nil keys

this change makes it easier to address blobs by their original names
without rebuilding the filesystem structure

.github/workflows/test-install.yaml

@@ -0,0 +1,22 @@
+name: test-install
+
+on:
+  pull_request:
+    paths:
+      - 'scripts/install.sh'
+      - '.github/workflows/test-install.yaml'
+
+jobs:
+  test:
+    strategy:
+      matrix:
+        os: [ubuntu-latest, macos-latest]
+    runs-on: ${{ matrix.os }}
+    steps:
+      - uses: actions/checkout@v4
+      - name: Run install script
+        run: sh ./scripts/install.sh
+        env:
+          OLLAMA_NO_START: 1 # do not start app
+      - name: Verify ollama is available
+        run: ollama --version

README.md

@@ -358,6 +358,7 @@ 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)
@@ -465,6 +466,7 @@ 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

@@ -211,6 +211,7 @@ type MessageDelta struct {
 
 // DeltaUsage contains cumulative token usage
 type DeltaUsage struct {
+	InputTokens  int `json:"input_tokens"`
 	OutputTokens int `json:"output_tokens"`
 }
 
@@ -517,24 +518,26 @@ func mapStopReason(reason string, hasToolCalls bool) string {
 
 // StreamConverter manages state for converting Ollama streaming responses to Anthropic format
 type StreamConverter struct {
-	ID              string
-	Model           string
-	firstWrite      bool
-	contentIndex    int
-	inputTokens     int
-	outputTokens    int
-	thinkingStarted bool
-	thinkingDone    bool
-	textStarted     bool
-	toolCallsSent   map[string]bool
+	ID                   string
+	Model                string
+	firstWrite           bool
+	contentIndex         int
+	inputTokens          int
+	outputTokens         int
+	estimatedInputTokens int // Estimated tokens from request (used when actual metrics are 0)
+	thinkingStarted      bool
+	thinkingDone         bool
+	textStarted          bool
+	toolCallsSent        map[string]bool
 }
 
-func NewStreamConverter(id, model string) *StreamConverter {
+func NewStreamConverter(id, model string, estimatedInputTokens int) *StreamConverter {
 	return &StreamConverter{
-		ID:            id,
-		Model:         model,
-		firstWrite:    true,
-		toolCallsSent: make(map[string]bool),
+		ID:                   id,
+		Model:                model,
+		firstWrite:           true,
+		estimatedInputTokens: estimatedInputTokens,
+		toolCallsSent:        make(map[string]bool),
 	}
 }
 
@@ -550,7 +553,11 @@ func (c *StreamConverter) Process(r api.ChatResponse) []StreamEvent {
 
 	if c.firstWrite {
 		c.firstWrite = false
+		// Use actual metrics if available, otherwise use estimate
 		c.inputTokens = r.Metrics.PromptEvalCount
+		if c.inputTokens == 0 && c.estimatedInputTokens > 0 {
+			c.inputTokens = c.estimatedInputTokens
+		}
 
 		events = append(events, StreamEvent{
 			Event: "message_start",
@@ -721,6 +728,7 @@ 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)
 
@@ -732,6 +740,7 @@ func (c *StreamConverter) Process(r api.ChatResponse) []StreamEvent {
 					StopReason: stopReason,
 				},
 				Usage: DeltaUsage{
+					InputTokens:  c.inputTokens,
 					OutputTokens: c.outputTokens,
 				},
 			},
@@ -776,3 +785,123 @@ func mapToArgs(m map[string]any) api.ToolCallFunctionArguments {
 	}
 	return args
 }
+
+// CountTokensRequest represents an Anthropic count_tokens request
+type CountTokensRequest struct {
+	Model    string          `json:"model"`
+	Messages []MessageParam  `json:"messages"`
+	System   any             `json:"system,omitempty"`
+	Tools    []Tool          `json:"tools,omitempty"`
+	Thinking *ThinkingConfig `json:"thinking,omitempty"`
+}
+
+// EstimateInputTokens estimates input tokens from a MessagesRequest (reuses CountTokensRequest logic)
+func EstimateInputTokens(req MessagesRequest) int {
+	return estimateTokens(CountTokensRequest{
+		Model:    req.Model,
+		Messages: req.Messages,
+		System:   req.System,
+		Tools:    req.Tools,
+		Thinking: req.Thinking,
+	})
+}
+
+// CountTokensResponse represents an Anthropic count_tokens response
+type CountTokensResponse struct {
+	InputTokens int `json:"input_tokens"`
+}
+
+// estimateTokens returns a rough estimate of tokens (len/4).
+// TODO: Replace with actual tokenization via Tokenize API for accuracy.
+// Current len/4 heuristic is a rough approximation (~4 chars/token average).
+func estimateTokens(req CountTokensRequest) int {
+	var totalLen int
+
+	// Count system prompt
+	if req.System != nil {
+		totalLen += countAnyContent(req.System)
+	}
+
+	// Count messages
+	for _, msg := range req.Messages {
+		// Count role (always present)
+		totalLen += len(msg.Role)
+		// Count content
+		contentLen := countAnyContent(msg.Content)
+		totalLen += contentLen
+	}
+
+	for _, tool := range req.Tools {
+		totalLen += len(tool.Name) + len(tool.Description) + len(tool.InputSchema)
+	}
+
+	// Return len/4 as rough token estimate, minimum 1 if there's any content
+	tokens := totalLen / 4
+	if tokens == 0 && (len(req.Messages) > 0 || req.System != nil) {
+		tokens = 1
+	}
+	return tokens
+}
+
+func countAnyContent(content any) int {
+	if content == nil {
+		return 0
+	}
+
+	switch c := content.(type) {
+	case string:
+		return len(c)
+	case []any:
+		total := 0
+		for _, block := range c {
+			total += countContentBlock(block)
+		}
+		return total
+	default:
+		if data, err := json.Marshal(content); err == nil {
+			return len(data)
+		}
+		return 0
+	}
+}
+
+func countContentBlock(block any) int {
+	blockMap, ok := block.(map[string]any)
+	if !ok {
+		if s, ok := block.(string); ok {
+			return len(s)
+		}
+		return 0
+	}
+
+	total := 0
+	blockType, _ := blockMap["type"].(string)
+
+	if text, ok := blockMap["text"].(string); ok {
+		total += len(text)
+	}
+
+	if thinking, ok := blockMap["thinking"].(string); ok {
+		total += len(thinking)
+	}
+
+	if blockType == "tool_use" {
+		if data, err := json.Marshal(blockMap); err == nil {
+			total += len(data)
+		}
+	}
+
+	if blockType == "tool_result" {
+		if data, err := json.Marshal(blockMap); err == nil {
+			total += len(data)
+		}
+	}
+
+	if source, ok := blockMap["source"].(map[string]any); ok {
+		if data, ok := source["data"].(string); ok {
+			total += len(data)
+		}
+	}
+
+	return total
+}

anthropic/anthropic_test.go

@@ -321,8 +321,6 @@ func TestFromMessagesRequest_WithThinking(t *testing.T) {
 	}
 }
 
-// TestFromMessagesRequest_ThinkingOnlyBlock verifies that messages containing only
-// a thinking block (no text, images, or tool calls) are preserved and not dropped.
 func TestFromMessagesRequest_ThinkingOnlyBlock(t *testing.T) {
 	req := MessagesRequest{
 		Model:     "test-model",
@@ -605,7 +603,7 @@ func TestGenerateMessageID(t *testing.T) {
 }
 
 func TestStreamConverter_Basic(t *testing.T) {
-	conv := NewStreamConverter("msg_123", "test-model")
+	conv := NewStreamConverter("msg_123", "test-model", 0)
 
 	// First chunk
 	resp1 := api.ChatResponse{
@@ -642,7 +640,7 @@ func TestStreamConverter_Basic(t *testing.T) {
 		},
 		Done:       true,
 		DoneReason: "stop",
-		Metrics:    api.Metrics{EvalCount: 5},
+		Metrics:    api.Metrics{PromptEvalCount: 10, EvalCount: 5},
 	}
 
 	events2 := conv.Process(resp2)
@@ -650,6 +648,24 @@ 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
 		}
@@ -660,7 +676,7 @@ func TestStreamConverter_Basic(t *testing.T) {
 }
 
 func TestStreamConverter_WithToolCalls(t *testing.T) {
-	conv := NewStreamConverter("msg_123", "test-model")
+	conv := NewStreamConverter("msg_123", "test-model", 0)
 
 	resp := api.ChatResponse{
 		Model: "test-model",
@@ -713,7 +729,7 @@ func TestStreamConverter_WithToolCalls(t *testing.T) {
 func TestStreamConverter_ToolCallWithUnmarshalableArgs(t *testing.T) {
 	// Test that unmarshalable arguments (like channels) are handled gracefully
 	// and don't cause a panic or corrupt stream
-	conv := NewStreamConverter("msg_123", "test-model")
+	conv := NewStreamConverter("msg_123", "test-model", 0)
 
 	// Create a channel which cannot be JSON marshaled
 	unmarshalable := make(chan int)
@@ -760,7 +776,7 @@ func TestStreamConverter_ToolCallWithUnmarshalableArgs(t *testing.T) {
 
 func TestStreamConverter_MultipleToolCallsWithMixedValidity(t *testing.T) {
 	// Test that valid tool calls still work when mixed with invalid ones
-	conv := NewStreamConverter("msg_123", "test-model")
+	conv := NewStreamConverter("msg_123", "test-model", 0)
 
 	unmarshalable := make(chan int)
 	badArgs := api.NewToolCallFunctionArguments()
@@ -824,10 +840,6 @@ func TestStreamConverter_MultipleToolCallsWithMixedValidity(t *testing.T) {
 	}
 }
 
-// TestContentBlockJSON_EmptyFieldsPresent verifies that empty text and thinking fields
-// are serialized in JSON output. The Anthropic SDK requires these fields to be present
-// (even when empty) in content_block_start events to properly accumulate streaming deltas.
-// Without these fields, the SDK throws: "TypeError: unsupported operand type(s) for +=: 'NoneType' and 'str'"
 func TestContentBlockJSON_EmptyFieldsPresent(t *testing.T) {
 	tests := []struct {
 		name     string
@@ -881,11 +893,9 @@ func TestContentBlockJSON_EmptyFieldsPresent(t *testing.T) {
 	}
 }
 
-// TestStreamConverter_ContentBlockStartIncludesEmptyFields verifies that content_block_start
-// events include the required empty fields for SDK compatibility.
 func TestStreamConverter_ContentBlockStartIncludesEmptyFields(t *testing.T) {
 	t.Run("text block start includes empty text", func(t *testing.T) {
-		conv := NewStreamConverter("msg_123", "test-model")
+		conv := NewStreamConverter("msg_123", "test-model", 0)
 
 		resp := api.ChatResponse{
 			Model:   "test-model",
@@ -919,7 +929,7 @@ func TestStreamConverter_ContentBlockStartIncludesEmptyFields(t *testing.T) {
 	})
 
 	t.Run("thinking block start includes empty thinking", func(t *testing.T) {
-		conv := NewStreamConverter("msg_123", "test-model")
+		conv := NewStreamConverter("msg_123", "test-model", 0)
 
 		resp := api.ChatResponse{
 			Model:   "test-model",
@@ -951,3 +961,105 @@ func TestStreamConverter_ContentBlockStartIncludesEmptyFields(t *testing.T) {
 		}
 	})
 }
+
+func TestEstimateTokens_SimpleMessage(t *testing.T) {
+	req := CountTokensRequest{
+		Model: "test-model",
+		Messages: []MessageParam{
+			{Role: "user", Content: "Hello, world!"},
+		},
+	}
+
+	tokens := estimateTokens(req)
+
+	// "user" (4) + "Hello, world!" (13) = 17 chars / 4 = 4 tokens
+	if tokens < 1 {
+		t.Errorf("expected at least 1 token, got %d", tokens)
+	}
+	// Sanity check: shouldn't be wildly off
+	if tokens > 10 {
+		t.Errorf("expected fewer than 10 tokens for short message, got %d", tokens)
+	}
+}
+
+func TestEstimateTokens_WithSystemPrompt(t *testing.T) {
+	req := CountTokensRequest{
+		Model:  "test-model",
+		System: "You are a helpful assistant.",
+		Messages: []MessageParam{
+			{Role: "user", Content: "Hello"},
+		},
+	}
+
+	tokens := estimateTokens(req)
+
+	// System prompt adds to count
+	if tokens < 5 {
+		t.Errorf("expected at least 5 tokens with system prompt, got %d", tokens)
+	}
+}
+
+func TestEstimateTokens_WithTools(t *testing.T) {
+	req := CountTokensRequest{
+		Model: "test-model",
+		Messages: []MessageParam{
+			{Role: "user", Content: "What's the weather?"},
+		},
+		Tools: []Tool{
+			{
+				Name:        "get_weather",
+				Description: "Get the current weather for a location",
+				InputSchema: json.RawMessage(`{"type":"object","properties":{"location":{"type":"string"}}}`),
+			},
+		},
+	}
+
+	tokens := estimateTokens(req)
+
+	// Tools add significant content
+	if tokens < 10 {
+		t.Errorf("expected at least 10 tokens with tools, got %d", tokens)
+	}
+}
+
+func TestEstimateTokens_WithThinking(t *testing.T) {
+	req := CountTokensRequest{
+		Model: "test-model",
+		Messages: []MessageParam{
+			{Role: "user", Content: "Hello"},
+			{
+				Role: "assistant",
+				Content: []any{
+					map[string]any{
+						"type":     "thinking",
+						"thinking": "Let me think about this carefully...",
+					},
+					map[string]any{
+						"type": "text",
+						"text": "Here is my response.",
+					},
+				},
+			},
+		},
+	}
+
+	tokens := estimateTokens(req)
+
+	// Thinking content should be counted
+	if tokens < 10 {
+		t.Errorf("expected at least 10 tokens with thinking content, got %d", tokens)
+	}
+}
+
+func TestEstimateTokens_EmptyContent(t *testing.T) {
+	req := CountTokensRequest{
+		Model:    "test-model",
+		Messages: []MessageParam{},
+	}
+
+	tokens := estimateTokens(req)
+
+	if tokens != 0 {
+		t.Errorf("expected 0 tokens for empty content, got %d", tokens)
+	}
+}

api/client.go

@@ -466,3 +466,25 @@ func (c *Client) Whoami(ctx context.Context) (*UserResponse, error) {
 	}
 	return &resp, nil
 }
+
+// AliasRequest is the request body for creating or updating a model alias.
+type AliasRequest struct {
+	Alias          string `json:"alias"`
+	Target         string `json:"target"`
+	PrefixMatching bool   `json:"prefix_matching,omitempty"`
+}
+
+// SetAliasExperimental creates or updates a model alias via the experimental aliases API.
+func (c *Client) SetAliasExperimental(ctx context.Context, req *AliasRequest) error {
+	return c.do(ctx, http.MethodPost, "/api/experimental/aliases", req, nil)
+}
+
+// AliasDeleteRequest is the request body for deleting a model alias.
+type AliasDeleteRequest struct {
+	Alias string `json:"alias"`
+}
+
+// DeleteAliasExperimental deletes a model alias via the experimental aliases API.
+func (c *Client) DeleteAliasExperimental(ctx context.Context, req *AliasDeleteRequest) error {
+	return c.do(ctx, http.MethodDelete, "/api/experimental/aliases", req, nil)
+}

cmd/cmd.go

@@ -29,6 +29,7 @@ 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"
@@ -52,7 +53,7 @@ import (
 	"github.com/ollama/ollama/x/imagegen"
 )
 
-const ConnectInstructions = "To sign in, navigate to:\n    %s\n\n"
+const ConnectInstructions = "If your browser did not open, 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) {
@@ -366,14 +367,25 @@ func loadOrUnloadModel(cmd *cobra.Command, opts *runOptions) error {
 		return err
 	} else if info.RemoteHost != "" {
 		// Cloud model, no need to load/unload
+
+		isCloud := strings.HasPrefix(info.RemoteHost, "https://ollama.com")
+
+		// Check if user is signed in for ollama.com cloud models
+		if isCloud {
+			if _, err := client.Whoami(cmd.Context()); err != nil {
+				return err
+			}
+		}
+
 		if opts.ShowConnect {
 			p.StopAndClear()
-			if strings.HasPrefix(info.RemoteHost, "https://ollama.com") {
+			if isCloud {
 				fmt.Fprintf(os.Stderr, "Connecting to '%s' on 'ollama.com' ⚡\n", info.RemoteModel)
 			} else {
 				fmt.Fprintf(os.Stderr, "Connecting to '%s' on '%s'\n", info.RemoteModel, info.RemoteHost)
 			}
 		}
+
 		return nil
 	}
 
@@ -663,6 +675,7 @@ func SigninHandler(cmd *cobra.Command, args []string) error {
 			fmt.Println()
 
 			if aErr.SigninURL != "" {
+				_ = browser.OpenURL(aErr.SigninURL)
 				fmt.Printf(ConnectInstructions, aErr.SigninURL)
 			}
 			return nil
@@ -1750,7 +1763,7 @@ func checkServerHeartbeat(cmd *cobra.Command, _ []string) error {
 			return err
 		}
 		if err := startApp(cmd.Context(), client); err != nil {
-			return fmt.Errorf("ollama server not responding - %w", err)
+			return err
 		}
 	}
 	return nil
@@ -1888,7 +1901,7 @@ 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

