bugfix: fix crash bug in token cache logic

This change fixes a problem in the token cache logic to avoid panics caused by empty token arrays
by ensuring at least one token remains on full cache hits in the relevant function. The happens
if there is an exact match in the cache on subsequent generations.

app/cmd/app/app.go

@@ -35,6 +35,7 @@ import (
 var (
 	wv           = &Webview{}
 	uiServerPort int
+	appStore     *store.Store
 )
 
 var debug = strings.EqualFold(os.Getenv("OLLAMA_DEBUG"), "true") || os.Getenv("OLLAMA_DEBUG") == "1"
@@ -208,6 +209,7 @@ func main() {
 	uiServerPort = port
 
 	st := &store.Store{}
+	appStore = st
 
 	// Enable CORS in development mode
 	if devMode {
@@ -294,8 +296,15 @@ func main() {
 
 	// Check for pending updates on startup (show tray notification if update is ready)
 	if updater.IsUpdatePending() {
-		slog.Debug("update pending on startup, showing tray notification")
-		UpdateAvailable("")
+		// On Windows, the tray is initialized in osRun(). Calling UpdateAvailable
+		// before that would dereference a nil tray callback.
+		// TODO: refactor so the update check runs after platform init on all platforms.
+		if runtime.GOOS == "windows" {
+			slog.Debug("update pending on startup, deferring tray notification until tray initialization")
+		} else {
+			slog.Debug("update pending on startup, showing tray notification")
+			UpdateAvailable("")
+		}
 	}
 
 	hasCompletedFirstRun, err := st.HasCompletedFirstRun()
@@ -360,8 +369,7 @@ func startHiddenTasks() {
 			slog.Info("deferring pending update for fast startup")
 		} else {
 			// Check if auto-update is enabled before automatically upgrading
-			st := &store.Store{}
-			settings, err := st.Settings()
+			settings, err := appStore.Settings()
 			if err != nil {
 				slog.Warn("failed to load settings for upgrade check", "error", err)
 			} else if !settings.AutoUpdateEnabled {

app/cmd/app/app_windows.go

@@ -154,6 +154,10 @@ func handleURLSchemeRequest(urlScheme string) {
 }
 
 func UpdateAvailable(ver string) error {
+	if app.t == nil {
+		slog.Debug("tray not yet initialized, skipping update notification")
+		return nil
+	}
 	return app.t.UpdateAvailable(ver)
 }
 
@@ -165,6 +169,14 @@ func osRun(shutdown func(), hasCompletedFirstRun, startHidden bool) {
 		log.Fatalf("Failed to start: %s", err)
 	}
 
+	// Check for pending updates now that the tray is initialized.
+	// The platform-independent check in app.go fires before osRun,
+	// when app.t is still nil, so we must re-check here.
+	if updater.IsUpdatePending() {
+		slog.Debug("update pending on startup, showing tray notification")
+		UpdateAvailable("")
+	}
+
 	signals := make(chan os.Signal, 1)
 	signal.Notify(signals, syscall.SIGINT, syscall.SIGTERM)
 

app/ui/app/src/components/StreamingMarkdownContent.test.ts

@@ -1,202 +0,0 @@
-import { describe, it, expect } from "vitest";
-import { sanitize } from "hast-util-sanitize";
-import { defaultSchema } from "rehype-sanitize";
-
-// Mirror the sanitizeSchema from StreamingMarkdownContent.tsx
-const sanitizeSchema = {
-  ...defaultSchema,
-  tagNames: [...(defaultSchema.tagNames || []), "ol-citation"],
-  attributes: {
-    ...defaultSchema.attributes,
-    div: [
-      ...(defaultSchema.attributes?.div || []),
-      ["className", "math", "math-display"],
-    ],
-    span: [
-      ...(defaultSchema.attributes?.span || []),
-      ["className", "math", "math-inline"],
-    ],
-    "ol-citation": ["cursor", "start", "end"],
-  },
-  strip: ["script", "style"],
-};
-
-// Helper to create a hast element node
-function h(
-  tagName: string,
-  properties: Record<string, unknown>,
-  children: any[] = [],
-): any {
-  return { type: "element", tagName, properties, children };
-}
-
-function text(value: string): any {
-  return { type: "text", value };
-}
-
-function root(...children: any[]): any {
-  return { type: "root", children };
-}
-
-describe("sanitizeSchema", () => {
-  it("should strip <style> tags and their content", () => {
-    const tree = root(
-      h("style", {}, [
-        text("body { background: red; } button { background: linear-gradient(blue, green); }"),
-      ]),
-      h("p", {}, [text("Hello world")]),
-    );
-
-    const result = sanitize(tree, sanitizeSchema);
-
-    // <style> should be completely stripped (including content)
-    const hasStyle = JSON.stringify(result).includes("background");
-    expect(hasStyle).toBe(false);
-
-    // <p> should survive
-    expect(result.children).toHaveLength(1);
-    expect(result.children[0].tagName).toBe("p");
-  });
-
-  it("should strip <script> tags and their content", () => {
-    const tree = root(
-      h("script", {}, [text("alert('xss')")]),
-      h("p", {}, [text("Safe content")]),
-    );
-
-    const result = sanitize(tree, sanitizeSchema);
-
-    const hasScript = JSON.stringify(result).includes("alert");
-    expect(hasScript).toBe(false);
-    expect(result.children).toHaveLength(1);
-    expect(result.children[0].tagName).toBe("p");
-  });
-
-  it("should strip <iframe> tags", () => {
-    const tree = root(
-      h("iframe", { src: "https://evil.com" }, []),
-      h("p", {}, [text("Safe content")]),
-    );
-
-    const result = sanitize(tree, sanitizeSchema);
-
-    const hasIframe = result.children.some(
-      (c: any) => c.tagName === "iframe",
-    );
-    expect(hasIframe).toBe(false);
-  });
-
-  it("should preserve math block elements (div.math.math-display)", () => {
-    const tree = root(
-      h("div", { className: ["math", "math-display"] }, [
-        text("E = mc^2"),
-      ]),
-    );
-
-    const result = sanitize(tree, sanitizeSchema);
-
-    expect(result.children).toHaveLength(1);
-    expect(result.children[0].tagName).toBe("div");
-    expect(result.children[0].properties.className).toEqual([
-      "math",
-      "math-display",
-    ]);
-  });
-
-  it("should preserve math inline elements (span.math.math-inline)", () => {
-    const tree = root(
-      h("span", { className: ["math", "math-inline"] }, [text("x^2")]),
-    );
-
-    const result = sanitize(tree, sanitizeSchema);
-
-    expect(result.children).toHaveLength(1);
-    expect(result.children[0].tagName).toBe("span");
-    expect(result.children[0].properties.className).toEqual([
-      "math",
-      "math-inline",
-    ]);
-  });
-
-  it("should preserve ol-citation elements with attributes", () => {
-    const tree = root(
-      h("ol-citation", { cursor: "1", start: "25", end: "30" }, []),
-    );
-
-    const result = sanitize(tree, sanitizeSchema);
-
-    expect(result.children).toHaveLength(1);
-    expect(result.children[0].tagName).toBe("ol-citation");
-    expect(result.children[0].properties.cursor).toBe("1");
-    expect(result.children[0].properties.start).toBe("25");
-    expect(result.children[0].properties.end).toBe("30");
-  });
-
-  it("should preserve code elements with language classes", () => {
-    const tree = root(
-      h("pre", {}, [
-        h("code", { className: ["language-python"] }, [
-          text("print('hello')"),
-        ]),
-      ]),
-    );
-
-    const result = sanitize(tree, sanitizeSchema);
-
-    expect(result.children).toHaveLength(1);
-    const code = result.children[0].children[0];
-    expect(code.tagName).toBe("code");
-    expect(code.properties.className).toEqual(["language-python"]);
-  });
-
-  it("should preserve standard markdown elements", () => {
-    const tree = root(
-      h("h1", {}, [text("Title")]),
-      h("p", {}, [
-        text("Some "),
-        h("strong", {}, [text("bold")]),
-        text(" and "),
-        h("em", {}, [text("italic")]),
-        text(" text."),
-      ]),
-      h("ul", {}, [
-        h("li", {}, [text("Item 1")]),
-        h("li", {}, [text("Item 2")]),
-      ]),
-      h("a", { href: "https://example.com" }, [text("A link")]),
-    );
-
-    const result = sanitize(tree, sanitizeSchema);
-
-    const tagNames = result.children.map((c: any) => c.tagName);
-    expect(tagNames).toEqual(["h1", "p", "ul", "a"]);
-  });
-
-  it("should strip model-generated HTML page that would corrupt the UI", () => {
-    // Simulate a model generating a full HTML page
-    const tree = root(
-      h("style", {}, [
-        text(`
-          * { margin: 0; padding: 0; }
-          button { background: linear-gradient(to right, #ff0000, #0000ff); }
-          .some-class { font-size: 72px; }
-        `),
-      ]),
-      h("div", {}, [
-        h("h1", {}, [text("My Generated Page")]),
-        h("p", {}, [text("This is model-generated content")]),
-      ]),
-    );
-
-    const result = sanitize(tree, sanitizeSchema);
-
-    // Style tag and its content should be gone
-    const serialized = JSON.stringify(result);
-    expect(serialized).not.toContain("linear-gradient");
-    expect(serialized).not.toContain("margin: 0");
-
-    // The safe content should remain
-    expect(serialized).toContain("My Generated Page");
-    expect(serialized).toContain("model-generated content");
-  });
-});

app/ui/app/src/components/StreamingMarkdownContent.tsx

@@ -1,36 +1,10 @@
 import React from "react";
-import { Streamdown, defaultRemarkPlugins, defaultRehypePlugins } from "streamdown";
-import rehypeSanitize, { defaultSchema } from "rehype-sanitize";
-import type { PluggableList } from "unified";
+import { Streamdown, defaultRemarkPlugins } from "streamdown";
 import remarkCitationParser from "@/utils/remarkCitationParser";
 import CopyButton from "./CopyButton";
 import type { BundledLanguage } from "shiki";
 import { highlighter } from "@/lib/highlighter";
 
-// Extend GitHub's default sanitization schema to support math rendering
-// and custom citation elements while stripping dangerous tags like <style>
-// and <script> that can leak from model-generated HTML content.
-const sanitizeSchema = {
-  ...defaultSchema,
-  tagNames: [
-    ...(defaultSchema.tagNames || []),
-    "ol-citation",
-  ],
-  attributes: {
-    ...defaultSchema.attributes,
-    div: [
-      ...(defaultSchema.attributes?.div || []),
-      ["className", "math", "math-display"],
-    ],
-    span: [
-      ...(defaultSchema.attributes?.span || []),
-      ["className", "math", "math-inline"],
-    ],
-    "ol-citation": ["cursor", "start", "end"],
-  },
-  strip: ["script", "style"],
-};
-
 interface StreamingMarkdownContentProps {
   content: string;
   isStreaming?: boolean;
@@ -161,18 +135,6 @@ const StreamingMarkdownContent: React.FC<StreamingMarkdownContentProps> =
       ];
     }, []);
 
-    // Build rehype plugins: keep defaults (harden, raw, katex) but add
-    // sanitization after raw HTML parsing to prevent model-generated HTML
-    // (e.g. <style> tags) from affecting the UI
-    const rehypePlugins: PluggableList = React.useMemo(() => {
-      return [
-        defaultRehypePlugins.harden,
-        defaultRehypePlugins.raw,
-        [rehypeSanitize, sanitizeSchema],
-        defaultRehypePlugins.katex,
-      ] as PluggableList;
-    }, []);
-
     return (
       <div
         className={`
@@ -249,7 +211,6 @@ const StreamingMarkdownContent: React.FC<StreamingMarkdownContentProps> =
             parseIncompleteMarkdown={isStreaming}
             isAnimating={isStreaming}
             remarkPlugins={remarkPlugins}
-            rehypePlugins={rehypePlugins}
             controls={false}
             components={{
               pre: CodeBlock,

app/updater/updater.go

@@ -289,6 +289,7 @@ func (u *Updater) TriggerImmediateCheck() {
 
 func (u *Updater) StartBackgroundUpdaterChecker(ctx context.Context, cb func(string) error) {
 	u.checkNow = make(chan struct{}, 1)
+	u.checkNow <- struct{}{} // Trigger first check after initial delay
 	go func() {
 		// Don't blast an update message immediately after startup
 		time.Sleep(UpdateCheckInitialDelay)
@@ -333,7 +334,7 @@ func (u *Updater) StartBackgroundUpdaterChecker(ctx context.Context, cb func(str
 				continue
 			}
 
-			// Download successful - show tray notification (regardless of toggle state)
+			// Download successful - show tray notification
 			err = cb(resp.UpdateVersion)
 			if err != nil {
 				slog.Warn("failed to register update available with tray", "error", err)

app/updater/updater_test.go

@@ -351,10 +351,13 @@ func TestTriggerImmediateCheck(t *testing.T) {
 
 	updater.StartBackgroundUpdaterChecker(ctx, cb)
 
-	// Wait for goroutine to start and pass initial delay
-	time.Sleep(10 * time.Millisecond)
+	// Wait for the initial check that fires after the initial delay
+	select {
+	case <-checkDone:
+	case <-time.After(2 * time.Second):
+		t.Fatal("initial check did not happen")
+	}
 
-	// With 1 hour interval, no check should have happened yet
 	initialCount := checkCount.Load()
 
 	// Trigger immediate check

convert/convert.go

@@ -320,7 +320,7 @@ func LoadModelMetadata(fsys fs.FS) (ModelKV, *Tokenizer, error) {
 		conv = &lfm2Model{}
 	case "Lfm2VlForConditionalGeneration":
 		conv = &lfm2VLTextModel{}
-	case "Qwen3NextForCausalLM":
+	case "Qwen3NextForCausalLM", "Qwen3_5ForConditionalGeneration", "Qwen3_5MoeForConditionalGeneration":
 		conv = &qwen3NextModel{}
 	case "NemotronHForCausalLM":
 		conv = &nemotronHModel{}

convert/convert_qwen3next.go

@@ -1,6 +1,7 @@
 package convert
 
 import (
+	"encoding/json"
 	"fmt"
 	"io/fs"
 	"math"
@@ -13,8 +14,21 @@ import (
 	"github.com/ollama/ollama/fs/ggml"
 )
 
-type qwen3NextModel struct {
-	ModelParameters
+type qwen3NextRopeScaling struct {
+	Type         string     `json:"type"`
+	Factor       ropeFactor `json:"factor"`
+	MropeSection []int32    `json:"mrope_section"`
+}
+
+type qwen3NextRopeParams struct {
+	MRopeInterleaved    bool    `json:"mrope_interleaved"`
+	MropeSection        []int32 `json:"mrope_section"`
+	RopeType            string  `json:"rope_type"`
+	RopeTheta           float32 `json:"rope_theta"`
+	PartialRotaryFactor float32 `json:"partial_rotary_factor"`
+}
+
+type qwen3NextTextConfig struct {
 	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
 	HiddenSize            uint32  `json:"hidden_size"`
 	NumHiddenLayers       uint32  `json:"num_hidden_layers"`
@@ -28,12 +42,13 @@ type qwen3NextModel struct {
 	// MoE config
 	NumExperts             uint32 `json:"num_experts"`
 	NumExpertsPerToken     uint32 `json:"num_experts_per_tok"`
-	NormTopkProb           bool   `json:"norm_topk_prob"`
+	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"`
+	FullAttentionInterval uint32   `json:"full_attention_interval"`
+	LayerTypes            []string `json:"layer_types"`
 
 	// Linear attention (Gated Delta Net) config
 	LinearConvKernelDim uint32 `json:"linear_conv_kernel_dim"`
@@ -43,16 +58,102 @@ type qwen3NextModel struct {
 	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"`
+	PartialRotaryFactor float32              `json:"partial_rotary_factor"`
+	RopeScaling         qwen3NextRopeScaling `json:"rope_scaling"`
+	RopeParameters      qwen3NextRopeParams  `json:"rope_parameters"`
+}
+
+type qwen3NextVisionConfig struct {
+	Depth                  uint32  `json:"depth"`
+	HiddenSize             uint32  `json:"hidden_size"`
+	NumHeads               uint32  `json:"num_heads"`
+	InChannels             uint32  `json:"in_channels"`
+	PatchSize              uint32  `json:"patch_size"`
+	SpatialMergeSize       uint32  `json:"spatial_merge_size"`
+	RMSNormEps             float32 `json:"layer_norm_epsilon"`
+	RopeTheta              float32 `json:"rope_theta"`
+	TemporalPatchSize      uint32  `json:"temporal_patch_size"`
+	DeepstackVisualIndexes []int32 `json:"deepstack_visual_indexes"`
+
+	Size struct {
+		ShortestEdge uint32 `json:"shortest_edge"`
+		LongestEdge  uint32 `json:"longest_edge"`
+	} `json:"size"`
+
+	ImageMean []float32 `json:"image_mean"`
+	ImageStd  []float32 `json:"image_std"`
+}
+
+type qwen3NextModel struct {
+	ModelParameters
+	qwen3NextTextConfig
+
+	TextConfig  *qwen3NextTextConfig  `json:"text_config"`
+	VisionModel qwen3NextVisionConfig `json:"vision_config"`
+
+	ImageTokenID       uint32 `json:"image_token_id"`
+	VisionStartTokenID uint32 `json:"vision_start_token_id"`
+	VisionEndTokenID   uint32 `json:"vision_end_token_id"`
 }
 
 var _ ModelConverter = (*qwen3NextModel)(nil)
 
