wip sampling

Only the last token's processing time is included in prompt processing,
giving an artificially high rate. In addition, the number of tokens
only included the tokens that miss the cache, instead of our historic
total tokens.

api/types.go

@@ -15,6 +15,7 @@ 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"
 )
@@ -569,6 +570,7 @@ 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"`
@@ -934,6 +936,10 @@ 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)
 	}
@@ -957,6 +963,14 @@ 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

llm/server.go

@@ -74,8 +74,7 @@ type LlamaServer interface {
 	Tokenize(ctx context.Context, content string) ([]int, error)
 	Detokenize(ctx context.Context, tokens []int) (string, error)
 	Close() error
-	VRAMSize() uint64 // Total VRAM across all GPUs
-	TotalSize() uint64
+	MemorySize() (total, vram uint64)
 	VRAMByGPU(id ml.DeviceID) uint64
 	Pid() int
 	GetPort() int
@@ -685,8 +684,9 @@ func (s *llamaServer) Load(ctx context.Context, systemInfo ml.SystemInfo, system
 	// Windows CUDA should not use mmap for best performance
 	// Linux  with a model larger than free space, mmap leads to thrashing
 	// For CPU loads we want the memory to be allocated, not FS cache
+	totalSize, _ := s.MemorySize()
 	if (runtime.GOOS == "windows" && len(gpus) > 0 && gpus[0].Library == "CUDA" && s.options.UseMMap == nil) ||
-		(runtime.GOOS == "linux" && systemInfo.FreeMemory < s.TotalSize() && s.options.UseMMap == nil) ||
+		(runtime.GOOS == "linux" && systemInfo.FreeMemory < totalSize && s.options.UseMMap == nil) ||
 		(len(gpus) == 0 && s.options.UseMMap == nil) ||
 		(len(gpus) > 0 && gpus[0].Library == "Vulkan" && s.options.UseMMap == nil) ||
 		(s.options.UseMMap != nil && !*s.options.UseMMap) {
@@ -1453,10 +1453,12 @@ type ImageData struct {
 }
 
 type CompletionRequest struct {
-	Prompt  string
-	Format  json.RawMessage
-	Images  []ImageData
-	Options *api.Options
+	Prompt          string
+	Format          json.RawMessage
+	Images          []ImageData
+	Options         *api.Options
+	Think           *api.ThinkValue
+	ExplicitOptions map[string]struct{}
 
 	Grammar  string // set before sending the request to the subprocess
 	Shift    bool
@@ -1518,6 +1520,7 @@ 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"`
@@ -1848,17 +1851,17 @@ func (s *llamaServer) GetDeviceInfos(ctx context.Context) []ml.DeviceInfo {
 	return nil
 }
 
-func (s *llmServer) VRAMSize() uint64 {
+func (s *llmServer) MemorySize() (total, vram uint64) {
 	if s.mem == nil {
-		return 0
+		return 0, 0
 	}
 
-	var mem uint64
-
 	for _, g := range s.mem.GPUs {
-		mem += g.Size()
+		vram += g.Size()
 	}
 
+	total = s.mem.InputWeights + s.mem.CPU.Size() + vram
+
 	// Some elements are always on CPU. However, if we have allocated all layers
 	// on the GPU then include the CPU components as well, to represent complete offloading.
 	noCPULayers := true
@@ -1869,25 +1872,11 @@ func (s *llmServer) VRAMSize() uint64 {
 		}
 	}
 	if noCPULayers {
-		mem += s.mem.InputWeights
-		mem += s.mem.CPU.Graph
+		vram += s.mem.InputWeights
+		vram += s.mem.CPU.Graph
 	}
 
-	return mem
-}
-
-func (s *llmServer) TotalSize() uint64 {
-	if s.mem == nil {
-		return 0
-	}
-
-	mem := s.mem.InputWeights
-	mem += s.mem.CPU.Size()
-	for _, g := range s.mem.GPUs {
-		mem += g.Size()
-	}
-
-	return mem
+	return total, vram
 }
 
 func (s *llmServer) VRAMByGPU(id ml.DeviceID) uint64 {

model/models/qwen3next/deltanet.go

@@ -41,8 +41,8 @@ type GatedDeltaNet struct {
 	SSMBeta      *nn.Linear  `gguf:"ssm_beta"`  // -> beta (qwen35)
 	SSMAlpha     *nn.Linear  `gguf:"ssm_alpha"` // -> alpha (qwen35)
 	SSMConv1D    *convKernel `gguf:"ssm_conv1d"`
-	SSMDT        ml.Tensor   `gguf:"ssm_dt"` // alpha bias
-	SSMA         ml.Tensor   `gguf:"ssm_a"`  // -A_log.exp()
+	SSMDT        ml.Tensor   `gguf:"ssm_dt,alt:ssm_dt.bias"` // alpha bias
+	SSMA         ml.Tensor   `gguf:"ssm_a"`                  // -A_log.exp()
 	SSMNorm      *nn.RMSNorm `gguf:"ssm_norm"`
 	SSMOut       *nn.Linear  `gguf:"ssm_out"`
 
@@ -135,6 +135,18 @@ func (gdn *GatedDeltaNet) Forward(ctx ml.Context, hiddenStates, _ ml.Tensor, cac
 	default:
 		return nil, errors.New("qwen3next: missing linear attention beta/alpha projections")
 	}
+	if gdn.SSMDT == nil {
+		return nil, errors.New("qwen3next: missing linear attention ssm_dt tensor")
+	}
+	if gdn.SSMA == nil {
+		return nil, errors.New("qwen3next: missing linear attention ssm_a tensor")
+	}
+	if gdn.SSMConv1D == nil || gdn.SSMConv1D.Weight == nil {
+		return nil, errors.New("qwen3next: missing linear attention ssm_conv1d tensor")
+	}
+	if gdn.SSMNorm == nil || gdn.SSMOut == nil {
+		return nil, errors.New("qwen3next: missing linear attention ssm_norm/ssm_out projections")
+	}
 
 	// Compute gate: softplus(alpha + dt_bias) * -A
 	alphaBiased := alpha.Add(ctx, gdn.SSMDT)

model/models/qwen3next/model.go

@@ -437,6 +437,46 @@ func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 	return m.Output.Forward(ctx, hiddenStates), nil
 }
 
+func (m *Model) Validate() error {
+	if m.Options == nil {
+		return fmt.Errorf("qwen3next: missing model options")
+	}
+	if len(m.Layers) != len(m.Options.isRecurrent) {
+		return fmt.Errorf("qwen3next: layer config mismatch: have %d layers, %d recurrent flags", len(m.Layers), len(m.Options.isRecurrent))
+	}
+
+	for i, layer := range m.Layers {
+		if !m.Options.isRecurrent[i] {
+			continue
+		}
+
+		gdn, ok := layer.Operator.(*GatedDeltaNet)
+		if !ok || gdn == nil {
+			return fmt.Errorf("qwen3next: layer %d expected recurrent operator", i)
+		}
+		if gdn.SSMQKV == nil || gdn.SSMQKVGate == nil {
+			return fmt.Errorf("qwen3next: layer %d missing attn_qkv/attn_gate projections", i)
+		}
+		if gdn.SSMBetaAlpha == nil && (gdn.SSMBeta == nil || gdn.SSMAlpha == nil) {
+			return fmt.Errorf("qwen3next: layer %d missing linear attention beta/alpha projections", i)
+		}
+		if gdn.SSMDT == nil {
+			return fmt.Errorf("qwen3next: layer %d missing ssm_dt tensor", i)
+		}
+		if gdn.SSMA == nil {
+			return fmt.Errorf("qwen3next: layer %d missing ssm_a tensor", i)
+		}
+		if gdn.SSMConv1D == nil || gdn.SSMConv1D.Weight == nil {
+			return fmt.Errorf("qwen3next: layer %d missing ssm_conv1d tensor", i)
+		}
+		if gdn.SSMNorm == nil || gdn.SSMOut == nil {
+			return fmt.Errorf("qwen3next: layer %d missing ssm_norm/ssm_out projections", i)
+		}
+	}
+
+	return nil
+}
+
 func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
 	m.positionCache = nil
 	if len(m.mropeSections) > 0 {
@@ -450,6 +490,64 @@ var (
 	_ model.MultimodalProcessor = (*Model)(nil)
 )
 
+func defaultVHeadReordered(arch string) bool {
+	return arch == "qwen35" || arch == "qwen35moe"
+}
+
+func inferRecurrentLayers(headCountKV []uint64, numLayers int, fullAttentionInterval uint32) ([]bool, error) {
+	isRecurrent := make([]bool, numLayers)
+
+	hasZero := false
+	hasFull := false
+	for i := range numLayers {
+		if i >= len(headCountKV) {
+			continue
+		}
+
+		if headCountKV[i] == 0 {
+			isRecurrent[i] = true
+			hasZero = true
+		} else {
+			hasFull = true
+		}
+	}
+	if hasZero && hasFull {
+		return isRecurrent, nil
+	}
+	if !hasFull {
+		return nil, fmt.Errorf("qwen3next: attention.head_count_kv must include at least one non-zero value")
+	}
+
+	// Compatibility path: older imports store a scalar KV head count and omit
+	// per-layer recurrent flags. Derive the hybrid layout from the interval.
+	interval := int(fullAttentionInterval)
+	if interval == 0 {
+		interval = min(4, numLayers)
+	}
+	if interval <= 0 {
+		return nil, fmt.Errorf("qwen3next: invalid block_count (%d)", numLayers)
+	}
+	if interval > numLayers {
+		return nil, fmt.Errorf("qwen3next: full_attention_interval (%d) exceeds block_count (%d)", interval, numLayers)
+	}
+
+	hasZero = false
+	hasFull = false
+	for i := range numLayers {
+		isRecurrent[i] = (i+1)%interval != 0
+		if isRecurrent[i] {
+			hasZero = true
+		} else {
+			hasFull = true
+		}
+	}
+	if !hasZero || !hasFull {
+		return nil, fmt.Errorf("qwen3next: full_attention_interval (%d) does not produce a mixed recurrent/full layout", interval)
+	}
+
+	return isRecurrent, nil
+}
+
 func New(c fs.Config) (model.Model, error) {
 	numLayers := int(c.Uint("block_count"))
 	layers := make([]Layer, numLayers)
@@ -460,26 +558,14 @@ func New(c fs.Config) (model.Model, error) {
 		HeadCountKV() []uint64
 	}
 
-	var isRecurrent []bool
 	var headCountKV []uint64
 	if hc, ok := c.(headCounts); ok {
 		headCountKV = hc.HeadCountKV()
 	}
 
-	isRecurrent = make([]bool, numLayers)
-	hasZero := false
-	hasFull := false
-	for i := range numLayers {
-		// If KV head count is 0, it's a recurrent layer
-		if i < len(headCountKV) && headCountKV[i] == 0 {
-			isRecurrent[i] = true
-			hasZero = true
-		} else if i < len(headCountKV) && headCountKV[i] > 0 {
-			hasFull = true
-		}
-	}
-	if !hasZero || !hasFull {
-		return nil, fmt.Errorf("qwen3next: invalid attention.head_count_kv array; expected mix of zero and non-zero values")
+	isRecurrent, err := inferRecurrentLayers(headCountKV, numLayers, c.Uint("full_attention_interval"))
+	if err != nil {
+		return nil, err
 	}
 