@@ -3,6 +3,7 @@ package cmd
 import (
 	"bytes"
 	"encoding/json"
+	"errors"
 	"fmt"
 	"io"
 	"net/http"
@@ -1553,7 +1554,7 @@ func TestShowInfoImageGen(t *testing.T) {
 		Details: api.ModelDetails{
 			Family:            "ZImagePipeline",
 			ParameterSize:     "10.3B",
-			QuantizationLevel: "FP8",
+			QuantizationLevel: "Q8",
 		},
 		Capabilities: []model.Capability{model.CapabilityImage},
 		Requires:     "0.14.0",
@@ -1565,7 +1566,7 @@ func TestShowInfoImageGen(t *testing.T) {
 	expect := "  Model\n" +
 		"    architecture    ZImagePipeline    \n" +
 		"    parameters      10.3B             \n" +
-		"    quantization    FP8               \n" +
+		"    quantization    Q8                \n" +
 		"    requires        0.14.0            \n" +
 		"\n" +
 		"  Capabilities\n" +
@@ -1659,3 +1660,103 @@ func TestRunOptions_Copy_Independence(t *testing.T) {
 		t.Error("Copy Think should not be affected by original modification")
 	}
 }
+
+func TestLoadOrUnloadModel_CloudModelAuth(t *testing.T) {
+	tests := []struct {
+		name          string
+		remoteHost    string
+		whoamiStatus  int
+		whoamiResp    any
+		expectedError string
+	}{
+		{
+			name:         "ollama.com cloud model - user signed in",
+			remoteHost:   "https://ollama.com",
+			whoamiStatus: http.StatusOK,
+			whoamiResp:   api.UserResponse{Name: "testuser"},
+		},
+		{
+			name:         "ollama.com cloud model - user not signed in",
+			remoteHost:   "https://ollama.com",
+			whoamiStatus: http.StatusUnauthorized,
+			whoamiResp: map[string]string{
+				"error":      "unauthorized",
+				"signin_url": "https://ollama.com/signin",
+			},
+			expectedError: "unauthorized",
+		},
+		{
+			name:         "non-ollama.com remote - no auth check",
+			remoteHost:   "https://other-remote.com",
+			whoamiStatus: http.StatusUnauthorized, // should not be called
+			whoamiResp:   nil,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			whoamiCalled := false
+			mockServer := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
+				switch r.URL.Path {
+				case "/api/show":
+					w.Header().Set("Content-Type", "application/json")
+					if err := json.NewEncoder(w).Encode(api.ShowResponse{
+						RemoteHost:  tt.remoteHost,
+						RemoteModel: "test-model",
+					}); err != nil {
+						http.Error(w, err.Error(), http.StatusInternalServerError)
+					}
+				case "/api/me":
+					whoamiCalled = true
+					w.Header().Set("Content-Type", "application/json")
+					w.WriteHeader(tt.whoamiStatus)
+					if tt.whoamiResp != nil {
+						if err := json.NewEncoder(w).Encode(tt.whoamiResp); err != nil {
+							http.Error(w, err.Error(), http.StatusInternalServerError)
+						}
+					}
+				default:
+					http.NotFound(w, r)
+				}
+			}))
+			defer mockServer.Close()
+
+			t.Setenv("OLLAMA_HOST", mockServer.URL)
+
+			cmd := &cobra.Command{}
+			cmd.SetContext(t.Context())
+
+			opts := &runOptions{
+				Model:       "test-cloud-model",
+				ShowConnect: false,
+			}
+
+			err := loadOrUnloadModel(cmd, opts)
+
+			if strings.HasPrefix(tt.remoteHost, "https://ollama.com") {
+				if !whoamiCalled {
+					t.Error("expected whoami to be called for ollama.com cloud model")
+				}
+			} else {
+				if whoamiCalled {
+					t.Error("whoami should not be called for non-ollama.com remote")
+				}
+			}
+
+			if tt.expectedError != "" {
+				if err == nil {
+					t.Errorf("expected error containing %q, got nil", tt.expectedError)
+				} else {
+					var authErr api.AuthorizationError
+					if !errors.As(err, &authErr) {
+						t.Errorf("expected AuthorizationError, got %T: %v", err, err)
+					}
+				}
+			} else {
+				if err != nil {
+					t.Errorf("expected no error, got %v", err)
+				}
+			}
+		})
+	}
+}

cmd/config/claude.go

@@ -1,25 +1,32 @@
 package config
 
 import (
+	"context"
 	"fmt"
 	"os"
 	"os/exec"
 	"path/filepath"
 	"runtime"
 
+	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
 )
 
-// Claude implements Runner for Claude Code integration
+// Claude implements Runner and AliasConfigurer for Claude Code integration
 type Claude struct{}
 
+// Compile-time check that Claude implements AliasConfigurer
+var _ AliasConfigurer = (*Claude)(nil)
+
 func (c *Claude) String() string { return "Claude Code" }
 
-func (c *Claude) args(model string) []string {
+func (c *Claude) args(model string, extra []string) []string {
+	var args []string
 	if model != "" {
-		return []string{"--model", model}
+		args = append(args, "--model", model)
 	}
-	return nil
+	args = append(args, extra...)
+	return args
 }
 
 func (c *Claude) findPath() (string, error) {
@@ -41,13 +48,13 @@ func (c *Claude) findPath() (string, error) {
 	return fallback, nil
 }
 
-func (c *Claude) Run(model string) error {
+func (c *Claude) Run(model string, args []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)...)
+	cmd := exec.Command(claudePath, c.args(model, args)...)
 	cmd.Stdin = os.Stdin
 	cmd.Stdout = os.Stdout
 	cmd.Stderr = os.Stderr
@@ -58,3 +65,104 @@ func (c *Claude) Run(model string) error {
 	)
 	return cmd.Run()
 }
+
+// ConfigureAliases sets up model aliases for Claude Code.
+// model: the model to use (if empty, user will be prompted to select)
+// aliases: existing alias configuration to preserve/update
+// Cloud-only: subagent routing (fast model) is gated to cloud models only until
+// there is a better strategy for prompt caching on local models.
+func (c *Claude) ConfigureAliases(ctx context.Context, model string, existingAliases map[string]string, force bool) (map[string]string, bool, error) {
+	aliases := make(map[string]string)
+	for k, v := range existingAliases {
+		aliases[k] = v
+	}
+
+	if model != "" {
+		aliases["primary"] = model
+	}
+
+	if !force && aliases["primary"] != "" {
+		client, _ := api.ClientFromEnvironment()
+		if isCloudModel(ctx, client, aliases["primary"]) {
+			if isCloudModel(ctx, client, aliases["fast"]) {
+				return aliases, false, nil
+			}
+		} else {
+			delete(aliases, "fast")
+			return aliases, false, nil
+		}
+	}
+
+	items, existingModels, cloudModels, client, err := listModels(ctx)
+	if err != nil {
+		return nil, false, err
+	}
+
+	fmt.Fprintf(os.Stderr, "\n%sModel Configuration%s\n\n", ansiBold, ansiReset)
+
+	if aliases["primary"] == "" || force {
+		primary, err := selectPrompt("Select model:", items)
+		fmt.Fprintf(os.Stderr, "\033[3A\033[J")
+		if err != nil {
+			return nil, false, err
+		}
+		if err := pullIfNeeded(ctx, client, existingModels, primary); err != nil {
+			return nil, false, err
+		}
+		if err := ensureAuth(ctx, client, cloudModels, []string{primary}); err != nil {
+			return nil, false, err
+		}
+		aliases["primary"] = primary
+	}
+
+	if isCloudModel(ctx, client, aliases["primary"]) {
+		if aliases["fast"] == "" || !isCloudModel(ctx, client, aliases["fast"]) {
+			aliases["fast"] = aliases["primary"]
+		}
+	} else {
+		delete(aliases, "fast")
+	}
+
+	return aliases, true, nil
+}
+
+// SetAliases syncs the configured aliases to the Ollama server using prefix matching.
+// Cloud-only: for local models (fast is empty), we delete any existing aliases to
+// prevent stale routing to a previous cloud model.
+func (c *Claude) SetAliases(ctx context.Context, aliases map[string]string) error {
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return err
+	}
+
+	prefixes := []string{"claude-sonnet-", "claude-haiku-"}
+
+	if aliases["fast"] == "" {
+		for _, prefix := range prefixes {
+			_ = client.DeleteAliasExperimental(ctx, &api.AliasDeleteRequest{Alias: prefix})
+		}
+		return nil
+	}
+
+	prefixAliases := map[string]string{
+		"claude-sonnet-": aliases["primary"],
+		"claude-haiku-":  aliases["fast"],
+	}
+
+	var errs []string
+	for prefix, target := range prefixAliases {
+		req := &api.AliasRequest{
+			Alias:          prefix,
+			Target:         target,
+			PrefixMatching: true,
+		}
+		if err := client.SetAliasExperimental(ctx, req); err != nil {
+			errs = append(errs, prefix)
+		}
+	}
+
+	if len(errs) > 0 {
+		return fmt.Errorf("failed to set aliases: %v", errs)
+	}
+	return nil
+}

cmd/config/claude_test.go

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

cmd/config/codex.go

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

cmd/config/codex_test.go

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

cmd/config/config.go

@@ -6,13 +6,15 @@ import (
 	"encoding/json"
 	"errors"
 	"fmt"
+	"log/slog"
 	"os"
 	"path/filepath"
 	"strings"
 )
 
 type integration struct {
-	Models []string `json:"models"`
+	Models  []string          `json:"models"`
+	Aliases map[string]string `json:"aliases,omitempty"`
 }
 
 type config struct {
@@ -20,6 +22,14 @@ 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
@@ -27,6 +37,46 @@ func configPath() (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 {
@@ -34,6 +84,11 @@ 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
@@ -79,8 +134,16 @@ func saveIntegration(appName string, models []string) error {
 		return err
 	}
 
-	cfg.Integrations[strings.ToLower(appName)] = &integration{
-		Models: models,
+	key := strings.ToLower(appName)
+	existing := cfg.Integrations[key]
+	var aliases map[string]string
+	if existing != nil && existing.Aliases != nil {
+		aliases = existing.Aliases
+	}
+
+	cfg.Integrations[key] = &integration{
+		Models:  models,
+		Aliases: aliases,
 	}
 
 	return save(cfg)
@@ -100,6 +163,29 @@ func loadIntegration(appName string) (*integration, error) {
 	return ic, nil
 }
 
+func saveAliases(appName string, aliases map[string]string) error {
+	if appName == "" {
+		return errors.New("app name cannot be empty")
+	}
+
+	cfg, err := load()
+	if err != nil {
+		return err
+	}
+
+	key := strings.ToLower(appName)
+	existing := cfg.Integrations[key]
+	if existing == nil {
+		existing = &integration{}
+	}
+
+	// Replace aliases entirely (not merge) so deletions are persisted
+	existing.Aliases = aliases
+
+	cfg.Integrations[key] = existing
+	return save(cfg)
+}
+
 func listIntegrations() ([]integration, error) {
 	cfg, err := load()
 	if err != nil {

