cmd: override stale entries for context window pi (#14655)

If the user is pointing at a remote OLLAMA_HOST, fail experimental safetensor
based create operations as we only support local creation currently.

api/types.go

@@ -15,7 +15,6 @@ import (
 	"github.com/google/uuid"
 
 	"github.com/ollama/ollama/envconfig"
-	"github.com/ollama/ollama/format"
 	"github.com/ollama/ollama/internal/orderedmap"
 	"github.com/ollama/ollama/types/model"
 )
@@ -570,7 +569,6 @@ type DebugInfo struct {
 
 type Metrics struct {
 	TotalDuration      time.Duration `json:"total_duration,omitempty"`
-	PeakMemory         uint64        `json:"peak_memory,omitempty"`
 	LoadDuration       time.Duration `json:"load_duration,omitempty"`
 	PromptEvalCount    int           `json:"prompt_eval_count,omitempty"`
 	PromptEvalDuration time.Duration `json:"prompt_eval_duration,omitempty"`
@@ -936,10 +934,6 @@ func (m *Metrics) Summary() {
 		fmt.Fprintf(os.Stderr, "total duration:       %v\n", m.TotalDuration)
 	}
 
-	if m.PeakMemory > 0 {
-		fmt.Fprintf(os.Stderr, "peak memory:          %s\n", formatPeakMemory(m.PeakMemory))
-	}
-
 	if m.LoadDuration > 0 {
 		fmt.Fprintf(os.Stderr, "load duration:        %v\n", m.LoadDuration)
 	}
@@ -963,14 +957,6 @@ func (m *Metrics) Summary() {
 	}
 }
 
-func formatPeakMemory(b uint64) string {
-	if b >= format.GibiByte {
-		return fmt.Sprintf("%.3f GiB", float64(b)/float64(format.GibiByte))
-	}
-
-	return format.HumanBytes2(b)
-}
-
 func (opts *Options) FromMap(m map[string]any) error {
 	valueOpts := reflect.ValueOf(opts).Elem() // names of the fields in the options struct
 	typeOpts := reflect.TypeOf(opts).Elem()   // types of the fields in the options struct
@@ -1077,7 +1063,7 @@ func DefaultOptions() Options {
 		TopP:             0.9,
 		TypicalP:         1.0,
 		RepeatLastN:      64,
-		RepeatPenalty:    1.1,
+		RepeatPenalty:    1.0,
 		PresencePenalty:  0.0,
 		FrequencyPenalty: 0.0,
 		Seed:             -1,

cmd/cmd.go

@@ -145,6 +145,12 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 	// Check for --experimental flag for safetensors model creation
 	experimental, _ := cmd.Flags().GetBool("experimental")
 	if experimental {
+		host := envconfig.Host()
+		h, _, _ := net.SplitHostPort(host.Host)
+		ip := net.ParseIP(h)
+		if ip == nil || (!ip.IsLoopback() && !ip.IsUnspecified()) {
+			return errors.New("remote safetensor model creation not yet supported")
+		}
 		// Get Modelfile content - either from -f flag or default to "FROM ."
 		var reader io.Reader
 		filename, err := getModelfileName(cmd)
@@ -214,6 +220,12 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		if filename == "" {
 			// No Modelfile found - check if current directory is an image gen model
 			if create.IsTensorModelDir(".") {
+				host := envconfig.Host()
+				h, _, _ := net.SplitHostPort(host.Host)
+				ip := net.ParseIP(h)
+				if ip == nil || (!ip.IsLoopback() && !ip.IsUnspecified()) {
+					return errors.New("remote safetensor model creation not yet supported")
+				}
 				quantize, _ := cmd.Flags().GetString("quantize")
 				return xcreateclient.CreateModel(xcreateclient.CreateOptions{
 					ModelName: modelName,
@@ -585,17 +597,6 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 	}
 	opts.WordWrap = !nowrap
 
-	useImagegen := false
-	if cmd.Flags().Lookup("imagegen") != nil {
-		useImagegen, err = cmd.Flags().GetBool("imagegen")
-		if err != nil {
-			return err
-		}
-	}
-	if useImagegen {
-		opts.Options["use_imagegen_runner"] = true
-	}
-
 	// Fill out the rest of the options based on information about the
 	// model.
 	client, err := api.ClientFromEnvironment()

cmd/config/droid_test.go

@@ -1277,7 +1277,8 @@ func TestDroidEdit_LocalModelDefaultMaxOutput(t *testing.T) {
 func TestDroidEdit_CloudModelLimitsUsed(t *testing.T) {
 	// Verify that every cloud model in cloudModelLimits has a valid output
 	// value that would be used for maxOutputTokens when isCloudModel returns true.
-	// :cloud suffix stripping must also work since that's how users specify them.
+	// Cloud suffix normalization must also work since integrations may see either
+	// :cloud or -cloud model names.
 	for name, expected := range cloudModelLimits {
 		t.Run(name, func(t *testing.T) {
 			l, ok := lookupCloudModelLimit(name)
@@ -1296,6 +1297,15 @@ func TestDroidEdit_CloudModelLimitsUsed(t *testing.T) {
 			if l2.Output != expected.Output {
 				t.Errorf(":cloud output = %d, want %d", l2.Output, expected.Output)
 			}
+			// Also verify -cloud suffix lookup
+			dashCloudName := name + "-cloud"
+			l3, ok := lookupCloudModelLimit(dashCloudName)
+			if !ok {
+				t.Fatalf("lookupCloudModelLimit(%q) returned false", dashCloudName)
+			}
+			if l3.Output != expected.Output {
+				t.Errorf("-cloud output = %d, want %d", l3.Output, expected.Output)
+			}
 		})
 	}
 }

cmd/config/integrations.go

@@ -81,6 +81,7 @@ var cloudModelLimits = map[string]cloudModelLimit{
 	"deepseek-v3.2":       {Context: 163_840, Output: 65_536},
 	"glm-4.6":             {Context: 202_752, Output: 131_072},
 	"glm-4.7":             {Context: 202_752, Output: 131_072},
+	"glm-5":               {Context: 202_752, Output: 131_072},
 	"gpt-oss:120b":        {Context: 131_072, Output: 131_072},
 	"gpt-oss:20b":         {Context: 131_072, Output: 131_072},
 	"kimi-k2:1t":          {Context: 262_144, Output: 262_144},
@@ -90,6 +91,7 @@ var cloudModelLimits = map[string]cloudModelLimit{
 	"qwen3-coder:480b":    {Context: 262_144, Output: 65_536},
 	"qwen3-coder-next":    {Context: 262_144, Output: 32_768},
 	"qwen3-next:80b":      {Context: 262_144, Output: 32_768},
+	"qwen3.5":             {Context: 262_144, Output: 32_768},
 }
 
 // recommendedVRAM maps local recommended models to their approximate VRAM requirement.

cmd/config/openclaw.go

@@ -502,7 +502,7 @@ func (c *Openclaw) Edit(models []string) error {
 		ollama = make(map[string]any)
 	}
 
-	ollama["baseUrl"] = envconfig.Host().String() + "/v1"
+	ollama["baseUrl"] = envconfig.Host().String()
 	// needed to register provider
 	ollama["apiKey"] = "ollama-local"
 	ollama["api"] = "ollama"

cmd/config/openclaw_test.go

@@ -589,7 +589,7 @@ const testOpenclawFixture = `{
     "providers": {
       "anthropic": {"apiKey": "xxx"},
       "ollama": {
-        "baseUrl": "http://127.0.0.1:11434/v1",
+        "baseUrl": "http://127.0.0.1:11434",
         "models": [{"id": "old-model", "customField": "preserved"}]
       }
     }

cmd/config/opencode.go

@@ -26,17 +26,15 @@ type cloudModelLimit struct {
 }
 
 // lookupCloudModelLimit returns the token limits for a cloud model.
-// It tries the exact name first, then strips the ":cloud" suffix.
+// It normalizes common cloud suffixes before checking the shared limit map.
 func lookupCloudModelLimit(name string) (cloudModelLimit, bool) {
+	// TODO(parthsareen): migrate to using cloud check instead.
+	for _, suffix := range []string{"-cloud", ":cloud"} {
+		name = strings.TrimSuffix(name, suffix)
+	}
 	if l, ok := cloudModelLimits[name]; ok {
 		return l, true
 	}
-	base := strings.TrimSuffix(name, ":cloud")
-	if base != name {
-		if l, ok := cloudModelLimits[base]; ok {
-			return l, true
-		}
-	}
 	return cloudModelLimit{}, false
 }
 
@@ -122,13 +120,18 @@ func (o *OpenCode) Edit(modelList []string) error {
 	if !ok {
 		ollama = map[string]any{
 			"npm":  "@ai-sdk/openai-compatible",
-			"name": "Ollama (local)",
+			"name": "Ollama",
 			"options": map[string]any{
 				"baseURL": envconfig.Host().String() + "/v1",
 			},
 		}
 	}
 
+	// Migrate legacy provider name
+	if name, _ := ollama["name"].(string); name == "Ollama (local)" {
+		ollama["name"] = "Ollama"
+	}
+
 	models, ok := ollama["models"].(map[string]any)
 	if !ok {
 		models = make(map[string]any)

cmd/config/opencode_test.go

@@ -3,6 +3,8 @@ package config
 import (
 	"encoding/json"
 	"fmt"
+	"net/http"
+	"net/http/httptest"
 	"os"
 	"path/filepath"
 	"testing"
@@ -232,6 +234,44 @@ func TestOpenCodeEdit(t *testing.T) {
 		}
 	})
 
+	t.Run("migrate Ollama (local) provider name", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(configPath, []byte(`{"provider":{"ollama":{"name":"Ollama (local)","npm":"@ai-sdk/openai-compatible","options":{"baseURL":"http://localhost:11434/v1"}}}}`), 0o644)
+
+		if err := o.Edit([]string{"llama3.2"}); err != nil {
+			t.Fatal(err)
+		}
+
+		data, _ := os.ReadFile(configPath)
+		var cfg map[string]any
+		json.Unmarshal(data, &cfg)
+		provider := cfg["provider"].(map[string]any)
+		ollama := provider["ollama"].(map[string]any)
+		if ollama["name"] != "Ollama" {
+			t.Errorf("provider name not migrated: got %q, want %q", ollama["name"], "Ollama")
+		}
+	})
+
+	t.Run("preserve custom provider name", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(configPath, []byte(`{"provider":{"ollama":{"name":"My Custom Ollama","npm":"@ai-sdk/openai-compatible","options":{"baseURL":"http://localhost:11434/v1"}}}}`), 0o644)
+
+		if err := o.Edit([]string{"llama3.2"}); err != nil {
+			t.Fatal(err)
+		}
+
+		data, _ := os.ReadFile(configPath)
+		var cfg map[string]any
+		json.Unmarshal(data, &cfg)
+		provider := cfg["provider"].(map[string]any)
+		ollama := provider["ollama"].(map[string]any)
+		if ollama["name"] != "My Custom Ollama" {
+			t.Errorf("custom provider name was changed: got %q, want %q", ollama["name"], "My Custom Ollama")
+		}
+	})
+
 	t.Run("remove model preserves non-ollama models", func(t *testing.T) {
 		cleanup()
 		os.MkdirAll(configDir, 0o755)
@@ -619,6 +659,54 @@ func TestOpenCodeEdit_CloudModelLimitStructure(t *testing.T) {
 	}
 }
 
+func TestOpenCodeEdit_BackfillsCloudModelLimitOnExistingEntry(t *testing.T) {
+	o := &OpenCode{}
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	srv := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
+		if r.URL.Path == "/api/show" {
+			fmt.Fprintf(w, `{"capabilities":[],"model_info":{},"remote_model":"glm-5"}`)
+			return
+		}
+		w.WriteHeader(http.StatusNotFound)
+	}))
+	defer srv.Close()
+	t.Setenv("OLLAMA_HOST", srv.URL)
+
+	configDir := filepath.Join(tmpDir, ".config", "opencode")
+	configPath := filepath.Join(configDir, "opencode.json")
+	os.MkdirAll(configDir, 0o755)
+	os.WriteFile(configPath, []byte(`{
+		"provider": {
+			"ollama": {
+				"models": {
+					"glm-5:cloud": {
+						"name": "glm-5:cloud",
+						"_launch": true
+					}
+				}
+			}
+		}
+	}`), 0o644)
+
+	if err := o.Edit([]string{"glm-5:cloud"}); err != nil {
+		t.Fatal(err)
+	}
+
+	entry := readOpenCodeModel(t, configPath, "glm-5:cloud")
+	limit, ok := entry["limit"].(map[string]any)
+	if !ok {
+		t.Fatal("cloud model limit was not added on re-edit")
+	}
+	if limit["context"] != float64(202_752) {
+		t.Errorf("context = %v, want 202752", limit["context"])
+	}
+	if limit["output"] != float64(131_072) {
+		t.Errorf("output = %v, want 131072", limit["output"])
+	}
+}
+
 func TestLookupCloudModelLimit(t *testing.T) {
 	tests := []struct {
 		name        string
@@ -628,6 +716,9 @@ func TestLookupCloudModelLimit(t *testing.T) {
 	}{
 		{"glm-4.7", true, 202_752, 131_072},
 		{"glm-4.7:cloud", true, 202_752, 131_072},
+		{"glm-5:cloud", true, 202_752, 131_072},
+		{"gpt-oss:120b-cloud", true, 131_072, 131_072},
+		{"gpt-oss:20b-cloud", true, 131_072, 131_072},
 		{"kimi-k2.5", true, 262_144, 262_144},
 		{"kimi-k2.5:cloud", true, 262_144, 262_144},
 		{"deepseek-v3.2", true, 163_840, 65_536},

cmd/config/pi.go

@@ -107,7 +107,8 @@ func (p *Pi) Edit(models []string) error {
 
 	// Build new models list:
 	// 1. Keep user-managed models (no _launch marker) - untouched
-	// 2. Keep ollama-managed models (_launch marker) that are still selected
+	// 2. Keep ollama-managed models (_launch marker) that are still selected,
+	//    except stale cloud entries that should be rebuilt below
 	// 3. Add new ollama-managed models
 	var newModels []any
 	for _, m := range existingModels {
@@ -117,7 +118,13 @@ func (p *Pi) Edit(models []string) error {
 				if !isPiOllamaModel(modelObj) {
 					newModels = append(newModels, m)
 				} else if selectedSet[id] {
-					// Ollama-managed and still selected - keep it
+					// Rebuild stale managed cloud entries so createConfig refreshes
+					// the whole entry instead of patching it in place.
+					if !hasContextWindow(modelObj) {
+						if _, ok := lookupCloudModelLimit(id); ok {
+							continue
+						}
+					}
 					newModels = append(newModels, m)
 					selectedSet[id] = false
 				}
@@ -199,12 +206,28 @@ func isPiOllamaModel(cfg map[string]any) bool {
 	return false
 }
 
+func hasContextWindow(cfg map[string]any) bool {
+	switch v := cfg["contextWindow"].(type) {
+	case float64:
+		return v > 0
+	case int:
+		return v > 0
+	case int64:
+		return v > 0
+	default:
+		return false
+	}
+}
+
 // createConfig builds Pi model config with capability detection
 func createConfig(ctx context.Context, client *api.Client, modelID string) map[string]any {
 	cfg := map[string]any{
 		"id":      modelID,
 		"_launch": true,
 	}
+	if l, ok := lookupCloudModelLimit(modelID); ok {
+		cfg["contextWindow"] = l.Context
+	}
 
 	resp, err := client.Show(ctx, &api.ShowRequest{Model: modelID})
 	if err != nil {
@@ -223,7 +246,8 @@ func createConfig(ctx context.Context, client *api.Client, modelID string) map[s
 		cfg["reasoning"] = true
 	}
 
-	// Extract context window from ModelInfo
+	// Extract context window from ModelInfo. For known cloud models, the
+	// pre-filled shared limit remains unless the server provides a positive value.
 	for key, val := range resp.ModelInfo {
 		if strings.HasSuffix(key, ".context_length") {
 			if ctxLen, ok := val.(float64); ok && ctxLen > 0 {

cmd/config/pi_test.go

@@ -192,6 +192,48 @@ func TestPiEdit(t *testing.T) {
 		}
 	})
 
+	t.Run("rebuilds stale existing managed cloud model", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+
+		existingConfig := `{
+			"providers": {
+				"ollama": {
+					"baseUrl": "http://localhost:11434/v1",
+					"api": "openai-completions",
+					"apiKey": "ollama",
+					"models": [
+						{"id": "glm-5:cloud", "_launch": true, "legacyField": "stale"}
+					]
+				}
+			}
+		}`
+		if err := os.WriteFile(configPath, []byte(existingConfig), 0o644); err != nil {
+			t.Fatal(err)
+		}
+
+		if err := pi.Edit([]string{"glm-5:cloud"}); err != nil {
+			t.Fatalf("Edit() error = %v", err)
+		}
+
+		cfg := readConfig()
+		providers := cfg["providers"].(map[string]any)
+		ollama := providers["ollama"].(map[string]any)
+		modelsArray := ollama["models"].([]any)
+		modelEntry := modelsArray[0].(map[string]any)
+
+		if modelEntry["contextWindow"] != float64(202_752) {
+			t.Errorf("contextWindow = %v, want 202752", modelEntry["contextWindow"])
+		}
+		input, ok := modelEntry["input"].([]any)
+		if !ok || len(input) != 1 || input[0] != "text" {
+			t.Errorf("input = %v, want [text]", modelEntry["input"])
+		}
+		if _, ok := modelEntry["legacyField"]; ok {
+			t.Error("legacyField should be removed when stale managed cloud entry is rebuilt")
+		}
+	})
+
 	t.Run("replaces old models with new ones", func(t *testing.T) {
 		cleanup()
 		os.MkdirAll(configDir, 0o755)
@@ -798,6 +840,60 @@ func TestCreateConfig(t *testing.T) {
 		}
 	})
 
+	t.Run("falls back to cloud context when show fails", func(t *testing.T) {
+		srv := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
+			w.WriteHeader(http.StatusNotFound)
+			fmt.Fprintf(w, `{"error":"model not found"}`)
+		}))
+		defer srv.Close()
+
+		u, _ := url.Parse(srv.URL)
+		client := api.NewClient(u, srv.Client())
+
+		cfg := createConfig(context.Background(), client, "kimi-k2.5:cloud")
+
+		if cfg["contextWindow"] != 262_144 {
+			t.Errorf("contextWindow = %v, want 262144", cfg["contextWindow"])
+		}
+	})
+
+	t.Run("falls back to cloud context when model info is empty", func(t *testing.T) {
+		srv := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
+			if r.URL.Path == "/api/show" {
+				fmt.Fprintf(w, `{"capabilities":[],"model_info":{}}`)
+				return
+			}
+			w.WriteHeader(http.StatusNotFound)
+		}))
+		defer srv.Close()
+
+		u, _ := url.Parse(srv.URL)
+		client := api.NewClient(u, srv.Client())
+
+		cfg := createConfig(context.Background(), client, "glm-5:cloud")
+
+		if cfg["contextWindow"] != 202_752 {
+			t.Errorf("contextWindow = %v, want 202752", cfg["contextWindow"])
+		}
+	})
+
+	t.Run("falls back to cloud context for dash cloud suffix", func(t *testing.T) {
+		srv := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
+			w.WriteHeader(http.StatusNotFound)
+			fmt.Fprintf(w, `{"error":"model not found"}`)
+		}))
+		defer srv.Close()
+
+		u, _ := url.Parse(srv.URL)
+		client := api.NewClient(u, srv.Client())
+
+		cfg := createConfig(context.Background(), client, "gpt-oss:120b-cloud")
+
+		if cfg["contextWindow"] != 131_072 {
+			t.Errorf("contextWindow = %v, want 131072", cfg["contextWindow"])
+		}
+	})
+
 	t.Run("skips zero context length", func(t *testing.T) {
 		srv := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
 			if r.URL.Path == "/api/show" {

docs/modelfile.mdx

@@ -152,7 +152,9 @@ PARAMETER <parameter> <parametervalue>
 | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | -------------------- |
 | num_ctx        | Sets the size of the context window used to generate the next token. (Default: 2048)                                                                                                                                                                                                                                                                                            | int        | num_ctx 4096         |
 | repeat_last_n  | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)                                                                                                                                                                                                                                                                   | int        | repeat_last_n 64     |
-| repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)                                                                                                                                                                                             | float      | repeat_penalty 1.1   |
+| repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.0)                                                                                                                                                                                             | float      | repeat_penalty 1.0   |
+| presence_penalty | Penalizes tokens that have already appeared in the generated text to reduce repetition. (Default: 0.0)                                                                                                                                                                                                                                                                       | float      | presence_penalty 1.5 |
+| frequency_penalty | Penalizes tokens based on how often they have appeared in the generated text. (Default: 0.0)                                                                                                                                                                                                                                                                                | float      | frequency_penalty 1.0 |
 | temperature    | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)                                                                                                                                                                                                                                                             | float      | temperature 0.7      |
 | seed           | Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0)                                                                                                                                                                                                               | int        | seed 42              |
 | stop           | Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate `stop` parameters in a modelfile.                                                                                                                                                              | string     | stop "AI assistant:" |

llm/server.go

@@ -1518,7 +1518,6 @@ type CompletionResponse struct {
 	PromptEvalDuration time.Duration `json:"prompt_eval_duration"`
 	EvalCount          int           `json:"eval_count"`
 	EvalDuration       time.Duration `json:"eval_duration"`
-	PeakMemory         uint64        `json:"peak_memory,omitempty"`
 
 	// Logprobs contains log probability information if requested
 	Logprobs []Logprob `json:"logprobs,omitempty"`

model/models/qwen3next/deltanet.go

@@ -454,6 +454,10 @@ func (gdn *GatedDeltaNet) deltaNetChunked(
 	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)
 
+	// Collect chunk outputs and concatenate at the end.
+	// Avoids SET on buffer-less intermediates under partial offload.
+	chunks := make([]ml.Tensor, nChunks)
+
 	for chunk := range nChunks {
 		qChunk := q.Slice(ctx, 2, chunk, chunk+1, 1)
 		vTChunk := vT.Slice(ctx, 2, chunk, chunk+1, 1)
@@ -475,14 +479,7 @@ func (gdn *GatedDeltaNet) deltaNetChunked(
 		vAttn := vTNewChunk.Mulmat(ctx, attnChunk)
 		coreAttnOutChunk := attnInter.Add(ctx, vAttn)
 
-		v = v.SetInplace(
+		chunks[chunk] = coreAttnOutChunk
-			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)
@@ -495,6 +492,20 @@ func (gdn *GatedDeltaNet) deltaNetChunked(
 		stateT = stateT.Add(ctx, kgdMulVNew)
 	}
 
+	// Use a balanced concat tree so concat work does not balloon on long prompts.
+	for len(chunks) > 1 {
+		merged := make([]ml.Tensor, 0, (len(chunks)+1)/2)
+		for i := 0; i < len(chunks); i += 2 {
+			if i+1 < len(chunks) {
+				merged = append(merged, chunks[i].Concat(ctx, chunks[i+1], 2))
+			} else {
+				merged = append(merged, chunks[i])
+			}
+		}
+		chunks = merged
+	}
+	v = chunks[0]
+
 	// Final reshape
 	coreAttnOut := v.Contiguous(ctx, headVDim, chunkSize*nChunks, numVHeads, nSeqs)
 

model/parsers/parsers.go

@@ -50,7 +50,7 @@ func ParserForName(name string) Parser {
 	case "qwen3-thinking":
 		p = &Qwen3Parser{hasThinkingSupport: true, defaultThinking: true}
 	case "qwen3.5":
-		p = &Qwen3Parser{hasThinkingSupport: true, defaultThinking: true}
+		p = &Qwen35Parser{}
 	case "qwen3-coder":
 		p = &Qwen3CoderParser{}
 	case "qwen3-vl-instruct":

model/parsers/qwen35.go

@@ -0,0 +1,238 @@
+package parsers
+
+import (
+	"context"
+	"log/slog"
+	"strings"
+	"unicode"
+
+	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/logutil"
+)
+
+type qwen35ParserState int
+
+const (
+	qwen35ParserStateCollectingThinking qwen35ParserState = iota
+	qwen35ParserStateThinkingDoneEatingWhitespace
+	qwen35ParserStateCollectingContent
+)
+
+const (
+	qwen35ThinkingOpenTag  = "<think>"
+	qwen35ThinkingCloseTag = "</think>"
+)
+
+// Qwen35Parser handles qwen3.5 reasoning extraction and delegates post-thinking
+// content (including XML tool calls) to Qwen3CoderParser.
+type Qwen35Parser struct {
+	toolParser Qwen3CoderParser
+
+	state  qwen35ParserState
+	buffer strings.Builder
+	// Some checkpoints may emit an explicit leading <think> even when the
+	// prompt already opened thinking. Strip at most one such tag.
+	allowLeadingThinkOpenTag bool
+}
+
+func (p *Qwen35Parser) HasToolSupport() bool {
+	return true
+}
+
+func (p *Qwen35Parser) HasThinkingSupport() bool {
+	return true
+}
+
+func (p *Qwen35Parser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
+	p.buffer.Reset()
+	p.toolParser = Qwen3CoderParser{}
+	p.toolParser.Init(tools, nil, nil)
+
+	thinkingEnabled := thinkValue != nil && thinkValue.Bool()
+	if thinkValue == nil {
+		thinkingEnabled = true
+	}
+
+	assistantPrefill := lastMessage != nil && lastMessage.Role == "assistant" && lastMessage.Content != ""
+	if thinkingEnabled && !assistantPrefill {
+		p.state = qwen35ParserStateCollectingThinking
+		p.allowLeadingThinkOpenTag = true
+	} else {
+		p.state = qwen35ParserStateCollectingContent
+		p.allowLeadingThinkOpenTag = false
+	}
+
+	return tools
+}
+
+type qwen35Event interface {
+	isQwen35Event()
+}
+
+type qwen35EventContent struct {
+	content string
+}
+
+func (qwen35EventContent) isQwen35Event() {}
+
+type qwen35EventThinkingContent struct {
+	content string
+}
+
+func (qwen35EventThinkingContent) isQwen35Event() {}
+
+func (p *Qwen35Parser) Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error) {
+	p.buffer.WriteString(s)
+	events := p.parseEvents()
+
+	var contentSb strings.Builder
+	var thinkingSb strings.Builder
+	for _, event := range events {
+		switch event := event.(type) {
+		case qwen35EventContent:
+			parsedContent, _, parsedCalls, err := p.toolParser.Add(event.content, done)
+			if err != nil {
+				slog.Warn("qwen3.5 tool call parsing failed", "error", err)
+				return "", "", nil, err
+			}
+			contentSb.WriteString(parsedContent)
+			calls = append(calls, parsedCalls...)
+		case qwen35EventThinkingContent:
+			thinkingSb.WriteString(event.content)
+		}
+	}
+
+	return contentSb.String(), thinkingSb.String(), calls, nil
+}
+
+func (p *Qwen35Parser) parseEvents() []qwen35Event {
+	var all []qwen35Event
+
+	keepLooping := true
+	for keepLooping {
+		var events []qwen35Event
+		events, keepLooping = p.eat()
+		if len(events) > 0 {
+			all = append(all, events...)
+		}
+	}
+
+	if len(all) > 0 {
+		slog.Log(context.TODO(), logutil.LevelTrace, "qwen3.5 events parsed", "events", all, "state", p.state, "buffer", p.buffer.String())
+	}
+
+	return all
+}
+
+func (p *Qwen35Parser) splitAtTag(tag string, trimAfter bool) (string, string) {
+	return splitAtTag(&p.buffer, tag, trimAfter)
+}
+
+func (p *Qwen35Parser) eatLeadingWhitespaceAndTransitionTo(nextState qwen35ParserState) ([]qwen35Event, bool) {
+	trimmed := strings.TrimLeftFunc(p.buffer.String(), unicode.IsSpace)
+	p.buffer.Reset()
+	if trimmed == "" {
+		return nil, false
+	}
+	p.state = nextState
+	p.buffer.WriteString(trimmed)
+	return nil, true
+}
+
+// maybeConsumeLeadingThinkOpenTag handles a single optional leading <think> tag.
+// Returns (handled, shouldContinueParsingNow).
+func (p *Qwen35Parser) maybeConsumeLeadingThinkOpenTag(acc string) (bool, bool) {
+	if !p.allowLeadingThinkOpenTag {
+		return false, false
+	}
+
+	trimmed := strings.TrimLeftFunc(acc, unicode.IsSpace)
+	if strings.HasPrefix(trimmed, qwen35ThinkingOpenTag) {
+		after := strings.TrimPrefix(trimmed, qwen35ThinkingOpenTag)
+		after = strings.TrimLeftFunc(after, unicode.IsSpace)
+		p.buffer.Reset()
+		p.buffer.WriteString(after)
+		if after == "" {
+			return true, false
+		}
+		p.allowLeadingThinkOpenTag = false
+		return true, true
+	}
+
+	if strings.HasPrefix(qwen35ThinkingOpenTag, trimmed) {
+		return true, false
+	}
+
+	p.allowLeadingThinkOpenTag = false
+	return false, false
+}
+
+func (p *Qwen35Parser) eat() ([]qwen35Event, bool) {
+	var events []qwen35Event
+
+	switch p.state {
+	case qwen35ParserStateCollectingThinking:
+		acc := p.buffer.String()
+
+		if handled, continueNow := p.maybeConsumeLeadingThinkOpenTag(acc); handled {
+			return events, continueNow
+		}
+
+		if strings.Contains(acc, qwen35ThinkingCloseTag) {
+			thinking, remaining := p.splitAtTag(qwen35ThinkingCloseTag, true)
+			if len(thinking) > 0 {
+				events = append(events, qwen35EventThinkingContent{content: thinking})
+			}
+			if remaining == "" {
+				p.state = qwen35ParserStateThinkingDoneEatingWhitespace
+			} else {
+				p.state = qwen35ParserStateCollectingContent
+			}
+			return events, true
+		} else if overlapLen := overlap(acc, qwen35ThinkingCloseTag); overlapLen > 0 {
+			beforePartialTag := acc[:len(acc)-overlapLen]
+			trailingWsLen := trailingWhitespaceLen(beforePartialTag)
+			ambiguousStart := len(beforePartialTag) - trailingWsLen
+
+			unambiguous := acc[:ambiguousStart]
+			ambiguous := acc[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, qwen35EventThinkingContent{content: unambiguous})
+			}
+			return events, false
+		}
+
+		whitespaceLen := trailingWhitespaceLen(acc)
+		ambiguousStart := len(acc) - whitespaceLen
+		unambiguous := acc[:ambiguousStart]
+		ambiguous := acc[ambiguousStart:]
+		p.buffer.Reset()
+		p.buffer.WriteString(ambiguous)
+		if len(unambiguous) > 0 {
+			events = append(events, qwen35EventThinkingContent{content: unambiguous})
+		}
+		return events, false
+
+	case qwen35ParserStateThinkingDoneEatingWhitespace:
+		return p.eatLeadingWhitespaceAndTransitionTo(qwen35ParserStateCollectingContent)
+
+	case qwen35ParserStateCollectingContent:
+		if p.buffer.Len() == 0 {
+			return events, false
+		}
+
+		content := p.buffer.String()
+		p.buffer.Reset()
+		if len(content) > 0 {
+			events = append(events, qwen35EventContent{content: content})
+		}
+		return events, false
+
+	default:
+		slog.Warn("qwen3.5 parser entered unknown state; resetting to content mode", "state", p.state)
+		p.state = qwen35ParserStateCollectingContent
+		return events, false
+	}
+}

model/parsers/qwen35_test.go

@@ -0,0 +1,382 @@
+package parsers
+
+import (
+	"testing"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestQwen35ParserXMLToolCall(t *testing.T) {
+	parser := ParserForName("qwen3.5")
+	if parser == nil {
+		t.Fatal("expected qwen3.5 parser")
+	}
+
+	tools := []api.Tool{
+		{
+			Function: api.ToolFunction{
+				Name: "get_weather",
+				Parameters: api.ToolFunctionParameters{
+					Properties: func() *api.ToolPropertiesMap {
+						props := api.NewToolPropertiesMap()
+						props.Set("location", api.ToolProperty{Type: api.PropertyType{"string"}})
+						props.Set("days", api.ToolProperty{Type: api.PropertyType{"integer"}})
+						return props
+					}(),
+				},
+			},
+		},
+	}
+
+	parser.Init(tools, nil, &api.ThinkValue{Value: false})
+	input := "<tool_call><function=get_weather><parameter=location>\nSan Francisco\n</parameter><parameter=days>\n3\n</parameter></function></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 != "" {
+		t.Fatalf("expected empty thinking, 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)
+	}
+
+	location, ok := calls[0].Function.Arguments.Get("location")
+	if !ok || location != "San Francisco" {
+		t.Fatalf("expected location %q, got %v", "San Francisco", location)
+	}
+
+	days, ok := calls[0].Function.Arguments.Get("days")
+	if !ok || days != 3 {
+		t.Fatalf("expected days %d, got %v", 3, days)
+	}
+}
+
+func TestQwen35ParserThinkingWithExplicitOpeningTag(t *testing.T) {
+	parser := ParserForName("qwen3.5")
+	if parser == nil {
+		t.Fatal("expected qwen3.5 parser")
+	}
+
+	parser.Init(nil, nil, &api.ThinkValue{Value: true})
+	content, thinking, calls, err := parser.Add("<think>\nLet me think...</think>Answer.", true)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+
+	if thinking != "Let me think..." {
+		t.Fatalf("expected thinking %q, got %q", "Let me think...", thinking)
+	}
+	if content != "Answer." {
+		t.Fatalf("expected content %q, got %q", "Answer.", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+}
+
+func TestQwen35ParserAssistantPrefillStartsInContent(t *testing.T) {
+	parser := ParserForName("qwen3.5")
+	if parser == nil {
+		t.Fatal("expected qwen3.5 parser")
+	}
+
+	last := &api.Message{Role: "assistant", Content: "Prefilled response start"}
+	parser.Init(nil, last, nil)
+
+	content, thinking, calls, err := parser.Add(" and continued", true)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+
+	if thinking != "" {
+		t.Fatalf("expected no thinking for assistant prefill continuation, got %q", thinking)
+	}
+	if content != " and continued" {
+		t.Fatalf("expected content %q, got %q", " and continued", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+}
+
+func TestQwen35ParserToolCallEmittedInThinkingIsNotParsed(t *testing.T) {
+	parser := ParserForName("qwen3.5")
+	if parser == nil {
+		t.Fatal("expected qwen3.5 parser")
+	}
+
+	tools := []api.Tool{
+		{
+			Function: api.ToolFunction{
+				Name: "get_weather",
+				Parameters: api.ToolFunctionParameters{
+					Properties: func() *api.ToolPropertiesMap {
+						props := api.NewToolPropertiesMap()
+						props.Set("location", api.ToolProperty{Type: api.PropertyType{"string"}})
+						return props
+					}(),
+				},
+			},
+		},
+	}
+
+	parser.Init(tools, nil, &api.ThinkValue{Value: true})
+	input := `Need weather lookup<tool_call><function=get_weather><parameter=location>
+SF
+</parameter></function></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)
+	}
+	expectedThinking := `Need weather lookup<tool_call><function=get_weather><parameter=location>
+SF
+</parameter></function></tool_call>`
+	if thinking != expectedThinking {
+		t.Fatalf("expected thinking %q, got %q", expectedThinking, thinking)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls before </think>, got %d", len(calls))
+	}
+}
+
+func TestQwen35ParserToolCallAfterThinkingCloseIsParsed(t *testing.T) {
+	parser := ParserForName("qwen3.5")
+	if parser == nil {
+		t.Fatal("expected qwen3.5 parser")
+	}
+
+	tools := []api.Tool{
+		{
+			Function: api.ToolFunction{
+				Name: "get_weather",
+				Parameters: api.ToolFunctionParameters{
+					Properties: func() *api.ToolPropertiesMap {
+						props := api.NewToolPropertiesMap()
+						props.Set("location", api.ToolProperty{Type: api.PropertyType{"string"}})
+						return props
+					}(),
+				},
+			},
+		},
+	}
+
+	parser.Init(tools, nil, &api.ThinkValue{Value: true})
+	input := `Need weather lookup</think><tool_call><function=get_weather><parameter=location>
+SF
+</parameter></function></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 != "Need weather lookup" {
+		t.Fatalf("expected thinking %q, got %q", "Need weather lookup", thinking)
+	}
+	if len(calls) != 1 {
+		t.Fatalf("expected 1 tool call after </think>, 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)
+	}
+
+	location, ok := calls[0].Function.Arguments.Get("location")
+	if !ok || location != "SF" {
+		t.Fatalf("expected location %q, got %v", "SF", location)
+	}
+}
+
+func TestQwen35ParserThinkingDisabledPassesContentThrough(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("Plain answer without think close tag.", true)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+
+	if thinking != "" {
+		t.Fatalf("expected empty thinking, got %q", thinking)
+	}
+	if content != "Plain answer without think close tag." {
+		t.Fatalf("expected content %q, got %q", "Plain answer without think close tag.", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+}
+
+func TestQwen35ParserThinkingDisabledWithCloseTagTreatsAsContent(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("</think>Some content after spurious tag.", true)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+
+	if thinking != "" {
+		t.Fatalf("expected empty thinking, got %q", thinking)
+	}
+	if content != "</think>Some content after spurious tag." {
+		t.Fatalf("expected content %q, got %q", "</think>Some content after spurious tag.", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+}
+
+func TestQwen35ParserLeadingThinkCloseProducesContent(t *testing.T) {
+	parser := ParserForName("qwen3.5")
+	if parser == nil {
+		t.Fatal("expected qwen3.5 parser")
+	}
+
+	parser.Init(nil, nil, &api.ThinkValue{Value: true})
+	content, thinking, calls, err := parser.Add("</think>The final answer.", true)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+
+	if thinking != "" {
+		t.Fatalf("expected empty thinking, got %q", thinking)
+	}
+	if content != "The final answer." {
+		t.Fatalf("expected content %q, got %q", "The final answer.", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+}
+
+func TestQwen35ParserStreamingSplitThinkCloseTag(t *testing.T) {
+	parser := ParserForName("qwen3.5")
+	if parser == nil {
+		t.Fatal("expected qwen3.5 parser")
+	}
+
+	parser.Init(nil, nil, &api.ThinkValue{Value: true})
+
+	content, thinking, calls, err := parser.Add("Reasoning text</thi", false)
+	if err != nil {
+		t.Fatalf("parse failed on first chunk: %v", err)
+	}
+	if thinking != "Reasoning text" {
+		t.Fatalf("expected thinking %q, got %q", "Reasoning text", thinking)
+	}
+	if content != "" {
+		t.Fatalf("expected empty content, got %q", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+
+	content, thinking, calls, err = parser.Add("nk>The final answer.", true)
+	if err != nil {
+		t.Fatalf("parse failed on second chunk: %v", err)
+	}
+	if thinking != "" {
+		t.Fatalf("expected no additional thinking on second chunk, got %q", thinking)
+	}
+	if content != "The final answer." {
+		t.Fatalf("expected content %q, got %q", "The final answer.", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+}
+
+func TestQwen35ParserStreamingEatsWhitespaceAfterThinkClose(t *testing.T) {
+	parser := ParserForName("qwen3.5")
+	if parser == nil {
+		t.Fatal("expected qwen3.5 parser")
+	}
+
+	parser.Init(nil, nil, &api.ThinkValue{Value: true})
+
+	content, thinking, calls, err := parser.Add("Reasoning</think>", false)
+	if err != nil {
+		t.Fatalf("parse failed on first chunk: %v", err)
+	}
+	if thinking != "Reasoning" {
+		t.Fatalf("expected thinking %q, got %q", "Reasoning", thinking)
+	}
+	if content != "" {
+		t.Fatalf("expected empty content, got %q", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+
+	content, thinking, calls, err = parser.Add("\n \t", false)
+	if err != nil {
+		t.Fatalf("parse failed on whitespace chunk: %v", err)
+	}
+	if thinking != "" {
+		t.Fatalf("expected no thinking on whitespace chunk, got %q", thinking)
+	}
+	if content != "" {
+		t.Fatalf("expected whitespace after </think> to be eaten, got content %q", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+
+	content, thinking, calls, err = parser.Add("The final answer.", true)
+	if err != nil {
+		t.Fatalf("parse failed on content chunk: %v", err)
+	}
+	if thinking != "" {
+		t.Fatalf("expected no additional thinking, got %q", thinking)
+	}
+	if content != "The final answer." {
+		t.Fatalf("expected content %q, got %q", "The final answer.", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+}
+
+func TestQwen35ParserThinkingTruncatedWithoutCloseTag(t *testing.T) {
+	parser := ParserForName("qwen3.5")
+	if parser == nil {
+		t.Fatal("expected qwen3.5 parser")
+	}
+
+	parser.Init(nil, nil, &api.ThinkValue{Value: true})
+	content, thinking, calls, err := parser.Add("Reasoning that never closes", true)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+
+	if thinking != "Reasoning that never closes" {
+		t.Fatalf("expected thinking %q, got %q", "Reasoning that never closes", thinking)
+	}
+	if content != "" {
+		t.Fatalf("expected empty content, got %q", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+}

model/renderers/glmocr.go

@@ -8,7 +8,21 @@ import (
 	"github.com/ollama/ollama/api"
 )
 
