model", modelPath, "error", err)
 		}
 	}
-	if textProcessor == nil {
+	if tokenizer == nil {
 		llamaModel, err = llama.LoadModelFromFile(modelPath, llama.ModelParams{VocabOnly: true})
 		if err != nil {
 			return nil, err
@@ -211,7 +211,7 @@ func NewLlamaServer(systemInfo ml.SystemInfo, gpus []ml.DeviceInfo, modelPath st
 
 	kvct := strings.ToLower(envconfig.KvCacheType())
 
-	if textProcessor == nil {
+	if tokenizer == nil {
 		flashAttention := ml.FlashAttentionAuto
 		if faUserSet {
 			if fa {
@@ -261,7 +261,7 @@ func NewLlamaServer(systemInfo ml.SystemInfo, gpus []ml.DeviceInfo, modelPath st
 	gpuLibs := ml.LibraryPaths(gpus)
 	status := NewStatusWriter(os.Stderr)
 	cmd, port, err := StartRunner(
-		textProcessor != nil,
+		tokenizer != nil,
 		modelPath,
 		gpuLibs,
 		status,
@@ -310,8 +310,8 @@ func NewLlamaServer(systemInfo ml.SystemInfo, gpus []ml.DeviceInfo, modelPath st
 		}
 	}()
 
-	if textProcessor != nil {
-		return &ollamaServer{llmServer: s, textProcessor: textProcessor}, nil
+	if tokenizer != nil {
+		return &ollamaServer{llmServer: s, tokenizer: tokenizer}, nil
 	} else {
 		return &llamaServer{llmServer: s, ggml: f}, nil
 	}
@@ -1201,8 +1201,7 @@ func (s *llmServer) initModel(ctx context.Context, req LoadRequest, operation Lo
 
 	resp, err := http.DefaultClient.Do(r)
 	if err != nil {
-		slog.Error("do load request", "error", err)
-		return nil, errors.New("model failed to load, this may be due to resource limitations or an internal error, check ollama server logs for details")
+		return nil, fmt.Errorf("do load request: %w", err)
 	}
 	defer resp.Body.Close()
 
@@ -1774,7 +1773,7 @@ func (s *llamaServer) Tokenize(ctx context.Context, content string) ([]int, erro
 }
 
 func (s *ollamaServer) Tokenize(ctx context.Context, content string) ([]int, error) {
-	tokens, err := s.textProcessor.Encode(content, false)
+	tokens, err := s.tokenizer.Encode(content, false)
 	if err != nil {
 		return nil, err
 	}
@@ -1809,7 +1808,7 @@ func (s *ollamaServer) Detokenize(ctx context.Context, tokens []int) (string, er
 		toks[i] = int32(t)
 	}
 
-	content, err := s.textProcessor.Decode(toks)
+	content, err := s.tokenizer.Decode(toks)
 	if err != nil {
 		return "", err
 	}
@@ -1903,10 +1902,6 @@ func (s *llmServer) VRAMByGPU(id ml.DeviceID) uint64 {
 	return 0
 }
 
-func (s *llmServer) ContextLength() int {
-	return s.options.NumCtx
-}
-
 func (s *ollamaServer) GetDeviceInfos(ctx context.Context) []ml.DeviceInfo {
 	devices, err := ml.GetDevicesFromRunner(ctx, s)
 	if err != nil {

ml/backend.go

@@ -170,7 +170,6 @@ type Tensor interface {
 	Cos(ctx Context) Tensor
 	Tanh(ctx Context) Tensor
 	GELU(ctx Context, up ...Tensor) Tensor
-	GELU_ERF(ctx Context) Tensor
 	QuickGELU(ctx Context, up ...Tensor) Tensor
 	SILU(ctx Context, up ...Tensor) Tensor
 	RELU(ctx Context, up ...Tensor) Tensor
@@ -207,32 +206,6 @@ type Tensor interface {
 	Stddev(ctx Context) Tensor
 	Sqr(ctx Context) Tensor
 	Sqrt(ctx Context) Tensor
-	Exp(ctx Context) Tensor
-	Neg(ctx Context) Tensor
-
-	// Clamp clamps values to [min, max] range
-	Clamp(ctx Context, min, max float32) Tensor
-
-	// Softplus computes ln(1 + exp(x))
-	Softplus(ctx Context) Tensor
-
-	// CumSum computes cumulative sum along dimension 0
-	CumSum(ctx Context) Tensor
-
-	// Diag creates a diagonal matrix from a 1D tensor
-	Diag(ctx Context) Tensor
-
-	// Tri converts a matrix to triangular form (0=upper+diag, 1=upper, 2=lower+diag, 3=lower)
-	Tri(ctx Context, triType int) Tensor
-
-	// Fill fills a tensor with a constant value (in-place)
-	Fill(ctx Context, value float32) Tensor
-
-	// Repeat4D repeats tensor to match target shape
-	Repeat4D(ctx Context, dim0, dim1, dim2, dim3 int) Tensor
-
-	// SolveTri solves a triangular system Ax = B
-	SolveTri(ctx Context, b Tensor, lower, left, unitDiag bool) Tensor
 
 	Interpolate(ctx Context, dims [4]int, samplingMode SamplingMode) Tensor
 }

ml/backend/ggml/ggml.go

@@ -378,7 +378,7 @@ func New(modelPath string, params ml.BackendParams) (ml.Backend, error) {
 		}
 	}
 
-	maxGraphNodes := max(1024, len(meta.Tensors().Items())*32)
+	maxGraphNodes := max(1024, len(meta.Tensors().Items())*8)
 
 	sched := C.ggml_backend_sched_new_ext(
 		(*C.ggml_backend_t)(unsafe.Pointer(&schedBackends[0])),
@@ -1581,13 +1581,6 @@ func (t *Tensor) GELU(ctx ml.Context, t2 ...ml.Tensor) ml.Tensor {
 	}
 }
 
-func (t *Tensor) GELU_ERF(ctx ml.Context) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_gelu_erf_inplace(ctx.(*Context).ctx, t.t),
-	}
-}
-
 func (t *Tensor) QuickGELU(ctx ml.Context, t2 ...ml.Tensor) ml.Tensor {
 	var tt *C.struct_ggml_tensor
 	if len(t2) > 0 {
@@ -1779,76 +1772,6 @@ func (t *Tensor) Sqrt(ctx ml.Context) ml.Tensor {
 	}
 }
 
-func (t *Tensor) Exp(ctx ml.Context) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_exp(ctx.(*Context).ctx, t.t),
-	}
-}
-
-func (t *Tensor) Neg(ctx ml.Context) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_neg(ctx.(*Context).ctx, t.t),
-	}
-}
-
-func (t *Tensor) Clamp(ctx ml.Context, min, max float32) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_clamp(ctx.(*Context).ctx, t.t, C.float(min), C.float(max)),
-	}
-}
-
-func (t *Tensor) Softplus(ctx ml.Context) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_softplus(ctx.(*Context).ctx, t.t),
-	}
-}
-
-func (t *Tensor) CumSum(ctx ml.Context) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_cumsum(ctx.(*Context).ctx, t.t),
-	}
-}
-
-func (t *Tensor) Diag(ctx ml.Context) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_diag(ctx.(*Context).ctx, t.t),
-	}
-}
-
-func (t *Tensor) Tri(ctx ml.Context, triType int) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_tri(ctx.(*Context).ctx, t.t, C.enum_ggml_tri_type(triType)),
-	}
-}
-
-func (t *Tensor) Fill(ctx ml.Context, value float32) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_fill_inplace(ctx.(*Context).ctx, t.t, C.float(value)),
-	}
-}
-
-func (t *Tensor) Repeat4D(ctx ml.Context, dim0, dim1, dim2, dim3 int) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_repeat_4d(ctx.(*Context).ctx, t.t, C.int64_t(dim0), C.int64_t(dim1), C.int64_t(dim2), C.int64_t(dim3)),
-	}
-}
-
-func (t *Tensor) SolveTri(ctx ml.Context, b ml.Tensor, lower, left, unitDiag bool) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_solve_tri(ctx.(*Context).ctx, t.t, b.(*Tensor).t, C._Bool(lower), C._Bool(left), C._Bool(unitDiag)),
-	}
-}
-
 func (t *Tensor) Interpolate(ctx ml.Context, dims [4]int, samplingMode ml.SamplingMode) ml.Tensor {
 	var mode C.uint32_t
 	switch samplingMode {

ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-device.cpp

@@ -1370,26 +1370,6 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_l2_norm(ggml_met
     return res;
 }
 
-ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_solve_tri(ggml_metal_library_t lib, const ggml_tensor * op) {
-    assert(op->op == GGML_OP_SOLVE_TRI);
-
-    GGML_ASSERT(ggml_is_contiguous(op->src[0]));
-    GGML_ASSERT(ggml_is_contiguous(op->src[1]));
-
-    char base[256];
-    char name[256];
-
-    snprintf(base, 256, "kernel_solve_tri_f32");
-    snprintf(name, 256, "%s", base);
-
-    ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
-    if (!res.pipeline) {
-        res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
-    }
-
-    return res;
-}
-
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_group_norm(ggml_metal_library_t lib, const ggml_tensor * op) {
     assert(op->op == GGML_OP_GROUP_NORM);
 

ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-device.h

@@ -133,7 +133,6 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k_merge       (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin               (ggml_metal_library_t lib, enum ggml_op op, int32_t n_fuse, bool row);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_l2_norm           (ggml_metal_library_t lib, const struct ggml_tensor * op);
-struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_solve_tri         (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_group_norm        (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_norm              (ggml_metal_library_t lib, const struct ggml_tensor * op, int32_t n_fuse);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_rope              (ggml_metal_library_t lib, const struct ggml_tensor * op);

ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-device.m

@@ -1023,17 +1023,6 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
             return has_simdgroup_reduction && ggml_is_contiguous_rows(op->src[0]);
         case GGML_OP_L2_NORM:
             return has_simdgroup_reduction && (op->ne[0] % 4 == 0 && ggml_is_contiguous_1(op->src[0]));
-        case GGML_OP_SOLVE_TRI:
-            return ggml_is_contiguous(op->src[0]) &&
-                ggml_is_contiguous(op->src[1]) &&
-                op->src[0]->type == GGML_TYPE_F32 &&
-                op->src[1]->type == GGML_TYPE_F32 &&
-                op->type == GGML_TYPE_F32;
-        case GGML_OP_COUNT_EQUAL:
-            return has_simdgroup_reduction &&
-                op->src[0]->type == GGML_TYPE_I32 &&
-                op->src[1]->type == GGML_TYPE_I32 &&
-                op->type == GGML_TYPE_I64;
         case GGML_OP_ARGMAX:
             return has_simdgroup_reduction;
         case GGML_OP_NORM:

ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-embed.metal

@@ -2385,27 +2385,6 @@ typedef struct {
     float    eps;
 } ggml_metal_kargs_l2_norm;
 
-typedef struct {
-    int32_t  ne00;
-    int32_t  ne01;
-    int32_t  ne02;
-    int32_t  ne03;
-    uint64_t nb00;
-    uint64_t nb01;
-    uint64_t nb02;
-    uint64_t nb03;
-    int32_t  ne10;
-    int32_t  ne11;
-    uint64_t nb10;
-    uint64_t nb11;
-    uint64_t nb12;
-    uint64_t nb13;
-    uint64_t nb0;
-    uint64_t nb1;
-    uint64_t nb2;
-    uint64_t nb3;
-} ggml_metal_kargs_solve_tri;
-
 typedef struct {
     int64_t  ne00;
     int64_t  ne01;
@@ -5834,66 +5813,6 @@ kernel void kernel_l2_norm_f32(
     }
 }
 
-kernel void kernel_solve_tri_f32(
-        constant ggml_metal_kargs_solve_tri & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint   tgpig[[threadgroup_position_in_grid]],
-        ushort tpitg[[thread_position_in_threadgroup]],
-        ushort   ntg[[threads_per_threadgroup]]) {
-    const uint64_t ncols = (uint64_t) args.ne10;
-    const uint64_t n_batches = (uint64_t) args.ne02 * (uint64_t) args.ne03;
-    const uint64_t nr = n_batches * ncols;
-
-    const uint64_t gid = (uint64_t) tgpig * (uint64_t) ntg + (uint64_t) tpitg;
-    if (gid >= nr) {
-        return;
-    }
-
-    const uint64_t i03 = gid / ((uint64_t) args.ne02 * ncols);
-    const uint64_t rem = gid - i03 * (uint64_t) args.ne02 * ncols;
-    const uint64_t i02 = rem / ncols;
-    const uint64_t i01 = rem - i02 * ncols;
-
-    const uint64_t sa0 = args.nb00 / sizeof(float);
-    const uint64_t sa1 = args.nb01 / sizeof(float);
-    const uint64_t sa2 = args.nb02 / sizeof(float);
-    const uint64_t sa3 = args.nb03 / sizeof(float);
-
-    const uint64_t sb0 = args.nb10 / sizeof(float);
-    const uint64_t sb1 = args.nb11 / sizeof(float);
-    const uint64_t sb2 = args.nb12 / sizeof(float);
-    const uint64_t sb3 = args.nb13 / sizeof(float);
-
-    const uint64_t sx0 = args.nb0 / sizeof(float);
-    const uint64_t sx1 = args.nb1 / sizeof(float);
-    const uint64_t sx2 = args.nb2 / sizeof(float);
-    const uint64_t sx3 = args.nb3 / sizeof(float);
-
-    device const float * A = (device const float *) src0;
-    device const float * B = (device const float *) src1;
-    device       float * X = (device       float *) dst;
-
-    const uint64_t A_base = i02 * sa2 + i03 * sa3;
-    const uint64_t B_base = i02 * sb2 + i03 * sb3;
-    const uint64_t X_base = i02 * sx2 + i03 * sx3;
-
-    const uint64_t n = (uint64_t) args.ne11;
-
-    for (uint64_t i00 = 0; i00 < n; ++i00) {
-        float sum = 0.0f;
-        for (uint64_t t = 0; t < i00; ++t) {
-            sum += A[A_base + i00 * sa1 + t * sa0] *
-                X[X_base + t * sx1 + i01 * sx0];
-        }
-
-        const float diag = A[A_base + i00 * sa1 + i00 * sa0];
-        X[X_base + i00 * sx1 + i01 * sx0] =
-            (B[B_base + i00 * sb1 + i01 * sb0] - sum) / diag;
-    }
-}
-
 kernel void kernel_group_norm_f32(
         constant ggml_metal_kargs_group_norm & args,
         device const float * src0,

ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-impl.h

@@ -500,27 +500,6 @@ typedef struct {
     float    eps;
 } ggml_metal_kargs_l2_norm;
 
-typedef struct {
-    int32_t  ne00;
-    int32_t  ne01;
-    int32_t  ne02;
-    int32_t  ne03;
-    uint64_t nb00;
-    uint64_t nb01;
-    uint64_t nb02;
-    uint64_t nb03;
-    int32_t  ne10;
-    int32_t  ne11;
-    uint64_t nb10;
-    uint64_t nb11;
-    uint64_t nb12;
-    uint64_t nb13;
-    uint64_t nb0;
-    uint64_t nb1;
-    uint64_t nb2;
-    uint64_t nb3;
-} ggml_metal_kargs_solve_tri;
-
 typedef struct {
     int64_t  ne00;
     int64_t  ne01;

ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-ops.cpp

@@ -357,10 +357,6 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
             {
                 n_fuse = ggml_metal_op_l2_norm(ctx, idx);
             } break;
-        case GGML_OP_SOLVE_TRI:
-            {
-                n_fuse = ggml_metal_op_solve_tri(ctx, idx);
-            } break;
         case GGML_OP_GROUP_NORM:
             {
                 n_fuse = ggml_metal_op_group_norm(ctx, idx);
@@ -2935,65 +2931,6 @@ int ggml_metal_op_l2_norm(ggml_metal_op_t ctx, int idx) {
     return 1;
 }
 
-int ggml_metal_op_solve_tri(ggml_metal_op_t ctx, int idx) {
-    ggml_tensor * op = ctx->node(idx);
-
-    ggml_metal_library_t lib = ctx->lib;
-    ggml_metal_encoder_t enc = ctx->enc;
-
-    GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
-    GGML_TENSOR_LOCALS( int32_t, ne1, op->src[1], ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb1, op->src[1], nb);
-    GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
-    GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
-
-    ggml_metal_kargs_solve_tri args = {
-        /*.ne00 =*/ ne00,
-        /*.ne01 =*/ ne01,
-        /*.ne02 =*/ ne02,
-        /*.ne03 =*/ ne03,
-        /*.nb00 =*/ nb00,
-        /*.nb01 =*/ nb01,
-        /*.nb02 =*/ nb02,
-        /*.nb03 =*/ nb03,
-        /*.ne10 =*/ ne10,
-        /*.ne11 =*/ ne11,
-        /*.nb10 =*/ nb10,
-        /*.nb11 =*/ nb11,
-        /*.nb12 =*/ nb12,
-        /*.nb13 =*/ nb13,
-        /*.nb0  =*/ nb0,
-        /*.nb1  =*/ nb1,
-        /*.nb2  =*/ nb2,
-        /*.nb3  =*/ nb3,
-    };
-
-    auto pipeline = ggml_metal_library_get_pipeline_solve_tri(lib, op);
-
-    const int64_t ncols = ne10;
-    const int64_t n_batches = (int64_t)ne02 * ne03;
-    const int64_t nr = n_batches * ncols;
-
-    int nth = 64;
-    nth = std::min(nth, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
-    if (nth < 1) {
-        nth = 1;
-    }
-
-    const int64_t n_tg = (nr + nth - 1) / nth;
-
-    ggml_metal_encoder_set_pipeline(enc, pipeline);
-    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[1]), 2);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         3);
-
-    ggml_metal_encoder_dispatch_threadgroups(enc, n_tg, 1, 1, nth, 1, 1);
-
-    return 1;
-}
-
 int ggml_metal_op_group_norm(ggml_metal_op_t ctx, int idx) {
     ggml_tensor * op = ctx->node(idx);
 

ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-ops.h

@@ -68,7 +68,6 @@ int ggml_metal_op_add_id            (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_flash_attn_ext    (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_bin               (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_l2_norm           (ggml_metal_op_t ctx, int idx);
-int ggml_metal_op_solve_tri         (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_group_norm        (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_norm              (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_rope              (ggml_metal_op_t ctx, int idx);

ml/backend/ggml/ggml/src/ggml-metal/ggml-metal.metal

@@ -3012,66 +3012,6 @@ kernel void kernel_l2_norm_f32(
     }
 }
 
-kernel void kernel_solve_tri_f32(
-        constant ggml_metal_kargs_solve_tri & args,
-        device const char * src0,
-        device const char * src1,
-        device       char * dst,
-        uint   tgpig[[threadgroup_position_in_grid]],
-        ushort tpitg[[thread_position_in_threadgroup]],
-        ushort   ntg[[threads_per_threadgroup]]) {
-    const uint64_t ncols = (uint64_t) args.ne10;
-    const uint64_t n_batches = (uint64_t) args.ne02 * (uint64_t) args.ne03;
-    const uint64_t nr = n_batches * ncols;
-
-    const uint64_t gid = (uint64_t) tgpig * (uint64_t) ntg + (uint64_t) tpitg;
-    if (gid >= nr) {
-        return;
-    }
-
-    const uint64_t i03 = gid / ((uint64_t) args.ne02 * ncols);
-    const uint64_t rem = gid - i03 * (uint64_t) args.ne02 * ncols;
-    const uint64_t i02 = rem / ncols;
-    const uint64_t i01 = rem - i02 * ncols;
-
-    const uint64_t sa0 = args.nb00 / sizeof(float);
-    const uint64_t sa1 = args.nb01 / sizeof(float);
-    const uint64_t sa2 = args.nb02 / sizeof(float);
-    const uint64_t sa3 = args.nb03 / sizeof(float);
-
-    const uint64_t sb0 = args.nb10 / sizeof(float);
-    const uint64_t sb1 = args.nb11 / sizeof(float);
-    const uint64_t sb2 = args.nb12 / sizeof(float);
-    const uint64_t sb3 = args.nb13 / sizeof(float);
-
-    const uint64_t sx0 = args.nb0 / sizeof(float);
-    const uint64_t sx1 = args.nb1 / sizeof(float);
-    const uint64_t sx2 = args.nb2 / sizeof(float);
-    const uint64_t sx3 = args.nb3 / sizeof(float);
-
-    device const float * A = (device const float *) src0;
-    device const float * B = (device const float *) src1;
-    device       float * X = (device       float *) dst;
-
-    const uint64_t A_base = i02 * sa2 + i03 * sa3;
-    const uint64_t B_base = i02 * sb2 + i03 * sb3;
-    const uint64_t X_base = i02 * sx2 + i03 * sx3;
-
-    const uint64_t n = (uint64_t) args.ne11;
-
-    for (uint64_t i00 = 0; i00 < n; ++i00) {
-        float sum = 0.0f;
-        for (uint64_t t = 0; t < i00; ++t) {
-            sum += A[A_base + i00 * sa1 + t * sa0] *
-                X[X_base + t * sx1 + i01 * sx0];
-        }
-
-        const float diag = A[A_base + i00 * sa1 + i00 * sa0];
-        X[X_base + i00 * sx1 + i01 * sx0] =
-            (B[B_base + i00 * sb1 + i01 * sb0] - sum) / diag;
-    }
-}
-
 kernel void kernel_group_norm_f32(
         constant ggml_metal_kargs_group_norm & args,
         device const float * src0,

model/model.go

@@ -23,6 +23,7 @@ import (
 	_ "github.com/ollama/ollama/ml/backend"
 	"github.com/ollama/ollama/ml/nn/pooling"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 var (
@@ -133,7 +134,7 @@ func New(modelPath string, params ml.BackendParams) (Model, error) {
 	return m, nil
 }
 
-func NewTextProcessor(s string) (TextProcessor, error) {
+func NewTextProcessor(s string) (tokenizer.Tokenizer, error) {
 	r, err := os.Open(s)
 	if err != nil {
 		return nil, err
@@ -150,7 +151,7 @@ func NewTextProcessor(s string) (TextProcessor, error) {
 		return nil, err
 	}
 
-	tp, ok := m.(TextProcessor)
+	tp, ok := m.(tokenizer.Tokenizer)
 	if !ok {
 		return nil, ErrUnsupportedTokenizer
 	}

model/model_test.go

@@ -56,18 +56,6 @@ type fakeTensor struct {
 	Name string
 }
 
-// Stub methods to satisfy ml.Tensor interface
-func (f *fakeTensor) Exp(ctx ml.Context) ml.Tensor                                 { return f }
-func (f *fakeTensor) Neg(ctx ml.Context) ml.Tensor                                 { return f }
-func (f *fakeTensor) Clamp(ctx ml.Context, _, _ float32) ml.Tensor                 { return f }
-func (f *fakeTensor) Softplus(ctx ml.Context) ml.Tensor                            { return f }
-func (f *fakeTensor) CumSum(ctx ml.Context) ml.Tensor                              { return f }
-func (f *fakeTensor) Diag(ctx ml.Context) ml.Tensor                                { return f }
-func (f *fakeTensor) Tri(ctx ml.Context, _ int) ml.Tensor                          { return f }
-func (f *fakeTensor) Fill(ctx ml.Context, _ float32) ml.Tensor                     { return f }
-func (f *fakeTensor) Repeat4D(ctx ml.Context, _, _, _, _ int) ml.Tensor            { return f }
-func (f *fakeTensor) SolveTri(ctx ml.Context, _ ml.Tensor, _, _, _ bool) ml.Tensor { return f }
-
 func (m *fakeBackend) Get(name string) ml.Tensor {
 	if slices.Contains(m.names, name) {
 		return &fakeTensor{Name: name}

model/models/bert/embed.go

@@ -10,11 +10,12 @@ import (
 	"github.com/ollama/ollama/ml/nn/pooling"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Model struct {
 	model.Base
-	model.TextProcessor
+	tokenizer.Tokenizer
 
 	TokenEmbedding     *nn.Embedding `gguf:"token_embd"`
 	TypeEmbedding      *nn.Embedding `gguf:"token_types"`
@@ -129,7 +130,7 @@ func (o Options) headDim() int {
 }
 
 func New(c fs.Config) (model.Model, error) {
-	vocab := &model.Vocabulary{
+	vocab := &tokenizer.Vocabulary{
 		Values: c.Strings("tokenizer.ggml.tokens"),
 		Scores: c.Floats("tokenizer.ggml.scores"),
 		Types:  c.Ints("tokenizer.ggml.token_type"),
@@ -153,17 +154,17 @@ func New(c fs.Config) (model.Model, error) {
 		},
 	}
 
-	var processor model.TextProcessor
+	var t tokenizer.Tokenizer
 	switch c.String("tokenizer.ggml.model", "bert") {
 	case "bert":
-		processor = model.NewWordPiece(vocab, true)
+		t = tokenizer.NewWordPiece(vocab, true)
 	default:
 		return nil, model.ErrUnsupportedTokenizer
 	}
 
 	return &Model{
-		TextProcessor: processor,
-		Layers:        make([]EncoderLayer, c.Uint("block_count")),
+		Tokenizer: t,
+		Layers:    make([]EncoderLayer, c.Uint("block_count")),
 		Options: Options{
 			hiddenSize:  int(c.Uint("embedding_length")),
 			numHeads:    int(c.Uint("attention.head_count")),

model/models/deepseek2/model.go

@@ -13,6 +13,7 @@ import (
 	"github.com/ollama/ollama/ml/nn/rope"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Options struct {
@@ -222,7 +223,7 @@ func (t *Layer) Forward(ctx ml.Context, hiddenStates, positions, outputs ml.Tens
 
 type Model struct {
 	model.Base
-	model.BytePairEncoding
+	tokenizer.Tokenizer
 
 	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
 	Layers         []Layer       `gguf:"blk"`
@@ -277,8 +278,8 @@ func New(c fs.Config) (model.Model, error) {
 	}
 
 	m := Model{
-		BytePairEncoding: model.NewBytePairEncoding(
-			&model.Vocabulary{
+		Tokenizer: tokenizer.NewBytePairEncoding(
+			&tokenizer.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),

model/models/deepseekocr/model.go

@@ -10,11 +10,12 @@ import (
 	"github.com/ollama/ollama/ml/nn"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Model struct {
 	model.Base
-	model.TextProcessor
+	tokenizer.Tokenizer
 
 	Sam    *samModel    `gguf:"s"`
 	Vision *visionModel `gguf:"v"`
@@ -134,8 +135,8 @@ func init() {
 		}
 
 		m := Model{
-			TextProcessor: model.NewBytePairEncoding(
-				&model.Vocabulary{
+			Tokenizer: tokenizer.NewBytePairEncoding(
+				&tokenizer.Vocabulary{
 					Values: c.Strings("tokenizer.ggml.tokens"),
 					Types:  c.Ints("tokenizer.ggml.token_type"),
 					Merges: c.Strings("tokenizer.ggml.merges"),

model/models/gemma2/model.go

@@ -10,6 +10,7 @@ import (
 	"github.com/ollama/ollama/ml/nn/rope"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Options struct {
@@ -27,7 +28,7 @@ func (o Options) applyRotaryPositionEmbeddings(ctx ml.Context, states, positions
 
 type Model struct {
 	model.Base
-	model.SentencePiece
+	tokenizer.Tokenizer
 
 	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
 	Layers         []Layer       `gguf:"blk"`
@@ -43,8 +44,8 @@ const (
 
 func New(c fs.Config) (model.Model, error) {
 	m := Model{
-		SentencePiece: model.NewSentencePiece(
-			&model.Vocabulary{
+		Tokenizer: tokenizer.NewSentencePiece(
+			&tokenizer.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Scores: c.Floats("tokenizer.ggml.scores"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),

model/models/gemma3/embed.go

@@ -7,11 +7,12 @@ import (
 	"github.com/ollama/ollama/ml/nn/pooling"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type embedModel struct {
 	model.Base
-	model.SentencePiece
+	tokenizer.Tokenizer
 
 	*TextModel
 	poolingType pooling.Type
@@ -31,8 +32,8 @@ func (m *embedModel) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, erro
 
 func newEmbedModel(c fs.Config) (model.Model, error) {
 	m := &embedModel{
-		SentencePiece: model.NewSentencePiece(
-			&model.Vocabulary{
+		Tokenizer: tokenizer.NewSentencePiece(
+			&tokenizer.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Scores: c.Floats("tokenizer.ggml.scores"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),

model/models/gemma3/model.go

@@ -12,11 +12,12 @@ import (
 	"github.com/ollama/ollama/ml/nn"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Model struct {
 	model.Base
-	model.TextProcessor
+	tokenizer.Tokenizer
 