cmd/config/config_cloud_test.go

@@ -0,0 +1,677 @@
+package config
+
+import (
+	"context"
+	"errors"
+	"os"
+	"path/filepath"
+	"testing"
+)
+
+func TestSetAliases_CloudModel(t *testing.T) {
+	// Test the SetAliases logic by checking the alias map behavior
+	aliases := map[string]string{
+		"primary": "kimi-k2.5:cloud",
+		"fast":    "kimi-k2.5:cloud",
+	}
+
+	// Verify fast is set (cloud model behavior)
+	if aliases["fast"] == "" {
+		t.Error("cloud model should have fast alias set")
+	}
+	if aliases["fast"] != aliases["primary"] {
+		t.Errorf("fast should equal primary for auto-set, got fast=%q primary=%q", aliases["fast"], aliases["primary"])
+	}
+}
+
+func TestSetAliases_LocalModel(t *testing.T) {
+	aliases := map[string]string{
+		"primary": "llama3.2:latest",
+	}
+	// Simulate local model behavior: fast should be empty
+	delete(aliases, "fast")
+
+	if aliases["fast"] != "" {
+		t.Error("local model should have empty fast alias")
+	}
+}
+
+func TestSaveAliases_ReplacesNotMerges(t *testing.T) {
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	// First save with both primary and fast
+	initial := map[string]string{
+		"primary": "cloud-model",
+		"fast":    "cloud-model",
+	}
+	if err := saveAliases("claude", initial); err != nil {
+		t.Fatalf("failed to save initial aliases: %v", err)
+	}
+
+	// Verify both are saved
+	loaded, err := loadIntegration("claude")
+	if err != nil {
+		t.Fatalf("failed to load: %v", err)
+	}
+	if loaded.Aliases["fast"] != "cloud-model" {
+		t.Errorf("expected fast=cloud-model, got %q", loaded.Aliases["fast"])
+	}
+
+	// Now save without fast (simulating switch to local model)
+	updated := map[string]string{
+		"primary": "local-model",
+		// fast intentionally missing
+	}
+	if err := saveAliases("claude", updated); err != nil {
+		t.Fatalf("failed to save updated aliases: %v", err)
+	}
+
+	// Verify fast is GONE (not merged/preserved)
+	loaded, err = loadIntegration("claude")
+	if err != nil {
+		t.Fatalf("failed to load after update: %v", err)
+	}
+	if loaded.Aliases["fast"] != "" {
+		t.Errorf("fast should be removed after saving without it, got %q", loaded.Aliases["fast"])
+	}
+	if loaded.Aliases["primary"] != "local-model" {
+		t.Errorf("primary should be updated to local-model, got %q", loaded.Aliases["primary"])
+	}
+}
+
+func TestSaveAliases_PreservesModels(t *testing.T) {
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	// First save integration with models
+	if err := saveIntegration("claude", []string{"model1", "model2"}); err != nil {
+		t.Fatalf("failed to save integration: %v", err)
+	}
+
+	// Then update aliases
+	aliases := map[string]string{"primary": "new-model"}
+	if err := saveAliases("claude", aliases); err != nil {
+		t.Fatalf("failed to save aliases: %v", err)
+	}
+
+	// Verify models are preserved
+	loaded, err := loadIntegration("claude")
+	if err != nil {
+		t.Fatalf("failed to load: %v", err)
+	}
+	if len(loaded.Models) != 2 || loaded.Models[0] != "model1" {
+		t.Errorf("models should be preserved, got %v", loaded.Models)
+	}
+}
+
+// TestSaveAliases_EmptyMap clears all aliases
+func TestSaveAliases_EmptyMap(t *testing.T) {
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	// Save with aliases
+	if err := saveAliases("claude", map[string]string{"primary": "model", "fast": "model"}); err != nil {
+		t.Fatalf("failed to save: %v", err)
+	}
+
+	// Save empty map
+	if err := saveAliases("claude", map[string]string{}); err != nil {
+		t.Fatalf("failed to save empty: %v", err)
+	}
+
+	loaded, err := loadIntegration("claude")
+	if err != nil {
+		t.Fatalf("failed to load: %v", err)
+	}
+	if len(loaded.Aliases) != 0 {
+		t.Errorf("aliases should be empty, got %v", loaded.Aliases)
+	}
+}
+
+// TestSaveAliases_NilMap handles nil gracefully
+func TestSaveAliases_NilMap(t *testing.T) {
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	// Save with aliases first
+	if err := saveAliases("claude", map[string]string{"primary": "model"}); err != nil {
+		t.Fatalf("failed to save: %v", err)
+	}
+
+	// Save nil map - should clear aliases
+	if err := saveAliases("claude", nil); err != nil {
+		t.Fatalf("failed to save nil: %v", err)
+	}
+
+	loaded, err := loadIntegration("claude")
+	if err != nil {
+		t.Fatalf("failed to load: %v", err)
+	}
+	if len(loaded.Aliases) > 0 {
+		t.Errorf("aliases should be nil or empty, got %v", loaded.Aliases)
+	}
+}
+
+// TestSaveAliases_EmptyAppName returns error
+func TestSaveAliases_EmptyAppName(t *testing.T) {
+	err := saveAliases("", map[string]string{"primary": "model"})
+	if err == nil {
+		t.Error("expected error for empty app name")
+	}
+}
+
+func TestSaveAliases_CaseInsensitive(t *testing.T) {
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	if err := saveAliases("Claude", map[string]string{"primary": "model1"}); err != nil {
+		t.Fatalf("failed to save: %v", err)
+	}
+
+	// Load with different case
+	loaded, err := loadIntegration("claude")
+	if err != nil {
+		t.Fatalf("failed to load: %v", err)
+	}
+	if loaded.Aliases["primary"] != "model1" {
+		t.Errorf("expected primary=model1, got %q", loaded.Aliases["primary"])
+	}
+
+	// Update with different case
+	if err := saveAliases("CLAUDE", map[string]string{"primary": "model2"}); err != nil {
+		t.Fatalf("failed to update: %v", err)
+	}
+
+	loaded, err = loadIntegration("claude")
+	if err != nil {
+		t.Fatalf("failed to load after update: %v", err)
+	}
+	if loaded.Aliases["primary"] != "model2" {
+		t.Errorf("expected primary=model2, got %q", loaded.Aliases["primary"])
+	}
+}
+
+// TestSaveAliases_CreatesIntegration creates integration if it doesn't exist
+func TestSaveAliases_CreatesIntegration(t *testing.T) {
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	// Save aliases for non-existent integration
+	if err := saveAliases("newintegration", map[string]string{"primary": "model"}); err != nil {
+		t.Fatalf("failed to save: %v", err)
+	}
+
+	loaded, err := loadIntegration("newintegration")
+	if err != nil {
+		t.Fatalf("failed to load: %v", err)
+	}
+	if loaded.Aliases["primary"] != "model" {
+		t.Errorf("expected primary=model, got %q", loaded.Aliases["primary"])
+	}
+}
+
+func TestConfigureAliases_AliasMap(t *testing.T) {
+	t.Run("cloud model auto-sets fast to primary", func(t *testing.T) {
+		aliases := make(map[string]string)
+		aliases["primary"] = "cloud-model"
+
+		// Simulate cloud model behavior
+		isCloud := true
+		if isCloud {
+			if aliases["fast"] == "" {
+				aliases["fast"] = aliases["primary"]
+			}
+		}
+
+		if aliases["fast"] != "cloud-model" {
+			t.Errorf("expected fast=cloud-model, got %q", aliases["fast"])
+		}
+	})
+
+	t.Run("cloud model preserves custom fast", func(t *testing.T) {
+		aliases := map[string]string{
+			"primary": "cloud-model",
+			"fast":    "custom-fast-model",
+		}
+
+		// Simulate cloud model behavior - should preserve existing fast
+		isCloud := true
+		if isCloud {
+			if aliases["fast"] == "" {
+				aliases["fast"] = aliases["primary"]
+			}
+		}
+
+		if aliases["fast"] != "custom-fast-model" {
+			t.Errorf("expected fast=custom-fast-model (preserved), got %q", aliases["fast"])
+		}
+	})
+
+	t.Run("local model clears fast", func(t *testing.T) {
+		aliases := map[string]string{
+			"primary": "local-model",
+			"fast":    "should-be-cleared",
+		}
+
+		// Simulate local model behavior
+		isCloud := false
+		if !isCloud {
+			delete(aliases, "fast")
+		}
+
+		if aliases["fast"] != "" {
+			t.Errorf("expected fast to be cleared, got %q", aliases["fast"])
+		}
+	})
+
+	t.Run("switching cloud to local clears fast", func(t *testing.T) {
+		// Start with cloud config
+		aliases := map[string]string{
+			"primary": "cloud-model",
+			"fast":    "cloud-model",
+		}
+
+		// Switch to local
+		aliases["primary"] = "local-model"
+		isCloud := false
+		if !isCloud {
+			delete(aliases, "fast")
+		}
+
+		if aliases["fast"] != "" {
+			t.Errorf("fast should be cleared when switching to local, got %q", aliases["fast"])
+		}
+		if aliases["primary"] != "local-model" {
+			t.Errorf("primary should be updated, got %q", aliases["primary"])
+		}
+	})
+
+	t.Run("switching local to cloud sets fast", func(t *testing.T) {
+		// Start with local config (no fast)
+		aliases := map[string]string{
+			"primary": "local-model",
+		}
+
+		// Switch to cloud
+		aliases["primary"] = "cloud-model"
+		isCloud := true
+		if isCloud {
+			if aliases["fast"] == "" {
+				aliases["fast"] = aliases["primary"]
+			}
+		}
+
+		if aliases["fast"] != "cloud-model" {
+			t.Errorf("fast should be set when switching to cloud, got %q", aliases["fast"])
+		}
+	})
+}
+
+func TestSetAliases_PrefixMapping(t *testing.T) {
+	// This tests the expected mapping without needing a real client
+	aliases := map[string]string{
+		"primary": "my-cloud-model",
+		"fast":    "my-fast-model",
+	}
+
+	expectedMappings := map[string]string{
+		"claude-sonnet-": aliases["primary"],
+		"claude-haiku-":  aliases["fast"],
+	}
+
+	if expectedMappings["claude-sonnet-"] != "my-cloud-model" {
+		t.Errorf("claude-sonnet- should map to primary")
+	}
+	if expectedMappings["claude-haiku-"] != "my-fast-model" {
+		t.Errorf("claude-haiku- should map to fast")
+	}
+}
+
+func TestSetAliases_LocalDeletesPrefixes(t *testing.T) {
+	aliases := map[string]string{
+		"primary": "local-model",
+		// fast is empty/missing - indicates local model
+	}
+
+	prefixesToDelete := []string{"claude-sonnet-", "claude-haiku-"}
+
+	// Verify the logic: when fast is empty, we should delete
+	if aliases["fast"] != "" {
+		t.Error("fast should be empty for local model")
+	}
+
+	// Verify we have the right prefixes to delete
+	if len(prefixesToDelete) != 2 {
+		t.Errorf("expected 2 prefixes to delete, got %d", len(prefixesToDelete))
+	}
+}
+
+// TestAtomicUpdate_ServerFailsConfigNotSaved simulates atomic update behavior
+func TestAtomicUpdate_ServerFailsConfigNotSaved(t *testing.T) {
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	// Simulate: server fails, config should NOT be saved
+	serverErr := errors.New("server unavailable")
+
+	if serverErr == nil {
+		t.Error("config should NOT be saved when server fails")
+	}
+}
+
+// TestAtomicUpdate_ServerSucceedsConfigSaved simulates successful atomic update
+func TestAtomicUpdate_ServerSucceedsConfigSaved(t *testing.T) {
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	// Simulate: server succeeds, config should be saved
+	var serverErr error
+	if serverErr != nil {
+		t.Fatal("server should succeed")
+	}
+
+	if err := saveAliases("claude", map[string]string{"primary": "model"}); err != nil {
+		t.Fatalf("saveAliases failed: %v", err)
+	}
+
+	// Verify it was actually saved
+	loaded, err := loadIntegration("claude")
+	if err != nil {
+		t.Fatalf("failed to load: %v", err)
+	}
+	if loaded.Aliases["primary"] != "model" {
+		t.Errorf("expected primary=model, got %q", loaded.Aliases["primary"])
+	}
+}
+
+func TestConfigFile_PreservesUnknownFields(t *testing.T) {
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	// Write config with extra fields
+	configPath := filepath.Join(tmpDir, ".ollama", "config.json")
+	os.MkdirAll(filepath.Dir(configPath), 0o755)
+
+	// Note: Our config struct only has Integrations, so top-level unknown fields
+	// won't be preserved by our current implementation. This test documents that.
+	initialConfig := `{
+  "integrations": {
+    "claude": {
+      "models": ["model1"],
+      "aliases": {"primary": "model1"},
+      "unknownField": "should be lost"
+    }
+  },
+  "topLevelUnknown": "will be lost"
+}`
+	os.WriteFile(configPath, []byte(initialConfig), 0o644)
+
+	// Update aliases
+	if err := saveAliases("claude", map[string]string{"primary": "model2"}); err != nil {
+		t.Fatalf("failed to save: %v", err)
+	}
+
+	// Read raw file to check
+	data, _ := os.ReadFile(configPath)
+	content := string(data)
+
+	// models should be preserved
+	if !contains(content, "model1") {
+		t.Error("models should be preserved")
+	}
+
+	// primary should be updated
+	if !contains(content, "model2") {
+		t.Error("primary should be updated to model2")
+	}
+}
+
+func contains(s, substr string) bool {
+	return len(s) >= len(substr) && (s == substr || len(s) > 0 && containsHelper(s, substr))
+}
+
+func containsHelper(s, substr string) bool {
+	for i := 0; i <= len(s)-len(substr); i++ {
+		if s[i:i+len(substr)] == substr {
+			return true
+		}
+	}
+	return false
+}
+
+func TestClaudeImplementsAliasConfigurer(t *testing.T) {
+	c := &Claude{}
+	var _ AliasConfigurer = c // Compile-time check
+}
+
+func TestModelNameEdgeCases(t *testing.T) {
+	testCases := []struct {
+		name  string
+		model string
+	}{
+		{"simple", "llama3.2"},
+		{"with tag", "llama3.2:latest"},
+		{"with cloud tag", "kimi-k2.5:cloud"},
+		{"with namespace", "library/llama3.2"},
+		{"with dots", "glm-4.7-flash"},
+		{"with numbers", "qwen3:8b"},
+	}
+
+	for _, tc := range testCases {
+		t.Run(tc.name, func(t *testing.T) {
+			tmpDir := t.TempDir()
+			setTestHome(t, tmpDir)
+
+			aliases := map[string]string{"primary": tc.model}
+			if err := saveAliases("claude", aliases); err != nil {
+				t.Fatalf("failed to save model %q: %v", tc.model, err)
+			}
+
+			loaded, err := loadIntegration("claude")
+			if err != nil {
+				t.Fatalf("failed to load: %v", err)
+			}
+			if loaded.Aliases["primary"] != tc.model {
+				t.Errorf("expected primary=%q, got %q", tc.model, loaded.Aliases["primary"])
+			}
+		})
+	}
+}
+
+func TestSwitchingScenarios(t *testing.T) {
+	t.Run("cloud to local removes fast", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+
+		// Initial cloud config
+		if err := saveAliases("claude", map[string]string{
+			"primary": "cloud-model",
+			"fast":    "cloud-model",
+		}); err != nil {
+			t.Fatal(err)
+		}
+
+		// Switch to local (no fast)
+		if err := saveAliases("claude", map[string]string{
+			"primary": "local-model",
+		}); err != nil {
+			t.Fatal(err)
+		}
+
+		loaded, _ := loadIntegration("claude")
+		if loaded.Aliases["fast"] != "" {
+			t.Errorf("fast should be removed, got %q", loaded.Aliases["fast"])
+		}
+		if loaded.Aliases["primary"] != "local-model" {
+			t.Errorf("primary should be local-model, got %q", loaded.Aliases["primary"])
+		}
+	})
+
+	t.Run("local to cloud adds fast", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+
+		// Initial local config
+		if err := saveAliases("claude", map[string]string{
+			"primary": "local-model",
+		}); err != nil {
+			t.Fatal(err)
+		}
+
+		// Switch to cloud (with fast)
+		if err := saveAliases("claude", map[string]string{
+			"primary": "cloud-model",
+			"fast":    "cloud-model",
+		}); err != nil {
+			t.Fatal(err)
+		}
+
+		loaded, _ := loadIntegration("claude")
+		if loaded.Aliases["fast"] != "cloud-model" {
+			t.Errorf("fast should be cloud-model, got %q", loaded.Aliases["fast"])
+		}
+	})
+
+	t.Run("cloud to different cloud updates both", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+
+		// Initial cloud config
+		if err := saveAliases("claude", map[string]string{
+			"primary": "cloud-model-1",
+			"fast":    "cloud-model-1",
+		}); err != nil {
+			t.Fatal(err)
+		}
+
+		// Switch to different cloud
+		if err := saveAliases("claude", map[string]string{
+			"primary": "cloud-model-2",
+			"fast":    "cloud-model-2",
+		}); err != nil {
+			t.Fatal(err)
+		}
+
+		loaded, _ := loadIntegration("claude")
+		if loaded.Aliases["primary"] != "cloud-model-2" {
+			t.Errorf("primary should be cloud-model-2, got %q", loaded.Aliases["primary"])
+		}
+		if loaded.Aliases["fast"] != "cloud-model-2" {
+			t.Errorf("fast should be cloud-model-2, got %q", loaded.Aliases["fast"])
+		}
+	})
+}
+
+func TestToolCapabilityFiltering(t *testing.T) {
+	t.Run("all models checked for tool capability", func(t *testing.T) {
+		// Both cloud and local models are checked for tool capability via Show API
+		// Only models with "tools" in capabilities are included
+		m := modelInfo{Name: "tool-model", Remote: false, ToolCapable: true}
+		if !m.ToolCapable {
+			t.Error("tool capable model should be marked as such")
+		}
+	})
+
+	t.Run("modelInfo includes ToolCapable field", func(t *testing.T) {
+		m := modelInfo{Name: "test", Remote: true, ToolCapable: true}
+		if !m.ToolCapable {
+			t.Error("ToolCapable field should be accessible")
+		}
+	})
+}
+
+func TestIsCloudModel_RequiresClient(t *testing.T) {
+	t.Run("nil client always returns false", func(t *testing.T) {
+		// isCloudModel now only uses Show API, no suffix detection
+		if isCloudModel(context.Background(), nil, "model:cloud") {
+			t.Error("nil client should return false regardless of suffix")
+		}
+		if isCloudModel(context.Background(), nil, "local-model") {
+			t.Error("nil client should return false")
+		}
+	})
+}
+
+func TestModelsAndAliasesMustStayInSync(t *testing.T) {
+	t.Run("saveAliases followed by saveIntegration keeps them in sync", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+
+		// Save aliases with one model
+		if err := saveAliases("claude", map[string]string{"primary": "model-a"}); err != nil {
+			t.Fatal(err)
+		}
+
+		// Save integration with same model (this is the pattern we use)
+		if err := saveIntegration("claude", []string{"model-a"}); err != nil {
+			t.Fatal(err)
+		}
+
+		loaded, _ := loadIntegration("claude")
+		if loaded.Aliases["primary"] != loaded.Models[0] {
+			t.Errorf("aliases.primary (%q) != models[0] (%q)", loaded.Aliases["primary"], loaded.Models[0])
+		}
+	})
+
+	t.Run("out of sync config is detectable", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+
+		// Simulate out-of-sync state (like manual edit or bug)
+		if err := saveIntegration("claude", []string{"old-model"}); err != nil {
+			t.Fatal(err)
+		}
+		if err := saveAliases("claude", map[string]string{"primary": "new-model"}); err != nil {
+			t.Fatal(err)
+		}
+
+		loaded, _ := loadIntegration("claude")
+
+		// They should be different (this is the bug state)
+		if loaded.Models[0] == loaded.Aliases["primary"] {
+			t.Error("expected out-of-sync state for this test")
+		}
+
+		// The fix: when updating aliases, also update models
+		if err := saveIntegration("claude", []string{loaded.Aliases["primary"]}); err != nil {
+			t.Fatal(err)
+		}
+
+		loaded, _ = loadIntegration("claude")
+		if loaded.Models[0] != loaded.Aliases["primary"] {
+			t.Errorf("after fix: models[0] (%q) should equal aliases.primary (%q)",
+				loaded.Models[0], loaded.Aliases["primary"])
+		}
+	})
+
+	t.Run("updating primary alias updates models too", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+
+		// Initial state
+		if err := saveIntegration("claude", []string{"initial-model"}); err != nil {
+			t.Fatal(err)
+		}
+		if err := saveAliases("claude", map[string]string{"primary": "initial-model"}); err != nil {
+			t.Fatal(err)
+		}
+
+		// Update aliases AND models together
+		newAliases := map[string]string{"primary": "updated-model"}
+		if err := saveAliases("claude", newAliases); err != nil {
+			t.Fatal(err)
+		}
+		if err := saveIntegration("claude", []string{newAliases["primary"]}); err != nil {
+			t.Fatal(err)
+		}
+
+		loaded, _ := loadIntegration("claude")
+		if loaded.Models[0] != "updated-model" {
+			t.Errorf("models[0] should be updated-model, got %q", loaded.Models[0])
+		}
+		if loaded.Aliases["primary"] != "updated-model" {
+			t.Errorf("aliases.primary should be updated-model, got %q", loaded.Aliases["primary"])
+		}
+	})
+}

cmd/config/config_test.go

@@ -46,6 +46,53 @@ func TestIntegrationConfig(t *testing.T) {
 		}
 	})
 
+	t.Run("save and load aliases", func(t *testing.T) {
+		models := []string{"llama3.2"}
+		if err := saveIntegration("claude", models); err != nil {
+			t.Fatal(err)
+		}
+		aliases := map[string]string{
+			"primary": "llama3.2:70b",
+			"fast":    "llama3.2:8b",
+		}
+		if err := saveAliases("claude", aliases); err != nil {
+			t.Fatal(err)
+		}
+
+		config, err := loadIntegration("claude")
+		if err != nil {
+			t.Fatal(err)
+		}
+		if config.Aliases == nil {
+			t.Fatal("expected aliases to be saved")
+		}
+		for k, v := range aliases {
+			if config.Aliases[k] != v {
+				t.Errorf("alias %s: expected %s, got %s", k, v, config.Aliases[k])
+			}
+		}
+	})
+
+	t.Run("saveIntegration preserves aliases", func(t *testing.T) {
+		if err := saveIntegration("claude", []string{"model-a"}); err != nil {
+			t.Fatal(err)
+		}
+		if err := saveAliases("claude", map[string]string{"primary": "model-a", "fast": "model-small"}); err != nil {
+			t.Fatal(err)
+		}
+
+		if err := saveIntegration("claude", []string{"model-b"}); err != nil {
+			t.Fatal(err)
+		}
+		config, err := loadIntegration("claude")
+		if err != nil {
+			t.Fatal(err)
+		}
+		if config.Aliases["primary"] != "model-a" {
+			t.Errorf("expected aliases to be preserved, got %v", config.Aliases)
+		}
+	})
+
 	t.Run("defaultModel returns first model", func(t *testing.T) {
 		saveIntegration("codex", []string{"model-a", "model-b"})
 
@@ -200,12 +247,10 @@ func TestLoadIntegration_CorruptedJSON(t *testing.T) {
 	tmpDir := t.TempDir()
 	setTestHome(t, tmpDir)
 
-	// Create corrupted config.json file
-	dir := filepath.Join(tmpDir, ".ollama", "config")
+	dir := filepath.Join(tmpDir, ".ollama")
 	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")
@@ -267,7 +312,7 @@ func TestConfigPath(t *testing.T) {
 		t.Fatal(err)
 	}
 
-	expected := filepath.Join(tmpDir, ".ollama", "config", "config.json")
+	expected := filepath.Join(tmpDir, ".ollama", "config.json")
 	if path != expected {
 		t.Errorf("expected %s, got %s", expected, path)
 	}
@@ -322,6 +367,183 @@ 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

@@ -39,7 +39,7 @@ type modelEntry struct {
 
 func (d *Droid) String() string { return "Droid" }
 
-func (d *Droid) Run(model string) error {
+func (d *Droid) Run(model string, args []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 +53,7 @@ func (d *Droid) Run(model string) error {
 		return fmt.Errorf("setup failed: %w", err)
 	}
 
-	cmd := exec.Command("droid")
+	cmd := exec.Command("droid", args...)
 	cmd.Stdin = os.Stdin
 	cmd.Stdout = os.Stdout
 	cmd.Stderr = os.Stderr

cmd/config/integrations.go

@@ -13,6 +13,7 @@ import (
 	"time"
 
 	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/progress"
 	"github.com/spf13/cobra"
 )
 
@@ -22,7 +23,7 @@ import (
 // Runner can run an integration with a model.
 
 type Runner interface {
-	Run(model string) error
+	Run(model string, args []string) error
 	// String returns the human-readable name of the integration
 	String() string
 }
@@ -38,13 +39,39 @@ type Editor interface {
 	Models() []string
 }
 
+// AliasConfigurer can configure model aliases (e.g., for subagent routing).
+// Integrations like Claude and Codex use this to route model requests to local models.
+type AliasConfigurer interface {
+	// ConfigureAliases prompts the user to configure aliases and returns the updated map.
+	ConfigureAliases(ctx context.Context, primaryModel string, existing map[string]string, force bool) (map[string]string, bool, error)
+	// SetAliases syncs the configured aliases to the server
+	SetAliases(ctx context.Context, aliases map[string]string) error
+}
+
 // integrations is the registry of available integrations.
 var integrations = map[string]Runner{
 	"claude":   &Claude{},
-	"clawdbot": &Clawdbot{},
+	"clawdbot": &Openclaw{},
 	"codex":    &Codex{},
+	"moltbot":  &Openclaw{},
 	"droid":    &Droid{},
 	"opencode": &OpenCode{},
+	"openclaw": &Openclaw{},
+}
+
+// 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,
 }
 
 func selectIntegration() (string, error) {
@@ -55,6 +82,9 @@ 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 {
@@ -83,152 +113,212 @@ func selectModels(ctx context.Context, name, current string) ([]string, error) {
 		return nil, err
 	}
 
-	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)
+	var existing []modelInfo
 	for _, m := range models.Models {
-		if m.RemoteModel != "" {
-			cloudModels[m.Name] = true
-		}
-		items = append(items, selectItem{Name: m.Name})
+		existing = append(existing, modelInfo{Name: m.Name, Remote: m.RemoteModel != ""})
 	}
 
-	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()
 	}
-	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
-		}
-	}
+	items, preChecked, existingModels, cloudModels := buildModelList(existing, preChecked, current)
 
-	// 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 })...)
+	if len(items) == 0 {
+		return nil, fmt.Errorf("no models available")
 	}
 
-	// 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 {
 			return nil, err
 		}
 	} else {
-		model, err := selectPrompt(fmt.Sprintf("Select model for %s:", r), items)
+		prompt := fmt.Sprintf("Select model for %s:", r)
+		if _, ok := r.(AliasConfigurer); ok {
+			prompt = fmt.Sprintf("Select Primary model for %s:", r)
+		}
+		model, err := selectPrompt(prompt, items)
 		if err != nil {
 			return nil, err
 		}
 		selected = []string{model}
 	}
 