-type GlmOcrRenderer struct{}
+type GlmOcrRenderer struct {
+	useImgTags bool
+}
+
+func (r *GlmOcrRenderer) renderContent(message api.Message, imageOffset int) (string, int) {
+	var sb strings.Builder
+	for range message.Images {
+		if r.useImgTags {
+			sb.WriteString(fmt.Sprintf("[img-%d]", imageOffset))
+			imageOffset++
+		}
+	}
+	sb.WriteString(message.Content)
+	return sb.String(), imageOffset
+}
 
 func (r *GlmOcrRenderer) Render(messages []api.Message, tools []api.Tool, thinkValue *api.ThinkValue) (string, error) {
 	var sb strings.Builder
@@ -38,11 +52,14 @@ func (r *GlmOcrRenderer) Render(messages []api.Message, tools []api.Tool, thinkV
 		thinkingExplicitlySet = true
 	}
 
+	imageOffset := 0
 	for i, message := range messages {
 		switch message.Role {
 		case "user":
 			sb.WriteString("<|user|>\n")
-			sb.WriteString(message.Content)
+			content, nextOffset := r.renderContent(message, imageOffset)
+			imageOffset = nextOffset
+			sb.WriteString(content)
 			if thinkingExplicitlySet && !enableThinking && !strings.HasSuffix(message.Content, "/nothink") {
 				sb.WriteString("/nothink")
 			}

model/renderers/glmocr_test.go

@@ -0,0 +1,99 @@
+package renderers
+
+import (
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestGlmOcrRenderer_Images(t *testing.T) {
+	tests := []struct {
+		name     string
+		renderer *GlmOcrRenderer
+		messages []api.Message
+		expected string
+	}{
+		{
+			name:     "use_img_tags_single_image",
+			renderer: &GlmOcrRenderer{useImgTags: true},
+			messages: []api.Message{
+				{
+					Role:    "user",
+					Content: "Describe this image.",
+					Images:  []api.ImageData{api.ImageData("img1")},
+				},
+			},
+			expected: "[gMASK]<sop><|user|>\n[img-0]Describe this image.<|assistant|>\n",
+		},
+		{
+			name:     "use_img_tags_multiple_images",
+			renderer: &GlmOcrRenderer{useImgTags: true},
+			messages: []api.Message{
+				{
+					Role:    "user",
+					Content: "Describe these images.",
+					Images:  []api.ImageData{api.ImageData("img1"), api.ImageData("img2")},
+				},
+			},
+			expected: "[gMASK]<sop><|user|>\n[img-0][img-1]Describe these images.<|assistant|>\n",
+		},
+		{
+			name:     "multi_turn_increments_image_offset",
+			renderer: &GlmOcrRenderer{useImgTags: true},
+			messages: []api.Message{
+				{
+					Role:    "user",
+					Content: "First image",
+					Images:  []api.ImageData{api.ImageData("img1")},
+				},
+				{
+					Role:    "assistant",
+					Content: "Processed.",
+				},
+				{
+					Role:    "user",
+					Content: "Second image",
+					Images:  []api.ImageData{api.ImageData("img2")},
+				},
+			},
+			expected: "[gMASK]<sop><|user|>\n[img-0]First image<|assistant|>\n<think></think>\nProcessed.\n<|user|>\n[img-1]Second image<|assistant|>\n",
+		},
+		{
+			name:     "default_no_img_tags",
+			renderer: &GlmOcrRenderer{},
+			messages: []api.Message{
+				{
+					Role:    "user",
+					Content: "No image tags expected.",
+					Images:  []api.ImageData{api.ImageData("img1")},
+				},
+			},
+			expected: "[gMASK]<sop><|user|>\nNo image tags expected.<|assistant|>\n",
+		},
+		{
+			name:     "no_images_content_unchanged",
+			renderer: &GlmOcrRenderer{useImgTags: true},
+			messages: []api.Message{
+				{
+					Role:    "user",
+					Content: "Text only message.",
+				},
+			},
+			expected: "[gMASK]<sop><|user|>\nText only message.<|assistant|>\n",
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			got, err := tt.renderer.Render(tt.messages, nil, nil)
+			if err != nil {
+				t.Fatalf("Render() error = %v", err)
+			}
+			if diff := cmp.Diff(tt.expected, got); diff != "" {
+				t.Fatalf("Render() mismatch (-want +got):\n%s", diff)
+			}
+		})
+	}
+}

model/renderers/qwen35.go

@@ -0,0 +1,194 @@
+package renderers
+
+import (
+	"fmt"
+	"strings"
+
+	"github.com/ollama/ollama/api"
+)
+
+const (
+	qwen35ThinkOpenTag  = "<think>"
+	qwen35ThinkCloseTag = "</think>"
+	qwen35ToolPostamble = `
+</tools>
+
+If you choose to call a function ONLY reply in the following format with NO suffix:
+
+<tool_call>
+<function=example_function_name>
+<parameter=example_parameter_1>
+value_1
+</parameter>
+<parameter=example_parameter_2>
+This is the value for the second parameter
+that can span
+multiple lines
+</parameter>
+</function>
+</tool_call>
+
+<IMPORTANT>
+Reminder:
+- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags
+- Required parameters MUST be specified
+- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after
+- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls
+</IMPORTANT>`
+)
+
+type Qwen35Renderer struct {
+	isThinking bool
+
+	emitEmptyThinkOnNoThink bool
+	useImgTags              bool
+}
+
+func (r *Qwen35Renderer) 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 {
+		if r.useImgTags {
+			subSb.WriteString(fmt.Sprintf("[img-%d]", imageOffset))
+			imageOffset++
+		} else {
+			subSb.WriteString("<|vision_start|><|image_pad|><|vision_end|>")
+		}
+	}
+	// TODO: support videos
+
+	subSb.WriteString(content.Content)
+	return subSb.String(), imageOffset
+}
+
+func splitQwen35ReasoningContent(content, messageThinking string, isThinking bool) (reasoning string, remaining string) {
+	if isThinking && messageThinking != "" {
+		return strings.TrimSpace(messageThinking), content
+	}
+
+	if idx := strings.Index(content, qwen35ThinkCloseTag); idx != -1 {
+		before := content[:idx]
+		if open := strings.LastIndex(before, qwen35ThinkOpenTag); open != -1 {
+			reasoning = before[open+len(qwen35ThinkOpenTag):]
+		} else {
+			reasoning = before
+		}
+		content = strings.TrimLeft(content[idx+len(qwen35ThinkCloseTag):], "\n")
+	}
+
+	return strings.TrimSpace(reasoning), content
+}
+
+func (r *Qwen35Renderer) 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")
+		sb.WriteString("# Tools\n\nYou have access to the following functions:\n\n<tools>")
+		for _, tool := range tools {
+			sb.WriteString("\n")
+			if b, err := marshalWithSpaces(tool); err == nil {
+				sb.Write(b)
+			}
+		}
+		sb.WriteString(qwen35ToolPostamble)
+		if len(messages) > 0 && messages[0].Role == "system" {
+			systemContent, _ := r.renderContent(messages[0], 0)
+			systemContent = strings.TrimSpace(systemContent)
+			if systemContent != "" {
+				sb.WriteString("\n\n")
+				sb.WriteString(systemContent)
+			}
+		}
+		sb.WriteString(imEndTag + "\n")
+	} else if len(messages) > 0 && messages[0].Role == "system" {
+		systemContent, _ := r.renderContent(messages[0], 0)
+		sb.WriteString(imStartTag + "system\n" + strings.TrimSpace(systemContent) + imEndTag + "\n")
+	}
+
+	multiStepTool := true
+	lastQueryIndex := len(messages) - 1 // so this is the last user message
+
+	for i := len(messages) - 1; i >= 0; i-- {
+		message := messages[i]
+		if multiStepTool && message.Role == "user" {
+			content, _ := r.renderContent(message, 0)
+			content = strings.TrimSpace(content)
+			if !(strings.HasPrefix(content, "<tool_response>") && strings.HasSuffix(content, "</tool_response>")) {
+				multiStepTool = false
+				lastQueryIndex = i
+			}
+		}
+	}
+
+	imageOffset := 0
+	for i, message := range messages {
+		content, nextImageOffset := r.renderContent(message, imageOffset)
+		imageOffset = nextImageOffset
+		content = strings.TrimSpace(content)
+
+		lastMessage := i == len(messages)-1
+		prefill := lastMessage && message.Role == "assistant"
+
+		if message.Role == "user" || (message.Role == "system" && i != 0) {
+			sb.WriteString(imStartTag + message.Role + "\n" + content + imEndTag + "\n")
+		} else if message.Role == "assistant" {
+			contentReasoning, content := splitQwen35ReasoningContent(content, message.Thinking, isThinking)
+
+			if isThinking && i > lastQueryIndex {
+				sb.WriteString(imStartTag + message.Role + "\n<think>\n" + contentReasoning + "\n</think>\n\n" + content)
+			} else {
+				sb.WriteString(imStartTag + message.Role + "\n" + content)
+			}
+
+			if len(message.ToolCalls) > 0 {
+				for j, toolCall := range message.ToolCalls {
+					if j == 0 {
+						if strings.TrimSpace(content) != "" {
+							sb.WriteString("\n\n")
+						}
+					} else {
+						sb.WriteString("\n")
+					}
+
+					sb.WriteString("<tool_call>\n<function=" + toolCall.Function.Name + ">\n")
+					for name, value := range toolCall.Function.Arguments.All() {
+						sb.WriteString("<parameter=" + name + ">\n")
+						sb.WriteString(formatToolCallArgument(value))
+						sb.WriteString("\n</parameter>\n")
+					}
+					sb.WriteString("</function>\n</tool_call>")
+				}
+			}
+
+			if !prefill {
+				sb.WriteString(imEndTag + "\n")
+			}
+		} else if message.Role == "tool" {
+			if i == 0 || messages[i-1].Role != "tool" {
+				sb.WriteString(imStartTag + "user")
+			}
+			sb.WriteString("\n<tool_response>\n" + content + "\n</tool_response>")
+			if i == len(messages)-1 || messages[i+1].Role != "tool" {
+				sb.WriteString(imEndTag + "\n")
+			}
+		}
+
+		// prefill at the end
+		if lastMessage && !prefill {
+			sb.WriteString(imStartTag + "assistant\n")
+			if isThinking {
+				sb.WriteString("<think>\n")
+			} else if r.emitEmptyThinkOnNoThink {
+				sb.WriteString("<think>\n\n</think>\n\n")
+			}
+		}
+	}
+
+	return sb.String(), nil
+}

model/renderers/qwen35_test.go

@@ -0,0 +1,389 @@
+package renderers
+
+import (
+	"strings"
+	"testing"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestQwen35RendererUsesXMLToolCallingFormat(t *testing.T) {
+	renderer := &Qwen35Renderer{isThinking: true}
+	msgs := []api.Message{
+		{Role: "system", Content: "You are a helpful assistant."},
+		{Role: "user", Content: "What's the weather in Paris?"},
+		{
+			Role:    "assistant",
+			Content: "I'll check.",
+			ToolCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "get_weather",
+						Arguments: testArgsOrdered([]orderedArg{
+							{Key: "location", Value: "Paris"},
+						}),
+					},
+				},
+			},
+		},
+		{Role: "tool", Content: "22C"},
+		{Role: "user", Content: "Thanks"},
+	}
+	tools := []api.Tool{
+		{
+			Type: "function",
+			Function: api.ToolFunction{
+				Name: "get_weather",
+				Parameters: api.ToolFunctionParameters{
+					Type: "object",
+					Properties: testPropsOrdered([]orderedProp{
+						{
+							Key: "location",
+							Value: api.ToolProperty{
+								Type: api.PropertyType{"string"},
+							},
+						},
+					}),
+					Required: []string{"location"},
+				},
+			},
+		},
+	}
+
+	got, err := renderer.Render(msgs, tools, nil)
+	if err != nil {
+		t.Fatalf("render failed: %v", err)
+	}
+
+	if !strings.Contains(got, "<tools>") {
+		t.Fatalf("expected tools section in prompt, got:\n%s", got)
+	}
+	if !strings.Contains(got, "<function=example_function_name>") {
+		t.Fatalf("expected xml-style tool call instructions, got:\n%s", got)
+	}
+
+	wantToolCall := "<tool_call>\n<function=get_weather>\n<parameter=location>\nParis\n</parameter>\n</function>\n</tool_call>"
+	if !strings.Contains(got, wantToolCall) {
+		t.Fatalf("expected xml tool call payload, got:\n%s", got)
+	}
+
+	toolsIdx := strings.Index(got, "# Tools")
+	systemIdx := strings.Index(got, "You are a helpful assistant.")
+	if toolsIdx == -1 || systemIdx == -1 || systemIdx < toolsIdx {
+		t.Fatalf("expected system prompt appended after tool instructions, got:\n%s", got)
+	}
+}
+
+func TestQwen35RendererNoThinkPrefill(t *testing.T) {
+	renderer := &Qwen35Renderer{isThinking: true, emitEmptyThinkOnNoThink: true}
+	msgs := []api.Message{
+		{Role: "user", Content: "hello"},
+	}
+
+	got, err := renderer.Render(msgs, nil, &api.ThinkValue{Value: false})
+	if err != nil {
+		t.Fatalf("render failed: %v", err)
+	}
+
+	if !strings.HasSuffix(got, "<|im_start|>assistant\n<think>\n\n</think>\n\n") {
+		t.Fatalf("expected explicit no-think prefill, got:\n%s", got)
+	}
+}
+
+func TestQwen35RendererBackToBackToolCallsAndResponses(t *testing.T) {
+	renderer := &Qwen35Renderer{isThinking: true}
+
+	msgs := []api.Message{
+		{Role: "system", Content: "You are a helpful assistant."},
+		{Role: "user", Content: "Run add and multiply."},
+		{
+			Role:     "assistant",
+			Content:  "I'll run both now.",
+			Thinking: "Need to call add and multiply.",
+			ToolCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "add",
+						Arguments: testArgsOrdered([]orderedArg{
+							{Key: "a", Value: 2},
+							{Key: "b", Value: 3},
+						}),
+					},
+				},
+				{
+					Function: api.ToolCallFunction{
+						Name: "multiply",
+						Arguments: testArgsOrdered([]orderedArg{
+							{Key: "x", Value: 4},
+							{Key: "y", Value: 5},
+						}),
+					},
+				},
+			},
+		},
+		{Role: "tool", Content: "5"},
+		{Role: "tool", Content: "20"},
+		{Role: "user", Content: "Summarize the results."},
+	}
+
+	got, err := renderer.Render(msgs, qwen35MathTools(), nil)
+	if err != nil {
+		t.Fatalf("render failed: %v", err)
+	}
+
+	if strings.Contains(got, "Need to call add and multiply.") {
+		t.Fatalf("did not expect historical reasoning block in this sequence, got:\n%s", got)
+	}
+
+	wantToolCalls := `<tool_call>
+<function=add>
+<parameter=a>
+2
+</parameter>
+<parameter=b>
+3
+</parameter>
+</function>
+</tool_call>
+<tool_call>
+<function=multiply>
+<parameter=x>
+4
+</parameter>
+<parameter=y>
+5
+</parameter>
+</function>
+</tool_call>`
+	if !strings.Contains(got, wantToolCalls) {
+		t.Fatalf("expected back-to-back tool calls, got:\n%s", got)
+	}
+
+	wantToolResponses := `<|im_start|>user
+<tool_response>
+5
+</tool_response>
+<tool_response>
+20
+</tool_response><|im_end|>`
+	if !strings.Contains(got, wantToolResponses) {
+		t.Fatalf("expected grouped back-to-back tool responses, got:\n%s", got)
+	}
+
+	if !strings.HasSuffix(got, "<|im_start|>assistant\n<think>\n") {
+		t.Fatalf("expected assistant thinking prefill at end, got:\n%s", got)
+	}
+}
+
+func TestQwen35RendererInterleavedThinkingAndTools(t *testing.T) {
+	renderer := &Qwen35Renderer{isThinking: true}
+
+	msgs := []api.Message{
+		{Role: "system", Content: "You are a helpful assistant."},
+		{Role: "user", Content: "Plan a picnic in Paris."},
+		{
+			Role:     "assistant",
+			Content:  "Checking weather first.",
+			Thinking: "Need weather before giving advice.",
+			ToolCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "get_weather",
+						Arguments: testArgsOrdered([]orderedArg{
+							{Key: "location", Value: "Paris"},
+						}),
+					},
+				},
+			},
+		},
+		{Role: "tool", Content: "22C"},
+		{
+			Role:     "assistant",
+			Content:  "Checking UV too.",
+			Thinking: "Need UV index for sunscreen advice.",
+			ToolCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "get_uv",
+						Arguments: testArgsOrdered([]orderedArg{
+							{Key: "location", Value: "Paris"},
+						}),
+					},
+				},
+			},
+		},
+		{Role: "tool", Content: "5"},
+	}
+
+	got, err := renderer.Render(msgs, qwen35WeatherUVTools(), nil)
+	if err != nil {
+		t.Fatalf("render failed: %v", err)
+	}
+
+	wantFirstTurn := `<|im_start|>assistant
+<think>
+Need weather before giving advice.
+</think>
+
+Checking weather first.
+
+<tool_call>
+<function=get_weather>
+<parameter=location>
+Paris
+</parameter>
+</function>
+</tool_call><|im_end|>`
+	if !strings.Contains(got, wantFirstTurn) {
+		t.Fatalf("expected first assistant thinking/tool sequence, got:\n%s", got)
+	}
+
+	wantSecondTurn := `<|im_start|>assistant
+<think>
+Need UV index for sunscreen advice.
+</think>
+
+Checking UV too.
+
+<tool_call>
+<function=get_uv>
+<parameter=location>
+Paris
+</parameter>
+</function>
+</tool_call><|im_end|>`
+	if !strings.Contains(got, wantSecondTurn) {
+		t.Fatalf("expected second assistant thinking/tool sequence, got:\n%s", got)
+	}
+
+	if !strings.HasSuffix(got, "<|im_start|>assistant\n<think>\n") {
+		t.Fatalf("expected assistant thinking prefill at end, got:\n%s", got)
+	}
+}
+
+func TestQwen35RendererAssistantPrefillWithThinking(t *testing.T) {
+	renderer := &Qwen35Renderer{isThinking: true}
+	msgs := []api.Message{
+		{Role: "user", Content: "Write two words."},
+		{
+			Role:     "assistant",
+			Thinking: "Keep it short.",
+			Content:  "Hello world",
+		},
+	}
+
+	got, err := renderer.Render(msgs, nil, nil)
+	if err != nil {
+		t.Fatalf("render failed: %v", err)
+	}
+
+	want := `<|im_start|>user
+Write two words.<|im_end|>
+<|im_start|>assistant
+<think>
+Keep it short.
+</think>
+
+Hello world`
+	if got != want {
+		t.Fatalf("unexpected prefill output\n--- got ---\n%s\n--- want ---\n%s", got, want)
+	}
+}
+
+func qwen35MathTools() []api.Tool {
+	return []api.Tool{
+		{
+			Type: "function",
+			Function: api.ToolFunction{
+				Name:        "add",
+				Description: "Add two numbers",
+				Parameters: api.ToolFunctionParameters{
+					Type: "object",
+					Properties: testPropsOrdered([]orderedProp{
+						{
+							Key: "a",
+							Value: api.ToolProperty{
+								Type: api.PropertyType{"integer"},
+							},
+						},
+						{
+							Key: "b",
+							Value: api.ToolProperty{
+								Type: api.PropertyType{"integer"},
+							},
+						},
+					}),
+					Required: []string{"a", "b"},
+				},
+			},
+		},
+		{
+			Type: "function",
+			Function: api.ToolFunction{
+				Name:        "multiply",
+				Description: "Multiply two numbers",
+				Parameters: api.ToolFunctionParameters{
+					Type: "object",
+					Properties: testPropsOrdered([]orderedProp{
+						{
+							Key: "x",
+							Value: api.ToolProperty{
+								Type: api.PropertyType{"integer"},
+							},
+						},
+						{
+							Key: "y",
+							Value: api.ToolProperty{
+								Type: api.PropertyType{"integer"},
+							},
+						},
+					}),
+					Required: []string{"x", "y"},
+				},
+			},
+		},
+	}
+}
+
+func qwen35WeatherUVTools() []api.Tool {
+	return []api.Tool{
+		{
+			Type: "function",
+			Function: api.ToolFunction{
+				Name:        "get_weather",
+				Description: "Get weather for a location",
+				Parameters: api.ToolFunctionParameters{
+					Type: "object",
+					Properties: testPropsOrdered([]orderedProp{
+						{
+							Key: "location",
+							Value: api.ToolProperty{
+								Type: api.PropertyType{"string"},
+							},
+						},
+					}),
+					Required: []string{"location"},
+				},
+			},
+		},
+		{
+			Type: "function",
+			Function: api.ToolFunction{
+				Name:        "get_uv",
+				Description: "Get UV index for a location",
+				Parameters: api.ToolFunctionParameters{
+					Type: "object",
+					Properties: testPropsOrdered([]orderedProp{
+						{
+							Key: "location",
+							Value: api.ToolProperty{
+								Type: api.PropertyType{"string"},
+							},
+						},
+					}),
+					Required: []string{"location"},
+				},
+			},
+		},
+	}
+}

model/renderers/renderer.go

@@ -57,7 +57,7 @@ func rendererForName(name string) Renderer {
 		renderer := &Qwen3VLRenderer{isThinking: true, useImgTags: RenderImgTags}
 		return renderer
 	case "qwen3.5":
-		renderer := &Qwen3VLRenderer{isThinking: true, emitEmptyThinkOnNoThink: true, useImgTags: RenderImgTags}
+		renderer := &Qwen35Renderer{isThinking: true, emitEmptyThinkOnNoThink: true, useImgTags: RenderImgTags}
 		return renderer
 	case "cogito":
 		renderer := &CogitoRenderer{isThinking: true}
@@ -86,7 +86,7 @@ func rendererForName(name string) Renderer {
 	case "glm-4.7":
 		return &GLM47Renderer{}
 	case "glm-ocr":
-		return &GlmOcrRenderer{}
+		return &GlmOcrRenderer{useImgTags: RenderImgTags}
 	case "lfm2":
 		return &LFM2Renderer{IsThinking: false, useImgTags: RenderImgTags}
 	case "lfm2-thinking":

runner/ollamarunner/runner.go

@@ -562,6 +562,7 @@ func (s *Server) forwardBatch(pendingBatch batchState) (nextBatch batchState, er
 					if errors.As(err, &reprocess) {
 						// Prepend these inputs to the sequence's inputs queue for reprocessing
 						seq.inputs = append(reprocess.Inputs, seq.inputs...)
+						seq.sampler.Reset()
 						// Skip this sequence but continue processing the rest
 						nextBatch.seqs[seqIdx] = nil // clear this sequence for this batch
 						err = nil
@@ -692,6 +693,12 @@ func (s *Server) computeBatch(activeBatch batchState) {
 		// (unless we take down the whole runner).
 		if len(seq.pendingInputs) > 0 {
 			seq.cache.Inputs = append(seq.cache.Inputs, seq.pendingInputs...)
+			for _, inp := range seq.pendingInputs {
+				if len(inp.Multimodal) != 0 {
+					continue
+				}
+				seq.sampler.Accept(inp.Token)
+			}
 			seq.pendingInputs = []*input.Input{}
 		}
 
@@ -892,6 +899,9 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 		req.Options.TopK,
 		req.Options.TopP,
 		req.Options.MinP,
+		req.Options.RepeatPenalty,
+		req.Options.PresencePenalty,
+		req.Options.FrequencyPenalty,
 		req.Options.Seed,
 		grammar,
 	)
@@ -938,6 +948,14 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 				return
 			}
 
+			seq.sampler.Reset()
+			for _, inp := range seq.cache.Inputs {
+				if len(inp.Multimodal) != 0 {
+					continue
+				}
+				seq.sampler.Accept(inp.Token)
+			}
+
 			s.seqs[i] = seq
 			s.cond.Signal()
 			found = true

sample/samplers.go

@@ -16,24 +16,49 @@ type token struct {
 	value float32 // The raw logit or probability from the model
 }
 
+const DefaultPenaltyLookback = 64
+
 type Sampler struct {
 	rng         *rand.Rand
 	topK        int
 	topP        float32
 	minP        float32
 	temperature float32
+	repeat      float32
+	presence    float32
+	frequency   float32
+	history     []int32
 	grammar     *GrammarSampler
 }
 
+func (s *Sampler) Reset() {
+	s.history = s.history[:0]
+}
+
+func (s *Sampler) Accept(token int32) {
+	s.history = append(s.history, token)
+	if len(s.history) > DefaultPenaltyLookback {
+		copy(s.history, s.history[len(s.history)-DefaultPenaltyLookback:])
+		s.history = s.history[:DefaultPenaltyLookback]
+	}
+}
+
 func (s *Sampler) Sample(logits []float32) (int32, error) {
 	if len(logits) == 0 {
 		return -1, errors.New("sample: no logits provided to sample")
 	}
 
+	counts := tokenCounts(s.history, len(logits))
+
 	tokens := make([]token, len(logits))
 	for i := range logits {
+		value := logits[i]
+		if count := counts[int32(i)]; count > 0 {
+			value = applyPenalty(value, count, s.repeat, s.presence, s.frequency)
+		}
+
 		tokens[i].id = int32(i)
-		tokens[i].value = logits[i]
+		tokens[i].value = value
 	}
 
 	t, err := s.sample(tokens)
@@ -55,8 +80,12 @@ func (s *Sampler) Sample(logits []float32) (int32, error) {
 		// we need to reset them before applying the grammar and
 		// sampling again
 		for i := range logits {
+			value := logits[i]
+			if count := counts[int32(i)]; count > 0 {
+				value = applyPenalty(value, count, s.repeat, s.presence, s.frequency)
+			}
 			tokens[i].id = int32(i)
-			tokens[i].value = logits[i]
+			tokens[i].value = value
 		}
 		s.grammar.Apply(tokens)
 		t, err = s.sample(tokens)
@@ -127,7 +156,7 @@ func (s *Sampler) sample(tokens []token) (token, error) {
 }
 
 // TODO(parthsareen): update sampler interface to use json unmarshal https://github.com/ollama/ollama/issues/9278
-func NewSampler(temperature float32, topK int, topP float32, minP float32, seed int, grammar *GrammarSampler) Sampler {
+func NewSampler(temperature float32, topK int, topP float32, minP float32, repeatPenalty float32, presencePenalty float32, frequencyPenalty float32, seed int, grammar *GrammarSampler) Sampler {
 	var rng *rand.Rand
 	if seed != -1 {
 		// PCG requires two parameters: sequence and stream
@@ -154,12 +183,19 @@ func NewSampler(temperature float32, topK int, topP float32, minP float32, seed
 		minP = 1.0
 	}
 
+	if repeatPenalty <= 0 {
+		repeatPenalty = 1.0
+	}
+
 	return Sampler{
 		rng:         rng,
 		topK:        topK,
 		topP:        topP,
 		minP:        minP,
 		temperature: temperature,
+		repeat:      repeatPenalty,
+		presence:    presencePenalty,
+		frequency:   frequencyPenalty,
 		grammar:     grammar,
 	}
 }

sample/samplers_benchmark_test.go

@@ -16,7 +16,7 @@ func BenchmarkWeightedSampler(b *testing.B) {
 				logits[i] = float32(rand.Float64()*10 - 5)
 			}
 
-			sampler := NewSampler(0.8, 0, 0, 0, 42, nil)
+			sampler := NewSampler(0.8, 0, 0, 0, 1, 0, 0, 42, nil)
 			b.ResetTimer()
 			for b.Loop() {
 				sampler.Sample(logits)
@@ -49,7 +49,7 @@ func BenchmarkWeightedSampler(b *testing.B) {
 
 	for _, tc := range configs {
 		b.Run("Config"+tc.name, func(b *testing.B) {
-			sampler := NewSampler(tc.temperature, tc.topK, tc.topP, tc.minP, tc.seed, nil)
+			sampler := NewSampler(tc.temperature, tc.topK, tc.topP, tc.minP, 1, 0, 0, tc.seed, nil)
 			sampler.Sample(logits)
 
 			b.ResetTimer()
@@ -62,7 +62,7 @@ func BenchmarkWeightedSampler(b *testing.B) {
 
 	// Test with combined transforms separately - topK influences performance greatly
 	b.Run("TransformCombined", func(b *testing.B) {
-		sampler := NewSampler(0.8, 50, 0.9, 0.05, 42, nil)
+		sampler := NewSampler(0.8, 50, 0.9, 0.05, 1, 0, 0, 42, nil)
 		b.ResetTimer()
 
 		for b.Loop() {
@@ -81,7 +81,7 @@ func BenchmarkGreedySampler(b *testing.B) {
 				logits[i] = float32(rand.Float64()*10 - 5)
 			}
 
-			sampler := NewSampler(0, -1, 0, 0, -1, nil)
+			sampler := NewSampler(0, -1, 0, 0, 1, 0, 0, -1, nil)
 			b.ResetTimer()
 
 			for b.Loop() {

sample/samplers_test.go

@@ -13,7 +13,7 @@ import (
 
 func TestWeighted(t *testing.T) {
 	logits := []float32{-10, 3, -10, -10}
-	sampler := NewSampler(0, 0, 0, 0, 0, nil)
+	sampler := NewSampler(0, 0, 0, 0, 1, 0, 0, 0, nil)
 	got, err := sampler.Sample(logits)
 	if err != nil {
 		t.Error(err)
@@ -25,7 +25,7 @@ func TestWeighted(t *testing.T) {
 	}
 
 	logits = []float32{-100, -10, 0, 10}
-	sampler = NewSampler(0, 0, 0, 0, 0, nil)
+	sampler = NewSampler(0, 0, 0, 0, 1, 0, 0, 0, nil)
 	got, err = sampler.Sample(logits)
 	if err != nil {
 		t.Error(err)
@@ -39,7 +39,7 @@ func TestWeighted(t *testing.T) {
 	// Test very high p
 	logits = []float32{1.0, 0.9999999999999999, 0.5, 0.1}
 	// Use extremely small topP to filter out all tokens
-	sampler = NewSampler(1.0, 0, 1e-10, 0, 0, nil)
+	sampler = NewSampler(1.0, 0, 1e-10, 0, 1, 0, 0, 0, nil)
 	got, err = sampler.Sample(logits)
 	if err != nil {
 		t.Error(err)
@@ -52,7 +52,7 @@ func TestWeighted(t *testing.T) {
 	}
 
 	logits = []float32{float32(math.NaN()), float32(math.NaN()), float32(math.NaN())}
-	sampler = NewSampler(1, 0, 0.95, 0.05, 0, nil)
+	sampler = NewSampler(1, 0, 0.95, 0.05, 1, 0, 0, 0, nil)
 	got, err = sampler.Sample(logits)
 	if err == nil {
 		t.Errorf("expected error, got %d", got)
@@ -151,8 +151,8 @@ func TestGrammar(t *testing.T) {
 
 func BenchmarkSample(b *testing.B) {
 	samplers := map[string]Sampler{
-		"Greedy":   NewSampler(0, 0, 0, 0, 0, nil), // Use NewSampler with temp=0 for greedy
+		"Greedy":   NewSampler(0, 0, 0, 0, 1, 0, 0, 0, nil), // Use NewSampler with temp=0 for greedy
-		"Weighted": NewSampler(0.5, 10, 0.9, 0.2, -1, nil),
+		"Weighted": NewSampler(0.5, 10, 0.9, 0.2, 1, 0, 0, -1, nil),
 	}
 
 	// Generate random logits for benchmarking

sample/transforms.go

@@ -25,6 +25,48 @@ func (h *tokenHeap) Pop() any {
 	return x
 }
 
+func tokenCounts(history []int32, vocabSize int) map[int32]int {
+	if len(history) == 0 {
+		return nil
+	}
+
+	start := 0
+	if len(history) > DefaultPenaltyLookback {
+		start = len(history) - DefaultPenaltyLookback
+	}
+
+	counts := make(map[int32]int, len(history)-start)
+	for _, token := range history[start:] {
+		if token < 0 || int(token) >= vocabSize {
+			continue
+		}
+		counts[token]++
+	}
+
+	return counts
+}
+
+func applyPenalty(logit float32, count int, repeatPenalty float32, presencePenalty float32, frequencyPenalty float32) float32 {
+	if repeatPenalty != 1.0 {
+		// Preserve ordering for negative logits when applying repeat penalty.
+		if logit < 0 {
+			logit *= repeatPenalty
+		} else {
+			logit /= repeatPenalty
+		}
+	}
+
+	if frequencyPenalty != 0 {
+		logit -= float32(count) * frequencyPenalty
+	}
+
+	if presencePenalty != 0 {
+		logit -= presencePenalty
+	}
+
+	return logit
+}
+
 // temperature applies scaling to the logits
 func temperature(ts []token, temp float32) {
 	// Ensure temperature clipping near 0 to avoid numerical instability

sample/transforms_test.go

@@ -295,6 +295,86 @@ func TestMinP(t *testing.T) {
 	}
 }
 
+func TestTokenCounts(t *testing.T) {
+	history := make([]int32, 70)
+	history[0] = 7
+	history[69] = 7
+
+	counts := tokenCounts(history, 8)
+	if got := counts[7]; got != 1 {
+		t.Fatalf("lookback mismatch: got %d want %d", got, 1)
+	}
+}
+
+func TestApplyPenalty(t *testing.T) {
+	logit := applyPenalty(5.0, 3, 1.0, 1.5, 0.5)
+	if math.Abs(float64(logit-2.0)) > 1e-6 {
+		t.Fatalf("unexpected penalty result: got %f want %f", logit, 2.0)
+	}
+
+	logit = applyPenalty(4.0, 1, 2.0, 0, 0)
+	if math.Abs(float64(logit-2.0)) > 1e-6 {
+		t.Fatalf("unexpected repeat penalty result for positive logits: got %f want %f", logit, 2.0)
+	}
+
+	logit = applyPenalty(-4.0, 1, 2.0, 0, 0)
+	if math.Abs(float64(logit-(-8.0))) > 1e-6 {
+		t.Fatalf("unexpected repeat penalty result for negative logits: got %f want %f", logit, -8.0)
+	}
+}
+
+func TestSamplerPresencePenalty(t *testing.T) {
+	logits := []float32{0.0, 5.0, 0.0}
+
+	baseline := NewSampler(0, 0, 1, 0, 1, 0, 0, -1, nil)
+	baseline.Accept(1)
+	got, err := baseline.Sample(logits)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if got != 1 {
+		t.Fatalf("unexpected baseline token: got %d want %d", got, 1)
+	}
+
+	presence := NewSampler(0, 0, 1, 0, 1, 6, 0, -1, nil)
+	presence.Accept(1)
+	got, err = presence.Sample(logits)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if got == 1 {
+		t.Fatalf("presence penalty did not change repeated token selection")
+	}
+}
+
+func TestSamplerFrequencyPenalty(t *testing.T) {
+	logits := []float32{0.0, 5.0, 4.0}
+
+	baseline := NewSampler(0, 0, 1, 0, 1, 0, 0, -1, nil)
+	baseline.Accept(1)
+	baseline.Accept(1)
+	baseline.Accept(1)
+	got, err := baseline.Sample(logits)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if got != 1 {
+		t.Fatalf("unexpected baseline token: got %d want %d", got, 1)
+	}
+
+	frequency := NewSampler(0, 0, 1, 0, 1, 0, 1.0, -1, nil)
+	frequency.Accept(1)
+	frequency.Accept(1)
+	frequency.Accept(1)
+	got, err = frequency.Sample(logits)
+	if err != nil {
+		t.Fatal(err)
+	}
+	if got != 2 {
+		t.Fatalf("frequency penalty did not demote repeated token as expected: got %d want %d", got, 2)
+	}
+}
+
 func BenchmarkTransforms(b *testing.B) {
 	// Generate random logits
 	tokens := make([]token, 1<<16)

server/create.go

@@ -65,11 +65,22 @@ func (s *Server) CreateHandler(c *gin.Context) {
 	config.Parser = r.Parser
 	config.Requires = r.Requires
 