 	*VisionModel `gguf:"v"`
 	*TextModel
@@ -54,7 +55,7 @@ func (p *MultiModalProjector) Forward(ctx ml.Context, visionOutputs ml.Tensor, i
 }
 
 func New(c fs.Config) (model.Model, error) {
-	vocabulary := model.Vocabulary{
+	vocabulary := tokenizer.Vocabulary{
 		Values: c.Strings("tokenizer.ggml.tokens"),
 		Scores: c.Floats("tokenizer.ggml.scores"),
 		Types:  c.Ints("tokenizer.ggml.token_type"),
@@ -70,19 +71,19 @@ func New(c fs.Config) (model.Model, error) {
 		),
 	}
 
-	var processor model.TextProcessor
+	var t tokenizer.Tokenizer
 	switch c.String("tokenizer.ggml.model") {
 	case "gpt2":
-		processor = model.NewBytePairEncoding(&vocabulary)
+		t = tokenizer.NewBytePairEncoding(&vocabulary)
 	default:
 		// Previous uploads of Gemma 3 on Ollama did not have token 106
 		// (i.e. "<end_of_turn>") so we need to add in case it's not already present
 		vocabulary.EOS = append(vocabulary.EOS, int32(c.Uint("tokenizer.ggml.eot_token_id", 106)))
-		processor = model.NewSentencePiece(&vocabulary)
+		t = tokenizer.NewSentencePiece(&vocabulary)
 	}
 
 	m := Model{
-		TextProcessor:  processor,
+		Tokenizer:      t,
 		ImageProcessor: newImageProcessor(c),
 		VisionModel:    newVisionModel(c),
 		TextModel:      newTextModel(c),

model/models/gemma3n/model.go

@@ -6,11 +6,12 @@ import (
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Model struct {
 	model.Base
-	model.SentencePiece
+	tokenizer.Tokenizer
 
 	*TextModel
 }
@@ -23,8 +24,8 @@ func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 func New(c fs.Config) (model.Model, error) {
 	m := Model{
 		TextModel: newTextModel(c),
-		SentencePiece: model.NewSentencePiece(
-			&model.Vocabulary{
+		Tokenizer: tokenizer.NewSentencePiece(
+			&tokenizer.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Scores: c.Floats("tokenizer.ggml.scores"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),

model/models/glm4moelite/model.go

@@ -10,6 +10,7 @@ import (
 	"github.com/ollama/ollama/ml/nn"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 var ErrOldModelFormat = errors.New("this model uses a weight format that is no longer supported; please re-download it")
@@ -198,7 +199,7 @@ func (t *Layer) Forward(ctx ml.Context, hiddenStates, positions, outputs ml.Tens
 
 type Model struct {
 	model.Base
-	model.BytePairEncoding
+	tokenizer.Tokenizer
 
 	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
 	Layers         []Layer       `gguf:"blk"`
@@ -236,8 +237,8 @@ func New(c fs.Config) (model.Model, error) {
 	}
 
 	m := Model{
-		BytePairEncoding: model.NewBytePairEncoding(
-			&model.Vocabulary{
+		Tokenizer: tokenizer.NewBytePairEncoding(
+			&tokenizer.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),

model/models/glmocr/imageprocessor.go

@@ -1,174 +0,0 @@
-package glmocr
-
-import (
-	"image"
-	"log/slog"
-	"math"
-
-	"github.com/ollama/ollama/fs"
-	"github.com/ollama/ollama/model/imageproc"
-)
-
-type ImageProcessor struct {
-	imageSize         int
-	patchSize         int
-	temporalPatchSize int
-	spatialMergeSize  int
-	minPixels         int
-	maxPixels         int
-	factor            int
-	imageMean         [3]float32
-	imageStd          [3]float32
-}
-
-func newImageProcessor(c fs.Config) ImageProcessor {
-	patchSize := int(c.Uint("vision.patch_size", 14))
-	spatialMergeSize := int(c.Uint("vision.spatial_merge_size", 2))
-	temporalPatchSize := int(c.Uint("vision.temporal_patch_size", 2))
-
-	// Read normalization values from config if available, otherwise use CLIP defaults
-	imageMean := c.Floats("vision.image_mean", imageproc.ClipDefaultMean[:])
-	imageStd := c.Floats("vision.image_std", imageproc.ClipDefaultSTD[:])
-
-	// Default max_pixels: 2048 * patchSize^2 * mergeSize^2 * temporal = ~3.2M pixels
-	// This limits to ~16k patches (4k output tokens) to keep memory stable without flash attention
-	defaultMaxPixels := 2048 * patchSize * patchSize * spatialMergeSize * spatialMergeSize * temporalPatchSize
-
-	return ImageProcessor{
-		imageSize:         int(c.Uint("vision.image_size", 336)),
-		patchSize:         patchSize,
-		temporalPatchSize: temporalPatchSize,
-		spatialMergeSize:  spatialMergeSize,
-		minPixels:         int(c.Uint("vision.min_pixels", uint32(8*patchSize*patchSize*spatialMergeSize*spatialMergeSize*temporalPatchSize))),
-		maxPixels:         int(c.Uint("vision.max_pixels", uint32(defaultMaxPixels))),
-		factor:            patchSize * spatialMergeSize,
-		imageMean:         [3]float32{imageMean[0], imageMean[1], imageMean[2]},
-		imageStd:          [3]float32{imageStd[0], imageStd[1], imageStd[2]},
-	}
-}
-
-func (p *ImageProcessor) SmartResize(height, width int) (int, int) {
-	factor := p.factor
-	temporalFactor := p.temporalPatchSize
-	numFrames := temporalFactor // single image
-
-	if height < factor || width < factor {
-		// Scale up small images
-		scale := float64(factor) / float64(min(height, width))
-		height = int(math.Ceil(float64(height) * scale))
-		width = int(math.Ceil(float64(width) * scale))
-	}
-
-	if temporalFactor <= 0 {
-		slog.Warn("temporal_patch_size must be > 0, defaulting to 1")
-		temporalFactor = 1
-	}
-	if numFrames < temporalFactor {
-		slog.Warn("num_frames must be >= temporal_patch_size, adjusting num_frames", "num_frames", numFrames, "temporal_patch_size", temporalFactor)
-		numFrames = temporalFactor
-	}
-	if aspectRatio := float64(max(height, width)) / float64(min(height, width)); aspectRatio > 200 {
-		slog.Warn("aspect ratio exceeds 200, image quality may be affected", "aspect_ratio", aspectRatio)
-	}
-
-	round := func(x float64) int { return int(math.RoundToEven(x)) }
-
-	hBar := round(float64(height)/float64(factor)) * factor
-	wBar := round(float64(width)/float64(factor)) * factor
-	tBar := round(float64(numFrames)/float64(temporalFactor)) * temporalFactor
-
-	if tBar*hBar*wBar > p.maxPixels {
-		beta := math.Sqrt(float64(numFrames*height*width) / float64(p.maxPixels))
-		hBar = int(math.Floor(float64(height)/beta/float64(factor))) * factor
-		wBar = int(math.Floor(float64(width)/beta/float64(factor))) * factor
-	} else if tBar*hBar*wBar < p.minPixels {
-		beta := math.Sqrt(float64(p.minPixels) / float64(numFrames*height*width))
-		hBar = int(math.Ceil(float64(height)*beta/float64(factor))) * factor
-		wBar = int(math.Ceil(float64(width)*beta/float64(factor))) * factor
-	}
-
-	return hBar, wBar
-}
-
-func (p *ImageProcessor) ProcessImage(img image.Image) ([]float32, *Grid, error) {
-	img = imageproc.Composite(img)
-
-	origWidth := img.Bounds().Dx()
-	origHeight := img.Bounds().Dy()
-
-	// Calculate smart resize dimensions
-	resizedHeight, resizedWidth := p.SmartResize(origHeight, origWidth)
-
-	// Resize image
-	resizedImg := imageproc.Resize(img, image.Point{X: resizedWidth, Y: resizedHeight}, imageproc.ResizeCatmullrom)
-
-	// Normalize pixels - output format is [C, H, W] with rescale and channelFirst
-	// We keep [C, H, W] for patch extraction
-	normalizedPixels := imageproc.Normalize(resizedImg, p.imageMean, p.imageStd, true, true)
-
-	// Calculate grid dimensions (after Conv2D patching)
-	grid := &Grid{
-		Height:      resizedHeight / p.patchSize,
-		Width:       resizedWidth / p.patchSize,
-		Temporal:    1, // Single image
-		ImageHeight: resizedHeight,
-		ImageWidth:  resizedWidth,
-	}
-
-	patches, err := p.createPatches(normalizedPixels, resizedHeight, resizedWidth, grid)
-	if err != nil {
-		return nil, nil, err
-	}
-
-	return patches, grid, nil
-}
-
-func (p *ImageProcessor) createPatches(pixels []float32, height, width int, grid *Grid) ([]float32, error) {
-	channels := 3
-	patchSize := p.patchSize
-	mergeSize := p.spatialMergeSize
-	temporalPatchSize := p.temporalPatchSize
-
-	numPatches := grid.Temporal * grid.Height * grid.Width
-	patchDim := channels * temporalPatchSize * patchSize * patchSize
-	result := make([]float32, numPatches*patchDim)
-	patchIndex := 0
-
-	// Single temporal frame handling (copies to all frames)
-	for range grid.Temporal {
-		for h := 0; h < grid.Height; h += mergeSize {
-			for w := 0; w < grid.Width; w += mergeSize {
-				for mh := range mergeSize {
-					for mw := range mergeSize {
-						baseOffset := patchIndex * patchDim
-						for c := range channels {
-							channelOffset := baseOffset + (c * temporalPatchSize * patchSize * patchSize)
-							for py := range patchSize {
-								for px := range patchSize {
-									y := (h+mh)*patchSize + py
-									x := (w+mw)*patchSize + px
-									srcIdx := c*height*width + y*width + x
-									dstIdx := channelOffset + (py * patchSize) + px
-									result[dstIdx] = pixels[srcIdx]
-								}
-							}
-
-							if temporalPatchSize > 1 {
-								frameSize := patchSize * patchSize
-								for tp := 1; tp < temporalPatchSize; tp++ {
-									currentFrameOffset := channelOffset + (tp * frameSize)
-									copy(result[currentFrameOffset:currentFrameOffset+frameSize],
-										result[channelOffset:channelOffset+frameSize])
-								}
-							}
-						}
-
-						patchIndex++
-					}
-				}
-			}
-		}
-	}
-
-	return result, nil
-}

model/models/glmocr/model.go

@@ -1,235 +0,0 @@
-package glmocr
-
-import (
-	"bytes"
-	"errors"
-	"image"
-	"slices"
-
-	"github.com/ollama/ollama/fs"
-	"github.com/ollama/ollama/kvcache"
-	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/model"
-	"github.com/ollama/ollama/model/input"
-)
-
-type Model struct {
-	model.Base
-	model.BytePairEncoding
-
-	*TextModel
-	*VisionModel     `gguf:"v"`
-	VisionDownsample *VisionDownsample `gguf:"mm.patch_merger"`
-	PatchMerger      *PatchMerger      `gguf:"mm"`
-
-	ImageProcessor
-
-	imageTokenID      int32
-	imageStartTokenID int32
-	imageEndTokenID   int32
-}
-
-var _ model.MultimodalProcessor = (*Model)(nil)
-
-func New(c fs.Config) (model.Model, error) {
-	eosTokenID := int32(c.Uint("tokenizer.ggml.eos_token_id"))
-	eosTokenIDs := c.Ints("tokenizer.ggml.eos_token_ids")
-	allEOS := append([]int32{eosTokenID}, eosTokenIDs...)
-
-	m := &Model{
-		BytePairEncoding: model.NewBytePairEncoding(
-			&model.Vocabulary{
-				Values: c.Strings("tokenizer.ggml.tokens"),
-				Types:  c.Ints("tokenizer.ggml.token_type"),
-				Merges: c.Strings("tokenizer.ggml.merges"),
-				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", false),
-				BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
-				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
-				EOS:    allEOS,
-			},
-			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
-		),
-		TextModel:         newTextModel(c),
-		VisionModel:       newVisionModel(c),
-		ImageProcessor:    newImageProcessor(c),
-		imageTokenID:      int32(c.Uint("image_token_id", 59280)),
-		imageStartTokenID: int32(c.Uint("image_start_token_id", 59256)),
-		imageEndTokenID:   int32(c.Uint("image_end_token_id", 59257)),
-	}
-
-	m.Cache = kvcache.NewCausalCache(m.TextModel.Shift)
-
-	return m, nil
-}
-
-func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input.Multimodal, error) {
-	if len(m.VisionModel.Blocks) == 0 {
-		return nil, model.ErrNoVisionModel
-	}
-
-	img, _, err := image.Decode(bytes.NewReader(multimodalData))
-	if err != nil {
-		return nil, err
-	}
-
-	f32s, grid, err := m.ImageProcessor.ProcessImage(img)
-	if err != nil {
-		return nil, err
-	}
-
-	// Create pixel values tensor from flattened patches
-	// Shape: [patchDim, numPatches]
-	patchDim := m.VisionModel.numChannels * m.temporalPatchSize * m.patchSize * m.patchSize
-	numPatches := grid.Temporal * grid.Height * grid.Width
-	pixelValues := ctx.Input().FromFloats(f32s, patchDim, numPatches)
-
-	// Forward through vision encoder
-	visionOutputs := m.VisionModel.Forward(ctx, pixelValues, grid)
-
-	// Forward through downsample (patch merger)
-	if m.VisionDownsample == nil || m.VisionDownsample.Weight == nil {
-		return nil, errors.New("glmocr: missing vision downsample weights")
-	}
-	visionOutputs = m.VisionDownsample.Forward(ctx, visionOutputs, grid, m.VisionModel.VisionModelOptions)
-
-	// Forward through patch merger (FC + LayerNorm + GELU + SwiGLU FFN)
-	if m.PatchMerger == nil {
-		return nil, errors.New("glmocr: missing patch merger weights")
-	}
-	visionOutputs = m.PatchMerger.Forward(ctx, visionOutputs, m.VisionModel.VisionModelOptions)
-
-	return []input.Multimodal{{Tensor: visionOutputs, Data: grid}}, nil
-}
-
-func (m *Model) PostTokenize(inputs []*input.Input) ([]*input.Input, error) {
-	var result []*input.Input
-
-	// Reset position cache
-	m.TextModel.positionCache = m.TextModel.positionCache[:0]
-	m.TextModel.ropeDelta = 0
-
-	pos := int32(0)
-	for _, inp := range inputs {
-		if inp.Multimodal == nil {
-			result = append(result, inp)
-			m.TextModel.positionCache = append(m.TextModel.positionCache, pos)
-			pos++
-			continue
-		}
-
-		// Get grid info for position calculation
-		grid := inp.Multimodal[0].Data.(*Grid)
-		mergedH := grid.Height / m.VisionModel.spatialMergeSize
-		mergedW := grid.Width / m.VisionModel.spatialMergeSize
-
-		// Add image start token
-		result = append(result, &input.Input{Token: m.imageStartTokenID})
-		m.TextModel.positionCache = append(m.TextModel.positionCache, pos)
-		pos++
-
-		// Add image tokens with multimodal data
-		// All image tokens share the same base position for temporal dimension
-		tokensPerGrid := inp.Multimodal[0].Tensor.Dim(1)
-		basePos := pos
-		sameBatch := tokensPerGrid - 1
-		if sameBatch < 0 {
-			sameBatch = 0
-		}
-		result = append(result, &input.Input{
-			Token:          m.imageTokenID,
-			Multimodal:     inp.Multimodal,
-			MultimodalHash: inp.MultimodalHash,
-			SameBatch:      sameBatch,
-		})
-		m.TextModel.positionCache = append(m.TextModel.positionCache, basePos)
-
-		// Add placeholder tokens for remaining positions
-		// All image tokens use the same base position (temporal stays constant)
-		for range tokensPerGrid - 1 {
-			result = append(result, &input.Input{Token: m.imageTokenID})
-			m.TextModel.positionCache = append(m.TextModel.positionCache, basePos)
-		}
-
-		// Advance position by max(mergedH, mergedW) after image tokens
-		pos = basePos + int32(max(mergedH, mergedW))
-
-		// Add image end token
-		result = append(result, &input.Input{Token: m.imageEndTokenID})
-		m.TextModel.positionCache = append(m.TextModel.positionCache, pos)
-		pos++
-	}
-
-	// Compute rope delta for continuation after the prefill segment:
-	// delta = (max_position_id + 1) - sequence_length
-	if len(m.TextModel.positionCache) > 0 {
-		last := m.TextModel.positionCache[len(m.TextModel.positionCache)-1]
-		m.TextModel.ropeDelta = last + 1 - int32(len(m.TextModel.positionCache))
-	}
-
-	return result, nil
-}
-
-func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
-	// Initial token embedding
-	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs).Duplicate(ctx)
-	ctx.Forward(hiddenStates)
-
-	// Build position slices for M-RoPE
-	positionSlice := func() [][]int32 {
-		s := [][]int32{
-			make([]int32, len(batch.Positions)), // temporal
-			make([]int32, len(batch.Positions)), // height
-			make([]int32, len(batch.Positions)), // width
-			make([]int32, len(batch.Positions)), // unused (zeros)
-		}
-		for i, position := range batch.Positions {
-			// Translate through position cache or continue sequence
-			if position < int32(len(m.TextModel.positionCache)) {
-				position = m.TextModel.positionCache[position]
-			} else if len(m.TextModel.positionCache) > 0 {
-				// Continue sequence after cached positions using ropeDelta
-				position = position + m.TextModel.ropeDelta
-			}
-
-			s[0][i] = position
-			s[1][i] = position
-			s[2][i] = position
-		}
-		return s
-	}()
-
-	// Inject vision embeddings and adjust positions for image tokens
-	for _, mi := range batch.Multimodal {
-		img := mi.Multimodal[0].Tensor
-		ctx.Forward(img.Copy(ctx, hiddenStates.View(ctx, mi.Index*hiddenStates.Stride(1), img.Dim(0)*img.Dim(1))))
-
-		if grid, ok := mi.Multimodal[0].Data.(*Grid); ok {
-			w := grid.Width / m.VisionModel.spatialMergeSize
-			for i := range img.Dim(1) {
-				positionSlice[1][mi.Index+i] += int32(i / w)
-				positionSlice[2][mi.Index+i] += int32(i % w)
-			}
-		}
-	}
-
-	positions := ctx.Input().FromInts(slices.Concat(positionSlice...), len(positionSlice[0])*len(positionSlice))
-
-	// Process through transformer layers
-	for i, layer := range m.TextModel.Layers {
-		m.Cache.SetLayer(i)
-
-		var lastLayerOutputs ml.Tensor
-		if i == len(m.TextModel.Layers)-1 {
-			lastLayerOutputs = batch.Outputs
-		}
-
-		hiddenStates = layer.Forward(ctx, hiddenStates, positions, lastLayerOutputs, m.Cache, m.TextModel.TextModelOptions)
-	}
-
-	hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.TextModel.eps)
-	return m.Output.Forward(ctx, hiddenStates), nil
-}
-
-func init() {
-	model.Register("glmocr", New)
-}

model/models/glmocr/model_text.go

@@ -1,190 +0,0 @@
-package glmocr
-
-import (
-	"math"
-
-	"github.com/ollama/ollama/fs"
-	"github.com/ollama/ollama/kvcache"
-	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/ml/nn"
-	"github.com/ollama/ollama/ml/nn/rope"
-)
-
-type TextModelOptions struct {
-	hiddenSize       int
-	numHeads         int
-	numKVHeads       int
-	headDim          int
-	rotaryDim        int
-	intermediateSize int
-	eps              float32
-	ropeBase         float32
-	mropeSections    []int
-}
-
-func (o *TextModelOptions) applyMRoPE(ctx ml.Context, states, positions ml.Tensor) ml.Tensor {
-	// With 4 sections for [temporal, height, width, unused]
-	return nn.RoPE(ctx, states, positions, o.rotaryDim, o.ropeBase, 1.0, rope.WithMRoPE(o.mropeSections))
-}
-
-type TextSelfAttention struct {
-	Query  *nn.Linear `gguf:"attn_q"`
-	Key    *nn.Linear `gguf:"attn_k"`
-	Value  *nn.Linear `gguf:"attn_v"`
-	Output *nn.Linear `gguf:"attn_out"`
-}
-
-func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache kvcache.Cache, opts *TextModelOptions) ml.Tensor {
-	batchSize := hiddenStates.Dim(1)
-
-	// Separate Q, K, V projections
-	q := sa.Query.Forward(ctx, hiddenStates)
-	k := sa.Key.Forward(ctx, hiddenStates)
-	v := sa.Value.Forward(ctx, hiddenStates)
-
-	// Reshape for GQA
-	q = q.Reshape(ctx, opts.headDim, opts.numHeads, batchSize)
-	k = k.Reshape(ctx, opts.headDim, opts.numKVHeads, batchSize)
-	v = v.Reshape(ctx, opts.headDim, opts.numKVHeads, batchSize)
-
-	// Apply M-RoPE (multi-resolution rotary position embeddings)
-	q = opts.applyMRoPE(ctx, q, positions)
-	k = opts.applyMRoPE(ctx, k, positions)
-
-	// Scaled dot-product attention with KV cache
-	scaleFactor := 1.0 / math.Sqrt(float64(opts.headDim))
-	kqv := nn.Attention(ctx, q, k, v, scaleFactor, cache)
-	// Reshape attention output: [headDim, numHeads, batchSize] -> [numHeads*headDim, batchSize]
-	// Note: numHeads * headDim = 16 * 128 = 2048, which is the attention hidden size
-	kqv = kqv.Reshape(ctx, opts.numHeads*opts.headDim, batchSize)
-
-	return sa.Output.Forward(ctx, kqv)
-}
-
-type TextMLP struct {
-	Gate *nn.Linear `gguf:"ffn_gate"`
-	Up   *nn.Linear `gguf:"ffn_up"`
-	Down *nn.Linear `gguf:"ffn_down"`
-}
-
-func (mlp *TextMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *TextModelOptions) ml.Tensor {
-	// SwiGLU: down(silu(gate(x)) * up(x))
-	gate := mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
-	return mlp.Down.Forward(ctx, gate)
-}
-
-type TextDecoderLayer struct {
-	// Input layernorm (before attention)
-	AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
-	SelfAttention *TextSelfAttention
-	// Post self-attention layernorm (after attention, before residual add)
-	PostAttnNorm *nn.RMSNorm `gguf:"post_attn_norm"`
-
-	// FFN input layernorm (after first residual, before MLP)
-	FFNNorm *nn.RMSNorm `gguf:"ffn_norm"`
-	MLP     *TextMLP
-	// Post MLP layernorm (after MLP, before residual add)
-	PostFFNNorm *nn.RMSNorm `gguf:"post_ffn_norm"`
-}
-
-func (l *TextDecoderLayer) Forward(ctx ml.Context, hiddenStates, positions, outputs ml.Tensor, cache kvcache.Cache, opts *TextModelOptions) ml.Tensor {
-	// Attention block
-	residual := hiddenStates
-	hiddenStates = l.AttentionNorm.Forward(ctx, hiddenStates, opts.eps)
-	hiddenStates = l.SelfAttention.Forward(ctx, hiddenStates, positions, cache, opts)
-	hiddenStates = l.PostAttnNorm.Forward(ctx, hiddenStates, opts.eps)
-
-	// Prune to output positions in final layer
-	if outputs != nil {
-		hiddenStates = hiddenStates.Rows(ctx, outputs)
-		residual = residual.Rows(ctx, outputs)
-	}
-
-	hiddenStates = hiddenStates.Add(ctx, residual)
-
-	// MLP block
-	residual = hiddenStates
-	hiddenStates = l.FFNNorm.Forward(ctx, hiddenStates, opts.eps)
-	hiddenStates = l.MLP.Forward(ctx, hiddenStates, opts)
-	hiddenStates = l.PostFFNNorm.Forward(ctx, hiddenStates, opts.eps)
-	hiddenStates = hiddenStates.Add(ctx, residual)
-
-	return hiddenStates
-}
-
-type TextModel struct {
-	TokenEmbedding *nn.Embedding      `gguf:"token_embd"`
-	Layers         []TextDecoderLayer `gguf:"blk"`
-	OutputNorm     *nn.RMSNorm        `gguf:"output_norm"`
-	Output         *nn.Linear         `gguf:"output,alt:token_embd"`
-
-	*TextModelOptions
-
-	// positionCache stores the M-RoPE position for each token in the sequence.
-	// This is needed because image tokens share the same base position but have
-	// different height/width offsets, and the end token position depends on the
-	// image grid dimensions.
-	positionCache []int32
-	ropeDelta     int32
-}
-
-func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
-	// Clear position cache when KV cache shifts
-	m.positionCache = nil
-	m.ropeDelta = 0
-	return m.applyMRoPE(ctx, key, shift), nil
-}
-
-func newTextModel(c fs.Config) *TextModel {
-	hiddenSize := int(c.Uint("embedding_length", 1536))
-	numHeads := int(c.Uint("attention.head_count", 16))
-	numKVHeads := int(c.Uint("attention.head_count_kv", 8))
-	intermediateSize := int(c.Uint("feed_forward_length", 4608))
-	eps := c.Float("attention.layer_norm_rms_epsilon", 1e-5)
-	ropeBase := c.Float("rope.freq_base", 10000)
-
-	headDim := int(c.Uint("attention.key_length", uint32(hiddenSize/numHeads)))
-	ropeDim := int(c.Uint("rope.dimension_count", uint32(headDim)))
-	if ropeDim <= 0 {
-		ropeDim = headDim
-	}
-
-	mropeSections := c.Ints("rope.mrope_section")
-	var sectionInts []int
-
-	if len(mropeSections) > 0 {
-		sectionInts = make([]int, len(mropeSections))
-		for i, section := range mropeSections {
-			sectionInts[i] = int(section)
-		}
-	} else {
-		// Default to GLM-OCR's HF ratio (2:3:3) scaled to rotaryDim/2.
-		// For rotaryDim=64 this yields [8, 12, 12].
-		total := ropeDim / 2
-		if total <= 0 {
-			total = 32
-		}
-		s0 := total * 2 / 8
-		s1 := total * 3 / 8
-		s2 := total - s0 - s1
-		sectionInts = []int{s0, s1, s2}
-	}
-
-	// GGML rope_multi: sector = (dim_pair) % sum(sections), mapping each pair to its position dim
-	rotaryDim := ropeDim
-
-	return &TextModel{
-		Layers: make([]TextDecoderLayer, c.Uint("block_count", 16)),
-		TextModelOptions: &TextModelOptions{
-			hiddenSize:       hiddenSize,
-			numHeads:         numHeads,
-			numKVHeads:       numKVHeads,
-			headDim:          headDim,
-			rotaryDim:        rotaryDim,
-			intermediateSize: intermediateSize,
-			eps:              eps,
-			ropeBase:         ropeBase,
-			mropeSections:    sectionInts,
-		},
-	}
-}