-	// if any model in selected is a cloud model, ensure signed in
+	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 err := ensureAuth(ctx, client, cloudModels, selected); err != nil {
+		return nil, err
+	}
+
+	return selected, nil
+}
+
+func pullIfNeeded(ctx context.Context, client *api.Client, existingModels map[string]bool, model string) error {
+	if existingModels[model] {
+		return nil
+	}
+	msg := fmt.Sprintf("Download %s?", model)
+	if ok, err := confirmPrompt(msg); err != nil {
+		return err
+	} else if !ok {
+		return errCancelled
+	}
+	fmt.Fprintf(os.Stderr, "\n")
+	if err := pullModel(ctx, client, model); err != nil {
+		return fmt.Errorf("failed to pull %s: %w", model, err)
+	}
+	return nil
+}
+
+func listModels(ctx context.Context) ([]selectItem, map[string]bool, map[string]bool, *api.Client, error) {
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return nil, nil, nil, nil, err
+	}
+
+	models, err := client.List(ctx)
+	if err != nil {
+		return nil, nil, nil, nil, err
+	}
+
+	var existing []modelInfo
+	for _, m := range models.Models {
+		existing = append(existing, modelInfo{
+			Name:   m.Name,
+			Remote: m.RemoteModel != "",
+		})
+	}
+
+	items, _, existingModels, cloudModels := buildModelList(existing, nil, "")
+
+	if len(items) == 0 {
+		return nil, nil, nil, nil, fmt.Errorf("no models available, run 'ollama pull <model>' first")
+	}
+
+	return items, existingModels, cloudModels, client, nil
+}
+
+func ensureAuth(ctx context.Context, client *api.Client, cloudModels map[string]bool, selected []string) error {
 	var selectedCloudModels []string
 	for _, m := range selected {
 		if cloudModels[m] {
 			selectedCloudModels = append(selectedCloudModels, m)
 		}
 	}
-	if len(selectedCloudModels) > 0 {
-		// ensure user is signed in
-		user, err := client.Whoami(ctx)
-		if err == nil && user != nil && user.Name != "" {
-			return selected, nil
-		}
+	if len(selectedCloudModels) == 0 {
+		return nil
+	}
 
-		var aErr api.AuthorizationError
-		if !errors.As(err, &aErr) || aErr.SigninURL == "" {
-			return nil, err
-		}
+	user, err := client.Whoami(ctx)
+	if err == nil && user != nil && user.Name != "" {
+		return nil
+	}
 
-		modelList := strings.Join(selectedCloudModels, ", ")
-		yes, err := confirmPrompt(fmt.Sprintf("sign in to use %s?", modelList))
-		if err != nil || !yes {
-			return nil, fmt.Errorf("%s requires sign in", modelList)
-		}
+	var aErr api.AuthorizationError
+	if !errors.As(err, &aErr) || aErr.SigninURL == "" {
+		return err
+	}
 
-		fmt.Fprintf(os.Stderr, "\nTo sign in, navigate to:\n    %s\n\n", aErr.SigninURL)
+	modelList := strings.Join(selectedCloudModels, ", ")
+	yes, err := confirmPrompt(fmt.Sprintf("sign in to use %s?", modelList))
+	if err != nil || !yes {
+		return fmt.Errorf("%s requires sign in", modelList)
+	}
 
-		// TODO(parthsareen): extract into auth package for cmd
-		// Auto-open browser (best effort, fail silently)
-		switch runtime.GOOS {
-		case "darwin":
-			_ = exec.Command("open", aErr.SigninURL).Start()
-		case "linux":
-			_ = exec.Command("xdg-open", aErr.SigninURL).Start()
-		case "windows":
-			_ = exec.Command("rundll32", "url.dll,FileProtocolHandler", aErr.SigninURL).Start()
-		}
+	fmt.Fprintf(os.Stderr, "\nTo sign in, navigate to:\n    %s\n\n", aErr.SigninURL)
 
-		spinnerFrames := []string{"|", "/", "-", "\\"}
-		frame := 0
+	switch runtime.GOOS {
+	case "darwin":
+		_ = exec.Command("open", aErr.SigninURL).Start()
+	case "linux":
+		_ = exec.Command("xdg-open", aErr.SigninURL).Start()
+	case "windows":
+		_ = exec.Command("rundll32", "url.dll,FileProtocolHandler", aErr.SigninURL).Start()
+	}
 
-		fmt.Fprintf(os.Stderr, "\033[90mwaiting for sign in to complete... %s\033[0m", spinnerFrames[0])
+	spinnerFrames := []string{"|", "/", "-", "\\"}
+	frame := 0
 
-		ticker := time.NewTicker(200 * time.Millisecond)
-		defer ticker.Stop()
+	fmt.Fprintf(os.Stderr, "\033[90mwaiting for sign in to complete... %s\033[0m", spinnerFrames[0])
 
-		for {
-			select {
-			case <-ctx.Done():
-				fmt.Fprintf(os.Stderr, "\r\033[K")
-				return nil, ctx.Err()
-			case <-ticker.C:
-				frame++
-				fmt.Fprintf(os.Stderr, "\r\033[90mwaiting for sign in to complete... %s\033[0m", spinnerFrames[frame%len(spinnerFrames)])
+	ticker := time.NewTicker(200 * time.Millisecond)
+	defer ticker.Stop()
 
-				// poll every 10th frame (~2 seconds)
-				if frame%10 == 0 {
-					u, err := client.Whoami(ctx)
-					if err == nil && u != nil && u.Name != "" {
-						fmt.Fprintf(os.Stderr, "\r\033[K\033[A\r\033[K\033[1msigned in:\033[0m %s\n", u.Name)
-						return selected, nil
-					}
+	for {
+		select {
+		case <-ctx.Done():
+			fmt.Fprintf(os.Stderr, "\r\033[K")
+			return ctx.Err()
+		case <-ticker.C:
+			frame++
+			fmt.Fprintf(os.Stderr, "\r\033[90mwaiting for sign in to complete... %s\033[0m", spinnerFrames[frame%len(spinnerFrames)])
+
+			// poll every 10th frame (~2 seconds)
+			if frame%10 == 0 {
+				u, err := client.Whoami(ctx)
+				if err == nil && u != nil && u.Name != "" {
+					fmt.Fprintf(os.Stderr, "\r\033[K\033[A\r\033[K\033[1msigned in:\033[0m %s\n", u.Name)
+					return nil
 				}
 			}
 		}
 	}
-
-	return selected, nil
 }
 
-func runIntegration(name, modelName string) error {
+func runIntegration(name, modelName string, args []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)
+	return r.Run(modelName, args)
+}
+
+// syncAliases syncs aliases to server and saves locally for an AliasConfigurer.
+func syncAliases(ctx context.Context, client *api.Client, ac AliasConfigurer, name, model string, existing map[string]string) error {
+	aliases := make(map[string]string)
+	for k, v := range existing {
+		aliases[k] = v
+	}
+	aliases["primary"] = model
+
+	if isCloudModel(ctx, client, model) {
+		if aliases["fast"] == "" || !isCloudModel(ctx, client, aliases["fast"]) {
+			aliases["fast"] = model
+		}
+	} else {
+		delete(aliases, "fast")
+	}
+
+	if err := ac.SetAliases(ctx, aliases); err != nil {
+		return err
+	}
+	return saveAliases(name, aliases)
 }
 
 // LaunchCmd returns the cobra command for launching integrations.
@@ -237,29 +327,52 @@ func LaunchCmd(checkServerHeartbeat func(cmd *cobra.Command, args []string) erro
 	var configFlag bool
 
 	cmd := &cobra.Command{
-		Use:   "launch [INTEGRATION]",
+		Use:   "launch [INTEGRATION] [-- [EXTRA_ARGS...]]",
 		Short: "Launch an integration with Ollama",
 		Long: `Launch an integration configured with Ollama models.
 
 Supported integrations:
   claude    Claude Code
-  clawdbot  Clawdbot
   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)`,
-		Args:    cobra.MaximumNArgs(1),
+  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,
 		PreRunE: checkServerHeartbeat,
 		RunE: func(cmd *cobra.Command, args []string) error {
+			// Extract integration name and args to pass through using -- separator
 			var name string
-			if len(args) > 0 {
-				name = args[0]
+			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]
+				}
 			} 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) {
@@ -275,16 +388,92 @@ 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])
+			// Handle AliasConfigurer integrations (claude, codex)
+			if ac, ok := r.(AliasConfigurer); ok {
+				client, err := api.ClientFromEnvironment()
+				if err != nil {
+					return err
+				}
+
+				// Validate --model flag if provided
+				if modelFlag != "" {
+					if _, err := client.Show(cmd.Context(), &api.ShowRequest{Name: modelFlag}); err != nil {
+						return fmt.Errorf("model %q not found", modelFlag)
+					}
+				}
+
+				var model string
+				var existingAliases map[string]string
+
+				// Load saved config
+				if cfg, err := loadIntegration(name); err == nil {
+					existingAliases = cfg.Aliases
+					if len(cfg.Models) > 0 {
+						model = cfg.Models[0]
+						// AliasConfigurer integrations use single model; sanitize if multiple
+						if len(cfg.Models) > 1 {
+							_ = saveIntegration(name, []string{model})
+						}
+					}
+				}
+
+				// --model flag overrides saved model
+				if modelFlag != "" {
+					model = modelFlag
+				}
+
+				// Validate saved model still exists
+				if model != "" && modelFlag == "" {
+					if _, err := client.Show(cmd.Context(), &api.ShowRequest{Name: model}); err != nil {
+						fmt.Fprintf(os.Stderr, "%sConfigured model %q not found%s\n\n", ansiGray, model, ansiReset)
+						model = ""
+					}
+				}
+
+				// If no valid model or --config flag, show picker
+				if model == "" || configFlag {
+					aliases, _, err := ac.ConfigureAliases(cmd.Context(), model, existingAliases, configFlag)
+					if errors.Is(err, errCancelled) {
+						return nil
+					}
+					if err != nil {
+						return err
+					}
+					model = aliases["primary"]
+					existingAliases = aliases
+				}
+
+				// Sync aliases and save
+				if err := syncAliases(cmd.Context(), client, ac, name, model, existingAliases); err != nil {
+					fmt.Fprintf(os.Stderr, "%sWarning: Could not sync aliases: %v%s\n", ansiGray, err, ansiReset)
+				}
+				if err := saveIntegration(name, []string{model}); err != nil {
+					return fmt.Errorf("failed to save: %w", err)
+				}
+
+				// Launch (unless --config without confirmation)
+				if configFlag {
+					if launch, _ := confirmPrompt(fmt.Sprintf("Launch %s now?", r)); launch {
+						return runIntegration(name, model, passArgs)
+					}
+					return nil
+				}
+				return runIntegration(name, model, passArgs)
+			}
+
+			// Validate --model flag for non-AliasConfigurer integrations
+			if modelFlag != "" {
+				client, err := api.ClientFromEnvironment()
+				if err != nil {
+					return err
+				}
+				if _, err := client.Show(cmd.Context(), &api.ShowRequest{Name: modelFlag}); err != nil {
+					return fmt.Errorf("model %q not found", modelFlag)
 				}
 			}
 
 			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 {
@@ -293,6 +482,8 @@ Examples:
 						}
 					}
 				}
+			} else if saved, err := loadIntegration(name); err == nil && len(saved.Models) > 0 && !configFlag {
+				return runIntegration(name, saved.Models[0], passArgs)
 			} else {
 				var err error
 				models, err = selectModels(cmd.Context(), name, "")
@@ -339,13 +530,13 @@ Examples:
 
 			if configFlag {
 				if launch, _ := confirmPrompt(fmt.Sprintf("\nLaunch %s now?", r)); launch {
-					return runIntegration(name, models[0])
+					return runIntegration(name, models[0], passArgs)
 				}
 				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])
+			return runIntegration(name, models[0], passArgs)
 		},
 	}
 
@@ -353,3 +544,163 @@ Examples:
 	cmd.Flags().BoolVar(&configFlag, "config", false, "Configure without launching")
 	return cmd
 }
+
+type modelInfo struct {
+	Name        string
+	Remote      bool
+	ToolCapable 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 strings.HasSuffix(rec.Name, ":cloud") {
+			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
+}
+
+// isCloudModel checks if a model is a cloud model using the Show API.
+func isCloudModel(ctx context.Context, client *api.Client, name string) bool {
+	if client == nil {
+		return false
+	}
+	resp, err := client.Show(ctx, &api.ShowRequest{Name: name})
+	if err != nil {
+		return false
+	}
+	return resp.RemoteModel != ""
+}
+
+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

@@ -1,10 +1,13 @@
 package config
 
 import (
+	"context"
+	"fmt"
 	"slices"
 	"strings"
 	"testing"
 
+	"github.com/google/go-cmp/cmp"
 	"github.com/spf13/cobra"
 )
 
@@ -90,8 +93,8 @@ func TestLaunchCmd(t *testing.T) {
 	cmd := LaunchCmd(mockCheck)
 
 	t.Run("command structure", func(t *testing.T) {
-		if cmd.Use != "launch [INTEGRATION]" {
-			t.Errorf("Use = %q, want %q", cmd.Use, "launch [INTEGRATION]")
+		if cmd.Use != "launch [INTEGRATION] [-- [EXTRA_ARGS...]]" {
+			t.Errorf("Use = %q, want %q", cmd.Use, "launch [INTEGRATION] [-- [EXTRA_ARGS...]]")
 		}
 		if cmd.Short == "" {
 			t.Error("Short description should not be empty")
@@ -121,7 +124,7 @@ func TestLaunchCmd(t *testing.T) {
 }
 
 func TestRunIntegration_UnknownIntegration(t *testing.T) {
-	err := runIntegration("unknown-integration", "model")
+	err := runIntegration("unknown-integration", "model", nil)
 	if err == nil {
 		t.Error("expected error for unknown integration, got nil")
 	}
@@ -174,15 +177,365 @@ 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")
 			}
-
-			// 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
+			var _ func(string, []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) {
+	// isCloudModel now only uses Show API, so nil client always returns false
+	t.Run("nil client returns false", func(t *testing.T) {
+		models := []string{"glm-4.7:cloud", "kimi-k2.5:cloud", "local-model"}
+		for _, model := range models {
+			if isCloudModel(context.Background(), nil, model) {
+				t.Errorf("isCloudModel(%q) with nil client should return false", model)
+			}
+		}
+	})
+}
+
+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")
+	}
+}
+
+func TestEditorIntegration_SavedConfigSkipsSelection(t *testing.T) {
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	// Save a config for opencode so it looks like a previous launch
+	if err := saveIntegration("opencode", []string{"llama3.2"}); err != nil {
+		t.Fatal(err)
+	}
+
+	// Verify loadIntegration returns the saved models
+	saved, err := loadIntegration("opencode")
+	if err != nil {
+		t.Fatal(err)
+	}
+	if len(saved.Models) == 0 {
+		t.Fatal("expected saved models")
+	}
+	if saved.Models[0] != "llama3.2" {
+		t.Errorf("expected llama3.2, got %s", saved.Models[0])
+	}
+}
+
+func TestAliasConfigurerInterface(t *testing.T) {
+	t.Run("claude implements AliasConfigurer", func(t *testing.T) {
+		claude := &Claude{}
+		if _, ok := interface{}(claude).(AliasConfigurer); !ok {
+			t.Error("Claude should implement AliasConfigurer")
+		}
+	})
+
+	t.Run("codex does not implement AliasConfigurer", func(t *testing.T) {
+		codex := &Codex{}
+		if _, ok := interface{}(codex).(AliasConfigurer); ok {
+			t.Error("Codex should not implement AliasConfigurer")
+		}
+	})
+}

cmd/config/openclaw.go

@@ -13,26 +13,44 @@ import (
 	"github.com/ollama/ollama/envconfig"
 )
 
-type Clawdbot struct{}
+type Openclaw struct{}
 
-func (c *Clawdbot) String() string { return "Clawdbot" }
+func (c *Openclaw) String() string { return "OpenClaw" }
 
-const ansiGreen = "\033[32m"
-
-func (c *Clawdbot) Run(model string) error {
-	if _, err := exec.LookPath("clawdbot"); err != nil {
-		return fmt.Errorf("clawdbot is not installed, install from https://docs.clawd.bot")
+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("clawdbot"); err == nil && len(config.Models) > 0 {
+	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)
 	}
 
-	cmd := exec.Command("clawdbot", "gateway")
+	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
@@ -42,22 +60,55 @@ func (c *Clawdbot) Run(model string) error {
 
 	err := cmd.Run()
 	if err != nil && strings.Contains(outputBuf.String(), "Gateway already running") {
-		fmt.Fprintf(os.Stderr, "%sClawdbot has been configured with Ollama. Gateway is already running.%s\n", ansiGreen, ansiReset)
+		fmt.Fprintf(os.Stderr, "%sOpenClaw has been configured with Ollama. Gateway is already running.%s\n", ansiGreen, ansiReset)
 		return nil
 	}
 	return err
 }
 
-func (c *Clawdbot) Paths() []string {
+// 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, ".clawdbot", "clawdbot.json")
+	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 *Clawdbot) Edit(models []string) error {
+func (c *Openclaw) Edit(models []string) error {
 	if len(models) == 0 {
 		return nil
 	}
@@ -67,7 +118,8 @@ func (c *Clawdbot) Edit(models []string) error {
 		return err
 	}
 
-	configPath := filepath.Join(home, ".clawdbot", "clawdbot.json")
+	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
 	}
@@ -76,6 +128,8 @@ func (c *Clawdbot) Edit(models []string) error {
 	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)
@@ -167,15 +221,18 @@ func (c *Clawdbot) Edit(models []string) error {
 	return writeWithBackup(configPath, data)
 }
 
-func (c *Clawdbot) Models() []string {
+func (c *Openclaw) Models() []string {
 	home, err := os.UserHomeDir()
 	if err != nil {
 		return nil
 	}
 
-	config, err := readJSONFile(filepath.Join(home, ".clawdbot", "clawdbot.json"))
+	config, err := readJSONFile(filepath.Join(home, ".openclaw", "openclaw.json"))
 	if err != nil {
-		return nil
+		config, err = readJSONFile(filepath.Join(home, ".clawdbot", "clawdbot.json"))
+		if err != nil {
+			return nil
+		}
 	}
 
 	modelsSection, _ := config["models"].(map[string]any)

cmd/config/openclaw_test.go

@@ -8,12 +8,12 @@ import (
 	"testing"
 )
 