-func (q *qwen3NextModel) parseMore(_ fs.FS) error {
+func (q *qwen3NextModel) parseMore(fsys fs.FS) error {
+	if q.TextConfig != nil {
+		q.qwen3NextTextConfig = *q.TextConfig
+	}
+
+	if q.RopeTheta == 0 {
+		q.RopeTheta = q.RopeParameters.RopeTheta
+	}
+	if q.PartialRotaryFactor == 0 {
+		q.PartialRotaryFactor = q.RopeParameters.PartialRotaryFactor
+	}
+
+	if q.RopeScaling.Type == "" && q.RopeParameters.RopeType != "" {
+		q.RopeScaling.Type = q.RopeParameters.RopeType
+	}
+
+	// Pull vision preprocessing fields when present.
+	if q.VisionModel.Depth > 0 {
+		if bts, err := fs.ReadFile(fsys, "preprocessor_config.json"); err == nil {
+			var pre 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"`
+			}
+			if json.Unmarshal(bts, &pre) == nil {
+				if q.VisionModel.PatchSize == 0 {
+					q.VisionModel.PatchSize = pre.PatchSize
+				}
+				if q.VisionModel.TemporalPatchSize == 0 {
+					q.VisionModel.TemporalPatchSize = pre.TemporalPatchSize
+				}
+				if q.VisionModel.SpatialMergeSize == 0 {
+					q.VisionModel.SpatialMergeSize = pre.MergeSize
+				}
+				if q.VisionModel.Size.ShortestEdge == 0 {
+					q.VisionModel.Size.ShortestEdge = pre.Size.ShortestEdge
+				}
+				if q.VisionModel.Size.LongestEdge == 0 {
+					q.VisionModel.Size.LongestEdge = pre.Size.LongestEdge
+				}
+				if len(q.VisionModel.ImageMean) == 0 {
+					q.VisionModel.ImageMean = pre.ImageMean
+				}
+				if len(q.VisionModel.ImageStd) == 0 {
+					q.VisionModel.ImageStd = pre.ImageStd
+				}
+			}
+		}
+	}
+
 	if q.NumHiddenLayers == 0 {
 		return fmt.Errorf("qwen3next: num_hidden_layers must be set")
 	}
@@ -74,36 +175,96 @@ func (q *qwen3NextModel) parseMore(_ fs.FS) error {
 	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)
+	if _, err := q.kvHeadCounts(); err != nil {
+		return err
 	}
 
 	return nil
 }
 
+func (q *qwen3NextModel) kvHeadCounts() ([]uint32, error) {
+	if len(q.LayerTypes) > 0 {
+		kv := make([]uint32, q.NumHiddenLayers)
+		hasFull := false
+		hasRecurrent := false
+		for i := range q.NumHiddenLayers {
+			layerType := ""
+			if i < uint32(len(q.LayerTypes)) {
+				layerType = q.LayerTypes[i]
+			}
+			if layerType == "full_attention" {
+				kv[i] = q.NumKeyValueHeads
+				hasFull = true
+			} else {
+				hasRecurrent = true
+			}
+		}
+		if !hasFull || !hasRecurrent {
+			return nil, fmt.Errorf("qwen3next: layer_types must include both full_attention and linear_attention")
+		}
+		return kv, nil
+	}
+
+	if q.FullAttentionInterval == 0 {
+		return nil, fmt.Errorf("qwen3next: full_attention_interval must be set")
+	}
+	if q.FullAttentionInterval > q.NumHiddenLayers {
+		return nil, fmt.Errorf("qwen3next: full_attention_interval (%d) exceeds num_hidden_layers (%d)", q.FullAttentionInterval, q.NumHiddenLayers)
+	}
+
+	kv := make([]uint32, q.NumHiddenLayers)
+	hasFull := false
+	for i := range q.NumHiddenLayers {
+		if (i+1)%q.FullAttentionInterval == 0 {
+			kv[i] = q.NumKeyValueHeads
+			hasFull = true
+		}
+	}
+	if !hasFull {
+		return nil, fmt.Errorf("qwen3next: head_count_kv would be all zeros (full_attention_interval=%d, num_hidden_layers=%d)", q.FullAttentionInterval, q.NumHiddenLayers)
+	}
+	return kv, nil
+}
+
+func (q *qwen3NextModel) ropeSections() []int32 {
+	if len(q.RopeParameters.MropeSection) > 0 {
+		return q.RopeParameters.MropeSection
+	}
+	return q.RopeScaling.MropeSection
+}
+
+func (q *qwen3NextModel) shouldReorderVHeads() bool {
+	modelType := strings.ToLower(q.ModelType)
+	if strings.Contains(modelType, "qwen3_next") || strings.Contains(modelType, "qwen3next") {
+		return false
+	}
+
+	for _, arch := range q.Architectures {
+		arch = strings.ToLower(arch)
+		if strings.Contains(arch, "qwen3next") || strings.Contains(arch, "qwen3_next") {
+			return false
+		}
+	}
+
+	// Default to qwen3.5 layout for all other qwen3next-family imports.
+	return true
+}
+
 func (q *qwen3NextModel) KV(t *Tokenizer) KV {
 	kv := q.ModelParameters.KV(t)
-	kv["general.architecture"] = "qwen3next"
-	kv["tokenizer.ggml.pre"] = "qwen2"
+
+	arch := "qwen35"
+	if q.NumExperts > 0 {
+		arch = "qwen35moe"
+	}
+	kv["general.architecture"] = arch
+	kv["tokenizer.ggml.pre"] = "qwen35"
 	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
@@ -113,18 +274,31 @@ func (q *qwen3NextModel) KV(t *Tokenizer) KV {
 	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 sections := q.ropeSections(); len(sections) > 0 {
+		kv["mrope_sections"] = sections
+		kv["rope.mrope_section"] = sections
+		kv["rope.dimension_sections"] = sections
+	}
+	if q.RopeParameters.MRopeInterleaved {
+		kv["rope.mrope_interleaved"] = true
+	}
+
+	if q.RopeScaling.Type != "" && q.RopeScaling.Type != "default" {
+		kv["rope.scaling.type"] = q.RopeScaling.Type
+		kv["rope.scaling.factor"] = q.RopeScaling.Factor
+	}
+
 	if q.NumExperts > 0 {
 		kv["expert_count"] = q.NumExperts
 		kv["expert_used_count"] = q.NumExpertsPerToken
-		kv["norm_top_k_prob"] = q.NormTopkProb
+		if q.NormTopkProb != nil {
+			kv["norm_top_k_prob"] = *q.NormTopkProb
+		}
 		if q.MoEIntermediateSize > 0 {
 			kv["expert_feed_forward_length"] = q.MoEIntermediateSize
 		}
@@ -133,33 +307,66 @@ func (q *qwen3NextModel) KV(t *Tokenizer) KV {
 		}
 	}
 
-	// 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.state_size"] = q.LinearKeyHeadDim
+	kv["ssm.group_count"] = q.LinearNumKeyHeads
+	kv["ssm.time_step_rank"] = q.LinearNumValueHeads
 	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)
+	if q.shouldReorderVHeads() {
+		kv["ssm.v_head_reordered"] = true
+	}
+	if q.FullAttentionInterval > 0 {
+		kv["full_attention_interval"] = q.FullAttentionInterval
 	}
-	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
+	if headCounts, err := q.kvHeadCounts(); err == nil {
+		kv["attention.head_count_kv"] = headCounts
+	}
+
+	if q.VisionModel.Depth > 0 {
+		kv["vision.block_count"] = q.VisionModel.Depth
+		kv["vision.embedding_length"] = q.VisionModel.HiddenSize
+		kv["vision.attention.head_count"] = q.VisionModel.NumHeads
+		kv["vision.num_channels"] = q.VisionModel.InChannels
+		if q.VisionModel.PatchSize > 0 {
+			kv["vision.patch_size"] = q.VisionModel.PatchSize
+		}
+		if q.VisionModel.SpatialMergeSize > 0 {
+			kv["vision.spatial_merge_size"] = q.VisionModel.SpatialMergeSize
+		}
+		if q.VisionModel.RMSNormEps > 0 {
+			kv["vision.attention.layer_norm_epsilon"] = q.VisionModel.RMSNormEps
+		}
+		if q.VisionModel.RopeTheta > 0 {
+			kv["vision.rope.freq_base"] = q.VisionModel.RopeTheta
+		}
+		if q.VisionModel.TemporalPatchSize > 0 {
+			kv["vision.temporal_patch_size"] = q.VisionModel.TemporalPatchSize
+		}
+		kv["vision.deepstack_visual_indexes"] = q.VisionModel.DeepstackVisualIndexes
+		if q.VisionModel.Size.ShortestEdge > 0 {
+			kv["vision.shortest_edge"] = q.VisionModel.Size.ShortestEdge
+		}
+		if q.VisionModel.Size.LongestEdge > 0 {
+			kv["vision.longest_edge"] = q.VisionModel.Size.LongestEdge
+		}
+		if len(q.VisionModel.ImageMean) > 0 {
+			kv["vision.image_mean"] = q.VisionModel.ImageMean
+		}
+		if len(q.VisionModel.ImageStd) > 0 {
+			kv["vision.image_std"] = q.VisionModel.ImageStd
+		}
+	}
+
+	if q.ImageTokenID > 0 {
+		kv["image_token_id"] = q.ImageTokenID
+	}
+	if q.VisionStartTokenID > 0 {
+		kv["vision_start_token_id"] = q.VisionStartTokenID
+	}
+	if q.VisionEndTokenID > 0 {
+		kv["vision_end_token_id"] = q.VisionEndTokenID
 	}
 
 	return kv
@@ -168,7 +375,6 @@ func (q *qwen3NextModel) KV(t *Tokenizer) 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{
@@ -185,16 +391,13 @@ func (q *qwen3NextModel) Tensors(ts []Tensor) []*ggml.Tensor {
 		}
 	}
 
-	// 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)
@@ -204,84 +407,299 @@ func (q *qwen3NextModel) Tensors(ts []Tensor) []*ggml.Tensor {
 		}
 
 		switch {
-		// Add 1 to norm weights (except ssm_norm which is linear_attn.norm)
-		// This matches the Python converter behavior for qwen3next
+		case strings.Contains(name, ".mlp.experts.gate_up_proj"):
+			out = append(out, slices.Collect(splitDim(t, 1,
+				split{Replacer: strings.NewReplacer(".mlp.experts.gate_up_proj", ".ffn_gate_exps.weight")},
+				split{Replacer: strings.NewReplacer(".mlp.experts.gate_up_proj", ".ffn_up_exps.weight")},
+			))...)
+
+		case strings.Contains(name, ".mlp.experts.down_proj"):
+			out = append(out, &ggml.Tensor{
+				Name:     strings.NewReplacer(".mlp.experts.down_proj", ".ffn_down_exps.weight").Replace(name),
+				Kind:     t.Kind(),
+				Shape:    slices.Clone(shape),
+				WriterTo: t,
+			})
+
+		case strings.HasPrefix(name, "v.blk.") && strings.Contains(name, ".attn_qkv"):
+			out = append(out, slices.Collect(splitDim(t, 0,
+				split{Replacer: strings.NewReplacer("attn_qkv", "attn_q")},
+				split{Replacer: strings.NewReplacer("attn_qkv", "attn_k")},
+				split{Replacer: strings.NewReplacer("attn_qkv", "attn_v")},
+			))...)
+
+		case strings.Contains(name, "patch_embed") && strings.HasSuffix(name, "weight"):
+			out = append(out, &ggml.Tensor{
+				Name:     name,
+				Kind:     t.Kind(),
+				Shape:    append([]uint64{shape[0] * shape[1]}, shape[2:]...),
+				WriterTo: t,
+			})
+
 		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,
-			})
+			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,
-			})
+			t.SetRepacker(q.repackSSMA())
+			out = append(out, &ggml.Tensor{Name: name, Kind: t.Kind(), Shape: slices.Clone(shape), WriterTo: t})
+
+		case strings.HasSuffix(name, ".attn_qkv.weight"):
+			if q.shouldReorderVHeads() {
+				t.SetRepacker(q.repackAttnQKV())
+			}
+			out = append(out, &ggml.Tensor{Name: name, Kind: t.Kind(), Shape: slices.Clone(shape), WriterTo: t})
+
+		case strings.HasSuffix(name, ".attn_gate.weight"):
+			if q.shouldReorderVHeads() {
+				// HF tensor layout is [out_features, in_features]; reorder rows.
+				t.SetRepacker(q.repackReorderDim(0, int(q.LinearValueHeadDim)))
+			}
+			out = append(out, &ggml.Tensor{Name: name, Kind: t.Kind(), Shape: slices.Clone(shape), WriterTo: t})
+
+		case strings.HasSuffix(name, ".ssm_beta.weight"), strings.HasSuffix(name, ".ssm_alpha.weight"):
+			if q.shouldReorderVHeads() {
+				// HF tensor layout is [out_features, in_features]; reorder rows.
+				t.SetRepacker(q.repackReorderDim(0, 1))
+			}
+			out = append(out, &ggml.Tensor{Name: name, Kind: t.Kind(), Shape: slices.Clone(shape), WriterTo: t})
+
+		case strings.HasSuffix(name, ".ssm_dt"):
+			if q.shouldReorderVHeads() {
+				t.SetRepacker(q.repackReorderDim(0, 1))
+			}
+			out = append(out, &ggml.Tensor{Name: name, Kind: t.Kind(), Shape: slices.Clone(shape), WriterTo: t})
+
+		case strings.HasSuffix(name, ".ssm_out.weight"):
+			if q.shouldReorderVHeads() {
+				// HF out_proj layout is [out_features, in_features]; reorder columns.
+				t.SetRepacker(q.repackReorderDim(1, int(q.LinearValueHeadDim)))
+			}
+			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]}
-				}
+			if q.shouldReorderVHeads() {
+				t.SetRepacker(q.repackConv1D())
 			}
-			out = append(out, &ggml.Tensor{
-				Name:     name,
-				Kind:     t.Kind(),
-				Shape:    newShape,
-				WriterTo: t,
-			})
+			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,
-			})
+			out = append(out, &ggml.Tensor{Name: name, Kind: t.Kind(), Shape: slices.Clone(shape), WriterTo: t})
 		}
 	}
 
 	return out
 }
 
+func (q *qwen3NextModel) repackReorderDim(dim, headDim int) Repacker {
+	return func(_ string, data []float32, shape []uint64) ([]float32, error) {
+		if !q.shouldReorderVHeads() {
+			return data, nil
+		}
+		numK := int(q.LinearNumKeyHeads)
+		numVPerK := int(q.LinearNumValueHeads / q.LinearNumKeyHeads)
+		return reorderHeadLayout(data, shape, dim, numK, numVPerK, headDim)
+	}
+}
+
+func (q *qwen3NextModel) repackAttnQKV() Repacker {
+	return func(_ string, data []float32, shape []uint64) ([]float32, error) {
+		if !q.shouldReorderVHeads() || len(shape) != 2 {
+			return data, nil
+		}
+
+		rows := int(shape[0])
+		cols := int(shape[1])
+		numK := int(q.LinearNumKeyHeads)
+		numV := int(q.LinearNumValueHeads)
+		headK := int(q.LinearKeyHeadDim)
+		headV := int(q.LinearValueHeadDim)
+		qDim := headK * numK
+		kDim := headK * numK
+		vDim := headV * numV
+		qkvDim := qDim + kDim + vDim
+
+		switch {
+		case rows == qkvDim:
+			// HF layout: [out_features, in_features]. Keep Q/K rows unchanged and
+			// reorder only V rows from grouped -> tiled head layout.
+			out := make([]float32, len(data))
+			qkRows := qDim + kDim
+			qkSize := qkRows * cols
+			copy(out[:qkSize], data[:qkSize])
+
+			vStart := qkSize
+			vEnd := vStart + vDim*cols
+			reorderedV, err := reorderHeadLayout(data[vStart:vEnd], []uint64{uint64(vDim), uint64(cols)}, 0, numK, numV/numK, headV)
+			if err != nil {
+				return nil, err
+			}
+			copy(out[vStart:vEnd], reorderedV)
+			copy(out[vEnd:], data[vEnd:])
+			return out, nil
+
+		case cols == qkvDim:
+			// Fallback for already-transposed [in_features, out_features] tensors.
+			out := make([]float32, len(data))
+			copy(out, data)
+			for r := range rows {
+				base := r * cols
+				vStart := base + qDim + kDim
+				vEnd := vStart + vDim
+				reorderedV, err := reorderHeadLayout(out[vStart:vEnd], []uint64{uint64(vDim)}, 0, numK, numV/numK, headV)
+				if err != nil {
+					return nil, err
+				}
+				copy(out[vStart:vEnd], reorderedV)
+			}
+			return out, nil
+
+		default:
+			return data, nil
+		}
+	}
+}
+
+func (q *qwen3NextModel) repackConv1D() Repacker {
+	return func(_ string, data []float32, shape []uint64) ([]float32, error) {
+		if !q.shouldReorderVHeads() {
+			return data, nil
+		}
+
+		normShape := slices.Clone(shape)
+		if len(shape) == 3 {
+			if shape[0] == 1 {
+				normShape = []uint64{shape[1], shape[2]}
+			} else if shape[1] == 1 {
+				normShape = []uint64{shape[0], shape[2]}
+			}
+		}
+		if len(normShape) != 2 {
+			return data, nil
+		}
+
+		rows := int(normShape[0])
+		cols := int(normShape[1])
+		numK := int(q.LinearNumKeyHeads)
+		numV := int(q.LinearNumValueHeads)
+		headK := int(q.LinearKeyHeadDim)
+		headV := int(q.LinearValueHeadDim)
+		qkChannels := 2 * headK * numK
+		totalChannels := qkChannels + headV*numV
+		if qkChannels <= 0 {
+			return data, nil
+		}
+
+		switch {
+		case rows == totalChannels:
+			// HF layout after squeeze: [channels, kernel]
+			out := make([]float32, len(data))
+			prefix := qkChannels * cols
+			copy(out[:prefix], data[:prefix])
+			reorderedV, err := reorderHeadLayout(data[prefix:], []uint64{uint64(totalChannels - qkChannels), uint64(cols)}, 0, numK, numV/numK, headV)
+			if err != nil {
+				return nil, err
+			}
+			copy(out[prefix:], reorderedV)
+			return out, nil
+		case cols == totalChannels:
+			// Fallback for transposed [kernel, channels]
+			out := make([]float32, len(data))
+			copy(out, data)
+			vChannels := totalChannels - qkChannels
+			for r := range rows {
+				base := r * cols
+				vStart := base + qkChannels
+				vEnd := vStart + vChannels
+				reorderedV, err := reorderHeadLayout(out[vStart:vEnd], []uint64{uint64(vChannels)}, 0, numK, numV/numK, headV)
+				if err != nil {
+					return nil, err
+				}
+				copy(out[vStart:vEnd], reorderedV)
+			}
+			return out, nil
+		default:
+			return data, nil
+		}
+	}
+}
+
+func (q *qwen3NextModel) repackSSMA() Repacker {
+	return func(_ string, data []float32, shape []uint64) ([]float32, error) {
+		result := make([]float32, len(data))
+		for i, v := range data {
+			result[i] = -float32(math.Exp(float64(v)))
+		}
+		if !q.shouldReorderVHeads() {
+			return result, nil
+		}
+		numK := int(q.LinearNumKeyHeads)
+		numVPerK := int(q.LinearNumValueHeads / q.LinearNumKeyHeads)
+		return reorderHeadLayout(result, shape, 0, numK, numVPerK, 1)
+	}
+}
+
+func reorderHeadLayout(data []float32, shape []uint64, dim int, numKHeads, numVPerK, headDim int) ([]float32, error) {
+	if len(shape) == 0 || numKHeads <= 0 || numVPerK <= 0 || headDim <= 0 {
+		return data, nil
+	}
+
+	dims := make([]int, len(shape))
+	for i := range shape {
+		dims[i] = int(shape[i])
+	}
+	if dim < 0 {
+		dim += len(dims)
+	}
+	if dim < 0 || dim >= len(dims) {
+		return data, nil
+	}
+
+	expected := numKHeads * numVPerK * headDim
+	if dims[dim] != expected {
+		return data, nil
+	}
+
+	newShape := make([]int, 0, len(dims)+2)
+	newShape = append(newShape, dims[:dim]...)
+	newShape = append(newShape, numKHeads, numVPerK, headDim)
+	newShape = append(newShape, dims[dim+1:]...)
+
+	var tt tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
+	if err := tt.Reshape(newShape...); err != nil {
+		return nil, err
+	}
+
+	perm := make([]int, len(newShape))
+	for i := range perm {
+		perm[i] = i
+	}
+	perm[dim], perm[dim+1] = perm[dim+1], perm[dim]
+
+	tt, err := tensor.Transpose(tt, perm...)
+	if err != nil {
+		return nil, err
+	}
+	tt = tensor.Materialize(tt)
+
+	total := 1
+	for _, d := range dims {
+		total *= d
+	}
+	if err := tt.Reshape(total); err != nil {
+		return nil, err
+	}
+	return native.VectorF32(tt.(*tensor.Dense))
+}
+
 type qkvzSplitSpec struct {
 	hidden    int
 	headKDim  int
@@ -369,7 +787,6 @@ func (q *qwen3NextModel) repackQKVZ(spec qkvzSplitSpec, extractGate bool) Repack
 		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
@@ -444,7 +861,6 @@ func (q *qwen3NextModel) repackQKVZ(spec qkvzSplitSpec, extractGate bool) Repack
 	}
 }
 