model/models/glmocr/model_vision.go

@@ -1,355 +0,0 @@
-package glmocr
-
-import (
-	"log/slog"
-	"math"
-	"slices"
-
-	"github.com/ollama/ollama/fs"
-	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/ml/nn"
-	"github.com/ollama/ollama/ml/nn/rope"
-)
-
-type Grid struct {
-	Height      int // Number of patches in height direction
-	Width       int // Number of patches in width direction
-	Temporal    int
-	ImageHeight int // Full image height in pixels
-	ImageWidth  int // Full image width in pixels
-}
-
-type VisionModelOptions struct {
-	hiddenSize        int
-	numHeads          int
-	headDim           int
-	numChannels       int
-	patchSize         int
-	temporalPatchSize int
-	imageSize         int
-	spatialMergeSize  int
-	outHiddenSize     int
-	intermediateSize  int
-	eps               float32
-}
-
-type VisionPatchEmbed struct {
-	Proj  *nn.Conv2D `gguf:"patch_embd_0"`
-	Proj1 *nn.Conv2D `gguf:"patch_embd_1"`
-	Bias  ml.Tensor  `gguf:"patch_embd.bias"`
-}
-
-func (pe *VisionPatchEmbed) Forward(ctx ml.Context, pixelValues ml.Tensor, grid *Grid, opts *VisionModelOptions) ml.Tensor {
-	_ = grid // patches are already in merge-block order
-
-	// pixelValues shape: [patchDim, numPatches]
-	numPatches := pixelValues.Shape()[1]
-
-	// Reshape to [patchSize*patchSize, temporalPatchSize, numChannels, numPatches]
-	pixelValues = pixelValues.Reshape(ctx, opts.patchSize*opts.patchSize, opts.temporalPatchSize, opts.numChannels, numPatches)
-	// Permute to [temporalPatchSize, patchSize*patchSize, numChannels, numPatches]
-	pixelValues = pixelValues.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
-
-	// Slice temporal frames for Conv2D (simulate Conv3D)
-	in0 := pixelValues.View(ctx, 0, 1, pixelValues.Stride(1), pixelValues.Dim(1), pixelValues.Stride(2), pixelValues.Dim(2), pixelValues.Stride(3), pixelValues.Dim(3)).Contiguous(ctx)
-	in0 = in0.Reshape(ctx, opts.patchSize, opts.patchSize, opts.numChannels, numPatches)
-
-	s0, s1 := opts.patchSize, opts.patchSize
-	p0, p1 := 0, 0
-	d0, d1 := 1, 1
-	hiddenStates := pe.Proj.Forward(ctx, in0, s0, s1, p0, p1, d0, d1)
-
-	if pe.Proj1 != nil && opts.temporalPatchSize > 1 {
-		in1 := pixelValues.View(ctx, pixelValues.Stride(0), 1, pixelValues.Stride(1), pixelValues.Dim(1), pixelValues.Stride(2), pixelValues.Dim(2), pixelValues.Stride(3), pixelValues.Dim(3)).Contiguous(ctx)
-		in1 = in1.Reshape(ctx, opts.patchSize, opts.patchSize, opts.numChannels, numPatches)
-		out1 := pe.Proj1.Forward(ctx, in1, s0, s1, p0, p1, d0, d1)
-		hiddenStates = hiddenStates.Add(ctx, out1)
-	}
-
-	// Flatten to [hidden_size, num_patches]
-	hiddenStates = hiddenStates.Reshape(ctx, opts.hiddenSize, numPatches)
-
-	// Add patch bias - reshape from [hidden_size] to [hidden_size, 1] for broadcasting
-	if pe.Bias != nil {
-		hiddenStates = hiddenStates.Add(ctx, pe.Bias.Reshape(ctx, opts.hiddenSize, 1))
-	}
-
-	return hiddenStates
-}
-
-type VisionSelfAttention struct {
-	QKV    *nn.Linear  `gguf:"attn_qkv"`
-	QNorm  *nn.RMSNorm `gguf:"attn_q_norm"`
-	KNorm  *nn.RMSNorm `gguf:"attn_k_norm"`
-	Output *nn.Linear  `gguf:"attn_out"`
-}
-
-func (sa *VisionSelfAttention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, opts *VisionModelOptions) ml.Tensor {
-	batchSize := hiddenStates.Dim(1)
-
-	// Combined QKV projection: [3*hidden_size, batch_size]
-	qkv := sa.QKV.Forward(ctx, hiddenStates)
-
-	// Split using ChunkSections along dim 0 (handles byte offsets correctly)
-	// ChunkSections returns views - must make contiguous before further operations
-	chunks := qkv.ChunkSections(ctx, 0, opts.hiddenSize, opts.hiddenSize, opts.hiddenSize)
-	q := chunks[0].Contiguous(ctx)
-	k := chunks[1].Contiguous(ctx)
-	v := chunks[2].Contiguous(ctx)
-
-	// Reshape for multi-head attention: [hiddenSize, N] -> [headDim, numHeads, N]
-	q = q.Reshape(ctx, opts.headDim, opts.numHeads, batchSize)
-	k = k.Reshape(ctx, opts.headDim, opts.numHeads, batchSize)
-	v = v.Reshape(ctx, opts.headDim, opts.numHeads, batchSize)
-
-	// Apply Q-norm and K-norm after head reshape
-	// Weights are [headDim]=64, tensor is [headDim, numHeads, N]
-	q = sa.QNorm.Forward(ctx, q, opts.eps)
-	k = sa.KNorm.Forward(ctx, k, opts.eps)
-
-	// Apply rotary position embeddings with vision-style 2D positions.
-	// ggml's vision RoPE uses two position dimensions (H/W) with half-rotation pairs.
-	// We provide H/W sections and leave the remaining sections empty.
-	ropeFreqBase := float32(10000.0)
-	section := opts.headDim / 4
-	if section <= 0 {
-		section = 1
-	}
-	sections := []int{section, section, 0, 0}
-	q = nn.RoPE(ctx, q, positions, opts.headDim/2, ropeFreqBase, 1.0, rope.WithVision(sections))
-	k = nn.RoPE(ctx, k, positions, opts.headDim/2, ropeFreqBase, 1.0, rope.WithVision(sections))
-
-	// Scale factor for scaled dot-product attention
-	scale := 1.0 / math.Sqrt(float64(opts.headDim))
-
-	// Try flash attention first (ScaledDotProductAttention), fall back to manual
-	if sdpa, ok := q.(ml.ScaledDotProductAttention); ok {
-		attention := sdpa.ScaledDotProductAttention(ctx, k, v, nil, nil, nil, scale, false)
-		attention = attention.Reshape(ctx, opts.hiddenSize, batchSize)
-		return sa.Output.Forward(ctx, attention)
-	}
-
-	slog.Warn("glmocr: vision attention falling back to manual attention",
-		"batchSize", batchSize, "numHeads", opts.numHeads,
-		"hint", "set OLLAMA_FLASH_ATTENTION=1 to enable flash attention")
-
-	// Manual attention fallback
-	// q, k, v are [headDim, numHeads, batchSize] - GGML treats as 4D with implicit dim 3 = 1
-	q = q.Permute(ctx, 0, 2, 1, 3)
-	k = k.Permute(ctx, 0, 2, 1, 3)
-	v = v.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
-
-	// Attention scores
-	kq := k.MulmatFullPrec(ctx, q)
-	kq = kq.Scale(ctx, scale)
-	kq = kq.Softmax(ctx)
-
-	// Attention output: v @ kq (note: v first)
-	kqv := v.Mulmat(ctx, kq)
-	attention := kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
-	attention = attention.Reshape(ctx, opts.hiddenSize, batchSize)
-
-	return sa.Output.Forward(ctx, attention)
-}
-
-type VisionMLP struct {
-	Gate *nn.Linear `gguf:"ffn_gate"`
-	Up   *nn.Linear `gguf:"ffn_up"`
-	Down *nn.Linear `gguf:"ffn_down"`
-}
-
-func (mlp *VisionMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor) ml.Tensor {
-	// SwiGLU: down(silu(gate(x)) * up(x))
-	gate := mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
-	return mlp.Down.Forward(ctx, gate)
-}
-
-type VisionBlock struct {
-	Norm1         *nn.RMSNorm `gguf:"ln1"`
-	SelfAttention *VisionSelfAttention
-	Norm2         *nn.RMSNorm `gguf:"ln2"`
-	MLP           *VisionMLP
-}
-
-func (b *VisionBlock) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, opts *VisionModelOptions) ml.Tensor {
-	// Pre-norm architecture
-	residual := hiddenStates
-	hiddenStates = b.Norm1.Forward(ctx, hiddenStates, opts.eps)
-	hiddenStates = b.SelfAttention.Forward(ctx, hiddenStates, positions, opts)
-	hiddenStates = hiddenStates.Add(ctx, residual)
-
-	residual = hiddenStates
-	hiddenStates = b.Norm2.Forward(ctx, hiddenStates, opts.eps)
-	hiddenStates = b.MLP.Forward(ctx, hiddenStates)
-	hiddenStates = hiddenStates.Add(ctx, residual)
-
-	return hiddenStates
-}
-
-type VisionDownsample struct {
-	*nn.Conv2D
-}
-
-func (d *VisionDownsample) Forward(ctx ml.Context, hiddenStates ml.Tensor, grid *Grid, opts *VisionModelOptions) ml.Tensor {
-	// Apply spatial downsampling via Conv2D
-	// Input: [hidden_size, num_patches] where patches are in merge-block order
-
-	if d.Conv2D == nil || d.Weight == nil {
-		slog.Error("VisionDownsample weights not loaded - model may be corrupted or incompatible")
-		return hiddenStates // Return input unchanged as fallback
-	}
-
-	merge := opts.spatialMergeSize
-	numOutputTokens := (grid.Height / merge) * (grid.Width / merge)
-
-	// Step 1: Reshape to [hidden_size, merge, merge, num_output_tokens]
-	hiddenStates = hiddenStates.Reshape(ctx, opts.hiddenSize, merge, merge, numOutputTokens)
-
-	// Step 2: Permute to [merge, merge, hidden_size, num_output_tokens]
-	// ggml semantics: result.ne[perm[i]] = input.ne[i]
-	// So permute(2,0,1,3) on [1024,2,2,N] gives: ne[2]=1024, ne[0]=2, ne[1]=2, ne[3]=N -> [2,2,1024,N]
-	hiddenStates = hiddenStates.Permute(ctx, 2, 0, 1, 3).Contiguous(ctx)
-
-	// Step 3: Apply Conv2D without bias (bias added after reshape)
-	// Note: ggml_conv_2d takes (kernel, input) - kernel must be receiver in ollama
-	s0, s1 := merge, merge
-	p0, p1 := 0, 0
-	d0, d1 := 1, 1
-	hiddenStates = d.Weight.Conv2D(ctx, hiddenStates, s0, s1, p0, p1, d0, d1)
-
-	// Step 4: Reshape to [out_hidden_size, num_output_tokens]
-	hiddenStates = hiddenStates.Reshape(ctx, opts.outHiddenSize, numOutputTokens)
-
-	// Step 5: Add bias after reshape
-	// Reshape bias from [out_hidden_size] to [out_hidden_size, 1] for proper broadcasting
-	if d.Bias != nil {
-		hiddenStates = hiddenStates.Add(ctx, d.Bias.Reshape(ctx, opts.outHiddenSize, 1))
-	}
-
-	return hiddenStates
-}
-
-type PatchMerger struct {
-	// GGUF tags align with mm.* keys used by the model
-	Proj     *nn.Linear    `gguf:"model.fc"`  // mm.model.fc.weight
-	PostLN   *nn.LayerNorm `gguf:"post_norm"` // mm.post_norm.weight/bias
-	GateProj *nn.Linear    `gguf:"gate"`      // mm.gate.weight
-	UpProj   *nn.Linear    `gguf:"up"`        // mm.up.weight
-	DownProj *nn.Linear    `gguf:"down"`      // mm.down.weight
-}
-
-func (m *PatchMerger) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *VisionModelOptions) ml.Tensor {
-	// Linear projection
-	hiddenStates = m.Proj.Forward(ctx, hiddenStates)
-
-	// Post-projection layer norm + GELU ERF
-	hiddenStates = m.PostLN.Forward(ctx, hiddenStates, opts.eps)
-	hiddenStates = hiddenStates.GELU_ERF(ctx)
-	// Force a copy to avoid in-place mutation issues with GELU_ERF
-	hiddenStates = hiddenStates.Contiguous(ctx)
-
-	// SwiGLU MLP: down(silu(gate(x)) * up(x))
-	gateOut := m.GateProj.Forward(ctx, hiddenStates)
-	upOut := m.UpProj.Forward(ctx, hiddenStates)
-	gate := gateOut.SILU(ctx, upOut)
-	return m.DownProj.Forward(ctx, gate)
-}
-
-type VisionModel struct {
-	PatchEmbed *VisionPatchEmbed
-	Blocks     []VisionBlock `gguf:"blk"`
-	PostLN     *nn.RMSNorm   `gguf:"post_ln"`
-	// Note: Downsample is applied at the model level so mm.patch_merger stays separate
-
-	*VisionModelOptions
-}
-
-func (m *VisionModel) Forward(ctx ml.Context, pixelValues ml.Tensor, grid *Grid) ml.Tensor {
-	// Extract patch embeddings from flattened patches
-	hiddenStates := m.PatchEmbed.Forward(ctx, pixelValues, grid, m.VisionModelOptions)
-
-	// Create position IDs for RoPE (spatial grid)
-	// Patches are already in merge-block order from preprocessing
-	positions := m.createPositions(ctx, grid)
-
-	// Process through vision blocks
-	for _, block := range m.Blocks {
-		hiddenStates = block.Forward(ctx, hiddenStates, positions, m.VisionModelOptions)
-	}
-
-	// Post-layernorm
-	hiddenStates = m.PostLN.Forward(ctx, hiddenStates, m.eps)
-
-	// Note: Downsample is now applied separately in Model.EncodeMultimodal
-	// so mm.patch_merger remains a distinct module
-
-	return hiddenStates
-}
-
-func (m *VisionModel) createPositions(ctx ml.Context, grid *Grid) ml.Tensor {
-	// Create spatial position IDs for vision RoPE
-	// Position layout: [height, width, height, width] - 4 sections for mrope
-	// Patches are in MERGE-BLOCK order after VisionPatchEmbed interleaving
-	// This follows the GLM-OCR rot_pos_emb layout
-	numPatches := grid.Height * grid.Width
-	mergeRatio := m.spatialMergeSize
-
-	// Build position arrays in merge-block order
-	// Each merge_ratio x merge_ratio block of patches is grouped together
-	hpos := make([]int32, numPatches)
-	wpos := make([]int32, numPatches)
-	ptr := 0
-	for y := 0; y < grid.Height; y += mergeRatio {
-		for x := 0; x < grid.Width; x += mergeRatio {
-			for dy := range mergeRatio {
-				for dx := range mergeRatio {
-					hpos[ptr] = int32(y + dy)
-					wpos[ptr] = int32(x + dx)
-					ptr++
-				}
-			}
-		}
-	}
-
-	// Build position arrays for 4 sections (mrope). ggml vision RoPE uses only H/W;
-	// keep remaining sections zeroed to match its conventions.
-	zeros := make([]int32, numPatches)
-	s := [][]int32{
-		hpos,  // Section 0: height
-		wpos,  // Section 1: width
-		zeros, // Section 2: unused
-		zeros, // Section 3: unused
-	}
-
-	return ctx.Input().FromInts(slices.Concat(s...), numPatches*4)
-}
-
-func newVisionModel(c fs.Config) *VisionModel {
-	hiddenSize := int(c.Uint("vision.embedding_length", 1024))
-	numHeads := int(c.Uint("vision.attention.head_count", 16))
-	numChannels := int(c.Uint("vision.num_channels", 3))
-	patchSize := int(c.Uint("vision.patch_size", 14))
-	temporalPatchSize := int(c.Uint("vision.temporal_patch_size", 2))
-	imageSize := int(c.Uint("vision.image_size", 336))
-	spatialMergeSize := int(c.Uint("vision.spatial_merge_size", 2))
-	outHiddenSize := int(c.Uint("vision.out_hidden_size", 1536))
-	intermediateSize := int(c.Uint("vision.intermediate_size", 4096))
-	eps := c.Float("vision.attention.layer_norm_rms_epsilon", 1e-5)
-
-	return &VisionModel{
-		Blocks: make([]VisionBlock, c.Uint("vision.block_count", 24)),
-		VisionModelOptions: &VisionModelOptions{
-			hiddenSize:        hiddenSize,
-			numHeads:          numHeads,
-			headDim:           hiddenSize / numHeads,
-			numChannels:       numChannels,
-			patchSize:         patchSize,
-			temporalPatchSize: temporalPatchSize,
-			imageSize:         imageSize,
-			spatialMergeSize:  spatialMergeSize,
-			outHiddenSize:     outHiddenSize,
-			intermediateSize:  intermediateSize,
-			eps:               eps,
-		},
-	}
-}

model/models/gptoss/model.go

@@ -12,11 +12,12 @@ import (
 	"github.com/ollama/ollama/ml/nn/rope"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Transformer struct {
 	model.Base
-	model.BytePairEncoding
+	tokenizer.Tokenizer
 
 	TokenEmbedding    *nn.Embedding      `gguf:"token_embd"`
 	TransformerBlocks []TransformerBlock `gguf:"blk"`
@@ -196,8 +197,8 @@ func (mlp *MLPBlock) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Optio
 func New(c fs.Config) (model.Model, error) {
 	m := Transformer{
 		TransformerBlocks: make([]TransformerBlock, c.Uint("block_count")),
-		BytePairEncoding: model.NewBytePairEncoding(
-			&model.Vocabulary{
+		Tokenizer: tokenizer.NewBytePairEncoding(
+			&tokenizer.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),

model/models/lfm2/model.go

@@ -10,6 +10,7 @@ import (
 	"github.com/ollama/ollama/ml/nn/rope"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Options struct {
@@ -59,7 +60,7 @@ func (o Options) applyRotaryPositionEmbeddings(ctx ml.Context, states, positions
 
 type Model struct {
 	model.Base
-	model.TextProcessor
+	tokenizer.Tokenizer
 
 	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
 	Layers         []Layer       `gguf:"blk"`
@@ -78,7 +79,7 @@ func New(c fs.Config) (model.Model, error) {
 		return nil, model.ErrUnsupportedTokenizer
 	}
 
-	vocabulary := model.Vocabulary{
+	vocabulary := tokenizer.Vocabulary{
 		Values: c.Strings("tokenizer.ggml.tokens"),
 		Scores: c.Floats("tokenizer.ggml.scores"),
 		Types:  c.Ints("tokenizer.ggml.token_type"),
@@ -104,8 +105,8 @@ func New(c fs.Config) (model.Model, error) {
 	}
 
 	m := Model{
-		TextProcessor: model.NewBytePairEncoding(&vocabulary, pretokenizers...),
-		Layers:        make([]Layer, c.Uint("block_count")),
+		Tokenizer: tokenizer.NewBytePairEncoding(&vocabulary, pretokenizers...),
+		Layers:    make([]Layer, c.Uint("block_count")),
 		Options: Options{
 			hiddenSize:            int(c.Uint("embedding_length")),
 			headDim:               int(c.Uint("attention.key_length")),

model/models/llama/model.go

@@ -11,6 +11,7 @@ import (
 	"github.com/ollama/ollama/ml/nn/rope"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Options struct {
@@ -25,7 +26,7 @@ func (o Options) applyRotaryPositionEmbeddings(ctx ml.Context, states, positions
 
 type Model struct {
 	model.Base
-	model.TextProcessor
+	tokenizer.Tokenizer
 
 	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
 	Layers         []Layer       `gguf:"blk"`
@@ -41,8 +42,8 @@ func New(c fs.Config) (model.Model, error) {
 		return nil, model.ErrUnsupportedModel
 	}
 
-	var processor model.TextProcessor
-	vocabulary := model.Vocabulary{
+	var processor tokenizer.Tokenizer
+	vocabulary := tokenizer.Vocabulary{
 		Values: c.Strings("tokenizer.ggml.tokens"),
 		Scores: c.Floats("tokenizer.ggml.scores"),
 		Types:  c.Ints("tokenizer.ggml.token_type"),
@@ -80,16 +81,16 @@ func New(c fs.Config) (model.Model, error) {
 				"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
 			}
 		}
-		processor = model.NewBytePairEncoding(&vocabulary, pretokenizers...)
+		processor = tokenizer.NewBytePairEncoding(&vocabulary, pretokenizers...)
 	case "llama":
-		processor = model.NewSentencePiece(&vocabulary)
+		processor = tokenizer.NewSentencePiece(&vocabulary)
 	default:
 		return nil, model.ErrUnsupportedTokenizer
 	}
 
 	m := Model{
-		TextProcessor: processor,
-		Layers:        make([]Layer, c.Uint("block_count")),
+		Tokenizer: processor,
+		Layers:    make([]Layer, c.Uint("block_count")),
 		Options: Options{
 			hiddenSize: int(c.Uint("embedding_length")),
 			numHeads:   int(c.Uint("attention.head_count")),

model/models/llama4/model.go

@@ -11,11 +11,12 @@ import (
 	"github.com/ollama/ollama/ml/nn"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Model struct {
 	model.Base
-	model.BytePairEncoding
+	tokenizer.Tokenizer
 	ImageProcessor
 
 	*VisionModel `gguf:"v"`
@@ -33,8 +34,8 @@ func (p *Projector) Forward(ctx ml.Context, visionOutputs ml.Tensor) ml.Tensor {
 
 func New(c fs.Config) (model.Model, error) {
 	m := Model{
-		BytePairEncoding: model.NewBytePairEncoding(
-			&model.Vocabulary{
+		Tokenizer: tokenizer.NewBytePairEncoding(
+			&tokenizer.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),

model/models/mistral3/model.go

@@ -11,11 +11,12 @@ import (
 	"github.com/ollama/ollama/ml/nn"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Model struct {
 	model.Base
-	model.BytePairEncoding
+	tokenizer.Tokenizer
 
 	*TextModel
 	*VisionModel         `gguf:"v"`
@@ -28,12 +29,12 @@ type Model struct {
 var _ model.MultimodalProcessor = (*Model)(nil)
 
 // Implement TextProcessor interface
-var _ model.TextProcessor = (*Model)(nil)
+var _ tokenizer.Tokenizer = (*Model)(nil)
 
 func New(c fs.Config) (model.Model, error) {
 	m := &Model{
-		BytePairEncoding: model.NewBytePairEncoding(
-			&model.Vocabulary{
+		Tokenizer: tokenizer.NewBytePairEncoding(
+			&tokenizer.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),

model/models/mllama/model.go

@@ -11,11 +11,12 @@ import (
 	"github.com/ollama/ollama/ml/nn"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Model struct {
 	model.Base
-	model.BytePairEncoding
+	tokenizer.Tokenizer
 
 	*VisionModel `gguf:"v"`
 	*TextModel
@@ -32,8 +33,8 @@ const (
 
 func New(c fs.Config) (model.Model, error) {
 	m := Model{
-		BytePairEncoding: model.NewBytePairEncoding(
-			&model.Vocabulary{
+		Tokenizer: tokenizer.NewBytePairEncoding(
+			&tokenizer.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),

model/models/models.go

@@ -8,7 +8,6 @@ import (
 	_ "github.com/ollama/ollama/model/models/gemma3"
 	_ "github.com/ollama/ollama/model/models/gemma3n"
 	_ "github.com/ollama/ollama/model/models/glm4moelite"
-	_ "github.com/ollama/ollama/model/models/glmocr"
 	_ "github.com/ollama/ollama/model/models/gptoss"
 	_ "github.com/ollama/ollama/model/models/lfm2"
 	_ "github.com/ollama/ollama/model/models/llama"
@@ -20,6 +19,5 @@ import (
 	_ "github.com/ollama/ollama/model/models/qwen2"
 	_ "github.com/ollama/ollama/model/models/qwen25vl"
 	_ "github.com/ollama/ollama/model/models/qwen3"
-	_ "github.com/ollama/ollama/model/models/qwen3next"
 	_ "github.com/ollama/ollama/model/models/qwen3vl"
 )

model/models/nomicbert/model.go

@@ -11,11 +11,12 @@ import (
 	"github.com/ollama/ollama/ml/nn/rope"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Model struct {
 	model.Base
-	model.TextProcessor
+	tokenizer.Tokenizer
 
 	TokenEmbedding     *nn.Embedding `gguf:"token_embd"`
 	TypeEmbedding      *nn.Embedding `gguf:"token_types"`
@@ -178,8 +179,8 @@ func New(c fs.Config) (model.Model, error) {
 	numHeads := int(c.Uint("attention.head_count"))
 	headDim := hiddenSize / numHeads
 
-	processor := model.NewWordPiece(
-		&model.Vocabulary{
+	tokenizer := tokenizer.NewWordPiece(
+		&tokenizer.Vocabulary{
 			Values: c.Strings("tokenizer.ggml.tokens"),
 			Scores: c.Floats("tokenizer.ggml.scores"),
 			Types:  c.Ints("tokenizer.ggml.token_type"),
@@ -219,8 +220,8 @@ func New(c fs.Config) (model.Model, error) {
 	}
 
 	return &Model{
-		TextProcessor: processor,
-		Layers:        layers,
+		Tokenizer: tokenizer,
+		Layers:    layers,
 		Options: Options{
 			hiddenSize:      hiddenSize,
 			numHeads:        numHeads,

model/models/olmo3/model.go

@@ -11,6 +11,7 @@ import (
 	"github.com/ollama/ollama/ml/nn/rope"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 const (
@@ -33,7 +34,7 @@ type Options struct {
 
 type Model struct {
 	model.Base
-	model.TextProcessor
+	tokenizer.Tokenizer
 
 	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
 	Layers         []Layer       `gguf:"blk"`
@@ -44,7 +45,7 @@ type Model struct {
 }
 
 func New(c fs.Config) (model.Model, error) {
-	vocabulary := model.Vocabulary{
+	vocabulary := tokenizer.Vocabulary{
 		Values: c.Strings("tokenizer.ggml.tokens"),
 		Scores: c.Floats("tokenizer.ggml.scores"),
 		Types:  c.Ints("tokenizer.ggml.token_type"),
@@ -58,14 +59,14 @@ func New(c fs.Config) (model.Model, error) {
 		),
 	}
 
-	processor := model.NewBytePairEncoding(
+	tokenizer := tokenizer.NewBytePairEncoding(
 		&vocabulary,
 		"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
 	)
 
 	m := Model{
-		TextProcessor: processor,
-		Layers:        make([]Layer, c.Uint("block_count")),
+		Tokenizer: tokenizer,
+		Layers:    make([]Layer, c.Uint("block_count")),
 		Options: Options{
 			hiddenSize:            int(c.Uint("embedding_length")),
 			numHeads:              int(c.Uint("attention.head_count")),

model/models/qwen2/model.go

@@ -13,6 +13,7 @@ import (
 	"github.com/ollama/ollama/ml/nn/rope"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Options struct {
@@ -92,7 +93,7 @@ func (d DecoderLayer) Forward(ctx ml.Context, hiddenStates, positions, outputs m
 
 type Model struct {
 	model.Base
-	model.BytePairEncoding
+	tokenizer.Tokenizer
 
 	TokenEmbedding *nn.Embedding  `gguf:"token_embd"`
 	Layers         []DecoderLayer `gguf:"blk"`
@@ -139,8 +140,8 @@ func New(c fs.Config) (model.Model, error) {
 	}
 	m := Model{
 		Layers: make([]DecoderLayer, c.Uint("block_count")),
-		BytePairEncoding: model.NewBytePairEncoding(
-			&model.Vocabulary{
+		Tokenizer: tokenizer.NewBytePairEncoding(
+			&tokenizer.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),

model/models/qwen25vl/model.go

@@ -10,11 +10,12 @@ import (
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Model struct {
 	model.Base
-	model.BytePairEncoding
+	tokenizer.Tokenizer
 
 	*TextModel
 	*VisionModel `gguf:"v"`
@@ -27,8 +28,8 @@ var _ model.MultimodalProcessor = (*Model)(nil)
 
 func New(c fs.Config) (model.Model, error) {
 	m := &Model{
-		BytePairEncoding: model.NewBytePairEncoding(
-			&model.Vocabulary{
+		Tokenizer: tokenizer.NewBytePairEncoding(
+			&tokenizer.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),

model/models/qwen3/embed.go

@@ -7,11 +7,12 @@ import (
 	"github.com/ollama/ollama/ml/nn/pooling"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type embedModel struct {
 	model.Base
-	model.BytePairEncoding
+	tokenizer.Tokenizer
 
