adjust recurrent cache checkpoints to 24 with interval 1664

convert/convert.go

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

convert/convert_qwen3next.go

@@ -1,6 +1,7 @@
 package convert
 
 import (
+	"encoding/json"
 	"fmt"
 	"io/fs"
 	"math"
@@ -13,8 +14,21 @@ import (
 	"github.com/ollama/ollama/fs/ggml"
 )
 
-type qwen3NextModel struct {
-	ModelParameters
+type qwen3NextRopeScaling struct {
+	Type         string     `json:"type"`
+	Factor       ropeFactor `json:"factor"`
+	MropeSection []int32    `json:"mrope_section"`
+}
+
+type qwen3NextRopeParams struct {
+	MRopeInterleaved    bool    `json:"mrope_interleaved"`
+	MropeSection        []int32 `json:"mrope_section"`
+	RopeType            string  `json:"rope_type"`
+	RopeTheta           float32 `json:"rope_theta"`
+	PartialRotaryFactor float32 `json:"partial_rotary_factor"`
+}
+
+type qwen3NextTextConfig struct {
 	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
 	HiddenSize            uint32  `json:"hidden_size"`
 	NumHiddenLayers       uint32  `json:"num_hidden_layers"`
@@ -28,12 +42,13 @@ type qwen3NextModel struct {
 	// MoE config
 	NumExperts             uint32 `json:"num_experts"`
 	NumExpertsPerToken     uint32 `json:"num_experts_per_tok"`
-	NormTopkProb           bool   `json:"norm_topk_prob"`
+	NormTopkProb           *bool  `json:"norm_topk_prob"`
 	MoEIntermediateSize    uint32 `json:"moe_intermediate_size"`
 	SharedExpertIntermSize uint32 `json:"shared_expert_intermediate_size"`
 
 	// Hybrid attention config
-	FullAttentionInterval uint32 `json:"full_attention_interval"`
+	FullAttentionInterval uint32   `json:"full_attention_interval"`
+	LayerTypes            []string `json:"layer_types"`
 
 	// Linear attention (Gated Delta Net) config
 	LinearConvKernelDim uint32 `json:"linear_conv_kernel_dim"`
@@ -43,67 +58,213 @@ type qwen3NextModel struct {
 	LinearValueHeadDim  uint32 `json:"linear_value_head_dim"`
 
 	// RoPE config
-	PartialRotaryFactor float32 `json:"partial_rotary_factor"`
-	RopeScaling         struct {
-		Type   string     `json:"type"`
-		Factor ropeFactor `json:"factor"`
-	} `json:"rope_scaling"`
+	PartialRotaryFactor float32              `json:"partial_rotary_factor"`
+	RopeScaling         qwen3NextRopeScaling `json:"rope_scaling"`
+	RopeParameters      qwen3NextRopeParams  `json:"rope_parameters"`
+}
+
+type qwen3NextVisionConfig struct {
+	Depth                  uint32  `json:"depth"`
+	HiddenSize             uint32  `json:"hidden_size"`
+	NumHeads               uint32  `json:"num_heads"`
+	InChannels             uint32  `json:"in_channels"`
+	PatchSize              uint32  `json:"patch_size"`
+	SpatialMergeSize       uint32  `json:"spatial_merge_size"`
+	RMSNormEps             float32 `json:"layer_norm_epsilon"`
+	RopeTheta              float32 `json:"rope_theta"`
+	TemporalPatchSize      uint32  `json:"temporal_patch_size"`
+	DeepstackVisualIndexes []int32 `json:"deepstack_visual_indexes"`
+
+	Size struct {
+		ShortestEdge uint32 `json:"shortest_edge"`
+		LongestEdge  uint32 `json:"longest_edge"`
+	} `json:"size"`
+
+	ImageMean []float32 `json:"image_mean"`
+	ImageStd  []float32 `json:"image_std"`
+}
+
+type qwen3NextModel struct {
+	ModelParameters
+	qwen3NextTextConfig
+
+	TextConfig  *qwen3NextTextConfig  `json:"text_config"`
+	VisionModel qwen3NextVisionConfig `json:"vision_config"`
+
+	ImageTokenID       uint32 `json:"image_token_id"`
+	VisionStartTokenID uint32 `json:"vision_start_token_id"`
+	VisionEndTokenID   uint32 `json:"vision_end_token_id"`
 }
 
 var _ ModelConverter = (*qwen3NextModel)(nil)
 