 	// Determine if MoE
@@ -543,7 +629,7 @@ func New(c fs.Config) (model.Model, error) {
 		ssmNGroup:             int(c.Uint("ssm.group_count")),
 		ssmDtRank:             int(c.Uint("ssm.time_step_rank")),
 		convKernelSize:        int(c.Uint("ssm.conv_kernel")),
-		vHeadReordered:        c.Bool("ssm.v_head_reordered", false),
+		vHeadReordered:        c.Bool("ssm.v_head_reordered", defaultVHeadReordered(c.Architecture())),
 		isRecurrent:           isRecurrent,
 		mropeSections: slices.Collect(func(yield func(int) bool) {
 			for _, section := range mropeSections {
@@ -555,7 +641,7 @@ func New(c fs.Config) (model.Model, error) {
 		mropeInterleaved: c.Bool("rope.mrope_interleaved", c.Bool("mrope_interleaved", false)),
 	}
 	if opts.numKVHeads == 0 {
-		return nil, fmt.Errorf("qwen3next: attention.head_count_kv array must include at least one non-zero value")
+		return nil, fmt.Errorf("qwen3next: attention.head_count_kv must include at least one non-zero value")
 	}
 
 	// Calculate cache dimensions

model/models/qwen3next/model_new_test.go

@@ -0,0 +1,65 @@
+package qwen3next
+
+import (
+	"slices"
+	"strings"
+	"testing"
+)
+
+func TestInferRecurrentLayersMixedKVArray(t *testing.T) {
+	got, err := inferRecurrentLayers([]uint64{0, 2, 0, 2}, 4, 0)
+	if err != nil {
+		t.Fatalf("inferRecurrentLayers() error = %v", err)
+	}
+
+	want := []bool{true, false, true, false}
+	if !slices.Equal(got, want) {
+		t.Fatalf("inferRecurrentLayers() = %v, want %v", got, want)
+	}
+}
+
+func TestInferRecurrentLayersScalarKVDefaultInterval(t *testing.T) {
+	got, err := inferRecurrentLayers([]uint64{2, 2, 2, 2, 2, 2, 2, 2}, 8, 0)
+	if err != nil {
+		t.Fatalf("inferRecurrentLayers() error = %v", err)
+	}
+
+	want := []bool{true, true, true, false, true, true, true, false}
+	if !slices.Equal(got, want) {
+		t.Fatalf("inferRecurrentLayers() = %v, want %v", got, want)
+	}
+}
+
+func TestInferRecurrentLayersScalarKVConfiguredInterval(t *testing.T) {
+	got, err := inferRecurrentLayers([]uint64{2, 2, 2, 2, 2, 2}, 6, 3)
+	if err != nil {
+		t.Fatalf("inferRecurrentLayers() error = %v", err)
+	}
+
+	want := []bool{true, true, false, true, true, false}
+	if !slices.Equal(got, want) {
+		t.Fatalf("inferRecurrentLayers() = %v, want %v", got, want)
+	}
+}
+
+func TestInferRecurrentLayersAllZeroRejects(t *testing.T) {
+	_, err := inferRecurrentLayers([]uint64{0, 0, 0, 0}, 4, 0)
+	if err == nil {
+		t.Fatal("inferRecurrentLayers() expected error, got nil")
+	}
+	if !strings.Contains(err.Error(), "must include at least one non-zero value") {
+		t.Fatalf("unexpected error = %v", err)
+	}
+}
+
+func TestDefaultVHeadReordered(t *testing.T) {
+	if !defaultVHeadReordered("qwen35") {
+		t.Fatal("defaultVHeadReordered(qwen35) = false, want true")
+	}
+	if !defaultVHeadReordered("qwen35moe") {
+		t.Fatal("defaultVHeadReordered(qwen35moe) = false, want true")
+	}
+	if defaultVHeadReordered("qwen3next") {
+		t.Fatal("defaultVHeadReordered(qwen3next) = true, want false")
+	}
+}

model/models/qwen3next/model_validate_test.go

@@ -0,0 +1,45 @@
+package qwen3next
+
+import (
+	"strings"
+	"testing"
+
+	"github.com/ollama/ollama/ml/nn"
+)
+
+func TestValidateRecurrentLayerRequiresSSMDT(t *testing.T) {
+	m := &Model{
+		Layers: []Layer{{
+			Operator: &GatedDeltaNet{
+				SSMQKV:     &nn.Linear{},
+				SSMQKVGate: &nn.Linear{},
+				SSMBeta:    &nn.Linear{},
+				SSMAlpha:   &nn.Linear{},
+			},
+		}},
+		Options: &Options{
+			isRecurrent: []bool{true},
+		},
+	}
+
+	err := m.Validate()
+	if err == nil {
+		t.Fatal("Validate() expected error, got nil")
+	}
+	if !strings.Contains(err.Error(), "missing ssm_dt") {
+		t.Fatalf("unexpected error = %v", err)
+	}
+}
+
+func TestValidateNonRecurrentSkipsLinearChecks(t *testing.T) {
+	m := &Model{
+		Layers: []Layer{{Operator: &FullAttention{}}},
+		Options: &Options{
+			isRecurrent: []bool{false},
+		},
+	}
+
+	if err := m.Validate(); err != nil {
+		t.Fatalf("Validate() error = %v", err)
+	}
+}

server/images.go

@@ -71,6 +71,10 @@ type Model struct {
 	Template *template.Template
 }
 
+func (m *Model) IsMLX() bool {
+	return m.Config.ModelFormat == "safetensors"
+}
+
 // Capabilities returns the capabilities that the model supports
 func (m *Model) Capabilities() []model.Capability {
 	capabilities := []model.Capability{}

server/prompt.go

@@ -30,42 +30,44 @@ func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.
 	lastMsgIdx := len(msgs) - 1
 	currMsgIdx := 0
 
-	// Start with all messages and remove from the front until it fits in context
-	for i := 0; i <= lastMsgIdx; i++ {
-		// Collect system messages from the portion we're about to skip
-		system = make([]api.Message, 0)
-		for j := range i {
-			if msgs[j].Role == "system" {
-				system = append(system, msgs[j])
+	if truncate {
+		// Start with all messages and remove from the front until it fits in context
+		for i := 0; i <= lastMsgIdx; i++ {
+			// Collect system messages from the portion we're about to skip
+			system = make([]api.Message, 0)
+			for j := range i {
+				if msgs[j].Role == "system" {
+					system = append(system, msgs[j])
+				}
 			}
-		}
 
-		p, err := renderPrompt(m, append(system, msgs[i:]...), tools, think)
-		if err != nil {
-			return "", nil, err
-		}
-
-		s, err := tokenize(ctx, p)
-		if err != nil {
-			return "", nil, err
-		}
-
-		ctxLen := len(s)
-		if m.ProjectorPaths != nil {
-			for _, msg := range msgs[i:] {
-				ctxLen += imageNumTokens * len(msg.Images)
+			p, err := renderPrompt(m, append(system, msgs[i:]...), tools, think)
+			if err != nil {
+				return "", nil, err
 			}
-		}
 
-		if !truncate || ctxLen <= opts.NumCtx {
-			currMsgIdx = i
-			break
-		}
+			s, err := tokenize(ctx, p)
+			if err != nil {
+				return "", nil, err
+			}
 
-		// Must always include at least the last message
-		if i == lastMsgIdx {
-			currMsgIdx = lastMsgIdx
-			break
+			ctxLen := len(s)
+			if m.ProjectorPaths != nil {
+				for _, msg := range msgs[i:] {
+					ctxLen += imageNumTokens * len(msg.Images)
+				}
+			}
+
+			if ctxLen <= opts.NumCtx {
+				currMsgIdx = i
+				break
+			}
+
+			// Must always include at least the last message
+			if i == lastMsgIdx {
+				currMsgIdx = lastMsgIdx
+				break
+			}
 		}
 	}
 

server/routes.go

@@ -130,6 +130,35 @@ func (s *Server) modelOptions(model *Model, requestOpts map[string]any) (api.Opt
 	return opts, nil
 }
 
+func explicitOptions(modelOpts, requestOpts map[string]any) map[string]struct{} {
+	keys := []string{
+		"temperature",
+		"top_p",
+		"min_p",
+		"top_k",
+		"repeat_last_n",
+		"repeat_penalty",
+		"presence_penalty",
+		"frequency_penalty",
+	}
+
+	explicit := make(map[string]struct{}, len(keys))
+	for _, key := range keys {
+		if optionSpecified(modelOpts, requestOpts, key) {
+			explicit[key] = struct{}{}
+		}
+	}
+	return explicit
+}
+
+func optionSpecified(modelOpts, requestOpts map[string]any, key string) bool {
+	if _, ok := requestOpts[key]; ok {
+		return true
+	}
+	_, ok := modelOpts[key]
+	return ok
+}
+
 // scheduleRunner schedules a runner after validating inputs such as capabilities and model options.
 // It returns the allocated runner, model instance, and consolidated options if successful and error otherwise.
 func (s *Server) scheduleRunner(ctx context.Context, name string, caps []model.Capability, requestOpts map[string]any, keepAlive *api.Duration) (llm.LlamaServer, *Model, *api.Options, error) {
@@ -484,7 +513,8 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 		// the real chat handler, but doing this as a stopgap to get renderer
 		// support for generate
 		if values.Messages != nil && values.Suffix == "" && req.Template == "" {
-			prompt, images, err = chatPrompt(c.Request.Context(), m, r.Tokenize, opts, values.Messages, []api.Tool{}, req.Think, req.Truncate == nil || *req.Truncate)
+			genTruncate := (req.Truncate == nil || *req.Truncate) && !m.IsMLX()
+			prompt, images, err = chatPrompt(c.Request.Context(), m, r.Tokenize, opts, values.Messages, []api.Tool{}, req.Think, genTruncate)
 			if err != nil {
 				c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 				return
@@ -538,14 +568,16 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 		var sb strings.Builder
 		defer close(ch)
 		if err := r.Completion(c.Request.Context(), llm.CompletionRequest{
-			Prompt:      prompt,
-			Images:      images,
-			Format:      req.Format,
-			Options:     opts,
-			Shift:       req.Shift == nil || *req.Shift,
-			Truncate:    req.Truncate == nil || *req.Truncate,
-			Logprobs:    req.Logprobs,
-			TopLogprobs: req.TopLogprobs,
+			Prompt:          prompt,
+			Images:          images,
+			Format:          req.Format,
+			Options:         opts,
+			Think:           req.Think,
+			ExplicitOptions: explicitOptions(m.Options, req.Options),
+			Shift:           req.Shift == nil || *req.Shift,
+			Truncate:        req.Truncate == nil || *req.Truncate,
+			Logprobs:        req.Logprobs,
+			TopLogprobs:     req.TopLogprobs,
 		}, func(cr llm.CompletionResponse) {
 			res := api.GenerateResponse{
 				Model:     req.Model,
@@ -557,6 +589,7 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 					PromptEvalDuration: cr.PromptEvalDuration,
 					EvalCount:          cr.EvalCount,
 					EvalDuration:       cr.EvalDuration,
+					PeakMemory:         cr.PeakMemory,
 				},
 				Logprobs: toAPILogprobs(cr.Logprobs),
 			}
@@ -1951,6 +1984,9 @@ func (s *Server) PsHandler(c *gin.Context) {
 		}
 		if v.llama != nil {
 			mr.ContextLength = v.llama.ContextLength()
+			total, vram := v.llama.MemorySize()
+			mr.Size = int64(total)
+			mr.SizeVRAM = int64(vram)
 		}
 		// The scheduler waits to set expiresAt, so if a model is loading it's
 		// possible that it will be set to the unix epoch. For those cases, just
@@ -2213,6 +2249,9 @@ func (s *Server) ChatHandler(c *gin.Context) {
 	}
 