 	*Model
 	poolingType pooling.Type
@@ -34,8 +35,8 @@ func newEmbed(c fs.Config) (model.Model, error) {
 		layers[i].MLP = &dense{}
 	}
 	m := embedModel{
-		BytePairEncoding: model.NewBytePairEncoding(
-			&model.Vocabulary{
+		Tokenizer: tokenizer.NewBytePairEncoding(
+			&tokenizer.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),

model/models/qwen3/model.go

@@ -12,6 +12,7 @@ import (
 	"github.com/ollama/ollama/ml/nn/rope"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Options struct {
@@ -159,7 +160,7 @@ func (d *Layer) Forward(ctx ml.Context, hiddenStates, positions, outputs ml.Tens
 
 type Model struct {
 	model.Base
-	model.BytePairEncoding
+	tokenizer.Tokenizer
 
 	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
 	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
@@ -218,8 +219,8 @@ func New(c fs.Config) (model.Model, error) {
 	}
 
 	m := Model{
-		BytePairEncoding: model.NewBytePairEncoding(
-			&model.Vocabulary{
+		Tokenizer: tokenizer.NewBytePairEncoding(
+			&tokenizer.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),

model/models/qwen3next/attention.go

@@ -1,103 +0,0 @@
-package qwen3next
-
-import (
-	"errors"
-	"math"
-
-	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/ml/nn"
-)
-
-// ErrUnsupportedBatchLayout is returned when the batch layout is incompatible
-// with the attention layer requirements.
-var ErrUnsupportedBatchLayout = errors.New("qwen3next: unsupported batch layout")
-
-// FullAttention implements gated attention with QK normalization and sigmoid-gated output.
-// Key differences from standard attention:
-// - Q projection outputs 2x size (Q + gate interleaved)
-// - Both Q and K have RMSNorm
-// - Output is gated: attn * sigmoid(gate)
-type FullAttention struct {
-	Query     *nn.Linear  `gguf:"attn_q"` // outputs [n_embd_head * 2, n_head]
-	QueryNorm *nn.RMSNorm `gguf:"attn_q_norm"`
-	Key       *nn.Linear  `gguf:"attn_k"`
-	KeyNorm   *nn.RMSNorm `gguf:"attn_k_norm"`
-	Value     *nn.Linear  `gguf:"attn_v"`
-	Output    *nn.Linear  `gguf:"attn_output"`
-}
-
-func (sa *FullAttention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache *HybridCache, opts *Options) (ml.Tensor, error) {
-	// Use Dim() instead of Shape() for consistent behavior during graph construction
-	hiddenDim := hiddenStates.Dim(0)
-	batchSize := hiddenStates.Dim(1)
-	nSeqs := hiddenStates.Dim(2) // 0 if 2D tensor
-
-	if cache != nil && cache.IsSupportedForBatch() {
-		seqTokens := cache.seqTokens()
-		seqs := cache.numSeqs()
-		if seqTokens > 0 && seqs > 0 {
-			if nSeqs > 0 {
-				// 3D tensor: [hiddenDim, seqTokens, nSeqs]
-				if batchSize != seqTokens || nSeqs != seqs {
-					return nil, ErrUnsupportedBatchLayout
-				}
-				hiddenStates = hiddenStates.Reshape(ctx, hiddenDim, seqTokens*seqs)
-				batchSize = seqTokens * seqs
-			} else if batchSize != seqTokens*seqs {
-				return nil, ErrUnsupportedBatchLayout
-			}
-		}
-	}
-	headDim := opts.headDim()
-	numHeads := opts.numHeads
-
-	// Q projection outputs query + gate interleaved
-	qFull := sa.Query.Forward(ctx, hiddenStates)
-
-	// Reshape to [headDim * 2, numHeads, batchSize]
-	qFull = qFull.Reshape(ctx, headDim*2, numHeads, batchSize)
-
-	// Split Q and gate along dimension 0
-	// Q: first headDim elements, gate: second headDim elements
-	query := qFull.Slice(ctx, 0, 0, headDim, 1)
-	gate := qFull.Slice(ctx, 0, headDim, headDim*2, 1)
-
-	// Make query contiguous for further operations
-	query = query.Contiguous(ctx, headDim, numHeads, batchSize)
-
-	// K and V projections
-	key := sa.Key.Forward(ctx, hiddenStates)
-	value := sa.Value.Forward(ctx, hiddenStates)
-
-	// Derive numKVHeads from tensor dimensions (per-layer value)
-	numKVHeads := key.Dim(0) / headDim
-
-	key = key.Reshape(ctx, headDim, numKVHeads, batchSize)
-	value = value.Reshape(ctx, headDim, numKVHeads, batchSize)
-
-	// Apply QK normalization
-	query = sa.QueryNorm.Forward(ctx, query, opts.eps)
-	key = sa.KeyNorm.Forward(ctx, key, opts.eps)
-
-	// Apply RoPE
-	query = opts.applyRotaryPositionEmbeddings(ctx, query, positions)
-	key = opts.applyRotaryPositionEmbeddings(ctx, key, positions)
-
-	// Standard attention computation
-	scale := opts.attentionScale
-	if scale == 0 {
-		scale = 1.0 / math.Sqrt(float64(headDim))
-	}
-	attention := nn.Attention(ctx, query, key, value, scale, cache)
-
-	// Flatten heads
-	attention = attention.Reshape(ctx, headDim*numHeads, batchSize)
-
-	// Apply sigmoid gate
-	// gate shape: [headDim, numHeads, batchSize] -> [headDim*numHeads, batchSize]
-	gate = gate.Contiguous(ctx, headDim*numHeads, batchSize)
-	gateSigmoid := gate.Sigmoid(ctx)
-	attention = attention.Mul(ctx, gateSigmoid)
-
-	return sa.Output.Forward(ctx, attention), nil
-}

model/models/qwen3next/cache.go

@@ -1,596 +0,0 @@
-package qwen3next
-
-import (
-	"math"
-	"slices"
-
-	"github.com/ollama/ollama/kvcache"
-	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/model/input"
-)
-
-var _ kvcache.Cache = (*HybridCache)(nil)
-
-// HybridCache stores:
-// - a standard causal KV cache for full attention layers
-// - per-sequence conv state for linear attention layers
-// - per-sequence delta state for linear attention layers
-//
-// Conv state shape (per layer, per sequence): [convKernelSize-1, convChannels]
-// Delta state shape (per layer, per sequence): [headVDim, headVDim * numVHeads]
-type HybridCache struct {
-	kv *kvcache.Causal
-
-	backend      ml.Backend
-	dtype        ml.DType
-	maxSequences int
-
-	// Conv state dimensions
-	convDim      int // convKernelSize - 1
-	convChannels int // d_inner + 2 * num_k_heads * head_k_dim
-
-	// Delta state dimensions
-	deltaStateSize int // headVDim * headVDim * numVHeads
-
-	// slot mapping for recurrent state (copy-on-write)
-	slotForSeq map[int]int
-	refCount   []int
-	freeSlots  []int
-
-	// per-layer conv state buffers (allocated lazily)
-	convCtxs   map[int]ml.Context
-	convStates map[int]ml.Tensor // [convDim*convChannels, maxSlots]
-
-	// per-layer delta state buffers (allocated lazily)
-	deltaCtxs   map[int]ml.Context
-	deltaStates map[int]ml.Tensor // [deltaStateSize, maxSlots]
-
-	// recurrent checkpoints (per slot)
-	checkpointCount     int
-	checkpointMinPos    int32
-	checkpointInterval  int32
-	checkpointCtxSize   int
-	checkpoints         map[int]*slotCheckpointStore
-	pendingRestore      map[int]checkpointRestore
-	curCheckpointPos    []int32
-	curCheckpointSlots  map[int]int
-	reserveCheckpoints  bool
-	checkpointConvCtxs  map[int]ml.Context
-	checkpointDeltaCtxs map[int]ml.Context
-	checkpointReserved  map[int]struct{}
-
-	// current forward batch (derived in StartForward)
-	curSeqs       []int
-	curSlots      []int
-	curSlotsInput ml.Tensor
-	curSeqTokens  int
-
-	// track if EnsureWritable has been called for this forward pass
-	writableEnsured bool
-	writableError   error
-}
-
-func NewHybridCache(
-	shift func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error),
-	convDim, convChannels, deltaStateSize int,
-) *HybridCache {
-	return &HybridCache{
-		kv:                  kvcache.NewCausalCache(shift),
-		convDim:             convDim,
-		convChannels:        convChannels,
-		deltaStateSize:      deltaStateSize,
-		slotForSeq:          make(map[int]int),
-		convCtxs:            make(map[int]ml.Context),
-		convStates:          make(map[int]ml.Tensor),
-		deltaCtxs:           make(map[int]ml.Context),
-		deltaStates:         make(map[int]ml.Tensor),
-		checkpointCount:     checkpointCountDefault,
-		checkpointMinPos:    checkpointMinPosDefault,
-		checkpointInterval:  checkpointIntervalDefault,
-		checkpoints:         make(map[int]*slotCheckpointStore),
-		pendingRestore:      make(map[int]checkpointRestore),
-		curCheckpointSlots:  make(map[int]int),
-		checkpointConvCtxs:  make(map[int]ml.Context),
-		checkpointDeltaCtxs: make(map[int]ml.Context),
-		checkpointReserved:  make(map[int]struct{}),
-	}
-}
-
-func (c *HybridCache) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int) {
-	c.backend = backend
-	c.dtype = dtype
-	c.maxSequences = maxSequences
-	c.checkpoints = make(map[int]*slotCheckpointStore)
-	c.pendingRestore = make(map[int]checkpointRestore)
-	c.curCheckpointPos = c.curCheckpointPos[:0]
-	c.curCheckpointSlots = make(map[int]int)
-	c.checkpointReserved = make(map[int]struct{})
-	c.checkpointCtxSize = c.checkpointCount * c.maxSequences
-	if c.checkpointCtxSize < 8 {
-		c.checkpointCtxSize = 8
-	}
-
-	// initialize slot allocator
-	c.refCount = make([]int, maxSequences)
-	c.freeSlots = c.freeSlots[:0]
-	for i := maxSequences - 1; i >= 0; i-- {
-		c.freeSlots = append(c.freeSlots, i)
-	}
-
-	c.kv.Init(backend, dtype, maxSequences, capacity, maxBatch)
-}
-
-func (c *HybridCache) Close() {
-	for _, ctx := range c.convCtxs {
-		ctx.Close()
-	}
-	for _, ctx := range c.deltaCtxs {
-		ctx.Close()
-	}
-	for _, ctx := range c.checkpointConvCtxs {
-		ctx.Close()
-	}
-	for _, ctx := range c.checkpointDeltaCtxs {
-		ctx.Close()
-	}
-	c.kv.Close()
-}
-
-func (c *HybridCache) SetConfig(config ml.CacheConfig) {
-	c.kv.SetConfig(config)
-}
-
-func (c *HybridCache) SetLayer(layer int) {
-	c.kv.SetLayer(layer)
-}
-
-func (c *HybridCache) Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor) {
-	return c.kv.Get(ctx)
-}
-
-func (c *HybridCache) Put(ctx ml.Context, key, value ml.Tensor) {
-	c.kv.Put(ctx, key, value)
-}
-
-func (c *HybridCache) StartForward(ctx ml.Context, batch input.Batch, reserve bool) error {
-	if err := c.kv.StartForward(ctx, batch, reserve); err != nil {
-		return err
-	}
-
-	// Derive equal-length sequence layout for recurrent layers
-	seqCounts := make(map[int]int)
-	c.curSeqs = c.curSeqs[:0]
-	for _, s := range batch.Sequences {
-		if _, ok := seqCounts[s]; !ok {
-			c.curSeqs = append(c.curSeqs, s)
-		}
-		seqCounts[s]++
-	}
-
-	if len(c.curSeqs) == 0 {
-		return nil
-	}
-
-	nTokens := len(batch.Sequences)
-	nSeqs := len(c.curSeqs)
-	want := nTokens / nSeqs
-	for _, s := range c.curSeqs {
-		if seqCounts[s] != want {
-			return kvcache.ErrNotSupported
-		}
-	}
-
-	c.curSeqTokens = want
-
-	// When reserving memory for estimation, use fake slot assignments
-	if reserve {
-		c.curSlots = c.curSlots[:0]
-		slots := make([]int32, nSeqs)
-		for i := range nSeqs {
-			c.curSlots = append(c.curSlots, i)
-			slots[i] = int32(i)
-		}
-		c.curSlotsInput = ctx.Input().FromInts(slots, len(slots))
-		c.reserveCheckpoints = true
-		c.planCheckpoints(batch)
-		return nil
-	}
-
-	// Ensure slots exist for sequences in this batch
-	c.curSlots = c.curSlots[:0]
-	var newSlots []int
-	for _, s := range c.curSeqs {
-		slot, ok := c.slotForSeq[s]
-		if !ok {
-			var err error
-			slot, err = c.allocSlot()
-			if err != nil {
-				return err
-			}
-			c.slotForSeq[s] = slot
-			c.refCount[slot] = 1
-			newSlots = append(newSlots, slot)
-		}
-		c.curSlots = append(c.curSlots, slot)
-	}
-
-	// Zero state for newly allocated slots
-	if len(newSlots) > 0 {
-		c.zeroSlots(ctx, newSlots)
-	}
-
-	// Create a tensor for the current slots
-	slots := make([]int32, len(c.curSlots))
-	for i, v := range c.curSlots {
-		slots[i] = int32(v)
-	}
-	c.curSlotsInput = ctx.Input().FromInts(slots, len(slots))
-
-	// Reset writable state for new forward pass
-	c.writableEnsured = false
-	c.writableError = nil
-	c.reserveCheckpoints = false
-	c.planCheckpoints(batch)
-
-	return nil
-}
-
-func (c *HybridCache) allocSlot() (int, error) {
-	if len(c.freeSlots) == 0 {
-		return 0, kvcache.ErrKvCacheFull
-	}
-	slot := c.freeSlots[len(c.freeSlots)-1]
-	c.freeSlots = c.freeSlots[:len(c.freeSlots)-1]
-	return slot, nil
-}
-
-func (c *HybridCache) freeSlot(slot int) {
-	if slot >= 0 && slot < c.maxSequences {
-		c.freeSlots = append(c.freeSlots, slot)
-	}
-}
-
-// zeroSlots zeros the recurrent state for the given slots across all layers.
-func (c *HybridCache) zeroSlots(ctx ml.Context, slots []int) {
-	if len(slots) == 0 {
-		return
-	}
-
-	inputCtx := ctx.Input()
-
-	slotIndices := make([]int32, len(slots))
-	for i, s := range slots {
-		slotIndices[i] = int32(s)
-	}
-	slotsTensor := inputCtx.FromInts(slotIndices, len(slotIndices))
-
-	// Zero conv states
-	if len(c.convStates) > 0 {
-		zeros := inputCtx.Zeros(ml.DTypeF32, c.convDim*c.convChannels, len(slots))
-		for _, buf := range c.convStates {
-			ctx.Forward(buf.SetRows(ctx, zeros, slotsTensor))
-		}
-	}
-
-	// Zero delta states
-	if len(c.deltaStates) > 0 {
-		zeros := inputCtx.Zeros(ml.DTypeF32, c.deltaStateSize, len(slots))
-		for _, buf := range c.deltaStates {
-			ctx.Forward(buf.SetRows(ctx, zeros, slotsTensor))
-		}
-	}
-}
-
-// EnsureWritable ensures sequences have private slots (copy-on-write).
-func (c *HybridCache) EnsureWritable(ctx ml.Context) error {
-	for i, seq := range c.curSeqs {
-		slot, ok := c.slotForSeq[seq]
-		if !ok {
-			continue
-		}
-
-		if slot < 0 || slot >= len(c.refCount) {
-			continue
-		}
-
-		if c.refCount[slot] <= 1 {
-			continue
-		}
-
-		newSlot, err := c.allocSlot()
-		if err != nil {
-			return err
-		}
-		c.refCount[slot]--
-		c.refCount[newSlot] = 1
-		c.slotForSeq[seq] = newSlot
-		c.curSlots[i] = newSlot
-
-		c.copyRecurrentState(ctx, slot, newSlot)
-		c.copyCheckpoints(ctx, slot, newSlot)
-	}
-
-	// Rebuild current slots tensor
-	slots := make([]int32, len(c.curSlots))
-	for i, v := range c.curSlots {
-		slots[i] = int32(v)
-	}
-	c.curSlotsInput = ctx.Input().FromInts(slots, len(slots))
-
-	return nil
-}
-
-func (c *HybridCache) copyRecurrentState(ctx ml.Context, srcSlot, dstSlot int) {
-	src := ctx.Input().FromInts([]int32{int32(srcSlot)}, 1)
-	dst := ctx.Input().FromInts([]int32{int32(dstSlot)}, 1)
-
-	for _, buf := range c.convStates {
-		rows := buf.Rows(ctx, src)
-		rowsF32 := rows.Cast(ctx, ml.DTypeF32)
-		ctx.Forward(buf.SetRows(ctx, rowsF32, dst))
-	}
-
-	for _, buf := range c.deltaStates {
-		rows := buf.Rows(ctx, src)
-		rowsF32 := rows.Cast(ctx, ml.DTypeF32)
-		ctx.Forward(buf.SetRows(ctx, rowsF32, dst))
-	}
-}
-
-func (c *HybridCache) CopyPrefix(srcSeq, dstSeq int, prefixLen int32) {
-	c.kv.CopyPrefix(srcSeq, dstSeq, prefixLen)
-
-	// Copy-on-write for recurrent state
-	if dstSlot, ok := c.slotForSeq[dstSeq]; ok {
-		if c.validSlot(dstSlot) {
-			c.refCount[dstSlot]--
-			if c.refCount[dstSlot] <= 0 {
-				c.refCount[dstSlot] = 0
-				c.freeSlot(dstSlot)
-			}
-		}
-		delete(c.slotForSeq, dstSeq)
-	}
-
-	srcSlot, ok := c.slotForSeq[srcSeq]
-	if !ok {
-		return
-	}
-
-	if c.validSlot(srcSlot) {
-		c.slotForSeq[dstSeq] = srcSlot
-		c.refCount[srcSlot]++
-	}
-}
-
-func (c *HybridCache) CanResume(seq int, pos int32) bool {
-	if !c.kv.CanResume(seq, pos) {
-		return false
-	}
-	if pos == 0 {
-		return true
-	}
-	return c.hasCheckpoint(seq, pos)
-}
-
-func (c *HybridCache) Remove(seq int, beginIndex, endIndex int32) error {
-	if beginIndex > 0 && endIndex != math.MaxInt32 {
-		return kvcache.ErrNotSupported
-	}
-
-	if beginIndex > 0 {
-		restore, ok := c.pendingRestore[seq]
-		if !ok || restore.pos+1 != beginIndex {
-			return kvcache.ErrNotSupported
-		}
-		if !c.restoreComplete(restore) {
-			return kvcache.ErrNotSupported
-		}
-		// If the recurrent slot is shared, detach it before applying a restore.
-		if slot, ok := c.slotForSeq[seq]; ok && c.validSlot(slot) && c.refCount[slot] > 1 {
-			newSlot, err := c.allocSlot()
-			if err != nil {
-				return err
-			}
-			ctx := c.backend.NewContext()
-			c.copyRecurrentState(ctx, slot, newSlot)
-			c.copyCheckpoints(ctx, slot, newSlot)
-			if len(c.convStates) > 0 || len(c.deltaStates) > 0 {
-				ctx.Compute()
-			}
-			ctx.Close()
-
-			c.refCount[slot]--
-			c.refCount[newSlot] = 1
-			c.slotForSeq[seq] = newSlot
-
-			restore.slot = newSlot
-			c.pendingRestore[seq] = restore
-		}
-	}
-
-	if err := c.kv.Remove(seq, beginIndex, endIndex); err != nil {
-		return err
-	}
-
-	if beginIndex > 0 {
-		restore := c.pendingRestore[seq]
-		delete(c.pendingRestore, seq)
-		return c.applyCheckpointRestore(restore)
-	}
-
-	// Removal invalidates recurrent state
-	slot, ok := c.slotForSeq[seq]
-	delete(c.pendingRestore, seq)
-	if !ok {
-		return nil
-	}
-
-	if !c.validSlot(slot) {
-		delete(c.slotForSeq, seq)
-		return nil
-	}
-
-	c.refCount[slot]--
-	if c.refCount[slot] <= 0 {
-		c.refCount[slot] = 0
-		c.clearCheckpoints(slot)
-		c.freeSlot(slot)
-	}
-	delete(c.slotForSeq, seq)
-
-	return nil
-}
-
-func (c *HybridCache) validSlot(slot int) bool {
-	return slot >= 0 && slot < len(c.refCount)
-}
-
-func (c *HybridCache) slotsTensor() ml.Tensor {
-	return c.curSlotsInput
-}
-
-// contiguousSlots returns the starting slot if current slots are contiguous and ordered.
-func (c *HybridCache) contiguousSlots() (int, bool) {
-	if len(c.curSlots) == 0 {
-		return 0, false
-	}
-	start := c.curSlots[0]
-	for i, s := range c.curSlots {
-		if s != start+i {
-			return 0, false
-		}
-	}
-	return start, true
-}
-
-func (c *HybridCache) seqTokens() int {
-	return c.curSeqTokens
-}
-
-func (c *HybridCache) numSeqs() int {
-	return len(c.curSeqs)
-}
-
-func (c *HybridCache) convBuffer(ctx ml.Context, layer int) ml.Tensor {
-	if buf, ok := c.convStates[layer]; ok {
-		return buf
-	}
-
-	if _, ok := c.convCtxs[layer]; !ok {
-		c.convCtxs[layer] = c.backend.NewContextSize(1).Layer(layer)
-	}
-
-	// Recurrent state must stay in F32 (ssm_conv kernels are F32-only).
-	buf := c.convCtxs[layer].Zeros(ml.DTypeF32, c.convDim*c.convChannels, c.maxSequences)
-	c.convStates[layer] = buf
-	return buf
-}
-
-func (c *HybridCache) deltaBuffer(ctx ml.Context, layer int) ml.Tensor {
-	if buf, ok := c.deltaStates[layer]; ok {
-		return buf
-	}
-
-	if _, ok := c.deltaCtxs[layer]; !ok {
-		c.deltaCtxs[layer] = c.backend.NewContextSize(1).Layer(layer)
-	}
-
-	// Recurrent delta state must stay in F32.
-	buf := c.deltaCtxs[layer].Zeros(ml.DTypeF32, c.deltaStateSize, c.maxSequences)
-	c.deltaStates[layer] = buf
-	return buf
-}
-
-func (c *HybridCache) ensureWritableOnce(ctx ml.Context) {
-	if !c.writableEnsured {
-		needsWritable := false
-		for _, seq := range c.curSeqs {
-			slot, ok := c.slotForSeq[seq]
-			if !ok {
-				continue
-			}
-			if slot >= 0 && slot < len(c.refCount) && c.refCount[slot] > 1 {
-				needsWritable = true
-				break
-			}
-		}
-
-		if needsWritable {
-			if err := c.EnsureWritable(ctx); err != nil {
-				c.writableError = err
-			}
-		}
-		c.writableEnsured = true
-	}
-}
-
-// ConvState returns the conv state for current batch sequences as [convDim, convChannels, nSeqs].
-func (c *HybridCache) ConvState(ctx ml.Context, layer int) (ml.Tensor, error) {
-	c.ensureWritableOnce(ctx)
-
-	if c.writableError != nil {
-		return nil, c.writableError
-	}
-
-	buf := c.convBuffer(ctx, layer)
-	cur := buf.Rows(ctx, c.slotsTensor())
-	return cur.Reshape(ctx, c.convDim, c.convChannels, c.numSeqs()), nil
-}
-
-// UpdateConvState writes a new conv state for current batch sequences.
-func (c *HybridCache) UpdateConvState(ctx ml.Context, layer int, newState ml.Tensor) {
-	buf := c.convBuffer(ctx, layer)
-	src := newState.Reshape(ctx, c.convDim*c.convChannels, c.numSeqs())
-	srcF32 := src.Cast(ctx, ml.DTypeF32)
-	if start, ok := c.contiguousSlots(); ok {
-		// Fast path: contiguous slots allow a single view + copy
-		offset := start * buf.Stride(1)
-		view := buf.View(ctx, offset, c.convDim*c.convChannels, buf.Stride(1), c.numSeqs())
-		ctx.Forward(srcF32.Copy(ctx, view))
-	} else {
-		ctx.Forward(buf.SetRows(ctx, srcF32, c.slotsTensor()))
-	}
-
-	c.captureConvCheckpoint(ctx, layer, srcF32)
-}
-
-// DeltaState returns the delta state for current batch sequences as [headVDim, headVDim*numVHeads, nSeqs].
-func (c *HybridCache) DeltaState(ctx ml.Context, layer int, headVDim, numVHeads int) (ml.Tensor, error) {
-	c.ensureWritableOnce(ctx)
-
-	if c.writableError != nil {
-		return nil, c.writableError
-	}
-
-	buf := c.deltaBuffer(ctx, layer)
-	cur := buf.Rows(ctx, c.slotsTensor())
-	return cur.Reshape(ctx, headVDim, headVDim*numVHeads, c.numSeqs()), nil
-}
-
-// UpdateDeltaState writes a new delta state for current batch sequences.
-func (c *HybridCache) UpdateDeltaState(ctx ml.Context, layer int, newState ml.Tensor) {
-	buf := c.deltaBuffer(ctx, layer)
-	src := newState.Reshape(ctx, c.deltaStateSize, c.numSeqs())
-	srcF32 := src.Cast(ctx, ml.DTypeF32)
-	if start, ok := c.contiguousSlots(); ok {
-		// Fast path: contiguous slots allow a single view + copy
-		offset := start * buf.Stride(1)
-		view := buf.View(ctx, offset, c.deltaStateSize, buf.Stride(1), c.numSeqs())
-		ctx.Forward(srcF32.Copy(ctx, view))
-	} else {
-		ctx.Forward(buf.SetRows(ctx, srcF32, c.slotsTensor()))
-	}
-
-	c.captureDeltaCheckpoint(ctx, layer, srcF32)
-}
-
-// IsSupportedForBatch returns true if the current batch layout supports recurrent layers.
-func (c *HybridCache) IsSupportedForBatch() bool {
-	return c.curSeqTokens > 0 && len(c.curSeqs) > 0
-}
-
-// Seqs returns the ordered unique sequences for the current forward pass.
-func (c *HybridCache) Seqs() []int {
-	return slices.Clone(c.curSeqs)
-}