-	for v := range r.Files {
+	for v, digest := range r.Files {
 		if !fs.ValidPath(v) {
 			c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": errFilePath.Error()})
 			return
 		}
+		if digest == "" {
+			c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": manifest.ErrInvalidDigestFormat.Error()})
+			return
+		}
+	}
+
+	for _, digest := range r.Adapters {
+		if digest == "" {
+			c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": manifest.ErrInvalidDigestFormat.Error()})
+			return
+		}
 	}
 
 	name := model.ParseName(cmp.Or(r.Model, r.Name))

server/prompt_test.go

@@ -3,6 +3,7 @@ package server
 import (
 	"bytes"
 	"context"
+	"strings"
 	"testing"
 
 	"github.com/google/go-cmp/cmp"
@@ -366,3 +367,33 @@ func TestChatPromptRendererDoesNotRewriteMessageContent(t *testing.T) {
 		t.Fatal("prompt is empty")
 	}
 }
+
+func TestChatPromptGLMOcrRendererAddsImageTags(t *testing.T) {
+	msgs := []api.Message{
+		{
+			Role:    "user",
+			Content: "extract text",
+			Images:  []api.ImageData{[]byte("img-1"), []byte("img-2")},
+		},
+	}
+
+	m := Model{
+		Config:         model.ConfigV2{Renderer: "glm-ocr"},
+		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 got, want := len(images), 2; got != want {
+		t.Fatalf("len(images) = %d, want %d", got, want)
+	}
+
+	if !strings.Contains(prompt, "<|user|>\n[img-0][img-1]extract text") {
+		t.Fatalf("prompt missing glm-ocr image tags, got: %q", prompt)
+	}
+}

server/routes.go

@@ -150,7 +150,7 @@ func (s *Server) scheduleRunner(ctx context.Context, name string, caps []model.C
 		return nil, nil, nil, fmt.Errorf("%s %w", name, err)
 	}
 
-	useImagegen, _ := requestOpts["use_imagegen_runner"].(bool)
+	// Deprecated runner override option; ignore if present.
 	delete(requestOpts, "use_imagegen_runner")
 
 	opts, err := s.modelOptions(model, requestOpts)
@@ -158,7 +158,7 @@ func (s *Server) scheduleRunner(ctx context.Context, name string, caps []model.C
 		return nil, nil, nil, err
 	}
 
-	runnerCh, errCh := s.sched.GetRunner(ctx, model, opts, keepAlive, useImagegen)
+	runnerCh, errCh := s.sched.GetRunner(ctx, model, opts, keepAlive)
 	var runner *runnerRef
 	select {
 	case runner = <-runnerCh:
@@ -370,12 +370,6 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 		}
 	}
 
-	// Validate Think value: string values currently only allowed for harmony/gptoss models
-	if req.Think != nil && req.Think.IsString() && m.Config.Parser != "harmony" {
-		c.JSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("think value %q is not supported for this model", req.Think.String())})
-		return
-	}
-
 	caps := []model.Capability{model.CapabilityCompletion}
 	if req.Suffix != "" {
 		caps = append(caps, model.CapabilityInsert)
@@ -558,7 +552,6 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 					PromptEvalDuration: cr.PromptEvalDuration,
 					EvalCount:          cr.EvalCount,
 					EvalDuration:       cr.EvalDuration,
-					PeakMemory:         cr.PeakMemory,
 				},
 				Logprobs: toAPILogprobs(cr.Logprobs),
 			}
@@ -2241,12 +2234,6 @@ func (s *Server) ChatHandler(c *gin.Context) {
 		return
 	}
 
-	// Validate Think value: string values currently only allowed for harmony/gptoss models
-	if req.Think != nil && req.Think.IsString() && m.Config.Parser != "harmony" {
-		c.JSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("think value %q is not supported for this model", req.Think.String())})
-		return
-	}
-
 	var thinkingState *thinking.Parser
 	openingTag, closingTag := thinking.InferTags(m.Template.Template)
 	if req.Think != nil && req.Think.Bool() && openingTag != "" && closingTag != "" {
@@ -2317,7 +2304,6 @@ func (s *Server) ChatHandler(c *gin.Context) {
 						PromptEvalDuration: r.PromptEvalDuration,
 						EvalCount:          r.EvalCount,
 						EvalDuration:       r.EvalDuration,
-						PeakMemory:         r.PeakMemory,
 					},
 					Logprobs: toAPILogprobs(r.Logprobs),
 				}

server/routes_create_test.go

@@ -144,6 +144,37 @@ func TestCreateFromBin(t *testing.T) {
 		filepath.Join(p, "blobs", "sha256-6bcdb8859d417753645538d7bbfbd7ca91a3f0c191aef5379c53c05e86b669dd"),
 		filepath.Join(p, "blobs", "sha256-89a2116c3a82d6a97f59f748d86ed4417214353fd178ee54df418fde32495fad"),
 	})
+
+	t.Run("empty file digest", func(t *testing.T) {
+		w := createRequest(t, s.CreateHandler, api.CreateRequest{
+			Name:   "my-gguf-model",
+			Files:  map[string]string{"0.gguf": ""},
+			Stream: &stream,
+		})
+
+		if w.Code != http.StatusBadRequest {
+			t.Fatalf("expected status 400, got %d", w.Code)
+		}
+		if !strings.Contains(w.Body.String(), "invalid digest format") {
+			t.Errorf("expected invalid digest format error, got:\n%s", w.Body.String())
+		}
+	})
+
+	t.Run("empty adapter digest", func(t *testing.T) {
+		w := createRequest(t, s.CreateHandler, api.CreateRequest{
+			Name:     "my-gguf-model",
+			Files:    map[string]string{"0.gguf": digest},
+			Adapters: map[string]string{"adapter.gguf": ""},
+			Stream:   &stream,
+		})
+
+		if w.Code != http.StatusBadRequest {
+			t.Fatalf("expected status 400, got %d", w.Code)
+		}
+		if !strings.Contains(w.Body.String(), "invalid digest format") {
+			t.Errorf("expected invalid digest format error, got:\n%s", w.Body.String())
+		}
+	})
 }
 
 func TestCreateFromModel(t *testing.T) {

server/sched.go

@@ -33,7 +33,6 @@ type LlmRequest struct {
 	successCh       chan *runnerRef
 	errCh           chan error
 	schedAttempts   uint
-	useImagegen     bool
 }
 
 type Scheduler struct {
@@ -106,7 +105,7 @@ func schedulerModelKey(m *Model) string {
 }
 
 // context must be canceled to decrement ref count and release the runner
-func (s *Scheduler) GetRunner(c context.Context, m *Model, opts api.Options, sessionDuration *api.Duration, useImagegen bool) (chan *runnerRef, chan error) {
+func (s *Scheduler) GetRunner(c context.Context, m *Model, opts api.Options, sessionDuration *api.Duration) (chan *runnerRef, chan error) {
 	if opts.NumCtx < 4 {
 		opts.NumCtx = 4
 	}
@@ -123,7 +122,6 @@ func (s *Scheduler) GetRunner(c context.Context, m *Model, opts api.Options, ses
 		sessionDuration: sessionDuration,
 		successCh:       make(chan *runnerRef, 1),
 		errCh:           make(chan error, 1),
-		useImagegen:     useImagegen,
 	}
 
 	key := schedulerModelKey(req.model)
@@ -593,20 +591,15 @@ iGPUScan:
 	return false
 }
 
-// loadMLX loads an experimental safetensors model using the unified MLX runner.
+// loadMLX loads an experimental safetensors model using MLX runners.
-// This supports both LLM (completion) and image generation models.
+// Image models use x/imagegen; LLM models use x/mlxrunner.
 func (s *Scheduler) loadMLX(req *LlmRequest) bool {
 	modelName := req.model.ShortName
 	var server llm.LlamaServer
 	var err error
 
-	isImagegen := false
 	if slices.Contains(req.model.Config.Capabilities, "image") {
-		server, err = imagegen.NewServer(modelName, imagegen.ModeImageGen)
+		server, err = imagegen.NewServer(modelName)
-		isImagegen = true
-	} else if req.useImagegen {
-		server, err = imagegen.NewServer(modelName, imagegen.ModeLLM)
-		isImagegen = true
 	} else {
 		server, err = mlxrunner.NewClient(modelName)
 	}
@@ -628,7 +621,7 @@ func (s *Scheduler) loadMLX(req *LlmRequest) bool {
 		llama:           server,
 		Options:         &req.opts,
 		loading:         false,
-		isImagegen:      isImagegen,
+		isImagegen:      slices.Contains(req.model.Config.Capabilities, "image"),
 		sessionDuration: sessionDuration,
 		totalSize:       totalSize,
 		vramSize:        vramSize,
@@ -737,8 +730,8 @@ func (runner *runnerRef) needsReload(ctx context.Context, req *LlmRequest) bool
 	runner.refMu.Lock()
 	defer runner.refMu.Unlock()
 
-	// Check if runner type (imagegen vs mlxrunner) matches what's requested
+	// Check if runner type (imagegen vs mlxrunner) matches what's requested.
-	wantImagegen := req.useImagegen || slices.Contains(req.model.Config.Capabilities, "image")
+	wantImagegen := slices.Contains(req.model.Config.Capabilities, "image")
 	if runner.isImagegen != wantImagegen {
 		return true
 	}

server/sched_test.go

@@ -408,10 +408,10 @@ func TestSchedGetRunner(t *testing.T) {
 	s.getSystemInfoFn = getSystemInfoFn
 	s.newServerFn = a.newServer
 	slog.Info("a")
-	successCh1a, errCh1a := s.GetRunner(a.ctx, a.req.model, a.req.opts, a.req.sessionDuration, false)
+	successCh1a, errCh1a := s.GetRunner(a.ctx, a.req.model, a.req.opts, a.req.sessionDuration)
 	require.Len(t, s.pendingReqCh, 1)
 	slog.Info("b")
-	successCh1b, errCh1b := s.GetRunner(b.ctx, b.req.model, b.req.opts, b.req.sessionDuration, false)
+	successCh1b, errCh1b := s.GetRunner(b.ctx, b.req.model, b.req.opts, b.req.sessionDuration)
 	require.Len(t, s.pendingReqCh, 1)
 	require.Empty(t, successCh1b)
 	require.Len(t, errCh1b, 1)
@@ -435,7 +435,7 @@ func TestSchedGetRunner(t *testing.T) {
 
 	c.req.model.ModelPath = "bad path"
 	slog.Info("c")
-	successCh1c, errCh1c := s.GetRunner(c.ctx, c.req.model, c.req.opts, c.req.sessionDuration, false)
+	successCh1c, errCh1c := s.GetRunner(c.ctx, c.req.model, c.req.opts, c.req.sessionDuration)
 	// Starts in pending channel, then should be quickly processed to return an error
 	time.Sleep(50 * time.Millisecond) // Long enough for the "a" model to expire and unload
 	require.Empty(t, successCh1c)
@@ -470,7 +470,7 @@ func TestSchedGetRunnerUsesDigestKeyWhenModelPathEmpty(t *testing.T) {
 	s.loadedMu.Unlock()
 
 	reqModel := &Model{Name: "safetensors-b", Digest: "sha-b"}
-	successCh, errCh := s.GetRunner(ctx, reqModel, opts, nil, false)
+	successCh, errCh := s.GetRunner(ctx, reqModel, opts, nil)
 
 	require.Empty(t, successCh)
 	require.Empty(t, errCh)
@@ -499,7 +499,7 @@ func TestSchedGetRunnerReusesSameDigestWhenModelPathEmpty(t *testing.T) {
 	s.loadedMu.Unlock()
 
 	reqCtx, cancelReq := context.WithCancel(ctx)
-	successCh, errCh := s.GetRunner(reqCtx, &Model{Name: "safetensors-a-copy", Digest: "sha-a"}, opts, nil, false)
+	successCh, errCh := s.GetRunner(reqCtx, &Model{Name: "safetensors-a-copy", Digest: "sha-a"}, opts, nil)
 	cancelReq()
 
 	select {
@@ -574,7 +574,7 @@ func TestSchedPrematureExpired(t *testing.T) {
 	s.getGpuFn = getGpuFn
 	s.getSystemInfoFn = getSystemInfoFn
 	s.newServerFn = scenario1a.newServer
-	successCh1a, errCh1a := s.GetRunner(scenario1a.ctx, scenario1a.req.model, scenario1a.req.opts, scenario1a.req.sessionDuration, false)
+	successCh1a, errCh1a := s.GetRunner(scenario1a.ctx, scenario1a.req.model, scenario1a.req.opts, scenario1a.req.sessionDuration)
 	require.Len(t, s.pendingReqCh, 1)
 	s.Run(ctx)
 	select {

x/create/create.go

@@ -288,6 +288,18 @@ func normalizeQuantType(quantize string) string {
 	}
 }
 
+func isStackedExpertWeight(name string) bool {
+	// Combined/stacked expert tensors may be emitted either as "...proj.weight" (per-expert)
+	// or "...proj" (pre-stacked packed tensor).
+	if strings.HasSuffix(name, ".bias") || strings.HasSuffix(name, ".scale") || strings.HasSuffix(name, ".qbias") {
+		return false
+	}
+
+	return strings.Contains(name, ".mlp.switch_mlp.") ||
+		strings.Contains(name, ".mlp.experts.") ||
+		strings.Contains(name, ".mlp.shared_experts.")
+}
+
 // GetTensorQuantization returns the appropriate quantization type for a tensor.
 // Returns "" if the tensor should not be quantized.
 // This implements mixed-precision quantization:
@@ -296,18 +308,25 @@ func normalizeQuantType(quantize string) string {
 //   - Down projection weights: int8 (more sensitive, would be Q6 in GGML but no MLX kernel)
 //   - Norms, embeddings, biases, routing gates: no quantization
 func GetTensorQuantization(name string, shape []int32, quantize string) string {
+	stackedExpert := isStackedExpertWeight(name)
+
 	// Use basic name-based check first
-	if !ShouldQuantize(name, "") {
+	if !stackedExpert && !ShouldQuantize(name, "") {
 		return ""
 	}
 
-	// Only quantize 2D tensors (linear layers) - skip 1D (biases, norms) and higher-D (convolutions if any)
+	// Quantize standard linear weights (2D). Also allow stacked expert weights (3D),
-	if len(shape) != 2 {
+	// e.g. qwen switch_mlp / experts combined tensors.
+	if len(shape) != 2 && !(len(shape) == 3 && stackedExpert) {
 		return ""
 	}
 
 	// Skip small tensors (less than 1024 elements) - not worth quantizing
-	if len(shape) >= 2 && int64(shape[0])*int64(shape[1]) < 1024 {
+	var elems int64 = 1
+	for _, d := range shape {
+		elems *= int64(d)
+	}
+	if elems < 1024 {
 		return ""
 	}
 

x/create/create_test.go

@@ -557,6 +557,10 @@ func TestShouldQuantizeTensor(t *testing.T) {
 		// 3D+ tensors should not be quantized
 		{"3D tensor", "conv.weight", []int32{64, 64, 3}, "fp8", false},
 		{"4D tensor", "conv2d.weight", []int32{64, 64, 3, 3}, "fp8", false},
+		{"stacked expert switch_mlp gate_up 3D int8", "model.layers.1.mlp.switch_mlp.gate_up_proj.weight", []int32{64, 22016, 4096}, "int8", true},
+		{"stacked expert experts down_proj 3D int8", "model.layers.1.mlp.experts.down_proj.weight", []int32{64, 4096, 14336}, "int8", true},
+		{"stacked expert combined gate_up 3D int8", "model.language_model.layers.0.mlp.experts.gate_up_proj", []int32{256, 1024, 2048}, "int8", true},
+		{"stacked expert combined down_proj 3D int8", "model.language_model.layers.0.mlp.experts.down_proj", []int32{256, 2048, 512}, "int8", true},
 
 		// Embeddings should not be quantized regardless of shape
 		{"embedding 2D", "embed_tokens.weight", []int32{32000, 4096}, "fp8", false},
@@ -619,6 +623,44 @@ func TestExpertGroupPrefix(t *testing.T) {
 	}
 }
 
+func TestGetTensorQuantization_StackedExpert3D(t *testing.T) {
+	gateUp := GetTensorQuantization(
+		"model.layers.1.mlp.switch_mlp.gate_up_proj.weight",
+		[]int32{64, 22016, 4096},
+		"int4",
+	)
+	if gateUp != "int4" {
+		t.Fatalf("gate_up_proj quantization = %q, want %q", gateUp, "int4")
+	}
+
+	down := GetTensorQuantization(
+		"model.layers.1.mlp.experts.down_proj.weight",
+		[]int32{64, 4096, 14336},
+		"int4",
+	)
+	if down != "int8" {
+		t.Fatalf("down_proj quantization = %q, want %q", down, "int8")
+	}
+
+	combinedGateUp := GetTensorQuantization(
+		"model.language_model.layers.0.mlp.experts.gate_up_proj",
+		[]int32{256, 1024, 2048},
+		"int8",
+	)
+	if combinedGateUp != "int8" {
+		t.Fatalf("combined gate_up_proj quantization = %q, want %q", combinedGateUp, "int8")
+	}
+
+	combinedDown := GetTensorQuantization(
+		"model.language_model.layers.0.mlp.experts.down_proj",
+		[]int32{256, 2048, 512},
+		"int4",
+	)
+	if combinedDown != "int8" {
+		t.Fatalf("combined down_proj quantization = %q, want %q", combinedDown, "int8")
+	}
+}
+
 func TestCreateSafetensorsModel_WithQuantize(t *testing.T) {
 	dir := t.TempDir()
 

x/imagegen/cmd/engine/README.md

@@ -10,17 +10,7 @@ go build -tags mlx -o engine ./x/imagegen/cmd/engine
 
 ## Text Generation
 
-```bash
+Text generation models are no longer supported by this engine.
-./engine -model /path/to/model -prompt "Hello" -max-tokens 100
-```
-
-Options:
-
-- `-temperature` - sampling temperature (default 0.7)
-- `-top-p` - nucleus sampling (default 0.9)
-- `-top-k` - top-k sampling (default 40)
-
-Supports: Llama, Gemma3, GPT-OSS
 
 ## Image Generation
 

x/imagegen/cmd/engine/main.go

@@ -18,9 +18,6 @@ import (
 	"github.com/ollama/ollama/x/imagegen"
 	"github.com/ollama/ollama/x/imagegen/mlx"
 	"github.com/ollama/ollama/x/imagegen/models/flux2"
-	"github.com/ollama/ollama/x/imagegen/models/gemma3"
-	"github.com/ollama/ollama/x/imagegen/models/gpt_oss"
-	"github.com/ollama/ollama/x/imagegen/models/llama"
 	"github.com/ollama/ollama/x/imagegen/models/zimage"
 	"github.com/ollama/ollama/x/imagegen/safetensors"
 )
@@ -170,11 +167,11 @@ func main() {
 			log.Fatal(err)
 		}
 
-		// Load image if provided and model supports it
+		// Load image if provided and model supports it.
 		var image *mlx.Array
 		if *imagePath != "" {
 			if mm, ok := m.(interface{ ImageSize() int32 }); ok {
-				image, err = gemma3.ProcessImage(*imagePath, mm.ImageSize())
+				image, err = imagegen.ProcessImage(*imagePath, mm.ImageSize())
 				if err != nil {
 					log.Fatal("load image:", err)
 				}
@@ -236,14 +233,8 @@ func load(modelPath string) (Model, error) {
 	}
 
 	switch kind {
-	case "gpt_oss":
-		return gpt_oss.Load(modelPath)
-	case "gemma3":
-		return gemma3.Load(modelPath)
-	case "gemma3_text":
-		return gemma3.LoadText(modelPath)
 	default:
-		return llama.Load(modelPath)
+		return nil, fmt.Errorf("model type %q is not supported by x/imagegen/cmd/engine", kind)
 	}
 }
 

x/imagegen/image_processor.go

@@ -1,6 +1,6 @@
 //go:build mlx
 
-package gemma3
+package imagegen
 
 import (
 	"fmt"
@@ -13,8 +13,8 @@ import (
 	"golang.org/x/image/draw"
 )
 
-// ProcessImage loads and preprocesses an image for the vision tower
+// ProcessImage loads and preprocesses an image for multimodal vision towers.
-// Returns [1, H, W, C] tensor in NHWC format normalized for SigLIP
+// Returns [1, H, W, C] tensor in NHWC format normalized for SigLIP.
 func ProcessImage(path string, imageSize int32) (*mlx.Array, error) {
 	f, err := os.Open(path)
 	if err != nil {
@@ -30,20 +30,20 @@ func ProcessImage(path string, imageSize int32) (*mlx.Array, error) {
 	return ProcessImageData(img, imageSize)
 }
 
-// ProcessImageData preprocesses an image.Image for the vision tower
+// ProcessImageData preprocesses an image.Image for multimodal vision towers.
 func ProcessImageData(img image.Image, imageSize int32) (*mlx.Array, error) {
-	// Resize to target size using bilinear interpolation
+	// Resize to target size using bilinear interpolation.
 	resized := image.NewRGBA(image.Rect(0, 0, int(imageSize), int(imageSize)))
 	draw.BiLinear.Scale(resized, resized.Bounds(), img, img.Bounds(), draw.Over, nil)
 
-	// Convert to float32 array [H, W, C] and normalize
+	// Convert to float32 array [H, W, C] and normalize.
-	// SigLIP normalization: (pixel / 255.0 - 0.5) / 0.5 = pixel / 127.5 - 1.0
+	// SigLIP normalization: (pixel / 255.0 - 0.5) / 0.5 = pixel / 127.5 - 1.0.
 	data := make([]float32, imageSize*imageSize*3)
 	idx := 0
 	for y := int32(0); y < imageSize; y++ {
 		for x := int32(0); x < imageSize; x++ {
 			r, g, b, _ := resized.At(int(x), int(y)).RGBA()
-			// RGBA returns 16-bit values, convert to 8-bit
+			// RGBA returns 16-bit values, convert to 8-bit.
 			data[idx] = float32(r>>8)/127.5 - 1.0
 			data[idx+1] = float32(g>>8)/127.5 - 1.0
 			data[idx+2] = float32(b>>8)/127.5 - 1.0
@@ -51,8 +51,8 @@ func ProcessImageData(img image.Image, imageSize int32) (*mlx.Array, error) {
 		}
 	}
 
-	// Create MLX array [1, H, W, C] for NHWC layout
+	// Create MLX array [1, H, W, C] for NHWC layout.
 	arr := mlx.NewArrayFloat32(data, []int32{1, imageSize, imageSize, 3})
-	mlx.Eval(arr) // Materialize to prevent use-after-free
+	mlx.Eval(arr) // Materialize to prevent use-after-free.
 	return arr, nil
 }

x/imagegen/llm.go

@@ -1,420 +0,0 @@
-//go:build mlx
-
-package imagegen
-
-import (
-	"encoding/json"
-	"errors"
-	"fmt"
-	"log/slog"
-	"net/http"
-	"strings"
-	"sync"
-	"time"
-
-	"github.com/ollama/ollama/x/imagegen/cache"
-	"github.com/ollama/ollama/x/imagegen/manifest"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/models/glm4_moe_lite"
-	"github.com/ollama/ollama/x/imagegen/tokenizer"
-)
-
-// TextModel is the interface for LLM text generation models.
-type TextModel interface {
-	Forward(tokens *mlx.Array, caches []cache.Cache) *mlx.Array
-	NewCache(maxSeqLen int32) []cache.Cache
-	Tokenizer() *tokenizer.Tokenizer
-	VocabSize() int32
-	MaxContextLength() int32
-	NumLayers() int
-}
-
-// llmState holds the state for LLM generation
-type llmState struct {
-	model TextModel
-}
-
-var llmMu sync.Mutex
-
-// Dedicated stream for generation (like mlx-lm's generation_stream)
-var generationStream *mlx.Stream
-
-// withStream runs fn with the generation stream as default
-func withStream(fn func()) {
-	// Lazy initialization of generationStream
-	if generationStream == nil {
-		generationStream = mlx.NewStream()
-	}
-	orig := mlx.GetDefaultStream()
-	mlx.SetDefaultStream(generationStream)
-	fn()
-	mlx.SetDefaultStream(orig)
-}
-
-// Decoder wraps model + cache for autoregressive generation.
-// This matches the pattern from cmd/engine/generate.go
-type Decoder struct {
-	model         TextModel
-	caches        []cache.Cache
-	vocabSize     int32
-	temp          float32
-	token         *mlx.Array   // Current token (kept across iterations)
-	oldCacheState []*mlx.Array // Preallocated slice for old cache state
-}
-
-func NewDecoder(m TextModel, temp float32) *Decoder {
-	caches := m.NewCache(0)
-	return &Decoder{
-		model:         m,
-		caches:        caches,
-		vocabSize:     m.VocabSize(),
-		temp:          temp,
-		oldCacheState: make([]*mlx.Array, 0, len(caches)*2),
-	}
-}
-
-func (d *Decoder) prefill(inputIDs []int32) int {
-	processed := 0
-
-	// Track old cache state to free after each chunk
-	var oldCacheState []*mlx.Array
-
-	// Process all-but-1 tokens in chunks, eval cache state for memory management
-	for len(inputIDs) > 1 {
-		chunkSize := min(2048, len(inputIDs)-1)
-		if chunkSize <= 0 {
-			break
-		}
-		chunk := inputIDs[:chunkSize]
-
-		// Save old cache state before forward
-		oldCacheState = oldCacheState[:0]
-		for _, c := range d.caches {
-			oldCacheState = append(oldCacheState, c.State()...)
-		}
-
-		var cacheState []*mlx.Array
-		withStream(func() {
-			x := mlx.NewArrayInt32(chunk, []int32{1, int32(len(chunk))})
-			d.model.Forward(x, d.caches)
-			for _, c := range d.caches {
-				cacheState = append(cacheState, c.State()...)
-			}
-		})
-		mlx.Eval(cacheState...)
-
-		// Free old cache state
-		for _, arr := range oldCacheState {
-			if arr != nil {
-				arr.Free()
-			}
-		}
-
-		inputIDs = inputIDs[chunkSize:]
-		processed += chunkSize
-	}
-
-	// Save old cache state before final step
-	oldCacheState = oldCacheState[:0]
-	for _, c := range d.caches {
-		oldCacheState = append(oldCacheState, c.State()...)
-	}
-
-	// Final token + sampling
-	withStream(func() {
-		x := mlx.NewArrayInt32(inputIDs, []int32{1, int32(len(inputIDs))})
-		mlx.Eval(x) // Materialize before any other evals
-		logits := d.model.Forward(x, d.caches)
-		d.token = sample(logits, d.temp, d.vocabSize)
-	})
-	// Keep cache state (token auto-kept by AsyncEval)
-	for _, c := range d.caches {
-		mlx.Keep(c.State()...)
-	}
-	mlx.AsyncEval(d.token)
-
-	// Free old cache state from before final step
-	for _, arr := range oldCacheState {
-		if arr != nil {
-			arr.Free()
-		}
-	}
-
-	mlx.ClearCache()
-
-	return processed + len(inputIDs)
-}
-
-func (d *Decoder) step() int32 {
-	prevToken := d.token
-
-	// Save old cache state (reuse preallocated slice)
-	d.oldCacheState = d.oldCacheState[:0]
-	for _, c := range d.caches {
-		d.oldCacheState = append(d.oldCacheState, c.State()...)
-	}
-
-	withStream(func() {
-		logits := d.model.Forward(mlx.Reshape(prevToken, 1, 1), d.caches)
-		d.token = sample(logits, d.temp, d.vocabSize)
-	})
-	// Keep token and new cache state so they survive cleanup
-	mlx.Keep(d.token)
-	for _, c := range d.caches {
-		mlx.Keep(c.State()...)
-	}
-	mlx.AsyncEval(d.token)
-
-	// Sync on previous token (GPU already working on next step)
-	val := prevToken.ItemInt32()
-
-	// Free old token and old cache state
-	prevToken.Free()
-	for _, arr := range d.oldCacheState {
-		arr.Free()
-	}
-	return val
-}
-
-// sample samples from logits using temperature scaling
-func sample(logits *mlx.Array, temp float32, vocabSize int32) *mlx.Array {
-	// Get last position logits: [1, L, vocab] -> [vocab]
-	shape := logits.Shape()
-	seqLen := shape[1]
-	lastLogits := mlx.Slice(logits, []int32{0, seqLen - 1, 0}, []int32{1, seqLen, vocabSize})
-	lastLogits = mlx.Reshape(lastLogits, vocabSize)
-
-	if temp <= 0 || temp < 0.01 {
-		// Greedy decoding
-		return mlx.Argmax(lastLogits, -1, false)
-	}
-
-	// Apply temperature scaling
-	scaled := mlx.DivScalar(lastLogits, temp)
-	return mlx.RandomCategorical(scaled, -1, 1)
-}
-
-// loadLLMModel loads a safetensors LLM model and its tokenizer from manifest storage.
-func (s *server) loadLLMModel() error {
-	// Load the manifest to get model information
-	modelManifest, err := manifest.LoadManifest(s.modelName)
-	if err != nil {
-		return fmt.Errorf("failed to load manifest: %w", err)
-	}
-
-	// Detect model architecture from config.json
-	configData, err := modelManifest.ReadConfig("config.json")
-	if err != nil {
-		return fmt.Errorf("failed to read config.json: %w", err)
-	}
-
-	var modelConfig struct {
-		Architectures []string `json:"architectures"`
-		ModelType     string   `json:"model_type"`
-	}
-	if err := json.Unmarshal(configData, &modelConfig); err != nil {
-		return fmt.Errorf("failed to parse config.json: %w", err)
-	}
-
-	arch := ""
-	if len(modelConfig.Architectures) > 0 {
-		arch = modelConfig.Architectures[0]
-	}
-	if arch == "" {
-		arch = modelConfig.ModelType
-	}
-
-	slog.Info("detected LLM architecture", "architecture", arch, "model_type", modelConfig.ModelType)
-
-	// Load the appropriate model based on architecture
-	var model TextModel
-	archLower := strings.ToLower(arch)
-
-	switch {
-	case strings.Contains(archLower, "glm4moelite"):
-		m, err := glm4_moe_lite.LoadFromManifest(modelManifest)
-		if err != nil {
-			return fmt.Errorf("failed to load glm4-moe-lite model: %w", err)
-		}
-		model = m
-		slog.Info("loaded glm4-moe-lite model", "vocab_size", m.VocabSize(), "layers", m.NumLayers())
-
-	default:
-		return fmt.Errorf("LLM architecture %q is not yet supported. "+
-			"Supported architectures: glm4-moe-lite. "+
-			"Please convert your model to GGUF format or use a supported architecture", arch)
-	}
-
-	s.llmModel = &llmState{
-		model: model,
-	}
-
-	return nil
-}
-
-// handleLLMCompletion handles LLM text generation requests.
-func (s *server) handleLLMCompletion(w http.ResponseWriter, r *http.Request, req Request) {
-	if s.llmModel == nil {
-		http.Error(w, "LLM model not loaded", http.StatusInternalServerError)
-		return
-	}
-
-	// Serialize generation requests
-	llmMu.Lock()
-	defer llmMu.Unlock()
-
-	if err := s.llmGenerate(w, r, req); err != nil {
-		slog.Error("LLM generation failed", "error", err)
-		// Don't send error if we've already started streaming
-	}
-}
-
-// llmGenerate runs the generation loop using the Decoder pattern from cmd/engine
-func (s *server) llmGenerate(w http.ResponseWriter, r *http.Request, req Request) error {
-	state := s.llmModel
-
-	// Set up streaming response
-	w.Header().Set("Content-Type", "application/x-ndjson")
-	w.Header().Set("Transfer-Encoding", "chunked")
-	flusher, ok := w.(http.Flusher)
-	if !ok {
-		return errors.New("streaming not supported")
-	}
-
-	tok := state.model.Tokenizer()
-
-	// The prompt is already formatted by the server using the model's renderer
-	// (see server/prompt.go renderPrompt), so we don't apply FormatPrompt here.
-	prompt := req.Prompt
-
-	// Tokenize the prompt
-	inputIDs := tok.Encode(prompt, true)
-	slog.Debug("tokenized prompt", "num_tokens", len(inputIDs))
-
-	// Generation parameters
-	maxTokens := int(state.model.MaxContextLength())
-	if maxTokens <= 0 {
-		maxTokens = 4096
-	}
-	if req.Options != nil && req.Options.NumPredict > 0 {
-		maxTokens = req.Options.NumPredict
-	}
-
-	temperature := float32(0.7)
-	if req.Options != nil && req.Options.Temperature > 0 {
-		temperature = float32(req.Options.Temperature)
-	}
-
-	// Enable MLX compilation for better performance
-	mlx.EnableCompile()
-
-	// Create decoder with fresh caches
-	dec := NewDecoder(state.model, temperature)
-
-	prefillStart := time.Now()
-	prefillTokens := dec.prefill(inputIDs)
-	// Prefill measurement includes time to first token
-	firstToken := dec.step()
-	prefillDuration := time.Since(prefillStart)
-	promptEvalDuration := prefillDuration
-
-	enc := json.NewEncoder(w)
-	ctx := r.Context()
-	generated := 0
-	stopReason := "max_tokens"
-
-	// Handle first token
-	generated++
-	if tok.IsEOS(firstToken) {
-		resp := Response{
-			Done:               true,
-			StopReason:         fmt.Sprintf("first_token_eos:%d", firstToken),
-			PromptEvalCount:    prefillTokens,
-			PromptEvalDuration: int(promptEvalDuration.Nanoseconds()),
-		}
-		enc.Encode(resp)
-		flusher.Flush()
-		return nil
-	}
-
-	text := tok.Decode([]int32{firstToken})
-	resp := Response{Content: text}
-	enc.Encode(resp)
-	flusher.Flush()
-
-	genStart := time.Now()
-
-	// Generation loop
-	for n := 1; n < maxTokens; n++ {
-		// Check for cancellation
-		select {
-		case <-ctx.Done():
-			stopReason = fmt.Sprintf("context_cancelled:%d", generated)
-			break
-		default:
-		}
-		if stopReason != "max_tokens" {
-			break
-		}
-
-		token := dec.step()
-		generated++
-
-		if tok.IsEOS(token) {
-			stopReason = fmt.Sprintf("eos_token:%d", token)
-			break
-		}
-
-		text := tok.Decode([]int32{token})
-
-		// Check for stop sequences
-		if req.Options != nil && len(req.Options.Stop) > 0 {
-			shouldStop := false
-			var matchedStop string
-			for _, stop := range req.Options.Stop {
-				if strings.Contains(text, stop) {
-					text = strings.Split(text, stop)[0]
-					shouldStop = true
-					matchedStop = stop
-					break
-				}
-			}
-			if shouldStop {
-				if text != "" {
-					resp := Response{Content: text}
-					enc.Encode(resp)
-					flusher.Flush()
-				}
-				stopReason = fmt.Sprintf("stop_sequence:%s", matchedStop)
-				break
-			}
-		}
-
-		resp := Response{Content: text}
-		enc.Encode(resp)
-		flusher.Flush()
-
-		// Periodically clear MLX cache
-		if n%256 == 0 {
-			mlx.ClearCache()
-		}
-	}
-
-	// Clean up
-	mlx.ClearCache()
-
-	// Send final response with stats
-	evalDuration := time.Since(genStart)
-	resp = Response{
-		Done:               true,
-		StopReason:         fmt.Sprintf("%s:generated=%d", stopReason, generated),
-		PromptEvalCount:    prefillTokens,
-		PromptEvalDuration: int(promptEvalDuration.Nanoseconds()),
-		EvalCount:          generated,
-		EvalDuration:       int(evalDuration.Nanoseconds()),
-	}
-	enc.Encode(resp)
-	flusher.Flush()
-
-	return nil
-}