-func (q *qwen3NextModel) parseMore(_ fs.FS) error {
-	if q.NumHiddenLayers == 0 {
-		return fmt.Errorf("qwen3next: num_hidden_layers must be set")
-	}
-	if q.NumAttentionHeads == 0 {
-		return fmt.Errorf("qwen3next: num_attention_heads must be set")
-	}
-	if q.NumKeyValueHeads == 0 {
-		return fmt.Errorf("qwen3next: num_key_value_heads must be set")
-	}
-	if q.HeadDim == 0 {
-		return fmt.Errorf("qwen3next: head_dim must be set")
-	}
-	if q.RopeTheta == 0 {
-		return fmt.Errorf("qwen3next: rope_theta must be set")
-	}
-	if q.PartialRotaryFactor <= 0 || q.PartialRotaryFactor > 1 {
-		return fmt.Errorf("qwen3next: partial_rotary_factor must be in (0,1], got %v", q.PartialRotaryFactor)
-	}
-	if q.LinearNumKeyHeads == 0 || q.LinearNumValueHeads == 0 || q.LinearKeyHeadDim == 0 || q.LinearValueHeadDim == 0 {
-		return fmt.Errorf("qwen3next: linear attention config must be set (linear_num_key_heads, linear_num_value_heads, linear_key_head_dim, linear_value_head_dim)")
-	}
-	if q.FullAttentionInterval == 0 {
-		return fmt.Errorf("qwen3next: full_attention_interval must be set")
-	}
-	if q.FullAttentionInterval > q.NumHiddenLayers {
-		return fmt.Errorf("qwen3next: full_attention_interval (%d) exceeds num_hidden_layers (%d)", q.FullAttentionInterval, q.NumHiddenLayers)
+func (q *qwen3NextModel) parseMore(fsys fs.FS) error {
+	if q.TextConfig != nil {
+		q.qwen3NextTextConfig = *q.TextConfig
 	}
 