model/models/qwen3next/checkpoints.go

@@ -1,498 +0,0 @@
-package qwen3next
-
-import (
-	"log/slog"
-	"math"
-
-	"github.com/ollama/ollama/kvcache"
-	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/model/input"
-)
-
-const (
-	checkpointCountDefault    = 32
-	checkpointMinPosDefault   = int32(16)
-	checkpointIntervalDefault = int32(1280)
-)
-
-// TODO(jmorganca): Add byte-serialized host-RAM checkpoints to reduce GPU
-// memory usage while preserving prefix reuse for recurrent state.
-
-type checkpointEntry struct {
-	pos   int32
-	conv  map[int]ml.Tensor
-	delta map[int]ml.Tensor
-}
-
-type slotCheckpointStore struct {
-	entries []checkpointEntry
-	size    int
-	next    int
-	lastPos int32
-}
-
-type checkpointRestore struct {
-	slot int
-	idx  int
-	pos  int32
-}
-
-func newSlotCheckpointStore(n int) *slotCheckpointStore {
-	entries := make([]checkpointEntry, n)
-	for i := range entries {
-		entries[i].pos = -1
-	}
-	return &slotCheckpointStore{
-		entries: entries,
-		lastPos: -1,
-	}
-}
-
-func (s *slotCheckpointStore) reset() {
-	s.size = 0
-	s.next = 0
-	s.lastPos = -1
-	for i := range s.entries {
-		s.entries[i].pos = -1
-	}
-}
-
-func (s *slotCheckpointStore) record(pos int32) int {
-	if len(s.entries) == 0 {
-		return -1
-	}
-	idx := s.next
-	s.next = (s.next + 1) % len(s.entries)
-	if s.size < len(s.entries) {
-		s.size++
-	}
-	s.entries[idx].pos = pos
-	s.lastPos = pos
-	return idx
-}
-
-func (s *slotCheckpointStore) bestIndex(targetPos int32) (int, int32, bool) {
-	bestIdx := -1
-	bestPos := int32(-1)
-	for i := range s.entries {
-		pos := s.entries[i].pos
-		if pos < 0 || pos >= targetPos {
-			continue
-		}
-		if pos > bestPos {
-			bestPos = pos
-			bestIdx = i
-		}
-	}
-	if bestIdx < 0 {
-		return -1, -1, false
-	}
-	return bestIdx, bestPos, true
-}
-
-func (s *slotCheckpointStore) pruneAfter(pos int32) {
-	if len(s.entries) == 0 {
-		s.size = 0
-		s.next = 0
-		s.lastPos = -1
-		return
-	}
-
-	size := 0
-	next := -1
-	minPos := int32(math.MaxInt32)
-	minIdx := 0
-	for i := range s.entries {
-		if s.entries[i].pos > pos {
-			s.entries[i].pos = -1
-		}
-		if s.entries[i].pos >= 0 {
-			size++
-			if s.entries[i].pos < minPos {
-				minPos = s.entries[i].pos
-				minIdx = i
-			}
-		} else if next == -1 {
-			next = i
-		}
-	}
-
-	s.size = size
-	if size == 0 {
-		s.next = 0
-		s.lastPos = -1
-		return
-	}
-	if next != -1 {
-		s.next = next
-	} else {
-		// Full ring: overwrite the oldest checkpoint next.
-		s.next = minIdx
-	}
-	s.lastPos = pos
-}
-
-func (s *slotCheckpointStore) window() (size int, minPos, maxPos, lastPos int32) {
-	minPos = int32(math.MaxInt32)
-	maxPos = int32(-1)
-	for i := range s.entries {
-		pos := s.entries[i].pos
-		if pos < 0 {
-			continue
-		}
-		size++
-		if pos < minPos {
-			minPos = pos
-		}
-		if pos > maxPos {
-			maxPos = pos
-		}
-	}
-	if size == 0 {
-		minPos = -1
-		maxPos = -1
-	}
-	return size, minPos, maxPos, s.lastPos
-}
-
-func (c *HybridCache) planCheckpoints(batch input.Batch) {
-	if c.checkpointCount == 0 || len(c.curSeqs) == 0 {
-		c.curCheckpointPos = c.curCheckpointPos[:0]
-		for k := range c.curCheckpointSlots {
-			delete(c.curCheckpointSlots, k)
-		}
-		return
-	}
-
-	if cap(c.curCheckpointPos) < len(c.curSeqs) {
-		c.curCheckpointPos = make([]int32, len(c.curSeqs))
-	} else {
-		c.curCheckpointPos = c.curCheckpointPos[:len(c.curSeqs)]
-	}
-	for i := range c.curCheckpointPos {
-		c.curCheckpointPos[i] = -1
-	}
-	for k := range c.curCheckpointSlots {
-		delete(c.curCheckpointSlots, k)
-	}
-
-	posMax := make(map[int]int32, len(c.curSeqs))
-	for i, seq := range batch.Sequences {
-		pos := batch.Positions[i]
-		if cur, ok := posMax[seq]; !ok || pos > cur {
-			posMax[seq] = pos
-		}
-	}
-
-	for i, seq := range c.curSeqs {
-		pos, ok := posMax[seq]
-		if !ok {
-			continue
-		}
-		if pos < c.checkpointMinPos {
-			continue
-		}
-		slot := c.curSlots[i]
-		store := c.checkpointStore(slot)
-		lastPos := store.lastPos
-		if lastPos < 0 || pos-lastPos >= c.checkpointInterval {
-			c.curCheckpointPos[i] = pos
-		}
-	}
-}
-
-func (c *HybridCache) checkpointStore(slot int) *slotCheckpointStore {
-	store, ok := c.checkpoints[slot]
-	if ok {
-		return store
-	}
-	store = newSlotCheckpointStore(c.checkpointCount)
-	c.checkpoints[slot] = store
-	return store
-}
-
-func (c *HybridCache) checkpointIndexForSlot(slot int, pos int32) int {
-	if c.checkpointCount == 0 {
-		return -1
-	}
-	if idx, ok := c.curCheckpointSlots[slot]; ok {
-		return idx
-	}
-	store := c.checkpointStore(slot)
-	idx := store.record(pos)
-	if idx >= 0 {
-		c.curCheckpointSlots[slot] = idx
-	}
-	return idx
-}
-
-func (c *HybridCache) hasCheckpoint(seq int, pos int32) bool {
-	if pos <= 0 {
-		return false
-	}
-	slot, ok := c.slotForSeq[seq]
-	if !ok {
-		return false
-	}
-	store, ok := c.checkpoints[slot]
-	if !ok {
-		return false
-	}
-	_, _, ok = store.bestIndex(pos)
-	return ok
-}
-
-func (c *HybridCache) PrepareRestore(seq int, targetPos int32) (int32, bool) {
-	if targetPos <= 0 {
-		return 0, false
-	}
-	slot, ok := c.slotForSeq[seq]
-	if !ok {
-		return 0, false
-	}
-	store, ok := c.checkpoints[slot]
-	if !ok {
-		slog.Debug("qwen3next: checkpoint miss", "seq", seq, "slot", slot, "target", targetPos, "size", 0)
-		return 0, false
-	}
-	idx, pos, ok := store.bestIndex(targetPos)
-	if !ok {
-		size, minPos, maxPos, lastPos := store.window()
-		slog.Debug("qwen3next: checkpoint miss", "seq", seq, "slot", slot, "target", targetPos, "size", size,
-			"min", minPos, "max", maxPos, "last", lastPos)
-		return 0, false
-	}
-	c.pendingRestore[seq] = checkpointRestore{
-		slot: slot,
-		idx:  idx,
-		pos:  pos,
-	}
-	return pos + 1, true
-}
-
-func (c *HybridCache) applyCheckpointRestore(restore checkpointRestore) error {
-	entry, ok := c.restoreEntry(restore)
-	if !ok {
-		return kvcache.ErrNotSupported
-	}
-
-	ctx := c.backend.NewContext()
-	defer ctx.Close()
-
-	slotIdx := ctx.Input().FromInts([]int32{int32(restore.slot)}, 1)
-	for layer, src := range entry.conv {
-		buf := c.convBuffer(ctx, layer)
-		ctx.Forward(buf.SetRows(ctx, src, slotIdx))
-	}
-	for layer, src := range entry.delta {
-		buf := c.deltaBuffer(ctx, layer)
-		ctx.Forward(buf.SetRows(ctx, src, slotIdx))
-	}
-
-	if len(entry.conv) > 0 || len(entry.delta) > 0 {
-		ctx.Compute()
-	}
-	store := c.checkpoints[restore.slot]
-	store.pruneAfter(restore.pos)
-	return nil
-}
-
-func (c *HybridCache) restoreComplete(restore checkpointRestore) bool {
-	_, ok := c.restoreEntry(restore)
-	return ok
-}
-
-func (c *HybridCache) restoreEntry(restore checkpointRestore) (*checkpointEntry, bool) {
-	store, ok := c.checkpoints[restore.slot]
-	if !ok || restore.idx < 0 || restore.idx >= len(store.entries) {
-		return nil, false
-	}
-	entry := &store.entries[restore.idx]
-	if entry.pos < 0 {
-		return nil, false
-	}
-	if !c.entryComplete(entry) {
-		return nil, false
-	}
-	return entry, true
-}
-
-func (c *HybridCache) entryComplete(entry *checkpointEntry) bool {
-	for layer := range c.convStates {
-		if entry.conv == nil || entry.conv[layer] == nil {
-			return false
-		}
-	}
-	for layer := range c.deltaStates {
-		if entry.delta == nil || entry.delta[layer] == nil {
-			return false
-		}
-	}
-	return true
-}
-
-func (c *HybridCache) clearCheckpoints(slot int) {
-	if store, ok := c.checkpoints[slot]; ok {
-		store.reset()
-	}
-}
-
-func (c *HybridCache) copyCheckpoints(ctx ml.Context, srcSlot, dstSlot int) {
-	if c.checkpointCount == 0 {
-		return
-	}
-	srcStore, ok := c.checkpoints[srcSlot]
-	if !ok || srcStore.size == 0 {
-		return
-	}
-	dstStore := c.checkpointStore(dstSlot)
-	dstStore.size = srcStore.size
-	dstStore.next = srcStore.next
-	dstStore.lastPos = srcStore.lastPos
-
-	for i := range srcStore.entries {
-		srcEntry := &srcStore.entries[i]
-		dstEntry := &dstStore.entries[i]
-		dstEntry.pos = srcEntry.pos
-		if srcEntry.conv != nil {
-			if dstEntry.conv == nil {
-				dstEntry.conv = make(map[int]ml.Tensor)
-			}
-			for layer, src := range srcEntry.conv {
-				dst := c.ensureCheckpointConv(layer, dstEntry)
-				ctx.Forward(src.Copy(ctx, dst))
-			}
-		}
-		if srcEntry.delta != nil {
-			if dstEntry.delta == nil {
-				dstEntry.delta = make(map[int]ml.Tensor)
-			}
-			for layer, src := range srcEntry.delta {
-				dst := c.ensureCheckpointDelta(layer, dstEntry)
-				ctx.Forward(src.Copy(ctx, dst))
-			}
-		}
-	}
-}
-
-func (c *HybridCache) captureConvCheckpoint(ctx ml.Context, layer int, src ml.Tensor) {
-	if c.checkpointCount == 0 {
-		return
-	}
-	if c.reserveCheckpoints {
-		c.reserveCheckpointConv(layer)
-		return
-	}
-	if len(c.curCheckpointPos) == 0 {
-		return
-	}
-	for i, pos := range c.curCheckpointPos {
-		if pos < 0 {
-			continue
-		}
-		slot := c.curSlots[i]
-		idx := c.checkpointIndexForSlot(slot, pos)
-		if idx < 0 {
-			continue
-		}
-		entry := &c.checkpoints[slot].entries[idx]
-		dst := c.ensureCheckpointConv(layer, entry)
-		seqSlice := src.Slice(ctx, 1, i, i+1, 1)
-		ctx.Forward(seqSlice.Copy(ctx, dst))
-	}
-}
-
-func (c *HybridCache) captureDeltaCheckpoint(ctx ml.Context, layer int, src ml.Tensor) {
-	if c.checkpointCount == 0 {
-		return
-	}
-	if c.reserveCheckpoints {
-		c.reserveCheckpointDelta(layer)
-		return
-	}
-	if len(c.curCheckpointPos) == 0 {
-		return
-	}
-	for i, pos := range c.curCheckpointPos {
-		if pos < 0 {
-			continue
-		}
-		slot := c.curSlots[i]
-		idx := c.checkpointIndexForSlot(slot, pos)
-		if idx < 0 {
-			continue
-		}
-		entry := &c.checkpoints[slot].entries[idx]
-		dst := c.ensureCheckpointDelta(layer, entry)
-		seqSlice := src.Slice(ctx, 1, i, i+1, 1)
-		ctx.Forward(seqSlice.Copy(ctx, dst))
-	}
-}
-
-func (c *HybridCache) ensureCheckpointConv(layer int, entry *checkpointEntry) ml.Tensor {
-	if entry.conv == nil {
-		entry.conv = make(map[int]ml.Tensor)
-	}
-	if t, ok := entry.conv[layer]; ok {
-		return t
-	}
-	ctx, ok := c.checkpointConvCtxs[layer]
-	if !ok {
-		ctx = c.backend.NewContextSize(c.checkpointCtxSize).Layer(layer)
-		c.checkpointConvCtxs[layer] = ctx
-	}
-	t := ctx.Zeros(ml.DTypeF32, c.convDim*c.convChannels, 1)
-	entry.conv[layer] = t
-	return t
-}
-
-func (c *HybridCache) ensureCheckpointDelta(layer int, entry *checkpointEntry) ml.Tensor {
-	if entry.delta == nil {
-		entry.delta = make(map[int]ml.Tensor)
-	}
-	if t, ok := entry.delta[layer]; ok {
-		return t
-	}
-	ctx, ok := c.checkpointDeltaCtxs[layer]
-	if !ok {
-		ctx = c.backend.NewContextSize(c.checkpointCtxSize).Layer(layer)
-		c.checkpointDeltaCtxs[layer] = ctx
-	}
-	t := ctx.Zeros(ml.DTypeF32, c.deltaStateSize, 1)
-	entry.delta[layer] = t
-	return t
-}
-
-func (c *HybridCache) reserveCheckpointConv(layer int) {
-	key := checkpointReserveKey(layer, 0)
-	if _, ok := c.checkpointReserved[key]; ok {
-		return
-	}
-	for slot := range c.maxSequences {
-		store := c.checkpointStore(slot)
-		for i := range store.entries {
-			entry := &store.entries[i]
-			_ = c.ensureCheckpointConv(layer, entry)
-		}
-	}
-	c.checkpointReserved[key] = struct{}{}
-}
-
-func (c *HybridCache) reserveCheckpointDelta(layer int) {
-	key := checkpointReserveKey(layer, 1)
-	if _, ok := c.checkpointReserved[key]; ok {
-		return
-	}
-	for slot := range c.maxSequences {
-		store := c.checkpointStore(slot)
-		for i := range store.entries {
-			entry := &store.entries[i]
-			_ = c.ensureCheckpointDelta(layer, entry)
-		}
-	}
-	c.checkpointReserved[key] = struct{}{}
-}
-
-func checkpointReserveKey(layer int, kind int) int {
-	return layer*2 + kind
-}

model/models/qwen3next/checkpoints_test.go

@@ -1,300 +0,0 @@
-package qwen3next
-
-import (
-	"errors"
-	"math"
-	"os"
-	"testing"
-
-	"github.com/ollama/ollama/fs/ggml"
-	"github.com/ollama/ollama/kvcache"
-	"github.com/ollama/ollama/ml"
-)
-
-func newTestBackend(tb testing.TB) ml.Backend {
-	tb.Helper()
-
-	f, err := os.CreateTemp(tb.TempDir(), "*.gguf")
-	if err != nil {
-		tb.Fatal(err)
-	}
-	if err := ggml.WriteGGUF(f, ggml.KV{"general.architecture": "test"}, nil); err != nil {
-		_ = f.Close()
-		tb.Fatal(err)
-	}
-	if err := f.Close(); err != nil {
-		tb.Fatal(err)
-	}
-
-	b, err := ml.NewBackend(f.Name(), ml.BackendParams{AllocMemory: true})
-	if err != nil {
-		tb.Fatal(err)
-	}
-	tb.Cleanup(func() {
-		b.Close()
-	})
-
-	return b
-}
-
-func TestSlotCheckpointStoreBestIndex(t *testing.T) {
-	store := newSlotCheckpointStore(2)
-	store.record(10)
-	store.record(20)
-
-	_, pos, ok := store.bestIndex(15)
-	if !ok || pos != 10 {
-		t.Fatalf("expected best pos 10, got pos=%d ok=%v", pos, ok)
-	}
-
-	store.record(30) // overwrite oldest (10)
-
-	if _, _, ok := store.bestIndex(15); ok {
-		t.Fatalf("expected no checkpoint for targetPos=15 after overwrite")
-	}
-
-	_, pos, ok = store.bestIndex(40)
-	if !ok || pos != 30 {
-		t.Fatalf("expected best pos 30, got pos=%d ok=%v", pos, ok)
-	}
-}
-
-func TestHybridCachePrepareRestore(t *testing.T) {
-	cache := NewHybridCache(nil, 1, 1, 1)
-	cache.checkpointCount = 3
-	cache.checkpoints = make(map[int]*slotCheckpointStore)
-	cache.pendingRestore = make(map[int]checkpointRestore)
-
-	cache.slotForSeq[1] = 0
-	store := cache.checkpointStore(0)
-	store.record(5)
-	store.record(9)
-	store.record(15)
-
-	restorePos, ok := cache.PrepareRestore(1, 12)
-	if !ok {
-		t.Fatalf("expected restore ok")
-	}
-	if restorePos != 10 {
-		t.Fatalf("expected restorePos 10, got %d", restorePos)
-	}
-	rest, ok := cache.pendingRestore[1]
-	if !ok {
-		t.Fatalf("expected pending restore entry")
-	}
-	if rest.pos != 9 {
-		t.Fatalf("expected pending restore pos 9, got %d", rest.pos)
-	}
-}
-
-func TestSlotCheckpointStorePruneAfter(t *testing.T) {
-	store := newSlotCheckpointStore(3)
-	store.record(10)
-	store.record(20)
-	store.record(30)
-
-	store.pruneAfter(20)
-
-	if store.lastPos != 20 {
-		t.Fatalf("expected lastPos 20, got %d", store.lastPos)
-	}
-
-	_, pos, ok := store.bestIndex(25)
-	if !ok || pos != 20 {
-		t.Fatalf("expected best pos 20 after prune, got pos=%d ok=%v", pos, ok)
-	}
-
-	_, pos, ok = store.bestIndex(35)
-	if !ok || pos != 20 {
-		t.Fatalf("expected pruned best pos 20 for targetPos=35, got pos=%d ok=%v", pos, ok)
-	}
-}
-
-func TestHybridCacheRestoreDetachesSharedSlot(t *testing.T) {
-	backend := newTestBackend(t)
-
-	cache := NewHybridCache(nil, 1, 2, 2)
-	cache.Init(backend, ml.DTypeF16, 2, 8, 2)
-
-	cache.slotForSeq[1] = 0
-	cache.slotForSeq[2] = 0
-	cache.refCount[0] = 2
-	cache.refCount[1] = 0
-	cache.freeSlots = []int{1}
-
-	store := cache.checkpointStore(0)
-	idx := store.record(9)
-	cache.pendingRestore[1] = checkpointRestore{slot: 0, idx: idx, pos: 9}
-
-	if err := cache.Remove(1, 10, math.MaxInt32); err != nil {
-		t.Fatalf("Remove failed: %v", err)
-	}
-
-	if cache.slotForSeq[1] == cache.slotForSeq[2] {
-		t.Fatalf("expected restore to detach shared slot, got same slot %d", cache.slotForSeq[1])
-	}
-	if cache.slotForSeq[1] != 1 {
-		t.Fatalf("expected seq 1 to move to slot 1, got %d", cache.slotForSeq[1])
-	}
-	if cache.slotForSeq[2] != 0 {
-		t.Fatalf("expected seq 2 to remain on slot 0, got %d", cache.slotForSeq[2])
-	}
-	if cache.refCount[0] != 1 || cache.refCount[1] != 1 {
-		t.Fatalf("unexpected refCounts: slot0=%d slot1=%d", cache.refCount[0], cache.refCount[1])
-	}
-	if _, ok := cache.pendingRestore[1]; ok {
-		t.Fatalf("expected pending restore to be cleared")
-	}
-}
-
-func TestHybridCacheRestoreRejectsIncompleteCheckpoint(t *testing.T) {
-	cache := NewHybridCache(nil, 1, 2, 2)
-	cache.checkpointCount = 3
-	cache.checkpoints = make(map[int]*slotCheckpointStore)
-	cache.pendingRestore = make(map[int]checkpointRestore)
-
-	cache.slotForSeq[1] = 0
-	cache.refCount = []int{1}
-	cache.freeSlots = nil
-
-	// Simulate that layer 0 has both conv and delta state (so entryComplete expects both)
-	cache.convStates[0] = nil  // placeholder to indicate layer 0 exists
-	cache.deltaStates[0] = nil // placeholder to indicate layer 0 exists
-
-	store := cache.checkpointStore(0)
-	idx := store.record(9)
-	entry := &store.entries[idx]
-	// Only set conv checkpoint, not delta - making it incomplete
-	entry.conv = map[int]ml.Tensor{0: nil}
-	// entry.delta is not set, so checkpoint is incomplete
-
-	cache.pendingRestore[1] = checkpointRestore{slot: 0, idx: idx, pos: 9}
-
-	err := cache.Remove(1, 10, math.MaxInt32)
-	if !errors.Is(err, kvcache.ErrNotSupported) {
-		t.Fatalf("expected ErrNotSupported for incomplete checkpoint, got %v", err)
-	}
-}
-
-func TestHybridCacheRestoreAcceptsCompleteCheckpoint(t *testing.T) {
-	cache := NewHybridCache(nil, 1, 2, 2)
-	cache.checkpointCount = 3
-	cache.checkpoints = make(map[int]*slotCheckpointStore)
-	cache.pendingRestore = make(map[int]checkpointRestore)
-
-	cache.slotForSeq[1] = 0
-	cache.refCount = []int{1}
-	cache.freeSlots = nil
-
-	// Don't set convStates/deltaStates - with no layers to check,
-	// entryComplete will return true as long as entry.pos >= 0
-
-	store := cache.checkpointStore(0)
-	idx := store.record(9)
-
-	cache.pendingRestore[1] = checkpointRestore{slot: 0, idx: idx, pos: 9}
-
-	// Test that restoreComplete returns true when no layers need checkpoints
-	restore := cache.pendingRestore[1]
-	if !cache.restoreComplete(restore) {
-		t.Fatalf("expected restoreComplete to return true for complete checkpoint")
-	}
-}
-
-func TestSlotCheckpointStoreRingBufferWrapAround(t *testing.T) {
-	// Test that ring buffer wrap-around reuses entries without clearing maps.
-	store := newSlotCheckpointStore(3)
-
-	// Fill the buffer
-	store.record(10)
-	store.record(20)
-	store.record(30)
-
-	// Create fake tensor data in the first entry's maps
-	store.entries[0].conv = make(map[int]ml.Tensor)
-	store.entries[0].conv[0] = nil // Simulated tensor reference
-	store.entries[0].delta = make(map[int]ml.Tensor)
-	store.entries[0].delta[0] = nil // Simulated tensor reference
-
-	// Record another entry, which should wrap around and overwrite entry 0
-	store.record(40)
-
-	// Verify the maps are still present (we reuse tensors)
-	if store.entries[0].conv == nil {
-		t.Fatalf("expected conv map to be preserved on reuse")
-	}
-	if store.entries[0].delta == nil {
-		t.Fatalf("expected delta map to be preserved on reuse")
-	}
-
-	// Verify the new position was recorded
-	if store.entries[0].pos != 40 {
-		t.Fatalf("expected entry 0 pos to be 40, got %d", store.entries[0].pos)
-	}
-}
-
-func TestSlotCheckpointStoreFullCapacity(t *testing.T) {
-	// Test behavior when buffer is exactly at capacity
-	store := newSlotCheckpointStore(2)
-
-	idx1 := store.record(10)
-	idx2 := store.record(20)
-
-	if idx1 != 0 || idx2 != 1 {
-		t.Fatalf("expected indices 0, 1, got %d, %d", idx1, idx2)
-	}
-
-	if store.size != 2 {
-		t.Fatalf("expected size 2, got %d", store.size)
-	}
-
-	// Verify both checkpoints are accessible
-	_, pos1, ok1 := store.bestIndex(15)
-	_, pos2, ok2 := store.bestIndex(25)
-
-	if !ok1 || pos1 != 10 {
-		t.Fatalf("expected best pos 10 for target 15, got pos=%d ok=%v", pos1, ok1)
-	}
-	if !ok2 || pos2 != 20 {
-		t.Fatalf("expected best pos 20 for target 25, got pos=%d ok=%v", pos2, ok2)
-	}
-}
-
-func TestSlotCheckpointStoreEmptyBuffer(t *testing.T) {
-	// Test behavior with zero-size buffer
-	store := newSlotCheckpointStore(0)
-
-	idx := store.record(10)
-	if idx != -1 {
-		t.Fatalf("expected record to return -1 for empty buffer, got %d", idx)
-	}
-
-	_, _, ok := store.bestIndex(15)
-	if ok {
-		t.Fatalf("expected no checkpoint for empty buffer")
-	}
-}
-
-func TestSlotCheckpointStorePruneAfterAll(t *testing.T) {
-	// Test pruning that removes all checkpoints
-	store := newSlotCheckpointStore(3)
-	store.record(10)
-	store.record(20)
-	store.record(30)
-
-	// Prune everything by setting threshold below all positions
-	store.pruneAfter(5)
-
-	if store.size != 0 {
-		t.Fatalf("expected size 0 after pruning all, got %d", store.size)
-	}
-	// When all checkpoints are pruned, lastPos is reset to -1
-	if store.lastPos != -1 {
-		t.Fatalf("expected lastPos -1 after pruning all, got %d", store.lastPos)
-	}
-
-	_, _, ok := store.bestIndex(100)
-	if ok {
-		t.Fatalf("expected no checkpoint after pruning all")
-	}
-}