x/imagegen/models/gemma3/gemma3.go

@@ -1,614 +0,0 @@
-//go:build mlx
-
-package gemma3
-
-import (
-	"encoding/json"
-	"fmt"
-	"math"
-	"os"
-	"path/filepath"
-
-	"github.com/ollama/ollama/x/imagegen/cache"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/nn"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-	"github.com/ollama/ollama/x/imagegen/tokenizer"
-)
-
-// TextConfig holds configuration for the text model
-type TextConfig struct {
-	HiddenSize            int32   `json:"hidden_size"`
-	NumHiddenLayers       int32   `json:"num_hidden_layers"`
-	IntermediateSize      int32   `json:"intermediate_size"`
-	NumAttentionHeads     int32   `json:"num_attention_heads"`
-	NumKeyValueHeads      int32   `json:"num_key_value_heads"`
-	HeadDim               int32   `json:"head_dim"`
-	VocabSize             int32   `json:"vocab_size"`
-	RMSNormEps            float32 `json:"rms_norm_eps"`
-	RopeTheta             float32 `json:"rope_theta"`
-	RopeLocalBaseFreq     float32 `json:"rope_local_base_freq"`
-	MaxPositionEmbeddings int32   `json:"max_position_embeddings"`
-	SlidingWindow         int32   `json:"sliding_window"`
-	SlidingWindowPattern  int32   `json:"sliding_window_pattern"`
-
-	// Computed fields
-	Scale float32 `json:"-"`
-}
-
-// TextModel is the Gemma 3 text-only model
-type TextModel struct {
-	EmbedTokens *nn.Embedding   `weight:"model.embed_tokens"`
-	Layers      []*DecoderLayer `weight:"model.layers"`
-	Norm        *nn.RMSNorm     `weight:"model.norm"`
-	Output      *nn.Linear      `weight:"-"` // Tied to EmbedTokens, set manually
-
-	// Precomputed (1 + weight) for Gemma-style RMSNorm to avoid allocation per forward
-	NormScaled *mlx.Array `weight:"-"`
-
-	tok *tokenizer.Tokenizer
-	*TextConfig
-}
-
-// DecoderLayer is a single transformer block
-type DecoderLayer struct {
-	InputNorm    *nn.RMSNorm `weight:"input_layernorm"`
-	Attention    *Attention
-	PostAttnNorm *nn.RMSNorm `weight:"post_attention_layernorm"`
-	PreFFNorm    *nn.RMSNorm `weight:"pre_feedforward_layernorm"`
-	MLP          *MLP
-	PostFFNorm   *nn.RMSNorm `weight:"post_feedforward_layernorm"`
-
-	// Precomputed (1 + weight) for Gemma-style RMSNorm
-	InputNormScaled    *mlx.Array `weight:"-"`
-	PostAttnNormScaled *mlx.Array `weight:"-"`
-	PreFFNormScaled    *mlx.Array `weight:"-"`
-	PostFFNormScaled   *mlx.Array `weight:"-"`
-
-	// Whether this layer uses sliding window attention
-	IsSliding bool
-	LayerIdx  int32
-}
-
-// Attention implements Gemma 3 attention with Q/K normalization
-type Attention struct {
-	QProj *nn.Linear  `weight:"self_attn.q_proj"`
-	KProj *nn.Linear  `weight:"self_attn.k_proj"`
-	VProj *nn.Linear  `weight:"self_attn.v_proj"`
-	OProj *nn.Linear  `weight:"self_attn.o_proj"`
-	QNorm *nn.RMSNorm `weight:"self_attn.q_norm"`
-	KNorm *nn.RMSNorm `weight:"self_attn.k_norm"`
-
-	// Precomputed (1 + weight) for Gemma-style RMSNorm
-	QNormScaled *mlx.Array `weight:"-"`
-	KNormScaled *mlx.Array `weight:"-"`
-}
-
-// MLP is the feed-forward network with GELU activation
-type MLP struct {
-	GateProj *nn.Linear `weight:"mlp.gate_proj"`
-	UpProj   *nn.Linear `weight:"mlp.up_proj"`
-	DownProj *nn.Linear `weight:"mlp.down_proj"`
-}
-
-// LoadText loads the text-only Gemma 3 model
-func LoadText(modelPath string) (*TextModel, error) {
-	data, err := os.ReadFile(filepath.Join(modelPath, "config.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load config: %w", err)
-	}
-	var cfg TextConfig
-	if err := json.Unmarshal(data, &cfg); err != nil {
-		return nil, fmt.Errorf("parse config: %w", err)
-	}
-
-	// Compute scale
-	cfg.Scale = float32(1.0 / math.Sqrt(float64(cfg.HeadDim)))
-
-	// Set defaults if not specified
-	if cfg.RopeTheta == 0 {
-		cfg.RopeTheta = 1000000
-	}
-	if cfg.RopeLocalBaseFreq == 0 {
-		cfg.RopeLocalBaseFreq = 10000
-	}
-	if cfg.RMSNormEps == 0 {
-		cfg.RMSNormEps = 1e-6
-	}
-
-	weights, err := safetensors.LoadModelWeights(modelPath)
-	if err != nil {
-		return nil, fmt.Errorf("load weights: %w", err)
-	}
-
-	tok, err := tokenizer.Load(filepath.Join(modelPath, "tokenizer.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load tokenizer: %w", err)
-	}
-
-	m := &TextModel{
-		Layers:     make([]*DecoderLayer, cfg.NumHiddenLayers),
-		TextConfig: &cfg,
-		tok:        tok,
-	}
-
-	// Initialize layer metadata
-	for i := range m.Layers {
-		m.Layers[i] = &DecoderLayer{
-			LayerIdx:  int32(i),
-			IsSliding: isLayerSliding(int32(i), cfg.SlidingWindowPattern),
-		}
-	}
-
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return nil, err
-	}
-
-	// Tied embeddings for output
-	m.Output = nn.NewLinear(m.EmbedTokens.Weight, nil)
-
-	mlx.Eval(mlx.Collect(m)...)
-	weights.ReleaseAll()
-
-	// Precompute (1 + weight) for Gemma-style RMSNorm to avoid per-forward allocation
-	precomputeGemmaScaledWeights(m)
-
-	return m, nil
-}
-
-// precomputeGemmaScaledWeights computes (1 + weight) for all RMSNorm layers
-// This avoids creating temporary arrays on every forward pass
-func precomputeGemmaScaledWeights(m *TextModel) {
-	m.NormScaled = mlx.AddScalar(m.Norm.Weight, 1.0)
-
-	for _, layer := range m.Layers {
-		layer.InputNormScaled = mlx.AddScalar(layer.InputNorm.Weight, 1.0)
-		layer.PostAttnNormScaled = mlx.AddScalar(layer.PostAttnNorm.Weight, 1.0)
-		layer.PreFFNormScaled = mlx.AddScalar(layer.PreFFNorm.Weight, 1.0)
-		layer.PostFFNormScaled = mlx.AddScalar(layer.PostFFNorm.Weight, 1.0)
-
-		layer.Attention.QNormScaled = mlx.AddScalar(layer.Attention.QNorm.Weight, 1.0)
-		layer.Attention.KNormScaled = mlx.AddScalar(layer.Attention.KNorm.Weight, 1.0)
-	}
-
-	// Eval all the precomputed weights
-	var scaled []*mlx.Array
-	scaled = append(scaled, m.NormScaled)
-	for _, layer := range m.Layers {
-		scaled = append(scaled, layer.InputNormScaled, layer.PostAttnNormScaled,
-			layer.PreFFNormScaled, layer.PostFFNormScaled,
-			layer.Attention.QNormScaled, layer.Attention.KNormScaled)
-	}
-	mlx.Eval(scaled...)
-}
-
-// isLayerSliding determines if a layer uses sliding window attention
-// Pattern N means: layers 0 to N-1 sliding, N full, N+1 to 2N-1 sliding, 2N full, etc.
-func isLayerSliding(layerIdx, pattern int32) bool {
-	if pattern <= 0 {
-		return false // No sliding window
-	}
-	// Layer is full attention if (layerIdx + 1) % pattern == 0
-	return (layerIdx+1)%pattern != 0
-}
-
-// Forward runs the text model forward pass
-func (m *TextModel) Forward(tokens *mlx.Array, caches []cache.Cache) *mlx.Array {
-	B, L := tokens.Shape()[0], tokens.Shape()[1]
-
-	// Get embeddings and scale by sqrt(hidden_size)
-	h := m.EmbedTokens.Forward(tokens)
-	h = mlx.MulScalar(h, float32(math.Sqrt(float64(m.HiddenSize))))
-
-	for i, layer := range m.Layers {
-		h = layer.Forward(h, caches[i], B, L, m.TextConfig)
-	}
-
-	// Final norm and output projection
-	return m.Output.Forward(mlx.RMSNorm(h, m.NormScaled, m.RMSNormEps))
-}
-
-// Forward runs a decoder layer
-func (l *DecoderLayer) Forward(x *mlx.Array, c cache.Cache, B, L int32, cfg *TextConfig) *mlx.Array {
-	// Pre-attention norm (use precomputed scaled weight)
-	normed := mlx.RMSNorm(x, l.InputNormScaled, cfg.RMSNormEps)
-
-	// Attention
-	attnOut := l.Attention.Forward(normed, c, B, L, l.IsSliding, cfg)
-
-	// Post-attention norm and residual
-	attnOut = mlx.RMSNorm(attnOut, l.PostAttnNormScaled, cfg.RMSNormEps)
-	h := mlx.Add(x, attnOut)
-
-	// Pre-FFN norm
-	normed = mlx.RMSNorm(h, l.PreFFNormScaled, cfg.RMSNormEps)
-
-	// MLP
-	mlpOut := l.MLP.Forward(normed)
-
-	// Post-FFN norm and residual
-	mlpOut = mlx.RMSNorm(mlpOut, l.PostFFNormScaled, cfg.RMSNormEps)
-	return mlx.Add(h, mlpOut)
-}
-
-// Forward runs attention with Q/K normalization
-func (a *Attention) Forward(x *mlx.Array, c cache.Cache, B, L int32, isSliding bool, cfg *TextConfig) *mlx.Array {
-	q := a.QProj.Forward(x)
-	k := a.KProj.Forward(x)
-	v := a.VProj.Forward(x)
-
-	// Reshape to [B, num_heads, L, head_dim]
-	q = mlx.AsStrided(q, []int32{B, cfg.NumAttentionHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumAttentionHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumAttentionHeads * cfg.HeadDim), 1}, 0)
-	k = mlx.AsStrided(k, []int32{B, cfg.NumKeyValueHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumKeyValueHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumKeyValueHeads * cfg.HeadDim), 1}, 0)
-	v = mlx.AsStrided(v, []int32{B, cfg.NumKeyValueHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumKeyValueHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumKeyValueHeads * cfg.HeadDim), 1}, 0)
-
-	// Q/K normalization after reshaping (use precomputed scaled weight)
-	q = mlx.RMSNorm(q, a.QNormScaled, cfg.RMSNormEps)
-	k = mlx.RMSNorm(k, a.KNormScaled, cfg.RMSNormEps)
-
-	// Apply RoPE with appropriate theta
-	ropeTheta := cfg.RopeTheta
-	if isSliding {
-		ropeTheta = cfg.RopeLocalBaseFreq
-	}
-	q = mlx.RoPE(q, int(cfg.HeadDim), false, ropeTheta, 1.0, c.Offset())
-	k = mlx.RoPE(k, int(cfg.HeadDim), false, ropeTheta, 1.0, c.Offset())
-
-	// Update cache
-	k, v = c.Update(k, v, int(L))
-
-	// Repeat K/V for GQA if needed
-	repeatFactor := cfg.NumAttentionHeads / cfg.NumKeyValueHeads
-	if repeatFactor > 1 {
-		k = nn.RepeatKV(k, repeatFactor)
-		v = nn.RepeatKV(v, repeatFactor)
-	}
-
-	// Attention
-	out := mlx.ScaledDotProductAttention(q, k, v, cfg.Scale, L > 1)
-	out = mlx.Reshape(mlx.Transpose(out, 0, 2, 1, 3), B, L, cfg.NumAttentionHeads*cfg.HeadDim)
-	return a.OProj.Forward(out)
-}
-
-// compiledGeluApprox is a singleton compiled GELU function shared across all layers
-var compiledGeluApprox *mlx.CompiledFunc
-
-// getCompiledGeluApprox returns the compiled GELU function, creating it once if needed
-func getCompiledGeluApprox() *mlx.CompiledFunc {
-	if compiledGeluApprox == nil {
-		compiledGeluApprox = mlx.CompileShapeless(func(inputs []*mlx.Array) []*mlx.Array {
-			return []*mlx.Array{geluApproxImpl(inputs[0])}
-		}, true)
-	}
-	return compiledGeluApprox
-}
-
-// Forward runs the MLP with GELU approximation (tanh variant)
-func (m *MLP) Forward(x *mlx.Array) *mlx.Array {
-	gate := getCompiledGeluApprox().Call(m.GateProj.Forward(x))[0]
-	return m.DownProj.Forward(mlx.Mul(gate, m.UpProj.Forward(x)))
-}
-
-// geluApproxImpl computes GELU using the tanh approximation (gelu_pytorch_tanh):
-// 0.5 * x * (1 + tanh(sqrt(2/pi) * (x + 0.044715 * x^3)))
-func geluApproxImpl(x *mlx.Array) *mlx.Array {
-	// Constants
-	const sqrt2OverPi = 0.7978845608028654 // sqrt(2/pi)
-	const coeff = 0.044715
-
-	// x^3
-	x3 := mlx.Mul(mlx.Mul(x, x), x)
-	// x + 0.044715 * x^3
-	inner := mlx.Add(x, mlx.MulScalar(x3, coeff))
-	// sqrt(2/pi) * (x + 0.044715 * x^3)
-	scaled := mlx.MulScalar(inner, sqrt2OverPi)
-	// tanh(...)
-	tanh := mlx.Tanh(scaled)
-	// 1 + tanh(...)
-	onePlusTanh := mlx.AddScalar(tanh, 1.0)
-	// 0.5 * x * (1 + tanh(...))
-	return mlx.Mul(mlx.MulScalar(x, 0.5), onePlusTanh)
-}
-
-// gemmaRMSNorm applies Gemma-style RMS normalization: x * rsqrt(mean(x^2) + eps) * (1 + weight)
-// Uses mlx.RMSNorm fast kernel with pre-computed (1 + weight)
-func gemmaRMSNorm(x, weight *mlx.Array, eps float32) *mlx.Array {
-	// Gemma uses (1 + weight) instead of weight
-	scaledWeight := mlx.AddScalar(weight, 1.0)
-	return mlx.RMSNorm(x, scaledWeight, eps)
-}
-
-// Interface methods
-func (m *TextModel) NumLayers() int          { return len(m.Layers) }
-func (m *TextModel) MaxContextLength() int32 { return m.MaxPositionEmbeddings }
-func (m *TextModel) VocabSize() int32        { return m.TextConfig.VocabSize }
-
-// Tokenizer returns the tokenizer wrapped to add BOS and apply chat template
-func (m *TextModel) Tokenizer() *tokenizer.Tokenizer {
-	return m.tok
-}
-
-// FormatPrompt applies the Gemma 3 chat template to a prompt
-func (m *TextModel) FormatPrompt(prompt string) string {
-	// Gemma 3 chat format: <start_of_turn>user\n{prompt}<end_of_turn>\n<start_of_turn>model\n
-	return fmt.Sprintf("<start_of_turn>user\n%s<end_of_turn>\n<start_of_turn>model\n", prompt)
-}
-
-func (m *TextModel) NewCache(maxSeqLen int32) []cache.Cache {
-	caches := make([]cache.Cache, len(m.Layers))
-	for i := range caches {
-		if m.Layers[i].IsSliding {
-			// Use rotating cache for sliding window layers
-			caches[i] = cache.NewRotatingKVCache(int(m.SlidingWindow))
-		} else {
-			// Use regular cache for global attention layers
-			caches[i] = cache.NewKVCache()
-		}
-	}
-	return caches
-}
-
-// Config holds config for the full multimodal model
-type Config struct {
-	TextConfig   TextConfig   `json:"text_config"`
-	VisionConfig VisionConfig `json:"vision_config"`
-
-	// Image token config (from config.json)
-	BOITokenIndex     int32 `json:"boi_token_index"`     // <start_of_image> = 255999
-	EOITokenIndex     int32 `json:"eoi_token_index"`     // <end_of_image> = 256000
-	ImageTokenIndex   int32 `json:"image_token_index"`   // <image_soft_token> = 262144
-	MMTokensPerImage  int32 `json:"mm_tokens_per_image"` // 256
-}
-
-// Model is the full Gemma 3 multimodal model
-type Model struct {
-	VisionTower *VisionTower         `weight:"vision_tower"`
-	Projector   *MultiModalProjector `weight:"multi_modal_projector"`
-	TextModel   *TextModel           `weight:"language_model"`
-	Config      *Config
-	tok         *tokenizer.Tokenizer
-}
-
-// Load loads the full multimodal Gemma 3 model
-func Load(modelPath string) (*Model, error) {
-	data, err := os.ReadFile(filepath.Join(modelPath, "config.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load config: %w", err)
-	}
-
-	var cfg Config
-	if err := json.Unmarshal(data, &cfg); err != nil {
-		return nil, fmt.Errorf("parse config: %w", err)
-	}
-
-	// Set defaults for text config (multimodal config often has incomplete text_config)
-	// These defaults match transformers.Gemma3TextConfig defaults
-	tc := &cfg.TextConfig
-	if tc.HeadDim == 0 {
-		tc.HeadDim = 256 // Gemma 3 uses head_dim=256
-	}
-	if tc.NumAttentionHeads == 0 {
-		// Gemma 3 4B uses 8 attention heads (cannot infer from hidden_size/head_dim)
-		tc.NumAttentionHeads = 8
-	}
-	if tc.NumKeyValueHeads == 0 {
-		// Gemma 3 4B uses 4 KV heads (GQA with 2:1 ratio)
-		tc.NumKeyValueHeads = 4
-	}
-	if tc.VocabSize == 0 {
-		tc.VocabSize = 262208 // Gemma 3 vocab size (not 262144!)
-	}
-	if tc.RopeTheta == 0 {
-		tc.RopeTheta = 1000000
-	}
-	if tc.RopeLocalBaseFreq == 0 {
-		tc.RopeLocalBaseFreq = 10000
-	}
-	if tc.RMSNormEps == 0 {
-		tc.RMSNormEps = 1e-6
-	}
-	if tc.SlidingWindowPattern == 0 {
-		tc.SlidingWindowPattern = 6
-	}
-	if tc.MaxPositionEmbeddings == 0 {
-		tc.MaxPositionEmbeddings = 131072 // Gemma 3 4B default
-	}
-
-	// Compute text model scale
-	tc.Scale = float32(1.0 / math.Sqrt(float64(tc.HeadDim)))
-
-	// Set defaults for image token config
-	if cfg.BOITokenIndex == 0 {
-		cfg.BOITokenIndex = 255999 // <start_of_image>
-	}
-	if cfg.EOITokenIndex == 0 {
-		cfg.EOITokenIndex = 256000 // <end_of_image>
-	}
-	if cfg.ImageTokenIndex == 0 {
-		cfg.ImageTokenIndex = 262144 // <image_soft_token>
-	}
-	if cfg.MMTokensPerImage == 0 {
-		cfg.MMTokensPerImage = 256
-	}
-
-	weights, err := safetensors.LoadModelWeights(modelPath)
-	if err != nil {
-		return nil, fmt.Errorf("load weights: %w", err)
-	}
-
-	tok, err := tokenizer.Load(filepath.Join(modelPath, "tokenizer.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load tokenizer: %w", err)
-	}
-
-	m := &Model{
-		VisionTower: &VisionTower{
-			Embeddings: &VisionEmbeddings{},
-			Encoder:    make([]*VisionEncoderLayer, cfg.VisionConfig.NumHiddenLayers),
-			Config:     &cfg.VisionConfig,
-		},
-		Projector: &MultiModalProjector{},
-		TextModel: &TextModel{
-			Layers:     make([]*DecoderLayer, cfg.TextConfig.NumHiddenLayers),
-			TextConfig: &cfg.TextConfig,
-		},
-		Config: &cfg,
-		tok:    tok,
-	}
-
-	// Initialize text layer metadata
-	for i := range m.TextModel.Layers {
-		m.TextModel.Layers[i] = &DecoderLayer{
-			LayerIdx:  int32(i),
-			IsSliding: isLayerSliding(int32(i), cfg.TextConfig.SlidingWindowPattern),
-		}
-	}
-
-	// Initialize vision encoder layers
-	for i := range m.VisionTower.Encoder {
-		m.VisionTower.Encoder[i] = &VisionEncoderLayer{}
-	}
-
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return nil, err
-	}
-
-	// Tied embeddings for text output
-	m.TextModel.Output = nn.NewLinear(m.TextModel.EmbedTokens.Weight, nil)
-	m.TextModel.tok = tok
-
-	mlx.Eval(mlx.Collect(m)...)
-	weights.ReleaseAll()
-
-	// Precompute (1 + weight) for Gemma-style RMSNorm
-	precomputeGemmaScaledWeights(m.TextModel)
-
-	// Precompute projector's scaled weight
-	m.Projector.SoftEmbNormScaled = mlx.AddScalar(m.Projector.SoftEmbNorm.Weight, 1.0)
-	mlx.Eval(m.Projector.SoftEmbNormScaled)
-
-	return m, nil
-}
-
-// Forward runs the text-only forward pass
-func (m *Model) Forward(tokens *mlx.Array, caches []cache.Cache) *mlx.Array {
-	return m.TextModel.Forward(tokens, caches)
-}
-
-// ForwardWithImage runs the multimodal forward pass
-// tokens: [B, L] input token IDs (with image placeholder tokens)
-// image: [B, H, W, C] preprocessed image tensor
-func (m *Model) ForwardWithImage(tokens *mlx.Array, image *mlx.Array, caches []cache.Cache) *mlx.Array {
-	B, L := tokens.Shape()[0], tokens.Shape()[1]
-	cfg := m.Config.TextConfig
-
-	// Find image token position FIRST before any eval that might free tokens
-	imageStartPos := int32(-1)
-	if image != nil && B == 1 {
-		tokenData := tokens.DataInt32() // This evals tokens
-		for i, t := range tokenData {
-			if t == m.Config.ImageTokenIndex {
-				imageStartPos = int32(i)
-				break
-			}
-		}
-	}
-
-	// Get text embeddings and scale
-	h := m.TextModel.EmbedTokens.Forward(tokens)
-	h = mlx.MulScalar(h, float32(math.Sqrt(float64(cfg.HiddenSize))))
-
-	// Process image if provided
-	if image != nil && imageStartPos >= 0 {
-		// Vision tower: [B, H, W, C] -> [B, num_patches, vision_hidden]
-		visionFeatures := m.VisionTower.Forward(image)
-
-		// Project to text space: [B, num_patches, vision_hidden] -> [B, 256, text_hidden]
-		imageEmbeds := m.Projector.Forward(visionFeatures, cfg.RMSNormEps)
-
-		// Eval h and imageEmbeds together so neither gets freed
-		mlx.Eval(h, imageEmbeds)
-
-		// Cast imageEmbeds to match text embeddings dtype (bf16)
-		if imageEmbeds.Dtype() != h.Dtype() {
-			imageEmbeds = mlx.AsType(imageEmbeds, h.Dtype())
-			mlx.Eval(imageEmbeds)
-		}
-
-		// Insert image embeddings at the known position
-		h = m.insertImageEmbeddingsAt(h, imageEmbeds, imageStartPos)
-	}
-
-	// Run through text model layers
-	for i, layer := range m.TextModel.Layers {
-		h = layer.Forward(h, caches[i], B, L, m.TextModel.TextConfig)
-	}
-
-	// Final norm and output projection
-	return m.TextModel.Output.Forward(mlx.RMSNorm(h, m.TextModel.NormScaled, cfg.RMSNormEps))
-}
-
-// insertImageEmbeddingsAt replaces image placeholder tokens with actual image embeddings
-// at a known position (to avoid re-scanning tokens after eval)
-// textEmbeds: [B, L, hidden_size] text embeddings
-// imageEmbeds: [B, 256, hidden_size] image embeddings from projector
-// startPos: starting position of image tokens in the sequence
-func (m *Model) insertImageEmbeddingsAt(textEmbeds, imageEmbeds *mlx.Array, startPos int32) *mlx.Array {
-	numImageTokens := imageEmbeds.Shape()[1]
-	L := textEmbeds.Shape()[1]
-
-	// Split text embeddings: [0:startPos] + imageEmbeds + [startPos+256:L]
-	afterStart := startPos + numImageTokens
-
-	// Slice before image tokens: textEmbeds[:, 0:startPos, :]
-	before := mlx.SliceAxis(textEmbeds, 1, 0, startPos)
-
-	// Slice after image tokens: textEmbeds[:, startPos+256:L, :]
-	after := mlx.SliceAxis(textEmbeds, 1, afterStart, L)
-
-	// Concatenate: before + imageEmbeds + after along axis 1
-	return mlx.Concatenate([]*mlx.Array{before, imageEmbeds, after}, 1)
-}
-
-// Interface methods for Model
-func (m *Model) NumLayers() int                         { return len(m.TextModel.Layers) }
-func (m *Model) MaxContextLength() int32                { return m.Config.TextConfig.MaxPositionEmbeddings }
-func (m *Model) VocabSize() int32                       { return m.Config.TextConfig.VocabSize }
-func (m *Model) Tokenizer() *tokenizer.Tokenizer     { return m.tok }
-func (m *Model) NewCache(maxSeqLen int32) []cache.Cache { return m.TextModel.NewCache(maxSeqLen) }
-func (m *Model) ImageSize() int32                       { return m.Config.VisionConfig.ImageSize }
-
-// FormatPrompt applies the Gemma 3 multimodal chat template
-func (m *Model) FormatPrompt(prompt string) string {
-	return fmt.Sprintf("<start_of_turn>user\n%s<end_of_turn>\n<start_of_turn>model\n", prompt)
-}
-
-// FormatPromptWithImage applies the Gemma 3 multimodal chat template with image
-func (m *Model) FormatPromptWithImage(prompt string) string {
-	return fmt.Sprintf("<start_of_turn>user\n<start_of_image>%s<end_of_turn>\n<start_of_turn>model\n", prompt)
-}
-
-// ExpandImageTokens expands <start_of_image> into 256 image placeholder tokens
-// Input tokens containing boi_token (255999) are expanded to:
-// boi_token + 256 * image_token + eoi_token
-func (m *Model) ExpandImageTokens(tokens []int32) []int32 {
-	result := make([]int32, 0, len(tokens)+int(m.Config.MMTokensPerImage)+1)
-
-	for _, t := range tokens {
-		if t == m.Config.BOITokenIndex {
-			// Expand: boi + 256 * image_token + eoi
-			result = append(result, m.Config.BOITokenIndex)
-			for i := int32(0); i < m.Config.MMTokensPerImage; i++ {
-				result = append(result, m.Config.ImageTokenIndex)
-			}
-			result = append(result, m.Config.EOITokenIndex)
-		} else {
-			result = append(result, t)
-		}
-	}
-
-	return result
-}

x/imagegen/models/gemma3/projector.go

@@ -1,50 +0,0 @@
-//go:build mlx
-
-package gemma3
-
-import (
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/nn"
-)
-
-// MultiModalProjector projects vision features to text embedding space
-type MultiModalProjector struct {
-	// mm_input_projection_weight: [vision_hidden, text_hidden]
-	InputProjection *mlx.Array  `weight:"mm_input_projection_weight"`
-	SoftEmbNorm     *nn.RMSNorm `weight:"mm_soft_emb_norm"`
-
-	// Precomputed (1 + weight) for Gemma-style RMSNorm
-	SoftEmbNormScaled *mlx.Array `weight:"-"`
-}
-
-// Forward projects vision features to text space
-// Input: [B, num_patches, vision_hidden] (e.g., [1, 4096, 1152])
-// Output: [B, num_image_tokens, text_hidden] (e.g., [1, 256, 2560])
-func (p *MultiModalProjector) Forward(visionFeatures *mlx.Array, eps float32) *mlx.Array {
-	// Average pool 4x4: [B, 4096, 1152] -> [B, 256, 1152]
-	// 4096 patches = 64x64 grid, pool to 16x16 = 256 tokens
-	B := visionFeatures.Shape()[0]
-	visionHidden := visionFeatures.Shape()[2]
-
-	// Reshape to [B, 64, 64, hidden]
-	gridSize := int32(64) // sqrt(4096)
-	pooledSize := int32(16) // 64/4
-	h := mlx.Reshape(visionFeatures, B, gridSize, gridSize, visionHidden)
-
-	// Reshape to [B, 16, 4, 16, 4, hidden] for 4x4 pooling
-	h = mlx.Reshape(h, B, pooledSize, 4, pooledSize, 4, visionHidden)
-
-	// Average over pooling dimensions (axes 2 and 4)
-	h = mlx.Mean(h, 4, false)
-	h = mlx.Mean(h, 2, false)
-
-	// h is now [B, 16, 16, hidden], reshape to [B, 256, hidden]
-	numTokens := pooledSize * pooledSize
-	h = mlx.Reshape(h, B, numTokens, visionHidden)
-
-	// Apply Gemma-style RMS norm (use precomputed 1 + weight)
-	h = mlx.RMSNorm(h, p.SoftEmbNormScaled, eps)
-
-	// Project to text space: [B, 256, vision_hidden] @ [vision_hidden, text_hidden]
-	return mlx.Linear(h, p.InputProjection)
-}

x/imagegen/models/gemma3/vision.go

@@ -1,138 +0,0 @@
-//go:build mlx
-
-package gemma3
-
-import (
-	"math"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/nn"
-)
-
-// VisionConfig holds configuration for the SigLIP vision tower
-type VisionConfig struct {
-	HiddenSize        int32 `json:"hidden_size"`
-	ImageSize         int32 `json:"image_size"`
-	IntermediateSize  int32 `json:"intermediate_size"`
-	NumAttentionHeads int32 `json:"num_attention_heads"`
-	NumHiddenLayers   int32 `json:"num_hidden_layers"`
-	PatchSize         int32 `json:"patch_size"`
-}
-
-// VisionTower is the SigLIP vision encoder
-type VisionTower struct {
-	Embeddings    *VisionEmbeddings     `weight:"vision_model.embeddings"`
-	Encoder       []*VisionEncoderLayer `weight:"vision_model.encoder.layers"`
-	PostLayerNorm *nn.LayerNorm         `weight:"vision_model.post_layernorm"`
-	Config        *VisionConfig
-}
-
-// VisionEmbeddings handles patch and position embeddings
-type VisionEmbeddings struct {
-	// PatchWeight: [O, C, kH, kW] from PyTorch, transposed to [O, kH, kW, C] for MLX
-	PatchWeight *mlx.Array    `weight:"patch_embedding.weight"`
-	PatchBias   *mlx.Array    `weight:"patch_embedding.bias"`
-	PosEmbed    *nn.Embedding `weight:"position_embedding"`
-}
-
-// VisionEncoderLayer is a single transformer encoder layer
-type VisionEncoderLayer struct {
-	LayerNorm1 *nn.LayerNorm     `weight:"layer_norm1"`
-	Attention  *VisionAttention  `weight:"self_attn"`
-	LayerNorm2 *nn.LayerNorm     `weight:"layer_norm2"`
-	MLP        *VisionMLP        `weight:"mlp"`
-}
-
-// VisionAttention implements multi-head self-attention
-type VisionAttention struct {
-	QProj   *nn.Linear `weight:"q_proj"`
-	KProj   *nn.Linear `weight:"k_proj"`
-	VProj   *nn.Linear `weight:"v_proj"`
-	OutProj *nn.Linear `weight:"out_proj"`
-}
-
-// VisionMLP is the feed-forward network
-type VisionMLP struct {
-	FC1 *nn.Linear `weight:"fc1"`
-	FC2 *nn.Linear `weight:"fc2"`
-}
-
-// Forward runs the vision tower on preprocessed images
-// Input: [B, H, W, C] normalized image tensor (NHWC layout for MLX)
-// Output: [B, num_patches, hidden_size]
-func (v *VisionTower) Forward(x *mlx.Array) *mlx.Array {
-	// Patch embedding conv: input [B, H, W, C], weight [O, kH, kW, C] -> [B, grid, grid, O]
-	// Weight comes as [O, C, kH, kW] from PyTorch, transpose to [O, kH, kW, C]
-	weight := mlx.Transpose(v.Embeddings.PatchWeight, 0, 2, 3, 1)
-	h := mlx.Conv2d(x, weight, v.Config.PatchSize, 0) // stride=patch_size, no padding
-
-	// Add bias: [O] -> [1, 1, 1, O] for broadcasting
-	bias := mlx.Reshape(v.Embeddings.PatchBias, 1, 1, 1, v.Embeddings.PatchBias.Shape()[0])
-	h = mlx.Add(h, bias)
-
-	// h is [B, grid, grid, hidden], flatten to [B, num_patches, hidden]
-	B := h.Shape()[0]
-	gridH, gridW := h.Shape()[1], h.Shape()[2]
-	hidden := h.Shape()[3]
-	numPatches := gridH * gridW
-	h = mlx.Reshape(h, B, numPatches, hidden)
-
-	// Add position embeddings
-	posIds := mlx.ArangeInt(0, numPatches, 1, mlx.DtypeInt32)
-	posEmbed := v.Embeddings.PosEmbed.Forward(posIds)
-	h = mlx.Add(h, posEmbed)
-
-	// Encoder layers
-	headDim := float32(v.Config.HiddenSize / v.Config.NumAttentionHeads)
-	scale := float32(1.0 / math.Sqrt(float64(headDim)))
-	for _, layer := range v.Encoder {
-		h = layer.Forward(h, v.Config, scale)
-	}
-
-	// Final layer norm
-	h = v.PostLayerNorm.Forward(h)
-
-	return h
-}
-
-// Forward runs a vision encoder layer
-func (l *VisionEncoderLayer) Forward(x *mlx.Array, cfg *VisionConfig, scale float32) *mlx.Array {
-	// Pre-norm attention
-	h := l.LayerNorm1.Forward(x)
-	h = l.Attention.Forward(h, cfg, scale)
-	x = mlx.Add(x, h)
-
-	// Pre-norm MLP
-	h = l.LayerNorm2.Forward(x)
-	h = l.MLP.Forward(h)
-	return mlx.Add(x, h)
-}
-
-// Forward runs multi-head self-attention
-func (a *VisionAttention) Forward(x *mlx.Array, cfg *VisionConfig, scale float32) *mlx.Array {
-	B, L := x.Shape()[0], x.Shape()[1]
-	headDim := cfg.HiddenSize / cfg.NumAttentionHeads
-
-	q := a.QProj.Forward(x)
-	k := a.KProj.Forward(x)
-	v := a.VProj.Forward(x)
-
-	// Reshape to [B, num_heads, L, head_dim]
-	q = mlx.Transpose(mlx.Reshape(q, B, L, cfg.NumAttentionHeads, headDim), 0, 2, 1, 3)
-	k = mlx.Transpose(mlx.Reshape(k, B, L, cfg.NumAttentionHeads, headDim), 0, 2, 1, 3)
-	v = mlx.Transpose(mlx.Reshape(v, B, L, cfg.NumAttentionHeads, headDim), 0, 2, 1, 3)
-
-	// Scaled dot-product attention (no causal mask for vision)
-	out := mlx.ScaledDotProductAttention(q, k, v, scale, false)
-
-	// Reshape back: [B, num_heads, L, head_dim] -> [B, L, hidden]
-	out = mlx.Reshape(mlx.Transpose(out, 0, 2, 1, 3), B, L, cfg.HiddenSize)
-
-	return a.OutProj.Forward(out)
-}
-
-// Forward runs the MLP with GELU activation
-func (m *VisionMLP) Forward(x *mlx.Array) *mlx.Array {
-	h := mlx.GELU(m.FC1.Forward(x))
-	return m.FC2.Forward(h)
-}