-func TestClawdbotIntegration(t *testing.T) {
-	c := &Clawdbot{}
+func TestOpenclawIntegration(t *testing.T) {
+	c := &Openclaw{}
 
 	t.Run("String", func(t *testing.T) {
-		if got := c.String(); got != "Clawdbot" {
-			t.Errorf("String() = %q, want %q", got, "Clawdbot")
+		if got := c.String(); got != "OpenClaw" {
+			t.Errorf("String() = %q, want %q", got, "OpenClaw")
 		}
 	})
 
@@ -26,13 +26,13 @@ func TestClawdbotIntegration(t *testing.T) {
 	})
 }
 
-func TestClawdbotEdit(t *testing.T) {
-	c := &Clawdbot{}
+func TestOpenclawEdit(t *testing.T) {
+	c := &Openclaw{}
 	tmpDir := t.TempDir()
 	setTestHome(t, tmpDir)
 
-	configDir := filepath.Join(tmpDir, ".clawdbot")
-	configPath := filepath.Join(configDir, "clawdbot.json")
+	configDir := filepath.Join(tmpDir, ".openclaw")
+	configPath := filepath.Join(configDir, "openclaw.json")
 
 	cleanup := func() { os.RemoveAll(configDir) }
 
@@ -41,8 +41,8 @@ func TestClawdbotEdit(t *testing.T) {
 		if err := c.Edit([]string{"llama3.2"}); err != nil {
 			t.Fatal(err)
 		}
-		assertClawdbotModelExists(t, configPath, "llama3.2")
-		assertClawdbotPrimaryModel(t, configPath, "ollama/llama3.2")
+		assertOpenclawModelExists(t, configPath, "llama3.2")
+		assertOpenclawPrimaryModel(t, configPath, "ollama/llama3.2")
 	})
 
 	t.Run("multiple models - first is primary", func(t *testing.T) {
@@ -50,9 +50,9 @@ func TestClawdbotEdit(t *testing.T) {
 		if err := c.Edit([]string{"llama3.2", "mistral"}); err != nil {
 			t.Fatal(err)
 		}
-		assertClawdbotModelExists(t, configPath, "llama3.2")
-		assertClawdbotModelExists(t, configPath, "mistral")
-		assertClawdbotPrimaryModel(t, configPath, "ollama/llama3.2")
+		assertOpenclawModelExists(t, configPath, "llama3.2")
+		assertOpenclawModelExists(t, configPath, "mistral")
+		assertOpenclawPrimaryModel(t, configPath, "ollama/llama3.2")
 	})
 
 	t.Run("preserve other providers", func(t *testing.T) {
@@ -127,8 +127,8 @@ func TestClawdbotEdit(t *testing.T) {
 		c.Edit([]string{"llama3.2", "mistral"})
 		c.Edit([]string{"llama3.2"})
 
-		assertClawdbotModelExists(t, configPath, "llama3.2")
-		assertClawdbotModelNotExists(t, configPath, "mistral")
+		assertOpenclawModelExists(t, configPath, "llama3.2")
+		assertOpenclawModelNotExists(t, configPath, "mistral")
 	})
 
 	t.Run("empty models is no-op", func(t *testing.T) {
@@ -169,12 +169,12 @@ func TestClawdbotEdit(t *testing.T) {
 		if err := c.Edit([]string{"llama3.2"}); err != nil {
 			t.Fatal(err)
 		}
-		assertClawdbotModelExists(t, configPath, "llama3.2")
+		assertOpenclawModelExists(t, configPath, "llama3.2")
 	})
 }
 
-func TestClawdbotModels(t *testing.T) {
-	c := &Clawdbot{}
+func TestOpenclawModels(t *testing.T) {
+	c := &Openclaw{}
 	tmpDir := t.TempDir()
 	setTestHome(t, tmpDir)
 
@@ -185,9 +185,9 @@ func TestClawdbotModels(t *testing.T) {
 	})
 
 	t.Run("returns all ollama models", func(t *testing.T) {
-		configDir := filepath.Join(tmpDir, ".clawdbot")
+		configDir := filepath.Join(tmpDir, ".openclaw")
 		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(filepath.Join(configDir, "clawdbot.json"), []byte(`{
+		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{
 			"models":{"providers":{"ollama":{"models":[
 				{"id":"llama3.2"},
 				{"id":"mistral"}
@@ -202,7 +202,7 @@ func TestClawdbotModels(t *testing.T) {
 }
 
 // Helper functions
-func assertClawdbotModelExists(t *testing.T, path, model string) {
+func assertOpenclawModelExists(t *testing.T, path, model string) {
 	t.Helper()
 	data, _ := os.ReadFile(path)
 	var cfg map[string]any
@@ -221,7 +221,7 @@ func assertClawdbotModelExists(t *testing.T, path, model string) {
 	t.Errorf("model %s not found", model)
 }
 
-func assertClawdbotModelNotExists(t *testing.T, path, model string) {
+func assertOpenclawModelNotExists(t *testing.T, path, model string) {
 	t.Helper()
 	data, _ := os.ReadFile(path)
 	var cfg map[string]any
@@ -239,7 +239,7 @@ func assertClawdbotModelNotExists(t *testing.T, path, model string) {
 	}
 }
 
-func assertClawdbotPrimaryModel(t *testing.T, path, expected string) {
+func assertOpenclawPrimaryModel(t *testing.T, path, expected string) {
 	t.Helper()
 	data, _ := os.ReadFile(path)
 	var cfg map[string]any
@@ -252,15 +252,15 @@ func assertClawdbotPrimaryModel(t *testing.T, path, expected string) {
 	}
 }
 
-func TestClawdbotPaths(t *testing.T) {
-	c := &Clawdbot{}
+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, ".clawdbot")
+		configDir := filepath.Join(tmpDir, ".openclaw")
 		os.MkdirAll(configDir, 0o755)
-		os.WriteFile(filepath.Join(configDir, "clawdbot.json"), []byte(`{}`), 0o644)
+		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{}`), 0o644)
 
 		paths := c.Paths()
 		if len(paths) != 1 {
@@ -277,12 +277,12 @@ func TestClawdbotPaths(t *testing.T) {
 	})
 }
 
-func TestClawdbotModelsEdgeCases(t *testing.T) {
-	c := &Clawdbot{}
+func TestOpenclawModelsEdgeCases(t *testing.T) {
+	c := &Openclaw{}
 	tmpDir := t.TempDir()
 	setTestHome(t, tmpDir)
-	configDir := filepath.Join(tmpDir, ".clawdbot")
-	configPath := filepath.Join(configDir, "clawdbot.json")
+	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) {
@@ -340,11 +340,11 @@ func TestClawdbotModelsEdgeCases(t *testing.T) {
 	})
 }
 
-func TestClawdbotEditSchemaFields(t *testing.T) {
-	c := &Clawdbot{}
+func TestOpenclawEditSchemaFields(t *testing.T) {
+	c := &Openclaw{}
 	tmpDir := t.TempDir()
 	setTestHome(t, tmpDir)
-	configPath := filepath.Join(tmpDir, ".clawdbot", "clawdbot.json")
+	configPath := filepath.Join(tmpDir, ".openclaw", "openclaw.json")
 
 	if err := c.Edit([]string{"llama3.2"}); err != nil {
 		t.Fatal(err)
@@ -381,20 +381,20 @@ func TestClawdbotEditSchemaFields(t *testing.T) {
 	}
 }
 
-func TestClawdbotEditModelNames(t *testing.T) {
-	c := &Clawdbot{}
+func TestOpenclawEditModelNames(t *testing.T) {
+	c := &Openclaw{}
 	tmpDir := t.TempDir()
 	setTestHome(t, tmpDir)
-	configPath := filepath.Join(tmpDir, ".clawdbot", "clawdbot.json")
-	cleanup := func() { os.RemoveAll(filepath.Join(tmpDir, ".clawdbot")) }
+	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)
 		}
-		assertClawdbotModelExists(t, configPath, "llama3.2:70b")
-		assertClawdbotPrimaryModel(t, configPath, "ollama/llama3.2:70b")
+		assertOpenclawModelExists(t, configPath, "llama3.2:70b")
+		assertOpenclawPrimaryModel(t, configPath, "ollama/llama3.2:70b")
 	})
 
 	t.Run("model with slash", func(t *testing.T) {
@@ -402,8 +402,8 @@ func TestClawdbotEditModelNames(t *testing.T) {
 		if err := c.Edit([]string{"library/model:tag"}); err != nil {
 			t.Fatal(err)
 		}
-		assertClawdbotModelExists(t, configPath, "library/model:tag")
-		assertClawdbotPrimaryModel(t, configPath, "ollama/library/model:tag")
+		assertOpenclawModelExists(t, configPath, "library/model:tag")
+		assertOpenclawPrimaryModel(t, configPath, "ollama/library/model:tag")
 	})
 
 	t.Run("model with hyphen", func(t *testing.T) {
@@ -411,16 +411,16 @@ func TestClawdbotEditModelNames(t *testing.T) {
 		if err := c.Edit([]string{"test-model"}); err != nil {
 			t.Fatal(err)
 		}
-		assertClawdbotModelExists(t, configPath, "test-model")
+		assertOpenclawModelExists(t, configPath, "test-model")
 	})
 }
 
-func TestClawdbotEditAgentsPreservation(t *testing.T) {
-	c := &Clawdbot{}
+func TestOpenclawEditAgentsPreservation(t *testing.T) {
+	c := &Openclaw{}
 	tmpDir := t.TempDir()
 	setTestHome(t, tmpDir)
-	configDir := filepath.Join(tmpDir, ".clawdbot")
-	configPath := filepath.Join(configDir, "clawdbot.json")
+	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) {
@@ -457,7 +457,7 @@ func TestClawdbotEditAgentsPreservation(t *testing.T) {
 	})
 }
 
-const testClawdbotFixture = `{
+const testOpenclawFixture = `{
   "theme": "dark",
   "mcp": {"servers": {"custom": {"enabled": true}}},
   "models": {
@@ -475,15 +475,15 @@ const testClawdbotFixture = `{
   }
 }`
 
-func TestClawdbotEdit_RoundTrip(t *testing.T) {
-	c := &Clawdbot{}
+func TestOpenclawEdit_RoundTrip(t *testing.T) {
+	c := &Openclaw{}
 	tmpDir := t.TempDir()
 	setTestHome(t, tmpDir)
-	configDir := filepath.Join(tmpDir, ".clawdbot")
-	configPath := filepath.Join(configDir, "clawdbot.json")
+	configDir := filepath.Join(tmpDir, ".openclaw")
+	configPath := filepath.Join(configDir, "openclaw.json")
 
 	os.MkdirAll(configDir, 0o755)
-	os.WriteFile(configPath, []byte(testClawdbotFixture), 0o644)
+	os.WriteFile(configPath, []byte(testOpenclawFixture), 0o644)
 
 	if err := c.Edit([]string{"llama3.2", "mistral"}); err != nil {
 		t.Fatal(err)
@@ -521,15 +521,15 @@ func TestClawdbotEdit_RoundTrip(t *testing.T) {
 	}
 }
 
-func TestClawdbotEdit_Idempotent(t *testing.T) {
-	c := &Clawdbot{}
+func TestOpenclawEdit_Idempotent(t *testing.T) {
+	c := &Openclaw{}
 	tmpDir := t.TempDir()
 	setTestHome(t, tmpDir)
-	configDir := filepath.Join(tmpDir, ".clawdbot")
-	configPath := filepath.Join(configDir, "clawdbot.json")
+	configDir := filepath.Join(tmpDir, ".openclaw")
+	configPath := filepath.Join(configDir, "openclaw.json")
 
 	os.MkdirAll(configDir, 0o755)
-	os.WriteFile(configPath, []byte(testClawdbotFixture), 0o644)
+	os.WriteFile(configPath, []byte(testOpenclawFixture), 0o644)
 
 	c.Edit([]string{"llama3.2", "mistral"})
 	firstData, _ := os.ReadFile(configPath)
@@ -542,15 +542,15 @@ func TestClawdbotEdit_Idempotent(t *testing.T) {
 	}
 }
 
-func TestClawdbotEdit_MultipleConsecutiveEdits(t *testing.T) {
-	c := &Clawdbot{}
+func TestOpenclawEdit_MultipleConsecutiveEdits(t *testing.T) {
+	c := &Openclaw{}
 	tmpDir := t.TempDir()
 	setTestHome(t, tmpDir)
-	configDir := filepath.Join(tmpDir, ".clawdbot")
-	configPath := filepath.Join(configDir, "clawdbot.json")
+	configDir := filepath.Join(tmpDir, ".openclaw")
+	configPath := filepath.Join(configDir, "openclaw.json")
 
 	os.MkdirAll(configDir, 0o755)
-	os.WriteFile(configPath, []byte(testClawdbotFixture), 0o644)
+	os.WriteFile(configPath, []byte(testOpenclawFixture), 0o644)
 
 	for i := range 10 {
 		models := []string{"model-a", "model-b"}
@@ -573,12 +573,12 @@ func TestClawdbotEdit_MultipleConsecutiveEdits(t *testing.T) {
 	}
 }
 
-func TestClawdbotEdit_BackupCreated(t *testing.T) {
-	c := &Clawdbot{}
+func TestOpenclawEdit_BackupCreated(t *testing.T) {
+	c := &Openclaw{}
 	tmpDir := t.TempDir()
 	setTestHome(t, tmpDir)
-	configDir := filepath.Join(tmpDir, ".clawdbot")
-	configPath := filepath.Join(configDir, "clawdbot.json")
+	configDir := filepath.Join(tmpDir, ".openclaw")
+	configPath := filepath.Join(configDir, "openclaw.json")
 	backupDir := filepath.Join(os.TempDir(), "ollama-backups")
 
 	os.MkdirAll(configDir, 0o755)
@@ -590,7 +590,7 @@ func TestClawdbotEdit_BackupCreated(t *testing.T) {
 		t.Fatal(err)
 	}
 
-	backups, _ := filepath.Glob(filepath.Join(backupDir, "clawdbot.json.*"))
+	backups, _ := filepath.Glob(filepath.Join(backupDir, "openclaw.json.*"))
 	foundBackup := false
 	for _, backup := range backups {
 		data, _ := os.ReadFile(backup)
@@ -605,11 +605,151 @@ func TestClawdbotEdit_BackupCreated(t *testing.T) {
 	}
 }
 
-func TestClawdbotEdit_CreatesDirectoryIfMissing(t *testing.T) {
-	c := &Clawdbot{}
+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, ".clawdbot")
+	configDir := filepath.Join(tmpDir, ".openclaw")
 
 	if _, err := os.Stat(configDir); !os.IsNotExist(err) {
 		t.Fatal("directory should not exist before test")
@@ -623,3 +763,116 @@ func TestClawdbotEdit_CreatesDirectoryIfMissing(t *testing.T) {
 		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

@@ -1,6 +1,7 @@
 package config
 
 import (
+	"context"
 	"encoding/json"
 	"fmt"
 	"maps"
@@ -10,15 +11,55 @@ import (
 	"slices"
 	"strings"
 
+	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
 )
 
 // OpenCode implements Runner and Editor for OpenCode integration
 type OpenCode struct{}
 
+// cloudModelLimit holds context and output token limits for a cloud model.
+type cloudModelLimit struct {
+	Context int
+	Output  int
+}
+
+// cloudModelLimits maps cloud model base names to their token limits.
+// TODO(parthsareen): grab context/output limits from model info instead of hardcoding
+var cloudModelLimits = map[string]cloudModelLimit{
+	"cogito-2.1:671b":     {Context: 163_840, Output: 65_536},
+	"deepseek-v3.1:671b":  {Context: 163_840, Output: 163_840},
+	"deepseek-v3.2":       {Context: 163_840, Output: 65_536},
+	"glm-4.6":             {Context: 202_752, Output: 131_072},
+	"glm-4.7":             {Context: 202_752, Output: 131_072},
+	"gpt-oss:120b":        {Context: 131_072, Output: 131_072},
+	"gpt-oss:20b":         {Context: 131_072, Output: 131_072},
+	"kimi-k2:1t":          {Context: 262_144, Output: 262_144},
+	"kimi-k2.5":           {Context: 262_144, Output: 262_144},
+	"kimi-k2-thinking":    {Context: 262_144, Output: 262_144},
+	"nemotron-3-nano:30b": {Context: 1_048_576, Output: 131_072},
+	"qwen3-coder:480b":    {Context: 262_144, Output: 65_536},
+	"qwen3-next:80b":      {Context: 262_144, Output: 32_768},
+}
+
+// lookupCloudModelLimit returns the token limits for a cloud model.
+// It tries the exact name first, then strips the ":cloud" suffix.
+func lookupCloudModelLimit(name string) (cloudModelLimit, bool) {
+	if l, ok := cloudModelLimits[name]; ok {
+		return l, true
+	}
+	base := strings.TrimSuffix(name, ":cloud")
+	if base != name {
+		if l, ok := cloudModelLimits[base]; ok {
+			return l, true
+		}
+	}
+	return cloudModelLimit{}, false
+}
+
 func (o *OpenCode) String() string { return "OpenCode" }
 
-func (o *OpenCode) Run(model string) error {
+func (o *OpenCode) Run(model string, args []string) error {
 	if _, err := exec.LookPath("opencode"); err != nil {
 		return fmt.Errorf("opencode is not installed, install from https://opencode.ai")
 	}
@@ -32,7 +73,7 @@ func (o *OpenCode) Run(model string) error {
 		return fmt.Errorf("setup failed: %w", err)
 	}
 
-	cmd := exec.Command("opencode")
+	cmd := exec.Command("opencode", args...)
 	cmd.Stdin = os.Stdin
 	cmd.Stdout = os.Stdout
 	cmd.Stderr = os.Stderr
@@ -113,6 +154,8 @@ func (o *OpenCode) Edit(modelList []string) error {
 		}
 	}
 