model/models/qwen3next/deltanet.go

@@ -1,472 +0,0 @@
-package qwen3next
-
-import (
-	"errors"
-	"log/slog"
-	"math"
-
-	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/ml/nn"
-)
-
-const chunkSize = 64
-
-// TriType constants for triangular matrix operations
-const (
-	TriTypeUpperDiag = 0
-	TriTypeUpper     = 1
-	TriTypeLowerDiag = 2
-	TriTypeLower     = 3
-)
-
-// convKernel wraps the 1D convolution kernel tensor
-type convKernel struct {
-	Weight ml.Tensor `gguf:"weight"`
-}
-
-// Masks holds pre-computed mask tensors for chunked attention
-type Masks struct {
-	Causal   ml.Tensor // Lower triangular [chunkSize, chunkSize]
-	Identity ml.Tensor // Diagonal [chunkSize, chunkSize]
-	Diag     ml.Tensor // causal + identity
-}
-
-// GatedDeltaNet implements linear attention with SSM convolution and recurrent state.
-// It implements the Operator interface directly.
-type GatedDeltaNet struct {
-	// Optimized path: pre-split QKV and gate
-	SSMQKV       *nn.Linear  `gguf:"attn_qkv"`  // -> Q, K, V (concatenated)
-	SSMQKVGate   *nn.Linear  `gguf:"attn_gate"` // -> Z gate
-	SSMBetaAlpha *nn.Linear  `gguf:"ssm_ba"`    // -> beta, alpha
-	SSMConv1D    *convKernel `gguf:"ssm_conv1d"`
-	SSMDT        ml.Tensor   `gguf:"ssm_dt"` // alpha bias
-	SSMA         ml.Tensor   `gguf:"ssm_a"`  // -A_log.exp()
-	SSMNorm      *nn.RMSNorm `gguf:"ssm_norm"`
-	SSMOut       *nn.Linear  `gguf:"ssm_out"`
-
-	// Layer index for cache access (set during model construction)
-	Layer int
-}
-
-// createMasks builds the constant mask tensors (called once, reused for all chunks)
-func createMasks(ctx ml.Context) *Masks {
-	ones := ctx.Input().Zeros(ml.DTypeF32, chunkSize, chunkSize)
-	ones = ones.Fill(ctx, 1.0)
-	causalMask := ones.Tri(ctx, TriTypeLower)
-
-	onesVec := ctx.Input().Zeros(ml.DTypeF32, chunkSize)
-	onesVec = onesVec.Fill(ctx, 1.0)
-	identity := onesVec.Diag(ctx)
-
-	diagMask := causalMask.Add(ctx, identity)
-
-	return &Masks{
-		Causal:   causalMask,
-		Identity: identity,
-		Diag:     diagMask,
-	}
-}
-
-func (gdn *GatedDeltaNet) Forward(ctx ml.Context, hiddenStates, _ ml.Tensor, cache *HybridCache, opts *Options) (ml.Tensor, error) {
-	layer := gdn.Layer
-	nSeqTokens := hiddenStates.Dim(1)
-	nSeqs := hiddenStates.Dim(2)
-	if cache != nil && cache.IsSupportedForBatch() {
-		seqTokens := cache.seqTokens()
-		seqs := cache.numSeqs()
-		if seqTokens > 0 && seqs > 0 {
-			if nSeqs > 1 {
-				if nSeqTokens != seqTokens || nSeqs != seqs {
-					return nil, ErrUnsupportedBatchLayout
-				}
-			} else {
-				if nSeqTokens != seqTokens*seqs {
-					return nil, ErrUnsupportedBatchLayout
-				}
-				hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0), seqTokens, seqs)
-				nSeqTokens = seqTokens
-				nSeqs = seqs
-			}
-		}
-	}
-
-	headKDim := opts.ssmDState
-	numKHeads := opts.ssmNGroup
-	numVHeads := opts.ssmDtRank
-	headVDim := opts.ssmDInner / numVHeads
-	convKernelSize := opts.convKernelSize
-
-	mixedBA := gdn.SSMBetaAlpha.Forward(ctx, hiddenStates)
-	qkvDim := headKDim*numKHeads*2 + headVDim*numVHeads
-
-	if gdn.SSMQKV == nil || gdn.SSMQKVGate == nil {
-		return nil, errors.New("qwen3next: missing attn_qkv/attn_gate projections (legacy ssm_in is not supported)")
-	}
-	// Optimized path: pre-split QKV and gate
-	qkvMixed := gdn.SSMQKV.Forward(ctx, hiddenStates).Reshape(ctx, qkvDim, nSeqTokens, nSeqs)
-	z := gdn.SSMQKVGate.Forward(ctx, hiddenStates)
-
-	baNewDim := 2 * numVHeads / numKHeads
-	mixedBAReshaped := mixedBA.Reshape(ctx, baNewDim, numKHeads, nSeqTokens, nSeqs)
-
-	// Split beta and alpha
-	betaSize := numVHeads / numKHeads
-	alphaSize := numVHeads / numKHeads
-
-	b := mixedBAReshaped.Slice(ctx, 0, 0, betaSize, 1)
-	a := mixedBAReshaped.Slice(ctx, 0, betaSize, betaSize+alphaSize, 1)
-
-	// Reshape to merge head dimensions
-	beta := b.Contiguous(ctx, numVHeads, 1, nSeqTokens, nSeqs)
-	alpha := a.Contiguous(ctx, numVHeads, nSeqTokens, nSeqs)
-
-	// Compute gate: softplus(alpha + dt_bias) * -A
-	alphaBiased := alpha.Add(ctx, gdn.SSMDT)
-	alphaSoftplus := alphaBiased.Softplus(ctx)
-	gate := alphaSoftplus.Mul(ctx, gdn.SSMA)
-	qkvMixed = qkvMixed.Permute(ctx, 1, 0, 2, 3)
-
-	// Get conv state from cache
-	convStates, err := cache.ConvState(ctx, layer)
-	if err != nil {
-		// Log this - if it happens, short-term context will be lost
-		slog.Warn("qwen3next: failed to get conv state, using zeros", "layer", layer, "error", err)
-		convStates = ctx.Input().Zeros(ml.DTypeF32, convKernelSize-1, qkvDim, nSeqs)
-	}
-
-	// Reshape conv states
-	convStates = convStates.Reshape(ctx, convKernelSize-1, qkvDim, nSeqs)
-
-	// Concatenate with input for convolution
-	convInput := convStates.Concat(ctx, qkvMixed, 0)
-
-	// Save new conv state (last convKernelSize-1 tokens)
-	lastConvStates := convInput.Slice(ctx, 0, nSeqTokens, nSeqTokens+convKernelSize-1, 1)
-	cache.UpdateConvState(ctx, layer, lastConvStates)
-
-	// Apply SSM convolution (kernel must be F32 for Metal)
-	convOutput := convInput.SSMConv(ctx, gdn.SSMConv1D.Weight)
-	convOutput = convOutput.SILU(ctx)
-
-	// Reshape for extraction
-	convQKVMix := convOutput.Contiguous(ctx, qkvDim, nSeqTokens*nSeqs)
-
-	// Extract convolved Q, K, V
-	qConv := convQKVMix.Slice(ctx, 0, 0, headKDim*numKHeads, 1)
-	kConv := convQKVMix.Slice(ctx, 0, headKDim*numKHeads, 2*headKDim*numKHeads, 1)
-	vConv := convQKVMix.Slice(ctx, 0, 2*headKDim*numKHeads, qkvDim, 1)
-
-	// Reshape to 4D
-	qConv = qConv.Contiguous(ctx, headKDim, numKHeads, nSeqTokens, nSeqs)
-	kConv = kConv.Contiguous(ctx, headKDim, numKHeads, nSeqTokens, nSeqs)
-	vConv = vConv.Contiguous(ctx, headVDim, numVHeads, nSeqTokens, nSeqs)
-
-	// Get delta state from cache
-	state, err := cache.DeltaState(ctx, layer, headVDim, numVHeads)
-	if err != nil {
-		// Log this - if it happens frequently, context will degrade
-		slog.Warn("qwen3next: failed to get delta state, using zeros", "layer", layer, "error", err)
-		state = ctx.Input().Zeros(ml.DTypeF32, headVDim, headVDim*numVHeads, nSeqs)
-	}
-	state = state.Reshape(ctx, headVDim, headVDim*numVHeads, 1, nSeqs)
-
-	// Repeat interleave Q and K if numKHeads != numVHeads
-	if numKHeads != numVHeads {
-		repeatFactor := numVHeads / numKHeads
-
-		qReshaped := qConv.Reshape(ctx, headKDim, 1, numKHeads*nSeqTokens*nSeqs)
-		kReshaped := kConv.Reshape(ctx, headKDim, 1, numKHeads*nSeqTokens*nSeqs)
-
-		qRepeated := qReshaped.Repeat4D(ctx, headKDim, repeatFactor, numKHeads*nSeqTokens*nSeqs, 1)
-		kRepeated := kReshaped.Repeat4D(ctx, headKDim, repeatFactor, numKHeads*nSeqTokens*nSeqs, 1)
-
-		qConv = qRepeated.Reshape(ctx, headKDim, numKHeads*repeatFactor, nSeqTokens, nSeqs)
-		kConv = kRepeated.Reshape(ctx, headKDim, numKHeads*repeatFactor, nSeqTokens, nSeqs)
-	}
-
-	// Choose computation mode based on sequence length
-	var attnOut ml.Tensor
-	if nSeqTokens == 1 {
-		attnOut = gdn.deltaNetAutoregressive(ctx, qConv, kConv, vConv, gate, beta, state, opts, layer, cache)
-	} else {
-		// Use pre-computed masks from opts (created once in Model.Forward)
-		attnOut = gdn.deltaNetChunked(ctx, qConv, kConv, vConv, gate, beta, state, opts.masks, opts, layer, cache)
-	}
-
-	// Apply gated normalization
-	attnOut2D := attnOut.Contiguous(ctx, headVDim, numVHeads*nSeqTokens*nSeqs)
-	z2D := z.Contiguous(ctx, headVDim, numVHeads*nSeqTokens*nSeqs)
-
-	// norm(attnOut, z) = RMSNorm(attnOut) * silu(z)
-	attnOutNorm := gdn.SSMNorm.Forward(ctx, attnOut2D, opts.eps)
-	zSilu := z2D.SILU(ctx)
-	attnOutGated := attnOutNorm.Mul(ctx, zSilu)
-
-	// Reshape for output projection
-	finalOutput := attnOutGated.Reshape(ctx, headVDim*numVHeads, nSeqTokens, nSeqs)
-
-	out := gdn.SSMOut.Forward(ctx, finalOutput)
-	return out.Reshape(ctx, out.Dim(0), nSeqTokens*nSeqs), nil
-}
-
-// deltaNetAutoregressive implements single-token state update.
-// NOTE: Assumes headKDim == headVDim (state shape is [headVDim, headVDim, numVHeads, nSeqs]).
-func (gdn *GatedDeltaNet) deltaNetAutoregressive(
-	ctx ml.Context,
-	q, k, v, gate, beta, state ml.Tensor,
-	opts *Options,
-	layer int,
-	cache *HybridCache,
-) ml.Tensor {
-	numVHeads := v.Dim(1)
-	headVDim := v.Dim(0)
-	nSeqs := q.Dim(3)
-
-	// L2 normalize Q and K
-	q = q.L2Norm(ctx, opts.eps)
-	k = k.L2Norm(ctx, opts.eps)
-
-	// Scale Q
-	scale := 1.0 / math.Sqrt(float64(headVDim))
-	q = q.Scale(ctx, scale)
-
-	// Sigmoid beta
-	beta = beta.Sigmoid(ctx)
-
-	// Reshape state: [headVDim, headVDim, numVHeads, nSeqs]
-	state = state.Reshape(ctx, headVDim, headVDim, numVHeads, nSeqs)
-
-	// Reshape gate and beta for broadcasting
-	gT := gate.Permute(ctx, 1, 0, 2, 3).Reshape(ctx, 1, 1, numVHeads, nSeqs)
-	betaT := beta.Permute(ctx, 1, 0, 2, 3).Reshape(ctx, 1, 1, numVHeads, nSeqs)
-
-	// Apply exponential to gate
-	gT = gT.Exp(ctx)
-
-	// state = state * g_t
-	state = state.Mul(ctx, gT)
-
-	// kv_mem = (state * k_t.unsqueeze(-1)).sum(dim=-2)
-	kTUnsqueezed := k.Reshape(ctx, 1, headVDim, numVHeads, nSeqs)
-	kvMem := state.Mul(ctx, kTUnsqueezed)
-	// Sum over dim=-2 (second dimension after permute)
-	kvMem = kvMem.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
-	kvMem = kvMem.SumRows(ctx)
-	kvMem = kvMem.Permute(ctx, 1, 0, 2, 3)
-
-	// v_t with singleton dimension
-	vT := v.Reshape(ctx, headVDim, 1, numVHeads, nSeqs)
-
-	// delta = (v_t - kv_mem) * beta_t
-	vDiff := vT.Sub(ctx, kvMem)
-	delta := vDiff.Mul(ctx, betaT)
-
-	// state = state + k_t.unsqueeze(-1) * delta
-	kTUnsqueezedBroad := kTUnsqueezed.Repeat4D(ctx, headVDim, headVDim, numVHeads, nSeqs)
-	kTDelta := kTUnsqueezedBroad.Mul(ctx, delta)
-	state = state.Add(ctx, kTDelta)
-
-	// core_attn_out = (state * q_t.unsqueeze(-1)).sum(dim=-2)
-	qTUnsqueezed := q.Reshape(ctx, 1, headVDim, numVHeads, nSeqs)
-	stateQ := state.Mul(ctx, qTUnsqueezed)
-	stateQ = stateQ.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
-	coreAttnOut := stateQ.SumRows(ctx)
-	coreAttnOut = coreAttnOut.Permute(ctx, 1, 0, 2, 3)
-
-	// Update delta state in cache
-	cache.UpdateDeltaState(ctx, layer, state.Reshape(ctx, headVDim, headVDim*numVHeads, nSeqs))
-
-	return coreAttnOut.Reshape(ctx, headVDim, numVHeads, 1, nSeqs)
-}
-
-// deltaNetChunked implements chunked computation for prefill.
-// NOTE: Assumes headKDim == headVDim (state shape is [headVDim, headVDim, numVHeads, nSeqs]).
-func (gdn *GatedDeltaNet) deltaNetChunked(
-	ctx ml.Context,
-	q, k, v, gate, beta, state ml.Tensor,
-	masks *Masks,
-	opts *Options,
-	layer int,
-	cache *HybridCache,
-) ml.Tensor {
-	headKDim := q.Dim(0)
-	numVHeads := v.Dim(1)
-	headVDim := v.Dim(0)
-	nTokens := q.Dim(2)
-	nSeqs := q.Dim(3)
-
-	// L2 normalize Q and K
-	q = q.L2Norm(ctx, opts.eps)
-	k = k.L2Norm(ctx, opts.eps)
-
-	// Scale Q
-	scale := 1.0 / math.Sqrt(float64(headVDim))
-	q = q.Scale(ctx, scale)
-
-	// Sigmoid beta
-	beta = beta.Sigmoid(ctx)
-
-	// Permute tensors for chunked computation
-	q = q.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx, headKDim, nTokens, numVHeads, nSeqs)
-	k = k.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx, headKDim, nTokens, numVHeads, nSeqs)
-	v = v.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx, headVDim, nTokens, numVHeads, nSeqs)
-	gate = gate.Permute(ctx, 2, 0, 3, 1).Contiguous(ctx, nTokens, 1, numVHeads, nSeqs)
-
-	beta = beta.Permute(ctx, 2, 0, 1, 3).Contiguous(ctx)
-	state = state.Reshape(ctx, headVDim, headVDim, numVHeads, nSeqs)
-
-	// Compute padding
-	pad := (chunkSize - nTokens%chunkSize) % chunkSize
-	nChunks := (nTokens + pad) / chunkSize
-
-	// Pad tensors
-	if pad > 0 {
-		q = q.Pad(ctx, 0, pad, 0, 0)
-		k = k.Pad(ctx, 0, pad, 0, 0)
-		v = v.Pad(ctx, 0, pad, 0, 0)
-		gate = gate.Pad(ctx, pad, 0, 0, 0)
-		beta = beta.Pad(ctx, 0, pad, 0, 0)
-	}
-
-	// Use pre-computed masks (passed in, not recreated)
-	causalMask := masks.Causal
-	identity := masks.Identity
-	diagMask := masks.Diag
-	identity4D := identity.Reshape(ctx, chunkSize, chunkSize, 1, 1)
-
-	// v_beta = v * beta, k_beta = k * beta
-	vBeta := v.Mul(ctx, beta)
-	kBeta := k.Mul(ctx, beta)
-
-	// Reshape for chunked computation
-	q = q.Reshape(ctx, headKDim, chunkSize, nChunks, numVHeads*nSeqs)
-	k = k.Reshape(ctx, headKDim, chunkSize, nChunks, numVHeads*nSeqs)
-	kBeta = kBeta.Reshape(ctx, headKDim, chunkSize, nChunks, numVHeads*nSeqs)
-	vBeta = vBeta.Reshape(ctx, headVDim, chunkSize, nChunks, numVHeads*nSeqs)
-
-	gate = gate.Reshape(ctx, chunkSize, 1, nChunks, numVHeads*nSeqs)
-
-	// g_cumsum = cumsum(gate)
-	gCumsum := gate.CumSum(ctx)
-
-	// Compute decay mask
-	gcsI := gCumsum.Reshape(ctx, chunkSize, 1, nChunks, numVHeads*nSeqs)
-	gcsJ := gCumsum.Reshape(ctx, 1, chunkSize, nChunks, numVHeads*nSeqs)
-	gcsBroadcast := gcsJ.Repeat4D(ctx, chunkSize, chunkSize, nChunks, numVHeads*nSeqs)
-	decayMask := gcsBroadcast.Sub(ctx, gcsI)
-
-	decayMask = decayMask.Mul(ctx, diagMask)
-	decayMask = decayMask.Exp(ctx)
-	decayMask = decayMask.Mul(ctx, diagMask)
-
-	// k @ k_beta^T
-	kMulKBeta := k.Mulmat(ctx, kBeta)
-
-	// k_decay = k @ k_beta^T * decay_mask
-	kDecay := kMulKBeta.Mul(ctx, decayMask)
-
-	// attn = -k_decay * causal_mask
-	attn := kDecay.Neg(ctx).Mul(ctx, causalMask)
-
-	// Triangular solve: (I - attn_lower)^-1 @ attn
-	attnLower := attn.Mul(ctx, causalMask)
-	lhs := attnLower.Neg(ctx).Add(ctx, identity4D)
-	linSolve := lhs.SolveTri(ctx, attn, true, true, false)
-	attn = linSolve.Mul(ctx, causalMask)
-	attn = attn.Add(ctx, identity4D)
-
-	// v = v_beta^T @ attn
-	vBetaT := vBeta.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
-	v = vBetaT.Mulmat(ctx, attn)
-
-	// Compute g_exp for state update
-	gCumsumT := gCumsum.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
-	gExp := gCumsumT.Exp(ctx)
-
-	// kbeta_gexp = k_beta * g_exp
-	kBetaGExp := kBeta.Mul(ctx, gExp)
-
-	// k_cumdecay = attn @ kbeta_gexp^T
-	kBetaGExpT := kBetaGExp.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
-	kCumdecay := attn.Mulmat(ctx, kBetaGExpT)
-	kCumdecay = kCumdecay.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
-
-	// Pre-compute attn_kq = (k @ q) * decay_mask * diag_mask
-	attnKQ := k.Mulmat(ctx, q)
-	attnKQ = attnKQ.Mul(ctx, decayMask)
-	attnKQ = attnKQ.Mul(ctx, diagMask)
-
-	// Pre-compute g_last and key_gdiff
-	// g_last = view of last element in g_cumsum along chunk_size dimension
-	// We need to get the last row of gCumsum: shape [chunkSize, 1, nChunks, H*n_seqs] -> [1, 1, nChunks, H*n_seqs]
-	gLast := gCumsum.Slice(ctx, 0, chunkSize-1, chunkSize, 1).Contiguous(ctx, 1, 1, nChunks, numVHeads*nSeqs)
-	gLastExp := gLast.Exp(ctx)
-
-	// g_diff = -(g_cumsum - g_last) = g_last - g_cumsum
-	gDiff := gCumsum.Neg(ctx).Add(ctx, gLast)
-	gDiffExp := gDiff.Exp(ctx)
-
-	// Reshapes g_diff_exp to [1, chunkSize, nChunks, ...]
-	gDiffExpReshaped := gDiffExp.Reshape(ctx, 1, chunkSize, nChunks, numVHeads*nSeqs)
-	keyGDiff := k.Mul(ctx, gDiffExpReshaped)
-	keyGDiffT := keyGDiff.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
-
-	// Process chunks and update state
-	var coreAttnOut ml.Tensor
-	newState := state
-
-	for chunk := range nChunks {
-		qChunk := q.Slice(ctx, 2, chunk, chunk+1, 1)
-		vChunk := v.Slice(ctx, 2, chunk, chunk+1, 1)
-		gExpChunk := gExp.Slice(ctx, 2, chunk, chunk+1, 1)
-		kCumdecayChunk := kCumdecay.Slice(ctx, 2, chunk, chunk+1, 1)
-		attnChunk := attnKQ.Slice(ctx, 2, chunk, chunk+1, 1) // Pre-computed!
-
-		// state^T - permute is needed but Contiguous creates a copy
-		stateT := newState.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx, headVDim, headVDim, 1, numVHeads*nSeqs)
-
-		// v_prime = k_cumdecay @ state
-		vPrime := stateT.Mulmat(ctx, kCumdecayChunk)
-
-		// v_new = v - v_prime
-		vNew := vChunk.Sub(ctx, vPrime)
-		vNewT := vNew.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
-
-		// attn_inter = (q * g_exp) @ state
-		qGExp := qChunk.Mul(ctx, gExpChunk)
-		attnInter := stateT.Mulmat(ctx, qGExp)
-
-		// core_attn_out = attn_inter + attn @ v_new
-		vAttn := vNewT.Mulmat(ctx, attnChunk)
-		coreAttnOutChunk := attnInter.Add(ctx, vAttn)
-
-		if coreAttnOut == nil {
-			coreAttnOut = coreAttnOutChunk
-		} else {
-			coreAttnOut = coreAttnOut.Concat(ctx, coreAttnOutChunk, 1)
-		}
-
-		// Update state for next chunk
-		gExpLastChunk := gLastExp.Slice(ctx, 2, chunk, chunk+1, 1)
-		kGDiffChunkT := keyGDiffT.Slice(ctx, 2, chunk, chunk+1, 1)
-		kgdMulVNew := vNewT.Mulmat(ctx, kGDiffChunkT)
-
-		// state = state * g_last + kgdmulvnew
-		gExpLastReshaped := gExpLastChunk.Contiguous(ctx).Reshape(ctx, 1, 1, numVHeads, nSeqs)
-		newState = newState.Mul(ctx, gExpLastReshaped)
-		newState = newState.Add(ctx, kgdMulVNew.Reshape(ctx, headVDim, headVDim, numVHeads, nSeqs))
-	}
-
-	// Final reshape
-	coreAttnOut = coreAttnOut.Contiguous(ctx, headVDim, chunkSize*nChunks, numVHeads, nSeqs)
-
-	// Slice to remove padding
-	if pad > 0 {
-		coreAttnOut = coreAttnOut.Slice(ctx, 1, 0, nTokens, 1)
-	}
-
-	// Update delta state in cache
-	cache.UpdateDeltaState(ctx, layer, newState.Reshape(ctx, headVDim, headVDim*numVHeads, nSeqs))
-
-	return coreAttnOut.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx, headVDim, numVHeads, nTokens, nSeqs)
-}

model/models/qwen3next/model.go

@@ -1,383 +0,0 @@
-package qwen3next
-
-import (
-	"cmp"
-	"fmt"
-	"math"
-
-	"github.com/ollama/ollama/fs"
-	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/ml/nn"
-	"github.com/ollama/ollama/ml/nn/rope"
-	"github.com/ollama/ollama/model"
-	"github.com/ollama/ollama/model/input"
-)
-
-// Options contains model configuration
-type Options struct {
-	hiddenSize  int
-	numHeads    int
-	numKVHeads  int
-	keyLength   int
-	valueLength int
-	ropeDim     int
-
-	eps                   float32
-	ropeBase              float32
-	ropeScale             float32
-	ropeType              string
-	originalContextLength int
-	attentionScale        float64
-
-	// MoE config
-	numExperts     int
-	numExpertsUsed int
-	normTopKProb   bool
-
-	// Linear attention (Gated Delta Net) config
-	ssmDInner      int // d_inner = head_v_dim * num_v_heads
-	ssmDState      int // head_k_dim
-	ssmNGroup      int // num_k_heads
-	ssmDtRank      int // num_v_heads
-	convKernelSize int // SSM conv kernel size
-
-	// Per-layer type from GGUF metadata
-	isRecurrent []bool
-
-	// Pre-computed masks for chunked attention (created once per forward pass)
-	masks *Masks
-}
-
-func (o Options) headDim() int {
-	return cmp.Or(o.keyLength, o.valueLength, o.hiddenSize/o.numHeads)
-}
-
-func (o Options) applyRotaryPositionEmbeddings(ctx ml.Context, states, positions ml.Tensor) ml.Tensor {
-	opts := []func(*rope.Options){rope.WithTypeNeoX()}
-	if o.ropeType == "yarn" {
-		attnFactor := float32(1.0 / (1.0 + 0.1*math.Log(float64(o.ropeScale))))
-		opts = append(opts,
-			rope.WithOriginalContextLength(o.originalContextLength),
-			rope.WithExtrapolationFactor(1.),
-			rope.WithAttentionFactor(attnFactor),
-		)
-	}
-	ropeDim := cmp.Or(o.ropeDim, o.headDim())
-	return nn.RoPE(ctx, states, positions, ropeDim, o.ropeBase, 1./o.ropeScale, opts...)
-}
-
-// Operator is the interface for attention-like operators
-type Operator interface {
-	Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache *HybridCache, opts *Options) (ml.Tensor, error)
-}
-
-// MLP is the interface for feedforward networks
-type MLP interface {
-	Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor
-}
-
-// sparse implements MoE with shared experts
-type sparse struct {
-	Router *nn.Linear      `gguf:"ffn_gate_inp"`
-	Gate   *nn.LinearBatch `gguf:"ffn_gate_exps"`
-	Up     *nn.LinearBatch `gguf:"ffn_up_exps"`
-	Down   *nn.LinearBatch `gguf:"ffn_down_exps"`
-
-	// Shared experts
-	SharedGateInp *nn.Linear `gguf:"ffn_gate_inp_shexp"`
-	SharedGate    *nn.Linear `gguf:"ffn_gate_shexp"`
-	SharedUp      *nn.Linear `gguf:"ffn_up_shexp"`
-	SharedDown    *nn.Linear `gguf:"ffn_down_shexp"`
-}
-
-func (mlp *sparse) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
-	hiddenDim, sequenceLength, batchSize := hiddenStates.Dim(0), hiddenStates.Dim(1), hiddenStates.Dim(2)
-	if batchSize == 0 {
-		batchSize = 1
-	}
-	hiddenStates2D := hiddenStates.Reshape(ctx, hiddenDim, sequenceLength*batchSize)
-
-	// Router logits
-	routerLogits := mlp.Router.Forward(ctx, hiddenStates2D)
-
-	// Softmax routing weights
-	routingWeights := routerLogits.Softmax(ctx)
-	selectedExperts := routingWeights.TopK(ctx, opts.numExpertsUsed)
-	routingWeights = routingWeights.Reshape(ctx, 1, opts.numExperts, hiddenStates2D.Dim(1)).Rows(ctx, selectedExperts)
-	if opts.normTopKProb {
-		routingWeights = routingWeights.Reshape(ctx, opts.numExpertsUsed, hiddenStates2D.Dim(1))
-		routingWeights = routingWeights.Div(ctx, routingWeights.SumRows(ctx))
-		routingWeights = routingWeights.Reshape(ctx, 1, opts.numExpertsUsed, hiddenStates2D.Dim(1))
-	}
-
-	hiddenStates3D := hiddenStates2D.Reshape(ctx, hiddenStates2D.Dim(0), 1, hiddenStates2D.Dim(1))
-
-	// Expert computation with SILU activation
-	gateOut := mlp.Gate.Forward(ctx, hiddenStates3D, selectedExperts)
-	upOut := mlp.Up.Forward(ctx, hiddenStates3D, selectedExperts)
-	experts := gateOut.SILU(ctx, upOut)
-	experts = mlp.Down.Forward(ctx, experts, selectedExperts)
-	experts = experts.Mul(ctx, routingWeights)
-
-	// Sum over experts
-	moeOut := experts.View(ctx, 0, experts.Dim(0), experts.Stride(2), experts.Dim(2))
-	for i := 1; i < opts.numExpertsUsed; i++ {
-		moeOut = moeOut.Add(ctx, experts.View(ctx, i*experts.Stride(1), experts.Dim(0), experts.Stride(2), experts.Dim(2)))
-	}
-
-	// Add shared experts if present
-	if mlp.SharedUp != nil {
-		sharedGate := mlp.SharedGate.Forward(ctx, hiddenStates2D)
-		sharedUp := mlp.SharedUp.Forward(ctx, hiddenStates2D)
-		sharedOut := sharedGate.SILU(ctx, sharedUp)
-		sharedOut = mlp.SharedDown.Forward(ctx, sharedOut)
-
-		// Apply shared expert gating
-		if mlp.SharedGateInp != nil {
-			sharedGateVal := mlp.SharedGateInp.Forward(ctx, hiddenStates2D)
-			sharedGateVal = sharedGateVal.Sigmoid(ctx)
-			// Broadcast gate to match dimensions
-			sharedGateVal = sharedGateVal.Repeat(ctx, 0, sharedOut.Dim(0))
-			sharedOut = sharedOut.Mul(ctx, sharedGateVal)
-		}
-
-		moeOut = moeOut.Add(ctx, sharedOut)
-	}
-
-	return moeOut
-}
-
-// dense implements standard feedforward
-type dense struct {
-	Gate *nn.Linear `gguf:"ffn_gate"`
-	Up   *nn.Linear `gguf:"ffn_up"`
-	Down *nn.Linear `gguf:"ffn_down"`
-}
-
-func (mlp *dense) Forward(ctx ml.Context, hiddenStates ml.Tensor, _ *Options) ml.Tensor {
-	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
-	return mlp.Down.Forward(ctx, hiddenStates)
-}
-
-// Layer represents a single transformer layer
-type Layer struct {
-	AttentionNorm     *nn.RMSNorm `gguf:"attn_norm"`
-	AttentionPostNorm *nn.RMSNorm `gguf:"post_attention_norm"` // Post-attention norm before FFN
-	Operator          Operator
-
-	FFNNorm *nn.RMSNorm `gguf:"ffn_norm"`
-	MLP     MLP
-}
-
-func (l *Layer) Forward(ctx ml.Context, layer int, hiddenStates, positions, outputs ml.Tensor, cache *HybridCache, opts *Options) (ml.Tensor, error) {
-	residual := hiddenStates
-
-	// Pre-attention norm
-	hiddenStates = l.AttentionNorm.Forward(ctx, hiddenStates, opts.eps)
-
-	// Attention (full or linear)
-	var err error
-	hiddenStates, err = l.Operator.Forward(ctx, hiddenStates, positions, cache, opts)
-	if err != nil {
-		return nil, err
-	}
-
-	// Output projection for last layer
-	if outputs != nil {
-		hiddenStates = hiddenStates.Rows(ctx, outputs)
-		residual = residual.Rows(ctx, outputs)
-	}
-
-	// First residual connection
-	hiddenStates = hiddenStates.Add(ctx, residual)
-
-	// Save for FFN residual
-	ffnResidual := hiddenStates
-
-	// Post-attention norm (before FFN)
-	hiddenStates = l.AttentionPostNorm.Forward(ctx, hiddenStates, opts.eps)
-
-	// FFN
-	hiddenStates = l.MLP.Forward(ctx, hiddenStates, opts)
-
-	// Second residual connection
-	return hiddenStates.Add(ctx, ffnResidual), nil
-}
-
-// Model is the main Qwen3-Next model
-type Model struct {
-	model.Base
-	model.BytePairEncoding
-
-	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
-	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
-	Output         *nn.Linear    `gguf:"output,alt:token_embd"`
-
-	Layers []Layer `gguf:"blk"`
-
-	*Options
-}
-
-func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
-	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
-
-	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
-
-	cache := m.Cache.(*HybridCache)
-
-	// Create masks once per forward pass
-	m.Options.masks = createMasks(ctx)
-
-	for i, layer := range m.Layers {
-		cache.SetLayer(i)
-
-		var outputs ml.Tensor
-		if i == len(m.Layers)-1 {
-			outputs = batch.Outputs
-		}
-
-		var err error
-		hiddenStates, err = layer.Forward(ctx, i, hiddenStates, positions, outputs, cache, m.Options)
-		if err != nil {
-			return nil, err
-		}
-	}
-
-	hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.eps)
-	return m.Output.Forward(ctx, hiddenStates), nil
-}
-
-func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
-	return m.applyRotaryPositionEmbeddings(ctx, key, shift), nil
-}
-
-var _ model.Model = (*Model)(nil)
-
-func New(c fs.Config) (model.Model, error) {
-	numLayers := int(c.Uint("block_count"))
-	layers := make([]Layer, numLayers)
-
-	// Get per-layer head counts (for detecting layer type)
-	type headCounts interface {
-		HeadCount() []uint64
-		HeadCountKV() []uint64
-	}
-
-	var isRecurrent []bool
-	var headCountKV []uint64
-	if hc, ok := c.(headCounts); ok {
-		headCountKV = hc.HeadCountKV()
-	}
-
-	isRecurrent = make([]bool, numLayers)
-	hasZero := false
-	hasFull := false
-	for i := range numLayers {
-		// If KV head count is 0, it's a recurrent layer
-		if i < len(headCountKV) && headCountKV[i] == 0 {
-			isRecurrent[i] = true
-			hasZero = true
-		} else if i < len(headCountKV) && headCountKV[i] > 0 {
-			hasFull = true
-		}
-	}
-	if !hasZero || !hasFull {
-		return nil, fmt.Errorf("qwen3next: invalid attention.head_count_kv array; expected mix of zero and non-zero values")
-	}
-
-	// Determine if MoE
-	isMoE := c.Uint("expert_count") > 0
-
-	for i := range layers {
-		if isRecurrent[i] {
-			layers[i].Operator = &GatedDeltaNet{Layer: i}
-		} else {
-			layers[i].Operator = &FullAttention{}
-		}
-
-		if isMoE {
-			layers[i].MLP = &sparse{}
-		} else {
-			layers[i].MLP = &dense{}
-		}
-	}
-
-	opts := &Options{
-		hiddenSize: int(c.Uint("embedding_length")),
-		numHeads:   int(c.Uint("attention.head_count")),
-		numKVHeads: func() int {
-			for _, v := range headCountKV {
-				if v > 0 {
-					return int(v)
-				}
-			}
-			return 0
-		}(),
-		keyLength:             int(c.Uint("attention.key_length")),
-		valueLength:           int(c.Uint("attention.value_length")),
-		ropeDim:               int(c.Uint("rope.dimension_count")),
-		eps:                   c.Float("attention.layer_norm_rms_epsilon"),
-		ropeType:              c.String("rope.scaling.type"),
-		ropeBase:              c.Float("rope.freq_base"),
-		ropeScale:             c.Float("rope.scaling.factor", 1),
-		originalContextLength: int(c.Uint("rope.scaling.original_context_length")),
-		attentionScale:        float64(c.Float("attention.scale")),
-		numExperts:            int(c.Uint("expert_count")),
-		numExpertsUsed:        int(c.Uint("expert_used_count")),
-		normTopKProb:          c.Bool("norm_top_k_prob", true),
-		ssmDInner:             int(c.Uint("ssm.inner_size")),
-		ssmDState:             int(c.Uint("ssm.state_size")),
-		ssmNGroup:             int(c.Uint("ssm.group_count")),
-		ssmDtRank:             int(c.Uint("ssm.time_step_rank")),
-		convKernelSize:        int(c.Uint("ssm.conv_kernel")),
-		isRecurrent:           isRecurrent,
-	}
-	if opts.numKVHeads == 0 {
-		return nil, fmt.Errorf("qwen3next: attention.head_count_kv array must include at least one non-zero value")
-	}
-
-	// Calculate cache dimensions
-	convDim := max(0, opts.convKernelSize-1)
-	convChannels := opts.ssmDInner + 2*opts.ssmNGroup*opts.ssmDState
-	headVDim := 0
-	numVHeads := opts.ssmDtRank
-	if numVHeads > 0 {
-		headVDim = opts.ssmDInner / numVHeads
-	}
-	deltaStateSize := headVDim * headVDim * numVHeads
-
-	// Validate dimension assumption: headKDim == headVDim is required for state computations
-	headKDim := opts.ssmDState
-	if headKDim != headVDim && headKDim > 0 && headVDim > 0 {
-		return nil, fmt.Errorf("qwen3next: headKDim (%d) != headVDim (%d) not supported; state computations require equal dimensions", headKDim, headVDim)
-	}
-
-	m := Model{
-		BytePairEncoding: model.NewBytePairEncoding(
-			&model.Vocabulary{
-				Values: c.Strings("tokenizer.ggml.tokens"),
-				Types:  c.Ints("tokenizer.ggml.token_type"),
-				Merges: c.Strings("tokenizer.ggml.merges"),
-				// Qwen3 tokenizers typically set add_bos_token=false and bos_token=null.
-				// Default to false when the GGUF key is missing to avoid injecting a spurious BOS.
-				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", false),
-				BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
-				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
-				EOS: append(
-					[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
-					c.Ints("tokenizer.ggml.eos_token_ids")...,
-				),
-			},
-			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
-		),
-		Layers:  layers,
-		Options: opts,
-	}
-
-	m.Cache = NewHybridCache(m.Shift, convDim, convChannels, deltaStateSize)
-	return &m, nil
-}
-
-func init() {
-	model.Register("qwen3next", New)
-}

model/models/qwen3vl/model.go

@@ -10,11 +10,12 @@ import (
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 type Model struct {
 	model.Base
-	model.TextProcessor
+	tokenizer.Tokenizer
 