x/imagegen/models/glm4_moe_lite/glm4_moe_lite.go

@@ -1,840 +0,0 @@
-//go:build mlx
-
-// Package glm4_moe_lite provides the GLM4-MoE-Lite implementation for MLX.
-// This model uses Multi-head Latent Attention (MLA) and Mixture of Experts (MoE).
-package glm4_moe_lite
-
-import (
-	"encoding/json"
-	"fmt"
-	"math"
-
-	"github.com/ollama/ollama/x/imagegen/cache"
-	"github.com/ollama/ollama/x/imagegen/manifest"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/nn"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-	"github.com/ollama/ollama/x/imagegen/tokenizer"
-)
-
-// RopeScaling holds RoPE scaling configuration
-type RopeScaling struct {
-	Factor       float32 `json:"factor"`
-	MscaleAllDim float32 `json:"mscale_all_dim"`
-}
-
-// Config holds GLM4-MoE-Lite model configuration
-type Config struct {
-	HiddenSize            int32   `json:"hidden_size"`
-	NumHiddenLayers       int32   `json:"num_hidden_layers"`
-	IntermediateSize      int32   `json:"intermediate_size"`
-	MoEIntermediateSize   int32   `json:"moe_intermediate_size"`
-	NumAttentionHeads     int32   `json:"num_attention_heads"`
-	NumKeyValueHeads      int32   `json:"num_key_value_heads"`
-	VocabSize             int32   `json:"vocab_size"`
-	RMSNormEps            float32 `json:"rms_norm_eps"`
-	RopeTheta             float32 `json:"rope_theta"`
-	MaxPositionEmbeddings int32   `json:"max_position_embeddings"`
-	AttentionBias         bool    `json:"attention_bias"`
-
-	// MLA (Multi-head Latent Attention) parameters
-	QLoraRank     int32 `json:"q_lora_rank"`
-	KVLoraRank    int32 `json:"kv_lora_rank"`
-	QKRopeHeadDim int32 `json:"qk_rope_head_dim"`
-	QKNopeHeadDim int32 `json:"qk_nope_head_dim"`
-	VHeadDim      int32 `json:"v_head_dim"`
-
-	// MoE parameters
-	NRoutedExperts      int32   `json:"n_routed_experts"`
-	NSharedExperts      int32   `json:"n_shared_experts"`
-	NumExpertsPerTok    int32   `json:"num_experts_per_tok"`
-	RoutedScalingFactor float32 `json:"routed_scaling_factor"`
-	NormTopKProb        bool    `json:"norm_topk_prob"`
-	FirstKDenseReplace  int32   `json:"first_k_dense_replace"`
-	NGroup              int32   `json:"n_group"`
-	TopKGroup           int32   `json:"topk_group"`
-
-	// RoPE scaling
-	RopeScaling *RopeScaling `json:"rope_scaling"`
-
-	// Quantization parameters (set during load based on model quantization)
-	QuantGroupSize int    `json:"-"` // Group size for quantization (default 64)
-	QuantBits      int    `json:"-"` // Bits per weight (4 or 8)
-	QuantMode      string `json:"-"` // Quantization mode ("affine", etc.)
-
-	// Computed fields
-	QHeadDim int32   `json:"-"` // qk_nope_head_dim + qk_rope_head_dim
-	Scale    float32 `json:"-"` // 1/sqrt(QHeadDim) with mscale adjustment
-}
-
-// MLAAttention implements Multi-head Latent Attention with absorption.
-// This uses absorbed MLA which operates in latent space for reduced KV cache.
-type MLAAttention struct {
-	// Low-rank query projections
-	QAProj      nn.LinearLayer `weight:"self_attn.q_a_proj"`
-	QALayerNorm *nn.RMSNorm    `weight:"self_attn.q_a_layernorm"`
-	QBProj      nn.LinearLayer `weight:"self_attn.q_b_proj"`
-
-	// Low-rank KV projections (with shared rope component)
-	KVAProjWithMQA nn.LinearLayer `weight:"self_attn.kv_a_proj_with_mqa"`
-	KVALayerNorm   *nn.RMSNorm    `weight:"self_attn.kv_a_layernorm"`
-
-	// Absorbed MLA projections (derived from kv_b_proj)
-	// EmbedQ: projects q_nope to latent space [num_heads, kv_lora_rank, qk_nope_head_dim]
-	// UnembedOut: projects attention output from latent space [num_heads, v_head_dim, kv_lora_rank]
-	EmbedQ     *nn.MultiLinear `weight:"-"`
-	UnembedOut *nn.MultiLinear `weight:"-"`
-
-	// Output projection
-	OProj nn.LinearLayer `weight:"self_attn.o_proj"`
-}
-
-// Forward computes absorbed MLA attention output.
-// This operates in latent space for reduced KV cache memory.
-func (a *MLAAttention) Forward(x *mlx.Array, c cache.Cache, B, L int32, cfg *Config) *mlx.Array {
-	// Query path: q_a_proj -> layernorm -> q_b_proj
-	q := a.QAProj.Forward(x)
-	q = a.QALayerNorm.Forward(q, cfg.RMSNormEps)
-	q = a.QBProj.Forward(q)
-
-	// Reshape Q: [B, L, num_heads * q_head_dim] -> [B, num_heads, L, q_head_dim]
-	q = mlx.Reshape(q, B, L, cfg.NumAttentionHeads, cfg.QHeadDim)
-	q = mlx.Transpose(q, 0, 2, 1, 3)
-
-	// Split Q into nope and rope parts
-	qNope := mlx.Slice(q, []int32{0, 0, 0, 0}, []int32{B, cfg.NumAttentionHeads, L, cfg.QKNopeHeadDim})
-	qPE := mlx.Slice(q, []int32{0, 0, 0, cfg.QKNopeHeadDim}, []int32{B, cfg.NumAttentionHeads, L, cfg.QHeadDim})
-
-	// KV path: get compressed KV and k_pe
-	compressedKV := a.KVAProjWithMQA.Forward(x)
-
-	// Split into compressed_kv and k_pe (shared rope component)
-	kvCompressed := mlx.Slice(compressedKV, []int32{0, 0, 0}, []int32{B, L, cfg.KVLoraRank})
-	kPE := mlx.Slice(compressedKV, []int32{0, 0, cfg.KVLoraRank}, []int32{B, L, cfg.KVLoraRank + cfg.QKRopeHeadDim})
-
-	// k_pe is shared across heads (MQA-style): [B, L, rope_dim] -> [B, 1, L, rope_dim]
-	kPE = mlx.Reshape(kPE, B, L, 1, cfg.QKRopeHeadDim)
-	kPE = mlx.Transpose(kPE, 0, 2, 1, 3)
-
-	// Apply layernorm to get kv latent representation
-	kvLatent := a.KVALayerNorm.Forward(kvCompressed, cfg.RMSNormEps)
-	// kvLatent: [B, L, kv_lora_rank] -> [B, 1, L, kv_lora_rank] for broadcasting
-	kvLatent = mlx.ExpandDims(kvLatent, 1)
-
-	// Apply RoPE to the rope parts
-	offset := 0
-	if c != nil {
-		offset = c.Offset()
-	}
-	qPE = mlx.RoPE(qPE, int(cfg.QKRopeHeadDim), true, cfg.RopeTheta, 1.0, offset)
-	kPE = mlx.RoPE(kPE, int(cfg.QKRopeHeadDim), true, cfg.RopeTheta, 1.0, offset)
-
-	// ABSORBED MLA: project q_nope to latent space
-	// qNope: [B, num_heads, L, qk_nope_head_dim]
-	// EmbedQ: [num_heads, kv_lora_rank, qk_nope_head_dim]
-	// Result: [B, num_heads, L, kv_lora_rank]
-	qLatent := a.EmbedQ.Forward(qNope)
-
-	// Keys = concat(kvLatent, kPE)
-	// kvLatent: [B, 1, L, kv_lora_rank]
-	// kPE: [B, 1, L, qk_rope_head_dim]
-	// keys: [B, 1, L, kv_lora_rank + qk_rope_head_dim]
-	keys := mlx.Concatenate([]*mlx.Array{kvLatent, kPE}, 3)
-
-	// Cache the smaller latent representation
-	// We cache keys (latent + rope) and use empty values since values are derived from keys
-	cachedL := L
-	if c != nil {
-		// Create placeholder values with 0 dims for cache (we don't actually use cached values)
-		placeholderValues := mlx.Zeros([]int32{B, 1, L, 0}, mlx.DtypeFloat32)
-		keys, _ = c.Update(keys, placeholderValues, int(L))
-		cachedL = int32(keys.Shape()[2])
-	}
-
-	// Values are the first kv_lora_rank dims of keys (slice off rope part)
-	values := mlx.Slice(keys, []int32{0, 0, 0, 0}, []int32{B, 1, cachedL, cfg.KVLoraRank})
-
-	// Queries = concat(qLatent, qPE)
-	// qLatent: [B, num_heads, L, kv_lora_rank]
-	// qPE: [B, num_heads, L, qk_rope_head_dim]
-	// queries: [B, num_heads, L, kv_lora_rank + qk_rope_head_dim]
-	queries := mlx.Concatenate([]*mlx.Array{qLatent, qPE}, 3)
-
-	// Attention in latent space
-	// queries: [B, num_heads, L, kv_lora_rank + rope_dim]
-	// keys: [B, 1, cachedL, kv_lora_rank + rope_dim]
-	// values: [B, 1, cachedL, kv_lora_rank]
-	out := mlx.ScaledDotProductAttention(queries, keys, values, cfg.Scale, L > 1)
-
-	// ABSORBED MLA: unembed from latent space
-	// out: [B, num_heads, L, kv_lora_rank]
-	// UnembedOut: [num_heads, v_head_dim, kv_lora_rank]
-	// Result: [B, num_heads, L, v_head_dim]
-	out = a.UnembedOut.Forward(out)
-
-	// Reshape back: [B, num_heads, L, v_head_dim] -> [B, L, num_heads * v_head_dim]
-	out = mlx.Reshape(mlx.Transpose(out, 0, 2, 1, 3), B, L, cfg.NumAttentionHeads*cfg.VHeadDim)
-
-	return a.OProj.Forward(out)
-}
-
-// DenseMLP implements the standard SwiGLU MLP for dense layers
-type DenseMLP struct {
-	GateProj nn.LinearLayer `weight:"mlp.gate_proj"`
-	UpProj   nn.LinearLayer `weight:"mlp.up_proj"`
-	DownProj nn.LinearLayer `weight:"mlp.down_proj"`
-}
-
-// Forward applies the SwiGLU MLP
-func (m *DenseMLP) Forward(x *mlx.Array) *mlx.Array {
-	gate := mlx.SiLU(m.GateProj.Forward(x))
-	up := m.UpProj.Forward(x)
-	return m.DownProj.Forward(mlx.Mul(gate, up))
-}
-
-// MoEGate implements the expert gating mechanism
-type MoEGate struct {
-	Gate                 nn.LinearLayer `weight:"mlp.gate"`
-	EScoreCorrectionBias *mlx.Array     `weight:"mlp.gate.e_score_correction_bias,optional"`
-}
-
-// Forward computes expert selection indices and scores
-func (g *MoEGate) Forward(x *mlx.Array, cfg *Config) (*mlx.Array, *mlx.Array) {
-	// Compute gate logits through linear layer (handles both quantized and non-quantized)
-	gates := g.Gate.Forward(x)
-
-	// Sigmoid scoring
-	scores := mlx.Sigmoid(gates)
-	origScores := scores
-
-	// Add correction bias if present
-	if g.EScoreCorrectionBias != nil {
-		scores = mlx.Add(scores, g.EScoreCorrectionBias)
-	}
-
-	// Group-wise expert selection (simplified for n_group=1)
-	// Select top-k experts
-	topK := cfg.NumExpertsPerTok
-	negScores := mlx.Neg(scores)
-	inds := mlx.Argpartition(negScores, int(topK)-1, -1)
-
-	shape := inds.Shape()
-	inds = mlx.Slice(inds, []int32{0, 0, 0}, []int32{shape[0], shape[1], topK})
-
-	// Get scores for selected experts
-	scores = mlx.TakeAlongAxis(origScores, inds, -1)
-
-	// Normalize if configured
-	if topK > 1 && cfg.NormTopKProb {
-		sumScores := mlx.Sum(scores, -1, true)
-		scores = mlx.Div(scores, sumScores)
-	}
-
-	// Apply routing scaling factor
-	scores = mlx.MulScalar(scores, cfg.RoutedScalingFactor)
-
-	return inds, scores
-}
-
-// SwitchMLP implements the MoE expert computation using stacked weights
-// Note: No weight tags - these are populated manually by stacking expert weights
-type SwitchMLP struct {
-	// Dequantized weights (used when GatherQMM not available)
-	GateWeight *mlx.Array
-	UpWeight   *mlx.Array
-	DownWeight *mlx.Array
-
-	// Quantized weights (used with GatherQMM for 4/8-bit affine)
-	GateWeightQ, GateScales, GateBiases *mlx.Array
-	UpWeightQ, UpScales, UpBiases       *mlx.Array
-	DownWeightQ, DownScales, DownBiases *mlx.Array
-
-	// Quantization bits per projection (supports mixed precision Q4/Q8)
-	GateBits int
-	UpBits   int
-	DownBits int
-
-	// Quantization group size per projection (detected from tensor shapes)
-	GateGroupSize int
-	UpGroupSize   int
-	DownGroupSize int
-
-	// If true, use GatherQMM with quantized weights
-	UseQuantized bool
-}
-
-// Forward applies the switched expert MLP
-func (s *SwitchMLP) Forward(x *mlx.Array, indices *mlx.Array, cfg *Config) *mlx.Array {
-	shape := x.Shape()
-	B, L := shape[0], shape[1]
-	topK := cfg.NumExpertsPerTok
-
-	// Expand x for expert computation: [B, L, D] -> [B, L, 1, 1, D]
-	xExpanded := mlx.ExpandDims(mlx.ExpandDims(x, -2), -2)
-
-	// Flatten for gather_mm: [B*L, 1, 1, D]
-	xFlat := mlx.Reshape(xExpanded, B*L, 1, 1, cfg.HiddenSize)
-
-	// Flatten indices: [B, L, topK] -> [B*L, topK]
-	idxFlat := mlx.Reshape(indices, B*L, topK)
-
-	// Sort for efficient gather (when we have many tokens)
-	doSort := B*L >= 64
-	var invOrder *mlx.Array
-	n := B * L * topK
-
-	if doSort {
-		idxAll := mlx.Flatten(idxFlat)
-		order := mlx.Argsort(idxAll, 0)
-		invOrder = mlx.Argsort(order, 0)
-		// Reorder x based on sorted indices
-		xFlat = mlx.ExpandDims(mlx.Take(mlx.Squeeze(xFlat, 1), mlx.FloorDivideScalar(order, topK), 0), 1)
-		idxFlat = mlx.Reshape(mlx.Take(idxAll, order, 0), n, 1)
-	}
-
-	var gate, up, hidden, down *mlx.Array
-
-	if s.UseQuantized {
-		// Use GatherQMM for quantized weights (faster, keeps weights quantized)
-		// Each projection may have different bits and group sizes (mixed precision: Q4 for gate/up, Q8 for down)
-		gate = mlx.GatherQMM(xFlat, s.GateWeightQ, s.GateScales, s.GateBiases,
-			nil, idxFlat, true, s.GateGroupSize, s.GateBits, cfg.QuantMode, doSort)
-		up = mlx.GatherQMM(xFlat, s.UpWeightQ, s.UpScales, s.UpBiases,
-			nil, idxFlat, true, s.UpGroupSize, s.UpBits, cfg.QuantMode, doSort)
-
-		hidden = mlx.Mul(mlx.SiLU(gate), up)
-
-		down = mlx.GatherQMM(hidden, s.DownWeightQ, s.DownScales, s.DownBiases,
-			nil, idxFlat, true, s.DownGroupSize, s.DownBits, cfg.QuantMode, doSort)
-	} else {
-		// Use GatherMM for dequantized/non-quantized weights
-		gate = mlx.GatherMM(xFlat, mlx.Transpose(s.GateWeight, 0, 2, 1), nil, idxFlat, doSort)
-		up = mlx.GatherMM(xFlat, mlx.Transpose(s.UpWeight, 0, 2, 1), nil, idxFlat, doSort)
-
-		hidden = mlx.Mul(mlx.SiLU(gate), up)
-
-		down = mlx.GatherMM(hidden, mlx.Transpose(s.DownWeight, 0, 2, 1), nil, idxFlat, doSort)
-	}
-
-	// Unsort if we sorted
-	if doSort {
-		down = mlx.Reshape(mlx.Take(mlx.Squeeze(mlx.Squeeze(down, 2), 1), invOrder, 0), B*L, topK, cfg.HiddenSize)
-	} else {
-		down = mlx.Squeeze(down, 2)
-	}
-
-	return mlx.Reshape(down, B, L, topK, cfg.HiddenSize)
-}
-
-// SharedExperts implements the shared expert MLP
-type SharedExperts struct {
-	GateProj nn.LinearLayer `weight:"mlp.shared_experts.gate_proj"`
-	UpProj   nn.LinearLayer `weight:"mlp.shared_experts.up_proj"`
-	DownProj nn.LinearLayer `weight:"mlp.shared_experts.down_proj"`
-}
-
-// Forward applies the shared expert MLP
-func (s *SharedExperts) Forward(x *mlx.Array) *mlx.Array {
-	gate := mlx.SiLU(s.GateProj.Forward(x))
-	up := s.UpProj.Forward(x)
-	return s.DownProj.Forward(mlx.Mul(gate, up))
-}
-
-// MoE implements the full Mixture of Experts layer
-type MoE struct {
-	Gate          *MoEGate
-	SwitchMLP     *SwitchMLP
-	SharedExperts *SharedExperts
-}
-
-// Forward applies the MoE layer
-func (m *MoE) Forward(x *mlx.Array, cfg *Config) *mlx.Array {
-	shape := x.Shape()
-	B, L := shape[0], shape[1]
-
-	// Get expert indices and scores
-	inds, scores := m.Gate.Forward(x, cfg)
-
-	// Apply routed experts
-	expertOut := m.SwitchMLP.Forward(x, inds, cfg)
-
-	// Weight by scores: [B, L, topK, D] * [B, L, topK, 1] -> sum over topK
-	scoresExpanded := mlx.ExpandDims(scores, -1)
-	y := mlx.Sum(mlx.Mul(expertOut, scoresExpanded), 2, false)
-
-	// Add shared experts if present
-	if m.SharedExperts != nil {
-		y = mlx.Add(y, m.SharedExperts.Forward(x))
-	}
-
-	return mlx.Reshape(y, B, L, cfg.HiddenSize)
-}
-
-// DenseBlock represents a dense transformer block (for first_k_dense_replace layers)
-type DenseBlock struct {
-	Attention              *MLAAttention
-	MLP                    *DenseMLP
-	InputLayerNorm         *nn.RMSNorm `weight:"input_layernorm"`
-	PostAttentionLayerNorm *nn.RMSNorm `weight:"post_attention_layernorm"`
-}
-
-// Forward applies the dense block
-func (b *DenseBlock) Forward(x *mlx.Array, c cache.Cache, B, L int32, cfg *Config) *mlx.Array {
-	// Pre-norm attention with residual
-	r := b.Attention.Forward(b.InputLayerNorm.Forward(x, cfg.RMSNormEps), c, B, L, cfg)
-	h := mlx.Add(x, r)
-
-	// Pre-norm MLP with residual
-	r = b.MLP.Forward(b.PostAttentionLayerNorm.Forward(h, cfg.RMSNormEps))
-	return mlx.Add(h, r)
-}
-
-// MoEBlock represents a MoE transformer block
-type MoEBlock struct {
-	Attention              *MLAAttention
-	MoE                    *MoE
-	InputLayerNorm         *nn.RMSNorm `weight:"input_layernorm"`
-	PostAttentionLayerNorm *nn.RMSNorm `weight:"post_attention_layernorm"`
-}
-
-// Forward applies the MoE block
-func (b *MoEBlock) Forward(x *mlx.Array, c cache.Cache, B, L int32, cfg *Config) *mlx.Array {
-	// Pre-norm attention with residual
-	r := b.Attention.Forward(b.InputLayerNorm.Forward(x, cfg.RMSNormEps), c, B, L, cfg)
-	h := mlx.Add(x, r)
-
-	// Pre-norm MoE with residual
-	r = b.MoE.Forward(b.PostAttentionLayerNorm.Forward(h, cfg.RMSNormEps), cfg)
-	return mlx.Add(h, r)
-}
-
-// Block interface for both dense and MoE blocks
-type Block interface {
-	Forward(x *mlx.Array, c cache.Cache, B, L int32, cfg *Config) *mlx.Array
-}
-
-// Model represents the complete GLM4-MoE-Lite model
-type Model struct {
-	EmbedTokens *nn.Embedding  `weight:"model.embed_tokens"`
-	Layers      []Block        `weight:"-"` // Loaded manually due to different block types
-	Norm        *nn.RMSNorm    `weight:"model.norm"`
-	LMHead      nn.LinearLayer `weight:"lm_head"`
-
-	tok *tokenizer.Tokenizer
-	*Config
-}
-
-// computeScale computes the attention scale.
-// Uses the full key head dimension (qkNopeHeadDim + qkRopeHeadDim) to match the Ollama runner.
-func computeScale(cfg *Config) float32 {
-	keyLength := cfg.QKNopeHeadDim + cfg.QKRopeHeadDim
-	scale := float32(1.0 / math.Sqrt(float64(keyLength)))
-	if cfg.RopeScaling != nil && cfg.RopeScaling.MscaleAllDim > 0 && cfg.RopeScaling.Factor > 1 {
-		s := 0.1*cfg.RopeScaling.MscaleAllDim*float32(math.Log(float64(cfg.RopeScaling.Factor))) + 1.0
-		scale *= s * s
-	}
-	return scale
-}
-
-// supportsGatherQMM returns true if the quantization mode has GatherQMM kernel support.
-// Currently only 4-bit and 8-bit affine quantization are supported.
-func supportsGatherQMM(mode string, bits int) bool {
-	return mode == "affine" && (bits == 4 || bits == 8)
-}
-
-// ExpertWeight holds a single expert's weight with optional quantization components.
-type ExpertWeight struct {
-	Weight    *mlx.Array // Quantized weight (if quantized) or dequantized weight
-	Scales    *mlx.Array // Quantization scales (nil if not quantized)
-	Biases    *mlx.Array // Quantization biases (nil if not quantized or mode doesn't use biases)
-	Bits      int        // Quantization bits (4 or 8), 0 if not quantized
-	GroupSize int        // Quantization group size, 0 if not quantized
-}
-
-// getQuantParams returns quantization parameters from model metadata.
-// Returns groupSize, bits, and mode for the model's quantization type.
-func getQuantParams(weights safetensors.WeightSource) (groupSize, bits int, mode string) {
-	groupSize, bits, mode = safetensors.QuantizationParams(weights.Quantization())
-	// Use metadata group_size if available (overrides default)
-	if gs := weights.GroupSize(); gs > 0 {
-		groupSize = gs
-	}
-	return groupSize, bits, mode
-}
-
-// loadExpertWeight loads an expert weight.
-// If useQuantized is true and the weight is quantized with a supported mode, returns quantized components.
-// Otherwise dequantizes and returns only the weight.
-func loadExpertWeight(weights safetensors.WeightSource, path string, useQuantized bool, cfg *Config) *ExpertWeight {
-	w, _ := weights.GetTensor(path + ".weight")
-	if w == nil {
-		return nil
-	}
-
-	// Check if this is a quantized weight by looking for scales
-	scalePath := path + ".weight_scale"
-	if weights.HasTensor(scalePath) {
-		scales, _ := weights.GetTensor(scalePath)
-		var qbiases *mlx.Array
-		qbiasPath := path + ".weight_qbias"
-		if weights.HasTensor(qbiasPath) {
-			qbiases, _ = weights.GetTensor(qbiasPath)
-		}
-
-		// Get quantization params from metadata
-		groupSize, bits, mode := getQuantParams(weights)
-
-		// Update config with group size (for GatherQMM calls)
-		if cfg.QuantGroupSize == 0 {
-			cfg.QuantGroupSize = groupSize
-		}
-
-		// If GatherQMM is supported and requested, return quantized components
-		if useQuantized && supportsGatherQMM(mode, bits) {
-			return &ExpertWeight{Weight: w, Scales: scales, Biases: qbiases, Bits: bits, GroupSize: groupSize}
-		}
-
-		// Otherwise dequantize
-		return &ExpertWeight{Weight: mlx.Dequantize(w, scales, qbiases, groupSize, bits, mode)}
-	}
-
-	return &ExpertWeight{Weight: w}
-}
-
-// sanitizeMLAWeights transforms kv_b_proj weights into absorbed MLA format.
-// Returns embed_q and unembed_out weights for per-head projections.
-//
-// kv_b_proj.weight shape: [num_heads * (qk_nope_head_dim + v_head_dim), kv_lora_rank]
-// Output:
-//   - embed_q: [num_heads, kv_lora_rank, qk_nope_head_dim] - projects q_nope to latent
-//   - unembed_out: [num_heads, v_head_dim, kv_lora_rank] - projects latent to output
-func sanitizeMLAWeights(weights safetensors.WeightSource, prefix string, cfg *Config) (*mlx.Array, *mlx.Array) {
-	path := prefix + ".self_attn.kv_b_proj"
-	w, err := weights.GetTensor(path + ".weight")
-	if err != nil || w == nil {
-		return nil, nil
-	}
-
-	// Check if quantized and dequantize
-	scalePath := path + ".weight_scale"
-	if weights.HasTensor(scalePath) {
-		scales, _ := weights.GetTensor(scalePath)
-		var qbiases *mlx.Array
-		qbiasPath := path + ".weight_qbias"
-		if weights.HasTensor(qbiasPath) {
-			qbiases, _ = weights.GetTensor(qbiasPath)
-		}
-
-		groupSize, bits, mode := getQuantParams(weights)
-		w = mlx.Dequantize(w, scales, qbiases, groupSize, bits, mode)
-	}
-
-	// w: [num_heads * (qk_nope_head_dim + v_head_dim), kv_lora_rank]
-	// Reshape to [num_heads, qk_nope_head_dim + v_head_dim, kv_lora_rank]
-	headDim := cfg.QKNopeHeadDim + cfg.VHeadDim
-	w = mlx.Reshape(w, cfg.NumAttentionHeads, headDim, cfg.KVLoraRank)
-
-	// Split into wk and wv
-	// wk: [num_heads, qk_nope_head_dim, kv_lora_rank]
-	// wv: [num_heads, v_head_dim, kv_lora_rank]
-	wk := mlx.Slice(w, []int32{0, 0, 0}, []int32{cfg.NumAttentionHeads, cfg.QKNopeHeadDim, cfg.KVLoraRank})
-	wv := mlx.Slice(w, []int32{0, cfg.QKNopeHeadDim, 0}, []int32{cfg.NumAttentionHeads, headDim, cfg.KVLoraRank})
-
-	// Transform for absorbed MLA:
-	// embed_q: transpose(wk) -> [num_heads, kv_lora_rank, qk_nope_head_dim]
-	// This allows: q_nope @ embed_q.T = q_nope @ wk (absorbed key projection)
-	embedQ := mlx.Transpose(wk, 0, 2, 1)
-
-	// unembed_out: wv stays [num_heads, v_head_dim, kv_lora_rank]
-	// This allows: latent_out @ unembed_out.T = latent_out @ wv.T (absorbed value projection)
-	unembedOut := wv
-
-	return embedQ, unembedOut
-}
-
-// StackedExpertWeights holds stacked weights for all experts.
-type StackedExpertWeights struct {
-	Weight    *mlx.Array // Stacked weights [num_experts, out, in] or [num_experts, out, in_packed]
-	Scales    *mlx.Array // Stacked scales (nil if not quantized)
-	Biases    *mlx.Array // Stacked biases (nil if not quantized)
-	Bits      int        // Quantization bits (4 or 8), 0 if not quantized
-	GroupSize int        // Quantization group size, 0 if not quantized
-}
-
-// collectAndStackExpertWeights loads and stacks expert weights for one projection type.
-func collectAndStackExpertWeights(
-	weights safetensors.WeightSource,
-	prefix string,
-	projName string,
-	numExperts int32,
-	useQuantized bool,
-	cfg *Config,
-) *StackedExpertWeights {
-	var w, s, b []*mlx.Array
-	var bits, groupSize int
-
-	for e := int32(0); e < numExperts; e++ {
-		path := fmt.Sprintf("%s.mlp.experts.%d.%s", prefix, e, projName)
-		ew := loadExpertWeight(weights, path, useQuantized, cfg)
-		if ew == nil {
-			continue
-		}
-		w = append(w, ew.Weight)
-		if ew.Scales != nil {
-			s = append(s, ew.Scales)
-		}
-		if ew.Biases != nil {
-			b = append(b, ew.Biases)
-		}
-		if e == 0 {
-			bits = ew.Bits
-			groupSize = ew.GroupSize
-		}
-	}
-
-	result := &StackedExpertWeights{Bits: bits, GroupSize: groupSize}
-	if len(w) > 0 {
-		result.Weight = mlx.Stack(w, 0)
-		if len(s) > 0 {
-			result.Scales = mlx.Stack(s, 0)
-		}
-		if len(b) > 0 {
-			result.Biases = mlx.Stack(b, 0)
-		}
-	}
-	return result
-}
-
-// sanitizeExpertWeights stacks individual expert weights into tensors.
-// If useQuantized is true and weights support GatherQMM, returns quantized components.
-// Otherwise returns dequantized weights with nil scales/biases.
-// Bits and GroupSize are detected per-weight to support mixed-precision (Q4 for gate/up, Q8 for down).
-func sanitizeExpertWeights(weights safetensors.WeightSource, prefix string, numExperts int32, useQuantized bool, cfg *Config) (gate, up, down *StackedExpertWeights) {
-	gate = collectAndStackExpertWeights(weights, prefix, "gate_proj", numExperts, useQuantized, cfg)
-	up = collectAndStackExpertWeights(weights, prefix, "up_proj", numExperts, useQuantized, cfg)
-	down = collectAndStackExpertWeights(weights, prefix, "down_proj", numExperts, useQuantized, cfg)
-	return gate, up, down
-}
-
-// LoadFromManifest loads a GLM4-MoE-Lite model from a manifest (Ollama blob storage).
-func LoadFromManifest(modelManifest *manifest.ModelManifest) (*Model, error) {
-	// Read config from manifest
-	configData, err := modelManifest.ReadConfig("config.json")
-	if err != nil {
-		return nil, fmt.Errorf("load config: %w", err)
-	}
-
-	var cfg Config
-	if err := json.Unmarshal(configData, &cfg); err != nil {
-		return nil, fmt.Errorf("parse config: %w", err)
-	}
-
-	// Compute derived fields
-	cfg.QHeadDim = cfg.QKNopeHeadDim + cfg.QKRopeHeadDim
-	cfg.Scale = computeScale(&cfg)
-
-	// Load weights from manifest blobs
-	weights, err := manifest.LoadWeightsFromManifest(modelManifest, "")
-	if err != nil {
-		return nil, fmt.Errorf("load weights: %w", err)
-	}
-
-	if err := weights.Load(0); err != nil {
-		return nil, fmt.Errorf("load weight data: %w", err)
-	}
-
-	// Set up quantization parameters (only if model is actually quantized)
-	// Note: QuantGroupSize will be detected dynamically from tensor shapes during weight loading
-	quantization := weights.Quantization()
-	useQuantized := false
-	if quantization != "" {
-		_, cfg.QuantBits, cfg.QuantMode = safetensors.QuantizationParams(quantization)
-		useQuantized = supportsGatherQMM(cfg.QuantMode, cfg.QuantBits)
-	}
-
-	// Load tokenizer from manifest with config files for EOS token detection
-	tokData, err := modelManifest.ReadConfig("tokenizer.json")
-	if err != nil {
-		return nil, fmt.Errorf("load tokenizer config: %w", err)
-	}
-
-	// Build tokenizer config with companion files for EOS/BOS token loading
-	tokConfig := &tokenizer.TokenizerConfig{
-		ConfigJSON: configData, // Already loaded above, contains eos_token_id
-	}
-
-	// Try to load generation_config.json if available (preferred source for EOS)
-	if genConfigData, err := modelManifest.ReadConfig("generation_config.json"); err == nil {
-		tokConfig.GenerationConfigJSON = genConfigData
-	}
-
-	// Try to load tokenizer_config.json if available
-	if tokConfigData, err := modelManifest.ReadConfig("tokenizer_config.json"); err == nil {
-		tokConfig.TokenizerConfigJSON = tokConfigData
-	}
-
-	tok, err := tokenizer.LoadFromBytesWithConfig(tokData, tokConfig)
-	if err != nil {
-		return nil, fmt.Errorf("parse tokenizer: %w", err)
-	}
-
-	m := &Model{
-		Layers: make([]Block, cfg.NumHiddenLayers),
-		Config: &cfg,
-		tok:    tok,
-	}
-
-	// Load embedding, norm, and lm_head
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return nil, err
-	}
-
-	// Load layers manually due to different block types
-	for i := int32(0); i < cfg.NumHiddenLayers; i++ {
-		prefix := fmt.Sprintf("model.layers.%d", i)
-
-		// Load attention (same for both block types)
-		attn := &MLAAttention{}
-		if err := safetensors.LoadModule(attn, weights, prefix); err != nil {
-			return nil, fmt.Errorf("layer %d attention: %w", i, err)
-		}
-
-		// Sanitize MLA weights for absorbed attention
-		embedQ, unembedOut := sanitizeMLAWeights(weights, prefix, &cfg)
-		attn.EmbedQ = nn.NewMultiLinear(embedQ)
-		attn.UnembedOut = nn.NewMultiLinear(unembedOut)
-
-		if i < cfg.FirstKDenseReplace {
-			// Dense block
-			block := &DenseBlock{Attention: attn}
-			if err := safetensors.LoadModule(block, weights, prefix); err != nil {
-				return nil, fmt.Errorf("layer %d dense: %w", i, err)
-			}
-			m.Layers[i] = block
-		} else {
-			// MoE block
-			block := &MoEBlock{Attention: attn}
-			if err := safetensors.LoadModule(block, weights, prefix); err != nil {
-				return nil, fmt.Errorf("layer %d moe block: %w", i, err)
-			}
-
-			// Stack expert weights (pass cfg so group sizes can be detected)
-			gate, up, down := sanitizeExpertWeights(weights, prefix, cfg.NRoutedExperts, useQuantized, &cfg)
-
-			switchMLP := &SwitchMLP{UseQuantized: useQuantized}
-			if useQuantized {
-				switchMLP.GateWeightQ = gate.Weight
-				switchMLP.GateScales = gate.Scales
-				switchMLP.GateBiases = gate.Biases
-				switchMLP.GateBits = gate.Bits
-				switchMLP.GateGroupSize = gate.GroupSize
-				switchMLP.UpWeightQ = up.Weight
-				switchMLP.UpScales = up.Scales
-				switchMLP.UpBiases = up.Biases
-				switchMLP.UpBits = up.Bits
-				switchMLP.UpGroupSize = up.GroupSize
-				switchMLP.DownWeightQ = down.Weight
-				switchMLP.DownScales = down.Scales
-				switchMLP.DownBiases = down.Biases
-				switchMLP.DownBits = down.Bits
-				switchMLP.DownGroupSize = down.GroupSize
-			} else {
-				switchMLP.GateWeight = gate.Weight
-				switchMLP.UpWeight = up.Weight
-				switchMLP.DownWeight = down.Weight
-			}
-
-			block.MoE = &MoE{
-				Gate:      &MoEGate{},
-				SwitchMLP: switchMLP,
-			}
-
-			// Load gate weights
-			if err := safetensors.LoadModule(block.MoE.Gate, weights, prefix); err != nil {
-				return nil, fmt.Errorf("layer %d gate: %w", i, err)
-			}
-
-			// Load shared experts if present
-			if cfg.NSharedExperts > 0 {
-				block.MoE.SharedExperts = &SharedExperts{}
-				if err := safetensors.LoadModule(block.MoE.SharedExperts, weights, prefix); err != nil {
-					return nil, fmt.Errorf("layer %d shared experts: %w", i, err)
-				}
-			}
-
-			m.Layers[i] = block
-		}
-	}
-
-	mlx.Eval(mlx.Collect(m)...)
-	weights.ReleaseAll()
-
-	return m, nil
-}
-
-// Forward computes the forward pass of the model
-func (m *Model) Forward(tokens *mlx.Array, caches []cache.Cache) *mlx.Array {
-	B, L := tokens.Shape()[0], tokens.Shape()[1]
-
-	h := m.EmbedTokens.Forward(tokens)
-
-	for i, layer := range m.Layers {
-		var c cache.Cache
-		if caches != nil {
-			c = caches[i]
-		}
-		h = layer.Forward(h, c, B, L, m.Config)
-	}
-
-	h = m.Norm.Forward(h, m.RMSNormEps)
-	return m.LMHead.Forward(h)
-}
-
-// Interface methods
-
-// NumLayers returns the number of transformer layers
-func (m *Model) NumLayers() int { return len(m.Layers) }
-
-// MaxContextLength returns the maximum context length
-func (m *Model) MaxContextLength() int32 { return m.MaxPositionEmbeddings }
-
-// VocabSize returns the vocabulary size
-func (m *Model) VocabSize() int32 { return m.Config.VocabSize }
-
-// Tokenizer returns the model's tokenizer
-func (m *Model) Tokenizer() *tokenizer.Tokenizer { return m.tok }
-
-// NewCache creates a new KV cache for the model
-func (m *Model) NewCache(maxSeqLen int32) []cache.Cache {
-	caches := make([]cache.Cache, len(m.Layers))
-	for i := range caches {
-		caches[i] = cache.NewKVCache()
-	}
-	return caches
-}
-
-// FormatPrompt applies the GLM-4 chat template with thinking enabled by default.
-// This follows the GLM-4.7 format with <think> tag for reasoning mode.
-func (m *Model) FormatPrompt(prompt string) string {
-	return "[gMASK]<sop><|user|>" + prompt + "<|assistant|><think>"
-}
-
-// FormatPromptWithThinking applies the GLM-4 chat template with explicit thinking control.
-// When think is true, the prompt ends with <think> to enable reasoning mode.
-// When think is false, the prompt ends with </think> to skip reasoning.
-func (m *Model) FormatPromptWithThinking(prompt string, think bool) string {
-	if think {
-		return "[gMASK]<sop><|user|>" + prompt + "<|assistant|><think>"
-	}
-	return "[gMASK]<sop><|user|>" + prompt + "<|assistant|></think>"
-}
-
-// NewRenderer returns a new Renderer for formatting multi-turn conversations.
-func (m *Model) NewRenderer() *Renderer {
-	return &Renderer{}
-}
-
-// NewParser returns a new Parser for extracting thinking and tool calls from output.
-func (m *Model) NewParser() *Parser {
-	return &Parser{}
-}