-// 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]))
@@ -471,10 +887,21 @@ func (q *qwen3NextModel) Replacements() []string {
 	return []string{
 		// Embeddings and output
 		"lm_head", "output",
+		"model.language_model.embed_tokens", "token_embd",
+		"model.language_model.norm", "output_norm",
+		"model.language_model.layers", "blk",
 		"model.embed_tokens", "token_embd",
 		"model.norm", "output_norm",
 		"model.layers", "blk",
 
+		// Vision
+		"model.visual", "v",
+		"patch_embed.proj", "patch_embed",
+		"blocks", "blk",
+		"attn.qkv", "attn_qkv",
+		"attn.proj", "attn_out",
+		"deepstack_merger_list", "deepstack_merger",
+
 		// Layer norms
 		"input_layernorm", "attn_norm",
 		"post_attention_layernorm", "post_attention_norm",
@@ -487,9 +914,16 @@ func (q *qwen3NextModel) Replacements() []string {
 		"self_attn.v_proj", "attn_v",
 		"self_attn.o_proj", "attn_output",
 
-		// Linear attention (Gated Delta Net)
+		// Linear attention (legacy qwen3next)
 		"linear_attn.in_proj_qkvz", "ssm_in",
 		"linear_attn.in_proj_ba", "ssm_ba",
+
+		// Linear attention (qwen35)
+		"linear_attn.in_proj_qkv", "attn_qkv",
+		"linear_attn.in_proj_z", "attn_gate",
+		"linear_attn.in_proj_a", "ssm_alpha",
+		"linear_attn.in_proj_b", "ssm_beta",
+
 		"linear_attn.conv1d", "ssm_conv1d",
 		"linear_attn.dt_bias", "ssm_dt",
 		"linear_attn.dt_proj", "ssm_dt",
@@ -497,14 +931,14 @@ func (q *qwen3NextModel) Replacements() []string {
 		"linear_attn.norm", "ssm_norm",
 		"linear_attn.out_proj", "ssm_out",
 
-		// MoE (experts are stacked via mergeTensors, not replaced here)
+		// MoE
 		"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)
+		// Dense FFN
 		"mlp.down_proj", "ffn_down",
 		"mlp.gate_proj", "ffn_gate",
 		"mlp.up_proj", "ffn_up",

convert/convert_qwen3next_test.go

@@ -0,0 +1,563 @@
+package convert
+
+import (
+	"bytes"
+	"encoding/binary"
+	"os"
+	"slices"
+	"strings"
+	"testing"
+
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+func boolPtr(v bool) *bool {
+	return &v
+}
+
+func readTensorData(t *testing.T, tensor *ggml.Tensor) []float32 {
+	t.Helper()
+
+	var b bytes.Buffer
+	if _, err := tensor.WriteTo(&b); err != nil {
+		t.Fatal(err)
+	}
+
+	numel := 1
+	for _, d := range tensor.Shape {
+		numel *= int(d)
+	}
+
+	values := make([]float32, numel)
+	if err := binary.Read(&b, binary.LittleEndian, &values); err != nil {
+		t.Fatal(err)
+	}
+
+	return values
+}
+
+func TestQwen3NextLegacyModelTypeDisablesReorder(t *testing.T) {
+	m := &qwen3NextModel{
+		ModelParameters: ModelParameters{
+			ModelType: "qwen3_next",
+		},
+	}
+
+	if m.shouldReorderVHeads() {
+		t.Fatalf("legacy qwen3_next model_type should not reorder v-head layout")
+	}
+}
+
+func TestQwen3NextLegacyArchitectureDisablesReorder(t *testing.T) {
+	m := &qwen3NextModel{
+		ModelParameters: ModelParameters{
+			Architectures: []string{"Qwen3NextForCausalLM"},
+		},
+	}
+
+	if m.shouldReorderVHeads() {
+		t.Fatalf("legacy Qwen3Next architecture should not reorder v-head layout")
+	}
+}
+
+func TestQwen3NextKVLegacyConfig(t *testing.T) {
+	m := &qwen3NextModel{
+		ModelParameters: ModelParameters{
+			ModelType: "qwen3_next",
+		},
+		qwen3NextTextConfig: qwen3NextTextConfig{
+			MaxPositionEmbeddings: 8192,
+			HiddenSize:            512,
+			NumHiddenLayers:       4,
+			IntermediateSize:      2048,
+			NumAttentionHeads:     8,
+			NumKeyValueHeads:      2,
+			HeadDim:               64,
+			RopeTheta:             1_000_000,
+			RMSNormEPS:            1e-6,
+
+			NumExperts:             8,
+			NumExpertsPerToken:     2,
+			NormTopkProb:           boolPtr(true),
+			MoEIntermediateSize:    256,
+			SharedExpertIntermSize: 512,
+
+			FullAttentionInterval: 2,
+
+			LinearConvKernelDim: 4,
+			LinearKeyHeadDim:    64,
+			LinearNumKeyHeads:   2,
+			LinearNumValueHeads: 4,
+			LinearValueHeadDim:  64,
+
+			PartialRotaryFactor: 0.25,
+		},
+	}
+
+	if err := m.parseMore(os.DirFS(t.TempDir())); err != nil {
+		t.Fatal(err)
+	}
+
+	kv := m.KV(&Tokenizer{Vocabulary: &Vocabulary{}})
+	if got, want := kv["general.architecture"], "qwen35moe"; got != want {
+		t.Fatalf("unexpected architecture: got %v want %v", got, want)
+	}
+	if got, want := kv["tokenizer.ggml.pre"], "qwen35"; got != want {
+		t.Fatalf("unexpected tokenizer pre: got %v want %v", got, want)
+	}
+
+	headCountKV, ok := kv["attention.head_count_kv"].([]uint32)
+	if !ok {
+		t.Fatalf("attention.head_count_kv has unexpected type: %T", kv["attention.head_count_kv"])
+	}
+	if got, want := headCountKV, []uint32{0, 2, 0, 2}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected attention.head_count_kv: got %v want %v", got, want)
+	}
+
+	if _, ok := kv["ssm.v_head_reordered"]; ok {
+		t.Fatalf("legacy qwen3next should not enable ssm.v_head_reordered")
+	}
+	if got, want := kv["norm_top_k_prob"], true; got != want {
+		t.Fatalf("unexpected norm_top_k_prob: got %v want %v", got, want)
+	}
+}
+
+func TestQwen35MoeOmitsNormTopKProbWhenUnset(t *testing.T) {
+	m := &qwen3NextModel{
+		ModelParameters: ModelParameters{
+			ModelType: "qwen3_5",
+		},
+		qwen3NextTextConfig: qwen3NextTextConfig{
+			MaxPositionEmbeddings: 4096,
+			HiddenSize:            512,
+			NumHiddenLayers:       4,
+			IntermediateSize:      2048,
+			NumAttentionHeads:     8,
+			NumKeyValueHeads:      2,
+			HeadDim:               64,
+			RopeTheta:             1_000_000,
+			RMSNormEPS:            1e-6,
+			NumExperts:            8,
+			NumExpertsPerToken:    2,
+			FullAttentionInterval: 2,
+			LinearConvKernelDim:   4,
+			LinearKeyHeadDim:      64,
+			LinearNumKeyHeads:     2,
+			LinearNumValueHeads:   4,
+			LinearValueHeadDim:    64,
+			PartialRotaryFactor:   0.25,
+		},
+	}
+
+	if err := m.parseMore(os.DirFS(t.TempDir())); err != nil {
+		t.Fatal(err)
+	}
+
+	kv := m.KV(&Tokenizer{Vocabulary: &Vocabulary{}})
+	if _, ok := kv["norm_top_k_prob"]; ok {
+		t.Fatalf("expected norm_top_k_prob to be omitted when not set in config")
+	}
+}
+
+func TestQwen35KVFromTextConfig(t *testing.T) {
+	m := &qwen3NextModel{
+		ModelParameters: ModelParameters{
+			ModelType: "qwen3_5",
+		},
+		TextConfig: &qwen3NextTextConfig{
+			MaxPositionEmbeddings: 16384,
+			HiddenSize:            1024,
+			NumHiddenLayers:       4,
+			IntermediateSize:      4096,
+			NumAttentionHeads:     8,
+			NumKeyValueHeads:      4,
+			HeadDim:               128,
+			RMSNormEPS:            1e-6,
+
+			LayerTypes: []string{
+				"linear_attention",
+				"full_attention",
+				"linear_attention",
+				"full_attention",
+			},
+
+			LinearConvKernelDim: 4,
+			LinearKeyHeadDim:    128,
+			LinearNumKeyHeads:   2,
+			LinearNumValueHeads: 4,
+			LinearValueHeadDim:  128,
+
+			RopeParameters: qwen3NextRopeParams{
+				MRopeInterleaved:    true,
+				MropeSection:        []int32{11, 11, 10},
+				RopeType:            "default",
+				RopeTheta:           10_000_000,
+				PartialRotaryFactor: 0.25,
+			},
+		},
+		VisionModel: qwen3NextVisionConfig{
+			Depth:                  2,
+			HiddenSize:             128,
+			NumHeads:               4,
+			InChannels:             3,
+			PatchSize:              16,
+			SpatialMergeSize:       2,
+			RMSNormEps:             1e-6,
+			RopeTheta:              10_000,
+			TemporalPatchSize:      2,
+			DeepstackVisualIndexes: []int32{1},
+		},
+		ImageTokenID:       1001,
+		VisionStartTokenID: 1002,
+		VisionEndTokenID:   1003,
+	}
+	m.VisionModel.Size.ShortestEdge = 224
+	m.VisionModel.Size.LongestEdge = 4096
+	m.VisionModel.ImageMean = []float32{0.5, 0.5, 0.5}
+	m.VisionModel.ImageStd = []float32{0.2, 0.2, 0.2}
+
+	if err := m.parseMore(os.DirFS(t.TempDir())); err != nil {
+		t.Fatal(err)
+	}
+
+	kv := m.KV(&Tokenizer{Vocabulary: &Vocabulary{}})
+	if got, want := kv["general.architecture"], "qwen35"; got != want {
+		t.Fatalf("unexpected architecture: got %v want %v", got, want)
+	}
+
+	headCountKV, ok := kv["attention.head_count_kv"].([]uint32)
+	if !ok {
+		t.Fatalf("attention.head_count_kv has unexpected type: %T", kv["attention.head_count_kv"])
+	}
+	if got, want := headCountKV, []uint32{0, 4, 0, 4}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected attention.head_count_kv: got %v want %v", got, want)
+	}
+
+	if got, ok := kv["ssm.v_head_reordered"].(bool); !ok || !got {
+		t.Fatalf("expected ssm.v_head_reordered=true, got %v (%T)", kv["ssm.v_head_reordered"], kv["ssm.v_head_reordered"])
+	}
+
+	mrope, ok := kv["mrope_sections"].([]int32)
+	if !ok {
+		t.Fatalf("mrope_sections has unexpected type: %T", kv["mrope_sections"])
+	}
+	if got, want := mrope, []int32{11, 11, 10}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected mrope_sections: got %v want %v", got, want)
+	}
+	ropeSections, ok := kv["rope.dimension_sections"].([]int32)
+	if !ok {
+		t.Fatalf("rope.dimension_sections has unexpected type: %T", kv["rope.dimension_sections"])
+	}
+	if got, want := ropeSections, []int32{11, 11, 10}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected rope.dimension_sections: got %v want %v", got, want)
+	}
+
+	if got, ok := kv["rope.mrope_interleaved"].(bool); !ok || !got {
+		t.Fatalf("expected rope.mrope_interleaved=true, got %v (%T)", kv["rope.mrope_interleaved"], kv["rope.mrope_interleaved"])
+	}
+
+	if got, want := kv["vision.block_count"], uint32(2); got != want {
+		t.Fatalf("unexpected vision.block_count: got %v want %v", got, want)
+	}
+}
+
+func TestQwen3NextReplacements(t *testing.T) {
+	r := strings.NewReplacer((&qwen3NextModel{}).Replacements()...)
+
+	if got, want := r.Replace("model.language_model.layers.1.linear_attn.in_proj_qkv.weight"), "blk.1.attn_qkv.weight"; got != want {
+		t.Fatalf("unexpected language-model replacement: got %q want %q", got, want)
+	}
+	if got, want := r.Replace("model.visual.blocks.0.attn.qkv.weight"), "v.blk.0.attn_qkv.weight"; got != want {
+		t.Fatalf("unexpected vision replacement: got %q want %q", got, want)
+	}
+	if got, want := r.Replace("model.layers.1.linear_attn.in_proj_qkvz.weight"), "blk.1.ssm_in.weight"; got != want {
+		t.Fatalf("unexpected legacy replacement: got %q want %q", got, want)
+	}
+}
+
+func TestQwen35ReordersVHeads(t *testing.T) {
+	m := &qwen3NextModel{
+		ModelParameters: ModelParameters{
+			ModelType: "qwen3_5",
+		},
+		qwen3NextTextConfig: qwen3NextTextConfig{
+			LinearNumKeyHeads:   2,
+			LinearNumValueHeads: 4,
+			LinearValueHeadDim:  1,
+		},
+	}
+
+	out := m.Tensors([]Tensor{
+		&fakeTensor{
+			name:  "blk.0.attn_gate.weight",
+			shape: []uint64{4, 2},
+			data:  []float32{0, 1, 2, 3, 4, 5, 6, 7},
+		},
+	})
+	if len(out) != 1 {
+		t.Fatalf("unexpected output tensor count: got %d want 1", len(out))
+	}
+
+	if got, want := readTensorData(t, out[0]), []float32{0, 1, 4, 5, 2, 3, 6, 7}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected data: got %v want %v", got, want)
+	}
+}
+
+func TestQwen35ReordersAttnQKVOutputDim(t *testing.T) {
+	m := &qwen3NextModel{
+		ModelParameters: ModelParameters{
+			ModelType: "qwen3_5",
+		},
+		qwen3NextTextConfig: qwen3NextTextConfig{
+			LinearNumKeyHeads:   2,
+			LinearNumValueHeads: 4,
+			LinearKeyHeadDim:    1,
+			LinearValueHeadDim:  1,
+		},
+	}
+
+	out := m.Tensors([]Tensor{
+		&fakeTensor{
+			name:  "blk.0.attn_qkv.weight",
+			shape: []uint64{8, 2}, // [out_features, in_features] (HF layout)
+			data: []float32{
+				0, 1, // q0
+				2, 3, // q1
+				4, 5, // k0
+				6, 7, // k1
+				10, 11, // v(k0,v0)
+				12, 13, // v(k0,v1)
+				20, 21, // v(k1,v0)
+				22, 23, // v(k1,v1)
+			},
+		},
+	})
+	if len(out) != 1 {
+		t.Fatalf("unexpected output tensor count: got %d want 1", len(out))
+	}
+
+	if got, want := readTensorData(t, out[0]), []float32{
+		0, 1, 2, 3, 4, 5, 6, 7,
+		10, 11, 20, 21, 12, 13, 22, 23,
+	}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected qkv data: got %v want %v", got, want)
+	}
+}
+
+func TestQwen35ReordersSsmOutInputDim(t *testing.T) {
+	m := &qwen3NextModel{
+		ModelParameters: ModelParameters{
+			ModelType: "qwen3_5",
+		},
+		qwen3NextTextConfig: qwen3NextTextConfig{
+			LinearNumKeyHeads:   2,
+			LinearNumValueHeads: 4,
+			LinearValueHeadDim:  1,
+		},
+	}
+
+	out := m.Tensors([]Tensor{
+		&fakeTensor{
+			name:  "blk.0.ssm_out.weight",
+			shape: []uint64{2, 4},
+			data:  []float32{0, 1, 2, 3, 4, 5, 6, 7},
+		},
+	})
+	if len(out) != 1 {
+		t.Fatalf("unexpected output tensor count: got %d want 1", len(out))
+	}
+
+	if got, want := readTensorData(t, out[0]), []float32{0, 2, 1, 3, 4, 6, 5, 7}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected ssm_out data: got %v want %v", got, want)
+	}
+}
+
+func TestQwen35ReordersSsmBetaRows(t *testing.T) {
+	m := &qwen3NextModel{
+		ModelParameters: ModelParameters{
+			ModelType: "qwen3_5",
+		},
+		qwen3NextTextConfig: qwen3NextTextConfig{
+			LinearNumKeyHeads:   2,
+			LinearNumValueHeads: 4,
+		},
+	}
+
+	out := m.Tensors([]Tensor{
+		&fakeTensor{
+			name:  "blk.0.ssm_beta.weight",
+			shape: []uint64{4, 2},
+			data:  []float32{0, 1, 2, 3, 4, 5, 6, 7},
+		},
+	})
+	if len(out) != 1 {
+		t.Fatalf("unexpected output tensor count: got %d want 1", len(out))
+	}
+
+	if got, want := readTensorData(t, out[0]), []float32{0, 1, 4, 5, 2, 3, 6, 7}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected ssm_beta data: got %v want %v", got, want)
+	}
+}
+
+func TestQwen35ReordersConv1DChannelDim(t *testing.T) {
+	m := &qwen3NextModel{
+		ModelParameters: ModelParameters{
+			ModelType: "qwen3_5",
+		},
+		qwen3NextTextConfig: qwen3NextTextConfig{
+			LinearNumKeyHeads:   2,
+			LinearNumValueHeads: 4,
+			LinearKeyHeadDim:    1,
+			LinearValueHeadDim:  1,
+		},
+	}
+
+	out := m.Tensors([]Tensor{
+		&fakeTensor{
+			name:  "blk.0.ssm_conv1d.weight",
+			shape: []uint64{8, 2}, // [channels, kernel] after squeeze
+			data: []float32{
+				0, 1, // q0
+				2, 3, // q1
+				4, 5, // k0
+				6, 7, // k1
+				10, 11, // v(k0,v0)
+				12, 13, // v(k0,v1)
+				20, 21, // v(k1,v0)
+				22, 23, // v(k1,v1)
+			},
+		},
+	})
+	if len(out) != 1 {
+		t.Fatalf("unexpected output tensor count: got %d want 1", len(out))
+	}
+
+	if got, want := readTensorData(t, out[0]), []float32{
+		0, 1, 2, 3, 4, 5, 6, 7,
+		10, 11, 20, 21, 12, 13, 22, 23,
+	}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected conv1d data: got %v want %v", got, want)
+	}
+}
+
+func TestLegacyQwen3NextDoesNotReorderVHeads(t *testing.T) {
+	m := &qwen3NextModel{
+		ModelParameters: ModelParameters{
+			ModelType: "qwen3_next",
+		},
+		qwen3NextTextConfig: qwen3NextTextConfig{
+			LinearNumKeyHeads:   2,
+			LinearNumValueHeads: 4,
+			LinearValueHeadDim:  1,
+		},
+	}
+
+	out := m.Tensors([]Tensor{
+		&fakeTensor{
+			name:  "blk.0.attn_gate.weight",
+			shape: []uint64{4, 1},
+			data:  []float32{0, 1, 2, 3},
+		},
+	})
+	if len(out) != 1 {
+		t.Fatalf("unexpected output tensor count: got %d want 1", len(out))
+	}
+
+	if got, want := readTensorData(t, out[0]), []float32{0, 1, 2, 3}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected data for legacy qwen3next: got %v want %v", got, want)
+	}
+}
+
+func TestQwen35MoePackedExperts(t *testing.T) {
+	m := &qwen3NextModel{
+		qwen3NextTextConfig: qwen3NextTextConfig{
+			NumHiddenLayers: 1,
+		},
+	}
+
+	out := m.Tensors([]Tensor{
+		&fakeTensor{
+			name:  "blk.0.mlp.experts.gate_up_proj",
+			shape: []uint64{2, 4, 3},
+			data: []float32{
+				0, 1, 2,
+				3, 4, 5,
+				6, 7, 8,
+				9, 10, 11,
+				12, 13, 14,
+				15, 16, 17,
+				18, 19, 20,
+				21, 22, 23,
+			},
+		},
+		&fakeTensor{
+			name:  "blk.0.mlp.experts.down_proj",
+			shape: []uint64{2, 5, 3},
+			data:  make([]float32, 2*5*3),
+		},
+	})
+
+	get := func(name string) *ggml.Tensor {
+		for _, tensor := range out {
+			if tensor.Name == name {
+				return tensor
+			}
+		}
+		return nil
+	}
+
+	gate := get("blk.0.ffn_gate_exps.weight")
+	if gate == nil {
+		t.Fatalf("missing tensor %q", "blk.0.ffn_gate_exps.weight")
+	}
+	if got, want := gate.Shape, []uint64{2, 2, 3}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected gate shape: got %v want %v", got, want)
+	}
+	if got, want := readTensorData(t, gate), []float32{
+		0, 1, 2, 3, 4, 5,
+		12, 13, 14, 15, 16, 17,
+	}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected gate values: got %v want %v", got, want)
+	}
+
+	up := get("blk.0.ffn_up_exps.weight")
+	if up == nil {
+		t.Fatalf("missing tensor %q", "blk.0.ffn_up_exps.weight")
+	}
+	if got, want := up.Shape, []uint64{2, 2, 3}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected up shape: got %v want %v", got, want)
+	}
+	if got, want := readTensorData(t, up), []float32{
+		6, 7, 8, 9, 10, 11,
+		18, 19, 20, 21, 22, 23,
+	}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected up values: got %v want %v", got, want)
+	}
+
+	down := get("blk.0.ffn_down_exps.weight")
+	if down == nil {
+		t.Fatalf("missing tensor %q", "blk.0.ffn_down_exps.weight")
+	}
+	if got, want := down.Shape, []uint64{2, 5, 3}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected down shape: got %v want %v", got, want)
+	}
+}
+
+func TestQwen35SharedExpertGateKeepsMatrixShape(t *testing.T) {
+	m := &qwen3NextModel{}
+
+	out := m.Tensors([]Tensor{
+		&fakeTensor{
+			name:  "blk.0.ffn_gate_inp_shexp.weight",
+			shape: []uint64{1, 4},
+			data:  []float32{0, 1, 2, 3},
+		},
+	})
+	if len(out) != 1 {
+		t.Fatalf("unexpected output tensor count: got %d want 1", len(out))
+	}
+
+	if got, want := out[0].Shape, []uint64{1, 4}; !slices.Equal(got, want) {
+		t.Fatalf("unexpected shared gate shape: got %v want %v", got, want)
+	}
+}