 	truncate := req.Truncate == nil || *req.Truncate
+	if m.IsMLX() {
+		truncate = false
+	}
 	prompt, images, err := chatPrompt(c.Request.Context(), m, r.Tokenize, opts, msgs, processedTools, req.Think, truncate)
 	if err != nil {
 		slog.Error("chat prompt error", "error", err)
@@ -2290,14 +2329,16 @@ func (s *Server) ChatHandler(c *gin.Context) {
 			// sets up new context given parent context per request
 			ctx, cancel := context.WithCancel(c.Request.Context())
 			err := r.Completion(ctx, llm.CompletionRequest{
-				Prompt:      prompt,
-				Images:      images,
-				Format:      currentFormat,
-				Options:     opts,
-				Shift:       req.Shift == nil || *req.Shift,
-				Truncate:    truncate,
-				Logprobs:    req.Logprobs,
-				TopLogprobs: req.TopLogprobs,
+				Prompt:          prompt,
+				Images:          images,
+				Format:          currentFormat,
+				Options:         opts,
+				Think:           req.Think,
+				ExplicitOptions: explicitOptions(m.Options, req.Options),
+				Shift:           req.Shift == nil || *req.Shift,
+				Truncate:        truncate,
+				Logprobs:        req.Logprobs,
+				TopLogprobs:     req.TopLogprobs,
 			}, func(r llm.CompletionResponse) {
 				res := api.ChatResponse{
 					Model:     req.Model,
@@ -2309,6 +2350,7 @@ func (s *Server) ChatHandler(c *gin.Context) {
 						PromptEvalDuration: r.PromptEvalDuration,
 						EvalCount:          r.EvalCount,
 						EvalDuration:       r.EvalDuration,
+						PeakMemory:         r.PeakMemory,
 					},
 					Logprobs: toAPILogprobs(r.Logprobs),
 				}

server/sched.go

@@ -231,7 +231,7 @@ func (s *Scheduler) processPending(ctx context.Context) {
 					}
 
 					// Check for experimental safetensors LLM models
-					if pending.model.Config.ModelFormat == "safetensors" {
+					if pending.model.IsMLX() {
 						if slices.Contains(pending.model.Config.Capabilities, "completion") {
 							// LLM model with safetensors format - use MLX runner
 							if s.loadMLX(pending) {
@@ -536,6 +536,7 @@ iGPUScan:
 		}
 	}
 
+	totalSize, vramSize := llama.MemorySize()
 	runner := &runnerRef{
 		model:           req.model,
 		modelPath:       req.model.ModelPath,
@@ -545,8 +546,8 @@ iGPUScan:
 		sessionDuration: sessionDuration,
 		gpus:            gpuIDs,
 		discreteGPUs:    discreteGPUs,
-		vramSize:        llama.VRAMSize(),
-		totalSize:       llama.TotalSize(),
+		totalSize:       totalSize,
+		vramSize:        vramSize,
 		loading:         true,
 		pid:             llama.Pid(),
 	}
@@ -619,6 +620,7 @@ func (s *Scheduler) loadMLX(req *LlmRequest) bool {
 		sessionDuration = req.sessionDuration.Duration
 	}
 
+	totalSize, vramSize := server.MemorySize()
 	runner := &runnerRef{
 		model:           req.model,
 		modelPath:       req.model.ModelPath,
@@ -628,8 +630,8 @@ func (s *Scheduler) loadMLX(req *LlmRequest) bool {
 		loading:         false,
 		isImagegen:      isImagegen,
 		sessionDuration: sessionDuration,
-		totalSize:       server.TotalSize(),
-		vramSize:        server.VRAMSize(),
+		totalSize:       totalSize,
+		vramSize:        vramSize,
 	}
 
 	s.loadedMu.Lock()
@@ -762,7 +764,7 @@ func (runner *runnerRef) needsReload(ctx context.Context, req *LlmRequest) bool
 	defer cancel()
 	if !reflect.DeepEqual(runner.model.AdapterPaths, req.model.AdapterPaths) || // have the adapters changed?
 		!reflect.DeepEqual(runner.model.ProjectorPaths, req.model.ProjectorPaths) || // have the projectors changed?
-		!reflect.DeepEqual(optsExisting, optsNew) || // have the runner options changed?
+		(!runner.model.IsMLX() && !reflect.DeepEqual(optsExisting, optsNew)) || // have the runner options changed?
 		runner.llama.Ping(ctx) != nil {
 		return true
 	}

server/sched_test.go

@@ -861,8 +861,7 @@ func (s *mockLlm) Close() error {
 	s.closeCalled = true
 	return s.closeResp
 }
-func (s *mockLlm) VRAMSize() uint64                                   { return s.vramSize }
-func (s *mockLlm) TotalSize() uint64                                  { return s.totalSize }
+func (s *mockLlm) MemorySize() (uint64, uint64)                       { return s.totalSize, s.vramSize }
 func (s *mockLlm) VRAMByGPU(id ml.DeviceID) uint64                    { return s.vramByGPU[id] }
 func (s *mockLlm) Pid() int                                           { return -1 }
 func (s *mockLlm) GetPort() int                                       { return -1 }

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)
-	if len(shape) != 2 {
+	// Quantize standard linear weights (2D). Also allow stacked expert weights (3D),
+	// 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/server.go

@@ -374,14 +374,9 @@ func (s *Server) Close() error {
 	return nil
 }
 
-// VRAMSize returns the estimated VRAM usage.
-func (s *Server) VRAMSize() uint64 {
-	return s.vramSize
-}
-
-// TotalSize returns the total memory usage.
-func (s *Server) TotalSize() uint64 {
-	return s.vramSize
+// MemorySize returns the total and VRAM memory usage.
+func (s *Server) MemorySize() (total, vram uint64) {
+	return s.vramSize, s.vramSize
 }
 
 // VRAMByGPU returns VRAM usage for a specific GPU.

x/mlxrunner/cache.go

@@ -30,21 +30,64 @@ type cacheSession struct {
 	remaining []int32
 }
 
+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 {
-			c.caches = make([]cache.Cache, m.NumLayers())
-			for i := range c.caches {
-				c.caches[i] = cache.NewKVCache()
-			}
+			return
+		}
+		c.caches = make([]cache.Cache, m.NumLayers())
+		for i := range c.caches {
+			c.caches[i] = cache.NewKVCache()
 		}
 	}
+	ensureCaches()
 
 	remaining := c.findRemaining(inputs)
+	ensureCaches()
 
 	return &cacheSession{
 		cache:     c,
@@ -56,18 +99,34 @@ 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 {
-		// Ensure that if we have run the forward pass and set the metadata
-		// that we also actually have the data
-		arrays := make([]*mlx.Array, 0, 2*len(s.caches))
-		for _, c := range s.caches {
-			k, v := c.State()
-			arrays = append(arrays, k, v)
-		}
-		mlx.AsyncEval(arrays...)
-
-		s.cache.tokens = append(s.inputs, s.outputs...)[:offset]
+	if len(s.caches) == 0 {
+		return
 	}
+
+	offset := -1
+	arrays := make([]*mlx.Array, 0, 2*len(s.caches))
+	for _, kv := range s.caches {
+		if kv == nil {
+			continue
+		}
+		if off := kv.Offset(); offset < 0 || off < offset {
+			offset = off
+		}
+		arrays = append(arrays, kv.Materialize()...)
+	}
+	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
@@ -78,12 +137,20 @@ func (c *kvCache) findRemaining(tokens []int32) []int32 {
 		prefix++
 	}
 
+	// Always keep at least one token to re-evaluate so the
+	// pipeline can seed token generation from it.
+	if prefix == len(tokens) && prefix > 0 {
+		prefix--
+	}
+
 	if prefix < len(c.tokens) {
-		trim := len(c.tokens) - prefix
-		for _, kv := range c.caches {
-			kv.Trim(trim)
+		if c.cachesCanTrim() {
+			c.trimToPrefix(prefix)
+		} 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 {
@@ -98,10 +165,21 @@ func (c *kvCache) log() {
 	if len(c.caches) == 0 {
 		return
 	}
+	offset := -1
 	var totalBytes int
 	for _, kv := range c.caches {
-		k, v := kv.State()
-		totalBytes += k.NumBytes() + v.NumBytes()
+		if kv == nil {
+			continue
+		}
+		if off := kv.Offset(); offset < 0 || off < offset {
+			offset = off
+		}
+		for _, a := range kv.Materialize() {
+			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

@@ -10,6 +10,8 @@ import (
 type Cache interface {
 	Update(keys, values *mlx.Array) (newKeys, newValues *mlx.Array)
 	State() (keys, values *mlx.Array)
+	Materialize() []*mlx.Array
+	CanTrim() bool
 	Trim(int) int
 	Clone() Cache
 	Free()
@@ -67,6 +69,20 @@ func (c *KVCache) State() (*mlx.Array, *mlx.Array) {
 		c.values.Slice(mlx.Slice(), mlx.Slice(), mlx.Slice(0, c.offset), mlx.Slice())
 }
 
+// Materialize returns the backing key/value buffers currently held by the cache.
+func (c *KVCache) Materialize() []*mlx.Array {
+	out := make([]*mlx.Array, 0, 2)
+	if c.keys != nil && c.keys.Valid() {
+		out = append(out, c.keys)
+	}
+	if c.values != nil && c.values.Valid() {
+		out = append(out, c.values)
+	}
+	return out
+}
+
+func (c *KVCache) CanTrim() bool { return true }
+
 func (c *KVCache) Trim(n int) int {
 	n = min(c.offset, n)
 	c.offset -= n
@@ -190,6 +206,8 @@ func (c *RotatingKVCache) State() (*mlx.Array, *mlx.Array) {
 	return c.keys, c.values
 }
 