 	*TextModel
 	*VisionModel `gguf:"v"`
@@ -172,8 +173,8 @@ func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 
 func New(c fs.Config) (model.Model, error) {
 	m := Model{
-		TextProcessor: model.NewBytePairEncoding(
-			&model.Vocabulary{
+		Tokenizer: tokenizer.NewBytePairEncoding(
+			&tokenizer.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),

model/parsers/glmocr.go

@@ -1,17 +0,0 @@
-package parsers
-
-import "github.com/ollama/ollama/api"
-
-// GlmOcrParser is the GLM46 parser with thinking disabled.
-type GlmOcrParser struct {
-	GLM46Parser
-}
-
-func (p *GlmOcrParser) HasThinkingSupport() bool {
-	return false
-}
-
-func (p *GlmOcrParser) Init(tools []api.Tool, _ *api.Message, _ *api.ThinkValue) []api.Tool {
-	p.tools = tools
-	return tools
-}

model/parsers/ministral.go

@@ -4,7 +4,6 @@ import (
 	"encoding/json"
 	"fmt"
 	"strings"
-	"unicode"
 
 	"github.com/ollama/ollama/api"
 )
@@ -18,34 +17,12 @@ const (
 	ministralCollectingToolArgs
 )
 
-// ministralEvent represents an event emitted during parsing
-type ministralEvent interface {
-	isMinistralEvent()
-}
-
-type ministralEventContent struct {
-	content string
-}
-
-type ministralEventThinking struct {
-	thinking string
-}
-
-type ministralEventToolCall struct {
-	name string
-	args string // raw JSON string
-}
-
-func (ministralEventContent) isMinistralEvent()  {}
-func (ministralEventThinking) isMinistralEvent() {}
-func (ministralEventToolCall) isMinistralEvent() {}
-
 type MinistralParser struct {
 	state              ministralParserState
 	buffer             strings.Builder
 	tools              []api.Tool
 	hasThinkingSupport bool
-	pendingToolName    string // stores tool name while collecting args
+	currentTool        *api.Tool
 }
 
 func (p *MinistralParser) HasToolSupport() bool {
@@ -86,251 +63,74 @@ func toolByName(tools []api.Tool, n string) (*api.Tool, error) {
 	return nil, fmt.Errorf("tool '%s' not found", n)
 }
 
-const (
-	ministralToolCallsTag = "[TOOL_CALLS]"
-	ministralThinkTag     = "[THINK]"
-	ministralThinkEndTag  = "[/THINK]"
-	ministralArgsTag      = "[ARGS]"
-)
-
-// eat consumes the parser's buffer, and returns a list of any unambiguous
-// events from the current parser state. The second return value indicates
-// whether to keep looping (true when state transitions, false when waiting
-// for more data).
-func (p *MinistralParser) eat() ([]ministralEvent, bool) {
-	var events []ministralEvent
-
-	switch p.state {
-	case ministralCollectingContent:
-		bufStr := p.buffer.String()
-
-		// Check for [TOOL_CALLS] tag
-		if strings.Contains(bufStr, ministralToolCallsTag) {
-			split := strings.SplitN(bufStr, ministralToolCallsTag, 2)
-			before := strings.TrimRightFunc(split[0], unicode.IsSpace)
-			if len(before) > 0 {
-				events = append(events, ministralEventContent{content: before})
-			}
-			after := split[1]
-			p.buffer.Reset()
-			p.buffer.WriteString(after)
-			p.state = ministralCollectingToolName
-			return events, true
-		}
-
-		// Check for [THINK] tag
-		if strings.Contains(bufStr, ministralThinkTag) {
-			split := strings.SplitN(bufStr, ministralThinkTag, 2)
-			before := strings.TrimRightFunc(split[0], unicode.IsSpace)
-			if len(before) > 0 {
-				events = append(events, ministralEventContent{content: before})
-			}
-			after := split[1]
-			p.buffer.Reset()
-			p.buffer.WriteString(after)
-			p.state = ministralCollectingThinkingContent
-			return events, true
-		}
-
-		// Check for partial tag overlap with [TOOL_CALLS] or [THINK]
-		overlapToolCalls := overlap(bufStr, ministralToolCallsTag)
-		overlapThink := overlap(bufStr, ministralThinkTag)
-		maxOverlap := max(overlapToolCalls, overlapThink)
-
-		if maxOverlap > 0 {
-			// Withhold the potential partial tag
-			beforePartialTag := bufStr[:len(bufStr)-maxOverlap]
-			trailingWS := trailingWhitespaceLen(beforePartialTag)
-			ambiguousStart := len(beforePartialTag) - trailingWS
-			unambiguous := bufStr[:ambiguousStart]
-			ambiguous := bufStr[ambiguousStart:]
-			p.buffer.Reset()
-			p.buffer.WriteString(ambiguous)
-			if len(unambiguous) > 0 {
-				events = append(events, ministralEventContent{content: unambiguous})
-			}
-			return events, false
-		}
-
-		// No tag found: emit content but withhold trailing whitespace
-		whitespaceLen := trailingWhitespaceLen(bufStr)
-		ambiguousStart := len(bufStr) - whitespaceLen
-		unambiguous := bufStr[:ambiguousStart]
-		ambiguous := bufStr[ambiguousStart:]
-		p.buffer.Reset()
-		p.buffer.WriteString(ambiguous)
-		if len(unambiguous) > 0 {
-			events = append(events, ministralEventContent{content: unambiguous})
-		}
-		return events, false
-
-	case ministralCollectingThinkingContent:
-		bufStr := p.buffer.String()
-
-		if strings.Contains(bufStr, ministralThinkEndTag) {
-			split := strings.SplitN(bufStr, ministralThinkEndTag, 2)
-			thinkingContent := split[0]
-			after := strings.TrimLeftFunc(split[1], unicode.IsSpace)
-			p.buffer.Reset()
-			p.buffer.WriteString(after)
-			if len(thinkingContent) > 0 {
-				events = append(events, ministralEventThinking{thinking: thinkingContent})
-			}
-			p.state = ministralCollectingContent
-			return events, true
-		}
-
-		// Check for partial overlap with [/THINK]
-		if overlapLen := overlap(bufStr, ministralThinkEndTag); overlapLen > 0 {
-			unambiguous := bufStr[:len(bufStr)-overlapLen]
-			ambiguous := bufStr[len(bufStr)-overlapLen:]
-			p.buffer.Reset()
-			p.buffer.WriteString(ambiguous)
-			if len(unambiguous) > 0 {
-				events = append(events, ministralEventThinking{thinking: unambiguous})
-			}
-			return events, false
-		}
-
-		// No tag found: emit all thinking content
-		p.buffer.Reset()
-		if len(bufStr) > 0 {
-			events = append(events, ministralEventThinking{thinking: bufStr})
-		}
-		return events, false
-
-	case ministralCollectingToolName:
-		bufStr := p.buffer.String()
-
-		if strings.Contains(bufStr, ministralArgsTag) {
-			split := strings.SplitN(bufStr, ministralArgsTag, 2)
-			toolName := split[0]
-			after := split[1]
-			p.pendingToolName = toolName
-			p.buffer.Reset()
-			p.buffer.WriteString(after)
-			p.state = ministralCollectingToolArgs
-			return events, true
-		}
-		// Wait for more data
-		return events, false
-
-	case ministralCollectingToolArgs:
-		bufStr := p.buffer.String()
-		jsonEnd := findJSONEnd(bufStr)
-
-		if jsonEnd != -1 {
-			jsonStr := bufStr[:jsonEnd+1]
-			remaining := bufStr[jsonEnd+1:]
-
-			events = append(events, ministralEventToolCall{
-				name: p.pendingToolName,
-				args: jsonStr,
-			})
-
-			p.pendingToolName = ""
-			p.buffer.Reset()
-			p.buffer.WriteString(remaining)
-			p.state = ministralCollectingContent
-			return events, true
-		}
-		// Wait for more data
-		return events, false
-
-	default:
-		panic("unexpected ministral event")
-	}
-}
-
-// parseEvents loops calling eat() until it returns false
-func (p *MinistralParser) parseEvents() []ministralEvent {
-	var all []ministralEvent
-	keepLooping := true
-	for keepLooping {
-		var events []ministralEvent
-		events, keepLooping = p.eat()
-		all = append(all, events...)
-	}
-	return all
-}
-
 func (p *MinistralParser) Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error) {
 	p.buffer.WriteString(s)
 
-	events := p.parseEvents()
-
-	var contentBuilder, thinkingBuilder strings.Builder
-	var toolCalls []api.ToolCall
-
-	for _, event := range events {
-		switch e := event.(type) {
-		case ministralEventContent:
-			contentBuilder.WriteString(e.content)
-		case ministralEventThinking:
-			thinkingBuilder.WriteString(e.thinking)
-		case ministralEventToolCall:
-			// Validate tool exists
-			tool, toolErr := toolByName(p.tools, e.name)
-			if toolErr != nil {
-				return contentBuilder.String(), thinkingBuilder.String(), toolCalls, toolErr
+	switch p.state {
+	case ministralCollectingContent:
+		if strings.Contains(p.buffer.String(), "[TOOL_CALLS]") {
+			before, _ := splitAtTag(&p.buffer, "[TOOL_CALLS]", false)
+			if before != "" {
+				return before, "", calls, nil
 			}
-			// Parse JSON arguments
+			p.state = ministralCollectingToolName
+		} else if strings.Contains(p.buffer.String(), "[THINK]") {
+			p.state = ministralCollectingThinkingContent
+			return "", "", calls, nil
+		} else {
+			p.buffer.Reset()
+			return s, "", calls, nil
+		}
+	case ministralCollectingThinkingContent:
+		if strings.Contains(p.buffer.String(), "[/THINK]") {
+			thinkingContent, after := splitAtTag(&p.buffer, "[/THINK]", true)
+			p.state = ministralCollectingContent
+			if after != "" {
+				p.buffer.Reset()
+				return after, thinkingContent, calls, nil
+			}
+			return "", thinkingContent, calls, nil
+		} else {
+			p.buffer.Reset()
+			return "", s, calls, nil
+		}
+	case ministralCollectingToolName:
+		if strings.Contains(p.buffer.String(), "[ARGS]") {
+			name, _ := splitAtTag(&p.buffer, "[ARGS]", false)
+
+			t, err := toolByName(p.tools, name)
+			if err != nil {
+				return "", "", calls, err
+			}
+			p.currentTool = t
+			p.state = ministralCollectingToolArgs
+			return "", "", calls, nil
+		}
+		return "", "", calls, nil
+	case ministralCollectingToolArgs:
+		if strings.Contains(p.buffer.String(), "}") {
+			before, _ := splitAtTag(&p.buffer, "}", false)
+			before += "}"
+
 			var args api.ToolCallFunctionArguments
-			if jsonErr := json.Unmarshal([]byte(e.args), &args); jsonErr != nil {
-				return contentBuilder.String(), thinkingBuilder.String(), toolCalls, jsonErr
+			if err := json.Unmarshal([]byte(before), &args); err != nil {
+				// todo - throw a better error
+				return "", "", calls, err
 			}
-			toolCalls = append(toolCalls, api.ToolCall{
+
+			p.state = ministralCollectingContent
+
+			call := api.ToolCall{
 				Function: api.ToolCallFunction{
-					Name:      tool.Function.Name,
+					Name:      p.currentTool.Function.Name,
 					Arguments: args,
 				},
-			})
+			}
+			calls = append(calls, call)
+			return "", "", calls, nil
 		}
+		return "", "", calls, nil
 	}
 
-	return contentBuilder.String(), thinkingBuilder.String(), toolCalls, nil
-}
-
-// findJSONEnd finds the index of the closing brace that completes a JSON object.
-// It properly handles nested objects, arrays, and strings (including escaped characters).
-// Returns -1 if the JSON is not yet complete.
-func findJSONEnd(s string) int {
-	depth := 0
-	inString := false
-	escaped := false
-
-	for i, r := range s {
-		if inString {
-			switch {
-			case escaped:
-				// If the previous character was a backslash, skip this character
-				escaped = false
-			case r == '\\':
-				// Mark the next character as escaped
-				escaped = true
-			case r == '"':
-				// End of string literal
-				inString = false
-			}
-			continue
-		}
-
-		switch r {
-		case '"':
-			// Start of string literal
-			inString = true
-		case '{', '[':
-			// Increase nesting level for objects and arrays
-			depth++
-		case '}', ']':
-			// Decrease nesting level
-			depth--
-			if depth == 0 {
-				// Reached the end of the root JSON structure
-				return i
-			}
-		}
-	}
-
-	return -1
+	return p.buffer.String(), thinking, calls, nil
 }

model/parsers/ministral_test.go

@@ -1,545 +0,0 @@
-package parsers
-
-import (
-	"reflect"
-	"testing"
-
-	"github.com/ollama/ollama/api"
-)
-
-func TestMinistralParserStreaming(t *testing.T) {
-	type step struct {
-		input      string
-		wantEvents []ministralEvent
-	}
-
-	cases := []struct {
-		desc  string
-		tools []api.Tool
-		steps []step
-		think bool // whether to enable thinking support
-	}{
-		// Content streaming
-		{
-			desc: "simple content",
-			steps: []step{
-				{input: "Hello, how can I help you?", wantEvents: []ministralEvent{
-					ministralEventContent{content: "Hello, how can I help you?"},
-				}},
-			},
-		},
-		{
-			desc: "streaming content word by word",
-			steps: []step{
-				{input: "Hello,", wantEvents: []ministralEvent{ministralEventContent{content: "Hello,"}}},
-				{input: " how", wantEvents: []ministralEvent{ministralEventContent{content: " how"}}},
-				{input: " can I help?", wantEvents: []ministralEvent{ministralEventContent{content: " can I help?"}}},
-			},
-		},
-
-		// Simple tool calls
-		{
-			desc:  "simple tool call",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "get_weather"}}},
-			steps: []step{
-				{input: `[TOOL_CALLS]get_weather[ARGS]{"location": "San Francisco"}`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "get_weather", args: `{"location": "San Francisco"}`},
-				}},
-			},
-		},
-		{
-			desc:  "tool call with nested object",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "create_entities"}}},
-			steps: []step{
-				{input: `[TOOL_CALLS]create_entities[ARGS]{"entities": [{"entityType": "Person", "name": "Jack", "observations": ["Works as a baker"]}]}`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "create_entities", args: `{"entities": [{"entityType": "Person", "name": "Jack", "observations": ["Works as a baker"]}]}`},
-				}},
-			},
-		},
-		{
-			desc:  "tool call with deeply nested objects",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "update_config"}}},
-			steps: []step{
-				{input: `[TOOL_CALLS]update_config[ARGS]{"settings": {"user": {"profile": {"name": "John", "age": 30}}, "theme": "dark"}}`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "update_config", args: `{"settings": {"user": {"profile": {"name": "John", "age": 30}}, "theme": "dark"}}`},
-				}},
-			},
-		},
-		{
-			desc:  "tool call with array of objects",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "process_items"}}},
-			steps: []step{
-				{input: `[TOOL_CALLS]process_items[ARGS]{"items": [{"id": 1}, {"id": 2}, {"id": 3}]}`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "process_items", args: `{"items": [{"id": 1}, {"id": 2}, {"id": 3}]}`},
-				}},
-			},
-		},
-		{
-			desc:  "tool call with escaped quotes in string",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "search"}}},
-			steps: []step{
-				{input: `[TOOL_CALLS]search[ARGS]{"query": "say \"hello\""}`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "search", args: `{"query": "say \"hello\""}`},
-				}},
-			},
-		},
-		{
-			desc:  "tool call with braces inside string",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "format"}}},
-			steps: []step{
-				{input: `[TOOL_CALLS]format[ARGS]{"template": "Hello {name}!"}`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "format", args: `{"template": "Hello {name}!"}`},
-				}},
-			},
-		},
-		{
-			desc:  "empty JSON object",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "no_args"}}},
-			steps: []step{
-				{input: `[TOOL_CALLS]no_args[ARGS]{}`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "no_args", args: `{}`},
-				}},
-			},
-		},
-		{
-			desc:  "JSON with newlines in string",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "write"}}},
-			steps: []step{
-				{input: `[TOOL_CALLS]write[ARGS]{"content": "line1\nline2\nline3"}`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "write", args: `{"content": "line1\nline2\nline3"}`},
-				}},
-			},
-		},
-		{
-			desc:  "backslash in string value",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "path"}}},
-			steps: []step{
-				{input: `[TOOL_CALLS]path[ARGS]{"dir": "C:\\Users\\test"}`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "path", args: `{"dir": "C:\\Users\\test"}`},
-				}},
-			},
-		},
-
-		// Content after tool call
-		{
-			desc:  "content after tool call",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "test"}}},
-			steps: []step{
-				// NOTE: It's unclear if this is valid Ministral output, but the parser
-				// currently treats text after a tool call as regular content. This test
-				// documents that behavior so we notice if it changes.
-				{input: `[TOOL_CALLS]test[ARGS]{"a": 1}some content after`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "test", args: `{"a": 1}`},
-					ministralEventContent{content: "some content after"},
-				}},
-			},
-		},
-
-		// Multiple tool calls
-		{
-			desc: "multiple tool calls in sequence",
-			tools: []api.Tool{
-				{Function: api.ToolFunction{Name: "get_weather"}},
-				{Function: api.ToolFunction{Name: "get_time"}},
-			},
-			steps: []step{
-				{input: `[TOOL_CALLS]get_weather[ARGS]{"location": "NYC"}[TOOL_CALLS]get_time[ARGS]{"timezone": "EST"}`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "get_weather", args: `{"location": "NYC"}`},
-					ministralEventToolCall{name: "get_time", args: `{"timezone": "EST"}`},
-				}},
-			},
-		},
-		{
-			desc: "multiple tool calls streamed separately",
-			tools: []api.Tool{
-				{Function: api.ToolFunction{Name: "tool_a"}},
-				{Function: api.ToolFunction{Name: "tool_b"}},
-			},
-			steps: []step{
-				{input: `[TOOL_CALLS]tool_a[ARGS]{"x": 1}`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "tool_a", args: `{"x": 1}`},
-				}},
-				{input: `[TOOL_CALLS]tool_b[ARGS]{"y": 2}`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "tool_b", args: `{"y": 2}`},
-				}},
-			},
-		},
-
-		// Streaming tool calls
-		{
-			desc:  "streaming tool call with nested objects",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "create_entities"}}},
-			steps: []step{
-				{input: "[TOOL_CALLS]create_entities[ARGS]", wantEvents: []ministralEvent{}},
-				{input: `{"entities": [{"entityType": "Person",`, wantEvents: []ministralEvent{}},
-				{input: ` "name": "Jack",`, wantEvents: []ministralEvent{}},
-				{input: ` "observations": ["Works`, wantEvents: []ministralEvent{}},
-				{input: ` as a baker"]}`, wantEvents: []ministralEvent{}},
-				{input: `]}`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "create_entities", args: `{"entities": [{"entityType": "Person", "name": "Jack", "observations": ["Works as a baker"]}]}`},
-				}},
-			},
-		},
-		{
-			desc:  "streaming with incomplete JSON waits for completion",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "test"}}},
-			steps: []step{
-				{input: "[TOOL_CALLS]test[ARGS]{", wantEvents: []ministralEvent{}},
-				{input: `"a": {`, wantEvents: []ministralEvent{}},
-				{input: `"b": 1`, wantEvents: []ministralEvent{}},
-				{input: `}`, wantEvents: []ministralEvent{}},
-				{input: `}`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "test", args: `{"a": {"b": 1}}`},
-				}},
-			},
-		},
-
-		// Partial tag handling
-		{
-			desc: "partial tool tag fakeout",
-			steps: []step{
-				{input: "abc[TOOL", wantEvents: []ministralEvent{ministralEventContent{content: "abc"}}},
-				{input: " not a tag", wantEvents: []ministralEvent{ministralEventContent{content: "[TOOL not a tag"}}},
-			},
-		},
-		{
-			desc:  "tool call tag split across chunks",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "test"}}},
-			steps: []step{
-				{input: "[TOOL_", wantEvents: []ministralEvent{}},
-				{input: "CALLS]test[ARGS]{}", wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "test", args: `{}`},
-				}},
-			},
-		},
-		{
-			desc:  "content before tool call",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "get_weather"}}},
-			steps: []step{
-				{input: "hello [TOOL_CALLS]get_weather[ARGS]{}", wantEvents: []ministralEvent{
-					ministralEventContent{content: "hello"},
-					ministralEventToolCall{name: "get_weather", args: `{}`},
-				}},
-			},
-		},
-		{
-			desc:  "whitespace between content and tool call is trimmed",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "test"}}},
-			steps: []step{
-				{input: "content \n [TOOL_CALLS]test[ARGS]{}", wantEvents: []ministralEvent{
-					ministralEventContent{content: "content"},
-					ministralEventToolCall{name: "test", args: `{}`},
-				}},
-			},
-		},
-		{
-			desc:  "tabs and newlines before tool call are trimmed",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "test"}}},
-			steps: []step{
-				{input: "content\t\n\t[TOOL_CALLS]test[ARGS]{}", wantEvents: []ministralEvent{
-					ministralEventContent{content: "content"},
-					ministralEventToolCall{name: "test", args: `{}`},
-				}},
-			},
-		},
-		{
-			desc:  "non-breaking space before tool call is trimmed",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "test"}}},
-			steps: []step{
-				// \u00a0 is non-breaking space, which unicode.IsSpace considers whitespace
-				{input: "content\u00a0[TOOL_CALLS]test[ARGS]{}", wantEvents: []ministralEvent{
-					ministralEventContent{content: "content"},
-					ministralEventToolCall{name: "test", args: `{}`},
-				}},
-			},
-		},
-		{
-			desc: "whitespace before THINK tag is trimmed",
-			steps: []step{
-				{input: "content \n [THINK]thinking[/THINK]after", wantEvents: []ministralEvent{
-					ministralEventContent{content: "content"},
-					ministralEventThinking{thinking: "thinking"},
-					ministralEventContent{content: "after"},
-				}},
-			},
-		},
-		{
-			desc: "trailing whitespace withheld then emitted",
-			steps: []step{
-				{input: "Hello ", wantEvents: []ministralEvent{ministralEventContent{content: "Hello"}}},
-				{input: "world", wantEvents: []ministralEvent{ministralEventContent{content: " world"}}},
-			},
-		},
-		{
-			desc: "trailing newline withheld then emitted",
-			steps: []step{
-				{input: "Hello\n", wantEvents: []ministralEvent{ministralEventContent{content: "Hello"}}},
-				{input: "world", wantEvents: []ministralEvent{ministralEventContent{content: "\nworld"}}},
-			},
-		},
-
-		// Thinking support
-		{
-			desc:  "thinking content",
-			think: true,
-			steps: []step{
-				{input: "thinking here[/THINK]", wantEvents: []ministralEvent{
-					ministralEventThinking{thinking: "thinking here"},
-				}},
-				{input: "content after", wantEvents: []ministralEvent{
-					ministralEventContent{content: "content after"},
-				}},
-			},
-		},
-		{
-			desc:  "thinking with whitespace after end tag",
-			think: true,
-			steps: []step{
-				{input: "my thoughts[/THINK]  \n  response", wantEvents: []ministralEvent{
-					ministralEventThinking{thinking: "my thoughts"},
-					ministralEventContent{content: "response"},
-				}},
-			},
-		},
-		{
-			desc:  "non-breaking space after think end tag is trimmed",
-			think: true,
-			steps: []step{
-				// \u00a0 is non-breaking space
-				{input: "thinking[/THINK]\u00a0response", wantEvents: []ministralEvent{
-					ministralEventThinking{thinking: "thinking"},
-					ministralEventContent{content: "response"},
-				}},
-			},
-		},
-		{
-			desc:  "partial think end tag",
-			think: true,
-			steps: []step{
-				{input: "thinking[/THI", wantEvents: []ministralEvent{ministralEventThinking{thinking: "thinking"}}},
-				{input: "NK]after", wantEvents: []ministralEvent{ministralEventContent{content: "after"}}},
-			},
-		},
-		{
-			desc:  "think tag fakeout",
-			think: true,
-			steps: []step{
-				{input: "thinking[/THI", wantEvents: []ministralEvent{ministralEventThinking{thinking: "thinking"}}},
-				{input: "not end tag", wantEvents: []ministralEvent{ministralEventThinking{thinking: "[/THInot end tag"}}},
-			},
-		},
-		{
-			desc:  "thinking then tool call",
-			think: true,
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "test"}}},
-			steps: []step{
-				{input: "let me think[/THINK][TOOL_CALLS]test[ARGS]{}", wantEvents: []ministralEvent{
-					ministralEventThinking{thinking: "let me think"},
-					ministralEventToolCall{name: "test", args: `{}`},
-				}},
-			},
-		},
-
-		// Content then THINK tag transition
-		{
-			desc: "content then think tag",
-			steps: []step{
-				{input: "content[THINK]thinking[/THINK]more", wantEvents: []ministralEvent{
-					ministralEventContent{content: "content"},
-					ministralEventThinking{thinking: "thinking"},
-					ministralEventContent{content: "more"},
-				}},
-			},
-		},
-
-		// Unicode handling
-		{
-			desc: "unicode content",
-			steps: []step{
-				{input: "你好 🌍 مرحبا", wantEvents: []ministralEvent{
-					ministralEventContent{content: "你好 🌍 مرحبا"},
-				}},
-			},
-		},
-		{
-			desc:  "unicode in tool args",
-			tools: []api.Tool{{Function: api.ToolFunction{Name: "greet"}}},
-			steps: []step{
-				{input: `[TOOL_CALLS]greet[ARGS]{"message": "你好 🌍"}`, wantEvents: []ministralEvent{
-					ministralEventToolCall{name: "greet", args: `{"message": "你好 🌍"}`},
-				}},
-			},
-		},
-	}
-
-	for _, tc := range cases {
-		t.Run(tc.desc, func(t *testing.T) {
-			parser := MinistralParser{}
-			parser.hasThinkingSupport = tc.think
-			parser.Init(tc.tools, nil, nil)
-
-			for i, step := range tc.steps {
-				parser.buffer.WriteString(step.input)
-				gotEvents := parser.parseEvents()
-
-				if len(gotEvents) == 0 && len(step.wantEvents) == 0 {
-					// avoid deep equal on empty vs. nil slices
-					continue
-				}
-
-				if !reflect.DeepEqual(gotEvents, step.wantEvents) {
-					t.Errorf("step %d: input %q: got events %#v, want %#v", i, step.input, gotEvents, step.wantEvents)
-				}
-			}
-		})
-	}
-}
-
-func TestMinistralParser_Errors(t *testing.T) {
-	t.Run("unknown tool returns error", func(t *testing.T) {
-		p := &MinistralParser{}
-		p.Init([]api.Tool{{Function: api.ToolFunction{Name: "known_tool"}}}, nil, nil)
-
-		_, _, _, err := p.Add(`[TOOL_CALLS]unknown_tool[ARGS]{"a": 1}`, true)
-		if err == nil {
-			t.Fatal("expected error for unknown tool")
-		}
-	})
-
-	t.Run("invalid JSON returns error", func(t *testing.T) {
-		p := &MinistralParser{}
-		p.Init([]api.Tool{{Function: api.ToolFunction{Name: "test"}}}, nil, nil)
-
-		_, _, _, err := p.Add(`[TOOL_CALLS]test[ARGS]{invalid json}`, true)
-		if err == nil {
-			t.Fatal("expected error for invalid JSON")
-		}
-	})
-}
-
-func TestFindJSONEnd(t *testing.T) {
-	tests := []struct {
-		name     string
-		input    string
-		expected int
-	}{
-		{
-			name:     "simple object",
-			input:    `{"a": 1}`,
-			expected: 7,
-		},
-		{
-			name:     "nested object",
-			input:    `{"a": {"b": 2}}`,
-			expected: 14,
-		},
-		{
-			name:     "array inside object",
-			input:    `{"items": [1, 2, 3]}`,
-			expected: 19,
-		},
-		{
-			name:     "braces in string",
-			input:    `{"template": "Hello {name}!"}`,
-			expected: 28,
-		},
-		{
-			name:     "escaped quotes",
-			input:    `{"msg": "say \"hi\""}`,
-			expected: 20,
-		},
-		{
-			name:     "incomplete object",
-			input:    `{"a": {"b": 1}`,
-			expected: -1,
-		},
-		{
-			name:     "deeply nested",
-			input:    `{"a": {"b": {"c": {"d": 1}}}}`,
-			expected: 28,
-		},
-		{
-			name:     "object with trailing content",
-			input:    `{"a": 1} extra`,
-			expected: 7,
-		},
-		{
-			name:     "array",
-			input:    `[{"a": 1}, {"b": 2}]`,
-			expected: 19,
-		},
-		{
-			name:     "escaped backslash before quote",
-			input:    `{"path": "C:\\"}`,
-			expected: 15,
-		},
-		{
-			name:     "empty string",
-			input:    "",
-			expected: -1,
-		},
-		{
-			name:     "no opening brace",
-			input:    "hello world",
-			expected: -1,
-		},
-		{
-			name:     "only opening brace",
-			input:    "{",
-			expected: -1,
-		},
-		{
-			name:     "unclosed string",
-			input:    `{"key": "unclosed`,
-			expected: -1,
-		},
-		{
-			name:     "double escaped backslash then quote",
-			input:    `{"path": "C:\\\\"}`,
-			expected: 17,
-		},
-		{
-			name:     "unicode in key and value",
-			input:    `{"키": "값"}`,
-			expected: 13,
-		},
-		{
-			name:     "nested arrays",
-			input:    `{"matrix": [[1, 2], [3, 4]]}`,
-			expected: 27,
-		},
-		{
-			name:     "mixed nesting",
-			input:    `{"a": [{"b": {"c": [1, 2, 3]}}]}`,
-			expected: 31,
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			result := findJSONEnd(tt.input)
-			if result != tt.expected {
-				t.Errorf("findJSONEnd(%q) = %d, want %d", tt.input, result, tt.expected)
-			}
-		})
-	}
-}
-
-func TestMinistralParser_HasToolSupport(t *testing.T) {
-	p := &MinistralParser{}
-	if !p.HasToolSupport() {
-		t.Error("expected HasToolSupport to return true")
-	}
-}
-
-func TestMinistralParser_HasThinkingSupport(t *testing.T) {
-	p := &MinistralParser{hasThinkingSupport: false}
-	if p.HasThinkingSupport() {
-		t.Error("expected HasThinkingSupport to return false")
-	}
-
-	p = &MinistralParser{hasThinkingSupport: true}
-	if !p.HasThinkingSupport() {
-		t.Error("expected HasThinkingSupport to return true")
-	}
-}