convert/tokenizer.go

@@ -101,6 +101,8 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
 			t.Pre = "deepseek-coder"
 		case "1ff7f41064896984db5d1bb6ff64fa4bc29007d08c1b439e505b7392777a319e":
 			t.Pre = "qwen2"
+		case "00431aed57e696b747435f734d1e3b9b1bfd931a121fb5cac7129e97c181e9ba":
+			t.Pre = "qwen35"
 		case "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855":
 			// noop, empty pretokenizer
 		default:

convert/tokenizer_test.go

@@ -386,6 +386,28 @@ func TestParseTokenizer(t *testing.T) {
 				Pre: "default",
 			},
 		},
+		{
+			name: "qwen35 pretokenizer",
+			fsys: createTokenizerFS(t, t.TempDir(), map[string]io.Reader{
+				"tokenizer.json": strings.NewReader(`{
+					"pre_tokenizer": {
+						"type": "Sequence",
+						"pretokenizers": [
+							{
+								"type": "Split",
+								"pattern": {
+									"Regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+								}
+							}
+						]
+					}
+				}`),
+			}),
+			want: &Tokenizer{
+				Vocabulary: &Vocabulary{Model: "gpt2"},
+				Pre:        "qwen35",
+			},
+		},
 	}
 
 	for _, tt := range cases {

fs/ggml/ggml.go

@@ -290,6 +290,7 @@ func (kv KV) OllamaEngineRequired() bool {
 		"olmo3",
 		"qwen25vl",
 		"qwen3", "qwen3moe",
+		"qwen35", "qwen35moe",
 		"qwen3next",
 		"qwen3vl", "qwen3vlmoe",
 		"glm4moelite",
@@ -868,7 +869,12 @@ func (f GGML) SupportsFlashAttention() bool {
 		return false
 	}
 
-	if arch := f.KV().Architecture(); slices.Contains([]string{"gemma2"}, arch) {
+	arch := f.KV().Architecture()
+	if slices.Contains([]string{"qwen35", "qwen35moe", "qwen3next"}, arch) {
+		return true
+	}
+
+	if slices.Contains([]string{"gemma2"}, arch) {
 		return false
 	}
 
@@ -892,6 +898,7 @@ func (f GGML) FlashAttention() bool {
 		"nemotron_h", "nemotron_h_moe",
 		"olmo3",
 		"qwen3", "qwen3moe",
+		"qwen35", "qwen35moe",
 		"qwen3next",
 		"qwen3vl", "qwen3vlmoe",
 	}, f.KV().String("general.architecture"))

fs/ggml/gguf.go

@@ -245,7 +245,22 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
 	padding := ggufPadding(offset, int64(alignment))
 	llm.tensorOffset = uint64(offset + padding)
 
+	// get file size to validate tensor bounds
+	fileSize, err := rs.Seek(0, io.SeekEnd)
+	if err != nil {
+		return fmt.Errorf("failed to determine file size: %w", err)
+	}
+
+	if _, err := rs.Seek(offset, io.SeekStart); err != nil {
+		return fmt.Errorf("failed to seek back after size check: %w", err)
+	}
+
 	for _, tensor := range llm.tensors {
+		tensorEnd := llm.tensorOffset + tensor.Offset + tensor.Size()
+		if tensorEnd > uint64(fileSize) {
+			return fmt.Errorf("tensor %q offset+size (%d) exceeds file size (%d)", tensor.Name, tensorEnd, fileSize)
+		}
+
 		offset, err := rs.Seek(0, io.SeekCurrent)
 		if err != nil {
 			return fmt.Errorf("failed to get current offset: %w", err)

fs/ggml/gguf_test.go

@@ -11,21 +11,21 @@ import (
 )
 
 func TestWriteGGUF(t *testing.T) {
-	b := bytes.NewBuffer(make([]byte, 2*3))
+	tensorData := make([]byte, 2*3*4) // 6 F32 elements = 24 bytes
 	for range 8 {
 		t.Run("shuffle", func(t *testing.T) {
 			t.Parallel()
 
 			ts := []*Tensor{
-				{Name: "token_embd.weight", Shape: []uint64{2, 3}, WriterTo: b},
-				{Name: "blk.0.ffn_norm.weight", Shape: []uint64{2, 3}, WriterTo: b},
-				{Name: "blk.0.attn_norm.weight", Shape: []uint64{2, 3}, WriterTo: b},
-				{Name: "blk.1.ffn_up.weight", Shape: []uint64{2, 3}, WriterTo: b},
-				{Name: "blk.2.ffn_norm.weight", Shape: []uint64{2, 3}, WriterTo: b},
-				{Name: "blk.1.ffn_down.weight", Shape: []uint64{2, 3}, WriterTo: b},
-				{Name: "blk.0.attn_k.weight", Shape: []uint64{2, 3}, WriterTo: b},
-				{Name: "output_norm.weight", Shape: []uint64{3, 2}, WriterTo: b},
-				{Name: "output.weight", Shape: []uint64{3, 2}, WriterTo: b},
+				{Name: "token_embd.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewReader(tensorData)},
+				{Name: "blk.0.ffn_norm.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewReader(tensorData)},
+				{Name: "blk.0.attn_norm.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewReader(tensorData)},
+				{Name: "blk.1.ffn_up.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewReader(tensorData)},
+				{Name: "blk.2.ffn_norm.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewReader(tensorData)},
+				{Name: "blk.1.ffn_down.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewReader(tensorData)},
+				{Name: "blk.0.attn_k.weight", Shape: []uint64{2, 3}, WriterTo: bytes.NewReader(tensorData)},
+				{Name: "output_norm.weight", Shape: []uint64{3, 2}, WriterTo: bytes.NewReader(tensorData)},
+				{Name: "output.weight", Shape: []uint64{3, 2}, WriterTo: bytes.NewReader(tensorData)},
 			}
 
 			rand.Shuffle(len(ts), func(i, j int) {
@@ -98,4 +98,32 @@ func TestWriteGGUF(t *testing.T) {
 			}
 		})
 	}
+
+	t.Run("truncated_tensor_data", func(t *testing.T) {
+		t.Parallel()
+
+		ts := []*Tensor{
+			{Name: "blk.0.attn.weight", Kind: 0, Shape: []uint64{512, 2}, WriterTo: bytes.NewBuffer(make([]byte, 32))},
+		}
+
+		w, err := os.CreateTemp(t.TempDir(), "truncated_*.bin")
+		if err != nil {
+			t.Fatal(err)
+		}
+		defer w.Close()
+
+		if err := WriteGGUF(w, KV{"general.architecture": "test"}, ts); err != nil {
+			t.Fatal(err)
+		}
+
+		r, err := os.Open(w.Name())
+		if err != nil {
+			t.Fatal(err)
+		}
+		defer r.Close()
+
+		if _, err := Decode(r, -1); err == nil {
+			t.Error("Decode should reject GGUF files where tensor data extends beyond file size")
+		}
+	})
 }

kvcache/recurrent.go

@@ -11,9 +11,9 @@ import (
 )
 
 const (
-	DefaultCheckpointCount    = 32
+	DefaultCheckpointCount    = 24
 	DefaultCheckpointMinPos   = int32(16)
-	DefaultCheckpointInterval = int32(1280)
+	DefaultCheckpointInterval = int32(1664)
 )
 
 var ErrInvalidRecurrentShape = errors.New("kvcache: invalid recurrent state shape")

ml/backend.go

@@ -195,6 +195,7 @@ type Tensor interface {
 	Concat(ctx Context, t2 Tensor, dim int) Tensor
 	Rows(ctx Context, t2 Tensor) Tensor
 	SetRows(ctx Context, src Tensor, idxs Tensor) Tensor
+	SetInplace(ctx Context, src Tensor, nb1, nb2, nb3, offset int) Tensor
 	Copy(ctx Context, t2 Tensor) Tensor
 	Duplicate(ctx Context) Tensor
 

ml/backend/ggml/ggml.go

@@ -1345,6 +1345,21 @@ func (t *Tensor) SetRows(ctx ml.Context, src ml.Tensor, idxs ml.Tensor) ml.Tenso
 	}
 }
 
+func (t *Tensor) SetInplace(ctx ml.Context, src ml.Tensor, nb1, nb2, nb3, offset int) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_set_inplace(
+			ctx.(*Context).ctx,
+			t.t,
+			src.(*Tensor).t,
+			C.size_t(nb1),
+			C.size_t(nb2),
+			C.size_t(nb3),
+			C.size_t(offset),
+		),
+	}
+}
+
 func (t *Tensor) Copy(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
 	return &Tensor{
 		b: t.b,

model/models/qwen3next/cache.go

@@ -2,595 +2,58 @@ 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)
+var (
+	_ kvcache.Cache           = (*HybridCache)(nil)
+	_ kvcache.CheckpointCache = (*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]
+// HybridCache adapts the shared recurrent cache base for Qwen3-Next naming.
 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
+	*kvcache.Recurrent
 }
 
 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{}),
-	}
+	base := kvcache.NewRecurrentCache(kvcache.RecurrentConfig{
+		Shift:               shift,
+		ConvDim:             convDim,
+		ConvChannels:        convChannels,
+		RecurrentStateSize:  deltaStateSize,
+		CheckpointLogPrefix: "qwen3next",
+	})
+	return &HybridCache{Recurrent: base}
 }
 
-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].
+// 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
+	return c.RecurrentState(ctx, layer, headVDim, headVDim*numVHeads)
 }
 
 // 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.UpdateRecurrentState(ctx, layer, newState)
+}
+
+func (c *HybridCache) seqTokens() int {
+	return c.SeqTokens()
+}
+
+func (c *HybridCache) numSeqs() int {
+	return c.NumSeqs()
+}
+
+// Keep qwen3next behavior for partial mid-sequence removals.
+func (c *HybridCache) Remove(seq int, beginIndex, endIndex int32) error {
+	if beginIndex > 0 && endIndex != math.MaxInt32 {
+		return kvcache.ErrNotSupported
 	}
-
-	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)
+	return c.Recurrent.Remove(seq, beginIndex, endIndex)
 }

model/models/qwen3next/checkpoints.go

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

model/models/qwen3next/checkpoints_test.go

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

model/models/qwen3next/deltanet.go

@@ -37,7 +37,9 @@ 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
+	SSMBetaAlpha *nn.Linear  `gguf:"ssm_ba"`    // -> beta, alpha (legacy qwen3next)
+	SSMBeta      *nn.Linear  `gguf:"ssm_beta"`  // -> beta (qwen35)
+	SSMAlpha     *nn.Linear  `gguf:"ssm_alpha"` // -> alpha (qwen35)
 	SSMConv1D    *convKernel `gguf:"ssm_conv1d"`
 	SSMDT        ml.Tensor   `gguf:"ssm_dt"` // alpha bias
 	SSMA         ml.Tensor   `gguf:"ssm_a"`  // -A_log.exp()
@@ -96,7 +98,6 @@ func (gdn *GatedDeltaNet) Forward(ctx ml.Context, hiddenStates, _ ml.Tensor, cac
 	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 {
@@ -106,24 +107,40 @@ func (gdn *GatedDeltaNet) Forward(ctx ml.Context, hiddenStates, _ ml.Tensor, cac
 	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)
+	var beta ml.Tensor
+	var alpha ml.Tensor
+	switch {
+	case gdn.SSMBetaAlpha != nil:
+		// Legacy qwen3next path: in_proj_ba packs beta/alpha grouped by K-head.
+		mixedBA := gdn.SSMBetaAlpha.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
+		betaSize := numVHeads / numKHeads
+		alphaSize := numVHeads / numKHeads
 
-	b := mixedBAReshaped.Slice(ctx, 0, 0, betaSize, 1)
-	a := mixedBAReshaped.Slice(ctx, 0, betaSize, betaSize+alphaSize, 1)
+		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)
+		// Keep beta layout consistent with qwen35.
+		// [1, numVHeads, nSeqTokens, nSeqs]
+		beta = b.Contiguous(ctx, 1, numVHeads, nSeqTokens, nSeqs)
+		alpha = a.Contiguous(ctx, numVHeads, nSeqTokens, nSeqs)
+
+	case gdn.SSMBeta != nil && gdn.SSMAlpha != nil:
+		// qwen35 path: beta/alpha are separate projections.
+		beta = gdn.SSMBeta.Forward(ctx, hiddenStates).Reshape(ctx, 1, numVHeads, nSeqTokens, nSeqs)
+		alpha = gdn.SSMAlpha.Forward(ctx, hiddenStates).Reshape(ctx, numVHeads, nSeqTokens, nSeqs)
+
+	default:
+		return nil, errors.New("qwen3next: missing linear attention beta/alpha projections")
+	}
 
 	// Compute gate: softplus(alpha + dt_bias) * -A
 	alphaBiased := alpha.Add(ctx, gdn.SSMDT)
 	alphaSoftplus := alphaBiased.Softplus(ctx)
 	gate := alphaSoftplus.Mul(ctx, gdn.SSMA)
+	gate = gate.Reshape(ctx, 1, numVHeads, nSeqTokens, nSeqs)
 	qkvMixed = qkvMixed.Permute(ctx, 1, 0, 2, 3)
 
 	// Get conv state from cache
@@ -172,16 +189,20 @@ func (gdn *GatedDeltaNet) Forward(ctx ml.Context, hiddenStates, _ ml.Tensor, cac
 