x/imagegen/models/glm4_moe_lite/parser.go

@@ -1,479 +0,0 @@
-//go:build mlx
-
-package glm4_moe_lite
-
-import (
-	"context"
-	"encoding/json"
-	"encoding/xml"
-	"fmt"
-	"log/slog"
-	"strings"
-	"unicode"
-
-	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/logutil"
-)
-
-type parserState int
-
-const (
-	parserState_LookingForThinkingOpen parserState = iota
-	parserState_ThinkingStartedEatingWhitespace
-	parserState_CollectingThinking
-	parserState_ThinkingDoneEatingWhitespace
-	parserState_CollectingContent
-	parserState_ToolStartedEatingWhitespace
-	parserState_CollectingToolContent
-)
-
-const (
-	thinkingOpenTag  = "<think>"
-	thinkingCloseTag = "</think>"
-	toolOpenTag      = "<tool_call>"
-	toolCloseTag     = "</tool_call>"
-)
-
-// Parser parses GLM4-MoE-Lite model output to extract thinking and tool calls.
-// GLM-4's prompt ends with <think> when thinking is enabled, so the parser
-// must start in CollectingThinking state (the model outputs thinking content directly).
-type Parser struct {
-	state  parserState
-	buffer strings.Builder
-	tools  []api.Tool
-}
-
-// HasToolSupport returns true as GLM4 supports tool calling.
-func (p *Parser) HasToolSupport() bool {
-	return true
-}
-
-// HasThinkingSupport returns true as GLM4 supports thinking mode.
-func (p *Parser) HasThinkingSupport() bool {
-	return true
-}
-
-// Init initializes the parser with tools and thinking configuration.
-func (p *Parser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
-	p.tools = tools
-	// When thinking is enabled (nil or true), the prompt ends with <think>,
-	// so model output starts directly with thinking content (no opening tag).
-	if thinkValue == nil || thinkValue.Bool() {
-		p.state = parserState_CollectingThinking
-	}
-	return tools
-}
-
-type parserEvent interface {
-	isParserEvent()
-}
-
-type eventContent struct {
-	content string
-}
-
-func (eventContent) isParserEvent() {}
-
-type eventRawToolCall struct {
-	raw string
-}
-
-func (eventRawToolCall) isParserEvent() {}
-
-type eventThinkingContent struct {
-	content string
-}
-
-func (eventThinkingContent) isParserEvent() {}
-
-// Add processes new output text and returns parsed content, thinking, and tool calls.
-func (p *Parser) Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error) {
-	p.buffer.WriteString(s)
-	events := p.parseEvents()
-
-	var toolCalls []api.ToolCall
-	var contentSb strings.Builder
-	var thinkingSb strings.Builder
-
-	for _, event := range events {
-		switch event := event.(type) {
-		case eventRawToolCall:
-			toolCall, err := parseToolCall(event, p.tools)
-			if err != nil {
-				slog.Warn("glm-4 tool call parsing failed", "error", err)
-				return "", "", nil, err
-			}
-			toolCalls = append(toolCalls, toolCall)
-		case eventThinkingContent:
-			thinkingSb.WriteString(event.content)
-		case eventContent:
-			contentSb.WriteString(event.content)
-		}
-	}
-
-	return contentSb.String(), thinkingSb.String(), toolCalls, nil
-}
-
-func (p *Parser) parseEvents() []parserEvent {
-	var all []parserEvent
-
-	keepLooping := true
-	for keepLooping {
-		var events []parserEvent
-		events, keepLooping = p.eat()
-		if len(events) > 0 {
-			all = append(all, events...)
-		}
-	}
-
-	if len(all) > 0 {
-		slog.Log(context.TODO(), logutil.LevelTrace, "glm-4 events parsed", "events", all, "state", p.state, "buffer", p.buffer.String())
-	}
-
-	return all
-}
-
-// eatLeadingWhitespaceAndTransitionTo consumes leading whitespace from the buffer
-// and transitions to the next state. Returns (nil, false) if only whitespace remains
-// in the buffer (needs more input), or (nil, true) if we successfully transitioned.
-func (p *Parser) eatLeadingWhitespaceAndTransitionTo(nextState parserState) ([]parserEvent, bool) {
-	trimmed := strings.TrimLeftFunc(p.buffer.String(), unicode.IsSpace)
-	p.buffer.Reset()
-	if trimmed == "" {
-		return nil, false // Still only whitespace, keep waiting for more input
-	}
-	p.state = nextState
-	p.buffer.WriteString(trimmed)
-	return nil, true // Successfully transitioned
-}
-
-// splitAtTag splits the buffer at the given tag, returns the content before (trimmed of trailing whitespace),
-// the content after (optionally trimmed of leading whitespace), and updates the buffer
-func (p *Parser) splitAtTag(tag string, trimAfter bool) (string, string) {
-	split := strings.SplitN(p.buffer.String(), tag, 2)
-	before := split[0]
-	before = strings.TrimRightFunc(before, unicode.IsSpace)
-	after := split[1]
-	if trimAfter {
-		after = strings.TrimLeftFunc(after, unicode.IsSpace)
-	}
-	p.buffer.Reset()
-	p.buffer.WriteString(after)
-	return before, after
-}
-
-func (p *Parser) eat() ([]parserEvent, bool) {
-	var events []parserEvent
-
-	switch p.state {
-	case parserState_LookingForThinkingOpen:
-		trimmed := strings.TrimLeftFunc(p.buffer.String(), unicode.IsSpace)
-		if strings.HasPrefix(trimmed, thinkingOpenTag) {
-			// Found <think> opening tag
-			after := strings.TrimPrefix(trimmed, thinkingOpenTag)
-			after = strings.TrimLeftFunc(after, unicode.IsSpace)
-			p.buffer.Reset()
-			p.buffer.WriteString(after)
-			if after == "" {
-				p.state = parserState_ThinkingStartedEatingWhitespace
-			} else {
-				p.state = parserState_CollectingThinking
-			}
-			return events, true
-		} else if strings.HasPrefix(thinkingOpenTag, trimmed) {
-			// Partial opening tag seen, keep accumulating
-			return events, false
-		} else if trimmed == "" {
-			// Only whitespace, keep accumulating
-			return events, false
-		} else {
-			// No thinking tag found, skip to content collection
-			p.state = parserState_CollectingContent
-			// Don't trim - we want to keep the original content
-			return events, true
-		}
-
-	case parserState_ThinkingStartedEatingWhitespace:
-		return p.eatLeadingWhitespaceAndTransitionTo(parserState_CollectingThinking)
-
-	case parserState_CollectingThinking:
-		acc := p.buffer.String()
-		if strings.Contains(acc, thinkingCloseTag) {
-			thinking, remaining := p.splitAtTag(thinkingCloseTag, true)
-			if len(thinking) > 0 {
-				events = append(events, eventThinkingContent{content: thinking})
-			}
-			if remaining == "" {
-				p.state = parserState_ThinkingDoneEatingWhitespace
-			} else {
-				p.state = parserState_CollectingContent
-			}
-			return events, true
-		} else if overlapLen := overlap(acc, thinkingCloseTag); overlapLen > 0 {
-			// Partial closing tag - withhold it along with any trailing whitespace before it
-			beforePartialTag := acc[:len(acc)-overlapLen]
-			trailingWsLen := trailingWhitespaceLen(beforePartialTag)
-			ambiguousStart := len(beforePartialTag) - trailingWsLen
-
-			unambiguous := acc[:ambiguousStart]
-			ambiguous := acc[ambiguousStart:]
-			p.buffer.Reset()
-			p.buffer.WriteString(ambiguous)
-			if len(unambiguous) > 0 {
-				events = append(events, eventThinkingContent{content: unambiguous})
-			}
-			return events, false
-		} else {
-			// Pure thinking content - withhold trailing whitespace (might precede closing tag)
-			whitespaceLen := trailingWhitespaceLen(acc)
-			ambiguousStart := len(acc) - whitespaceLen
-
-			unambiguous := acc[:ambiguousStart]
-			ambiguous := acc[ambiguousStart:]
-			p.buffer.Reset()
-			p.buffer.WriteString(ambiguous)
-			if len(unambiguous) > 0 {
-				events = append(events, eventThinkingContent{content: unambiguous})
-			}
-			return events, false
-		}
-
-	case parserState_ThinkingDoneEatingWhitespace:
-		return p.eatLeadingWhitespaceAndTransitionTo(parserState_CollectingContent)
-
-	case parserState_CollectingContent:
-		if strings.Contains(p.buffer.String(), toolOpenTag) {
-			before, after := p.splitAtTag(toolOpenTag, true)
-			if len(before) > 0 {
-				events = append(events, eventContent{content: before})
-			}
-			if after == "" {
-				p.state = parserState_ToolStartedEatingWhitespace
-			} else {
-				p.state = parserState_CollectingToolContent
-			}
-			return events, true
-		} else if overlapLen := overlap(p.buffer.String(), toolOpenTag); overlapLen > 0 {
-			beforePartialTag := p.buffer.String()[:len(p.buffer.String())-overlapLen]
-			trailingWsLen := trailingWhitespaceLen(beforePartialTag)
-			ambiguousStart := len(beforePartialTag) - trailingWsLen
-
-			unambiguous := p.buffer.String()[:ambiguousStart]
-			ambiguous := p.buffer.String()[ambiguousStart:]
-			p.buffer.Reset()
-			p.buffer.WriteString(ambiguous)
-			if len(unambiguous) > 0 {
-				events = append(events, eventContent{content: unambiguous})
-			}
-			return events, false
-		} else {
-			whitespaceLen := trailingWhitespaceLen(p.buffer.String())
-			ambiguousStart := len(p.buffer.String()) - whitespaceLen
-
-			unambiguous := p.buffer.String()[:ambiguousStart]
-			ambiguous := p.buffer.String()[ambiguousStart:]
-			p.buffer.Reset()
-			p.buffer.WriteString(ambiguous)
-			if len(unambiguous) > 0 {
-				events = append(events, eventContent{content: unambiguous})
-			}
-			return events, false
-		}
-
-	case parserState_ToolStartedEatingWhitespace:
-		return p.eatLeadingWhitespaceAndTransitionTo(parserState_CollectingToolContent)
-
-	case parserState_CollectingToolContent:
-		acc := p.buffer.String()
-		if strings.Contains(acc, toolCloseTag) {
-			toolContent, _ := p.splitAtTag(toolCloseTag, true)
-			if len(toolContent) == 0 {
-				slog.Warn("glm4 tool call closing tag found but no content before it")
-			}
-			events = append(events, eventRawToolCall{raw: toolContent})
-			p.state = parserState_CollectingContent
-			return events, true
-		} else {
-			// Keep accumulating - tool calls are not streamed
-			// We just wait for the closing tag
-			return events, false
-		}
-
-	default:
-		panic("unreachable")
-	}
-}
-
-// overlap returns the length of the overlap between the end of s and the start of tag.
-func overlap(s, tag string) int {
-	for i := 1; i <= len(tag) && i <= len(s); i++ {
-		if strings.HasSuffix(s, tag[:i]) {
-			return i
-		}
-	}
-	return 0
-}
-
-// trailingWhitespaceLen returns the length of trailing whitespace in s.
-func trailingWhitespaceLen(s string) int {
-	trimmed := strings.TrimRightFunc(s, unicode.IsSpace)
-	return len(s) - len(trimmed)
-}
-
-// ToolCallXML represents the structure of a GLM-4 tool call for XML parsing
-type ToolCallXML struct {
-	XMLName xml.Name `xml:"tool_call"`
-	Content string   `xml:",chardata"` // Function name (text nodes between tags)
-	Keys    []string `xml:"arg_key"`   // All arg_key elements in document order
-	Values  []string `xml:"arg_value"` // All arg_value elements in document order
-}
-
-// escapeContent escapes XML entities in text content while preserving arg_key/arg_value tags
-func escapeContent(s string) string {
-	var result strings.Builder
-	inTag := false
-
-	for i := range len(s) {
-		ch := s[i]
-
-		if ch == '<' {
-			// Check if this is a known tag
-			if strings.HasPrefix(s[i:], "<arg_key>") ||
-				strings.HasPrefix(s[i:], "</arg_key>") ||
-				strings.HasPrefix(s[i:], "<arg_value>") ||
-				strings.HasPrefix(s[i:], "</arg_value>") {
-				inTag = true
-			}
-		}
-
-		if inTag {
-			result.WriteByte(ch)
-			if ch == '>' {
-				inTag = false
-			}
-		} else {
-			// Escape special characters in text content
-			switch ch {
-			case '&':
-				result.WriteString("&amp;")
-			case '<':
-				result.WriteString("&lt;")
-			case '>':
-				result.WriteString("&gt;")
-			default:
-				result.WriteByte(ch)
-			}
-		}
-	}
-
-	return result.String()
-}
-
-func parseToolCall(raw eventRawToolCall, tools []api.Tool) (api.ToolCall, error) {
-	// Escape any unescaped entities in text content
-	escaped := escapeContent(raw.raw)
-
-	// Wrap the content in a root element to make it valid XML
-	xmlString := "<tool_call>" + escaped + "</tool_call>"
-
-	// Parse XML into struct
-	var parsed ToolCallXML
-	if err := xml.Unmarshal([]byte(xmlString), &parsed); err != nil {
-		return api.ToolCall{}, fmt.Errorf("failed to parse XML: %w", err)
-	}
-
-	// Extract and trim function name
-	functionName := strings.TrimSpace(parsed.Content)
-	if functionName == "" {
-		return api.ToolCall{}, fmt.Errorf("empty function name")
-	}
-
-	// Verify keys and values are paired correctly
-	if len(parsed.Keys) != len(parsed.Values) {
-		return api.ToolCall{}, fmt.Errorf("mismatched arg_key and arg_value counts: %d keys, %d values", len(parsed.Keys), len(parsed.Values))
-	}
-
-	// Find the matching tool to get parameter types
-	var matchedTool *api.Tool
-	for i := range tools {
-		if tools[i].Function.Name == functionName {
-			matchedTool = &tools[i]
-			break
-		}
-	}
-
-	// Build arguments map by pairing keys and values
-	toolCall := api.ToolCall{
-		Function: api.ToolCallFunction{
-			Name:      functionName,
-			Arguments: api.NewToolCallFunctionArguments(),
-		},
-	}
-
-	for i := range parsed.Keys {
-		key := strings.TrimSpace(parsed.Keys[i])
-		value := parsed.Values[i] // Don't trim here - parseValue handles it
-
-		// Look up parameter type
-		var paramType api.PropertyType
-		if matchedTool != nil && matchedTool.Function.Parameters.Properties != nil {
-			if prop, ok := matchedTool.Function.Parameters.Properties.Get(key); ok {
-				// Handle anyOf by collecting all types from the union
-				if len(prop.AnyOf) > 0 {
-					for _, anyOfProp := range prop.AnyOf {
-						paramType = append(paramType, anyOfProp.Type...)
-					}
-				} else {
-					paramType = prop.Type
-				}
-			}
-		}
-
-		// Parse value with type coercion
-		toolCall.Function.Arguments.Set(key, parseValue(value, paramType))
-	}
-
-	return toolCall, nil
-}
-
-// parseValue parses a string value and coerces it to the appropriate type based on paramType.
-func parseValue(value string, paramType api.PropertyType) any {
-	value = strings.TrimSpace(value)
-
-	// If no type specified, return as string
-	if len(paramType) == 0 {
-		return value
-	}
-
-	// Try to parse based on specified types
-	for _, t := range paramType {
-		switch t {
-		case "boolean":
-			if value == "true" {
-				return true
-			}
-			if value == "false" {
-				return false
-			}
-		case "integer":
-			var i int64
-			if _, err := fmt.Sscanf(value, "%d", &i); err == nil {
-				return i
-			}
-		case "number":
-			var f float64
-			if _, err := fmt.Sscanf(value, "%f", &f); err == nil {
-				return f
-			}
-		case "array", "object":
-			// Try to parse as JSON
-			var result any
-			if err := json.Unmarshal([]byte(value), &result); err == nil {
-				return result
-			}
-		}
-	}
-
-	// Default to string
-	return value
-}

x/imagegen/models/glm4_moe_lite/parser_test.go

@@ -1,192 +0,0 @@
-//go:build mlx
-
-package glm4_moe_lite
-
-import (
-	"testing"
-
-	"github.com/ollama/ollama/api"
-)
-
-func TestParserThinking(t *testing.T) {
-	tests := []struct {
-		name          string
-		input         string
-		thinkEnabled  bool
-		wantContent   string
-		wantThinking  string
-		wantToolCalls int
-	}{
-		{
-			name:         "thinking enabled - simple thinking then content",
-			input:        "Let me think about this...</think>Here is my answer.",
-			thinkEnabled: true,
-			wantThinking: "Let me think about this...",
-			wantContent:  "Here is my answer.",
-		},
-		{
-			name:         "thinking enabled - only thinking",
-			input:        "I need to consider multiple factors...",
-			thinkEnabled: true,
-			wantThinking: "I need to consider multiple factors...",
-			wantContent:  "",
-		},
-		{
-			name:         "thinking disabled - direct content",
-			input:        "Here is my direct answer.",
-			thinkEnabled: false,
-			wantThinking: "",
-			wantContent:  "Here is my direct answer.",
-		},
-		{
-			name:          "thinking with tool call",
-			input:         "Let me search for that...</think>I'll use a tool.<tool_call>search<arg_key>query</arg_key><arg_value>test</arg_value></tool_call>",
-			thinkEnabled:  true,
-			wantThinking:  "Let me search for that...",
-			wantContent:   "I'll use a tool.",
-			wantToolCalls: 1,
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			p := &Parser{}
-
-			var thinkValue *api.ThinkValue
-			if tt.thinkEnabled {
-				thinkValue = &api.ThinkValue{Value: true}
-			} else {
-				thinkValue = &api.ThinkValue{Value: false}
-			}
-
-			// Define tools for tool call tests
-			props := api.NewToolPropertiesMap()
-			props.Set("query", api.ToolProperty{Type: api.PropertyType{"string"}})
-			tools := []api.Tool{
-				{
-					Function: api.ToolFunction{
-						Name: "search",
-						Parameters: api.ToolFunctionParameters{
-							Properties: props,
-						},
-					},
-				},
-			}
-
-			p.Init(tools, nil, thinkValue)
-
-			content, thinking, calls, err := p.Add(tt.input, true)
-			if err != nil {
-				t.Fatalf("unexpected error: %v", err)
-			}
-
-			if thinking != tt.wantThinking {
-				t.Errorf("thinking = %q, want %q", thinking, tt.wantThinking)
-			}
-			if content != tt.wantContent {
-				t.Errorf("content = %q, want %q", content, tt.wantContent)
-			}
-			if len(calls) != tt.wantToolCalls {
-				t.Errorf("len(calls) = %d, want %d", len(calls), tt.wantToolCalls)
-			}
-		})
-	}
-}
-
-func TestParserToolCall(t *testing.T) {
-	p := &Parser{}
-
-	props := api.NewToolPropertiesMap()
-	props.Set("location", api.ToolProperty{Type: api.PropertyType{"string"}})
-	props.Set("unit", api.ToolProperty{Type: api.PropertyType{"string"}})
-	tools := []api.Tool{
-		{
-			Function: api.ToolFunction{
-				Name: "get_weather",
-				Parameters: api.ToolFunctionParameters{
-					Properties: props,
-				},
-			},
-		},
-	}
-
-	// Initialize with thinking disabled
-	tv := &api.ThinkValue{Value: false}
-	p.Init(tools, nil, tv)
-
-	input := "<tool_call>get_weather<arg_key>location</arg_key><arg_value>San Francisco</arg_value><arg_key>unit</arg_key><arg_value>celsius</arg_value></tool_call>"
-
-	_, _, calls, err := p.Add(input, true)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if len(calls) != 1 {
-		t.Fatalf("expected 1 tool call, got %d", len(calls))
-	}
-
-	call := calls[0]
-	if call.Function.Name != "get_weather" {
-		t.Errorf("function name = %q, want %q", call.Function.Name, "get_weather")
-	}
-
-	location, ok := call.Function.Arguments.Get("location")
-	if !ok || location != "San Francisco" {
-		t.Errorf("location = %v, want %q", location, "San Francisco")
-	}
-
-	unit, ok := call.Function.Arguments.Get("unit")
-	if !ok || unit != "celsius" {
-		t.Errorf("unit = %v, want %q", unit, "celsius")
-	}
-}
-
-func TestOverlap(t *testing.T) {
-	tests := []struct {
-		s    string
-		tag  string
-		want int
-	}{
-		{"hello<", "</think>", 1},
-		{"hello</", "</think>", 2},
-		{"hello</t", "</think>", 3},
-		{"hello</th", "</think>", 4},
-		{"hello</thi", "</think>", 5},
-		{"hello</thin", "</think>", 6},
-		{"hello</think", "</think>", 7},
-		{"hello</think>", "</think>", 8}, // Complete tag at end returns full length
-		{"hello", "</think>", 0},
-		{"", "</think>", 0},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.s+"_"+tt.tag, func(t *testing.T) {
-			got := overlap(tt.s, tt.tag)
-			if got != tt.want {
-				t.Errorf("overlap(%q, %q) = %d, want %d", tt.s, tt.tag, got, tt.want)
-			}
-		})
-	}
-}
-
-func TestTrailingWhitespaceLen(t *testing.T) {
-	tests := []struct {
-		s    string
-		want int
-	}{
-		{"hello   ", 3},
-		{"hello\n\t ", 3},
-		{"hello", 0},
-		{"", 0},
-		{"   ", 3},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.s, func(t *testing.T) {
-			got := trailingWhitespaceLen(tt.s)
-			if got != tt.want {
-				t.Errorf("trailingWhitespaceLen(%q) = %d, want %d", tt.s, got, tt.want)
-			}
-		})
-	}
-}

x/imagegen/models/glm4_moe_lite/render.go

@@ -1,175 +0,0 @@
-//go:build mlx
-
-package glm4_moe_lite
-
-import (
-	"encoding/json"
-	"fmt"
-	"strings"
-
-	"github.com/ollama/ollama/api"
-)
-
-// Renderer renders messages for GLM4-MoE-Lite models.
-//
-// GLM-4 Thinking Modes (ref: https://docs.z.ai/guides/capabilities/thinking-mode):
-//
-//  1. INTERLEAVED THINKING
-//     The model thinks between tool calls and after receiving tool results.
-//     This enables complex step-by-step reasoning: interpreting each tool output
-//     before deciding what to do next. Thinking blocks are preserved and returned
-//     with tool results to maintain reasoning continuity.
-//
-//  2. PRESERVED THINKING
-//     The model retains reasoning content from previous assistant turns in context.
-//     This preserves reasoning continuity across multi-turn conversations. The
-//     upstream API has a "clear_thinking" parameter to control this:
-//     - clear_thinking=true:  clears reasoning from previous turns (outputs </think>)
-//     - clear_thinking=false: preserves <think>...</think> blocks from previous turns
-//
-//  3. TURN-LEVEL THINKING
-//     Controls whether the model should reason on each turn. The upstream API
-//     uses "enable_thinking" parameter:
-//     - enable_thinking=true:  outputs <think> to start reasoning
-//     - enable_thinking=false: outputs </think> to skip reasoning
-//
-// OLLAMA DEFAULTS:
-//   - Thinking is ENABLED by default (thinkValue=nil or true outputs <think>)
-//   - Thinking is PRESERVED by default (reasoning content from previous turns is always
-//     included in <think>...</think> blocks, equivalent to clear_thinking=false)
-//   - Users can disable thinking per-turn via thinkValue=false
-type Renderer struct{}
-
-// Render renders messages into the GLM4 chat format.
-func (r *Renderer) Render(messages []api.Message, tools []api.Tool, thinkValue *api.ThinkValue) (string, error) {
-	var sb strings.Builder
-
-	sb.WriteString("[gMASK]<sop>")
-
-	if len(tools) > 0 {
-		sb.WriteString("<|system|>\n")
-		sb.WriteString("# Tools\n\n")
-		sb.WriteString("You may call one or more functions to assist with the user query.\n\n")
-		sb.WriteString("You are provided with function signatures within <tools></tools> XML tags:\n")
-		sb.WriteString("<tools>\n")
-		for _, tool := range tools {
-			d, _ := json.Marshal(tool)
-			sb.WriteString(formatToolJSON(d))
-			sb.WriteString("\n")
-		}
-		sb.WriteString("</tools>\n\n")
-		sb.WriteString("For each function call, output the function name and arguments within the following XML format:\n")
-		sb.WriteString("<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call>")
-	}
-
-	think := true
-	if thinkValue != nil && !thinkValue.Bool() {
-		think = false
-	}
-
-	for i, message := range messages {
-		switch message.Role {
-		case "user":
-			sb.WriteString("<|user|>")
-			sb.WriteString(message.Content)
-		case "assistant":
-			sb.WriteString("<|assistant|>")
-			if message.Thinking != "" {
-				sb.WriteString("<think>" + message.Thinking + "</think>")
-			} else {
-				sb.WriteString("</think>")
-			}
-			if message.Content != "" {
-				sb.WriteString(message.Content)
-			}
-			if len(message.ToolCalls) > 0 {
-				for _, toolCall := range message.ToolCalls {
-					sb.WriteString("<tool_call>" + toolCall.Function.Name)
-					sb.WriteString(renderToolArguments(toolCall.Function.Arguments))
-					sb.WriteString("</tool_call>")
-				}
-			}
-		case "tool":
-			if i == 0 || messages[i-1].Role != "tool" {
-				sb.WriteString("<|observation|>")
-			}
-			sb.WriteString("<tool_response>")
-			sb.WriteString(message.Content)
-			sb.WriteString("</tool_response>")
-		case "system":
-			sb.WriteString("<|system|>")
-			sb.WriteString(message.Content)
-		}
-	}
-
-	sb.WriteString("<|assistant|>")
-	if think {
-		sb.WriteString("<think>")
-	} else {
-		sb.WriteString("</think>")
-	}
-
-	return sb.String(), nil
-}
-
-// renderToolArguments converts tool call arguments to GLM4 XML format.
-func renderToolArguments(args api.ToolCallFunctionArguments) string {
-	var sb strings.Builder
-	for key, value := range args.All() {
-		sb.WriteString("<arg_key>" + key + "</arg_key>")
-		var valueStr string
-		if str, ok := value.(string); ok {
-			valueStr = str
-		} else {
-			jsonBytes, err := json.Marshal(value)
-			if err != nil {
-				valueStr = fmt.Sprintf("%v", value)
-			} else {
-				valueStr = string(jsonBytes)
-			}
-		}
-
-		sb.WriteString("<arg_value>" + valueStr + "</arg_value>")
-	}
-
-	return sb.String()
-}
-
-// formatToolJSON formats JSON for GLM4 tool definitions by adding spaces after : and ,
-func formatToolJSON(raw []byte) string {
-	var sb strings.Builder
-	sb.Grow(len(raw) + len(raw)/10)
-
-	inString := false
-	escaped := false
-	for i := range raw {
-		ch := raw[i]
-		sb.WriteByte(ch)
-
-		if inString {
-			if escaped {
-				escaped = false
-				continue
-			}
-			if ch == '\\' {
-				escaped = true
-				continue
-			}
-			if ch == '"' {
-				inString = false
-			}
-			continue
-		}
-
-		if ch == '"' {
-			inString = true
-			continue
-		}
-
-		if ch == ':' || ch == ',' {
-			sb.WriteByte(' ')
-		}
-	}
-
-	return sb.String()
-}

x/imagegen/models/glm4_moe_lite/render_test.go

@@ -1,205 +0,0 @@
-//go:build mlx
-
-package glm4_moe_lite
-
-import (
-	"strings"
-	"testing"
-
-	"github.com/ollama/ollama/api"
-)
-
-func TestRendererSimple(t *testing.T) {
-	r := &Renderer{}
-
-	messages := []api.Message{
-		{Role: "user", Content: "Hello"},
-	}
-
-	// Thinking enabled (default)
-	result, err := r.Render(messages, nil, nil)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	expected := "[gMASK]<sop><|user|>Hello<|assistant|><think>"
-	if result != expected {
-		t.Errorf("result = %q, want %q", result, expected)
-	}
-}
-
-func TestRendererThinkingDisabled(t *testing.T) {
-	r := &Renderer{}
-
-	messages := []api.Message{
-		{Role: "user", Content: "Hello"},
-	}
-
-	tv := &api.ThinkValue{Value: false}
-
-	result, err := r.Render(messages, nil, tv)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	expected := "[gMASK]<sop><|user|>Hello<|assistant|></think>"
-	if result != expected {
-		t.Errorf("result = %q, want %q", result, expected)
-	}
-}
-
-func TestRendererMultiTurn(t *testing.T) {
-	r := &Renderer{}
-
-	messages := []api.Message{
-		{Role: "user", Content: "What is 2+2?"},
-		{Role: "assistant", Content: "4", Thinking: "Let me calculate: 2+2=4"},
-		{Role: "user", Content: "And 3+3?"},
-	}
-
-	result, err := r.Render(messages, nil, nil)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	// Check key parts
-	if !strings.Contains(result, "[gMASK]<sop>") {
-		t.Error("missing [gMASK]<sop> prefix")
-	}
-	if !strings.Contains(result, "<|user|>What is 2+2?") {
-		t.Error("missing first user message")
-	}
-	if !strings.Contains(result, "<|assistant|><think>Let me calculate: 2+2=4</think>4") {
-		t.Error("missing assistant message with thinking")
-	}
-	if !strings.Contains(result, "<|user|>And 3+3?") {
-		t.Error("missing second user message")
-	}
-	if !strings.HasSuffix(result, "<|assistant|><think>") {
-		t.Errorf("should end with <|assistant|><think>, got suffix: %q", result[len(result)-30:])
-	}
-}
-
-func TestRendererWithSystem(t *testing.T) {
-	r := &Renderer{}
-
-	messages := []api.Message{
-		{Role: "system", Content: "You are a helpful assistant."},
-		{Role: "user", Content: "Hello"},
-	}
-
-	result, err := r.Render(messages, nil, nil)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if !strings.Contains(result, "<|system|>You are a helpful assistant.") {
-		t.Error("missing system message")
-	}
-}
-
-func TestRendererWithTools(t *testing.T) {
-	r := &Renderer{}
-
-	messages := []api.Message{
-		{Role: "user", Content: "What's the weather?"},
-	}
-
-	props := api.NewToolPropertiesMap()
-	props.Set("location", api.ToolProperty{Type: api.PropertyType{"string"}, Description: "The city"})
-	tools := []api.Tool{
-		{
-			Function: api.ToolFunction{
-				Name:        "get_weather",
-				Description: "Get the weather for a location",
-				Parameters: api.ToolFunctionParameters{
-					Type:       "object",
-					Properties: props,
-					Required:   []string{"location"},
-				},
-			},
-		},
-	}
-
-	result, err := r.Render(messages, tools, nil)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	// Check for tool system prompt
-	if !strings.Contains(result, "<|system|>") {
-		t.Error("missing system tag for tools")
-	}
-	if !strings.Contains(result, "# Tools") {
-		t.Error("missing tools header")
-	}
-	if !strings.Contains(result, "<tools>") {
-		t.Error("missing tools tag")
-	}
-	if !strings.Contains(result, "get_weather") {
-		t.Error("missing tool name")
-	}
-	if !strings.Contains(result, "</tools>") {
-		t.Error("missing closing tools tag")
-	}
-}
-
-func TestRendererWithToolCalls(t *testing.T) {
-	r := &Renderer{}
-
-	args := api.NewToolCallFunctionArguments()
-	args.Set("location", "San Francisco")
-
-	messages := []api.Message{
-		{Role: "user", Content: "What's the weather in SF?"},
-		{
-			Role: "assistant",
-			ToolCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: args,
-					},
-				},
-			},
-		},
-		{Role: "tool", Content: "Sunny, 72F"},
-	}
-
-	result, err := r.Render(messages, nil, nil)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if !strings.Contains(result, "<tool_call>get_weather") {
-		t.Error("missing tool call")
-	}
-	if !strings.Contains(result, "<arg_key>location</arg_key>") {
-		t.Error("missing arg_key")
-	}
-	if !strings.Contains(result, "<arg_value>San Francisco</arg_value>") {
-		t.Error("missing arg_value")
-	}
-	if !strings.Contains(result, "</tool_call>") {
-		t.Error("missing tool call closing tag")
-	}
-	if !strings.Contains(result, "<|observation|>") {
-		t.Error("missing observation tag")
-	}
-	if !strings.Contains(result, "<tool_response>Sunny, 72F</tool_response>") {
-		t.Error("missing tool response")
-	}
-}
-
-func TestFormatToolJSON(t *testing.T) {
-	input := []byte(`{"name":"test","value":123}`)
-	result := formatToolJSON(input)
-
-	// Should add spaces after : and ,
-	if !strings.Contains(result, ": ") {
-		t.Error("should add space after colon")
-	}
-	if !strings.Contains(result, ", ") {
-		t.Error("should add space after comma")
-	}
-}