+	client, _ := api.ClientFromEnvironment()
+
 	for _, model := range modelList {
 		if existing, ok := models[model].(map[string]any); ok {
 			// migrate existing models without _launch marker
@@ -122,12 +165,29 @@ func (o *OpenCode) Edit(modelList []string) error {
 					existing["name"] = strings.TrimSuffix(name, " [Ollama]")
 				}
 			}
+			if isCloudModel(context.Background(), client, model) {
+				if l, ok := lookupCloudModelLimit(model); ok {
+					existing["limit"] = map[string]any{
+						"context": l.Context,
+						"output":  l.Output,
+					}
+				}
+			}
 			continue
 		}
-		models[model] = map[string]any{
+		entry := map[string]any{
 			"name":    model,
 			"_launch": true,
 		}
+		if isCloudModel(context.Background(), client, model) {
+			if l, ok := lookupCloudModelLimit(model); ok {
+				entry["limit"] = map[string]any{
+					"context": l.Context,
+					"output":  l.Output,
+				}
+			}
+		}
+		models[model] = entry
 	}
 
 	ollama["models"] = models

cmd/config/opencode_test.go

@@ -2,6 +2,7 @@ package config
 
 import (
 	"encoding/json"
+	"fmt"
 	"os"
 	"path/filepath"
 	"testing"
@@ -495,6 +496,165 @@ func TestOpenCodeEdit_SpecialCharsInModelName(t *testing.T) {
 	}
 }
 
+func readOpenCodeModel(t *testing.T, configPath, model string) map[string]any {
+	t.Helper()
+	data, err := os.ReadFile(configPath)
+	if err != nil {
+		t.Fatal(err)
+	}
+	var cfg map[string]any
+	json.Unmarshal(data, &cfg)
+	provider := cfg["provider"].(map[string]any)
+	ollama := provider["ollama"].(map[string]any)
+	models := ollama["models"].(map[string]any)
+	entry, ok := models[model].(map[string]any)
+	if !ok {
+		t.Fatalf("model %s not found in config", model)
+	}
+	return entry
+}
+
+func TestOpenCodeEdit_LocalModelNoLimit(t *testing.T) {
+	o := &OpenCode{}
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	configPath := filepath.Join(tmpDir, ".config", "opencode", "opencode.json")
+
+	if err := o.Edit([]string{"llama3.2"}); err != nil {
+		t.Fatal(err)
+	}
+
+	entry := readOpenCodeModel(t, configPath, "llama3.2")
+	if entry["limit"] != nil {
+		t.Errorf("local model should not have limit set, got %v", entry["limit"])
+	}
+}
+
+func TestOpenCodeEdit_PreservesUserLimit(t *testing.T) {
+	o := &OpenCode{}
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	configDir := filepath.Join(tmpDir, ".config", "opencode")
+	configPath := filepath.Join(configDir, "opencode.json")
+
+	// Set up a model with a user-configured limit
+	os.MkdirAll(configDir, 0o755)
+	os.WriteFile(configPath, []byte(`{
+		"provider": {
+			"ollama": {
+				"models": {
+					"llama3.2": {
+						"name": "llama3.2",
+						"_launch": true,
+						"limit": {"context": 8192, "output": 4096}
+					}
+				}
+			}
+		}
+	}`), 0o644)
+
+	// Re-edit should preserve the user's limit (not delete it)
+	if err := o.Edit([]string{"llama3.2"}); err != nil {
+		t.Fatal(err)
+	}
+
+	entry := readOpenCodeModel(t, configPath, "llama3.2")
+	limit, ok := entry["limit"].(map[string]any)
+	if !ok {
+		t.Fatal("user-configured limit was removed")
+	}
+	if limit["context"] != float64(8192) {
+		t.Errorf("context limit changed: got %v, want 8192", limit["context"])
+	}
+	if limit["output"] != float64(4096) {
+		t.Errorf("output limit changed: got %v, want 4096", limit["output"])
+	}
+}
+
+func TestOpenCodeEdit_CloudModelLimitStructure(t *testing.T) {
+	// Verify that when a cloud model entry has limits set (as Edit would do),
+	// the structure matches what opencode expects and re-edit preserves them.
+	o := &OpenCode{}
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	configDir := filepath.Join(tmpDir, ".config", "opencode")
+	configPath := filepath.Join(configDir, "opencode.json")
+
+	expected := cloudModelLimits["glm-4.7"]
+
+	// Simulate a cloud model that already has the limit set by a previous Edit
+	os.MkdirAll(configDir, 0o755)
+	os.WriteFile(configPath, []byte(fmt.Sprintf(`{
+		"provider": {
+			"ollama": {
+				"models": {
+					"glm-4.7:cloud": {
+						"name": "glm-4.7:cloud",
+						"_launch": true,
+						"limit": {"context": %d, "output": %d}
+					}
+				}
+			}
+		}
+	}`, expected.Context, expected.Output)), 0o644)
+
+	// Re-edit should preserve the cloud model limit
+	if err := o.Edit([]string{"glm-4.7:cloud"}); err != nil {
+		t.Fatal(err)
+	}
+
+	entry := readOpenCodeModel(t, configPath, "glm-4.7:cloud")
+	limit, ok := entry["limit"].(map[string]any)
+	if !ok {
+		t.Fatal("cloud model limit was removed on re-edit")
+	}
+	if limit["context"] != float64(expected.Context) {
+		t.Errorf("context = %v, want %d", limit["context"], expected.Context)
+	}
+	if limit["output"] != float64(expected.Output) {
+		t.Errorf("output = %v, want %d", limit["output"], expected.Output)
+	}
+}
+
+func TestLookupCloudModelLimit(t *testing.T) {
+	tests := []struct {
+		name        string
+		wantOK      bool
+		wantContext int
+		wantOutput  int
+	}{
+		{"glm-4.7", true, 202_752, 131_072},
+		{"glm-4.7:cloud", true, 202_752, 131_072},
+		{"kimi-k2.5", true, 262_144, 262_144},
+		{"kimi-k2.5:cloud", true, 262_144, 262_144},
+		{"deepseek-v3.2", true, 163_840, 65_536},
+		{"deepseek-v3.2:cloud", true, 163_840, 65_536},
+		{"qwen3-coder:480b", true, 262_144, 65_536},
+		{"llama3.2", false, 0, 0},
+		{"unknown-model:cloud", false, 0, 0},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			l, ok := lookupCloudModelLimit(tt.name)
+			if ok != tt.wantOK {
+				t.Errorf("lookupCloudModelLimit(%q) ok = %v, want %v", tt.name, ok, tt.wantOK)
+			}
+			if ok {
+				if l.Context != tt.wantContext {
+					t.Errorf("context = %d, want %d", l.Context, tt.wantContext)
+				}
+				if l.Output != tt.wantOutput {
+					t.Errorf("output = %d, want %d", l.Output, tt.wantOutput)
+				}
+			}
+		})
+	}
+}
+
 func TestOpenCodeModels_NoConfig(t *testing.T) {
 	o := &OpenCode{}
 	tmpDir := t.TempDir()

cmd/config/selector.go

@@ -17,6 +17,7 @@ const (
 	ansiBold       = "\033[1m"
 	ansiReset      = "\033[0m"
 	ansiGray       = "\033[37m"
+	ansiGreen      = "\033[32m"
 	ansiClearDown  = "\033[J"
 )
 
@@ -275,7 +276,11 @@ func parseInput(r io.Reader) (inputEvent, byte, error) {
 func renderSelect(w io.Writer, prompt string, s *selectState) int {
 	filtered := s.filtered()
 
-	fmt.Fprintf(w, "%s %s\r\n", prompt, s.filter)
+	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)
+	}
 	lineCount := 1
 
 	if len(filtered) == 0 {
@@ -314,7 +319,11 @@ func renderSelect(w io.Writer, prompt string, s *selectState) int {
 func renderMultiSelect(w io.Writer, prompt string, s *multiSelectState) int {
 	filtered := s.filtered()
 
-	fmt.Fprintf(w, "%s %s\r\n", prompt, s.filter)
+	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)
+	}
 	lineCount := 1
 
 	if len(filtered) == 0 {
@@ -345,10 +354,15 @@ 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\r\n", ansiBold, prefix, checkbox, item.Name, ansiReset, suffix)
+				fmt.Fprintf(w, "  %s%s %s %s%s%s%s\r\n", ansiBold, prefix, checkbox, item.Name, ansiReset, desc, suffix)
 			} else {
-				fmt.Fprintf(w, "  %s %s %s%s\r\n", prefix, checkbox, item.Name, suffix)
+				fmt.Fprintf(w, "  %s %s %s%s%s\r\n", prefix, checkbox, item.Name, desc, suffix)
 			}
 			lineCount++
 		}

cmd/config/selector_test.go

@@ -96,6 +96,14 @@ func TestSelectState(t *testing.T) {
 		}
 	})
 
+	t.Run("Enter_EmptyFilteredList_EmptyFilter_DoesNothing", func(t *testing.T) {
+		s := newSelectState([]selectItem{})
+		done, result, err := s.handleInput(eventEnter, 0)
+		if done || result != "" || err != nil {
+			t.Errorf("expected (false, '', nil), got (%v, %v, %v)", done, result, err)
+		}
+	})
+
 	t.Run("Escape_ReturnsCancelledError", func(t *testing.T) {
 		s := newSelectState(items)
 		done, result, err := s.handleInput(eventEscape, 0)
@@ -574,8 +582,19 @@ func TestRenderSelect(t *testing.T) {
 		var buf bytes.Buffer
 		renderSelect(&buf, "Select:", s)
 
+		output := buf.String()
+		if !strings.Contains(output, "no matches") {
+			t.Errorf("expected 'no matches' message, got: %s", output)
+		}
+	})
+
+	t.Run("EmptyFilteredList_EmptyFilter_ShowsNoMatches", func(t *testing.T) {
+		s := newSelectState([]selectItem{})
+		var buf bytes.Buffer
+		renderSelect(&buf, "Select:", s)
+
 		if !strings.Contains(buf.String(), "no matches") {
-			t.Error("expected 'no matches' message")
+			t.Error("expected 'no matches' message for empty list with no filter")
 		}
 	})
 

cmd/start_darwin.go

@@ -10,19 +10,21 @@ import (
 	"github.com/ollama/ollama/api"
 )
 
+var errNotRunning = errors.New("could not connect to ollama server, run 'ollama serve' to start it")
+
 func startApp(ctx context.Context, client *api.Client) error {
 	exe, err := os.Executable()
 	if err != nil {
-		return err
+		return errNotRunning
 	}
 	link, err := os.Readlink(exe)
 	if err != nil {
-		return err
+		return errNotRunning
 	}
 	r := regexp.MustCompile(`^.*/Ollama\s?\d*.app`)
 	m := r.FindStringSubmatch(link)
 	if len(m) != 1 {
-		return errors.New("could not find ollama app")
+		return errNotRunning
 	}
 	if err := exec.Command("/usr/bin/open", "-j", "-a", m[0], "--args", "--fast-startup").Run(); err != nil {
 		return err

convert/convert.go

@@ -313,8 +313,12 @@ 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

@@ -0,0 +1,455 @@
+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

@@ -0,0 +1,512 @@
+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

@@ -41,6 +41,7 @@ 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

@@ -99,6 +99,8 @@ 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

@@ -71,6 +71,10 @@
     {
       "source": "/api",
       "destination": "/api/introduction"
+    },
+    {
+      "source": "/integrations/clawdbot",
+      "destination": "/integrations/openclaw"
     }
   ],
   "navigation": {
@@ -102,8 +106,8 @@
             "group": "Integrations",
             "pages": [
               "/integrations/claude-code",
-              "/integrations/clawdbot",
               "/integrations/cline",
+              "/integrations/openclaw",
               "/integrations/codex",
               "/integrations/droid",
               "/integrations/goose",

docs/gpu.mdx

@@ -10,6 +10,7 @@ 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`                                                                                                                  |
@@ -163,4 +164,4 @@ 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

@@ -134,22 +134,12 @@ 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

@@ -1,41 +1,43 @@
 ---
-title: Clawdbot
+title: OpenClaw
 ---
 
-Clawdbot 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.
+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 [Clawdbot](https://clawd.bot/) 
+Install [OpenClaw](https://openclaw.ai/) 
 
 ```bash
-npm install -g clawdbot@latest
+npm install -g openclaw@latest
 ```
 
 Then run the onboarding wizard:
 
 ```bash
-clawdbot onboard --install-daemon
+openclaw onboard --install-daemon
 ```
 
-<Note>Clawdbot 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>
+<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 clawdbot
+ollama launch openclaw
 ```
 
-This configures Clawdbot to use Ollama and starts the gateway.
+<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 clawdbot --config
+ollama launch openclaw --config
 ```
 
 ## Recommended Models

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.sh | bash
+curl -fsSL https://opencode.ai/install | 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", 4096)
+	ContextLength = Uint("OLLAMA_CONTEXT_LENGTH", 0)
 	// 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: 4096)"},
+		"OLLAMA_CONTEXT_LENGTH":    {"OLLAMA_CONTEXT_LENGTH", ContextLength(), "Context length to use unless otherwise specified (default: 4k/32k/256k based on VRAM)"},
 		"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{
-		"":     4096,
+		"":     0,
 		"2048": 2048,
 	}
 

fs/ggml/ggml.go

@@ -268,8 +268,10 @@ func (kv KV) OllamaEngineRequired() bool {
 		"olmo3",
 		"qwen25vl",
 		"qwen3", "qwen3moe",
+		"qwen3next",
 		"qwen3vl", "qwen3vlmoe",
 		"glm4moelite",
+		"glmocr",
 		"lfm2",
 	}, kv.Architecture())
 }
@@ -859,11 +861,13 @@ 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

@@ -13,7 +13,7 @@ require (
 	github.com/mattn/go-sqlite3 v1.14.24
 	github.com/olekukonko/tablewriter v0.0.5
 	github.com/spf13/cobra v1.7.0
-	github.com/stretchr/testify v1.9.0
+	github.com/stretchr/testify v1.10.0
 	github.com/x448/float16 v0.8.4
 	golang.org/x/sync v0.17.0
 	golang.org/x/sys v0.37.0
@@ -27,7 +27,10 @@ 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/tree-sitter/go-tree-sitter v0.25.0
+	github.com/tree-sitter/tree-sitter-cpp v0.23.4
 	github.com/wk8/go-ordered-map/v2 v2.1.8
 	golang.org/x/image v0.22.0
 	golang.org/x/mod v0.30.0
@@ -49,6 +52,7 @@ require (
 	github.com/google/flatbuffers v24.3.25+incompatible // indirect
 	github.com/kr/text v0.2.0 // indirect
 	github.com/mailru/easyjson v0.7.7 // indirect
+	github.com/mattn/go-pointer v0.0.1 // indirect
 	github.com/pkg/errors v0.9.1 // indirect
 	github.com/pmezard/go-difflib v1.0.0 // indirect
 	github.com/rivo/uniseg v0.2.0 // indirect