 	// Repeat interleave Q and K if numKHeads != numVHeads
 	if numKHeads != numVHeads {
-		repeatFactor := numVHeads / numKHeads
+		if opts.vHeadReordered {
+			qConv = qConv.Repeat4D(ctx, headKDim, numVHeads, nSeqTokens, nSeqs)
+			kConv = kConv.Repeat4D(ctx, headKDim, numVHeads, nSeqTokens, nSeqs)
+		} else {
+			repeatFactor := numVHeads / numKHeads
+			qReshaped := qConv.Reshape(ctx, headKDim, 1, numKHeads*nSeqTokens*nSeqs)
+			kReshaped := kConv.Reshape(ctx, headKDim, 1, numKHeads*nSeqTokens*nSeqs)
 
-		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)
 
-		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)
+			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
@@ -189,7 +210,9 @@ func (gdn *GatedDeltaNet) Forward(ctx ml.Context, hiddenStates, _ ml.Tensor, cac
 	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)
+		if opts.masks == nil {
+			opts.masks = createMasks(ctx)
+		}
 		attnOut = gdn.deltaNetChunked(ctx, qConv, kConv, vConv, gate, beta, state, opts.masks, opts, layer, cache)
 	}
 
@@ -310,9 +333,9 @@ func (gdn *GatedDeltaNet) deltaNetChunked(
 	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)
+	// gate/beta: [1, numVHeads, nTokens, nSeqs] -> [1, nTokens, numVHeads, nSeqs]
+	gate = gate.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx, 1, nTokens, numVHeads, nSeqs)
+	beta = beta.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx, 1, nTokens, numVHeads, nSeqs)
 	state = state.Reshape(ctx, headVDim, headVDim, numVHeads, nSeqs)
 
 	// Compute padding
@@ -324,7 +347,7 @@ func (gdn *GatedDeltaNet) deltaNetChunked(
 		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)
+		gate = gate.Pad(ctx, 0, pad, 0, 0)
 		beta = beta.Pad(ctx, 0, pad, 0, 0)
 	}
 
@@ -344,10 +367,12 @@ func (gdn *GatedDeltaNet) deltaNetChunked(
 	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)
+	// Reshape gate and cumsum over chunk axis.
+	// [1, chunkSize, nChunks, H*nSeqs] -> transpose -> [chunkSize, 1, nChunks, H*nSeqs]
+	gate = gate.Reshape(ctx, 1, chunkSize, nChunks, numVHeads*nSeqs)
 
 	// g_cumsum = cumsum(gate)
-	gCumsum := gate.CumSum(ctx)
+	gCumsum := gate.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx, chunkSize, 1, nChunks, numVHeads*nSeqs).CumSum(ctx)
 
 	// Compute decay mask
 	gcsI := gCumsum.Reshape(ctx, chunkSize, 1, nChunks, numVHeads*nSeqs)
@@ -411,60 +436,64 @@ func (gdn *GatedDeltaNet) deltaNetChunked(
 	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
+	// Process chunks and update state.
+	// Keep a transposed view of v and recurrent state across chunks so the
+	// chunk loop does not need extra transpose+contiguous nodes.
+	vT := v.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx, chunkSize, headVDim, nChunks, numVHeads*nSeqs)
+	stateT := state.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx, headVDim, headVDim, 1, numVHeads*nSeqs)
 
 	for chunk := range nChunks {
 		qChunk := q.Slice(ctx, 2, chunk, chunk+1, 1)
-		vChunk := v.Slice(ctx, 2, chunk, chunk+1, 1)
+		vTChunk := vT.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'_t = k_cumdecay @ state_t
+		vTPrime := kCumdecayChunk.Mulmat(ctx, stateT)
 
-		// 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)
+		// v_t_new = v_t - v'_t
+		vTNewChunk := vTChunk.Sub(ctx, vTPrime)
 
 		// 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)
+		vAttn := vTNewChunk.Mulmat(ctx, attnChunk)
 		coreAttnOutChunk := attnInter.Add(ctx, vAttn)
 
-		if coreAttnOut == nil {
-			coreAttnOut = coreAttnOutChunk
-		} else {
-			coreAttnOut = coreAttnOut.Concat(ctx, coreAttnOutChunk, 1)
-		}
+		v = v.SetInplace(
+			ctx,
+			coreAttnOutChunk,
+			v.Stride(1),
+			v.Stride(2),
+			v.Stride(3),
+			chunk*v.Stride(2),
+		)
 
 		// 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)
+		// kgdmulvnew = key_gdiff_t @ v_new_t
+		kgdMulVNew := kGDiffChunkT.Mulmat(ctx, vTNewChunk)
 
-		// 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))
+		// stateT = stateT * g_last + kgdmulvnew
+		stateT = stateT.Mul(ctx, gExpLastChunk)
+		stateT = stateT.Add(ctx, kgdMulVNew)
 	}
 
 	// Final reshape
-	coreAttnOut = coreAttnOut.Contiguous(ctx, headVDim, chunkSize*nChunks, numVHeads, nSeqs)
+	coreAttnOut := v.Contiguous(ctx, headVDim, chunkSize*nChunks, numVHeads, nSeqs)
 
 	// Slice to remove padding
 	if pad > 0 {
 		coreAttnOut = coreAttnOut.Slice(ctx, 1, 0, nTokens, 1)
 	}
 
+	// Convert stateT back to cache layout [S_v, S_v, H_v, nSeqs]
+	newState := stateT.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx, headVDim, headVDim, numVHeads, nSeqs)
+
 	// Update delta state in cache
 	cache.UpdateDeltaState(ctx, layer, newState.Reshape(ctx, headVDim, headVDim*numVHeads, nSeqs))
 

model/models/qwen3next/model.go

@@ -1,9 +1,12 @@
 package qwen3next
 
 import (
+	"bytes"
 	"cmp"
 	"fmt"
+	"image"
 	"math"
+	"slices"
 
 	"github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/ml"
@@ -11,6 +14,7 @@ import (
 	"github.com/ollama/ollama/ml/nn/rope"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/model/models/qwen3vl"
 	"github.com/ollama/ollama/tokenizer"
 )
 
@@ -41,10 +45,15 @@ type Options struct {
 	ssmNGroup      int // num_k_heads
 	ssmDtRank      int // num_v_heads
 	convKernelSize int // SSM conv kernel size
+	vHeadReordered bool
 
 	// Per-layer type from GGUF metadata
 	isRecurrent []bool
 
+	// RoPE mode config (used by qwen35/qwen35moe)
+	mropeSections    []int
+	mropeInterleaved bool
+
 	// Pre-computed masks for chunked attention (created once per forward pass)
 	masks *Masks
 }
@@ -54,7 +63,17 @@ func (o Options) headDim() int {
 }
 
 func (o Options) applyRotaryPositionEmbeddings(ctx ml.Context, states, positions ml.Tensor) ml.Tensor {
-	opts := []func(*rope.Options){rope.WithTypeNeoX()}
+	var opts []func(*rope.Options)
+	if len(o.mropeSections) > 0 {
+		if o.mropeInterleaved {
+			opts = append(opts, rope.WithInterleaveMRoPE(o.mropeSections))
+		} else {
+			opts = append(opts, rope.WithMRoPE(o.mropeSections))
+		}
+	} else {
+		opts = append(opts, rope.WithTypeNeoX())
+	}
+
 	if o.ropeType == "yarn" {
 		attnFactor := float32(1.0 / (1.0 + 0.1*math.Log(float64(o.ropeScale))))
 		opts = append(opts,
@@ -214,20 +233,190 @@ type Model struct {
 	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
 	Output         *nn.Linear    `gguf:"output,alt:token_embd"`
 
-	Layers []Layer `gguf:"blk"`
+	Layers []Layer              `gguf:"blk"`
+	Vision *qwen3vl.VisionModel `gguf:"v"`
+
+	ImageProcessor *qwen3vl.ImageProcessor
 
 	*Options
+
+	positionCache    []int32
+	imageToken       int32
+	visionStart      int32
+	visionEnd        int32
+	spatialMergeSize uint32
+}
+
+func (m *Model) mapPosition(id int32) int32 {
+	if id < int32(len(m.positionCache)) {
+		return m.positionCache[id]
+	}
+	if len(m.positionCache) > 0 {
+		return id - int32(len(m.positionCache)) + m.positionCache[len(m.positionCache)-1] + 1
+	}
+	return id
+}
+
+func (m *Model) buildPositions(ctx ml.Context, batch input.Batch) ml.Tensor {
+	if len(m.mropeSections) == 0 {
+		return ctx.Input().FromInts(batch.Positions, len(batch.Positions))
+	}
+
+	// ggml MRoPE expects [time, height, width, extra] for each token.
+	positionSlice := [][]int32{
+		make([]int32, len(batch.Positions)),
+		make([]int32, len(batch.Positions)),
+		make([]int32, len(batch.Positions)),
+		make([]int32, len(batch.Positions)),
+	}
+
+	for i, id := range batch.Positions {
+		p := m.mapPosition(id)
+		positionSlice[0][i] = p
+		positionSlice[1][i] = p
+		positionSlice[2][i] = p
+	}
+
+	if m.Vision != nil {
+		for _, mi := range batch.Multimodal {
+			grid, ok := mi.Multimodal[0].Data.(*qwen3vl.Grid)
+			if !ok {
+				continue
+			}
+			w := max(1, grid.Width/int(m.spatialMergeSize))
+			for i := range mi.Multimodal[0].Tensor.Dim(1) {
+				positionSlice[1][mi.Index+i] += int32(i / w)
+				positionSlice[2][mi.Index+i] += int32(i % w)
+			}
+		}
+	}
+
+	return ctx.Input().FromInts(slices.Concat(positionSlice...), len(positionSlice[0])*len(positionSlice))
+}
+
+func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input.Multimodal, error) {
+	if m.Vision == nil || m.ImageProcessor == nil || len(m.Vision.Layers) == 0 {
+		return nil, model.ErrNoVisionModel
+	}
+
+	img, _, err := image.Decode(bytes.NewReader(multimodalData))
+	if err != nil {
+		return nil, err
+	}
+
+	pixelValues, grid, err := m.ImageProcessor.ProcessImage(ctx, img)
+	if err != nil {
+		return nil, err
+	}
+
+	visionOutputs, deepstackVisualEmbeds := m.Vision.Forward(ctx, pixelValues, grid)
+	mm := []input.Multimodal{{Tensor: visionOutputs, Data: grid}}
+	for i := range deepstackVisualEmbeds {
+		mm = append(mm, input.Multimodal{Tensor: deepstackVisualEmbeds[i]})
+	}
+
+	return mm, nil
+}
+
+func (m *Model) PostTokenize(inputs []*input.Input) ([]*input.Input, error) {
+	m.positionCache = m.positionCache[:0]
+	var result []*input.Input
+	appendInput := func(inp *input.Input, position int32) {
+		result = append(result, inp)
+		m.positionCache = append(m.positionCache, position)
+	}
+
+	var p int32
+	for _, inp := range inputs {
+		if inp.Multimodal == nil {
+			appendInput(inp, p)
+			p++
+			continue
+		}
+
+		grid := inp.Multimodal[0].Data.(*qwen3vl.Grid)
+		tokensPerGrid := inp.Multimodal[0].Tensor.Dim(1)
+
+		appendInput(&input.Input{
+			Token:     m.visionStart,
+			SameBatch: tokensPerGrid + 1,
+		}, p)
+		p++
+
+		appendInput(&input.Input{
+			Token:          m.imageToken,
+			Multimodal:     inp.Multimodal,
+			MultimodalHash: inp.MultimodalHash,
+		}, p)
+
+		for range tokensPerGrid - 1 {
+			appendInput(&input.Input{
+				Token: m.imageToken,
+			}, p)
+		}
+
+		gridSpan := max(grid.Width/int(m.spatialMergeSize), grid.Height/int(m.spatialMergeSize))
+		p = p + int32(gridSpan)
+		appendInput(&input.Input{
+			Token: m.visionEnd,
+		}, p)
+		p++
+	}
+
+	return result, nil
 }
 
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
-	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
+	positions := m.buildPositions(ctx, batch)
 
 	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
+	if len(batch.Multimodal) > 0 {
+		hiddenStates = hiddenStates.Duplicate(ctx)
+
+		var deepstackVisualEmbeds []ml.Tensor
+		for _, mi := range batch.Multimodal {
+			visionOutputs := mi.Multimodal[0].Tensor
+			ctx.Forward(visionOutputs.Copy(ctx, hiddenStates.View(ctx, mi.Index*hiddenStates.Stride(1), visionOutputs.Dim(0)*visionOutputs.Dim(1))))
+
+			if len(mi.Multimodal[1:]) > len(deepstackVisualEmbeds) {
+				deepstackVisualEmbeds = append(deepstackVisualEmbeds, make([]ml.Tensor, len(mi.Multimodal[1:])-len(deepstackVisualEmbeds))...)
+			}
+			for i, mm := range mi.Multimodal[1:] {
+				if deepstackVisualEmbeds[i] == nil {
+					deepstackVisualEmbeds[i] = ctx.Input().Zeros(mm.Tensor.DType(), hiddenStates.Shape()...)
+				}
+				ctx.Forward(mm.Tensor.Copy(ctx, deepstackVisualEmbeds[i].View(ctx, mi.Index*deepstackVisualEmbeds[i].Stride(1), mm.Tensor.Dim(0)*mm.Tensor.Dim(1))))
+			}
+		}
+
+		cache := m.Cache.(*HybridCache)
+		m.Options.masks = nil
+		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
+			}
+			if i < len(deepstackVisualEmbeds) {
+				hiddenStates = hiddenStates.Add(ctx, deepstackVisualEmbeds[i])
+			}
+		}
+
+		hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.eps)
+		return m.Output.Forward(ctx, hiddenStates), nil
+	}
 
 	cache := m.Cache.(*HybridCache)
 
-	// Create masks once per forward pass
-	m.Options.masks = createMasks(ctx)
+	// Masks are allocated lazily only for chunked recurrent prefill.
+	m.Options.masks = nil
 
 	for i, layer := range m.Layers {
 		cache.SetLayer(i)
@@ -249,10 +438,17 @@ func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 }
 
 func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	m.positionCache = nil
+	if len(m.mropeSections) > 0 {
+		shift = shift.Repeat(ctx, 1, 4).Reshape(ctx, -1)
+	}
 	return m.applyRotaryPositionEmbeddings(ctx, key, shift), nil
 }
 
-var _ model.Model = (*Model)(nil)
+var (
+	_ model.Model               = (*Model)(nil)
+	_ model.MultimodalProcessor = (*Model)(nil)
+)
 
 func New(c fs.Config) (model.Model, error) {
 	numLayers := int(c.Uint("block_count"))
@@ -303,6 +499,22 @@ func New(c fs.Config) (model.Model, error) {
 		}
 	}
 
+	mropeSections := c.Ints("mrope_sections", nil)
+	if len(mropeSections) == 0 {
+		mropeSections = c.Ints("rope.mrope_section", nil)
+	}
+	if len(mropeSections) == 0 {
+		mropeSections = c.Ints("rope.dimension_sections", nil)
+	}
+	if len(mropeSections) > 4 {
+		mropeSections = mropeSections[:4]
+	}
+
+	ropeType := c.String("rope.scaling.type")
+	if ropeType == "" {
+		ropeType = c.String("rope.type")
+	}
+
 	opts := &Options{
 		hiddenSize: int(c.Uint("embedding_length")),
 		numHeads:   int(c.Uint("attention.head_count")),
@@ -318,7 +530,7 @@ func New(c fs.Config) (model.Model, error) {
 		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"),
+		ropeType:              ropeType,
 		ropeBase:              c.Float("rope.freq_base"),
 		ropeScale:             c.Float("rope.scaling.factor", 1),
 		originalContextLength: int(c.Uint("rope.scaling.original_context_length")),
@@ -331,7 +543,16 @@ func New(c fs.Config) (model.Model, error) {
 		ssmNGroup:             int(c.Uint("ssm.group_count")),
 		ssmDtRank:             int(c.Uint("ssm.time_step_rank")),
 		convKernelSize:        int(c.Uint("ssm.conv_kernel")),
+		vHeadReordered:        c.Bool("ssm.v_head_reordered", false),
 		isRecurrent:           isRecurrent,
+		mropeSections: slices.Collect(func(yield func(int) bool) {
+			for _, section := range mropeSections {
+				if !yield(int(section)) {
+					return
+				}
+			}
+		}),
+		mropeInterleaved: c.Bool("rope.mrope_interleaved", c.Bool("mrope_interleaved", false)),
 	}
 	if opts.numKVHeads == 0 {
 		return nil, fmt.Errorf("qwen3next: attention.head_count_kv array must include at least one non-zero value")
@@ -353,6 +574,19 @@ func New(c fs.Config) (model.Model, error) {
 		return nil, fmt.Errorf("qwen3next: headKDim (%d) != headVDim (%d) not supported; state computations require equal dimensions", headKDim, headVDim)
 	}
 