+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 +1,220 @@
+//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) setStateMaterialized(dst **mlx.Array, v *mlx.Array) {
+	if v == nil || !v.Valid() {
+		return
+	}
+	if *dst == v {
+		return
+	}
+
+	// Break dependency chains so recurrent state does not retain the full
+	// per-token compute graph over time.
+	snap := mlx.Snapshot(v)
+	mlx.Eval(snap)
+
+	old := *dst
+	*dst = snap
+	mlx.Pin(snap)
+
+	// Drop references to the previous cached state root and transient incoming
+	// graph root now that a detached snapshot is retained in cache. Actual
+	// cleanup happens at the runner's normal sweep points.
+	if old != nil && old != snap {
+		mlx.Unpin(old)
+	}
+	if v != snap && v != old {
+		mlx.Unpin(v)
+	}
+}
+
+func (c *RecurrentCache) setStateRaw(dst **mlx.Array, v *mlx.Array) {
+	if v == nil || !v.Valid() {
+		return
+	}
+	if *dst == v {
+		return
+	}
+
+	old := *dst
+	*dst = v
+	mlx.Pin(v)
+	if old != nil && old != v {
+		mlx.Unpin(old)
+	}
+}
+
+func (c *RecurrentCache) setStateDetached(dst **mlx.Array, v *mlx.Array, ensureContiguous bool) {
+	if v == nil || !v.Valid() {
+		return
+	}
+	if *dst == v {
+		return
+	}
+
+	root := v
+	if ensureContiguous {
+		root = mlx.Contiguous(v, false)
+	}
+	detached := mlx.Detach(root)
+
+	old := *dst
+	*dst = detached
+	mlx.Pin(detached)
+	if old != nil && old != detached {
+		mlx.Unpin(old)
+	}
+
+	// Intentionally do not force-release root/v here. In the fast path, the detached
+	// handle aliases the same MLX value and may still be lazily computed. Releasing the
+	// source handles can invalidate the cached state before the next eval/sweep point.
+}
+
+func snapshotPinned(a *mlx.Array) *mlx.Array {
+	if a == nil || !a.Valid() {
+		return nil
+	}
+	snap := mlx.Snapshot(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.setStateRaw(&c.convState, mlx.Zeros(dtype, batch, c.convTail, c.convDim))
+	}
+	if needDelta {
+		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.setStateMaterialized(&c.convState, v)
+}
+
+// SetConvStateFast stores conv state without forcing an immediate snapshot/eval.
+// Use only for decode hot paths that accept higher transient memory until the next
+// sync/sweep point. The conv-state input is usually a slice view, so request a
+// compact contiguous copy to avoid pinning the whole source buffer.
+func (c *RecurrentCache) SetConvStateFast(v *mlx.Array) {
+	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.setStateMaterialized(&c.deltaState, v)
+}
+
+// SetDeltaStateFast stores delta state without forcing an immediate snapshot/eval.
+// Use only for decode hot paths that accept higher transient memory until the next
+// sync/sweep point.
+func (c *RecurrentCache) SetDeltaStateFast(v *mlx.Array) {
+	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
+}
+
+// Materialize returns the recurrent state roots (conv and delta) held by the cache.
+func (c *RecurrentCache) Materialize() []*mlx.Array {
+	out := make([]*mlx.Array, 0, 2)
+	if c.convState != nil && c.convState.Valid() {
+		out = append(out, c.convState)
+	}
+	if c.deltaState != nil && c.deltaState.Valid() {
+		out = append(out, c.deltaState)
+	}
+	return out
+}
+
+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

@@ -8,7 +8,6 @@ import (
 	"fmt"
 	"io"
 	"log/slog"
-	"math"
 	"math/rand"
 	"net"
 	"net/http"
@@ -19,25 +18,27 @@ import (
 	"strconv"
 	"strings"
 	"sync"
+	"sync/atomic"
 	"time"
 
+	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/llm"
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/x/imagegen"
-	"github.com/ollama/ollama/x/imagegen/manifest"
 )
 
 // Client wraps an MLX runner subprocess to implement llm.LlamaServer for LLM models.
 type Client struct {
-	port        int
-	modelName   string
-	vramSize    uint64
-	done        chan error
-	client      *http.Client
-	lastErr     string
-	lastErrLock sync.Mutex
-	mu          sync.Mutex
-	cmd         *exec.Cmd
+	port          int
+	modelName     string
+	contextLength atomic.Int64
+	memory        atomic.Uint64
+	done          chan error
+	client        *http.Client
+	lastErr       string
+	lastErrLock   sync.Mutex
+	mu            sync.Mutex
+	cmd           *exec.Cmd
 }
 
 // NewClient spawns a new MLX runner subprocess for LLM models and waits until it's ready.
@@ -98,18 +99,9 @@ func NewClient(modelName string) (*Client, error) {
 		slog.Debug("mlx subprocess library path", "LD_LIBRARY_PATH", pathEnvVal)
 	}
 
-	// Estimate VRAM based on tensor size from manifest
-	var vramSize uint64
-	if modelManifest, err := manifest.LoadManifest(modelName); err == nil {
-		vramSize = uint64(modelManifest.TotalTensorSize())
-	} else {
-		vramSize = 8 * 1024 * 1024 * 1024
-	}
-
 	c := &Client{
 		port:      port,
 		modelName: modelName,
-		vramSize:  vramSize,
 		done:      make(chan error, 1),
 		client:    &http.Client{Timeout: 10 * time.Minute},
 		cmd:       cmd,
@@ -190,15 +182,34 @@ func (c *Client) waitUntilRunning() error {
 // completionRequest is a properly-tagged version of llm.CompletionRequest for JSON serialization.
 type completionRequest struct {
 	Prompt  string          `json:"prompt"`
+	Think   *bool           `json:"think,omitempty"`
 	Options *completionOpts `json:"options,omitempty"`
 }
 
 type completionOpts struct {
-	Temperature float32 `json:"temperature,omitempty"`
-	TopP        float32 `json:"top_p,omitempty"`
-	MinP        float32 `json:"min_p,omitempty"`
-	TopK        int     `json:"top_k,omitempty"`
-	NumPredict  int     `json:"num_predict,omitempty"`
+	Temperature      *float32 `json:"temperature,omitempty"`
+	TopP             *float32 `json:"top_p,omitempty"`
+	MinP             *float32 `json:"min_p,omitempty"`
+	TopK             *int     `json:"top_k,omitempty"`
+	RepeatLastN      *int     `json:"repeat_last_n,omitempty"`
+	RepeatPenalty    *float32 `json:"repeat_penalty,omitempty"`
+	PresencePenalty  *float32 `json:"presence_penalty,omitempty"`
+	FrequencyPenalty *float32 `json:"frequency_penalty,omitempty"`
+	NumPredict       int      `json:"num_predict,omitempty"`
+}
+
+type CompletionResponse struct {
+	Content    string
+	Done       bool
+	DoneReason int
+
+	PromptEvalCount    int
+	PromptEvalDuration time.Duration
+	EvalCount          int
+	EvalDuration       time.Duration
+	PeakMemory         uint64
+
+	Error *api.StatusError
 }
 
 // Close terminates the subprocess.
@@ -222,16 +233,27 @@ func (c *Client) Close() error {
 
 // Completion implements llm.LlamaServer.
 func (c *Client) Completion(ctx context.Context, req llm.CompletionRequest, fn func(llm.CompletionResponse)) error {
+	var think *bool
+	if req.Think != nil {
+		enabled := req.Think.Bool()
+		think = &enabled
+	}
+
 	creq := completionRequest{
 		Prompt: req.Prompt,
+		Think:  think,
 	}
 	if req.Options != nil {
 		creq.Options = &completionOpts{
-			Temperature: req.Options.Temperature,
-			TopP:        req.Options.TopP,
-			MinP:        req.Options.MinP,
-			TopK:        req.Options.TopK,
-			NumPredict:  req.Options.NumPredict,
+			Temperature:      float32Ptr(req.Options.Temperature, hasExplicitOption(req.ExplicitOptions, "temperature")),
+			TopP:             float32Ptr(req.Options.TopP, hasExplicitOption(req.ExplicitOptions, "top_p")),
+			MinP:             float32Ptr(req.Options.MinP, hasExplicitOption(req.ExplicitOptions, "min_p")),
+			TopK:             intPtr(req.Options.TopK, hasExplicitOption(req.ExplicitOptions, "top_k")),
+			RepeatLastN:      intPtr(req.Options.RepeatLastN, hasExplicitOption(req.ExplicitOptions, "repeat_last_n")),
+			RepeatPenalty:    float32Ptr(req.Options.RepeatPenalty, hasExplicitOption(req.ExplicitOptions, "repeat_penalty")),
+			PresencePenalty:  float32Ptr(req.Options.PresencePenalty, hasExplicitOption(req.ExplicitOptions, "presence_penalty")),
+			FrequencyPenalty: float32Ptr(req.Options.FrequencyPenalty, hasExplicitOption(req.ExplicitOptions, "frequency_penalty")),
+			NumPredict:       req.Options.NumPredict,
 		}
 	}
 
@@ -260,28 +282,25 @@ func (c *Client) Completion(ctx context.Context, req llm.CompletionRequest, fn f
 
 	scanner := bufio.NewScanner(resp.Body)
 	for scanner.Scan() {
-		var raw struct {
-			Content            string `json:"content,omitempty"`
-			Done               bool   `json:"done"`
-			DoneReason         int    `json:"done_reason,omitempty"`
-			PromptEvalCount    int    `json:"prompt_eval_count,omitempty"`
-			PromptEvalDuration int    `json:"prompt_eval_duration,omitempty"`
-			EvalCount          int    `json:"eval_count,omitempty"`
-			EvalDuration       int    `json:"eval_duration,omitempty"`
-		}
+		var raw CompletionResponse
 		if err := json.Unmarshal(scanner.Bytes(), &raw); err != nil {
 			slog.Debug("mlx response parse error", "error", err, "line", string(scanner.Bytes()))
 			continue
 		}
 
+		if raw.Error != nil {
+			return *raw.Error
+		}
+
 		cresp := llm.CompletionResponse{
 			Content:            raw.Content,
 			Done:               raw.Done,
 			DoneReason:         llm.DoneReason(raw.DoneReason),
 			PromptEvalCount:    raw.PromptEvalCount,
-			PromptEvalDuration: time.Duration(raw.PromptEvalDuration),
+			PromptEvalDuration: raw.PromptEvalDuration,
 			EvalCount:          raw.EvalCount,
-			EvalDuration:       time.Duration(raw.EvalDuration),
+			EvalDuration:       raw.EvalDuration,
+			PeakMemory:         raw.PeakMemory,
 		}
 
 		fn(cresp)
@@ -293,8 +312,27 @@ func (c *Client) Completion(ctx context.Context, req llm.CompletionRequest, fn f
 	return scanner.Err()
 }
 
+func hasExplicitOption(explicit map[string]struct{}, key string) bool {
+	_, ok := explicit[key]
+	return ok
+}
+
+func float32Ptr(v float32, ok bool) *float32 {
+	if !ok {
+		return nil
+	}
+	return &v
+}
+
+func intPtr(v int, ok bool) *int {
+	if !ok {
+		return nil
+	}
+	return &v
+}
+
 func (c *Client) ContextLength() int {
-	return math.MaxInt
+	return int(c.contextLength.Load())
 }
 
 // Detokenize implements llm.LlamaServer.
@@ -347,9 +385,16 @@ func (c *Client) Pid() int {
 	return -1
 }
 
+type statusResponse struct {
+	Status        int
+	Progress      int
+	ContextLength int
+	Memory        uint64
+}
+
 // Ping implements llm.LlamaServer.
 func (c *Client) Ping(ctx context.Context) error {
-	reqURL := fmt.Sprintf("http://127.0.0.1:%d/health", c.port)
+	reqURL := fmt.Sprintf("http://127.0.0.1:%d/v1/status", c.port)
 	req, err := http.NewRequestWithContext(ctx, "GET", reqURL, nil)
 	if err != nil {
 		return err
@@ -362,6 +407,15 @@ func (c *Client) Ping(ctx context.Context) error {
 	if resp.StatusCode != http.StatusOK {
 		return fmt.Errorf("health check failed: %d", resp.StatusCode)
 	}
+
+	var status statusResponse
+	if err := json.NewDecoder(resp.Body).Decode(&status); err != nil {
+		return err
+	}
+
+	c.contextLength.Store(int64(status.ContextLength))
+	c.memory.Store(status.Memory)
+
 	return nil
 }
 
@@ -388,19 +442,24 @@ func (c *Client) Tokenize(ctx context.Context, content string) ([]int, error) {
 	return tokens, nil
 }
 