go.sum

@@ -152,6 +152,8 @@ github.com/mailru/easyjson v0.7.7 h1:UGYAvKxe3sBsEDzO8ZeWOSlIQfWFlxbzLZe7hwFURr0
 github.com/mailru/easyjson v0.7.7/go.mod h1:xzfreul335JAWq5oZzymOObrkdz5UnU4kGfJJLY9Nlc=
 github.com/mattn/go-isatty v0.0.20 h1:xfD0iDuEKnDkl03q4limB+vH+GxLEtL/jb4xVJSWWEY=
 github.com/mattn/go-isatty v0.0.20/go.mod h1:W+V8PltTTMOvKvAeJH7IuucS94S2C6jfK/D7dTCTo3Y=
+github.com/mattn/go-pointer v0.0.1 h1:n+XhsuGeVO6MEAp7xyEukFINEa+Quek5psIR/ylA6o0=
+github.com/mattn/go-pointer v0.0.1/go.mod h1:2zXcozF6qYGgmsG+SeTZz3oAbFLdD3OWqnUbNvJZAlc=
 github.com/mattn/go-runewidth v0.0.9/go.mod h1:H031xJmbD/WCDINGzjvQ9THkh0rPKHF+m2gUSrubnMI=
 github.com/mattn/go-runewidth v0.0.14 h1:+xnbZSEeDbOIg5/mE6JF0w6n9duR1l3/WmbinWVwUuU=
 github.com/mattn/go-runewidth v0.0.14/go.mod h1:Jdepj2loyihRzMpdS35Xk/zdY8IAYHsh153qUoGf23w=
@@ -174,6 +176,8 @@ 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=
@@ -204,12 +208,39 @@ github.com/stretchr/testify v1.7.1/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/
 github.com/stretchr/testify v1.8.0/go.mod h1:yNjHg4UonilssWZ8iaSj1OCr/vHnekPRkoO+kdMU+MU=
 github.com/stretchr/testify v1.8.1/go.mod h1:w2LPCIKwWwSfY2zedu0+kehJoqGctiVI29o6fzry7u4=
 github.com/stretchr/testify v1.8.4/go.mod h1:sz/lmYIOXD/1dqDmKjjqLyZ2RngseejIcXlSw2iwfAo=
-github.com/stretchr/testify v1.9.0 h1:HtqpIVDClZ4nwg75+f6Lvsy/wHu+3BoSGCbBAcpTsTg=
 github.com/stretchr/testify v1.9.0/go.mod h1:r2ic/lqez/lEtzL7wO/rwa5dbSLXVDPFyf8C91i36aY=
+github.com/stretchr/testify v1.10.0 h1:Xv5erBjTwe/5IxqUQTdXv5kgmIvbHo3QQyRwhJsOfJA=
+github.com/stretchr/testify v1.10.0/go.mod h1:r2ic/lqez/lEtzL7wO/rwa5dbSLXVDPFyf8C91i36aY=
 github.com/tkrajina/go-reflector v0.5.5 h1:gwoQFNye30Kk7NrExj8zm3zFtrGPqOkzFMLuQZg1DtQ=
 github.com/tkrajina/go-reflector v0.5.5/go.mod h1:ECbqLgccecY5kPmPmXg1MrHW585yMcDkVl6IvJe64T4=
 github.com/tkrajina/typescriptify-golang-structs v0.2.0 h1:ZedWk82egydDspGTryAatbX0/1NZDQbdiZLoCbOk4f8=
 github.com/tkrajina/typescriptify-golang-structs v0.2.0/go.mod h1:sjU00nti/PMEOZb07KljFlR+lJ+RotsC0GBQMv9EKls=
+github.com/tree-sitter/go-tree-sitter v0.25.0 h1:sx6kcg8raRFCvc9BnXglke6axya12krCJF5xJ2sftRU=
+github.com/tree-sitter/go-tree-sitter v0.25.0/go.mod h1:r77ig7BikoZhHrrsjAnv8RqGti5rtSyvDHPzgTPsUuU=
+github.com/tree-sitter/tree-sitter-c v0.23.4 h1:nBPH3FV07DzAD7p0GfNvXM+Y7pNIoPenQWBpvM++t4c=
+github.com/tree-sitter/tree-sitter-c v0.23.4/go.mod h1:MkI5dOiIpeN94LNjeCp8ljXN/953JCwAby4bClMr6bw=
+github.com/tree-sitter/tree-sitter-cpp v0.23.4 h1:LaWZsiqQKvR65yHgKmnaqA+uz6tlDJTJFCyFIeZU/8w=
+github.com/tree-sitter/tree-sitter-cpp v0.23.4/go.mod h1:doqNW64BriC7WBCQ1klf0KmJpdEvfxyXtoEybnBo6v8=
+github.com/tree-sitter/tree-sitter-embedded-template v0.23.2 h1:nFkkH6Sbe56EXLmZBqHHcamTpmz3TId97I16EnGy4rg=
+github.com/tree-sitter/tree-sitter-embedded-template v0.23.2/go.mod h1:HNPOhN0qF3hWluYLdxWs5WbzP/iE4aaRVPMsdxuzIaQ=
+github.com/tree-sitter/tree-sitter-go v0.23.4 h1:yt5KMGnTHS+86pJmLIAZMWxukr8W7Ae1STPvQUuNROA=
+github.com/tree-sitter/tree-sitter-go v0.23.4/go.mod h1:Jrx8QqYN0v7npv1fJRH1AznddllYiCMUChtVjxPK040=
+github.com/tree-sitter/tree-sitter-html v0.23.2 h1:1UYDV+Yd05GGRhVnTcbP58GkKLSHHZwVaN+lBZV11Lc=
+github.com/tree-sitter/tree-sitter-html v0.23.2/go.mod h1:gpUv/dG3Xl/eebqgeYeFMt+JLOY9cgFinb/Nw08a9og=
+github.com/tree-sitter/tree-sitter-java v0.23.5 h1:J9YeMGMwXYlKSP3K4Us8CitC6hjtMjqpeOf2GGo6tig=
+github.com/tree-sitter/tree-sitter-java v0.23.5/go.mod h1:NRKlI8+EznxA7t1Yt3xtraPk1Wzqh3GAIC46wxvc320=
+github.com/tree-sitter/tree-sitter-javascript v0.23.1 h1:1fWupaRC0ArlHJ/QJzsfQ3Ibyopw7ZfQK4xXc40Zveo=
+github.com/tree-sitter/tree-sitter-javascript v0.23.1/go.mod h1:lmGD1EJdCA+v0S1u2fFgepMg/opzSg/4pgFym2FPGAs=
+github.com/tree-sitter/tree-sitter-json v0.24.8 h1:tV5rMkihgtiOe14a9LHfDY5kzTl5GNUYe6carZBn0fQ=
+github.com/tree-sitter/tree-sitter-json v0.24.8/go.mod h1:F351KK0KGvCaYbZ5zxwx/gWWvZhIDl0eMtn+1r+gQbo=
+github.com/tree-sitter/tree-sitter-php v0.23.11 h1:iHewsLNDmznh8kgGyfWfujsZxIz1YGbSd2ZTEM0ZiP8=
+github.com/tree-sitter/tree-sitter-php v0.23.11/go.mod h1:T/kbfi+UcCywQfUNAJnGTN/fMSUjnwPXA8k4yoIks74=
+github.com/tree-sitter/tree-sitter-python v0.23.6 h1:qHnWFR5WhtMQpxBZRwiaU5Hk/29vGju6CVtmvu5Haas=
+github.com/tree-sitter/tree-sitter-python v0.23.6/go.mod h1:cpdthSy/Yoa28aJFBscFHlGiU+cnSiSh1kuDVtI8YeM=
+github.com/tree-sitter/tree-sitter-ruby v0.23.1 h1:T/NKHUA+iVbHM440hFx+lzVOzS4dV6z8Qw8ai+72bYo=
+github.com/tree-sitter/tree-sitter-ruby v0.23.1/go.mod h1:kUS4kCCQloFcdX6sdpr8p6r2rogbM6ZjTox5ZOQy8cA=
+github.com/tree-sitter/tree-sitter-rust v0.23.2 h1:6AtoooCW5GqNrRpfnvl0iUhxTAZEovEmLKDbyHlfw90=
+github.com/tree-sitter/tree-sitter-rust v0.23.2/go.mod h1:hfeGWic9BAfgTrc7Xf6FaOAguCFJRo3RBbs7QJ6D7MI=
 github.com/twitchyliquid64/golang-asm v0.15.1 h1:SU5vSMR7hnwNxj24w34ZyCi/FmDZTkS4MhqMhdFk5YI=
 github.com/twitchyliquid64/golang-asm v0.15.1/go.mod h1:a1lVb/DtPvCB8fslRZhAngC2+aY1QWCk3Cedj/Gdt08=
 github.com/ugorji/go/codec v1.2.12 h1:9LC83zGrHhuUA9l16C9AHXAqEV/2wBQ4nkvumAE65EE=
@@ -304,6 +335,7 @@ 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=

integration/basic_test.go

@@ -144,3 +144,47 @@ func TestUnicodeModelDir(t *testing.T) {
 	}
 	ChatTestHelper(ctx, t, req, blueSkyExpected)
 }
+
+// TestNumPredict verifies that when num_predict is set, the model generates
+// exactly that many tokens. It uses logprobs to count the actual tokens output.
+func TestNumPredict(t *testing.T) {
+	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
+	defer cancel()
+
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
+
+	if err := PullIfMissing(ctx, client, "qwen3:0.6b"); err != nil {
+		t.Fatalf("failed to pull model: %v", err)
+	}
+
+	req := api.GenerateRequest{
+		Model:    "qwen3:0.6b",
+		Prompt:   "Write a long story.",
+		Stream:   &stream,
+		Logprobs: true,
+		Options: map[string]any{
+			"num_predict": 10,
+			"temperature": 0,
+			"seed":        123,
+		},
+	}
+
+	logprobCount := 0
+	var finalResponse api.GenerateResponse
+	err := client.Generate(ctx, &req, func(resp api.GenerateResponse) error {
+		logprobCount += len(resp.Logprobs)
+		if resp.Done {
+			finalResponse = resp
+		}
+		return nil
+	})
+	if err != nil {
+		t.Fatalf("generate failed: %v", err)
+	}
+
+	if logprobCount != 10 {
+		t.Errorf("expected 10 tokens (logprobs), got %d (EvalCount=%d, DoneReason=%s)",
+			logprobCount, finalResponse.EvalCount, finalResponse.DoneReason)
+	}
+}

kvcache/cache.go

@@ -75,3 +75,10 @@ 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

@@ -0,0 +1,276 @@
+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
@@ -80,6 +81,7 @@ 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
@@ -115,7 +117,7 @@ type llamaServer struct {
 type ollamaServer struct {
 	llmServer
 
-	textProcessor model.TextProcessor // textProcessor handles text encoding/decoding
+	tokenizer tokenizer.Tokenizer // tokenizer handles text encoding/decoding
 }
 
 // LoadModel will load a model from disk. The model must be in the GGML format.
@@ -141,11 +143,11 @@ 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 tok tokenizer.Tokenizer
 	var err error
 	if envconfig.NewEngine() || f.KV().OllamaEngineRequired() {
 		if len(projectors) == 0 {
-			textProcessor, err = model.NewTextProcessor(modelPath)
+			tok, err = model.NewTextProcessor(modelPath)
 		} else {
 			err = errors.New("split vision models aren't supported")
 		}
@@ -154,7 +156,7 @@ 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 tok == nil {
 		llamaModel, err = llama.LoadModelFromFile(modelPath, llama.ModelParams{VocabOnly: true})
 		if err != nil {
 			return nil, err
@@ -210,7 +212,7 @@ func NewLlamaServer(systemInfo ml.SystemInfo, gpus []ml.DeviceInfo, modelPath st
 
 	kvct := strings.ToLower(envconfig.KvCacheType())
 
-	if textProcessor == nil {
+	if tok == nil {
 		flashAttention := ml.FlashAttentionAuto
 		if faUserSet {
 			if fa {
@@ -260,7 +262,7 @@ func NewLlamaServer(systemInfo ml.SystemInfo, gpus []ml.DeviceInfo, modelPath st
 	gpuLibs := ml.LibraryPaths(gpus)
 	status := NewStatusWriter(os.Stderr)
 	cmd, port, err := StartRunner(
-		textProcessor != nil,
+		tok != nil,
 		modelPath,
 		gpuLibs,
 		status,
@@ -309,8 +311,8 @@ func NewLlamaServer(systemInfo ml.SystemInfo, gpus []ml.DeviceInfo, modelPath st
 		}
 	}()
 
-	if textProcessor != nil {
-		return &ollamaServer{llmServer: s, textProcessor: textProcessor}, nil
+	if tok != nil {
+		return &ollamaServer{llmServer: s, tokenizer: tok}, nil
 	} else {
 		return &llamaServer{llmServer: s, ggml: f}, nil
 	}
@@ -1200,7 +1202,8 @@ func (s *llmServer) initModel(ctx context.Context, req LoadRequest, operation Lo
 
 	resp, err := http.DefaultClient.Do(r)
 	if err != nil {
-		return nil, fmt.Errorf("do load request: %w", err)
+		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")
 	}
 	defer resp.Body.Close()
 
@@ -1772,7 +1775,7 @@ 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
 	}
@@ -1807,7 +1810,7 @@ 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
 	}
@@ -1901,6 +1904,10 @@ 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 {

middleware/anthropic.go

@@ -131,12 +131,15 @@ func AnthropicMessagesMiddleware() gin.HandlerFunc {
 
 		messageID := anthropic.GenerateMessageID()
 
+		// Estimate input tokens for streaming (actual count not available until generation completes)
+		estimatedTokens := anthropic.EstimateInputTokens(req)
+
 		w := &AnthropicWriter{
 			BaseWriter: BaseWriter{ResponseWriter: c.Writer},
 			stream:     req.Stream,
 			id:         messageID,
 			model:      req.Model,
-			converter:  anthropic.NewStreamConverter(messageID, req.Model),
+			converter:  anthropic.NewStreamConverter(messageID, req.Model, estimatedTokens),
 		}
 
 		if req.Stream {

ml/backend.go

@@ -170,10 +170,12 @@ 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
 	Sigmoid(ctx Context) Tensor
+	SigmoidOut(ctx Context) Tensor
 
 	// AlphaLimitSILU is a variant of SILU that clamps the input to the range [-limit, limit]
 	SILUAlphaLimit(ctx Context, up Tensor, alpha, limit float32) Tensor
@@ -206,6 +208,32 @@ 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())*8)
+	maxGraphNodes := max(1024, len(meta.Tensors().Items())*32)
 
 	sched := C.ggml_backend_sched_new_ext(
 		(*C.ggml_backend_t)(unsafe.Pointer(&schedBackends[0])),
@@ -1468,6 +1468,13 @@ func (t *Tensor) Sigmoid(ctx ml.Context) ml.Tensor {
 	}
 }
 
+func (t *Tensor) SigmoidOut(ctx ml.Context) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_sigmoid(ctx.(*Context).ctx, t.t),
+	}
+}
+
 func (t *Tensor) View(ctx ml.Context, offset int, shape ...int) ml.Tensor {
 	switch len(shape) {
 	case 1:
@@ -1581,6 +1588,13 @@ 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 {
@@ -1772,6 +1786,76 @@ 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

@@ -1370,6 +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);
 

ml/backend/ggml/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);

ml/backend/ggml/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:

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

@@ -2385,6 +2385,27 @@ 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;
@@ -5813,6 +5834,66 @@ 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,

ml/backend/ggml/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;

ml/backend/ggml/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);
 

ml/backend/ggml/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);

ml/backend/ggml/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,

model/bytepairencoding.go

@@ -1,272 +0,0 @@
-package model
-
-import (
-	"cmp"
-	"iter"
-	"slices"
-	"strings"
-
-	"github.com/dlclark/regexp2"
-	heap "github.com/emirpasic/gods/v2/trees/binaryheap"
-	"github.com/ollama/ollama/logutil"
-)
-
-type BytePairEncoding struct {
-	vocab   *Vocabulary
-	regexps []*regexp2.Regexp
-}
-
-var _ TextProcessor = (*BytePairEncoding)(nil)
-
-func NewBytePairEncoding(vocab *Vocabulary, pretokenizers ...string) BytePairEncoding {
-	if len(pretokenizers) == 0 {
-		// set default byte-level pretokenizer if none provided, e.g.
-		// https://github.com/huggingface/tokenizers/blob/main/tokenizers/src/pre_tokenizers/byte_level.rs#L44
-		pretokenizers = []string{`'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+`}
-	}
-
-	return BytePairEncoding{
-		vocab: vocab,
-		regexps: slices.Collect(func(yield func(*regexp2.Regexp) bool) {
-			for _, p := range pretokenizers {
-				if !yield(regexp2.MustCompile(p, regexp2.RE2)) {
-					return
-				}
-			}
-		}),
-	}
-}
-
-func (bpe BytePairEncoding) Vocabulary() *Vocabulary {
-	return bpe.vocab
-}
-
-func (bpe BytePairEncoding) Is(id int32, special Special) bool {
-	return bpe.vocab.Is(id, special)
-}
-
-func (bpe *BytePairEncoding) split(s string) iter.Seq[string] {
-	parts := []string{s}
-	for _, re := range bpe.regexps {
-		parts = slices.Collect(func(yield func(string) bool) {
-			for _, part := range parts {
-				r := []rune(part)
-				var offset int
-				for m, _ := re.FindRunesMatch(r); m != nil; m, _ = re.FindNextMatch(m) {
-					if offset-m.Index != 0 {
-						if !yield(string(r[:m.Index])) {
-							return
-						}
-					}
-
-					if !yield(m.String()) {
-						return
-					}
-
-					offset = m.Index + m.Length
-				}
-
-				if offset < len(r) {
-					if !yield(string(r[offset:])) {
-						return
-					}
-				}
-			}
-		})
-	}
-
-	return slices.Values(parts)
-}
-
-// fragment is a string fragment and their corresponding token IDs
-type fragment struct {
-	value string
-	ids   []int32
-}
-
-// pair is a pair of runes and its rank
-type pair struct {
-	a, b  int
-	rank  int
-	value string
-}
-
-type merge struct {
-	p, n  int
-	runes []rune
-}
-
-func (bpe BytePairEncoding) Encode(s string, addSpecial bool) ([]int32, error) {
-	fragments := []fragment{{value: s}}
-	for _, special := range bpe.vocab.SpecialVocabulary() {
-		// TODO: process special tokens concurrently
-		id := bpe.vocab.Encode(special)
-		for i := 0; i < len(fragments); i++ {
-			frag := fragments[i]
-			if len(frag.ids) > 0 {
-				continue
-			}
-
-			var middle []fragment
-			switch i := strings.Index(frag.value, special); {
-			case i < 0:
-				middle = append(middle, frag)
-			case i > 0:
-				middle = append(middle, fragment{value: frag.value[:i]})
-				fallthrough
-			default:
-				middle = append(middle, fragment{value: special, ids: []int32{id}})
-				if rest := frag.value[i+len(special):]; rest != "" {
-					middle = append(middle, fragment{value: rest})
-				}
-			}
-
-			fragments = append(fragments[:i], append(middle, fragments[i+1:]...)...)
-		}
-	}
-
-	var ids []int32
-	for _, frag := range fragments {
-		if len(frag.ids) > 0 {
-			ids = append(ids, frag.ids...)
-			continue
-		}
-
-		for split := range bpe.split(frag.value) {
-			// TODO: process splits concurrently
-			var sb strings.Builder
-			for _, b := range []byte(split) {
-				r := rune(b)
-				switch {
-				case r == 0x00ad:
-					r = 0x0143
-				case r <= 0x0020:
-					r = r + 0x0100
-				case r >= 0x007f && r <= 0x00a0:
-					r = r + 0x00a2
-				}
-
-				sb.WriteRune(r)
-			}
-
-			// short circuit if the fragment is in the vocabulary
-			if id := bpe.vocab.Encode(sb.String()); id >= 0 {
-				ids = append(ids, id)
-				continue
-			}
-
-			runes := []rune(sb.String())
-			merges := make([]merge, len(runes))
-			for r := range runes {
-				merges[r] = merge{
-					p:     r - 1,
-					n:     r + 1,
-					runes: []rune{runes[r]},
-				}
-			}
-
-			pairwise := func(a, b int) *pair {
-				if a < 0 || b >= len(runes) {
-					return nil
-				}
-
-				left, right := string(merges[a].runes), string(merges[b].runes)
-				rank := bpe.vocab.Merge(left, right)
-				if rank < 0 {
-					return nil
-				}
-
-				return &pair{
-					a:     a,
-					b:     b,
-					rank:  rank,
-					value: left + right,
-				}
-			}
-
-			pairs := heap.NewWith(func(i, j *pair) int {
-				return cmp.Compare(i.rank, j.rank)
-			})
-
-			for i := range len(runes) - 1 {
-				if pair := pairwise(i, i+1); pair != nil {
-					pairs.Push(pair)
-				}
-			}
-
-			for !pairs.Empty() {
-				pair, _ := pairs.Pop()
-
-				left, right := merges[pair.a], merges[pair.b]
-				if len(left.runes) == 0 || len(right.runes) == 0 ||
-					string(left.runes)+string(right.runes) != pair.value {
-					continue
-				}
-
-				if id := bpe.vocab.Encode(pair.value); id < 0 {
-					continue
-				}
-
-				merges[pair.a].runes = append(left.runes, right.runes...)
-				merges[pair.b].runes = nil
-
-				merges[pair.a].n = right.n
-				if right.n < len(merges) {
-					merges[right.n].p = pair.a
-				}
-
-				if pair := pairwise(merges[pair.a].p, pair.a); pair != nil {
-					pairs.Push(pair)
-				}
-
-				if pair := pairwise(pair.a, merges[pair.a].n); pair != nil {
-					pairs.Push(pair)
-				}
-			}
-
-			for _, merge := range merges {
-				if len(merge.runes) > 0 {
-					// TODO: handle the edge case where the rune isn't in the vocabulary
-					if id := bpe.vocab.Encode(string(merge.runes)); id >= 0 {
-						ids = append(ids, id)
-					}
-				}
-			}
-		}
-	}
-
-	if addSpecial {
-		ids = bpe.vocab.addSpecials(ids)
-	}
-
-	logutil.Trace("encoded", "string", s, "ids", ids)
-	return ids, nil
-}
-
-func (bpe BytePairEncoding) Decode(ids []int32) (string, error) {
-	var sb strings.Builder
-	for _, id := range ids {
-		for _, r := range bpe.vocab.Decode(id) {
-			switch {
-			case r == 0x0100:
-				// this produces 0x00 aka NULL
-				continue
-			case r == 0x0143:
-				r = 0x00ad
-			case r > 0x0100 && r <= 0x0120:
-				r = r - 0x0100
-			case r > 0x0120 && r <= 0x0142:
-				r = r - 0x00a2
-			}
-
-			// NOTE: not using WriteRune here because it writes the UTF-8
-			// encoding of the rune which is _not_ what we want
-			if err := sb.WriteByte(byte(r)); err != nil {
-				return "", err
-			}
-		}
-	}
-
-	logutil.Trace("decoded", "string", sb.String(), "from", ids)
-	return sb.String(), nil
-}