+	var vision *qwen3vl.VisionModel
+	var imageProcessor *qwen3vl.ImageProcessor
+	if c.Uint("vision.block_count", 0) > 0 {
+		vision = qwen3vl.NewVisionModel(c)
+		processor := qwen3vl.NewImageProcessor(c)
+		imageProcessor = &processor
+	}
+
+	spatialMergeSize := c.Uint("vision.spatial_merge_size", 2)
+	if spatialMergeSize == 0 {
+		spatialMergeSize = 2
+	}
+
 	m := Model{
 		Tokenizer: tokenizer.NewBytePairEncoding(
 			&tokenizer.Vocabulary{
@@ -371,8 +605,14 @@ 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}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
 		),
-		Layers:  layers,
-		Options: opts,
+		Layers:           layers,
+		Vision:           vision,
+		ImageProcessor:   imageProcessor,
+		Options:          opts,
+		imageToken:       int32(c.Uint("image_token_id", 151655)),
+		visionStart:      int32(c.Uint("vision_start_token_id", 151652)),
+		visionEnd:        int32(c.Uint("vision_end_token_id", 151653)),
+		spatialMergeSize: spatialMergeSize,
 	}
 
 	m.Cache = NewHybridCache(m.Shift, convDim, convChannels, deltaStateSize)
@@ -380,5 +620,7 @@ func New(c fs.Config) (model.Model, error) {
 }
 
 func init() {
+	model.Register("qwen35", New)
+	model.Register("qwen35moe", New)
 	model.Register("qwen3next", New)
 }

model/models/qwen3next/model_posttokenize_test.go

@@ -0,0 +1,101 @@
+package qwen3next
+
+import (
+	"testing"
+
+	"github.com/ollama/ollama/ml/backend/ggml"
+	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/model/models/qwen3vl"
+)
+
+type fakeTensor struct {
+	*ggml.Tensor
+	dims []int
+}
+
+func (t *fakeTensor) Dim(i int) int {
+	return t.dims[i]
+}
+
+func makeImageInput(hash uint64, width, height, tokens int) *input.Input {
+	return &input.Input{
+		Multimodal: []input.Multimodal{{
+			Tensor: &fakeTensor{dims: []int{1, tokens, 1, 1}},
+			Data:   &qwen3vl.Grid{Width: width, Height: height},
+		}},
+		MultimodalHash: hash,
+	}
+}
+
+func TestPostTokenizeMultiImageSpans(t *testing.T) {
+	m := &Model{
+		imageToken:       10,
+		visionStart:      11,
+		visionEnd:        12,
+		spatialMergeSize: 2,
+	}
+
+	inputs := []*input.Input{
+		{Token: 100},
+		makeImageInput(1, 8, 4, 4),
+		makeImageInput(2, 4, 8, 4),
+		{Token: 200},
+	}
+
+	got, err := m.PostTokenize(inputs)
+	if err != nil {
+		t.Fatalf("PostTokenize() error = %v", err)
+	}
+
+	want := []struct {
+		token     int32
+		hash      uint64
+		sameBatch int
+		hasMM     bool
+	}{
+		{token: 100},
+		{token: 11, sameBatch: 5},
+		{token: 10, hash: 1, hasMM: true},
+		{token: 10},
+		{token: 10},
+		{token: 10},
+		{token: 12},
+		{token: 11, sameBatch: 5},
+		{token: 10, hash: 2, hasMM: true},
+		{token: 10},
+		{token: 10},
+		{token: 10},
+		{token: 12},
+		{token: 200},
+	}
+
+	if len(got) != len(want) {
+		t.Fatalf("len(got) = %d, want %d", len(got), len(want))
+	}
+
+	for i := range want {
+		if got[i].Token != want[i].token {
+			t.Fatalf("got[%d].Token = %d, want %d", i, got[i].Token, want[i].token)
+		}
+		if got[i].MultimodalHash != want[i].hash {
+			t.Fatalf("got[%d].MultimodalHash = %d, want %d", i, got[i].MultimodalHash, want[i].hash)
+		}
+		if got[i].SameBatch != want[i].sameBatch {
+			t.Fatalf("got[%d].SameBatch = %d, want %d", i, got[i].SameBatch, want[i].sameBatch)
+		}
+		hasMM := len(got[i].Multimodal) > 0
+		if hasMM != want[i].hasMM {
+			t.Fatalf("got[%d].hasMM = %v, want %v", i, hasMM, want[i].hasMM)
+		}
+	}
+
+	wantPositions := []int32{0, 1, 2, 2, 2, 2, 6, 7, 8, 8, 8, 8, 12, 13}
+	if len(m.positionCache) != len(wantPositions) {
+		t.Fatalf("len(positionCache) = %d, want %d", len(m.positionCache), len(wantPositions))
+	}
+	for i := range wantPositions {
+		if m.positionCache[i] != wantPositions[i] {
+			t.Fatalf("positionCache[%d] = %d, want %d", i, m.positionCache[i], wantPositions[i])
+		}
+	}
+}

model/models/qwen3vl/imageprocessor.go

@@ -24,8 +24,8 @@ type ImageProcessor struct {
 	imageStd          []float32
 }
 
-// newImageProcessor creates a new image processor with default values
-func newImageProcessor(c fs.Config) ImageProcessor {
+// NewImageProcessor creates a new image processor with default values.
+func NewImageProcessor(c fs.Config) ImageProcessor {
 	patchSize := int(c.Uint("vision.patch_size", 14))
 	mergeSize := int(c.Uint("vision.spatial_merge_size", 2))
 

model/models/qwen3vl/model.go

@@ -56,60 +56,46 @@ var (
 	tokenVisionEnd   int32 = 151653
 )
 
-type modelInput struct {
-	*input.Input
-	position int32
-}
-
 // PostTokenize arranges Qwen 3 VL's inputs for the forward pass
 func (m *Model) PostTokenize(inputs []*input.Input) ([]*input.Input, error) {
 	m.positionCache = m.positionCache[:0]
-	return slices.Collect(func(yield func(*input.Input) bool) {
-		for i := range inputs {
-			s := []modelInput{{Input: inputs[i]}}
-			if mm := inputs[i].Multimodal; mm != nil {
-				t := mm[0].Tensor
-				s = slices.Repeat([]modelInput{
-					{
-						position: int32(i + 1),
-						Input:    &input.Input{Token: tokenVision},
-					},
-				}, t.Dim(1)+1+1)
+	var result []*input.Input
+	appendInput := func(inp *input.Input, position int32) {
+		result = append(result, inp)
+		m.positionCache = append(m.positionCache, position)
+	}
 
-				s[0] = modelInput{
-					Input:    &input.Input{Token: tokenVisionStart},
-					position: int32(i),
-				}
-
-				s[len(s)-1] = modelInput{
-					Input:    &input.Input{Token: tokenVisionEnd},
-					position: int32(i + mm[0].Data.(*Grid).Width/m.spatialMergeSize + 1),
-				}
-
-				s[1] = modelInput{
-					Input: &input.Input{
-						Token:          tokenVision,
-						Multimodal:     inputs[i].Multimodal,
-						MultimodalHash: inputs[i].MultimodalHash,
-						SameBatch:      t.Dim(1),
-					},
-					position: int32(i + 1),
-				}
-			}
-
-			for _, e := range s {
-				position := e.position
-				if position == 0 && len(m.positionCache) > 0 {
-					position = m.positionCache[len(m.positionCache)-1] + 1
-				}
-
-				m.positionCache = append(m.positionCache, position)
-				if !yield(e.Input) {
-					return
-				}
-			}
+	var p int32
+	for _, inp := range inputs {
+		if inp.Multimodal == nil {
+			appendInput(inp, p)
+			p++
+			continue
 		}
-	}), nil
+
+		grid := inp.Multimodal[0].Data.(*Grid)
+		tokensPerGrid := inp.Multimodal[0].Tensor.Dim(1)
+
+		appendInput(&input.Input{Token: tokenVisionStart}, p)
+		p++
+
+		appendInput(&input.Input{
+			Token:          tokenVision,
+			Multimodal:     inp.Multimodal,
+			MultimodalHash: inp.MultimodalHash,
+			SameBatch:      tokensPerGrid,
+		}, p)
+
+		for range tokensPerGrid - 1 {
+			appendInput(&input.Input{Token: tokenVision}, p)
+		}
+
+		p = p + int32(grid.Width/m.spatialMergeSize)
+		appendInput(&input.Input{Token: tokenVisionEnd}, p)
+		p++
+	}
+
+	return result, nil
 }
 
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
@@ -143,9 +129,13 @@ func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 			}
 		}
 
-		deepstackVisualEmbeds = make([]ml.Tensor, len(mi.Multimodal[1:]))
+		if len(mi.Multimodal[1:]) > len(deepstackVisualEmbeds) {
+			deepstackVisualEmbeds = append(deepstackVisualEmbeds, make([]ml.Tensor, len(mi.Multimodal[1:])-len(deepstackVisualEmbeds))...)
+		}
 		for i, mm := range mi.Multimodal[1:] {
-			deepstackVisualEmbeds[i] = ctx.Input().Zeros(mm.Tensor.DType(), hiddenStates.Shape()...)
+			if deepstackVisualEmbeds[i] == nil {
+				deepstackVisualEmbeds[i] = ctx.Input().Zeros(mm.Tensor.DType(), hiddenStates.Shape()...)
+			}
 			ctx.Forward(mm.Tensor.Copy(ctx, deepstackVisualEmbeds[i].View(ctx, mi.Index*deepstackVisualEmbeds[i].Stride(1), mm.Tensor.Dim(0)*mm.Tensor.Dim(1))))
 		}
 	}
@@ -189,8 +179,8 @@ 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}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
 		),
 		TextModel:      newTextModel(c),
-		VisionModel:    newVisionModel(c),
-		ImageProcessor: newImageProcessor(c),
+		VisionModel:    NewVisionModel(c),
+		ImageProcessor: NewImageProcessor(c),
 	}
 
 	m.Cache = kvcache.NewCausalCache(func(ctx ml.Context, layer int, key, positions ml.Tensor) (ml.Tensor, error) {

model/models/qwen3vl/model_vision.go

@@ -238,8 +238,8 @@ func (m *VisionModel) Forward(ctx ml.Context, pixelValues ml.Tensor, grid *Grid)
 	return hiddenStates, deepstackStates
 }
 
-// newVisionModel creates a new instance of the Qwen vision model
-func newVisionModel(c fs.Config) *VisionModel {
+// NewVisionModel creates a new instance of the Qwen vision model.
+func NewVisionModel(c fs.Config) *VisionModel {
 	deepstackVisualIndexes := c.Ints("vision.deepstack_visual_indexes")
 	model := &VisionModel{
 		Layers:          make([]VisionEncoderLayer, c.Uint("vision.block_count", 32)),

model/parsers/parsers.go

@@ -49,6 +49,8 @@ func ParserForName(name string) Parser {
 		p = &Qwen3Parser{hasThinkingSupport: false, defaultThinking: false}
 	case "qwen3-thinking":
 		p = &Qwen3Parser{hasThinkingSupport: true, defaultThinking: true}
+	case "qwen3.5":
+		p = &Qwen3Parser{hasThinkingSupport: true, defaultThinking: true}
 	case "qwen3-coder":
 		p = &Qwen3CoderParser{}
 	case "qwen3-vl-instruct":

model/parsers/parsers_test.go

@@ -59,6 +59,7 @@ func TestBuiltInParsersStillWork(t *testing.T) {
 		{"qwen3-coder"},
 		{"lfm2"},
 		{"lfm2-thinking"},
+		{"qwen3.5"},
 		{"harmony"},
 	}
 

model/parsers/qwen3.go

@@ -204,6 +204,24 @@ func (p *Qwen3Parser) eat() ([]qwen3Event, bool) {
 			p.maybeThinkingOpenAtBOL = false
 		}
 
+		thinkingCloseIdx := strings.Index(acc, qwen3ThinkingCloseTag)
+		toolOpenIdx := strings.Index(acc, qwen3ToolOpenTag)
+
+		// If a tool call starts before </think>, treat that as the end of thinking
+		// for parsing purposes and continue in tool-call mode.
+		if toolOpenIdx != -1 && (thinkingCloseIdx == -1 || toolOpenIdx < thinkingCloseIdx) {
+			before, after := p.splitAtTag(qwen3ToolOpenTag, true)
+			if len(before) > 0 {
+				events = append(events, qwen3EventThinkingContent{content: before})
+			}
+			if after == "" {
+				p.state = qwen3ParserStateToolStartedEatingWhitespace
+			} else {
+				p.state = qwen3ParserStateCollectingToolContent
+			}
+			return events, true
+		}
+
 		if strings.Contains(acc, qwen3ThinkingCloseTag) {
 			thinking, remaining := p.splitAtTag(qwen3ThinkingCloseTag, true)
 			if len(thinking) > 0 {
@@ -215,7 +233,7 @@ func (p *Qwen3Parser) eat() ([]qwen3Event, bool) {
 				p.state = qwen3ParserStateCollectingContent
 			}
 			return events, true
-		} else if overlapLen := overlap(acc, qwen3ThinkingCloseTag); overlapLen > 0 {
+		} else if overlapLen := max(overlap(acc, qwen3ThinkingCloseTag), overlap(acc, qwen3ToolOpenTag)); overlapLen > 0 {
 			beforePartialTag := acc[:len(acc)-overlapLen]
 			trailingWsLen := trailingWhitespaceLen(beforePartialTag)
 			ambiguousStart := len(beforePartialTag) - trailingWsLen

model/parsers/qwen3_test.go

@@ -145,3 +145,88 @@ func TestQwen3ParserToolCall(t *testing.T) {
 		t.Fatalf("expected unit %q, got %v", "celsius", unit)
 	}
 }
+
+func TestQwen3ParserThinkingWithToolCallBeforeThinkingClose(t *testing.T) {
+	parser := &Qwen3Parser{hasThinkingSupport: true, defaultThinking: true}
+	parser.Init(nil, nil, &api.ThinkValue{Value: true})
+
+	input := "Let me think<tool_call>{\"name\":\"get_weather\",\"arguments\":{\"location\":\"San Francisco\",\"unit\":\"celsius\"}}</tool_call>"
+	content, thinking, calls, err := parser.Add(input, true)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+
+	if content != "" {
+		t.Fatalf("expected empty content, got %q", content)
+	}
+	if thinking != "Let me think" {
+		t.Fatalf("expected thinking %q, got %q", "Let me think", thinking)
+	}
+	if len(calls) != 1 {
+		t.Fatalf("expected 1 tool call, got %d", len(calls))
+	}
+	if calls[0].Function.Name != "get_weather" {
+		t.Fatalf("expected tool name %q, got %q", "get_weather", calls[0].Function.Name)
+	}
+}
+
+func TestQwen3ParserThinkingWithSplitToolOpenTag(t *testing.T) {
+	parser := &Qwen3Parser{hasThinkingSupport: true, defaultThinking: true}
+	parser.Init(nil, nil, &api.ThinkValue{Value: true})
+
+	content, thinking, calls, err := parser.Add("Let me think<tool_ca", false)
+	if err != nil {
+		t.Fatalf("parse failed on first chunk: %v", err)
+	}
+	if content != "" || thinking != "Let me think" || len(calls) != 0 {
+		t.Fatalf(
+			"expected content=%q thinking=%q calls=%d, got content=%q thinking=%q calls=%d",
+			"",
+			"Let me think",
+			0,
+			content,
+			thinking,
+			len(calls),
+		)
+	}
+
+	content, thinking, calls, err = parser.Add("ll>{\"name\":\"get_weather\",\"arguments\":{\"location\":\"SF\"}}</tool_call>", true)
+	if err != nil {
+		t.Fatalf("parse failed on second chunk: %v", err)
+	}
+	if content != "" {
+		t.Fatalf("expected empty content, got %q", content)
+	}
+	if thinking != "" {
+		t.Fatalf("expected no additional thinking on second chunk, got %q", thinking)
+	}
+	if len(calls) != 1 {
+		t.Fatalf("expected 1 tool call, got %d", len(calls))
+	}
+	if calls[0].Function.Name != "get_weather" {
+		t.Fatalf("expected tool name %q, got %q", "get_weather", calls[0].Function.Name)
+	}
+}
+
+func TestQwen35ParserRespectsNoThink(t *testing.T) {
+	parser := ParserForName("qwen3.5")
+	if parser == nil {
+		t.Fatal("expected qwen3.5 parser")
+	}
+
+	parser.Init(nil, nil, &api.ThinkValue{Value: false})
+	content, thinking, calls, err := parser.Add("Hello! How can I help you today?", true)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+
+	if thinking != "" {
+		t.Fatalf("expected no thinking, got %q", thinking)
+	}
+	if content != "Hello! How can I help you today?" {
+		t.Fatalf("expected content %q, got %q", "Hello! How can I help you today?", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+}

model/parsers/qwen3vl.go

@@ -180,7 +180,22 @@ func (p *Qwen3VLParser) eat() ([]qwenEvent, bool) {
 			return events, false
 		}
 	case CollectingThinkingContent:
-		if strings.Contains(p.buffer.String(), thinkingCloseTag) {
+		acc := p.buffer.String()
+		thinkingCloseIdx := strings.Index(acc, thinkingCloseTag)
+		toolOpenIdx := strings.Index(acc, toolOpenTag)
+
+		// If a tool call starts before </think>, treat that as the end of thinking
+		// for parsing purposes and continue in tool-call mode.
+		if toolOpenIdx != -1 && (thinkingCloseIdx == -1 || toolOpenIdx < thinkingCloseIdx) {
+			before, _ := splitAtTag(&p.buffer, toolOpenTag, false)
+			if len(before) > 0 {
+				events = append(events, qwenEventThinkingContent{content: before})
+			}
+			p.state = CollectingToolContent
+			return events, true
+		}
+
+		if strings.Contains(acc, thinkingCloseTag) {
 			thinking, remaining := splitAtTag(&p.buffer, thinkingCloseTag, true)
 			if len(thinking) > 0 {
 				events = append(events, qwenEventThinkingContent{content: thinking})
@@ -191,13 +206,13 @@ func (p *Qwen3VLParser) eat() ([]qwenEvent, bool) {
 				p.state = CollectingContent
 			}
 			return events, true
-		} else if overlapLen := overlap(p.buffer.String(), thinkingCloseTag); overlapLen > 0 {
-			beforePartialTag := p.buffer.String()[:len(p.buffer.String())-overlapLen]
+		} else if overlapLen := max(overlap(acc, thinkingCloseTag), overlap(acc, toolOpenTag)); overlapLen > 0 {
+			beforePartialTag := acc[:len(acc)-overlapLen]
 			trailingWhitespaceLen := trailingWhitespaceLen(beforePartialTag)
 			ambiguousStart := len(beforePartialTag) - trailingWhitespaceLen
 
-			unambiguous := p.buffer.String()[:ambiguousStart]
-			ambiguous := p.buffer.String()[ambiguousStart:]
+			unambiguous := acc[:ambiguousStart]
+			ambiguous := acc[ambiguousStart:]
 			p.buffer.Reset()
 			p.buffer.WriteString(ambiguous)
 			if len(unambiguous) > 0 {
@@ -205,11 +220,11 @@ func (p *Qwen3VLParser) eat() ([]qwenEvent, bool) {
 			}
 			return events, false
 		} else {
-			whitespaceLen := trailingWhitespaceLen(p.buffer.String())
-			ambiguousStart := len(p.buffer.String()) - whitespaceLen
+			whitespaceLen := trailingWhitespaceLen(acc)
+			ambiguousStart := len(acc) - whitespaceLen
 