-// TotalSize implements llm.LlamaServer.
-func (c *Client) TotalSize() uint64 {
-	return c.vramSize
+func (c *Client) currentMemory() uint64 {
+	ctx, cancel := context.WithTimeout(context.Background(), time.Second)
+	defer cancel()
+	if err := c.Ping(ctx); err != nil {
+		slog.Warn("failed to get current memory", "error", err)
+	}
+	return c.memory.Load()
+}
+
+// MemorySize implements llm.LlamaServer.
+func (c *Client) MemorySize() (total, vram uint64) {
+	mem := c.currentMemory()
+	return mem, mem
 }
 
 // VRAMByGPU implements llm.LlamaServer.
 func (c *Client) VRAMByGPU(id ml.DeviceID) uint64 {
-	return c.vramSize
-}
-
-// VRAMSize implements llm.LlamaServer.
-func (c *Client) VRAMSize() uint64 {
-	return c.vramSize
+	return c.currentMemory()
 }
 
 // WaitUntilRunning implements llm.LlamaServer.

x/mlxrunner/client_test.go

@@ -0,0 +1,167 @@
+package mlxrunner
+
+import (
+	"context"
+	"encoding/json"
+	"io"
+	"net/http"
+	"strings"
+	"testing"
+
+	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/llm"
+)
+
+func TestCompletionForwardsThink(t *testing.T) {
+	boolPtr := func(v bool) *bool { return &v }
+
+	testCases := []struct {
+		name  string
+		think *api.ThinkValue
+		want  *bool
+	}{
+		{name: "unset", think: nil, want: nil},
+		{name: "enabled", think: &api.ThinkValue{Value: true}, want: boolPtr(true)},
+		{name: "disabled", think: &api.ThinkValue{Value: false}, want: boolPtr(false)},
+		{name: "level maps to enabled", think: &api.ThinkValue{Value: "high"}, want: boolPtr(true)},
+	}
+
+	for _, tc := range testCases {
+		t.Run(tc.name, func(t *testing.T) {
+			var got completionRequest
+
+			rt := roundTripFunc(func(r *http.Request) (*http.Response, error) {
+				if r.URL.Path != "/completion" {
+					t.Fatalf("request path = %q, want %q", r.URL.Path, "/completion")
+				}
+
+				if err := json.NewDecoder(r.Body).Decode(&got); err != nil {
+					return nil, err
+				}
+
+				return &http.Response{
+					StatusCode: http.StatusOK,
+					Header:     make(http.Header),
+					Body:       io.NopCloser(strings.NewReader("{\"done\":true}\n")),
+					Request:    r,
+				}, nil
+			})
+
+			c := &Client{
+				port: 11434,
+				client: &http.Client{
+					Transport: rt,
+				},
+			}
+
+			err := c.Completion(context.Background(), llm.CompletionRequest{
+				Prompt: "hello",
+				Think:  tc.think,
+			}, func(llm.CompletionResponse) {})
+			if err != nil {
+				t.Fatalf("completion request failed: %v", err)
+			}
+
+			if got.Prompt != "hello" {
+				t.Fatalf("prompt = %q, want %q", got.Prompt, "hello")
+			}
+
+			switch {
+			case tc.want == nil && got.Think != nil:
+				t.Fatalf("think = %v, want nil", *got.Think)
+			case tc.want != nil && got.Think == nil:
+				t.Fatalf("think = nil, want %v", *tc.want)
+			case tc.want != nil && got.Think != nil && *tc.want != *got.Think:
+				t.Fatalf("think = %v, want %v", *got.Think, *tc.want)
+			}
+		})
+	}
+}
+
+func TestCompletionForwardsOnlySpecifiedSamplingOptions(t *testing.T) {
+	var got completionRequest
+
+	rt := roundTripFunc(func(r *http.Request) (*http.Response, error) {
+		if err := json.NewDecoder(r.Body).Decode(&got); err != nil {
+			return nil, err
+		}
+
+		return &http.Response{
+			StatusCode: http.StatusOK,
+			Header:     make(http.Header),
+			Body:       io.NopCloser(strings.NewReader("{\"done\":true}\n")),
+			Request:    r,
+		}, nil
+	})
+
+	c := &Client{
+		port: 11434,
+		client: &http.Client{
+			Transport: rt,
+		},
+	}
+
+	opts := &api.Options{
+		Temperature:      1.0,
+		TopP:             0.95,
+		MinP:             0.1,
+		TopK:             20,
+		RepeatLastN:      128,
+		RepeatPenalty:    1.2,
+		PresencePenalty:  1.5,
+		FrequencyPenalty: 0.25,
+		NumPredict:       64,
+	}
+
+	err := c.Completion(context.Background(), llm.CompletionRequest{
+		Prompt:  "hello",
+		Options: opts,
+		ExplicitOptions: map[string]struct{}{
+			"temperature":      {},
+			"top_k":            {},
+			"repeat_penalty":   {},
+			"presence_penalty": {},
+		},
+	}, func(llm.CompletionResponse) {})
+	if err != nil {
+		t.Fatalf("completion request failed: %v", err)
+	}
+
+	if got.Options == nil {
+		t.Fatal("options = nil, want serialized options")
+	}
+
+	if got.Options.Temperature == nil || *got.Options.Temperature != opts.Temperature {
+		t.Fatalf("temperature = %v, want %v", got.Options.Temperature, opts.Temperature)
+	}
+	if got.Options.TopK == nil || *got.Options.TopK != opts.TopK {
+		t.Fatalf("top_k = %v, want %v", got.Options.TopK, opts.TopK)
+	}
+	if got.Options.RepeatPenalty == nil || *got.Options.RepeatPenalty != opts.RepeatPenalty {
+		t.Fatalf("repeat_penalty = %v, want %v", got.Options.RepeatPenalty, opts.RepeatPenalty)
+	}
+	if got.Options.PresencePenalty == nil || *got.Options.PresencePenalty != opts.PresencePenalty {
+		t.Fatalf("presence_penalty = %v, want %v", got.Options.PresencePenalty, opts.PresencePenalty)
+	}
+	if got.Options.TopP != nil {
+		t.Fatalf("top_p = %v, want nil", *got.Options.TopP)
+	}
+	if got.Options.MinP != nil {
+		t.Fatalf("min_p = %v, want nil", *got.Options.MinP)
+	}
+	if got.Options.RepeatLastN != nil {
+		t.Fatalf("repeat_last_n = %v, want nil", *got.Options.RepeatLastN)
+	}
+	if got.Options.FrequencyPenalty != nil {
+		t.Fatalf("frequency_penalty = %v, want nil", *got.Options.FrequencyPenalty)
+	}
+	if got.Options.NumPredict != opts.NumPredict {
+		t.Fatalf("num_predict = %d, want %d", got.Options.NumPredict, opts.NumPredict)
+	}
+}
+
+type roundTripFunc func(*http.Request) (*http.Response, error)
+
+func (f roundTripFunc) RoundTrip(r *http.Request) (*http.Response, error) {
+	return f(r)
+}

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/gated_delta_metal.go

@@ -0,0 +1,275 @@
+//go:build mlx
+
+package mlx
+
+// #include <stdlib.h>
+// #include "generated.h"
+import "C"
+
+import (
+	"sync"
+	"sync/atomic"
+	"unsafe"
+)
+
+var (
+	gatedDeltaMetalKernelOnce sync.Once
+	gatedDeltaMetalKernel     C.mlx_fast_metal_kernel
+	gatedDeltaMetalDisabled   atomic.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.Store(true)
+		freeInputs()
+		return
+	}
+	defer freeInputs()
+
+	outputs, freeOutputs, ok := cStringVector([]string{"y", "state_out"})
+	if !ok {
+		gatedDeltaMetalDisabled.Store(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.Load() {
+		return nil, nil, false
+	}
+	if q == nil || k == nil || v == nil || g == nil || beta == nil || state == nil {
+		return nil, nil, false
+	}
+	if !q.Valid() || !k.Valid() || !v.Valid() || !g.Valid() || !beta.Valid() || !state.Valid() {
+		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.Load() {
+		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.Store(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.Store(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.Store(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.Store(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.Store(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.Store(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.Store(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
+}

x/mlxrunner/mlx/memory.go

@@ -64,6 +64,10 @@ func PeakMemory() int {
 	return int(peak)
 }
 
+func ResetPeakMemory() {
+	C.mlx_reset_peak_memory()
+}
+
 type Memory struct{}
 
 func (Memory) LogValue() slog.Value {

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.go

@@ -93,6 +93,12 @@ func (t *Array) Divide(other *Array) *Array {
 	return out
 }
 
+func (t *Array) Cumsum(axis int, reverse, inclusive bool) *Array {
+	out := New("CUMSUM")
+	C.mlx_cumsum(&out.ctx, t.ctx, C.int(axis), C.bool(reverse), C.bool(inclusive), DefaultStream().ctx)
+	return out
+}
+
 func (t *Array) ExpandDims(axis int) *Array {
 	out := New("EXPAND_DIMS")
 	C.mlx_expand_dims(&out.ctx, t.ctx, C.int(axis), DefaultStream().ctx)
@@ -123,12 +129,30 @@ func (t *Array) GatherMM(other, lhs, rhs *Array, sorted bool) *Array {
 	return out
 }
 
+func (t *Array) GreaterEqual(other *Array) *Array {
+	out := New("GREATER_EQUAL")
+	C.mlx_greater_equal(&out.ctx, t.ctx, other.ctx, DefaultStream().ctx)
+	return out
+}
+
 func (t *Array) Logsumexp(keepDims bool) *Array {
 	out := New("LOGSUMEXP")
 	C.mlx_logsumexp(&out.ctx, t.ctx, C.bool(keepDims), DefaultStream().ctx)
 	return out
 }
 
+func (t *Array) Less(other *Array) *Array {
+	out := New("LESS")
+	C.mlx_less(&out.ctx, t.ctx, other.ctx, DefaultStream().ctx)
+	return out
+}
+
+func (t *Array) LogicalOr(other *Array) *Array {
+	out := New("LOGICAL_OR")
+	C.mlx_logical_or(&out.ctx, t.ctx, other.ctx, DefaultStream().ctx)
+	return out
+}
+
 func (t *Array) Matmul(other *Array) *Array {
 	out := New("MATMUL")
 	C.mlx_matmul(&out.ctx, t.ctx, other.ctx, DefaultStream().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 +429,27 @@ func Collect(v any) []*Array {
 	return arrays
 }
 
+// Snapshot copies an array into a fresh leaf value with no Go-side graph inputs.
+func Snapshot(a *Array) *Array {
+	if a == nil || !a.Valid() {
+		return a
+	}
+	out := New("SNAPSHOT")
+	C.mlx_copy(&out.ctx, a.ctx, DefaultStream().ctx)
+	return out
+}
+
+// Detach returns a new Array handle that shares the same MLX value but does
+// not retain Go-side graph input references.
+func Detach(a *Array) *Array {
+	if a == nil || !a.Valid() {
+		return a
+	}
+	out := New("DETACH")
+	C.mlx_array_set(&out.ctx, a.ctx)
+	return out
+}
+
 func collect(v reflect.Value, arrays *[]*Array, seen map[uintptr]bool) {
 	if !v.IsValid() {
 		return

x/mlxrunner/model/base/base.go

@@ -20,6 +20,7 @@ type Model interface {
 	Unembed(x *mlx.Array) *mlx.Array
 	NumLayers() int
 	Tokenizer() *tokenizer.Tokenizer
+	MaxContextLength() int
 