-	hasFull := false
-	for i := range q.NumHiddenLayers {
-		if (i+1)%q.FullAttentionInterval == 0 {
-			hasFull = true
-			break
+	if q.RopeTheta == 0 {
+		q.RopeTheta = q.RopeParameters.RopeTheta
+	}
+	if q.PartialRotaryFactor == 0 {
+		q.PartialRotaryFactor = q.RopeParameters.PartialRotaryFactor
+	}
+
+	if q.RopeScaling.Type == "" && q.RopeParameters.RopeType != "" {
+		q.RopeScaling.Type = q.RopeParameters.RopeType
+	}
+
+	// Pull vision preprocessing fields when present.
+	if q.VisionModel.Depth > 0 {
+		if bts, err := fs.ReadFile(fsys, "preprocessor_config.json"); err == nil {
+			var pre struct {
+				Size struct {
+					ShortestEdge uint32 `json:"shortest_edge"`
+					LongestEdge  uint32 `json:"longest_edge"`
+				} `json:"size"`
+				PatchSize         uint32    `json:"patch_size"`
+				TemporalPatchSize uint32    `json:"temporal_patch_size"`
+				MergeSize         uint32    `json:"merge_size"`
+				ImageMean         []float32 `json:"image_mean"`
+				ImageStd          []float32 `json:"image_std"`
+			}
+			if json.Unmarshal(bts, &pre) == nil {
+				if q.VisionModel.PatchSize == 0 {
+					q.VisionModel.PatchSize = pre.PatchSize
+				}
+				if q.VisionModel.TemporalPatchSize == 0 {
+					q.VisionModel.TemporalPatchSize = pre.TemporalPatchSize
+				}
+				if q.VisionModel.SpatialMergeSize == 0 {
+					q.VisionModel.SpatialMergeSize = pre.MergeSize
+				}
+				if q.VisionModel.Size.ShortestEdge == 0 {
+					q.VisionModel.Size.ShortestEdge = pre.Size.ShortestEdge
+				}
+				if q.VisionModel.Size.LongestEdge == 0 {
+					q.VisionModel.Size.LongestEdge = pre.Size.LongestEdge
+				}
+				if len(q.VisionModel.ImageMean) == 0 {
+					q.VisionModel.ImageMean = pre.ImageMean
+				}
+				if len(q.VisionModel.ImageStd) == 0 {
+					q.VisionModel.ImageStd = pre.ImageStd
+				}
+			}
 		}
 	}
-	if !hasFull {
-		return fmt.Errorf("qwen3next: head_count_kv would be all zeros (full_attention_interval=%d, num_hidden_layers=%d)", q.FullAttentionInterval, q.NumHiddenLayers)
+
+	if q.NumHiddenLayers == 0 {
+		return fmt.Errorf("qwen35: num_hidden_layers must be set")
+	}
+	if q.NumAttentionHeads == 0 {
+		return fmt.Errorf("qwen35: num_attention_heads must be set")
+	}
+	if q.NumKeyValueHeads == 0 {
+		return fmt.Errorf("qwen35: num_key_value_heads must be set")
+	}
+	if q.HeadDim == 0 {
+		return fmt.Errorf("qwen35: head_dim must be set")
+	}
+	if q.RopeTheta == 0 {
+		return fmt.Errorf("qwen35: rope_theta must be set")
+	}
+	if q.PartialRotaryFactor <= 0 || q.PartialRotaryFactor > 1 {
+		return fmt.Errorf("qwen35: partial_rotary_factor must be in (0,1], got %v", q.PartialRotaryFactor)
+	}
+	if q.LinearNumKeyHeads == 0 || q.LinearNumValueHeads == 0 || q.LinearKeyHeadDim == 0 || q.LinearValueHeadDim == 0 {
+		return fmt.Errorf("qwen35: linear attention config must be set (linear_num_key_heads, linear_num_value_heads, linear_key_head_dim, linear_value_head_dim)")
+	}
+	if _, err := q.kvHeadCounts(); err != nil {
+		return err
 	}
 