-			unambiguous := p.buffer.String()[:ambiguousStart]
-			ambiguous := p.buffer.String()[ambiguousStart:]
+			unambiguous := acc[:ambiguousStart]
+			ambiguous := acc[ambiguousStart:]
 			p.buffer.Reset()
 			p.buffer.WriteString(ambiguous)
 			if len(unambiguous) > 0 {

model/parsers/qwen3vl_thinking_test.go

@@ -98,8 +98,12 @@ func TestQwen3VLThinkingParserStreaming(t *testing.T) {
 			desc: "nested thinking and tool call (outside thinking, inside tool call)",
 			steps: []step{
 				{
-					input:      "I'm thinking<tool_call>I'm nested tool call</tool_call></think>",
-					wantEvents: []qwenEvent{qwenEventThinkingContent{content: "I'm thinking<tool_call>I'm nested tool call</tool_call>"}},
+					input: "I'm thinking<tool_call>I'm nested tool call</tool_call></think>",
+					wantEvents: []qwenEvent{
+						qwenEventThinkingContent{content: "I'm thinking"},
+						qwenEventRawToolCall{raw: "I'm nested tool call"},
+						qwenEventContent{content: "</think>"},
+					},
 				},
 			},
 		},
@@ -109,8 +113,7 @@ func TestQwen3VLThinkingParserStreaming(t *testing.T) {
 				{
 					input: "<tool_call>I'm nested tool call<think>I'm thinking</think></tool_call>",
 					wantEvents: []qwenEvent{
-						qwenEventThinkingContent{content: "<tool_call>I'm nested tool call<think>I'm thinking"},
-						qwenEventContent{content: "</tool_call>"},
+						qwenEventRawToolCall{raw: "I'm nested tool call<think>I'm thinking</think>"},
 					},
 				},
 			},
@@ -121,8 +124,8 @@ func TestQwen3VLThinkingParserStreaming(t *testing.T) {
 				{
 					input: "I'm thinking<tool_call>I'm NOT a nested tool call</think></tool_call><tool_call>I'm nested tool call 2<think></tool_call></think>",
 					wantEvents: []qwenEvent{
-						qwenEventThinkingContent{content: "I'm thinking<tool_call>I'm NOT a nested tool call"},
-						qwenEventContent{content: "</tool_call>"},
+						qwenEventThinkingContent{content: "I'm thinking"},
+						qwenEventRawToolCall{raw: "I'm NOT a nested tool call</think>"},
 						qwenEventRawToolCall{raw: "I'm nested tool call 2<think>"},
 						qwenEventContent{content: "</think>"},
 					},

model/renderers/lfm2.go

@@ -3,6 +3,7 @@ package renderers
 import (
 	"bytes"
 	"encoding/json"
+	"fmt"
 	"sort"
 	"strings"
 
@@ -192,21 +193,25 @@ func lfm2RenderToolCalls(calls []api.ToolCall) string {
 	return sb.String()
 }
 
-func (r *LFM2Renderer) renderMessageContent(message api.Message) string {
+func (r *LFM2Renderer) renderMessageContent(message api.Message, imageOffset int) string {
 	content := lfm2RenderContent(message.Content, r.useImgTags)
 	if len(message.Images) == 0 {
 		return content
 	}
 
-	// chatPrompt may already have inserted [img] / [img-n] placeholders.
-	if strings.Contains(content, "[img]") || strings.Contains(content, "[img-") || strings.Contains(content, "<image>") {
-		return content
-	}
-
 	var sb strings.Builder
-	placeholder := lfm2ImagePlaceholder(r.useImgTags)
-	for range message.Images {
-		sb.WriteString(placeholder)
+	if r.useImgTags {
+		for i := range message.Images {
+			sb.WriteString(fmt.Sprintf("[img-%d]", imageOffset+i))
+		}
+	} else {
+		placeholder := lfm2ImagePlaceholder(false)
+		if strings.Contains(content, placeholder) {
+			return content
+		}
+		for range message.Images {
+			sb.WriteString(placeholder)
+		}
 	}
 	sb.WriteString(content)
 	return sb.String()
@@ -262,6 +267,11 @@ func (r *LFM2Renderer) Render(messages []api.Message, tools []api.Tool, thinkVal
 		}
 	}
 
+	imageOffset := 0
+	for i := range startIdx {
+		imageOffset += len(messages[i].Images)
+	}
+
 	for i := startIdx; i < len(messages); i++ {
 		message := messages[i]
 		lastMessage := i == len(messages)-1
@@ -271,7 +281,8 @@ func (r *LFM2Renderer) Render(messages []api.Message, tools []api.Tool, thinkVal
 		sb.WriteString(message.Role)
 		sb.WriteString("\n")
 
-		content := r.renderMessageContent(message)
+		content := r.renderMessageContent(message, imageOffset)
+		imageOffset += len(message.Images)
 		if message.Role == "assistant" && !keepPastThinking && i != lastAssistantIndex {
 			if idx := strings.LastIndex(content, "</think>"); idx >= 0 {
 				content = strings.TrimSpace(content[idx+len("</think>"):])

model/renderers/lfm2_test.go

@@ -236,16 +236,6 @@ func TestLFM2Renderer_Images(t *testing.T) {
 				Content: "Describe this image.",
 				Images:  []api.ImageData{api.ImageData("img1")},
 			},
-			expected: "<|startoftext|><|im_start|>user\n[img]Describe this image.<|im_end|>\n<|im_start|>assistant\n",
-		},
-		{
-			name:     "existing_indexed_img_placeholder_not_duplicated",
-			renderer: &LFM2Renderer{useImgTags: true},
-			message: api.Message{
-				Role:    "user",
-				Content: "[img-0]Describe this image.",
-				Images:  []api.ImageData{api.ImageData("img1")},
-			},
 			expected: "<|startoftext|><|im_start|>user\n[img-0]Describe this image.<|im_end|>\n<|im_start|>assistant\n",
 		},
 		{

model/renderers/qwen3vl.go

@@ -1,6 +1,7 @@
 package renderers
 
 import (
+	"fmt"
 	"strings"
 
 	"github.com/ollama/ollama/api"
@@ -9,10 +10,11 @@ import (
 type Qwen3VLRenderer struct {
 	isThinking bool
 
-	useImgTags bool
+	emitEmptyThinkOnNoThink bool
+	useImgTags              bool
 }
 
-func (r *Qwen3VLRenderer) renderContent(content api.Message) string {
+func (r *Qwen3VLRenderer) renderContent(content api.Message, imageOffset int) (string, int) {
 	// This assumes all images are at the front of the message - same assumption as ollama/ollama/runner.go
 	var subSb strings.Builder
 	for range content.Images {
@@ -20,7 +22,8 @@ func (r *Qwen3VLRenderer) renderContent(content api.Message) string {
 		// model backends, and so we should eventually parameterize this or
 		// only output a placeholder such as [img]
 		if r.useImgTags {
-			subSb.WriteString("[img]")
+			subSb.WriteString(fmt.Sprintf("[img-%d]", imageOffset))
+			imageOffset++
 		} else {
 			subSb.WriteString("<|vision_start|><|image_pad|><|vision_end|>")
 		}
@@ -28,12 +31,17 @@ func (r *Qwen3VLRenderer) renderContent(content api.Message) string {
 	// TODO: support videos
 
 	subSb.WriteString(content.Content)
-	return subSb.String()
+	return subSb.String(), imageOffset
 }
 
-func (r *Qwen3VLRenderer) Render(messages []api.Message, tools []api.Tool, _ *api.ThinkValue) (string, error) {
+func (r *Qwen3VLRenderer) Render(messages []api.Message, tools []api.Tool, think *api.ThinkValue) (string, error) {
 	var sb strings.Builder
 
+	isThinking := r.isThinking
+	if think != nil {
+		isThinking = think.Bool()
+	}
+
 	if len(tools) > 0 {
 		sb.WriteString(imStartTag + "system\n")
 		if len(messages) > 0 && messages[0].Role == "system" {
@@ -57,7 +65,7 @@ func (r *Qwen3VLRenderer) Render(messages []api.Message, tools []api.Tool, _ *ap
 		message := messages[i]
 		if multiStepTool && message.Role == "user" {
 			// Check if content starts with <tool_response> and ends with </tool_response>
-			content := r.renderContent(message)
+			content, _ := r.renderContent(message, 0)
 			if !(strings.HasPrefix(content, "<tool_response>") && strings.HasSuffix(content, "</tool_response>")) {
 				multiStepTool = false
 				lastQueryIndex = i
@@ -65,8 +73,10 @@ func (r *Qwen3VLRenderer) Render(messages []api.Message, tools []api.Tool, _ *ap
 		}
 	}
 
+	imageOffset := 0
 	for i, message := range messages {
-		content := r.renderContent(message)
+		content, nextImageOffset := r.renderContent(message, imageOffset)
+		imageOffset = nextImageOffset
 
 		lastMessage := i == len(messages)-1
 		prefill := lastMessage && message.Role == "assistant"
@@ -76,13 +86,13 @@ func (r *Qwen3VLRenderer) Render(messages []api.Message, tools []api.Tool, _ *ap
 		} else if message.Role == "assistant" {
 			contentReasoning := ""
 
-			if r.isThinking {
+			if isThinking {
 				if message.Thinking != "" {
 					contentReasoning = message.Thinking
 				}
 			}
 
-			if r.isThinking && i > lastQueryIndex {
+			if isThinking && i > lastQueryIndex {
 				if i == len(messages)-1 || contentReasoning != "" {
 					sb.WriteString("<|im_start|>" + message.Role + "\n<think>\n" + strings.Trim(contentReasoning, "\n")) // do we want to add a new line here?
 					if content != "" {
@@ -125,8 +135,10 @@ func (r *Qwen3VLRenderer) Render(messages []api.Message, tools []api.Tool, _ *ap
 		// prefill at the end
 		if lastMessage && !prefill {
 			sb.WriteString("<|im_start|>assistant\n")
-			if r.isThinking {
+			if isThinking {
 				sb.WriteString("<think>\n")
+			} else if r.emitEmptyThinkOnNoThink {
+				sb.WriteString("<think>\n\n</think>\n\n")
 			}
 		}
 	}

model/renderers/qwen3vl_nonthinking_test.go

@@ -101,7 +101,7 @@ Let me analyze this image.`,
 			},
 			useImgTags: true,
 			expected: `<|im_start|>user
-[img]Describe this image.<|im_end|>
+[img-0]Describe this image.<|im_end|>
 <|im_start|>assistant
 Let me analyze this image.`,
 		},
@@ -123,7 +123,7 @@ Let me analyze this image.`,
 			},
 			useImgTags: true,
 			expected: `<|im_start|>user
-[img][img]Describe these images.<|im_end|>
+[img-0][img-1]Describe these images.<|im_end|>
 <|im_start|>assistant
 Let me analyze this image.`,
 		},

model/renderers/qwen3vl_thinking_test.go

@@ -1,6 +1,7 @@
 package renderers
 
 import (
+	"strings"
 	"testing"
 
 	"github.com/google/go-cmp/cmp"
@@ -370,3 +371,74 @@ func TestFormatToolCallArgumentThinkingVL(t *testing.T) {
 		})
 	}
 }
+
+func TestQwen3VLRendererThinkOverride(t *testing.T) {
+	msgs := []api.Message{
+		{Role: "user", Content: "Hello"},
+	}
+
+	renderThinking, err := (&Qwen3VLRenderer{isThinking: true}).Render(msgs, nil, nil)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if !strings.Contains(renderThinking, "<|im_start|>assistant\n<think>\n") {
+		t.Fatalf("expected default thinking renderer to emit <think>, got:\n%s", renderThinking)
+	}
+
+	renderNonThinking, err := (&Qwen3VLRenderer{isThinking: true}).Render(msgs, nil, &api.ThinkValue{Value: false})
+	if err != nil {
+		t.Fatal(err)
+	}
+	if strings.Contains(renderNonThinking, "<think>") {
+		t.Fatalf("expected think=false override to suppress <think>, got:\n%s", renderNonThinking)
+	}
+
+	renderForcedThinking, err := (&Qwen3VLRenderer{isThinking: false}).Render(msgs, nil, &api.ThinkValue{Value: true})
+	if err != nil {
+		t.Fatal(err)
+	}
+	if !strings.Contains(renderForcedThinking, "<|im_start|>assistant\n<think>\n") {
+		t.Fatalf("expected think=true override to emit <think>, got:\n%s", renderForcedThinking)
+	}
+}
+
+func TestQwen3VLRendererThinkOverrideWithExplicitNoThinkPrefill(t *testing.T) {
+	msgs := []api.Message{
+		{Role: "user", Content: "Hello"},
+	}
+
+	renderNonThinking, err := (&Qwen3VLRenderer{
+		isThinking:              true,
+		emitEmptyThinkOnNoThink: true,
+	}).Render(msgs, nil, &api.ThinkValue{Value: false})
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	if !strings.Contains(renderNonThinking, "<|im_start|>assistant\n<think>\n\n</think>\n\n") {
+		t.Fatalf("expected explicit think=false prefill block, got:\n%s", renderNonThinking)
+	}
+}
+
+func TestQwenRendererNameNoThinkBehaviorSplit(t *testing.T) {
+	msgs := []api.Message{
+		{Role: "user", Content: "Hello"},
+	}
+	thinkFalse := &api.ThinkValue{Value: false}
+
+	qwen35Rendered, err := RenderWithRenderer("qwen3.5", msgs, nil, thinkFalse)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if !strings.Contains(qwen35Rendered, "<|im_start|>assistant\n<think>\n\n</think>\n\n") {
+		t.Fatalf("expected qwen3.5 renderer to emit explicit no-think prefill, got:\n%s", qwen35Rendered)
+	}
+
+	qwen3VLRendered, err := RenderWithRenderer("qwen3-vl-thinking", msgs, nil, thinkFalse)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if strings.Contains(qwen3VLRendered, "<|im_start|>assistant\n<think>\n\n</think>\n\n") {
+		t.Fatalf("expected qwen3-vl-thinking renderer to keep legacy non-empty no-think behavior, got:\n%s", qwen3VLRendered)
+	}
+}

model/renderers/renderer.go

@@ -56,6 +56,9 @@ func rendererForName(name string) Renderer {
 	case "qwen3-vl-thinking":
 		renderer := &Qwen3VLRenderer{isThinking: true, useImgTags: RenderImgTags}
 		return renderer
+	case "qwen3.5":
+		renderer := &Qwen3VLRenderer{isThinking: true, emitEmptyThinkOnNoThink: true, useImgTags: RenderImgTags}
+		return renderer
 	case "cogito":
 		renderer := &CogitoRenderer{isThinking: true}
 		return renderer

model/renderers/renderer_test.go

@@ -29,17 +29,27 @@ func TestRegisterCustomRenderer(t *testing.T) {
 }
 
 func TestBuiltInRendererStillWorks(t *testing.T) {
-	// Test that qwen3-coder still works
+	tests := []struct {
+		name string
+	}{
+		{name: "qwen3-coder"},
+		{name: "qwen3.5"},
+	}
+
 	messages := []api.Message{
 		{Role: "user", Content: "Hello"},
 	}
 
-	result, err := RenderWithRenderer("qwen3-coder", messages, nil, nil)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-	if result == "" {
-		t.Error("expected non-empty result from qwen3-coder renderer")
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			result, err := RenderWithRenderer(tt.name, messages, nil, nil)
+			if err != nil {
+				t.Fatalf("unexpected error: %v", err)
+			}
+			if result == "" {
+				t.Fatalf("expected non-empty result from %s renderer", tt.name)
+			}
+		})
 	}
 }
 

server/prompt.go

@@ -86,6 +86,11 @@ func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.
 				ID:   len(images),
 				Data: i,
 			}
+			images = append(images, imgData)
+
+			if m.Config.Renderer != "" {
+				continue
+			}
 
 			imgTag := fmt.Sprintf("[img-%d]", imgData.ID)
 			if !strings.Contains(prompt, "[img]") {
@@ -93,8 +98,6 @@ func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.
 			} else {
 				prompt = strings.Replace(prompt, "[img]", imgTag, 1)
 			}
-
-			images = append(images, imgData)
 		}
 		msgs[currMsgIdx+cnt].Content = prefix + prompt
 	}

server/prompt_test.go

@@ -9,6 +9,7 @@ import (
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/template"
+	"github.com/ollama/ollama/types/model"
 )
 
 func TestChatPrompt(t *testing.T) {
@@ -330,3 +331,38 @@ func TestChatPromptTokenizeCalls(t *testing.T) {
 		})
 	}
 }
+
+func TestChatPromptRendererDoesNotRewriteMessageContent(t *testing.T) {
+	msgs := []api.Message{
+		{
+			Role:    "user",
+			Content: "what do these photos have in common?",
+			Images:  []api.ImageData{[]byte("img-1"), []byte("img-2"), []byte("img-3")},
+		},
+	}
+	originalContent := msgs[0].Content
+
+	m := Model{
+		Config:         model.ConfigV2{Renderer: "qwen3-vl-instruct"},
+		ProjectorPaths: []string{"vision"},
+	}
+	opts := api.Options{Runner: api.Runner{NumCtx: 8192}}
+	think := false
+
+	prompt, images, err := chatPrompt(t.Context(), &m, mockRunner{}.Tokenize, &opts, msgs, nil, &api.ThinkValue{Value: think}, true)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	if msgs[0].Content != originalContent {
+		t.Fatalf("renderer path should not mutate message content: got %q, want %q", msgs[0].Content, originalContent)
+	}
+
+	if got, want := len(images), 3; got != want {
+		t.Fatalf("len(images) = %d, want %d", got, want)
+	}
+
+	if prompt == "" {
+		t.Fatal("prompt is empty")
+	}
+}

server/quantization.go

@@ -6,6 +6,7 @@ import (
 	"log/slog"
 	"maps"
 	"os"
+	"slices"
 	"strings"
 	"unsafe"
 
@@ -33,6 +34,9 @@ func (q quantizer) WriteTo(w io.Writer) (int64, error) {
 		slog.Warn("file read error", "tensor", q.from.Name, "file", q.Name(), "error", err)
 		return 0, fmt.Errorf("unable to read tensor %s from %s: %s", q.from.Name, q.Name(), err)
 	}
+	if uint64(len(data)) < q.from.Size() {
+		return 0, fmt.Errorf("tensor %s data size %d is less than expected %d from shape %v", q.from.Name, len(data), q.from.Size(), q.from.Shape)
+	}
 	var f32s []float32
 	newType := fsggml.TensorType(q.to.Kind)
 	if fsggml.TensorType(q.from.Kind) == fsggml.TensorTypeF32 {
@@ -58,7 +62,7 @@ func useMoreBits(iLayer, nLayers int) bool {
 	return iLayer < (nLayers/8) || iLayer >= 7*nLayers/8 || (iLayer-nLayers/8)%3 == 2
 }
 
-func qwen3nextQuantType(name string) (fsggml.TensorType, bool) {
+func qwen3LinearAttnQuantType(name string) (fsggml.TensorType, bool) {
 	switch {
 	// Full attention
 	case strings.HasSuffix(name, ".attn_q.weight"):
@@ -79,6 +83,10 @@ func qwen3nextQuantType(name string) (fsggml.TensorType, bool) {
 	// SSM
 	case strings.HasSuffix(name, ".ssm_ba.weight"):
 		return fsggml.TensorTypeQ4_K, true
+	case strings.HasSuffix(name, ".ssm_beta.weight"):
+		return fsggml.TensorTypeQ4_K, true
+	case strings.HasSuffix(name, ".ssm_alpha.weight"):
+		return fsggml.TensorTypeQ4_K, true
 	case strings.HasSuffix(name, ".ssm_out.weight"):
 		return fsggml.TensorTypeQ4_K, true
 