x/imagegen/models/gpt_oss/gpt_oss.go

@@ -1,487 +0,0 @@
-//go:build mlx
-
-package gpt_oss
-
-import (
-	"encoding/json"
-	"fmt"
-	"math"
-	"os"
-	"path/filepath"
-
-	"github.com/ollama/ollama/x/imagegen/cache"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/nn"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-	"github.com/ollama/ollama/x/imagegen/tokenizer"
-)
-
-// RopeScaling holds YaRN or other RoPE scaling configuration
-type RopeScaling struct {
-	RopeType                      string  `json:"rope_type"`
-	Factor                        float32 `json:"factor"`
-	OriginalMaxPositionEmbeddings int32   `json:"original_max_position_embeddings"`
-	BetaFast                      float32 `json:"beta_fast"`
-	BetaSlow                      float32 `json:"beta_slow"`
-}
-
-type Config struct {
-	HiddenSize        int32        `json:"hidden_size"`
-	NumHiddenLayers   int32        `json:"num_hidden_layers"`
-	IntermediateSize  int32        `json:"intermediate_size"`
-	NumAttentionHeads int32        `json:"num_attention_heads"`
-	NumKeyValueHeads  int32        `json:"num_key_value_heads"`
-	VocabSize         int32        `json:"vocab_size"`
-	RMSNormEps        float32      `json:"rms_norm_eps"`
-	RopeTheta         float32      `json:"rope_theta"`
-	HeadDim           int32        `json:"head_dim"`
-	SlidingWindow     int32        `json:"sliding_window"`
-	NumLocalExperts   int32        `json:"num_local_experts"`
-	NumExpertsPerTok  int32        `json:"num_experts_per_tok"`
-	LayerTypes        []string     `json:"layer_types"`
-	SwiGLULimit       float32      `json:"swiglu_limit"`
-	RopeScaling       *RopeScaling `json:"rope_scaling"`
-	Scale             float32      `json:"-"` // computed: 1/sqrt(HeadDim)
-}
-
-type Attention struct {
-	QProj      *nn.Linear `weight:"self_attn.q_proj"`
-	KProj      *nn.Linear `weight:"self_attn.k_proj"`
-	VProj      *nn.Linear `weight:"self_attn.v_proj"`
-	OProj      *nn.Linear `weight:"self_attn.o_proj"`
-	Sinks      *mlx.Array `weight:"self_attn.sinks,optional"`
-	YarnFreqs  *mlx.Array // computed
-	YarnMscale float32
-}
-
-// swiGLU applies the GPT-OSS custom SwiGLU activation.
-// Formula: (gate * sigmoid(alpha * gate)) * (up + 1)
-// with clipping: gate to [None, limit], up to [-limit, limit]
-func swiGLU(gate, up *mlx.Array, alpha, limit float32) *mlx.Array {
-	// Clip gate to [None, limit]
-	gateClipped := mlx.ClipScalar(gate, 0, limit, false, true)
-
-	// Clip up to [-limit, limit]
-	upClipped := mlx.ClipScalar(up, -limit, limit, true, true)
-
-	// glu_scaled = alpha * gate_clipped
-	gluScaled := mlx.MulScalar(gateClipped, alpha)
-
-	// sig = sigmoid(glu_scaled)
-	sig := mlx.Sigmoid(gluScaled)
-
-	// out_glu = gate_clipped * sig
-	outGlu := mlx.Mul(gateClipped, sig)
-
-	// result = out_glu * (up_clipped + 1)
-	return mlx.Mul(outGlu, mlx.AddScalar(upClipped, 1.0))
-}
-
-// compiledSwiGLU is a singleton compiled SwiGLU function shared across all layers
-var compiledSwiGLU *mlx.CompiledFunc
-
-// getCompiledSwiGLU returns the compiled SwiGLU function, creating it once if needed
-func getCompiledSwiGLU() *mlx.CompiledFunc {
-	if compiledSwiGLU == nil {
-		const alpha float32 = 1.702
-		const limit float32 = 7.0
-		compiledSwiGLU = mlx.CompileShapeless(func(inputs []*mlx.Array) []*mlx.Array {
-			return []*mlx.Array{swiGLU(inputs[0], inputs[1], alpha, limit)}
-		}, true) // shapeless=true so it works for any input size
-	}
-	return compiledSwiGLU
-}
-
-// ComputeYarnFreqs computes YaRN-modified RoPE frequencies
-// Based on mlx-lm's YarnRoPE implementation
-func ComputeYarnFreqs(dims int32, base, scalingFactor float32, origMaxPos int32, betaFast, betaSlow float32) (*mlx.Array, float32) {
-	// yarn_find_correction_dim
-	yarnFindCorrectionDim := func(numRotations float64) float64 {
-		return float64(dims) * math.Log(float64(origMaxPos)/(numRotations*2*math.Pi)) / (2 * math.Log(float64(base)))
-	}
-
-	// yarn_find_correction_range
-	low := int(math.Floor(yarnFindCorrectionDim(float64(betaFast))))
-	high := int(math.Ceil(yarnFindCorrectionDim(float64(betaSlow))))
-	if low < 0 {
-		low = 0
-	}
-	if high > int(dims)-1 {
-		high = int(dims) - 1
-	}
-
-	// yarn_get_mscale
-	yarnGetMscale := func(scale, mscale float64) float64 {
-		if scale <= 1 {
-			return 1.0
-		}
-		return 0.1*mscale*math.Log(scale) + 1.0
-	}
-	mscale := float32(yarnGetMscale(float64(scalingFactor), 1.0) / yarnGetMscale(float64(scalingFactor), 0.0))
-
-	// Compute frequencies
-	// freq_extra = base ** (arange(0, dims, 2) / dims)
-	// freq_inter = scaling_factor * freq_extra
-	halfDims := dims / 2
-	freqData := make([]float32, halfDims)
-	for i := int32(0); i < halfDims; i++ {
-		exp := float64(2*i) / float64(dims)
-		freqExtra := math.Pow(float64(base), exp)
-		freqInter := float64(scalingFactor) * freqExtra
-
-		// linear ramp mask
-		var freqMask float64
-		if low == high {
-			freqMask = 0.0
-		} else {
-			t := (float64(i) - float64(low)) / float64(high-low)
-			if t < 0 {
-				t = 0
-			}
-			if t > 1 {
-				t = 1
-			}
-			freqMask = 1.0 - t
-		}
-
-		// Combined frequency: (inter * extra) / (inter * mask + extra * (1 - mask))
-		freqData[i] = float32((freqInter * freqExtra) / (freqInter*freqMask + freqExtra*(1-freqMask)))
-	}
-
-	return mlx.NewArray(freqData, []int32{halfDims}), mscale
-}
-
-// initYarn initializes YaRN RoPE if configured
-func (a *Attention) initYarn(cfg *Config) {
-	a.YarnMscale = 1.0
-	if cfg.RopeScaling != nil && cfg.RopeScaling.RopeType == "yarn" {
-		a.YarnFreqs, a.YarnMscale = ComputeYarnFreqs(
-			cfg.HeadDim,
-			cfg.RopeTheta,
-			cfg.RopeScaling.Factor,
-			cfg.RopeScaling.OriginalMaxPositionEmbeddings,
-			cfg.RopeScaling.BetaFast,
-			cfg.RopeScaling.BetaSlow,
-		)
-	}
-}
-
-func (a *Attention) Forward(x *mlx.Array, c cache.Cache, B, L int32, mask *mlx.Array, maskMode string, cfg *Config) *mlx.Array {
-	q := a.QProj.Forward(x)
-	k := a.KProj.Forward(x)
-	v := a.VProj.Forward(x)
-
-	// Reshape via AsStrided: [B, L, n_heads * head_dim] -> [B, n_heads, L, head_dim]
-	q = mlx.AsStrided(q, []int32{B, cfg.NumAttentionHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumAttentionHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumAttentionHeads * cfg.HeadDim), 1}, 0)
-	k = mlx.AsStrided(k, []int32{B, cfg.NumKeyValueHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumKeyValueHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumKeyValueHeads * cfg.HeadDim), 1}, 0)
-	v = mlx.AsStrided(v, []int32{B, cfg.NumKeyValueHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumKeyValueHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumKeyValueHeads * cfg.HeadDim), 1}, 0)
-
-	offset := 0
-	if c != nil {
-		offset = c.Offset()
-	}
-	if a.YarnFreqs != nil {
-		if a.YarnMscale != 1.0 {
-			q = mlx.MulScalar(q, a.YarnMscale)
-		}
-		q = mlx.RoPEWithFreqs(q, a.YarnFreqs, int(cfg.HeadDim), false, 1.0, offset)
-		k = mlx.RoPEWithFreqs(k, a.YarnFreqs, int(cfg.HeadDim), false, 1.0, offset)
-	} else {
-		q = mlx.RoPE(q, int(cfg.HeadDim), false, cfg.RopeTheta, 1.0, offset)
-		k = mlx.RoPE(k, int(cfg.HeadDim), false, cfg.RopeTheta, 1.0, offset)
-	}
-
-	if c != nil {
-		k, v = c.Update(k, v, int(L))
-	}
-
-	out := mlx.ScaledDotProductAttentionWithSinks(q, k, v, cfg.Scale, maskMode, mask, a.Sinks)
-	out = mlx.Reshape(mlx.Transpose(out, 0, 2, 1, 3), B, L, cfg.NumAttentionHeads*cfg.HeadDim)
-	return a.OProj.Forward(out)
-}
-
-// CreateSlidingWindowMask creates a causal mask with sliding window
-// Mirrors mlx-lm's create_causal_mask with window_size
-func CreateSlidingWindowMask(seqLen, queryStart, keyStart, keyLen, windowSize int) *mlx.Array {
-	// Build mask aligned to actual cache length (may be rotated)
-	// rinds covers existing keys: [keyStart, keyStart+keyLen)
-	// linds covers new queries: [queryStart, queryStart+seqLen)
-	rinds := mlx.Arange(float32(keyStart), float32(keyStart+keyLen), 1)     // [keyLen]
-	linds := mlx.Arange(float32(queryStart), float32(queryStart+seqLen), 1) // [seqLen]
-
-	linds = mlx.ExpandDims(linds, 1) // [seqLen, 1]
-	rinds = mlx.ExpandDims(rinds, 0) // [1, keyLen]
-
-	causalMask := mlx.GreaterEqual(linds, rinds) // [seqLen, keyLen]
-	windowLimit := mlx.AddScalar(rinds, float32(windowSize))
-	windowMask := mlx.LessArray(linds, windowLimit) // [seqLen, keyLen]
-
-	return mlx.LogicalAnd(causalMask, windowMask)
-}
-
-// MoE represents the Mixture of Experts SwiGLU layer with quantized experts.
-type MoE struct {
-	Router     *nn.Linear `weight:"mlp.router"`
-	TopK       int32
-	HiddenSize int32
-	GroupSize  int
-	Bits       int
-	// Expert weights (loaded manually via sanitizeExpertWeights)
-	GateBlocks, GateScales, GateBias *mlx.Array
-	UpBlocks, UpScales, UpBias       *mlx.Array
-	DownBlocks, DownScales, DownBias *mlx.Array
-}
-
-func (moe *MoE) Forward(x *mlx.Array, B, L int32) *mlx.Array {
-	logits := moe.Router.Forward(x)
-	neg := mlx.Neg(logits)
-	part := mlx.Argpartition(neg, int(moe.TopK)-1, -1)
-	topKIdx := mlx.Slice(part, []int32{0, 0, 0}, []int32{B, L, moe.TopK})
-	topKVal := mlx.TakeAlongAxis(logits, topKIdx, -1)
-	weights := mlx.Softmax(topKVal, -1)
-
-	xFlat := mlx.Reshape(x, B*L, 1, 1, moe.HiddenSize)
-	idxFlat := mlx.Reshape(topKIdx, B*L, moe.TopK)
-
-	doSort := B*L >= 64
-	var invOrder *mlx.Array
-	sorted := false
-	n := B * L * moe.TopK
-
-	if doSort {
-		idxAll := mlx.Flatten(idxFlat)
-		order := mlx.Argsort(idxAll, 0)
-		invOrder = mlx.Argsort(order, 0)
-		xFlat = mlx.ExpandDims(mlx.Take(mlx.Squeeze(xFlat, 1), mlx.FloorDivideScalar(order, moe.TopK), 0), 1)
-		idxFlat = mlx.Reshape(mlx.Take(idxAll, order, 0), n, 1)
-		sorted = true
-	}
-
-	gate := mlx.GatherQMM(xFlat, moe.GateBlocks, moe.GateScales, nil, nil, idxFlat, true, moe.GroupSize, moe.Bits, "mxfp4", sorted)
-	up := mlx.GatherQMM(xFlat, moe.UpBlocks, moe.UpScales, nil, nil, idxFlat, true, moe.GroupSize, moe.Bits, "mxfp4", sorted)
-
-	if moe.GateBias != nil {
-		gate = mlx.Add(gate, mlx.ExpandDims(mlx.Take(moe.GateBias, idxFlat, 0), 2))
-	}
-	if moe.UpBias != nil {
-		up = mlx.Add(up, mlx.ExpandDims(mlx.Take(moe.UpBias, idxFlat, 0), 2))
-	}
-
-	hidden := getCompiledSwiGLU().Call(gate, up)[0]
-
-	down := mlx.GatherQMM(hidden, moe.DownBlocks, moe.DownScales, nil, nil, idxFlat, true, moe.GroupSize, moe.Bits, "mxfp4", sorted)
-	if moe.DownBias != nil {
-		down = mlx.Add(down, mlx.ExpandDims(mlx.Take(moe.DownBias, idxFlat, 0), 2))
-	}
-
-	if doSort {
-		down = mlx.Reshape(mlx.Take(mlx.Squeeze(mlx.Squeeze(down, 2), 1), invOrder, 0), B*L, moe.TopK, moe.HiddenSize)
-	} else {
-		down = mlx.Squeeze(down, 2)
-	}
-
-	ewFlat := mlx.Reshape(weights, B*L, moe.TopK, 1)
-	return mlx.Reshape(mlx.Sum(mlx.Mul(down, ewFlat), 1, false), B, L, moe.HiddenSize)
-}
-
-type Block struct {
-	Attention    *Attention
-	MLP          *MoE
-	InputNorm    *nn.RMSNorm `weight:"input_layernorm"`
-	PostAttnNorm *nn.RMSNorm `weight:"post_attention_layernorm"`
-	LayerType    string      // "sliding_attention" or "full_attention"
-}
-
-func (b *Block) Forward(x *mlx.Array, c cache.Cache, B, L int32, mask *mlx.Array, maskMode string, cfg *Config) *mlx.Array {
-	h := mlx.Add(x, b.Attention.Forward(b.InputNorm.Forward(x, cfg.RMSNormEps), c, B, L, mask, maskMode, cfg))
-	return mlx.Add(h, b.MLP.Forward(b.PostAttnNorm.Forward(h, cfg.RMSNormEps), B, L))
-}
-
-type Model struct {
-	EmbedTokens *nn.Embedding `weight:"model.embed_tokens"`
-	Layers      []*Block      `weight:"-"` // loaded manually due to MoE sanitization
-	Norm        *nn.RMSNorm   `weight:"model.norm"`
-	LMHead      *nn.Linear    `weight:"lm_head"`
-
-	tok *tokenizer.Tokenizer
-	*Config
-}
-
-func (m *Model) Tokenizer() *tokenizer.Tokenizer { return m.tok }
-func (m *Model) NumLayers() int                     { return len(m.Layers) }
-func (m *Model) VocabSize() int32                   { return m.Config.VocabSize }
-
-func (m *Model) NewCache(int32) []cache.Cache {
-	caches := make([]cache.Cache, len(m.Layers))
-	for i, layer := range m.Layers {
-		if layer.LayerType == "sliding_attention" && m.SlidingWindow > 0 {
-			caches[i] = cache.NewRotatingKVCache(int(m.SlidingWindow))
-		} else {
-			caches[i] = cache.NewKVCache()
-		}
-	}
-	return caches
-}
-
-func (m *Model) Forward(tokens *mlx.Array, caches []cache.Cache) *mlx.Array {
-	B, L := tokens.Shape()[0], tokens.Shape()[1]
-	x := m.EmbedTokens.Forward(tokens)
-
-	// Find representative cache indices for sliding window attention
-	var swaIdx int = -1
-	for i, layer := range m.Layers {
-		if layer.LayerType == "sliding_attention" {
-			swaIdx = i
-			break
-		}
-	}
-
-	// Create masks once at model level
-	var fullMask, swaMask *mlx.Array
-	var fullMaskMode, swaMaskMode string
-
-	if L > 1 {
-		fullMaskMode = "causal"
-		if swaIdx >= 0 && m.SlidingWindow > 0 && caches != nil {
-			c := caches[swaIdx]
-			offset := c.Offset()
-			windowSize := int(m.SlidingWindow)
-			cacheLen := min(int(L), windowSize)
-			if offset > 0 {
-				cacheLen = min(c.Len()+int(L), windowSize)
-			}
-			if int(L) > windowSize {
-				swaMask = CreateSlidingWindowMask(int(L), offset, offset+int(L)-cacheLen, cacheLen, windowSize)
-			} else {
-				swaMaskMode = "causal"
-			}
-		} else {
-			swaMaskMode = "causal"
-		}
-	}
-
-	for i, layer := range m.Layers {
-		var c cache.Cache
-		if caches != nil {
-			c = caches[i]
-		}
-		mask, maskMode := fullMask, fullMaskMode
-		if layer.LayerType == "sliding_attention" {
-			mask, maskMode = swaMask, swaMaskMode
-		}
-		x = layer.Forward(x, c, B, L, mask, maskMode, m.Config)
-	}
-
-	return m.LMHead.Forward(m.Norm.Forward(x, m.RMSNormEps))
-}
-
-// sanitizeExpertWeights splits merged gate_up weights into separate gate/up arrays.
-// MXFP4 quantized weights require contiguous memory - strided views give wrong results.
-func sanitizeExpertWeights(weights *safetensors.ModelWeights, prefix string) (moe *MoE) {
-	gateUpBlocks, _ := weights.GetTensor(prefix + ".mlp.experts.gate_up_proj_blocks")
-	gateUpScales, _ := weights.GetTensor(prefix + ".mlp.experts.gate_up_proj_scales")
-	gateUpBias, _ := weights.GetTensor(prefix + ".mlp.experts.gate_up_proj_bias")
-	downBlocks, _ := weights.GetTensor(prefix + ".mlp.experts.down_proj_blocks")
-	downScales, _ := weights.GetTensor(prefix + ".mlp.experts.down_proj_scales")
-	downBias, _ := weights.GetTensor(prefix + ".mlp.experts.down_proj_bias")
-
-	moe = &MoE{GroupSize: 32, Bits: 4, DownScales: downScales, DownBias: downBias}
-
-	if gateUpBlocks != nil {
-		gub := mlx.FlattenRange(mlx.View(gateUpBlocks, int(mlx.DtypeUint32)), -2, -1)
-		s := gub.Shape()
-		moe.GateBlocks = mlx.Contiguous(mlx.SliceStride(gub, []int32{0, 0, 0}, []int32{s[0], s[1], s[2]}, []int32{1, 2, 1}))
-		moe.UpBlocks = mlx.Contiguous(mlx.SliceStride(gub, []int32{0, 1, 0}, []int32{s[0], s[1], s[2]}, []int32{1, 2, 1}))
-	}
-	if gateUpScales != nil {
-		s := gateUpScales.Shape()
-		moe.GateScales = mlx.Contiguous(mlx.SliceStride(gateUpScales, []int32{0, 0, 0}, []int32{s[0], s[1], s[2]}, []int32{1, 2, 1}))
-		moe.UpScales = mlx.Contiguous(mlx.SliceStride(gateUpScales, []int32{0, 1, 0}, []int32{s[0], s[1], s[2]}, []int32{1, 2, 1}))
-	}
-	if gateUpBias != nil {
-		s := gateUpBias.Shape()
-		moe.GateBias = mlx.Contiguous(mlx.SliceStride(gateUpBias, []int32{0, 0}, []int32{s[0], s[1]}, []int32{1, 2}))
-		moe.UpBias = mlx.Contiguous(mlx.SliceStride(gateUpBias, []int32{0, 1}, []int32{s[0], s[1]}, []int32{1, 2}))
-	}
-	if downBlocks != nil {
-		moe.DownBlocks = mlx.FlattenRange(mlx.View(downBlocks, int(mlx.DtypeUint32)), -2, -1)
-	}
-	return moe
-}
-
-func Load(modelPath string) (*Model, error) {
-	data, err := os.ReadFile(filepath.Join(modelPath, "config.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load config: %w", err)
-	}
-	var cfg Config
-	if err := json.Unmarshal(data, &cfg); err != nil {
-		return nil, fmt.Errorf("parse config: %w", err)
-	}
-	cfg.Scale = float32(1.0 / math.Sqrt(float64(cfg.HeadDim)))
-
-	weights, err := safetensors.LoadModelWeights(modelPath)
-	if err != nil {
-		return nil, fmt.Errorf("load weights: %w", err)
-	}
-
-	tok, err := tokenizer.Load(filepath.Join(modelPath, "tokenizer.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load tokenizer: %w", err)
-	}
-
-	m := &Model{
-		Layers: make([]*Block, cfg.NumHiddenLayers),
-		Config: &cfg,
-		tok:    tok,
-	}
-
-	// Load simple weights via struct tags
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return nil, err
-	}
-
-	// Load layers with custom MoE handling
-	for i := int32(0); i < cfg.NumHiddenLayers; i++ {
-		prefix := fmt.Sprintf("model.layers.%d", i)
-		layer := &Block{}
-		if err := safetensors.LoadModule(layer, weights, prefix); err != nil {
-			return nil, fmt.Errorf("layer %d: %w", i, err)
-		}
-
-		// Initialize attention YaRN
-		layer.Attention.initYarn(&cfg)
-
-		// Load MoE with weight sanitization
-		moe := sanitizeExpertWeights(weights, prefix)
-		moe.Router = layer.MLP.Router // Router was loaded by LoadModule
-		moe.TopK = cfg.NumExpertsPerTok
-		moe.HiddenSize = cfg.HiddenSize
-		layer.MLP = moe
-
-		// Set layer type
-		layer.LayerType = "full_attention"
-		if int(i) < len(cfg.LayerTypes) {
-			layer.LayerType = cfg.LayerTypes[i]
-		}
-
-		m.Layers[i] = layer
-	}
-
-	// Release safetensors BEFORE eval - lazy arrays have captured data,
-	// this reduces peak memory by freeing mmap during materialization
-	weights.ReleaseAll()
-	mlx.Eval(mlx.Collect(m)...)
-
-	return m, nil
-}
-
-func (m *Model) MaxContextLength() int32 {
-	if m.RopeScaling != nil && m.RopeScaling.OriginalMaxPositionEmbeddings > 0 {
-		return m.RopeScaling.OriginalMaxPositionEmbeddings
-	}
-	return 131072
-}

x/imagegen/models/llama/llama.go

@@ -1,152 +0,0 @@
-//go:build mlx
-
-package llama
-
-import (
-	"encoding/json"
-	"fmt"
-	"math"
-	"os"
-	"path/filepath"
-
-	"github.com/ollama/ollama/x/imagegen/cache"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/nn"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-	"github.com/ollama/ollama/x/imagegen/tokenizer"
-)
-
-type Config struct {
-	HiddenSize            int32   `json:"hidden_size"`
-	NumHiddenLayers       int32   `json:"num_hidden_layers"`
-	IntermediateSize      int32   `json:"intermediate_size"`
-	NumAttentionHeads     int32   `json:"num_attention_heads"`
-	NumKeyValueHeads      int32   `json:"num_key_value_heads"`
-	VocabSize             int32   `json:"vocab_size"`
-	RMSNormEps            float32 `json:"rms_norm_eps"`
-	RopeTheta             float32 `json:"rope_theta"`
-	MaxPositionEmbeddings int32   `json:"max_position_embeddings"`
-	HeadDim               int32   `json:"-"`
-	Scale                 float32 `json:"-"`
-}
-
-type Model struct {
-	EmbedTokens *nn.Embedding `weight:"model.embed_tokens"`
-	Layers      []*Layer      `weight:"model.layers"`
-	Norm        *nn.RMSNorm   `weight:"model.norm"`
-	Output      *nn.Linear    `weight:"lm_head,optional"`
-
-	tok *tokenizer.Tokenizer
-	*Config
-}
-
-type Layer struct {
-	Attention     *Attention
-	MLP           *MLP
-	AttentionNorm *nn.RMSNorm `weight:"input_layernorm"`
-	MLPNorm       *nn.RMSNorm `weight:"post_attention_layernorm"`
-}
-
-type Attention struct {
-	QProj *nn.Linear `weight:"self_attn.q_proj"`
-	KProj *nn.Linear `weight:"self_attn.k_proj"`
-	VProj *nn.Linear `weight:"self_attn.v_proj"`
-	OProj *nn.Linear `weight:"self_attn.o_proj"`
-}
-
-type MLP struct {
-	GateProj *nn.Linear `weight:"mlp.gate_proj"`
-	UpProj   *nn.Linear `weight:"mlp.up_proj"`
-	DownProj *nn.Linear `weight:"mlp.down_proj"`
-}
-
-func Load(modelPath string) (*Model, error) {
-	data, err := os.ReadFile(filepath.Join(modelPath, "config.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load config: %w", err)
-	}
-	var cfg Config
-	if err := json.Unmarshal(data, &cfg); err != nil {
-		return nil, fmt.Errorf("parse config: %w", err)
-	}
-	cfg.HeadDim = cfg.HiddenSize / cfg.NumAttentionHeads
-	cfg.Scale = float32(1.0 / math.Sqrt(float64(cfg.HeadDim)))
-
-	weights, err := safetensors.LoadModelWeights(modelPath)
-	if err != nil {
-		return nil, fmt.Errorf("load weights: %w", err)
-	}
-
-	tok, err := tokenizer.Load(filepath.Join(modelPath, "tokenizer.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load tokenizer: %w", err)
-	}
-
-	m := &Model{
-		Layers: make([]*Layer, cfg.NumHiddenLayers),
-		Config: &cfg,
-		tok:    tok,
-	}
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return nil, err
-	}
-	m.Output = nn.NewLinear(m.EmbedTokens.Weight, nil)
-
-	mlx.Eval(mlx.Collect(m)...)
-	weights.ReleaseAll()
-
-	return m, nil
-}
-
-func (m *Model) Forward(tokens *mlx.Array, caches []cache.Cache) *mlx.Array {
-	B, L := tokens.Shape()[0], tokens.Shape()[1]
-	h := m.EmbedTokens.Forward(tokens)
-	for i, layer := range m.Layers {
-		h = layer.Forward(h, caches[i], B, L, m.Config)
-	}
-	return m.Output.Forward(m.Norm.Forward(h, m.RMSNormEps))
-}
-
-func (l *Layer) Forward(x *mlx.Array, c cache.Cache, B, L int32, cfg *Config) *mlx.Array {
-	h := mlx.Add(x, l.Attention.Forward(l.AttentionNorm.Forward(x, cfg.RMSNormEps), c, B, L, cfg))
-	return mlx.Add(h, l.MLP.Forward(l.MLPNorm.Forward(h, cfg.RMSNormEps)))
-}
-
-func (a *Attention) Forward(x *mlx.Array, c cache.Cache, B, L int32, cfg *Config) *mlx.Array {
-	q := a.QProj.Forward(x)
-	k := a.KProj.Forward(x)
-	v := a.VProj.Forward(x)
-
-	q = mlx.AsStrided(q, []int32{B, cfg.NumAttentionHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumAttentionHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumAttentionHeads * cfg.HeadDim), 1}, 0)
-	k = mlx.AsStrided(k, []int32{B, cfg.NumKeyValueHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumKeyValueHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumKeyValueHeads * cfg.HeadDim), 1}, 0)
-	v = mlx.AsStrided(v, []int32{B, cfg.NumKeyValueHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumKeyValueHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumKeyValueHeads * cfg.HeadDim), 1}, 0)
-
-	q = mlx.RoPE(q, int(cfg.HeadDim), false, cfg.RopeTheta, 1.0, c.Offset())
-	k = mlx.RoPE(k, int(cfg.HeadDim), false, cfg.RopeTheta, 1.0, c.Offset())
-
-	k, v = c.Update(k, v, int(L))
-	out := mlx.ScaledDotProductAttention(q, k, v, cfg.Scale, L > 1)
-	out = mlx.Reshape(mlx.Transpose(out, 0, 2, 1, 3), B, L, cfg.NumAttentionHeads*cfg.HeadDim)
-	return a.OProj.Forward(out)
-}
-
-func (m *MLP) Forward(x *mlx.Array) *mlx.Array {
-	return m.DownProj.Forward(mlx.Mul(mlx.SiLU(m.GateProj.Forward(x)), m.UpProj.Forward(x)))
-}
-
-// Interface methods
-func (m *Model) NumLayers() int                     { return len(m.Layers) }
-func (m *Model) MaxContextLength() int32            { return m.MaxPositionEmbeddings }
-func (m *Model) VocabSize() int32                   { return m.Config.VocabSize }
-func (m *Model) Tokenizer() *tokenizer.Tokenizer { return m.tok }
-
-func (m *Model) NewCache(maxSeqLen int32) []cache.Cache {
-	caches := make([]cache.Cache, len(m.Layers))
-	for i := range caches {
-		caches[i] = cache.NewKVCache()
-	}
-	return caches
-}

x/imagegen/runner.go

@@ -39,19 +39,23 @@ func Execute(args []string) error {
 		return fmt.Errorf("--port is required")
 	}
 
-	// Initialize MLX
+	// Detect model type from capabilities
+	mode := detectModelMode(*modelName)
+	slog.Info("starting mlx runner", "model", *modelName, "port", *port, "mode", mode)
+
+	if mode != ModeImageGen {
+		return fmt.Errorf("imagegen runner only supports image generation models")
+	}
+
+	// Initialize MLX only for image generation mode.
 	if err := mlx.InitMLX(); err != nil {
 		slog.Error("unable to initialize MLX", "error", err)
 		return err
 	}
 	slog.Info("MLX library initialized")
 
-	// Detect model type from capabilities
-	mode := detectModelMode(*modelName)
-	slog.Info("starting mlx runner", "model", *modelName, "port", *port, "mode", mode)
-
 	// Create and start server
-	server, err := newServer(*modelName, *port, mode)
+	server, err := newServer(*modelName, *port)
 	if err != nil {
 		return fmt.Errorf("failed to create server: %w", err)
 	}
@@ -61,12 +65,6 @@ func Execute(args []string) error {
 	mux.HandleFunc("/health", server.healthHandler)
 	mux.HandleFunc("/completion", server.completionHandler)
 
-	// LLM-specific endpoints
-	if mode == ModeLLM {
-		mux.HandleFunc("/tokenize", server.tokenizeHandler)
-		mux.HandleFunc("/embedding", server.embeddingHandler)
-	}
-
 	httpServer := &http.Server{
 		Addr:    fmt.Sprintf("127.0.0.1:%d", *port),
 		Handler: mux,
@@ -112,34 +110,22 @@ func detectModelMode(modelName string) ModelMode {
 
 // server holds the model and handles HTTP requests.
 type server struct {
-	mode      ModelMode
 	modelName string
 	port      int
 
-	// Image generation model (when mode == ModeImageGen)
+	// Image generation model.
 	imageModel ImageModel
-
-	// LLM model (when mode == ModeLLM)
-	llmModel *llmState
 }
 
-// newServer creates a new server instance and loads the appropriate model.
+// newServer creates a new server instance for image generation models.
-func newServer(modelName string, port int, mode ModelMode) (*server, error) {
+func newServer(modelName string, port int) (*server, error) {
 	s := &server{
-		mode:      mode,
 		modelName: modelName,
 		port:      port,
 	}
 
-	switch mode {
+	if err := s.loadImageModel(); err != nil {
-	case ModeImageGen:
+		return nil, fmt.Errorf("failed to load image model: %w", err)
-		if err := s.loadImageModel(); err != nil {
-			return nil, fmt.Errorf("failed to load image model: %w", err)
-		}
-	case ModeLLM:
-		if err := s.loadLLMModel(); err != nil {
-			return nil, fmt.Errorf("failed to load LLM model: %w", err)
-		}
 	}
 
 	return s, nil
@@ -163,41 +149,5 @@ func (s *server) completionHandler(w http.ResponseWriter, r *http.Request) {
 		return
 	}
 
-	switch s.mode {
+	s.handleImageCompletion(w, r, req)
-	case ModeImageGen:
-		s.handleImageCompletion(w, r, req)
-	case ModeLLM:
-		s.handleLLMCompletion(w, r, req)
-	}
-}
-
-func (s *server) tokenizeHandler(w http.ResponseWriter, r *http.Request) {
-	if s.llmModel == nil {
-		http.Error(w, "LLM model not loaded", http.StatusInternalServerError)
-		return
-	}
-
-	var req struct {
-		Content string `json:"content"`
-	}
-	if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
-		http.Error(w, err.Error(), http.StatusBadRequest)
-		return
-	}
-
-	tok := s.llmModel.model.Tokenizer()
-	tokens := tok.Encode(req.Content, false)
-
-	// Convert int32 to int for JSON response
-	intTokens := make([]int, len(tokens))
-	for i, t := range tokens {
-		intTokens[i] = int(t)
-	}
-
-	w.Header().Set("Content-Type", "application/json")
-	json.NewEncoder(w).Encode(map[string][]int{"tokens": intTokens})
-}
-
-func (s *server) embeddingHandler(w http.ResponseWriter, r *http.Request) {
-	http.Error(w, "embeddings not yet implemented for MLX models", http.StatusNotImplemented)
 }

x/imagegen/server.go

@@ -30,13 +30,12 @@ import (
 // Server wraps an MLX runner subprocess to implement llm.LlamaServer.
 //
 // This implementation is compatible with Ollama's scheduler and can be loaded/unloaded
-// like any other model. It supports both LLM (safetensors) and image generation models.
+// like any other model. It is used for image generation models.
 type Server struct {
 	mu          sync.Mutex
 	cmd         *exec.Cmd
 	port        int
 	modelName   string
-	mode        ModelMode
 	vramSize    uint64
 	done        chan error
 	client      *http.Client
@@ -45,7 +44,7 @@ type Server struct {
 }
 
 // NewServer spawns a new MLX runner subprocess and waits until it's ready.
-func NewServer(modelName string, mode ModelMode) (*Server, error) {
+func NewServer(modelName string) (*Server, error) {
 	// Validate platform support before attempting to start
 	if err := CheckPlatformSupport(); err != nil {
 		return nil, err
@@ -119,7 +118,6 @@ func NewServer(modelName string, mode ModelMode) (*Server, error) {
 		cmd:       cmd,
 		port:      port,
 		modelName: modelName,
-		mode:      mode,
 		vramSize:  vramSize,
 		done:      make(chan error, 1),
 		client:    &http.Client{Timeout: 10 * time.Minute},
@@ -145,7 +143,7 @@ func NewServer(modelName string, mode ModelMode) (*Server, error) {
 		}
 	}()
 
-	slog.Info("starting mlx runner subprocess", "exe", exe, "model", modelName, "port", port, "mode", mode)
+	slog.Info("starting mlx runner subprocess", "exe", exe, "model", modelName, "port", port)
 	if err := cmd.Start(); err != nil {
 		return nil, fmt.Errorf("failed to start mlx runner: %w", err)
 	}
@@ -396,36 +394,7 @@ func (s *Server) Embedding(ctx context.Context, input string) ([]float32, int, e
 
 // Tokenize tokenizes the input content.
 func (s *Server) Tokenize(ctx context.Context, content string) ([]int, error) {
-	body, err := json.Marshal(map[string]string{"content": content})
+	return nil, errors.New("tokenization not supported for image generation models")
-	if err != nil {
-		return nil, err
-	}
-
-	url := fmt.Sprintf("http://127.0.0.1:%d/tokenize", s.port)
-	req, err := http.NewRequestWithContext(ctx, "POST", url, bytes.NewReader(body))
-	if err != nil {
-		return nil, err
-	}
-	req.Header.Set("Content-Type", "application/json")
-
-	resp, err := s.client.Do(req)
-	if err != nil {
-		return nil, err
-	}
-	defer resp.Body.Close()
-
-	if resp.StatusCode != http.StatusOK {
-		return nil, fmt.Errorf("tokenize failed: %d", resp.StatusCode)
-	}
-
-	var result struct {
-		Tokens []int `json:"tokens"`
-	}
-	if err := json.NewDecoder(resp.Body).Decode(&result); err != nil {
-		return nil, err
-	}
-
-	return result.Tokens, nil
 }
 
 // Detokenize converts tokens back to text.

x/mlxrunner/cache.go

@@ -30,21 +30,80 @@ type cacheSession struct {
 	remaining []int32
 }
 
+func appendCacheState(dst []*mlx.Array, c cache.Cache) []*mlx.Array {
+	if c == nil {
+		return dst
+	}
+
+	keys, values := c.State()
+	if keys != nil && keys.Valid() {
+		dst = append(dst, keys)
+	}
+	if values != nil && values.Valid() {
+		dst = append(dst, values)
+	}
+
+	return dst
+}
+
+func (c *kvCache) free() {
+	for i, kv := range c.caches {
+		if kv == nil {
+			continue
+		}
+		kv.Free()
+		c.caches[i] = nil
+	}
+	c.caches = nil
+	c.tokens = nil
+}
+
+func (c *kvCache) cachesCanTrim() bool {
+	for _, kv := range c.caches {
+		if kv == nil {
+			continue
+		}
+		if !kv.CanTrim() {
+			return false
+		}
+	}
+	return true
+}
+
+func (c *kvCache) trimToPrefix(prefix int) {
+	for _, kv := range c.caches {
+		if kv == nil || !kv.CanTrim() {
+			continue
+		}
+		if trim := kv.Offset() - prefix; trim > 0 {
+			kv.Trim(trim)
+		}
+	}
+	if prefix < len(c.tokens) {
+		c.tokens = c.tokens[:prefix]
+	}
+}
+
 // 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 {
+	ensureCaches := func() {
+		if len(c.caches) != 0 {
+			return
+		}
 		if cacheFactory, ok := m.(interface{ NewCaches() []cache.Cache }); ok {
 			c.caches = cacheFactory.NewCaches()
-		} else {
+			return
-			c.caches = make([]cache.Cache, m.NumLayers())
+		}
-			for i := range c.caches {
+		c.caches = make([]cache.Cache, m.NumLayers())
-				c.caches[i] = cache.NewKVCache()
+		for i := range c.caches {
-			}
+			c.caches[i] = cache.NewKVCache()
 		}
 	}
+	ensureCaches()
 
 	remaining := c.findRemaining(inputs)
+	ensureCaches()
 
 	return &cacheSession{
 		cache:     c,
@@ -56,18 +115,36 @@ func (c *kvCache) begin(m base.Model, inputs []int32) *cacheSession {
 
 // close saves the token state if the forward pass ran.
 func (s *cacheSession) close() {
-	if offset := s.caches[0].Offset(); offset > 0 {
+	if len(s.caches) == 0 {
-		// Ensure that if we have run the forward pass and set the metadata
+		return
-		// 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]
 	}
+
+	offset := -1
+	arrays := make([]*mlx.Array, 0, 2*len(s.caches))
+	for _, kv := range s.caches {
+		if kv == nil {
+			continue
+		}
+		// Mixed cache types (e.g. recurrent + KV) can transiently report different
+		// offsets, so use the minimum as the safe reusable token prefix.
+		if off := kv.Offset(); offset < 0 || off < offset {
+			offset = off
+		}
+		arrays = appendCacheState(arrays, kv)
+	}
+	if offset <= 0 {
+		return
+	}
+
+	// Ensure that if we have run the forward pass and set the metadata
+	// that we also actually have the data.
+	mlx.AsyncEval(arrays...)
+
+	stored := append(s.inputs, s.outputs...)
+	if offset > len(stored) {
+		offset = len(stored)
+	}
+	s.cache.tokens = stored[:offset]
 }
 
 // findRemaining finds the longest common prefix between tokens and the cached
@@ -85,11 +162,13 @@ func (c *kvCache) findRemaining(tokens []int32) []int32 {
 	}
 
 	if prefix < len(c.tokens) {
-		trim := len(c.tokens) - prefix
+		if c.cachesCanTrim() {
-		for _, kv := range c.caches {
+			c.trimToPrefix(prefix)
-			kv.Trim(trim)
+		} else {
+			c.free()
+			slog.Info("Cache miss", "left", len(tokens), "matched", prefix, "reason", "non_trimmable_divergence")
+			return tokens
 		}
-		c.tokens = c.tokens[:prefix]
 	}
 
 	if prefix == 0 {
@@ -104,10 +183,21 @@ func (c *kvCache) log() {
 	if len(c.caches) == 0 {
 		return
 	}
+	offset := -1
 	var totalBytes int
 	for _, kv := range c.caches {
-		k, v := kv.State()
+		if kv == nil {
-		totalBytes += k.NumBytes() + v.NumBytes()
+			continue
+		}
+		if off := kv.Offset(); offset < 0 || off < offset {
+			offset = off
+		}
+		for _, a := range appendCacheState(nil, kv) {
+			totalBytes += a.NumBytes()
+		}
 	}
-	logutil.Trace(fmt.Sprintf("kv cache tokens: %d, size: %s", c.caches[0].Offset(), mlx.PrettyBytes(totalBytes)))
+	if offset < 0 {
+		return
+	}
+	logutil.Trace(fmt.Sprintf("kv cache tokens: %d, size: %s", offset, mlx.PrettyBytes(totalBytes)))
 }

x/mlxrunner/cache/cache.go

@@ -9,7 +9,9 @@ import (
 
 type Cache interface {
 	Update(keys, values *mlx.Array) (newKeys, newValues *mlx.Array)
+	// State returns the cache-owned state roots that should be kept/evaluated.
 	State() (keys, values *mlx.Array)
+	CanTrim() bool
 	Trim(int) int
 	Clone() Cache
 	Free()
@@ -60,13 +62,15 @@ func (c *KVCache) Update(keys, values *mlx.Array) (*mlx.Array, *mlx.Array) {
 }
 
 func (c *KVCache) State() (*mlx.Array, *mlx.Array) {
-	if c.offset == c.keys.Dim(2) {
+	if c.keys == nil || c.values == nil {
-		return c.keys, c.values
+		return nil, nil
 	}
 	return c.keys.Slice(mlx.Slice(), mlx.Slice(), mlx.Slice(0, c.offset), mlx.Slice()),
 		c.values.Slice(mlx.Slice(), mlx.Slice(), mlx.Slice(0, c.offset), mlx.Slice())
 }
 
+func (c *KVCache) CanTrim() bool { return true }
+
 func (c *KVCache) Trim(n int) int {
 	n = min(c.offset, n)
 	c.offset -= n
@@ -183,13 +187,15 @@ func (c *RotatingKVCache) update(keys, values *mlx.Array) (*mlx.Array, *mlx.Arra
 }
 
 func (c *RotatingKVCache) State() (*mlx.Array, *mlx.Array) {
-	if c.offset < c.keys.Dim(2) {
+	if c.keys == nil || c.values == nil {
-		return c.keys.Slice(mlx.Slice(), mlx.Slice(), mlx.Slice(0, c.offset), mlx.Slice()),
+		return nil, nil
-			c.values.Slice(mlx.Slice(), mlx.Slice(), mlx.Slice(0, c.offset), mlx.Slice())
 	}
-	return c.keys, c.values
+	return c.keys.Slice(mlx.Slice(), mlx.Slice(), mlx.Slice(0, c.offset), mlx.Slice()),
+		c.values.Slice(mlx.Slice(), mlx.Slice(), mlx.Slice(0, c.offset), mlx.Slice())
 }
 