 	return nil
 }
 
+func (q *qwen3NextModel) kvHeadCounts() ([]uint32, error) {
+	if len(q.LayerTypes) > 0 {
+		kv := make([]uint32, q.NumHiddenLayers)
+		hasFull := false
+		hasRecurrent := false
+		for i := range q.NumHiddenLayers {
+			layerType := ""
+			if i < uint32(len(q.LayerTypes)) {
+				layerType = q.LayerTypes[i]
+			}
+			if layerType == "full_attention" {
+				kv[i] = q.NumKeyValueHeads
+				hasFull = true
+			} else {
+				hasRecurrent = true
+			}
+		}
+		if !hasFull || !hasRecurrent {
+			return nil, fmt.Errorf("qwen35: layer_types must include both full_attention and linear_attention")
+		}
+		return kv, nil
+	}
+
+	if q.FullAttentionInterval == 0 {
+		return nil, fmt.Errorf("qwen35: full_attention_interval must be set")
+	}
+	if q.FullAttentionInterval > q.NumHiddenLayers {
+		return nil, fmt.Errorf("qwen35: full_attention_interval (%d) exceeds num_hidden_layers (%d)", q.FullAttentionInterval, q.NumHiddenLayers)
+	}
+
+	kv := make([]uint32, q.NumHiddenLayers)
+	hasFull := false
+	for i := range q.NumHiddenLayers {
+		if (i+1)%q.FullAttentionInterval == 0 {
+			kv[i] = q.NumKeyValueHeads
+			hasFull = true
+		}
+	}
+	if !hasFull {
+		return nil, fmt.Errorf("qwen35: head_count_kv would be all zeros (full_attention_interval=%d, num_hidden_layers=%d)", q.FullAttentionInterval, q.NumHiddenLayers)
+	}
+	return kv, nil
+}
+
+func (q *qwen3NextModel) ropeSections() []int32 {
+	if len(q.RopeParameters.MropeSection) > 0 {
+		return q.RopeParameters.MropeSection
+	}
+	return q.RopeScaling.MropeSection
+}
+
+func (q *qwen3NextModel) shouldReorderVHeads() bool {
+	modelType := strings.ToLower(q.ModelType)
+	if strings.Contains(modelType, "qwen3_next") || strings.Contains(modelType, "qwen3next") {
+		return false
+	}
+
+	for _, arch := range q.Architectures {
+		arch = strings.ToLower(arch)
+		if strings.Contains(arch, "qwen3next") || strings.Contains(arch, "qwen3_next") {
+			return false
+		}
+	}
+
+	// Default to qwen3.5 layout for all other qwen3next-family imports.
+	return true
+}
+
 func (q *qwen3NextModel) KV(t *Tokenizer) KV {
 	kv := q.ModelParameters.KV(t)
-	kv["general.architecture"] = "qwen3next"
-	kv["tokenizer.ggml.pre"] = "qwen2"
+
+	arch := "qwen35"
+	if q.NumExperts > 0 {
+		arch = "qwen35moe"
+	}
+	kv["general.architecture"] = arch
+	kv["tokenizer.ggml.pre"] = "qwen35"
 	kv["block_count"] = q.NumHiddenLayers
 	kv["context_length"] = q.MaxPositionEmbeddings
 	kv["embedding_length"] = q.HiddenSize
 	kv["feed_forward_length"] = q.IntermediateSize
 	kv["attention.head_count"] = q.NumAttentionHeads
+
 	headDim := q.HeadDim
 	if headDim == 0 && q.NumAttentionHeads > 0 {
 		headDim = q.HiddenSize / q.NumAttentionHeads
@@ -113,18 +274,31 @@ func (q *qwen3NextModel) KV(t *Tokenizer) KV {
 	kv["attention.layer_norm_rms_epsilon"] = q.RMSNormEPS
 	kv["rope.freq_base"] = q.RopeTheta
 
-	// RoPE dimension count (partial rotary)
-	// partial_rotary_factor = 0.25 means only 25% of head_dim uses RoPE
 	partialRotary := q.PartialRotaryFactor
 	if partialRotary > 0 && partialRotary <= 1 {
 		kv["rope.dimension_count"] = uint32(float32(headDim) * partialRotary)
 	}
 
-	// MoE config
+	if sections := q.ropeSections(); len(sections) > 0 {
+		kv["mrope_sections"] = sections
+		kv["rope.mrope_section"] = sections
+		kv["rope.dimension_sections"] = sections
+	}
+	if q.RopeParameters.MRopeInterleaved {
+		kv["rope.mrope_interleaved"] = true
+	}
+
+	if q.RopeScaling.Type != "" && q.RopeScaling.Type != "default" {
+		kv["rope.scaling.type"] = q.RopeScaling.Type
+		kv["rope.scaling.factor"] = q.RopeScaling.Factor
+	}
+
 	if q.NumExperts > 0 {
 		kv["expert_count"] = q.NumExperts
 		kv["expert_used_count"] = q.NumExpertsPerToken
-		kv["norm_top_k_prob"] = q.NormTopkProb
+		if q.NormTopkProb != nil {
+			kv["norm_top_k_prob"] = *q.NormTopkProb
+		}
 		if q.MoEIntermediateSize > 0 {
 			kv["expert_feed_forward_length"] = q.MoEIntermediateSize
 		}
@@ -133,33 +307,66 @@ func (q *qwen3NextModel) KV(t *Tokenizer) KV {
 		}
 	}
 