@@ -287,8 +295,8 @@ func newType(t *fsggml.Tensor, kv fsggml.KV, qs *quantizeState, ftype fsggml.Fil
 
 	newType := fsggml.TensorType(t.Kind)
 	if quantize {
-		if kv.Architecture() == "qwen3next" && (ftype == fsggml.FileTypeQ4_K_M || ftype == fsggml.FileTypeQ4_K_S) {
-			if qt, ok := qwen3nextQuantType(name); ok {
+		if slices.Contains([]string{"qwen3next", "qwen35", "qwen35moe"}, kv.Architecture()) && (ftype == fsggml.FileTypeQ4_K_M || ftype == fsggml.FileTypeQ4_K_S) {
+			if qt, ok := qwen3LinearAttnQuantType(name); ok {
 				return qt
 			}
 		}

server/quantization_test.go

@@ -166,6 +166,60 @@ func TestGetTensorNewType(t *testing.T) {
 	}
 }
 
+func TestQwen3LinearAttentionQuantOverride(t *testing.T) {
+	cases := []struct {
+		name     string
+		arch     string
+		tensor   string
+		fileType fsggml.FileType
+		expected fsggml.TensorType
+	}{
+		{
+			name:     "qwen35_beta",
+			arch:     "qwen35",
+			tensor:   "blk.0.ssm_beta.weight",
+			fileType: fsggml.FileTypeQ4_K_M,
+			expected: fsggml.TensorTypeQ4_K,
+		},
+		{
+			name:     "qwen35_alpha",
+			arch:     "qwen35",
+			tensor:   "blk.0.ssm_alpha.weight",
+			fileType: fsggml.FileTypeQ4_K_M,
+			expected: fsggml.TensorTypeQ4_K,
+		},
+		{
+			name:     "qwen35moe_attn_qkv",
+			arch:     "qwen35moe",
+			tensor:   "blk.0.attn_qkv.weight",
+			fileType: fsggml.FileTypeQ4_K_M,
+			expected: fsggml.TensorTypeQ4_K,
+		},
+		{
+			name:     "non_qwen35_falls_back",
+			arch:     "foo",
+			tensor:   "blk.0.attn_qkv.weight",
+			fileType: fsggml.FileTypeQ4_K_M,
+			expected: fsggml.TensorTypeQ5_K,
+		},
+	}
+
+	for _, tt := range cases {
+		t.Run(tt.name, func(t *testing.T) {
+			kv := fsggml.KV{"general.architecture": tt.arch}
+			got := newType(&fsggml.Tensor{
+				Name:  tt.tensor,
+				Shape: []uint64{256, 256},
+				Kind:  uint32(fsggml.TensorTypeF16),
+			}, kv, &quantizeState{}, tt.fileType)
+
+			if got != tt.expected {
+				t.Fatalf("unexpected tensor type for %s (%s): got %s want %s", tt.tensor, tt.arch, got, tt.expected)
+			}
+		})
+	}
+}
+
 func TestQuantizeModel(t *testing.T) {
 	cases := []struct {
 		name                string
@@ -173,6 +227,7 @@ func TestQuantizeModel(t *testing.T) {
 		tensors             []*fsggml.Tensor
 		newType             string
 		expectedTensorTypes map[string]fsggml.TensorType
+		expectErr           bool
 	}{
 		{
 			name: "f16_q4_k",
@@ -253,6 +308,36 @@ func TestQuantizeModel(t *testing.T) {
 				"output.weight":     fsggml.TensorTypeQ8_0,
 			},
 		},
+		{
+			name: "f32_short_data",
+			kv: map[string]any{
+				"general.architecture": "foo",
+			},
+			tensors: []*fsggml.Tensor{
+				{
+					Name: "blk.0.attn.weight", Kind: uint32(fsggml.TensorTypeF32),
+					Offset: uint64(0), Shape: []uint64{512, 2},
+					WriterTo: bytes.NewReader(make([]byte, 32)),
+				},
+			},
+			newType:   "Q4_K",
+			expectErr: true,
+		},
+		{
+			name: "f16_short_data",
+			kv: map[string]any{
+				"general.architecture": "foo",
+			},
+			tensors: []*fsggml.Tensor{
+				{
+					Name: "blk.0.attn.weight", Kind: uint32(fsggml.TensorTypeF16),
+					Offset: uint64(0), Shape: []uint64{512, 2},
+					WriterTo: bytes.NewReader(make([]byte, 32)),
+				},
+			},
+			newType:   "Q4_K",
+			expectErr: true,
+		},
 	}
 
 	for _, tt := range cases {
@@ -264,6 +349,9 @@ func TestQuantizeModel(t *testing.T) {
 			}
 			defer fp.Close()
 			meta, err := fsggml.Decode(fp, -1)
+			if tt.expectErr && err != nil {
+				return
+			}
 			if err != nil {
 				t.Fatal(err.Error())
 			}
@@ -283,6 +371,12 @@ func TestQuantizeModel(t *testing.T) {
 			}
 
 			err = quantize(fp, tmp, meta, ftype, progress)
+			if tt.expectErr {
+				if err == nil {
+					t.Fatal("expected quantize to return an error")
+				}
+				return
+			}
 			if err != nil {
 				t.Fatalf("error during quantize: %s", err)
 			}

server/sched.go

@@ -447,7 +447,7 @@ func (s *Scheduler) load(req *LlmRequest, f *ggml.GGML, systemInfo ml.SystemInfo
 
 	// Some architectures are not safe with num_parallel > 1.
 	// ref: https://github.com/ollama/ollama/issues/4165
-	if slices.Contains([]string{"mllama", "qwen3vl", "qwen3vlmoe", "qwen3next", "lfm2", "lfm2moe", "nemotron_h", "nemotron_h_moe"}, req.model.Config.ModelFamily) && numParallel != 1 {
+	if slices.Contains([]string{"mllama", "qwen3vl", "qwen3vlmoe", "qwen35", "qwen35moe", "qwen3next", "lfm2", "lfm2moe", "nemotron_h", "nemotron_h_moe"}, req.model.Config.ModelFamily) && numParallel != 1 {
 		numParallel = 1
 		slog.Warn("model architecture does not currently support parallel requests", "architecture", req.model.Config.ModelFamily)
 	}

x/mlxrunner/cache.go

@@ -9,59 +9,104 @@ import (
 	"github.com/ollama/ollama/logutil"
 	"github.com/ollama/ollama/x/mlxrunner/cache"
 	"github.com/ollama/ollama/x/mlxrunner/mlx"
+	"github.com/ollama/ollama/x/mlxrunner/model/base"
 )
 
-// CacheEntry stores a single sequence
-type CacheEntry struct {
-	Tokens []int32
-	Caches []cache.Cache
+type kvCache struct {
+	// For now we only support a single entry, so this is just one sequence
+	tokens []int32
+	caches []cache.Cache
 }
 
-// FindNearestCache finds the longest common prefix between tokens and the cached sequence
-func (r *Runner) FindNearestCache(tokens []int32) ([]cache.Cache, []int32) {
-	if r.cache == nil {
-		slog.Info("Cache miss", "left", len(tokens))
-		return nil, tokens
+// cacheSession manages caches for a single pipeline run.
+// Callers should append generated tokens to outputs and
+// defer close to save the cache state.
+type cacheSession struct {
+	cache   *kvCache
+	inputs  []int32
+	outputs []int32
+
+	caches    []cache.Cache
+	remaining []int32
+}
+
+// begin prepares caches for a new request. It finds the nearest
+// matching cache or creates new caches if none match.
+func (c *kvCache) begin(m base.Model, inputs []int32) *cacheSession {
+	if len(c.caches) == 0 {
+		if cacheFactory, ok := m.(interface{ NewCaches() []cache.Cache }); ok {
+			c.caches = cacheFactory.NewCaches()
+		} else {
+			c.caches = make([]cache.Cache, m.NumLayers())
+			for i := range c.caches {
+				c.caches[i] = cache.NewKVCache()
+			}
+		}
 	}
 
-	// Find longest common prefix
+	remaining := c.findRemaining(inputs)
+
+	return &cacheSession{
+		cache:     c,
+		inputs:    inputs,
+		caches:    c.caches,
+		remaining: remaining,
+	}
+}
+
+// close saves the token state if the forward pass ran.
+func (s *cacheSession) close() {
+	if offset := s.caches[0].Offset(); offset > 0 {
+		// Ensure that if we have run the forward pass and set the metadata
+		// that we also actually have the data
+		arrays := make([]*mlx.Array, 0, 2*len(s.caches))
+		for _, c := range s.caches {
+			k, v := c.State()
+			arrays = append(arrays, k, v)
+		}
+		mlx.AsyncEval(arrays...)
+
+		s.cache.tokens = append(s.inputs, s.outputs...)[:offset]
+	}
+}
+
+// findRemaining finds the longest common prefix between tokens and the cached
+// sequence, trims stale cache entries, and returns the remaining tokens.
+func (c *kvCache) findRemaining(tokens []int32) []int32 {
 	prefix := 0
-	for prefix < len(tokens) && prefix < len(r.cache.Tokens) && tokens[prefix] == r.cache.Tokens[prefix] {
+	for prefix < len(tokens) && prefix < len(c.tokens) && tokens[prefix] == c.tokens[prefix] {
 		prefix++
 	}
 
-	switch {
-	case prefix == 0:
-		for _, c := range r.cache.Caches {
-			c.Free()
+	if prefix == len(tokens) && prefix > 0 {
+		// Leave one token to run through the model so we can sample a response.
+		prefix--
+	}
+
+	if prefix < len(c.tokens) {
+		trim := len(c.tokens) - prefix
+		for _, kv := range c.caches {
+			kv.Trim(trim)
 		}
-		r.cache = nil
+		c.tokens = c.tokens[:prefix]
+	}
+
+	if prefix == 0 {
 		slog.Info("Cache miss", "left", len(tokens))
-		return nil, tokens
-	case prefix < len(r.cache.Tokens):
-		trim := len(r.cache.Tokens) - prefix
-		for _, c := range r.cache.Caches {
-			c.Trim(trim)
-		}
-		r.cache.Tokens = r.cache.Tokens[:prefix]
+	} else {
+		slog.Info("Cache hit", "total", len(tokens), "cached", prefix, "left", len(tokens[prefix:]))
 	}
-
-	slog.Info("Cache hit", "total", len(tokens), "cached", prefix, "left", len(tokens[prefix:]))
-	return r.cache.Caches, tokens[prefix:]
+	return tokens[prefix:]
 }
 
-func (r *Runner) InsertCache(tokens []int32, caches []cache.Cache) {
-	r.cache = &CacheEntry{
-		Tokens: tokens,
-		Caches: caches,
+func (c *kvCache) log() {
+	if len(c.caches) == 0 {
+		return
 	}
-}
-
-func (c *CacheEntry) LogCache() {
 	var totalBytes int
-	for _, kv := range c.Caches {
+	for _, kv := range c.caches {
 		k, v := kv.State()
 		totalBytes += k.NumBytes() + v.NumBytes()
 	}
-	logutil.Trace(fmt.Sprintf("kv cache tokens: %d, size: %s", c.Caches[0].Offset(), mlx.PrettyBytes(totalBytes)))
+	logutil.Trace(fmt.Sprintf("kv cache tokens: %d, size: %s", c.caches[0].Offset(), mlx.PrettyBytes(totalBytes)))
 }

x/mlxrunner/pipeline.go

@@ -10,7 +10,6 @@ import (
 	"time"
 
 	"github.com/ollama/ollama/logutil"
-	"github.com/ollama/ollama/x/mlxrunner/cache"
 	"github.com/ollama/ollama/x/mlxrunner/mlx"
 )
 
@@ -19,6 +18,23 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 		return errors.New("model not loaded")
 	}
 
+	var (
+		sample, logprobs         *mlx.Array
+		nextSample, nextLogprobs *mlx.Array
+	)
+
+	defer func() {
+		mlx.Unpin(sample, logprobs)
+		mlx.Unpin(nextSample, nextLogprobs)
+		mlx.Sweep()
+		mlx.ClearCache()
+
+		if slog.Default().Enabled(context.TODO(), logutil.LevelTrace) {
+			mlx.LogArrays()
+			r.cache.log()
+		}
+	}()
+
 	enableCompile := true
 	if modelCompile, ok := r.Model.(interface{ EnableCompile() bool }); ok {
 		enableCompile = modelCompile.EnableCompile()
@@ -30,22 +46,19 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 	}
 
 	inputs := r.Tokenizer.Encode(request.Prompt, true)
+	session := r.cache.begin(r.Model, inputs)
+	defer session.close()
 
-	caches, tokens := r.FindNearestCache(inputs)
-	if len(caches) == 0 {
-		if cacheFactory, ok := r.Model.(interface{ NewCaches() []cache.Cache }); ok {
-			caches = cacheFactory.NewCaches()
-		} else {
-			caches = make([]cache.Cache, r.Model.NumLayers())
-			for i := range caches {
-				caches[i] = cache.NewKVCache()
-			}
-		}
-	}
+	caches := session.caches
+	tokens := session.remaining
 
 	total, processed := len(tokens), 0
 	slog.Info("Prompt processing progress", "processed", processed, "total", total)
 	for total-processed > 1 {
+		if err := request.Ctx.Err(); err != nil {
+			return err
+		}
+
 		n := min(2<<10, total-processed-1)
 		r.Model.Forward(mlx.FromValues(tokens[processed:processed+n], n).ExpandDims(0), caches)
 		mlx.Sweep()
@@ -76,15 +89,18 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 		return sample, logprobs
 	}
 
-	sample, logprobs := step(mlx.FromValues(tokens[processed:], total-processed))
+	sample, logprobs = step(mlx.FromValues(tokens[processed:], total-processed))
 
 	var b bytes.Buffer
 
 	now := time.Now()
 	final := Response{Done: true, PromptTokens: total, CompletionTokens: request.Options.MaxTokens, DoneReason: 1}
-	outputs := make([]int32, 0, request.Options.MaxTokens)
 	for i := range request.Options.MaxTokens {
-		nextSample, nextLogprobs := step(sample)
+		if err := request.Ctx.Err(); err != nil {
+			return err
+		}
+
+		nextSample, nextLogprobs = step(sample)
 
 		if i == 0 {
 			slog.Info("Prompt processing progress", "processed", total, "total", total)
@@ -94,43 +110,40 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 		}
 
 		output := int32(sample.Int())
-		outputs = append(outputs, output)
+		session.outputs = append(session.outputs, output)
 
 		if r.Tokenizer.IsEOS(output) {
-			mlx.Unpin(nextSample, nextLogprobs)
 			final.Token = int(output)
 			final.DoneReason = 0
 			final.CompletionTokens = i
 			break
 		}
 
-		request.Responses <- Response{
+		select {
+		case <-request.Ctx.Done():
+			return request.Ctx.Err()
+		case request.Responses <- Response{
 			Text:  r.Decode(output, &b),
 			Token: int(output),
+		}:
 		}
 
 		mlx.Unpin(sample, logprobs)
+		sample, logprobs = nextSample, nextLogprobs
+		nextSample, nextLogprobs = nil, nil
+
 		if i%256 == 0 {
 			mlx.ClearCache()
 		}
-
-		sample, logprobs = nextSample, nextLogprobs
 	}
 
-	mlx.Unpin(sample, logprobs)
 	final.CompletionTokensDuration = time.Since(now)
-	request.Responses <- final
-	r.InsertCache(append(inputs, outputs...), caches)
-	mlx.Sweep()
-
-	if slog.Default().Enabled(context.TODO(), logutil.LevelTrace) {
-		mlx.LogArrays()
-		if r.cache != nil {
-			r.cache.LogCache()
-		}
+	select {
+	case <-request.Ctx.Done():
+		return request.Ctx.Err()
+	case request.Responses <- final:
+		return nil
 	}
-
-	return nil
 }
 
 func (r Runner) Decode(sample int32, b *bytes.Buffer) string {

x/mlxrunner/runner.go

@@ -12,7 +12,6 @@ import (
 
 	"golang.org/x/sync/errgroup"
 
-	"github.com/ollama/ollama/x/mlxrunner/cache"
 	"github.com/ollama/ollama/x/mlxrunner/mlx"
 	"github.com/ollama/ollama/x/mlxrunner/model"
 	"github.com/ollama/ollama/x/mlxrunner/model/base"
@@ -25,8 +24,9 @@ type Request struct {
 	Responses chan Response
 	Pipeline  func(Request) error
 
+	Ctx context.Context
+
 	sample.Sampler
-	caches []cache.Cache
 }
 
 type TextCompletionsRequest struct {
@@ -61,7 +61,7 @@ type Runner struct {
 	Model     base.Model
 	Tokenizer *tokenizer.Tokenizer
 	Requests  chan Request
-	cache     *CacheEntry
+	cache     kvCache
 }
 
 func (r *Runner) Load(modelName string) error {
@@ -157,7 +157,7 @@ func (r *Runner) Run(host, port string, mux http.Handler) error {
 				return nil
 			case request := <-r.Requests:
 				if err := request.Pipeline(request); err != nil {
-					break
+					slog.Info("Request terminated", "error", err)
 				}
 
 				close(request.Responses)

x/mlxrunner/server.go

@@ -5,6 +5,7 @@ package mlxrunner
 import (
 	"bytes"
 	"cmp"
+	"context"
 	"encoding/json"
 	"flag"
 	"fmt"
@@ -98,19 +99,36 @@ func Execute(args []string) error {
 			request.Options.TopK,
 		)
 
-		runner.Requests <- request
+		var cancel context.CancelFunc
+		request.Ctx, cancel = context.WithCancel(r.Context())
+		defer cancel()
+
+		select {
+		case <-r.Context().Done():
+			return
+		case runner.Requests <- request:
+		}
 
 		w.Header().Set("Content-Type", "application/jsonl")
 		w.WriteHeader(http.StatusOK)
 		enc := json.NewEncoder(w)
-		for response := range request.Responses {
-			if err := enc.Encode(response); err != nil {
-				slog.Error("Failed to encode response", "error", err)
+		for {
+			select {
+			case <-r.Context().Done():
 				return
-			}
+			case response, ok := <-request.Responses:
+				if !ok {
+					return
+				}
 
-			if f, ok := w.(http.Flusher); ok {
-				f.Flush()
+				if err := enc.Encode(response); err != nil {
+					slog.Error("Failed to encode response", "error", err)
+					return
+				}
+
+				if f, ok := w.(http.Flusher); ok {
+					f.Flush()
+				}
 			}
 		}
 	})