 	// LoadWeights receives all tensors loaded from the manifest and assigns
 	// them to model fields. Model-specific logic (MLA absorption, expert

x/mlxrunner/pipeline.go

@@ -6,18 +6,30 @@ import (
 	"bytes"
 	"context"
 	"errors"
+	"fmt"
 	"log/slog"
+	"net/http"
 	"time"
 
+	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/logutil"
 	"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")
 	}
 
+	ctx := request.Ctx
+	if ctx == nil {
+		ctx = context.Background()
+	}
+
 	var (
 		sample, logprobs         *mlx.Array
 		nextSample, nextLogprobs *mlx.Array
@@ -44,43 +56,72 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 	} else {
 		mlx.DisableCompile()
 	}
+	mlx.ResetPeakMemory()
 
 	inputs := r.Tokenizer.Encode(request.Prompt, true)
+	if len(inputs) == 0 {
+		return errors.New("empty prompt")
+	}
+
+	if len(inputs) >= r.contextLength {
+		return api.StatusError{
+			StatusCode:   http.StatusBadRequest,
+			ErrorMessage: fmt.Sprintf("input length (%d tokens) exceeds the model's maximum context length (%d tokens)", len(inputs), r.contextLength),
+		}
+	}
+
+	// Cap generation to stay within the model's context length
+	maxGenerate := r.contextLength - len(inputs)
+	if request.Options.MaxTokens <= 0 {
+		request.Options.MaxTokens = maxGenerate
+	} else {
+		request.Options.MaxTokens = min(request.Options.MaxTokens, maxGenerate)
+	}
+
 	session := r.cache.begin(r.Model, inputs)
 	defer session.close()
-
 	caches := session.caches
 	tokens := session.remaining
+	history := append([]int32(nil), session.inputs...)
+	prefillChunk := prefillChunkSize()
 
+	materializeCaches := func() {
+		state := make([]*mlx.Array, 0, 2*len(caches))
+		for _, c := range caches {
+			if c == nil {
+				continue
+			}
+			state = append(state, c.Materialize()...)
+		}
+		if len(state) == 0 {
+			return
+		}
+		mlx.Eval(state...)
+	}
+
+	now := time.Now()
 	total, processed := len(tokens), 0
-	slog.Info("Prompt processing progress", "processed", processed, "total", total)
 	for total-processed > 1 {
-		if err := request.Ctx.Err(); err != nil {
+		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 {
-			s := make([]*mlx.Array, 2*len(caches))
-			for i, c := range caches {
-				s[2*i], s[2*i+1] = c.State()
-			}
-			return s
-		}()...)
+		materializeCaches()
 		processed += n
 		slog.Info("Prompt processing progress", "processed", processed, "total", total)
 		mlx.ClearCache()
 	}
 
-	step := func(token *mlx.Array) (*mlx.Array, *mlx.Array) {
+	step := func(token *mlx.Array, history []int32) (*mlx.Array, *mlx.Array) {
 		fwd := r.Model.Forward(token.ExpandDims(0), caches)
 		logits := r.Model.Unembed(fwd)
 		logits = logits.Slice(mlx.Slice(), mlx.Slice(logits.Dim(1)-1), mlx.Slice()).Squeeze(1)
 
 		logprobs := logits.Subtract(logits.Logsumexp(true))
-		sample := request.Sample(logprobs)
+		sample := request.Sample(logprobs, history)
 
 		mlx.Pin(sample, logprobs)
 		mlx.Sweep()
@@ -89,45 +130,42 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 		return sample, logprobs
 	}
 
-	sample, logprobs = step(mlx.FromValues(tokens[processed:], total-processed))
+	sample, logprobs = step(mlx.FromValues(tokens[processed:], total-processed), history)
 
 	var b bytes.Buffer
 
-	now := time.Now()
-	final := Response{Done: true, PromptTokens: total, CompletionTokens: request.Options.MaxTokens, DoneReason: 1}
+	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
 		}
 
-		nextSample, nextLogprobs = step(sample)
-
 		if i == 0 {
-			slog.Info("Prompt processing progress", "processed", total, "total", total)
 			mlx.Eval(sample)
-			final.PromptTokensDuration = time.Since(now)
+			final.PromptEvalDuration = time.Since(now)
 			now = time.Now()
 		}
 
 		output := int32(sample.Int())
 		session.outputs = append(session.outputs, output)
+		history = append(history, output)
 
 		if r.Tokenizer.IsEOS(output) {
-			final.Token = int(output)
 			final.DoneReason = 0
-			final.CompletionTokens = i
+			final.EvalCount = i
 			break
 		}
 
 		select {
 		case <-request.Ctx.Done():
 			return request.Ctx.Err()
-		case request.Responses <- Response{
-			Text:  r.Decode(output, &b),
-			Token: int(output),
+		case request.Responses <- CompletionResponse{
+			Content: r.Decode(output, &b),
 		}:
 		}
 
+		nextSample, nextLogprobs = step(sample, history)
+
 		mlx.Unpin(sample, logprobs)
 		sample, logprobs = nextSample, nextLogprobs
 		nextSample, nextLogprobs = nil, nil
@@ -137,10 +175,11 @@ func (r *Runner) TextGenerationPipeline(request Request) error {
 		}
 	}
 
-	final.CompletionTokensDuration = time.Since(now)
+	final.EvalDuration = time.Since(now)
+	final.PeakMemory = uint64(mlx.PeakMemory())
 	select {
-	case <-request.Ctx.Done():
-		return request.Ctx.Err()
+	case <-ctx.Done():
+		return ctx.Err()
 	case request.Responses <- final:
 		return nil
 	}

x/mlxrunner/runner.go

@@ -4,14 +4,15 @@ package mlxrunner
 
 import (
 	"context"
+	"errors"
 	"log/slog"
 	"net"
 	"net/http"
 	"strings"
-	"time"
 
 	"golang.org/x/sync/errgroup"
 
+	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/x/mlxrunner/mlx"
 	"github.com/ollama/ollama/x/mlxrunner/model"
 	"github.com/ollama/ollama/x/mlxrunner/model/base"
@@ -21,7 +22,7 @@ import (
 
 type Request struct {
 	TextCompletionsRequest
-	Responses chan Response
+	Responses chan CompletionResponse
 	Pipeline  func(Request) error
 
 	Ctx context.Context
@@ -31,37 +32,29 @@ type Request struct {
 
 type TextCompletionsRequest struct {
 	Prompt  string `json:"prompt"`
+	Think   *bool  `json:"think,omitempty"`
 	Options struct {
-		Temperature float32 `json:"temperature"`
-		TopP        float32 `json:"top_p"`
-		MinP        float32 `json:"min_p"`
-		TopK        int     `json:"top_k"`
-		MaxTokens   int     `json:"max_tokens"`
+		Temperature      *float32 `json:"temperature"`
+		TopP             *float32 `json:"top_p"`
+		MinP             *float32 `json:"min_p"`
+		TopK             *int     `json:"top_k"`
+		RepeatLastN      *int     `json:"repeat_last_n"`
+		RepeatPenalty    *float32 `json:"repeat_penalty"`
+		PresencePenalty  *float32 `json:"presence_penalty"`
+		FrequencyPenalty *float32 `json:"frequency_penalty"`
+		MaxTokens        int      `json:"max_tokens"`
 
 		// Deprecated: use MaxTokens instead
 		NumPredict int `json:"num_predict"`
 	} `json:"options"`
 }
 
-type Response struct {
-	Text       string    `json:"content,omitempty"`
-	Token      int       `json:"token,omitempty"`
-	Logprobs   []float32 `json:"logprobs,omitempty"`
-	Done       bool      `json:"done,omitempty"`
-	DoneReason int       `json:"done_reason,omitempty"`
-
-	PromptTokens             int           `json:"prompt_eval_count,omitempty"`
-	PromptTokensDuration     time.Duration `json:"prompt_eval_duration,omitempty"`
-	CompletionTokens         int           `json:"eval_count,omitempty"`
-	CompletionTokensDuration time.Duration `json:"eval_duration,omitempty"`
-	TotalTokens              int           `json:"total_tokens,omitempty"`
-}
-
 type Runner struct {
-	Model     base.Model
-	Tokenizer *tokenizer.Tokenizer
-	Requests  chan Request
-	cache     kvCache
+	Model         base.Model
+	Tokenizer     *tokenizer.Tokenizer
+	Requests      chan Request
+	cache         kvCache
+	contextLength int
 }
 
 func (r *Runner) Load(modelName string) error {
@@ -90,6 +83,7 @@ func (r *Runner) Load(modelName string) error {
 
 	r.Model = m
 	r.Tokenizer = m.Tokenizer()
+	r.contextLength = m.MaxContextLength()
 	return nil
 }
 
@@ -158,6 +152,17 @@ func (r *Runner) Run(host, port string, mux http.Handler) error {
 			case request := <-r.Requests:
 				if err := request.Pipeline(request); err != nil {
 					slog.Info("Request terminated", "error", err)
+					var statusErr api.StatusError
+					if !errors.As(err, &statusErr) {
+						statusErr = api.StatusError{
+							StatusCode:   http.StatusInternalServerError,
+							ErrorMessage: err.Error(),
+						}
+					}
+					select {
+					case request.Responses <- CompletionResponse{Error: &statusErr}:
+					case <-request.Ctx.Done():
+					}
 				}
 
 				close(request.Responses)

x/mlxrunner/sample/sample.go

@@ -9,69 +9,204 @@ import (
 )
 
 type Sampler interface {
-	Sample(*mlx.Array) *mlx.Array
+	Sample(*mlx.Array, []int32) *mlx.Array
 }
 
-func New(temp, top_p, min_p float32, top_k int) Sampler {
-	if temp == 0 {
-		return greedy{}
-	}
-
+func New(temp, top_p, min_p float32, top_k, repeatLastN int, repeatPenalty, presencePenalty, frequencyPenalty float32) Sampler {
 	var samplers []Sampler
-	if top_p > 0 && top_p < 1 {
-		samplers = append(samplers, TopP(top_p))
+	if repeatLastN > 0 && (repeatPenalty != 1 || presencePenalty != 0 || frequencyPenalty != 0) {
+		samplers = append(samplers, Penalty{
+			RepeatLastN:      repeatLastN,
+			RepeatPenalty:    repeatPenalty,
+			PresencePenalty:  presencePenalty,
+			FrequencyPenalty: frequencyPenalty,
+		})
 	}
 
-	if min_p != 0 {
-		samplers = append(samplers, MinP(min_p))
+	if temp == 0 {
+		samplers = append(samplers, greedy{})
+	} else {
+		samplers = append(samplers, Distribution{
+			Temperature: temp,
+			TopK:        top_k,
+			TopP:        top_p,
+			MinP:        min_p,
+		})
 	}
-
-	if top_k > 0 {
-		samplers = append(samplers, TopK(top_k))
-	}
-
-	samplers = append(samplers, Temperature(temp))
 	return chain(samplers)
 }
 
 type greedy struct{}
 
-func (greedy) Sample(logits *mlx.Array) *mlx.Array {
+func (greedy) Sample(logits *mlx.Array, _ []int32) *mlx.Array {
 	return logits.Argmax(-1, false)
 }
 
 type chain []Sampler
 
-func (c chain) Sample(logits *mlx.Array) *mlx.Array {
+func (c chain) Sample(logits *mlx.Array, history []int32) *mlx.Array {
 	for _, sampler := range c {
-		logits = sampler.Sample(logits)
+		logits = sampler.Sample(logits, history)
 	}
 	return logits
 }
 