model/parsers/parsers.go

@@ -3,7 +3,6 @@ package parsers
 import (
 	"strings"
 	"unicode"
-	"unicode/utf8"
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/harmony"
@@ -71,8 +70,6 @@ func ParserForName(name string) Parser {
 		return &FunctionGemmaParser{}
 	case "glm-4.7":
 		return &GLM47Parser{}
-	case "glm-ocr":
-		return &GlmOcrParser{}
 	case "lfm2":
 		return &LFM2Parser{hasThinkingSupport: false}
 	case "lfm2-thinking":
@@ -117,33 +114,3 @@ func splitAtTag(sb *strings.Builder, tag string, trimAfter bool) (string, string
 	sb.WriteString(after)
 	return before, after // return events
 }
-
-// overlap returns the longest overlap between the suffix of s and the prefix of delim
-func overlap(s, delim string) int {
-	max := min(len(delim), len(s))
-	for i := max; i > 0; i-- {
-		if strings.HasSuffix(s, delim[:i]) {
-			return i
-		}
-	}
-	return 0
-}
-
-// trailingWhitespaceLen returns the length in bytes of trailing whitespace in s
-func trailingWhitespaceLen(s string) int {
-	remaining := s
-	total := 0
-	for len(remaining) > 0 {
-		r, size := utf8.DecodeLastRuneInString(remaining)
-		// if it's an invalid utf8 rune, assume it isn't whitespace
-		if r == utf8.RuneError && size == 1 {
-			break
-		}
-		if !unicode.IsSpace(r) {
-			break
-		}
-		total += size
-		remaining = remaining[:len(remaining)-size]
-	}
-	return total
-}

model/parsers/qwen3coder.go

@@ -11,6 +11,7 @@ import (
 	"strconv"
 	"strings"
 	"unicode"
+	"unicode/utf8"
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/logutil"
@@ -193,6 +194,36 @@ func eat(p *Qwen3CoderParser) ([]qwenEvent, bool) {
 	}
 }
 
+// TODO(drifkin): move this to a shared location
+// longest overlap between suffix of s and prefix of delim
+func overlap(s, delim string) int {
+	max := min(len(delim), len(s))
+	for i := max; i > 0; i-- {
+		if strings.HasSuffix(s, delim[:i]) {
+			return i
+		}
+	}
+	return 0
+}
+
+func trailingWhitespaceLen(s string) int {
+	remaining := s
+	total := 0
+	for len(remaining) > 0 {
+		r, size := utf8.DecodeLastRuneInString(remaining)
+		// if it's an invalid utf8 rune, assume it isn't whitespace
+		if r == utf8.RuneError && size == 1 {
+			break
+		}
+		if !unicode.IsSpace(r) {
+			break
+		}
+		total += size
+		remaining = remaining[:len(remaining)-size]
+	}
+	return total
+}
+
 type XMLFunctionCall struct {
 	XMLName    xml.Name       `xml:"function"`
 	Name       string         `xml:"name,attr"`

model/renderers/glmocr.go

@@ -1,109 +0,0 @@
-package renderers
-
-import (
-	"encoding/json"
-	"fmt"
-	"strings"
-
-	"github.com/ollama/ollama/api"
-)
-
-type GlmOcrRenderer struct{}
-
-func (r *GlmOcrRenderer) Render(messages []api.Message, tools []api.Tool, thinkValue *api.ThinkValue) (string, error) {
-	var sb strings.Builder
-
-	sb.WriteString("[gMASK]<sop>")
-
-	if len(tools) > 0 {
-		sb.WriteString("<|system|>\n")
-		sb.WriteString("# Tools\n\n")
-		sb.WriteString("You may call one or more functions to assist with the user query.\n\n")
-		sb.WriteString("You are provided with function signatures within <tools></tools> XML tags:\n")
-		sb.WriteString("<tools>\n")
-		for _, tool := range tools {
-			d, _ := json.Marshal(tool)
-			sb.WriteString(formatGLM47ToolJSON(d))
-			sb.WriteString("\n")
-		}
-		sb.WriteString("</tools>\n\n")
-		sb.WriteString("For each function call, output the function name and arguments within the following XML format:\n")
-		sb.WriteString("<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call>")
-	}
-
-	enableThinking := false
-	thinkingExplicitlySet := false
-	if thinkValue != nil {
-		enableThinking = thinkValue.Bool()
-		thinkingExplicitlySet = true
-	}
-
-	for i, message := range messages {
-		switch message.Role {
-		case "user":
-			sb.WriteString("<|user|>\n")
-			sb.WriteString(message.Content)
-			if thinkingExplicitlySet && !enableThinking && !strings.HasSuffix(message.Content, "/nothink") {
-				sb.WriteString("/nothink")
-			}
-		case "assistant":
-			sb.WriteString("<|assistant|>\n")
-			if message.Thinking != "" {
-				sb.WriteString("<think>" + strings.TrimSpace(message.Thinking) + "</think>")
-			} else {
-				sb.WriteString("<think></think>")
-			}
-			if message.Content != "" {
-				sb.WriteString("\n" + strings.TrimSpace(message.Content))
-			}
-			if len(message.ToolCalls) > 0 {
-				for _, toolCall := range message.ToolCalls {
-					sb.WriteString("\n<tool_call>" + toolCall.Function.Name)
-					sb.WriteString(renderGlmOcrToolArguments(toolCall.Function.Arguments))
-					sb.WriteString("</tool_call>")
-				}
-			}
-			sb.WriteString("\n")
-		case "tool":
-			if i == 0 || messages[i-1].Role != "tool" {
-				sb.WriteString("<|observation|>")
-			}
-			sb.WriteString("\n<tool_response>\n")
-			sb.WriteString(message.Content)
-			sb.WriteString("\n</tool_response>\n")
-		case "system":
-			sb.WriteString("<|system|>\n")
-			sb.WriteString(message.Content)
-			sb.WriteString("\n")
-		}
-	}
-
-	sb.WriteString("<|assistant|>\n")
-	if thinkingExplicitlySet && !enableThinking {
-		sb.WriteString("<think></think>\n")
-	}
-
-	return sb.String(), nil
-}
-
-func renderGlmOcrToolArguments(args api.ToolCallFunctionArguments) string {
-	var sb strings.Builder
-	for key, value := range args.All() {
-		sb.WriteString("<arg_key>" + key + "</arg_key>")
-		var valueStr string
-		if str, ok := value.(string); ok {
-			valueStr = str
-		} else {
-			jsonBytes, err := json.Marshal(value)
-			if err != nil {
-				valueStr = fmt.Sprintf("%v", value)
-			} else {
-				valueStr = string(jsonBytes)
-			}
-		}
-
-		sb.WriteString("<arg_value>" + valueStr + "</arg_value>")
-	}
-
-	return sb.String()
-}

model/renderers/qwen3coder.go

@@ -167,12 +167,12 @@ func (r *Qwen3CoderRenderer) Render(messages []api.Message, tools []api.Tool, _
 
 			// only start a new user block if this is the first tool response
 			if i == 0 || filteredMessages[i-1].Role != "tool" {
-				sb.WriteString(imStartTag + "user")
+				sb.WriteString(imStartTag + "user\n")
 			}
 
-			sb.WriteString("\n<tool_response>\n")
+			sb.WriteString("<tool_response>\n")
 			sb.WriteString(message.Content)
-			sb.WriteString("\n</tool_response>")
+			sb.WriteString("\n</tool_response>\n")
 
 			// close the user block only if this is the last tool response
 			if i == len(filteredMessages)-1 || filteredMessages[i+1].Role != "tool" {

model/renderers/qwen3coder_test.go

@@ -1,7 +1,6 @@
 package renderers
 
 import (
-	"strings"
 	"testing"
 
 	"github.com/google/go-cmp/cmp"
@@ -128,7 +127,8 @@ fahrenheit
 <|im_start|>user
 <tool_response>
 {"location": "San Francisco, CA", "temperature": 68, "condition": "partly cloudy", "humidity": 65, "wind_speed": 12}
-</tool_response><|im_end|>
+</tool_response>
+<|im_end|>
 <|im_start|>user
 That sounds nice! What about New York?<|im_end|>
 <|im_start|>assistant
@@ -233,7 +233,8 @@ I'll call double(1) and triple(2) for you.
 </tool_response>
 <tool_response>
 {"number": 6}
-</tool_response><|im_end|>
+</tool_response>
+<|im_end|>
 <|im_start|>assistant
 `,
 		},
@@ -279,7 +280,8 @@ call tool<|im_end|>
 <|im_start|>user
 <tool_response>
 {"payload": {"foo": "bar"}}
-</tool_response><|im_end|>
+</tool_response>
+<|im_end|>
 <|im_start|>assistant
 `,
 		},
@@ -335,31 +337,6 @@ func TestFormatToolCallArgument(t *testing.T) {
 	}
 }
 
-func TestQwen3CoderRendererToolResponseNoTrailingNewline(t *testing.T) {
-	msgs := []api.Message{
-		{Role: "user", Content: "call tool"},
-		{Role: "assistant", ToolCalls: []api.ToolCall{
-			{Function: api.ToolCallFunction{
-				Name:      "echo",
-				Arguments: testArgs(map[string]any{"payload": "ok"}),
-			}},
-		}},
-		{Role: "tool", Content: "{\"payload\":\"ok\"}", ToolName: "echo"},
-	}
-
-	rendered, err := (&Qwen3CoderRenderer{}).Render(msgs, nil, nil)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	if strings.Contains(rendered, "</tool_response>\n<|im_end|>") {
-		t.Fatalf("expected no newline after </tool_response>, got:\n%s", rendered)
-	}
-	if !strings.Contains(rendered, "</tool_response><|im_end|>") {
-		t.Fatalf("expected </tool_response> to be immediately followed by <|im_end|>, got:\n%s", rendered)
-	}
-}
-
 func TestQwen3ToolDefinitionTypes(t *testing.T) {
 	tests := []struct {
 		name         string

model/renderers/renderer.go

@@ -82,8 +82,6 @@ func rendererForName(name string) Renderer {
 		return &FunctionGemmaRenderer{}
 	case "glm-4.7":
 		return &GLM47Renderer{}
-	case "glm-ocr":
-		return &GlmOcrRenderer{}
 	case "lfm2":
 		return &LFM2Renderer{IsThinking: false}
 	case "lfm2-thinking":

runner/ollamarunner/cache.go

@@ -124,17 +124,8 @@ func (c *InputCache) LoadCacheSlot(prompt []*input.Input, cachePrompt bool) (*In
 	}
 
 	if c.cache != nil {
-		if numPast > 0 {
-			// Recurrent caches use checkpoints to pick a safe resume position.
-			if cc, ok := c.cache.(kvcache.CheckpointCache); ok {
-				if restored, ok := cc.PrepareRestore(slot.Id, numPast); ok {
-					numPast = restored
-				} else {
-					numPast = 0
-				}
-			} else if !c.cache.CanResume(slot.Id, numPast) {
-				numPast = 0
-			}
+		if numPast > 0 && !c.cache.CanResume(slot.Id, numPast) {
+			numPast = 0
 		}
 
 		err = c.cache.Remove(slot.Id, numPast, math.MaxInt32)

runner/ollamarunner/runner.go

@@ -37,6 +37,7 @@ import (
 	"github.com/ollama/ollama/model/input"
 	"github.com/ollama/ollama/runner/common"
 	"github.com/ollama/ollama/sample"
+	"github.com/ollama/ollama/tokenizer"
 
 	_ "github.com/ollama/ollama/model/models"
 )
@@ -210,9 +211,9 @@ func (s *Server) NewSequence(prompt string, images []llm.ImageData, params NewSe
 }
 
 // calculateLogprobs converts raw logits to log probabilities and finds top K tokens
-func calculateLogprobs(logits []float32, selectedToken int32, topK int, textProcessor model.TextProcessor) []llm.Logprob {
+func calculateLogprobs(logits []float32, selectedToken int32, topK int, tokenizer tokenizer.Tokenizer) []llm.Logprob {
 	decoder := func(tokenID int) string {
-		text, _ := textProcessor.Decode([]int32{int32(tokenID)})
+		text, _ := tokenizer.Decode([]int32{int32(tokenID)})
 		return text
 	}
 	return common.CalculateLogprobs(logits, int(selectedToken), topK, decoder)
@@ -242,7 +243,7 @@ func (s *Server) inputs(prompt string, images []llm.ImageData) ([]*input.Input,
 
 	for i, part := range parts {
 		// text - tokenize
-		tokens, err := s.model.(model.TextProcessor).Encode(part, i == 0)
+		tokens, err := s.model.(tokenizer.Tokenizer).Encode(part, i == 0)
 		if err != nil {
 			return nil, nil, nil, err
 		}
@@ -740,11 +741,7 @@ func (s *Server) computeBatch(activeBatch batchState) {
 		if seq == nil || nextBatchTokens[i] == nil {
 			continue
 		}
-		// If the sequence was replaced while this batch was computing, discard results.
-		if activeBatch.seqs[i] != seq {
-			logutil.Trace("computeBatch: sequence replaced, discarding its results", "batchID", activeBatch.id, "seqIdx", i)
-			continue
-		}
+
 		seq.lastUpdatedAt = t
 		if seq.numPredicted == 1 {
 			seq.processingDuration = seq.lastUpdatedAt.Sub(seq.startedAt)
@@ -770,7 +767,7 @@ func (s *Server) computeBatch(activeBatch batchState) {
 		nextBatchTokens[i].Token = token
 
 		// if it's an end of sequence token, break
-		if s.model.(model.TextProcessor).Is(token, model.SpecialEOS) {
+		if s.model.(tokenizer.Tokenizer).Is(token, tokenizer.SpecialEOS) {
 			// TODO (jmorganca): we should send this back
 			// as it's important for the /api/generate context
 			// seq.responses <- piece
@@ -779,14 +776,14 @@ func (s *Server) computeBatch(activeBatch batchState) {
 			continue
 		}
 
-		piece, err := s.model.(model.TextProcessor).Decode([]int32{token})
+		piece, err := s.model.(tokenizer.Tokenizer).Decode([]int32{token})
 		if err != nil {
 			panic("failed to decode token")
 		}
 
 		// Calculate logprobs if requested (after EOS check to avoid logprobs for EOS tokens)
 		if seq.logprobs {
-			logprobs := calculateLogprobs(logits, token, seq.topLogprobs, s.model.(model.TextProcessor))
+			logprobs := calculateLogprobs(logits, token, seq.topLogprobs, s.model.(tokenizer.Tokenizer))
 			seq.pendingLogprobs = append(seq.pendingLogprobs, logprobs...)
 		}
 
@@ -877,7 +874,7 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 	var grammar *sample.GrammarSampler
 	var err error
 	if req.Grammar != "" {
-		grammar, err = sample.NewGrammarSampler(s.model.(model.TextProcessor), req.Grammar)
+		grammar, err = sample.NewGrammarSampler(s.model.(tokenizer.Tokenizer), req.Grammar)
 		if err != nil {
 			http.Error(w, "failed to load model vocabulary required for format", http.StatusInternalServerError)
 			return
@@ -1362,7 +1359,7 @@ func (s *Server) info(w http.ResponseWriter, r *http.Request) {
 		// Dummy load to get the backend wired up
 		f, err := os.CreateTemp("", "*.bin")
 		if err != nil {
-			http.Error(w, fmt.Sprintf("failed to initialize backend: %v", err), http.StatusInternalServerError)
+			http.Error(w, fmt.Sprintf("failed to initialize baackend: %v", err), http.StatusInternalServerError)
 			return
 		}
 		defer f.Close()
@@ -1372,13 +1369,13 @@ func (s *Server) info(w http.ResponseWriter, r *http.Request) {
 			"general.architecture": "llama",
 			"tokenizer.ggml.model": "gpt2",
 		}, nil); err != nil {
-			http.Error(w, fmt.Sprintf("failed to initialize backend: %v", err), http.StatusInternalServerError)
+			http.Error(w, fmt.Sprintf("failed to initialize baackend: %v", err), http.StatusInternalServerError)
 			return
 		}
 
 		m, err = model.New(f.Name(), ml.BackendParams{NumThreads: runtime.NumCPU(), AllocMemory: false, GPULayers: ml.GPULayersList{{}}})
 		if err != nil {
-			http.Error(w, fmt.Sprintf("failed to initialize backend: %v", err), http.StatusInternalServerError)
+			http.Error(w, fmt.Sprintf("failed to initialize baackend: %v", err), http.StatusInternalServerError)
 			return
 		}
 		slog.Debug("dummy model load took", "duration", time.Since(startLoad))

runner/runner.go

@@ -3,7 +3,7 @@ package runner
 import (
 	"github.com/ollama/ollama/runner/llamarunner"
 	"github.com/ollama/ollama/runner/ollamarunner"
-	"github.com/ollama/ollama/x/mlxrunner"
+	imagerunner "github.com/ollama/ollama/x/imagegen/runner"
 )
 
 func Execute(args []string) error {
@@ -12,18 +12,18 @@ func Execute(args []string) error {
 	}
 
 	var newRunner bool
-	var mlxRunner bool
+	var imageRunner bool
 	if len(args) > 0 && args[0] == "--ollama-engine" {
 		args = args[1:]
 		newRunner = true
 	}
-	if len(args) > 0 && args[0] == "--mlx-engine" {
+	if len(args) > 0 && args[0] == "--image-engine" {
 		args = args[1:]
-		mlxRunner = true
+		imageRunner = true
 	}
 
-	if mlxRunner {
-		return mlxrunner.Execute(args)
+	if imageRunner {
+		return imagerunner.Execute(args)
 	} else if newRunner {
 		return ollamarunner.Execute(args)
 	} else {

sample/samplers.go

@@ -7,7 +7,7 @@ import (
 	"slices"
 
 	"github.com/ollama/ollama/llama"
-	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 // token represents information about a single token during sampling
@@ -168,15 +168,15 @@ type GrammarSampler struct {
 	grammar *llama.Grammar
 }
 
-func NewGrammarSampler(model model.TextProcessor, grammarStr string) (*GrammarSampler, error) {
-	vocabIds := make([]uint32, len(model.Vocabulary().Values))
-	pieces := make([]string, len(model.Vocabulary().Values))
-	for i := range model.Vocabulary().Values {
-		pieces[i], _ = model.Decode([]int32{int32(i)})
+func NewGrammarSampler(tokenizer tokenizer.Tokenizer, grammarStr string) (*GrammarSampler, error) {
+	vocabIds := make([]uint32, len(tokenizer.Vocabulary().Values))
+	pieces := make([]string, len(tokenizer.Vocabulary().Values))
+	for i := range tokenizer.Vocabulary().Values {
+		pieces[i], _ = tokenizer.Decode([]int32{int32(i)})
 		vocabIds[i] = uint32(i)
 	}
 
-	grammar := llama.NewGrammar(grammarStr, vocabIds, pieces, model.Vocabulary().EOS)
+	grammar := llama.NewGrammar(grammarStr, vocabIds, pieces, tokenizer.Vocabulary().EOS)
 	if grammar == nil {
 		return nil, errors.New("sample: failed to initialize grammar")
 	}

sample/samplers_test.go

@@ -8,7 +8,7 @@ import (
 	"path/filepath"
 	"testing"
 
-	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/tokenizer"
 )
 
 func TestWeighted(t *testing.T) {
@@ -60,10 +60,10 @@ func TestWeighted(t *testing.T) {
 	}
 }
 
-func modelHelper(t testing.TB) model.BytePairEncoding {
+func modelHelper(t testing.TB) tokenizer.Tokenizer {
 	t.Helper()
 
-	f, err := os.Open(filepath.Join("..", "model", "testdata", "llama3.2", "encoder.json"))
+	f, err := os.Open(filepath.Join("..", "testdata", "testdata", "llama3.2", "encoder.json"))
 	if err != nil {
 		t.Fatal(err)
 	}
@@ -81,8 +81,8 @@ func modelHelper(t testing.TB) model.BytePairEncoding {
 
 	merges := make([]string, 0, 1)
 	// Only need vocab for Grammar Test
-	return model.NewBytePairEncoding(
-		&model.Vocabulary{
+	return tokenizer.NewBytePairEncoding(
+		&tokenizer.Vocabulary{
 			Values: tokens,
 			Types:  make([]int32, len(vocab)),
 			Merges: merges,

server/prompt.go

@@ -27,12 +27,14 @@ func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.
 	// Clip images are represented as 768 tokens, each an embedding
 	imageNumTokens := 768
 
-	lastMsgIdx := len(msgs) - 1
-	currMsgIdx := 0
+	n := len(msgs) - 1
+	// in reverse, find all messages that fit into context window
+	for i := n; i >= 0; i-- {
+		// always include the last message
+		if i == n {
+			continue
+		}
 
-	// Start with all messages and remove from the front until it fits in context
-	for i := 0; i <= lastMsgIdx; i++ {
-		// Collect system messages from the portion we're about to skip
 		system = make([]api.Message, 0)
 		for j := range i {
 			if msgs[j].Role == "system" {
@@ -52,26 +54,20 @@ func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.
 
 		ctxLen := len(s)
 		if m.ProjectorPaths != nil {
-			for _, msg := range msgs[i:] {
-				ctxLen += imageNumTokens * len(msg.Images)
+			for _, m := range msgs[i:] {
+				ctxLen += imageNumTokens * len(m.Images)
 			}
 		}
 
-		if !truncate || ctxLen <= opts.NumCtx {
-			currMsgIdx = i
-			break
-		}
-
-		// Must always include at least the last message
-		if i == lastMsgIdx {
-			currMsgIdx = lastMsgIdx
+		if truncate && ctxLen > opts.NumCtx {
+			slog.Debug("truncating input messages which exceed context length", "truncated", len(msgs[i:]))
 			break
+		} else {
+			n = i
 		}
 	}
 
-	if currMsgIdx > 0 {
-		slog.Debug("truncating input messages which exceed context length", "truncated", len(msgs[currMsgIdx:]))
-	}
+	currMsgIdx := n
 
 	for cnt, msg := range msgs[currMsgIdx:] {
 		if slices.Contains(m.Config.ModelFamilies, "mllama") && len(msg.Images) > 1 {

server/prompt_test.go

@@ -2,7 +2,6 @@ package server
 
 import (
 	"bytes"
-	"context"
 	"testing"
 
 	"github.com/google/go-cmp/cmp"
@@ -265,68 +264,3 @@ func TestChatPrompt(t *testing.T) {
 		})
 	}
 }
-
-func TestChatPromptTokenizeCalls(t *testing.T) {
-	tmpl, err := template.Parse(`
-{{- if .System }}{{ .System }} {{ end }}
-{{- if .Prompt }}{{ .Prompt }} {{ end }}
-{{- if .Response }}{{ .Response }} {{ end }}`)
-	if err != nil {
-		t.Fatal(err)
-	}
-	model := Model{Template: tmpl}
-
-	cases := []struct {
-		name         string
-		limit        int
-		msgs         []api.Message
-		maxTokenizes int
-	}{
-		{
-			name:  "all messages fit",
-			limit: 2048,
-			msgs: []api.Message{
-				{Role: "user", Content: "message 1"},
-				{Role: "assistant", Content: "response 1"},
-				{Role: "user", Content: "message 2"},
-				{Role: "assistant", Content: "response 2"},
-				{Role: "user", Content: "message 3"},
-			},
-			maxTokenizes: 1,
-		},
-		{
-			name:  "truncate to last message",
-			limit: 5,
-			msgs: []api.Message{
-				{Role: "user", Content: "message 1"},
-				{Role: "assistant", Content: "response 1"},
-				{Role: "user", Content: "message 2"},
-				{Role: "assistant", Content: "response 2"},
-				{Role: "user", Content: "message 3"},
-			},
-			maxTokenizes: 5,
-		},
-	}
-
-	for _, tt := range cases {
-		t.Run(tt.name, func(t *testing.T) {
-			tokenizeCount := 0
-			countingTokenize := func(ctx context.Context, s string) ([]int, error) {
-				tokenizeCount++
-				tokens, err := mockRunner{}.Tokenize(ctx, s)
-				return tokens, err
-			}
-
-			opts := api.Options{Runner: api.Runner{NumCtx: tt.limit}}
-			think := false
-			_, _, err := chatPrompt(t.Context(), &model, countingTokenize, &opts, tt.msgs, nil, &api.ThinkValue{Value: think}, true)
-			if err != nil {
-				t.Fatal(err)
-			}
-
-			if tokenizeCount > tt.maxTokenizes {
-				t.Errorf("tokenize called %d times, expected at most %d", tokenizeCount, tt.maxTokenizes)
-			}
-		})
-	}
-}