+func (c *RotatingKVCache) CanTrim() bool { return true }
+
 func (c *RotatingKVCache) Trim(n int) int {
 	n = min(c.offset, n)
 	c.offset -= n

x/mlxrunner/cache/recurrent.go

@@ -0,0 +1,161 @@
+//go:build mlx
+
+package cache
+
+import "github.com/ollama/ollama/x/mlxrunner/mlx"
+
+// RecurrentCache stores state for linear-recurrent layers.
+//
+// Conv state shape: [B, convTail, convDim]
+// Delta state shape: [B, numVHeads, headVDim, headKDim]
+type RecurrentCache struct {
+	convState  *mlx.Array
+	deltaState *mlx.Array
+	offset     int
+
+	convTail  int
+	convDim   int
+	numVHeads int
+	headVDim  int
+	headKDim  int
+}
+
+func (c *RecurrentCache) setStateRaw(old, v *mlx.Array) *mlx.Array {
+	if v == nil || !v.Valid() {
+		return old
+	}
+	if old == v {
+		return old
+	}
+
+	mlx.Pin(v)
+	if old != nil && old != v {
+		mlx.Unpin(old)
+	}
+
+	return v
+}
+
+func (c *RecurrentCache) setStateDetached(old, v *mlx.Array, ensureContiguous bool) *mlx.Array {
+	if v == nil || !v.Valid() {
+		return old
+	}
+	if old == v {
+		return old
+	}
+
+	root := v
+	if ensureContiguous {
+		root = mlx.Contiguous(v, false)
+	}
+	detached := root.Clone()
+
+	mlx.Pin(detached)
+	if old != nil && old != detached {
+		mlx.Unpin(old)
+	}
+
+	return detached
+}
+
+func snapshotPinned(a *mlx.Array) *mlx.Array {
+	if a == nil || !a.Valid() {
+		return nil
+	}
+	snap := mlx.Copy(a)
+	mlx.Eval(snap)
+	mlx.Pin(snap)
+	return snap
+}
+
+func NewRecurrentCache(convTail, convDim, numVHeads, headVDim, headKDim int32) *RecurrentCache {
+	return &RecurrentCache{
+		convTail:  int(convTail),
+		convDim:   int(convDim),
+		numVHeads: int(numVHeads),
+		headVDim:  int(headVDim),
+		headKDim:  int(headKDim),
+	}
+}
+
+func (c *RecurrentCache) ensure(batch int, dtype mlx.DType) {
+	if batch <= 0 {
+		batch = 1
+	}
+
+	needConv := c.convState == nil || !c.convState.Valid() || c.convState.DType() != dtype ||
+		c.convState.Dim(0) != batch || c.convState.Dim(1) != c.convTail || c.convState.Dim(2) != c.convDim
+	needDelta := c.deltaState == nil || !c.deltaState.Valid() || c.deltaState.DType() != dtype ||
+		c.deltaState.Dim(0) != batch || c.deltaState.Dim(1) != c.numVHeads || c.deltaState.Dim(2) != c.headVDim || c.deltaState.Dim(3) != c.headKDim
+	if !needConv && !needDelta {
+		return
+	}
+
+	if needConv {
+		c.convState = c.setStateRaw(c.convState, mlx.Zeros(dtype, batch, c.convTail, c.convDim))
+	}
+	if needDelta {
+		c.deltaState = c.setStateRaw(c.deltaState, mlx.Zeros(dtype, batch, c.numVHeads, c.headVDim, c.headKDim))
+	}
+}
+
+func (c *RecurrentCache) ConvState(batch int, dtype mlx.DType) *mlx.Array {
+	c.ensure(batch, dtype)
+	return c.convState
+}
+
+func (c *RecurrentCache) SetConvState(v *mlx.Array) {
+	c.convState = c.setStateDetached(c.convState, v, true)
+}
+
+func (c *RecurrentCache) DeltaState(batch int, dtype mlx.DType) *mlx.Array {
+	c.ensure(batch, dtype)
+	return c.deltaState
+}
+
+func (c *RecurrentCache) SetDeltaState(v *mlx.Array) {
+	c.deltaState = c.setStateDetached(c.deltaState, v, false)
+}
+
+func (c *RecurrentCache) Advance(n int) {
+	c.offset += n
+}
+
+func (c *RecurrentCache) Update(keys, values *mlx.Array) (*mlx.Array, *mlx.Array) {
+	return keys, values
+}
+
+func (c *RecurrentCache) State() (*mlx.Array, *mlx.Array) {
+	return c.convState, c.deltaState
+}
+
+func (c *RecurrentCache) CanTrim() bool { return false }
+
+func (c *RecurrentCache) Trim(n int) int {
+	// Recurrent state is not directly trimmable. Divergent prefixes must drop the cache.
+	_ = n
+	return 0
+}
+
+func (c *RecurrentCache) Clone() Cache {
+	clone := &RecurrentCache{
+		offset:     c.offset,
+		convTail:   c.convTail,
+		convDim:    c.convDim,
+		numVHeads:  c.numVHeads,
+		headVDim:   c.headVDim,
+		headKDim:   c.headKDim,
+		convState:  snapshotPinned(c.convState),
+		deltaState: snapshotPinned(c.deltaState),
+	}
+	return clone
+}
+
+func (c *RecurrentCache) Free() {
+	mlx.Unpin(c.convState, c.deltaState)
+	c.convState, c.deltaState = nil, nil
+	c.offset = 0
+}
+
+func (c *RecurrentCache) Offset() int { return c.offset }
+func (c *RecurrentCache) Len() int    { return c.offset }

x/mlxrunner/client.go

@@ -202,7 +202,6 @@ type CompletionResponse struct {
 	PromptEvalDuration time.Duration
 	EvalCount          int
 	EvalDuration       time.Duration
-	PeakMemory         uint64
 
 	Error *api.StatusError
 }
@@ -284,7 +283,6 @@ func (c *Client) Completion(ctx context.Context, req llm.CompletionRequest, fn f
 			PromptEvalDuration: raw.PromptEvalDuration,
 			EvalCount:          raw.EvalCount,
 			EvalDuration:       raw.EvalDuration,
-			PeakMemory:         raw.PeakMemory,
 		}
 
 		fn(cresp)

x/mlxrunner/imports.go

@@ -7,4 +7,6 @@ import (
 	_ "github.com/ollama/ollama/x/models/glm4_moe_lite"
 	_ "github.com/ollama/ollama/x/models/llama"
 	_ "github.com/ollama/ollama/x/models/qwen3"
+	_ "github.com/ollama/ollama/x/models/qwen3_5"
+	_ "github.com/ollama/ollama/x/models/qwen3_5_moe"
 )

x/mlxrunner/mlx/array.go

@@ -20,7 +20,7 @@ import (
 type Array struct {
 	ctx    C.mlx_array
 	name   string
-	pinned bool
+	pinned int
 }
 
 var arrays []*Array
@@ -129,7 +129,7 @@ func (t *Array) Clone() *Array {
 func Pin(s ...*Array) {
 	for _, t := range s {
 		if t != nil {
-			t.pinned = true
+			t.pinned++
 		}
 	}
 }
@@ -138,7 +138,7 @@ func Pin(s ...*Array) {
 func Unpin(s ...*Array) {
 	for _, t := range s {
 		if t != nil {
-			t.pinned = false
+			t.pinned--
 		}
 	}
 }
@@ -148,7 +148,7 @@ func Unpin(s ...*Array) {
 func Sweep() {
 	n := 0
 	for _, t := range arrays {
-		if t.pinned && t.Valid() {
+		if t.pinned > 0 && t.Valid() {
 			arrays[n] = t
 			n++
 		} else if t.Valid() {
@@ -175,7 +175,7 @@ func (t *Array) String() string {
 func (t *Array) LogValue() slog.Value {
 	attrs := []slog.Attr{
 		slog.String("name", t.name),
-		slog.Bool("pinned", t.pinned),
+		slog.Int("pinned", t.pinned),
 	}
 	if t.Valid() {
 		attrs = append(attrs,

x/mlxrunner/mlx/gated_delta.go

@@ -0,0 +1,370 @@
+//go:build mlx
+
+package mlx
+
+// #include <stdlib.h>
+// #include "generated.h"
+import "C"
+
+import (
+	"sync"
+	"unsafe"
+)
+
+var (
+	gatedDeltaMetalKernelOnce sync.Once
+	gatedDeltaMetalKernel     C.mlx_fast_metal_kernel
+	gatedDeltaMetalDisabled   bool
+)
+
+const gatedDeltaMetalKernelSource = `
+auto n = thread_position_in_grid.z;
+auto b_idx = n / Hv;
+auto hv_idx = n % Hv;
+auto hk_idx = hv_idx / (Hv / Hk);
+constexpr int n_per_t = Dk / 32;
+
+// q, k: [B, T, Hk, Dk]
+auto q_ = q + b_idx * T * Hk * Dk + hk_idx * Dk;
+auto k_ = k + b_idx * T * Hk * Dk + hk_idx * Dk;
+
+// v, y: [B, T, Hv, Dv]
+auto v_ = v + b_idx * T * Hv * Dv + hv_idx * Dv;
+y += b_idx * T * Hv * Dv + hv_idx * Dv;
+
+auto dk_idx = thread_position_in_threadgroup.x;
+auto dv_idx = thread_position_in_grid.y;
+
+// state_in, state_out: [B, Hv, Dv, Dk]
+auto i_state = state_in + (n * Dv + dv_idx) * Dk;
+auto o_state = state_out + (n * Dv + dv_idx) * Dk;
+
+float state[n_per_t];
+for (int i = 0; i < n_per_t; ++i) {
+  auto s_idx = n_per_t * dk_idx + i;
+  state[i] = static_cast<float>(i_state[s_idx]);
+}
+
+// g: [B, T, Hv]
+auto g_ = g + b_idx * T * Hv;
+auto beta_ = beta + b_idx * T * Hv;
+
+for (int t = 0; t < T; ++t) {
+  float kv_mem = 0.0f;
+  for (int i = 0; i < n_per_t; ++i) {
+    auto s_idx = n_per_t * dk_idx + i;
+    state[i] = state[i] * g_[hv_idx];
+    kv_mem += state[i] * k_[s_idx];
+  }
+  kv_mem = simd_sum(kv_mem);
+
+  auto delta = (v_[dv_idx] - kv_mem) * beta_[hv_idx];
+
+  float out = 0.0f;
+  for (int i = 0; i < n_per_t; ++i) {
+    auto s_idx = n_per_t * dk_idx + i;
+    state[i] = state[i] + k_[s_idx] * delta;
+    out += state[i] * q_[s_idx];
+  }
+  out = simd_sum(out);
+  if (thread_index_in_simdgroup == 0) {
+    y[dv_idx] = static_cast<InT>(out);
+  }
+
+  q_ += Hk * Dk;
+  k_ += Hk * Dk;
+  v_ += Hv * Dv;
+  y += Hv * Dv;
+  g_ += Hv;
+  beta_ += Hv;
+}
+
+for (int i = 0; i < n_per_t; ++i) {
+  auto s_idx = n_per_t * dk_idx + i;
+  o_state[s_idx] = static_cast<InT>(state[i]);
+}
+`
+
+func cStringVector(values []string) (C.mlx_vector_string, func(), bool) {
+	vec := C.mlx_vector_string_new()
+	ok := true
+	for _, s := range values {
+		cs := C.CString(s)
+		if C.mlx_vector_string_append_value(vec, cs) != 0 {
+			ok = false
+		}
+		C.free(unsafe.Pointer(cs))
+		if !ok {
+			break
+		}
+	}
+	cleanup := func() {
+		C.mlx_vector_string_free(vec)
+	}
+	return vec, cleanup, ok
+}
+
+func initGatedDeltaMetalKernel() {
+	inputs, freeInputs, ok := cStringVector([]string{"q", "k", "v", "g", "beta", "state_in", "T"})
+	if !ok {
+		gatedDeltaMetalDisabled = true
+		freeInputs()
+		return
+	}
+	defer freeInputs()
+
+	outputs, freeOutputs, ok := cStringVector([]string{"y", "state_out"})
+	if !ok {
+		gatedDeltaMetalDisabled = true
+		freeOutputs()
+		return
+	}
+	defer freeOutputs()
+
+	cName := C.CString("gated_delta_step")
+	defer C.free(unsafe.Pointer(cName))
+	cSource := C.CString(gatedDeltaMetalKernelSource)
+	defer C.free(unsafe.Pointer(cSource))
+	cHeader := C.CString("")
+	defer C.free(unsafe.Pointer(cHeader))
+
+	gatedDeltaMetalKernel = C.mlx_fast_metal_kernel_new(
+		cName,
+		inputs,
+		outputs,
+		cSource,
+		cHeader,
+		C.bool(true),
+		C.bool(false),
+	)
+}
+
+// gatedDeltaKernel runs a fused Metal kernel for the qwen3.5 recurrent update.
+// It returns ok=false on unsupported shapes/devices or kernel setup/apply failure.
+func gatedDeltaKernel(q, k, v, g, beta, state *Array) (y, nextState *Array, ok bool) {
+	if gatedDeltaMetalDisabled {
+		return nil, nil, false
+	}
+	if q == nil || k == nil || v == nil || g == nil || beta == nil || state == nil {
+		return nil, nil, false
+	}
+
+	qd := q.Dims()
+	kd := k.Dims()
+	vd := v.Dims()
+	gd := g.Dims()
+	bd := beta.Dims()
+	sd := state.Dims()
+	if len(qd) != 4 || len(kd) != 4 || len(vd) != 4 || len(gd) != 3 || len(bd) != 3 || len(sd) != 4 {
+		return nil, nil, false
+	}
+
+	B, T, Hk, Dk := qd[0], qd[1], qd[2], qd[3]
+	if T <= 0 || Hk <= 0 || Dk <= 0 || Dk%32 != 0 {
+		return nil, nil, false
+	}
+	if kd[0] != B || kd[1] != T || kd[2] != Hk || kd[3] != Dk {
+		return nil, nil, false
+	}
+	Hv, Dv := vd[2], vd[3]
+	if vd[0] != B || vd[1] != T || Hv <= 0 || Dv <= 0 || Hv%Hk != 0 {
+		return nil, nil, false
+	}
+	if gd[0] != B || gd[1] != T || gd[2] != Hv {
+		return nil, nil, false
+	}
+	if bd[0] != B || bd[1] != T || bd[2] != Hv {
+		return nil, nil, false
+	}
+	if sd[0] != B || sd[1] != Hv || sd[2] != Dv || sd[3] != Dk {
+		return nil, nil, false
+	}
+
+	dtype := q.DType()
+	if k.DType() != dtype || v.DType() != dtype || g.DType() != dtype || beta.DType() != dtype || state.DType() != dtype {
+		return nil, nil, false
+	}
+
+	gatedDeltaMetalKernelOnce.Do(initGatedDeltaMetalKernel)
+	if gatedDeltaMetalDisabled {
+		return nil, nil, false
+	}
+
+	cfg := C.mlx_fast_metal_kernel_config_new()
+	defer C.mlx_fast_metal_kernel_config_free(cfg)
+
+	cInT := C.CString("InT")
+	defer C.free(unsafe.Pointer(cInT))
+	if C.mlx_fast_metal_kernel_config_add_template_arg_dtype(cfg, cInT, C.mlx_dtype(dtype)) != 0 {
+		gatedDeltaMetalDisabled = true
+		return nil, nil, false
+	}
+	for _, tpl := range []struct {
+		name  string
+		value int
+	}{
+		{name: "Dk", value: Dk},
+		{name: "Dv", value: Dv},
+		{name: "Hk", value: Hk},
+		{name: "Hv", value: Hv},
+	} {
+		cn := C.CString(tpl.name)
+		rc := C.mlx_fast_metal_kernel_config_add_template_arg_int(cfg, cn, C.int(tpl.value))
+		C.free(unsafe.Pointer(cn))
+		if rc != 0 {
+			gatedDeltaMetalDisabled = true
+			return nil, nil, false
+		}
+	}
+
+	yShape := []C.int{C.int(B), C.int(T), C.int(Hv), C.int(Dv)}
+	stateShape := []C.int{C.int(B), C.int(Hv), C.int(Dv), C.int(Dk)}
+	if C.mlx_fast_metal_kernel_config_add_output_arg(cfg, unsafe.SliceData(yShape), C.size_t(len(yShape)), C.mlx_dtype(dtype)) != 0 {
+		gatedDeltaMetalDisabled = true
+		return nil, nil, false
+	}
+	if C.mlx_fast_metal_kernel_config_add_output_arg(cfg, unsafe.SliceData(stateShape), C.size_t(len(stateShape)), C.mlx_dtype(dtype)) != 0 {
+		gatedDeltaMetalDisabled = true
+		return nil, nil, false
+	}
+	if C.mlx_fast_metal_kernel_config_set_grid(cfg, 32, C.int(Dv), C.int(B*Hv)) != 0 {
+		gatedDeltaMetalDisabled = true
+		return nil, nil, false
+	}
+	threadY := Dv
+	if threadY > 4 {
+		threadY = 4
+	}
+	if C.mlx_fast_metal_kernel_config_set_thread_group(cfg, 32, C.int(threadY), 1) != 0 {
+		gatedDeltaMetalDisabled = true
+		return nil, nil, false
+	}
+
+	tScalar := FromValue(T)
+	inputs := []C.mlx_array{
+		q.ctx,
+		k.ctx,
+		v.ctx,
+		g.ctx,
+		beta.ctx,
+		state.ctx,
+		tScalar.ctx,
+	}
+	inVec := C.mlx_vector_array_new_data(unsafe.SliceData(inputs), C.size_t(len(inputs)))
+	defer C.mlx_vector_array_free(inVec)
+
+	outVec := C.mlx_vector_array_new()
+	defer C.mlx_vector_array_free(outVec)
+	if C.mlx_fast_metal_kernel_apply(&outVec, gatedDeltaMetalKernel, inVec, cfg, DefaultStream().ctx) != 0 {
+		gatedDeltaMetalDisabled = true
+		return nil, nil, false
+	}
+	if int(C.mlx_vector_array_size(outVec)) < 2 {
+		return nil, nil, false
+	}
+
+	y = New("GATED_DELTA_METAL_Y")
+	nextState = New("GATED_DELTA_METAL_STATE")
+	C.mlx_vector_array_get(&y.ctx, outVec, 0)
+	C.mlx_vector_array_get(&nextState.ctx, outVec, 1)
+	return y, nextState, true
+}
+
+func repeatHeadsForGatedDelta(x *Array, repeatFactor int) *Array {
+	if repeatFactor <= 1 {
+		return x
+	}
+	shape := x.Dims()
+	x = ExpandDims(x, 3)
+	x = Tile(x, []int32{1, 1, 1, int32(repeatFactor), 1})
+	return Reshape(x, int32(shape[0]), int32(shape[1]), int32(shape[2]*repeatFactor), int32(shape[3]))
+}
+
+func gatedDeltaFallback(q, k, v, g, beta, state *Array) (y, nextState *Array) {
+	if q == nil || k == nil || v == nil || g == nil || beta == nil || state == nil {
+		return nil, nil
+	}
+
+	qd := q.Dims()
+	kd := k.Dims()
+	vd := v.Dims()
+	gd := g.Dims()
+	bd := beta.Dims()
+	sd := state.Dims()
+	if len(qd) != 4 || len(kd) != 4 || len(vd) != 4 || len(gd) != 3 || len(bd) != 3 || len(sd) != 4 {
+		return nil, nil
+	}
+
+	B, T, Hk, Dk := int32(qd[0]), int32(qd[1]), int32(qd[2]), int32(qd[3])
+	Hv, Dv := int32(vd[2]), int32(vd[3])
+	if T <= 0 || Hk <= 0 || Dk <= 0 || Hv <= 0 || Dv <= 0 || Hv%Hk != 0 {
+		return nil, nil
+	}
+	if kd[0] != int(B) || kd[1] != int(T) || kd[2] != int(Hk) || kd[3] != int(Dk) {
+		return nil, nil
+	}
+	if vd[0] != int(B) || vd[1] != int(T) {
+		return nil, nil
+	}
+	if gd[0] != int(B) || gd[1] != int(T) || gd[2] != int(Hv) {
+		return nil, nil
+	}
+	if bd[0] != int(B) || bd[1] != int(T) || bd[2] != int(Hv) {
+		return nil, nil
+	}
+	if sd[0] != int(B) || sd[1] != int(Hv) || sd[2] != int(Dv) || sd[3] != int(Dk) {
+		return nil, nil
+	}
+
+	repeatFactor := int(Hv / Hk)
+	q = repeatHeadsForGatedDelta(q, repeatFactor)
+	k = repeatHeadsForGatedDelta(k, repeatFactor)
+
+	nextState = state
+	if T == 1 {
+		qt := Squeeze(q, 1)
+		kt := Squeeze(k, 1)
+		vt := Squeeze(v, 1)
+		gt := Squeeze(g, 1)
+		bt := Squeeze(beta, 1)
+
+		nextState = Mul(nextState, ExpandDims(ExpandDims(gt, -1), -1))
+		kvMem := Sum(Mul(nextState, ExpandDims(kt, 2)), -1, false)
+		delta := Mul(Sub(vt, kvMem), ExpandDims(bt, -1))
+		nextState = Add(nextState, Mul(ExpandDims(kt, 2), ExpandDims(delta, -1)))
+		yt := Sum(Mul(nextState, ExpandDims(qt, 2)), -1, false)
+		return ExpandDims(yt, 1), nextState
+	}
+
+	outs := make([]*Array, 0, T)
+	for t := int32(0); t < T; t++ {
+		qt := Squeeze(SliceStartStop(q, []int32{0, t, 0, 0}, []int32{B, t + 1, Hv, Dk}), 1)
+		kt := Squeeze(SliceStartStop(k, []int32{0, t, 0, 0}, []int32{B, t + 1, Hv, Dk}), 1)
+		vt := Squeeze(SliceStartStop(v, []int32{0, t, 0, 0}, []int32{B, t + 1, Hv, Dv}), 1)
+		gt := Squeeze(SliceStartStop(g, []int32{0, t, 0}, []int32{B, t + 1, Hv}), 1)
+		bt := Squeeze(SliceStartStop(beta, []int32{0, t, 0}, []int32{B, t + 1, Hv}), 1)
+
+		nextState = Mul(nextState, ExpandDims(ExpandDims(gt, -1), -1))
+		kvMem := Sum(Mul(nextState, ExpandDims(kt, 2)), -1, false)
+		delta := Mul(Sub(vt, kvMem), ExpandDims(bt, -1))
+		nextState = Add(nextState, Mul(ExpandDims(kt, 2), ExpandDims(delta, -1)))
+		yt := Sum(Mul(nextState, ExpandDims(qt, 2)), -1, false)
+		outs = append(outs, ExpandDims(yt, 1))
+	}
+	return Concatenate(outs, 1), nextState
+}
+
+// GatedDelta runs the recurrent update operation.
+//
+// It uses the fused Metal kernel when available and otherwise falls back to a
+// backend-agnostic MLX implementation with identical inputs/outputs.
+func GatedDelta(q, k, v, g, beta, state *Array) (y, nextState *Array) {
+	if y, nextState, ok := gatedDeltaKernel(q, k, v, g, beta, state); ok {
+		return y, nextState
+	}
+	y, nextState = gatedDeltaFallback(q, k, v, g, beta, state)
+	if y == nil || nextState == nil {
+		panic("mlx.GatedDelta: fallback failed (invalid inputs or unsupported shapes)")
+	}
+	return y, nextState
+}

x/mlxrunner/mlx/mlx.go

@@ -19,7 +19,7 @@ func doEval(outputs []*Array, async bool) {
 	defer C.mlx_vector_array_free(vector)
 
 	for _, output := range outputs {
-		if output.Valid() {
+		if output != nil && output.Valid() {
 			C.mlx_vector_array_append_value(vector, output.ctx)
 		}
 	}

x/mlxrunner/mlx/ops_extra.go

@@ -113,6 +113,35 @@ func Where(condition, a, b *Array) *Array {
 	return out
 }
 
+func Conv1d(x, weight *Array, bias *Array, stride, padding, dilation, groups int32) *Array {
+	out := New("CONV1D")
+	C.mlx_conv1d(
+		&out.ctx,
+		x.ctx,
+		weight.ctx,
+		C.int(stride),
+		C.int(padding),
+		C.int(dilation),
+		C.int(groups),
+		DefaultStream().ctx,
+	)
+	if bias != nil && bias.Valid() {
+		out = Add(out, bias)
+	}
+	return out
+}
+
+func Contiguous(a *Array, allowColMajor bool) *Array {
+	out := New("CONTIGUOUS")
+	C.mlx_contiguous(&out.ctx, a.ctx, C.bool(allowColMajor), DefaultStream().ctx)
+	return out
+}
+
+func DepthwiseConv1d(x, weight *Array, bias *Array) *Array {
+	groups := int32(x.Dim(x.NumDims() - 1))
+	return Conv1d(x, weight, bias, 1, 0, 1, groups)
+}
+
 // Convenience wrappers (function-style for the model code)
 
 func Stack(arrays []*Array, axis int) *Array {
@@ -271,6 +300,24 @@ func Sigmoid(a *Array) *Array {
 	return a.Sigmoid()
 }
 
+func Exp(a *Array) *Array {
+	out := New("EXP")
+	C.mlx_exp(&out.ctx, a.ctx, DefaultStream().ctx)
+	return out
+}
+
+func Log(a *Array) *Array {
+	out := New("LOG")
+	C.mlx_log(&out.ctx, a.ctx, DefaultStream().ctx)
+	return out
+}
+
+func SoftmaxAxis(a *Array, axis int, precise bool) *Array {
+	out := New("SOFTMAX_AXIS")
+	C.mlx_softmax_axis(&out.ctx, a.ctx, C.int(axis), C.bool(precise), DefaultStream().ctx)
+	return out
+}
+
 func ScaledDotProductAttentionCausal(q, k, v *Array, scale float32, causalMask bool) *Array {
 	mask := New("")
 	sinks := New("")
@@ -288,7 +335,11 @@ func ScaledDotProductAttentionCausal(q, k, v *Array, scale float32, causalMask b
 
 func RMSNormFn(x, weight *Array, eps float32) *Array {
 	out := New("FAST_RMSNORM")
-	C.mlx_fast_rms_norm(&out.ctx, x.ctx, weight.ctx, C.float(eps), DefaultStream().ctx)
+	var w C.mlx_array
+	if weight != nil {
+		w = weight.ctx
+	}
+	C.mlx_fast_rms_norm(&out.ctx, x.ctx, w, C.float(eps), DefaultStream().ctx)
 	return out
 }
 
@@ -378,6 +429,15 @@ func Collect(v any) []*Array {
 	return arrays
 }
 
+func Copy(a *Array) *Array {
+	if a == nil || !a.Valid() {
+		return a
+	}
+	out := New("COPY")
+	C.mlx_copy(&out.ctx, a.ctx, DefaultStream().ctx)
+	return out
+}
+
 func collect(v reflect.Value, arrays *[]*Array, seen map[uintptr]bool) {
 	if !v.IsValid() {
 		return

x/mlxrunner/pipeline.go

@@ -16,11 +16,26 @@ import (
 	"github.com/ollama/ollama/x/mlxrunner/mlx"
 )
 
+func prefillChunkSize() int {
+	return 2 << 10
+}
+
 func (r *Runner) TextGenerationPipeline(request Request) error {
 	if r.Model == nil {
 		return errors.New("model not loaded")
 	}
 
+	enableCompile := true
+	if modelCompile, ok := r.Model.(interface{ EnableCompile() bool }); ok {
+		enableCompile = modelCompile.EnableCompile()
+	}
+	if enableCompile {
+		mlx.EnableCompile()
+	} else {
+		mlx.DisableCompile()
+	}
+	mlx.ResetPeakMemory()
+	ctx := request.Ctx
 	var (
 		sample, logprobs         *mlx.Array
 		nextSample, nextLogprobs *mlx.Array
@@ -36,19 +51,9 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 			mlx.LogArrays()
 			r.cache.log()
 		}
+		slog.Info("peak memory", "size", mlx.PrettyBytes(mlx.PeakMemory()))
 	}()
 
-	enableCompile := true
-	if modelCompile, ok := r.Model.(interface{ EnableCompile() bool }); ok {
-		enableCompile = modelCompile.EnableCompile()
-	}
-	if enableCompile {
-		mlx.EnableCompile()
-	} else {
-		mlx.DisableCompile()
-	}
-	mlx.ResetPeakMemory()
-
 	inputs := r.Tokenizer.Encode(request.Prompt, true)
 	if len(inputs) == 0 {
 		return errors.New("empty prompt")
@@ -73,24 +78,30 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 	defer session.close()
 	caches := session.caches
 	tokens := session.remaining
+	prefillChunk := prefillChunkSize()
+
+	materializeCaches := func() {
+		state := make([]*mlx.Array, 0, 2*len(caches))
+		for _, c := range caches {
+			state = appendCacheState(state, c)
+		}
+		if len(state) == 0 {
+			return
+		}
+		mlx.Eval(state...)
+	}
 
 	now := time.Now()
 	total, processed := len(tokens), 0
 	for total-processed > 1 {
-		if err := request.Ctx.Err(); err != nil {
+		if err := ctx.Err(); err != nil {
 			return err
 		}
 
-		n := min(2<<10, total-processed-1)
+		n := min(prefillChunk, total-processed-1)
 		r.Model.Forward(mlx.FromValues(tokens[processed:processed+n], n).ExpandDims(0), caches)
 		mlx.Sweep()
-		mlx.Eval(func() []*mlx.Array {
+		materializeCaches()
-			s := make([]*mlx.Array, 2*len(caches))
-			for i, c := range caches {
-				s[2*i], s[2*i+1] = c.State()
-			}
-			return s
-		}()...)
 		processed += n
 		slog.Info("Prompt processing progress", "processed", processed, "total", total)
 		mlx.ClearCache()
@@ -117,7 +128,7 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 
 	final := CompletionResponse{Done: true, PromptEvalCount: len(inputs), EvalCount: request.Options.MaxTokens, DoneReason: 1}
 	for i := range request.Options.MaxTokens {
-		if err := request.Ctx.Err(); err != nil {
+		if err := ctx.Err(); err != nil {
 			return err
 		}
 
@@ -139,8 +150,8 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 		}
 
 		select {
-		case <-request.Ctx.Done():
+		case <-ctx.Done():
-			return request.Ctx.Err()
+			return ctx.Err()
 		case request.Responses <- CompletionResponse{
 			Content: r.Decode(output, &b),
 		}:
@@ -156,10 +167,9 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 	}
 
 	final.EvalDuration = time.Since(now)
-	final.PeakMemory = uint64(mlx.PeakMemory())
 	select {
-	case <-request.Ctx.Done():
+	case <-ctx.Done():
-		return request.Ctx.Err()
+		return ctx.Err()
 	case request.Responses <- final:
 		return nil
 	}

x/models/nn/nn.go

@@ -15,6 +15,40 @@ type LinearLayer interface {
 	OutputDim() int32
 }
 
+// Conv1d applies 1D convolution over NLC input.
+type Conv1d struct {
+	Weight   *mlx.Array
+	Bias     *mlx.Array
+	Stride   int32
+	Padding  int32
+	Dilation int32
+	Groups   int32
+}
+
+func NewConv1d(weight, bias *mlx.Array, stride, padding, dilation, groups int32) *Conv1d {
+	if stride <= 0 {
+		stride = 1
+	}
+	if dilation <= 0 {
+		dilation = 1
+	}
+	if groups <= 0 {
+		groups = 1
+	}
+	return &Conv1d{
+		Weight:   weight,
+		Bias:     bias,
+		Stride:   stride,
+		Padding:  padding,
+		Dilation: dilation,
+		Groups:   groups,
+	}
+}
+
+func (c *Conv1d) Forward(x *mlx.Array) *mlx.Array {
+	return mlx.Conv1d(x, c.Weight, c.Bias, c.Stride, c.Padding, c.Dilation, c.Groups)
+}
+
 // Linear applies an affine transformation: y = x @ W.T + b
 type Linear struct {
 	Weight *mlx.Array

x/models/qwen3_5/qwen3_5_test.go

@@ -0,0 +1,159 @@
+//go:build mlx
+
+package qwen3_5
+
+import (
+	"testing"
+
+	"github.com/ollama/ollama/x/mlxrunner/cache"
+	"github.com/ollama/ollama/x/mlxrunner/mlx"
+)
+
+func TestParseConfigNestedDefaults(t *testing.T) {
+	data := []byte(`{
+		"model_type": "Qwen3_5MoeForConditionalGeneration",
+		"text_config": {
+			"hidden_size": 4096,
+			"intermediate_size": 14336,
+			"num_hidden_layers": 8,
+			"num_attention_heads": 32,
+			"num_key_value_heads": 8,
+			"head_dim": 128,
+			"linear_num_value_heads": 64,
+			"linear_num_key_heads": 16,
+			"linear_key_head_dim": 128,
+			"linear_value_head_dim": 128,
+			"linear_conv_kernel_dim": 4,
+			"num_experts": 16,
+			"num_experts_per_tok": 4,
+			"moe_intermediate_size": 2048,
+			"shared_expert_intermediate_size": 4096,
+			"rope_parameters": {
+				"rope_theta": 500000,
+				"partial_rotary_factor": 0.5
+			}
+		}
+	}`)
+
+	cfg, err := parseConfig(data)
+	if err != nil {
+		t.Fatalf("parseConfig failed: %v", err)
+	}
+
+	if cfg.RopeTheta != 500000 {
+		t.Fatalf("rope theta mismatch: got %v", cfg.RopeTheta)
+	}
+	if cfg.RopeDim != 64 {
+		t.Fatalf("rope dim mismatch: got %d want 64", cfg.RopeDim)
+	}
+	if cfg.FullAttentionInterval != 4 {
+		t.Fatalf("full_attention_interval default mismatch: got %d want 4", cfg.FullAttentionInterval)
+	}
+	if !cfg.NormTopKProb {
+		t.Fatalf("norm_topk_prob should default to true for MoE")
+	}
+}
+
+func TestLayerSelectionHelpers(t *testing.T) {
+	cfg := &Config{
+		NumHiddenLayers:       6,
+		FullAttentionInterval: 3,
+		NumExperts:            8,
+		DecoderSparseStep:     2,
+		MLPOnlyLayers:         []int32{1},
+	}
+
+	if !layerIsLinear(cfg, 0) {
+		t.Fatalf("layer 0 should be linear")
+	}
+	if layerIsLinear(cfg, 2) {
+		t.Fatalf("layer 2 should be full attention")
+	}
+
+	if layerUsesMoE(cfg, 1) {
+		t.Fatalf("layer 1 should be forced dense by mlp_only_layers")
+	}
+	if !layerUsesMoE(cfg, 3) {
+		t.Fatalf("layer 3 should use moe with decoder_sparse_step=2")
+	}
+}
+
+func TestResolveTensorPathLayout(t *testing.T) {
+	dummy := mlx.New("dummy")
+
+	tests := []struct {
+		name          string
+		key           string
+		wantContainer string
+		wantModel     string
+	}{
+		{
+			name:          "standard",
+			key:           "model.embed_tokens.weight",
+			wantContainer: "",
+			wantModel:     "model.",
+		},
+		{
+			name:          "nested language model with inner model",
+			key:           "model.language_model.model.embed_tokens.weight",
+			wantContainer: "model.language_model.",
+			wantModel:     "model.",
+		},
+		{
+			name:          "nested language model without inner model",
+			key:           "model.language_model.embed_tokens.weight",
+			wantContainer: "model.language_model.",
+			wantModel:     "",
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			layout := resolveTensorPathLayout(map[string]*mlx.Array{
+				tt.key: dummy,
+			})
+
+			if layout.containerPrefix != tt.wantContainer || layout.modelPrefix != tt.wantModel {
+				t.Fatalf(
+					"resolveTensorPathLayout() = {%q %q}, want {%q %q}",
+					layout.containerPrefix,
+					layout.modelPrefix,
+					tt.wantContainer,
+					tt.wantModel,
+				)
+			}
+		})
+	}
+}
+
+func TestNewCachesLayout(t *testing.T) {
+	m := &Model{
+		Config: &Config{
+			LinearConvKernelDim: 4,
+			LinearNumKeyHeads:   2,
+			LinearKeyHeadDim:    8,
+			LinearNumValueHeads: 4,
+			LinearValueHeadDim:  16,
+		},
+		Layers: []*Layer{
+			{IsLinear: true},
+			{IsLinear: false},
+			{IsLinear: true},
+		},
+	}
+
+	caches := m.NewCaches()
+	if len(caches) != len(m.Layers) {
+		t.Fatalf("len(caches) = %d, want %d", len(caches), len(m.Layers))
+	}
+
+	if _, ok := caches[0].(*cache.RecurrentCache); !ok {
+		t.Fatalf("cache[0] = %T, want *cache.RecurrentCache", caches[0])
+	}
+	if _, ok := caches[1].(*cache.KVCache); !ok {
+		t.Fatalf("cache[1] = %T, want *cache.KVCache", caches[1])
+	}
+	if _, ok := caches[2].(*cache.RecurrentCache); !ok {
+		t.Fatalf("cache[2] = %T, want *cache.RecurrentCache", caches[2])
+	}
+}

x/models/qwen3_5_moe/qwen3_5_moe.go

@@ -0,0 +1,16 @@
+//go:build mlx
+
+// Package qwen3_5_moe registers Qwen 3.5 MoE architecture aliases.
+package qwen3_5_moe
+
+import (
+	"github.com/ollama/ollama/x/mlxrunner/model/base"
+	"github.com/ollama/ollama/x/models/qwen3_5"
+)
+
+func init() {
+	base.Register("Qwen3_5MoeForConditionalGeneration", qwen3_5.NewModel)
+	base.Register("Qwen3_5MoeForCausalLM", qwen3_5.NewModel)
+	base.Register("Qwen3NextMoeForConditionalGeneration", qwen3_5.NewModel)
+	base.Register("Qwen3NextMoeForCausalLM", qwen3_5.NewModel)
+}