-type Temperature float32
-
-func (t Temperature) Sample(logits *mlx.Array) *mlx.Array {
-	return mlx.DivScalar(logits, float32(t)).Categorical(-1)
+type Distribution struct {
+	Temperature float32
+	TopK        int
+	TopP        float32
+	MinP        float32
 }
 
-type TopP float32
+func (d Distribution) Sample(logits *mlx.Array, _ []int32) *mlx.Array {
+	filtered, indices := d.filter(logits)
+	sample := filtered.Categorical(-1)
+	if indices == nil {
+		return sample
+	}
 
-func (p TopP) Sample(logprobs *mlx.Array) *mlx.Array {
-	// TODO: implement
-	return logprobs
+	positions := sample.ExpandDims(1)
+	return indices.TakeAlongAxis(positions, -1).Squeeze(1)
 }
 
-type MinP float32
+func (d Distribution) filter(logits *mlx.Array) (*mlx.Array, *mlx.Array) {
+	candidates := logits
+	var candidateIndices *mlx.Array
 
-func (p MinP) Sample(logprobs *mlx.Array) *mlx.Array {
-	// TODO: implement
-	return logprobs
+	if d.TopK > 0 && d.TopK < logits.Dim(logits.NumDims()-1) {
+		partitions := logits.Negative().ArgpartitionAxis(d.TopK-1, -1)
+		switch logits.NumDims() {
+		case 1:
+			candidateIndices = partitions.Slice(mlx.Slice(0, d.TopK))
+		default:
+			candidateIndices = partitions.Slice(mlx.Slice(), mlx.Slice(0, d.TopK))
+		}
+		candidates = logits.TakeAlongAxis(candidateIndices, -1)
+	}
+
+	if d.Temperature != 1 {
+		candidates = mlx.DivScalar(candidates, d.Temperature)
+	}
+
+	if !d.needsProbabilityFilters() {
+		return candidates, candidateIndices
+	}
+
+	order := candidates.Negative().ArgsortAxis(-1)
+	sortedLogits := candidates.TakeAlongAxis(order, -1)
+	sortedProbs := mlx.SoftmaxAxis(candidates, -1, true).TakeAlongAxis(order, -1)
+
+	remove := d.topPRemovalMask(sortedProbs)
+	if d.MinP > 0 {
+		minPRemove := d.minPRemovalMask(sortedProbs)
+		if remove == nil {
+			remove = minPRemove
+		} else {
+			remove = remove.LogicalOr(minPRemove)
+		}
+	}
+
+	if remove == nil {
+		return candidates, candidateIndices
+	}
+
+	negInf := mlx.FromValue(float32(math.Inf(-1)))
+	filtered := mlx.Where(remove, negInf, sortedLogits)
+	return candidates.PutAlongAxis(order, filtered, -1), candidateIndices
 }
 
-type TopK int
-
-func (k TopK) Sample(logprobs *mlx.Array) *mlx.Array {
-	mask := logprobs.Negative().ArgpartitionAxis(int(k)-1, -1).Slice(mlx.Slice(), mlx.Slice(int(k), 0))
-	return logprobs.PutAlongAxis(mask, mlx.FromValue(float32(math.Inf(-1))), -1)
+func (d Distribution) needsProbabilityFilters() bool {
+	return (d.TopP > 0 && d.TopP < 1) || d.MinP > 0
+}
+
+func (d Distribution) topPRemovalMask(sortedProbs *mlx.Array) *mlx.Array {
+	if d.TopP <= 0 || d.TopP >= 1 {
+		return nil
+	}
+
+	threshold := mlx.NewScalarArray(d.TopP)
+	prevCum := sortedProbs.Cumsum(-1, false, true).Subtract(sortedProbs)
+	return prevCum.GreaterEqual(threshold)
+}
+
+func (d Distribution) minPRemovalMask(sortedProbs *mlx.Array) *mlx.Array {
+	if d.MinP <= 0 {
+		return nil
+	}
+
+	var maxProb *mlx.Array
+	switch sortedProbs.NumDims() {
+	case 1:
+		maxProb = sortedProbs.Slice(mlx.Slice(0, 1))
+	default:
+		maxProb = sortedProbs.Slice(mlx.Slice(), mlx.Slice(0, 1))
+	}
+
+	threshold := mlx.MulScalar(maxProb, d.MinP)
+	return sortedProbs.Less(threshold)
+}
+
+type Penalty struct {
+	RepeatLastN      int
+	RepeatPenalty    float32
+	PresencePenalty  float32
+	FrequencyPenalty float32
+}
+
+func (p Penalty) Sample(logprobs *mlx.Array, history []int32) *mlx.Array {
+	if len(history) == 0 {
+		return logprobs
+	}
+
+	window := p.RepeatLastN
+	if window <= 0 || window > len(history) {
+		window = len(history)
+	}
+
+	counts := make(map[int32]int, window)
+	order := make([]int32, 0, window)
+	for _, token := range history[len(history)-window:] {
+		if token < 0 {
+			continue
+		}
+		if counts[token] == 0 {
+			order = append(order, token)
+		}
+		counts[token]++
+	}
+	if len(order) == 0 {
+		return logprobs
+	}
+
+	indexShape := []int32{int32(len(order))}
+	valueShape := []int{len(order)}
+	if logprobs.NumDims() > 1 {
+		indexShape = []int32{1, int32(len(order))}
+		valueShape = []int{1, len(order)}
+	}
+
+	indices := mlx.NewArrayInt32(order, indexShape)
+	selected := logprobs.TakeAlongAxis(indices, -1)
+	mlx.Eval(selected)
+
+	values := selected.Floats()
+	for i, token := range order {
+		v := values[i]
+		if p.RepeatPenalty != 1 {
+			if v < 0 {
+				v *= p.RepeatPenalty
+			} else {
+				v /= p.RepeatPenalty
+			}
+		}
+		if p.PresencePenalty != 0 {
+			v -= p.PresencePenalty
+		}
+		if p.FrequencyPenalty != 0 {
+			v -= p.FrequencyPenalty * float32(counts[token])
+		}
+		values[i] = v
+	}
+
+	return logprobs.PutAlongAxis(indices, mlx.FromValues(values, valueShape...), -1)
 }

x/mlxrunner/sample/sample_test.go

@@ -0,0 +1,104 @@
+//go:build mlx
+
+package sample
+
+import (
+	"math"
+	"testing"
+
+	"github.com/ollama/ollama/x/mlxrunner/mlx"
+)
+
+func TestPenaltySample(t *testing.T) {
+	if err := mlx.CheckInit(); err != nil {
+		t.Skipf("MLX not available: %v", err)
+	}
+
+	logprobs := mlx.FromValues([]float32{
+		1.0, -2.0, 3.0, 4.0,
+	}, 1, 4)
+
+	got := Penalty{
+		RepeatLastN:      3,
+		RepeatPenalty:    2.0,
+		PresencePenalty:  1.5,
+		FrequencyPenalty: 0.25,
+	}.Sample(logprobs, []int32{2, 1, 2})
+
+	mlx.Eval(got)
+
+	want := []float32{1.0, -5.75, -0.5, 4.0}
+	values := got.Floats()
+	if len(values) != len(want) {
+		t.Fatalf("len(values) = %d, want %d", len(values), len(want))
+	}
+
+	for i := range want {
+		if math.Abs(float64(values[i]-want[i])) > 1e-5 {
+			t.Fatalf("values[%d] = %v, want %v", i, values[i], want[i])
+		}
+	}
+}
+
+func TestPenaltySampleHonorsRepeatWindow(t *testing.T) {
+	if err := mlx.CheckInit(); err != nil {
+		t.Skipf("MLX not available: %v", err)
+	}
+
+	logprobs := mlx.FromValues([]float32{
+		1.0, 2.0, 3.0,
+	}, 1, 3)
+
+	got := Penalty{
+		RepeatLastN:     1,
+		PresencePenalty: 1.0,
+	}.Sample(logprobs, []int32{0, 1})
+
+	mlx.Eval(got)
+
+	want := []float32{1.0, 1.0, 3.0}
+	values := got.Floats()
+	for i := range want {
+		if math.Abs(float64(values[i]-want[i])) > 1e-5 {
+			t.Fatalf("values[%d] = %v, want %v", i, values[i], want[i])
+		}
+	}
+}
+
+func TestDistributionFilterTopP(t *testing.T) {
+	if err := mlx.CheckInit(); err != nil {
+		t.Skipf("MLX not available: %v", err)
+	}
+
+	logits := mlx.FromValues([]float32{
+		10.0, 9.0, 1.0, 0.0,
+	}, 1, 4)
+
+	filtered, indices := Distribution{
+		Temperature: 1.0,
+		TopK:        2,
+		TopP:        0.55,
+	}.filter(logits)
+
+	got := materializeFilteredLogits(filtered, indices, 4)
+	mlx.Eval(got)
+
+	values := got.Floats()
+	if values[0] != 10.0 {
+		t.Fatalf("values[0] = %v, want 10", values[0])
+	}
+	for i := 1; i < len(values); i++ {
+		if !math.IsInf(float64(values[i]), -1) {
+			t.Fatalf("values[%d] = %v, want -Inf", i, values[i])
+		}
+	}
+}
+
+func materializeFilteredLogits(filtered, indices *mlx.Array, width int) *mlx.Array {
+	if indices == nil {
+		return filtered
+	}
+
+	base := mlx.AddScalar(mlx.Zeros(mlx.DTypeFloat32, 1, width), float32(math.Inf(-1)))
+	return base.PutAlongAxis(indices, filtered, -1)
+}

x/mlxrunner/server.go

@@ -16,12 +16,89 @@ import (
 	"strconv"
 	"time"
 
+	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/logutil"
 	"github.com/ollama/ollama/x/mlxrunner/mlx"
+	"github.com/ollama/ollama/x/mlxrunner/model/base"
 	"github.com/ollama/ollama/x/mlxrunner/sample"
+	"github.com/ollama/ollama/x/models/qwen3_5"
 )
 