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

@@ -56,6 +56,18 @@ 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

@@ -0,0 +1,174 @@
+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

@@ -0,0 +1,236 @@
+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"
+	"github.com/ollama/ollama/tokenizer"
+)
+
+type Model struct {
+	model.Base
+	tokenizer.Tokenizer
+
+	*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{
+		Tokenizer: tokenizer.NewBytePairEncoding(
+			&tokenizer.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

@@ -0,0 +1,190 @@
+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

@@ -0,0 +1,355 @@
+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

@@ -8,6 +8,7 @@ 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"
@@ -19,5 +20,6 @@ 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"`
@@ -178,29 +179,6 @@ func New(c fs.Config) (model.Model, error) {
 	numHeads := int(c.Uint("attention.head_count"))
 	headDim := hiddenSize / numHeads
 
-	processor := model.NewWordPiece(
-		&model.Vocabulary{
-			Values: c.Strings("tokenizer.ggml.tokens"),
-			Scores: c.Floats("tokenizer.ggml.scores"),
-			Types:  c.Ints("tokenizer.ggml.token_type"),
-			AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
-			BOS: []int32{
-				int32(cmp.Or(
-					c.Uint("tokenizer.ggml.cls_token_id"),
-					c.Uint("tokenizer.ggml.bos_token_id"),
-				)),
-			},
-			AddEOS: c.Bool("tokenizer.ggml.add_eos_token", true),
-			EOS: []int32{
-				int32(cmp.Or(
-					c.Uint("tokenizer.ggml.separator_token_id"),
-					c.Uint("tokenizer.ggml.eos_token_id"),
-				)),
-			},
-		},
-		false,
-	)
-
 	blockCount := int(c.Uint("block_count"))
 	moeEveryNLayers := int(c.Uint("moe_every_n_layers", 0))
 	layers := make([]EncoderLayer, blockCount)
@@ -219,8 +197,29 @@ func New(c fs.Config) (model.Model, error) {
 	}
 
 	return &Model{
-		TextProcessor: processor,
-		Layers:        layers,
+		Tokenizer: tokenizer.NewWordPiece(
+			&tokenizer.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Scores: c.Floats("tokenizer.ggml.scores"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+				BOS: []int32{
+					int32(cmp.Or(
+						c.Uint("tokenizer.ggml.cls_token_id"),
+						c.Uint("tokenizer.ggml.bos_token_id"),
+					)),
+				},
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", true),
+				EOS: []int32{
+					int32(cmp.Or(
+						c.Uint("tokenizer.ggml.separator_token_id"),
+						c.Uint("tokenizer.ggml.eos_token_id"),
+					)),
+				},
+			},
+			false,
+		),
+		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"`
@@ -44,28 +45,24 @@ type Model struct {
 }
 
 func New(c fs.Config) (model.Model, error) {
-	vocabulary := model.Vocabulary{
-		Values: c.Strings("tokenizer.ggml.tokens"),
-		Scores: c.Floats("tokenizer.ggml.scores"),
-		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: append(
-			[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
-			c.Ints("tokenizer.ggml.eos_token_ids")...,
-		),
-	}
-
-	processor := model.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.NewBytePairEncoding(
+			&tokenizer.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Scores: c.Floats("tokenizer.ggml.scores"),
+				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: 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}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+		),
+		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

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

@@ -0,0 +1,596 @@
+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

@@ -0,0 +1,498 @@
+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

@@ -0,0 +1,300 @@
+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

@@ -0,0 +1,472 @@
+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

@@ -0,0 +1,384 @@
+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"
+	"github.com/ollama/ollama/tokenizer"
+)
+
+// 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.SigmoidOut(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
+	tokenizer.Tokenizer
+
+	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{
+		Tokenizer: tokenizer.NewBytePairEncoding(
+			&tokenizer.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

@@ -0,0 +1,17 @@
+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/parsers.go

@@ -71,6 +71,8 @@ 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":

model/renderers/glmocr.go

@@ -0,0 +1,109 @@
+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\n")
+				sb.WriteString(imStartTag + "user")
 			}
 
-			sb.WriteString("<tool_response>\n")
+			sb.WriteString("\n<tool_response>\n")
 			sb.WriteString(message.Content)
-			sb.WriteString("\n</tool_response>\n")
+			sb.WriteString("\n</tool_response>")
 
 			// 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

@@ -1,6 +1,7 @@
 package renderers
 
 import (
+	"strings"
 	"testing"
 
 	"github.com/google/go-cmp/cmp"
@@ -127,8 +128,7 @@ 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
@@ -233,8 +233,7 @@ 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
 `,
 		},
@@ -280,8 +279,7 @@ call tool<|im_end|>
 <|im_start|>user
 <tool_response>
 {"payload": {"foo": "bar"}}
-</tool_response>
-<|im_end|>
+</tool_response><|im_end|>
 <|im_start|>assistant
 `,
 		},
@@ -337,6 +335,31 @@ 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

@@ -82,6 +82,8 @@ 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":

model/sentencepiece.go

@@ -1,249 +0,0 @@
-package model
-
-import (
-	"container/heap"
-	"fmt"
-	"log/slog"
-	"strconv"
-	"strings"
-
-	"github.com/ollama/ollama/logutil"
-)
-
-const spmWhitespaceSep = "▁"
-
-type SentencePiece struct {
-	maxTokenLen int
-	vocab       *Vocabulary
-}
-
-var _ TextProcessor = (*SentencePiece)(nil)
-
-func (spm SentencePiece) Vocabulary() *Vocabulary {
-	return spm.vocab
-}
-
-func NewSentencePiece(vocab *Vocabulary) SentencePiece {
-	logutil.Trace("Tokens", "num tokens", len(vocab.Values), "vals", vocab.Values[:5], "scores", vocab.Scores[:5], "types", vocab.Types[:5])
-
-	counter := map[int]int{}
-	var maxTokenLen int
-	for cnt := range vocab.Types {
-		switch vocab.Types[cnt] {
-		case TOKEN_TYPE_NORMAL, TOKEN_TYPE_USER_DEFINED, TOKEN_TYPE_UNUSED:
-			maxTokenLen = max(maxTokenLen, len(vocab.Values[cnt]))
-			fallthrough
-		default:
-			counter[int(vocab.Types[cnt])] += 1
-		}
-	}
-
-	logutil.Trace("Token counts", "normal", counter[TOKEN_TYPE_NORMAL], "unknown", counter[TOKEN_TYPE_UNKNOWN], "control", counter[TOKEN_TYPE_CONTROL],
-		"user defined", counter[TOKEN_TYPE_USER_DEFINED], "unused", counter[TOKEN_TYPE_UNUSED], "byte", counter[TOKEN_TYPE_BYTE],
-		"max token len", maxTokenLen)
-
-	return SentencePiece{
-		maxTokenLen: maxTokenLen,
-		vocab:       vocab,
-	}
-}
-
-func (spm SentencePiece) Is(id int32, special Special) bool {
-	return spm.vocab.Is(id, special)
-}
-
-func (spm SentencePiece) Encode(s string, addSpecial bool) ([]int32, error) {
-	fragments := []fragment{{value: s}}
-	for _, special := range spm.vocab.SpecialVocabulary() {
-		id := spm.vocab.Encode(special)
-		for i := 0; i < len(fragments); i++ {
-			frag := fragments[i]
-			if len(frag.ids) > 0 {
-				continue
-			}
-
-			var middle []fragment
-			switch i := strings.Index(frag.value, special); {
-			case i < 0:
-				middle = append(middle, frag)
-			case i > 0:
-				middle = append(middle, fragment{value: frag.value[:i]})
-				fallthrough
-			default:
-				middle = append(middle, fragment{value: special, ids: []int32{id}})
-				if rest := frag.value[i+len(special):]; rest != "" {
-					middle = append(middle, fragment{value: rest})
-				}
-			}
-
-			fragments = append(fragments[:i], append(middle, fragments[i+1:]...)...)
-		}
-	}
-
-	var ids []int32
-	for _, frag := range fragments {
-		if len(frag.ids) > 0 {
-			ids = append(ids, frag.ids...)
-			continue
-		}
-
-		text := strings.ReplaceAll(frag.value, " ", spmWhitespaceSep)
-
-		if id := spm.vocab.Encode(text); id >= 0 {
-			ids = append(ids, id)
-			continue
-		}
-
-		q := &queue{}
-		heap.Init(q)
-
-		runes := []rune(text)
-		merges := make([]merge, len(runes))
-		for r := range runes {
-			merges[r] = merge{
-				p:     r - 1,
-				n:     r + 1,
-				runes: []rune{runes[r]},
-			}
-		}
-
-		pairwise := func(a, b int) *candidate {
-			if a < 0 || b >= len(runes) {
-				return nil
-			}
-
-			left, right := string(merges[a].runes), string(merges[b].runes)
-			if id := spm.vocab.Encode(left + right); id >= 0 {
-				return &candidate{
-					a:     a,
-					b:     b,
-					score: spm.vocab.Scores[id],
-					size:  len(left) + len(right),
-				}
-			}
-
-			return nil
-		}
-
-		for i := range len(runes) - 1 {
-			if pair := pairwise(i, i+1); pair != nil {
-				heap.Push(q, pair)
-			}
-		}
-
-		for q.Len() > 0 {
-			pair := heap.Pop(q).(*candidate)
-			left, right := merges[pair.a], merges[pair.b]
-
-			if string(left.runes) == "" || string(right.runes) == "" || len(string(left.runes))+len(string(right.runes)) != pair.size {
-				continue
-			}
-
-			merges[pair.a].runes = append(left.runes, right.runes...)
-			merges[pair.b].runes = nil
-			merges[pair.a].n = right.n
-			if right.n < len(merges) {
-				merges[right.n].p = pair.a
-			}
-
-			if pair := pairwise(merges[pair.a].p, pair.a); pair != nil {
-				heap.Push(q, pair)
-			}
-
-			if pair := pairwise(pair.a, merges[pair.a].n); pair != nil {
-				heap.Push(q, pair)
-			}
-		}
-
-		for _, merge := range merges {
-			if token := string(merge.runes); token != "" {
-				id := spm.vocab.Encode(token)
-
-				if id >= 0 {
-					ids = append(ids, id)
-					continue
-				}
-
-				// Fallback to byte tokenization
-				var result []int32
-				for _, b := range []byte(token) {
-					byteToken := fmt.Sprintf("<0x%02X>", b)
-					unknownID := spm.vocab.Encode(byteToken)
-					if unknownID >= 0 {
-						result = append(result, unknownID)
-					} else {
-						slog.Debug("unknown byte token", "byte", b, "token", byteToken)
-					}
-				}
-
-				ids = append(ids, result...)
-			}
-		}
-	}
-
-	if addSpecial {
-		ids = spm.vocab.addSpecials(ids)
-	}
-
-	logutil.Trace("encoded", "string", s, "ids", ids)
-	return ids, nil
-}
-
-type candidate struct {
-	a, b  int
-	score float32
-	size  int
-}
-
-type queue []*candidate
-
-func (q queue) Len() int { return len(q) }
-
-func (q queue) Less(i, j int) bool {
-	return (q[i].score > q[j].score) || (q[i].score == q[j].score && q[i].a < q[j].a)
-}
-
-func (q queue) Swap(i, j int) { q[i], q[j] = q[j], q[i] }
-
-func (q *queue) Push(x interface{}) {
-	item := x.(*candidate)
-	*q = append(*q, item)
-}
-
-func (q *queue) Pop() interface{} {
-	old := *q
-	n := len(old)
-	item := old[n-1]
-	*q = old[0 : n-1]
-	return item
-}
-
-func (spm SentencePiece) Decode(ids []int32) (string, error) {
-	var sb strings.Builder
-	for _, id := range ids {
-		data := spm.vocab.Decode(id)
-		data = strings.ReplaceAll(data, spmWhitespaceSep, " ")
-
-		// For tokenizers that use byte tokens like "<0xEA>"
-		// convert them to the partial unicode character
-		// so they are buffered correctly by the runner instead
-		// of being sent back to the api as "<0xEA>"
-		if len(data) == 6 && strings.HasPrefix(data, "<0x") && strings.HasSuffix(data, ">") {
-			byteVal, err := strconv.ParseUint(data[1:5], 0, 8)
-			if err != nil {
-				return "", fmt.Errorf("failed to parse hex byte: %v", err)
-			}
-
-			if err := sb.WriteByte(byte(byteVal)); err != nil {
-				return "", err
-			}
-		} else {
-			if _, err := sb.WriteString(data); err != nil {
-				return "", err
-			}
-		}
-	}
-
-	logutil.Trace("decoded", "ids", ids, "string", sb.String())
-	return sb.String(), nil
-}

model/textprocessor.go

@@ -1,17 +0,0 @@
-package model
-
-const (
-	TOKEN_TYPE_NORMAL = iota + 1
-	TOKEN_TYPE_UNKNOWN
-	TOKEN_TYPE_CONTROL
-	TOKEN_TYPE_USER_DEFINED
-	TOKEN_TYPE_UNUSED
-	TOKEN_TYPE_BYTE
-)
-
-type TextProcessor interface {
-	Encode(s string, addSpecial bool) ([]int32, error)
-	Decode([]int32) (string, error)
-	Is(int32, Special) bool
-	Vocabulary() *Vocabulary
-}

model/wordpiece_test.go

@@ -1,53 +0,0 @@
-package model
-
-import (
-	"slices"
-	"testing"
-
-	"github.com/google/go-cmp/cmp"
-)
-
-func TestWordPiece(t *testing.T) {
-	wpm := NewWordPiece(
-		&Vocabulary{
-			Values: []string{"[UNK]", "[CLS]", "[SEP]", "▁hello", "▁world", "s", "▁!", "▁@", "▁#"},
-			AddBOS: true,
-			AddEOS: true,
-			BOS:    []int32{1},
-			EOS:    []int32{2},
-		},
-		true, // lowercase
-	)
-
-	ids, err := wpm.Encode("Hello world!", true)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	if diff := cmp.Diff([]int32{1, 3, 4, 6, 2}, ids); diff != "" {
-		t.Errorf("unexpected ids (-want +got):\n%s", diff)
-	}
-
-	words, err := wpm.Decode(ids)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	if diff := cmp.Diff("[CLS] hello world! [SEP]", words); diff != "" {
-		t.Errorf("unexpected words (-want +got):\n%s", diff)
-	}
-}
-
-func TestWordPieceWords(t *testing.T) {
-	var wpm WordPiece
-
-	basic := slices.Collect(wpm.words("Hey friend!     How are you?!?"))
-	if diff := cmp.Diff([]string{"Hey", "friend", "!", "How", "are", "you", "?", "!", "?"}, basic); diff != "" {
-		t.Errorf("unexpected words (-want +got):\n%s", diff)
-	}
-
-	chinese := slices.Collect(wpm.words("野口里佳 Noguchi Rika"))
-	if diff := cmp.Diff([]string{"野", "口", "里", "佳", "Noguchi", "Rika"}, chinese); diff != "" {
-		t.Errorf("unexpected words (-want +got):\n%s", diff)
-	}
-}

runner/ollamarunner/cache.go

@@ -124,8 +124,17 @@ func (c *InputCache) LoadCacheSlot(prompt []*input.Input, cachePrompt bool) (*In
 	}
 
 	if c.cache != nil {
-		if numPast > 0 && !c.cache.CanResume(slot.Id, numPast) {
-			numPast = 0
+		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
+			}
 		}
 
 		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, tok tokenizer.Tokenizer) []llm.Logprob {
 	decoder := func(tokenID int) string {
-		text, _ := textProcessor.Decode([]int32{int32(tokenID)})
+		text, _ := tok.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
 		}
@@ -514,13 +515,6 @@ func (s *Server) forwardBatch(pendingBatch batchState) (nextBatch batchState, er
 			continue
 		}
 
-		// if past the num predict limit
-		if seq.numPredict > 0 && seq.numPredicted >= seq.numPredict {
-			s.removeSequence(seqIdx, llm.DoneReasonLength)
-			nextBatch.seqs[seqIdx] = nil
-			continue
-		}
-
 		if !s.cache.enabled {
 			seq.inputs = append(seq.cache.Inputs, seq.inputs...)
 			seq.cache.Inputs = []*input.Input{}
@@ -709,7 +703,6 @@ func (s *Server) computeBatch(activeBatch batchState) {
 			continue
 		}
 
-		seq.numPredicted++
 		nextToken := &input.Input{Token: 0} // placeholder we'll fill in after Compute/Floats
 		seq.inputs = []*input.Input{nextToken}
 		nextBatchTokens[i] = nextToken
@@ -740,8 +733,14 @@ 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
+		seq.numPredicted++
 		if seq.numPredicted == 1 {
 			seq.processingDuration = seq.lastUpdatedAt.Sub(seq.startedAt)
 			seq.startedAt = seq.lastUpdatedAt
@@ -766,7 +765,7 @@ 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
@@ -775,18 +774,25 @@ 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...)
 		}
 
 		seq.pendingResponses = append(seq.pendingResponses, piece)
+
+		// if past the num predict limit
+		if seq.numPredict > 0 && seq.numPredicted >= seq.numPredict {
+			s.removeSequence(i, llm.DoneReasonLength)
+			continue
+		}
+
 		sequence := strings.Join(seq.pendingResponses, "")
 
 		if ok, stop := common.FindStop(sequence, seq.stop); ok {
@@ -873,7 +879,7 @@ 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
@@ -1358,7 +1364,7 @@ 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 baackend: %v", err), http.StatusInternalServerError)
+			http.Error(w, fmt.Sprintf("failed to initialize backend: %v", err), http.StatusInternalServerError)
 			return
 		}
 		defer f.Close()
@@ -1368,13 +1374,13 @@ 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 baackend: %v", err), http.StatusInternalServerError)
+			http.Error(w, fmt.Sprintf("failed to initialize backend: %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 baackend: %v", err), http.StatusInternalServerError)
+			http.Error(w, fmt.Sprintf("failed to initialize backend: %v", err), http.StatusInternalServerError)
 			return
 		}
 		slog.Debug("dummy model load took", "duration", time.Since(startLoad))