+type samplingConfig struct {
+	temperature      float32
+	topP             float32
+	minP             float32
+	topK             int
+	repeatLastN      int
+	repeatPenalty    float32
+	presencePenalty  float32
+	frequencyPenalty float32
+}
+
+func defaultSamplingConfig(m base.Model, think *bool) samplingConfig {
+	if _, ok := m.(*qwen3_5.Model); ok {
+		cfg := samplingConfig{
+			temperature:      1.0,
+			topP:             0.95,
+			minP:             0.0,
+			topK:             20,
+			repeatLastN:      64,
+			repeatPenalty:    1.0,
+			presencePenalty:  1.5,
+			frequencyPenalty: 0.0,
+		}
+		if think != nil && !*think {
+			cfg.temperature = 0.7
+			cfg.topP = 0.8
+		}
+		return cfg
+	}
+
+	opts := api.DefaultOptions()
+	return samplingConfig{
+		temperature:      opts.Temperature,
+		topP:             opts.TopP,
+		minP:             opts.MinP,
+		topK:             opts.TopK,
+		repeatLastN:      opts.RepeatLastN,
+		repeatPenalty:    opts.RepeatPenalty,
+		presencePenalty:  opts.PresencePenalty,
+		frequencyPenalty: opts.FrequencyPenalty,
+	}
+}
+
+func resolveSamplingConfig(m base.Model, req Request) samplingConfig {
+	cfg := defaultSamplingConfig(m, req.Think)
+
+	if req.Options.Temperature != nil {
+		cfg.temperature = *req.Options.Temperature
+	}
+	if req.Options.TopP != nil {
+		cfg.topP = *req.Options.TopP
+	}
+	if req.Options.MinP != nil {
+		cfg.minP = *req.Options.MinP
+	}
+	if req.Options.TopK != nil {
+		cfg.topK = *req.Options.TopK
+	}
+	if req.Options.RepeatLastN != nil {
+		cfg.repeatLastN = *req.Options.RepeatLastN
+	}
+	if req.Options.RepeatPenalty != nil {
+		cfg.repeatPenalty = *req.Options.RepeatPenalty
+	}
+	if req.Options.PresencePenalty != nil {
+		cfg.presencePenalty = *req.Options.PresencePenalty
+	}
+	if req.Options.FrequencyPenalty != nil {
+		cfg.frequencyPenalty = *req.Options.FrequencyPenalty
+	}
+
+	return cfg
+}
+
 func Execute(args []string) error {
 	slog.SetDefault(logutil.NewLogger(os.Stderr, envconfig.LogLevel()))
 
@@ -50,9 +127,11 @@ func Execute(args []string) error {
 
 	mux := http.NewServeMux()
 	mux.HandleFunc("GET /v1/status", func(w http.ResponseWriter, r *http.Request) {
-		if err := json.NewEncoder(w).Encode(map[string]any{
-			"status":   0,
-			"progress": 100,
+		if err := json.NewEncoder(w).Encode(statusResponse{
+			Status:        0,
+			Progress:      100,
+			ContextLength: runner.contextLength,
+			Memory:        uint64(mlx.ActiveMemory() + mlx.CacheMemory()),
 		}); err != nil {
 			slog.Error("Failed to encode response", "error", err)
 			http.Error(w, "Internal Server Error", http.StatusInternalServerError)
@@ -78,7 +157,7 @@ func Execute(args []string) error {
 	})
 
 	mux.HandleFunc("POST /v1/completions", func(w http.ResponseWriter, r *http.Request) {
-		request := Request{Responses: make(chan Response)}
+		request := Request{Responses: make(chan CompletionResponse)}
 
 		if err := json.NewDecoder(r.Body).Decode(&request.TextCompletionsRequest); err != nil {
 			slog.Error("Failed to decode request", "error", err)
@@ -87,16 +166,19 @@ func Execute(args []string) error {
 		}
 
 		request.Options.MaxTokens = cmp.Or(request.Options.MaxTokens, request.Options.NumPredict)
-		if request.Options.MaxTokens < 1 {
-			request.Options.MaxTokens = 16 << 10
-		}
+
+		sampling := resolveSamplingConfig(runner.Model, request)
 
 		request.Pipeline = runner.TextGenerationPipeline
 		request.Sampler = sample.New(
-			request.Options.Temperature,
-			request.Options.TopP,
-			request.Options.MinP,
-			request.Options.TopK,
+			sampling.temperature,
+			sampling.topP,
+			sampling.minP,
+			sampling.topK,
+			sampling.repeatLastN,
+			sampling.repeatPenalty,
+			sampling.presencePenalty,
+			sampling.frequencyPenalty,
 		)
 
 		var cancel context.CancelFunc

x/mlxrunner/server_test.go

@@ -0,0 +1,172 @@
+//go:build mlx
+
+package mlxrunner
+
+import (
+	"testing"
+
+	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/x/mlxrunner/cache"
+	"github.com/ollama/ollama/x/mlxrunner/mlx"
+	"github.com/ollama/ollama/x/mlxrunner/model/base"
+	"github.com/ollama/ollama/x/models/qwen3_5"
+	"github.com/ollama/ollama/x/tokenizer"
+)
+
+type stubModel struct{}
+
+func (stubModel) Forward(*mlx.Array, []cache.Cache) *mlx.Array { return nil }
+func (stubModel) Unembed(*mlx.Array) *mlx.Array                { return nil }
+func (stubModel) NumLayers() int                               { return 0 }
+func (stubModel) Tokenizer() *tokenizer.Tokenizer              { return nil }
+func (stubModel) LoadWeights(map[string]*mlx.Array) error      { return nil }
+
+func TestResolveSamplingConfigDefaults(t *testing.T) {
+	trueValue := true
+	falseValue := false
+
+	tests := []struct {
+		name  string
+		model base.Model
+		req   Request
+		want  samplingConfig
+	}{
+		{
+			name:  "generic model uses api defaults",
+			model: stubModel{},
+			req:   Request{},
+			want: samplingConfig{
+				temperature:      0.8,
+				topP:             0.9,
+				minP:             0.0,
+				topK:             40,
+				repeatLastN:      64,
+				repeatPenalty:    1.1,
+				presencePenalty:  0.0,
+				frequencyPenalty: 0.0,
+			},
+		},
+		{
+			name:  "qwen3.5 defaults to thinking profile when think unset",
+			model: &qwen3_5.Model{},
+			req:   Request{},
+			want: samplingConfig{
+				temperature:      1.0,
+				topP:             0.95,
+				minP:             0.0,
+				topK:             20,
+				repeatLastN:      64,
+				repeatPenalty:    1.0,
+				presencePenalty:  1.5,
+				frequencyPenalty: 0.0,
+			},
+		},
+		{
+			name:  "qwen3.5 thinking disabled defaults",
+			model: &qwen3_5.Model{},
+			req:   Request{TextCompletionsRequest: TextCompletionsRequest{Think: &falseValue}},
+			want: samplingConfig{
+				temperature:      0.7,
+				topP:             0.8,
+				minP:             0.0,
+				topK:             20,
+				repeatLastN:      64,
+				repeatPenalty:    1.0,
+				presencePenalty:  1.5,
+				frequencyPenalty: 0.0,
+			},
+		},
+		{
+			name:  "qwen3.5 thinking enabled defaults",
+			model: &qwen3_5.Model{},
+			req:   Request{TextCompletionsRequest: TextCompletionsRequest{Think: &trueValue}},
+			want: samplingConfig{
+				temperature:      1.0,
+				topP:             0.95,
+				minP:             0.0,
+				topK:             20,
+				repeatLastN:      64,
+				repeatPenalty:    1.0,
+				presencePenalty:  1.5,
+				frequencyPenalty: 0.0,
+			},
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			if got := resolveSamplingConfig(tt.model, tt.req); got != tt.want {
+				t.Fatalf("resolveSamplingConfig() = %+v, want %+v", got, tt.want)
+			}
+		})
+	}
+}
+
+func TestResolveSamplingConfigOverridesSpecifiedValues(t *testing.T) {
+	trueValue := true
+	temperature := float32(0.4)
+	topP := float32(0.6)
+	minP := float32(0.05)
+	topK := 12
+	repeatLastN := 32
+	repeatPenalty := float32(1.1)
+	presencePenalty := float32(0.7)
+	frequencyPenalty := float32(0.2)
+
+	got := resolveSamplingConfig(stubModel{}, Request{
+		TextCompletionsRequest: TextCompletionsRequest{
+			Think: &trueValue,
+			Options: struct {
+				Temperature      *float32 `json:"temperature"`
+				TopP             *float32 `json:"top_p"`
+				MinP             *float32 `json:"min_p"`
+				TopK             *int     `json:"top_k"`
+				RepeatLastN      *int     `json:"repeat_last_n"`
+				RepeatPenalty    *float32 `json:"repeat_penalty"`
+				PresencePenalty  *float32 `json:"presence_penalty"`
+				FrequencyPenalty *float32 `json:"frequency_penalty"`
+				MaxTokens        int      `json:"max_tokens"`
+				NumPredict       int      `json:"num_predict"`
+			}{
+				Temperature:      &temperature,
+				TopP:             &topP,
+				MinP:             &minP,
+				TopK:             &topK,
+				RepeatLastN:      &repeatLastN,
+				RepeatPenalty:    &repeatPenalty,
+				PresencePenalty:  &presencePenalty,
+				FrequencyPenalty: &frequencyPenalty,
+			},
+		},
+	})
+
+	want := samplingConfig{
+		temperature:      temperature,
+		topP:             topP,
+		minP:             minP,
+		topK:             topK,
+		repeatLastN:      repeatLastN,
+		repeatPenalty:    repeatPenalty,
+		presencePenalty:  presencePenalty,
+		frequencyPenalty: frequencyPenalty,
+	}
+	if got != want {
+		t.Fatalf("resolveSamplingConfig() = %+v, want %+v", got, want)
+	}
+}
+
+func TestResolveSamplingConfigMatchesGenericDefaults(t *testing.T) {
+	want := api.DefaultOptions()
+	got := defaultSamplingConfig(stubModel{}, nil)
+
+	if got.temperature != want.Temperature ||
+		got.topP != want.TopP ||
+		got.minP != want.MinP ||
+		got.topK != want.TopK ||
+		got.repeatLastN != want.RepeatLastN ||
+		got.repeatPenalty != want.RepeatPenalty ||
+		got.presencePenalty != want.PresencePenalty ||
+		got.frequencyPenalty != want.FrequencyPenalty {
+		t.Fatalf("defaultSamplingConfig() = %+v, want api defaults %+v", got, want)
+	}
+}

x/models/gemma3/gemma3.go

@@ -430,6 +430,10 @@ func (m *Model) NumLayers() int {
 	return len(m.Layers)
 }
 
+func (m *Model) MaxContextLength() int {
+	return int(m.MaxPositionEmbeddings)
+}
+
 func (m *Model) Tokenizer() *tokenizer.Tokenizer {
 	return m.tok
 }

x/models/glm4_moe_lite/glm4_moe_lite.go

@@ -733,7 +733,7 @@ func (m *Model) Unembed(x *mlx.Array) *mlx.Array {
 func (m *Model) NumLayers() int { return len(m.Layers) }
 
 // MaxContextLength returns the maximum context length
-func (m *Model) MaxContextLength() int32 { return m.MaxPositionEmbeddings }
+func (m *Model) MaxContextLength() int { return int(m.MaxPositionEmbeddings) }
 
 // VocabSize returns the vocabulary size
 func (m *Model) VocabSize() int32 { return m.Config.VocabSize }

x/models/llama/llama.go

@@ -262,6 +262,10 @@ func (m *Model) NumLayers() int {
 	return len(m.Layers)
 }
 
+func (m *Model) MaxContextLength() int {
+	return int(m.MaxPositionEmbeddings)
+}
+
 func (m *Model) Tokenizer() *tokenizer.Tokenizer {
 	return m.tok
 }

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/qwen3.go

@@ -279,6 +279,10 @@ func (m *Model) NumLayers() int {
 	return len(m.Layers)
 }
 
+func (m *Model) MaxContextLength() int {
+	return int(m.MaxPositionEmbeddings)
+}
+
 func (m *Model) Tokenizer() *tokenizer.Tokenizer {
 	return m.tok
 }

x/models/qwen3_5/qwen3_5_test.go

@@ -0,0 +1,166 @@
+//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 TestModelRuntimeDefaults(t *testing.T) {
+	m := &Model{}
+	if m.DisablePromptCache() {
+		t.Fatal("DisablePromptCache() = true, want false")
+	}
+}
+
+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)
+}