-	// SSM/Linear attention config
-	// d_inner = linear_value_head_dim * linear_num_value_heads
 	dInner := q.LinearValueHeadDim * q.LinearNumValueHeads
 	kv["ssm.inner_size"] = dInner
-	kv["ssm.state_size"] = q.LinearKeyHeadDim        // head_k_dim
-	kv["ssm.group_count"] = q.LinearNumKeyHeads      // num_k_heads
-	kv["ssm.time_step_rank"] = q.LinearNumValueHeads // num_v_heads
+	kv["ssm.state_size"] = q.LinearKeyHeadDim
+	kv["ssm.group_count"] = q.LinearNumKeyHeads
+	kv["ssm.time_step_rank"] = q.LinearNumValueHeads
 	kv["ssm.conv_kernel"] = q.LinearConvKernelDim
-	interval := q.FullAttentionInterval
-	kv["full_attention_interval"] = interval
-
-	// Build per-layer KV head count array to identify layer types
-	// 0 = recurrent (linear attention), non-zero = full attention
-	kvHeadCounts := make([]uint32, q.NumHiddenLayers)
-	for i := range q.NumHiddenLayers {
-		// Full attention every full_attention_interval layers (starting at interval-1)
-		if interval > 0 && (i+1)%interval == 0 {
-			kvHeadCounts[i] = q.NumKeyValueHeads
-		}
-		// else stays 0 (recurrent layer)
+	if q.shouldReorderVHeads() {
+		kv["ssm.v_head_reordered"] = true
+	}
+	if q.FullAttentionInterval > 0 {
+		kv["full_attention_interval"] = q.FullAttentionInterval
 	}
-	kv["attention.head_count_kv"] = kvHeadCounts
 
-	// RoPE scaling
-	if q.RopeScaling.Type != "" {
-		kv["rope.scaling.type"] = q.RopeScaling.Type
-		kv["rope.scaling.factor"] = q.RopeScaling.Factor
+	if headCounts, err := q.kvHeadCounts(); err == nil {
+		kv["attention.head_count_kv"] = headCounts
+	}
+
+	if q.VisionModel.Depth > 0 {
+		kv["vision.block_count"] = q.VisionModel.Depth
+		kv["vision.embedding_length"] = q.VisionModel.HiddenSize
+		kv["vision.attention.head_count"] = q.VisionModel.NumHeads
+		kv["vision.num_channels"] = q.VisionModel.InChannels
+		if q.VisionModel.PatchSize > 0 {
+			kv["vision.patch_size"] = q.VisionModel.PatchSize
+		}
+		if q.VisionModel.SpatialMergeSize > 0 {
+			kv["vision.spatial_merge_size"] = q.VisionModel.SpatialMergeSize
+		}
+		if q.VisionModel.RMSNormEps > 0 {
+			kv["vision.attention.layer_norm_epsilon"] = q.VisionModel.RMSNormEps
+		}
+		if q.VisionModel.RopeTheta > 0 {
+			kv["vision.rope.freq_base"] = q.VisionModel.RopeTheta
+		}
+		if q.VisionModel.TemporalPatchSize > 0 {
+			kv["vision.temporal_patch_size"] = q.VisionModel.TemporalPatchSize
+		}
+		kv["vision.deepstack_visual_indexes"] = q.VisionModel.DeepstackVisualIndexes
+		if q.VisionModel.Size.ShortestEdge > 0 {
+			kv["vision.shortest_edge"] = q.VisionModel.Size.ShortestEdge
+		}
+		if q.VisionModel.Size.LongestEdge > 0 {
+			kv["vision.longest_edge"] = q.VisionModel.Size.LongestEdge
+		}
+		if len(q.VisionModel.ImageMean) > 0 {
+			kv["vision.image_mean"] = q.VisionModel.ImageMean
+		}
+		if len(q.VisionModel.ImageStd) > 0 {
+			kv["vision.image_std"] = q.VisionModel.ImageStd
+		}
+	}
+
+	if q.ImageTokenID > 0 {
+		kv["image_token_id"] = q.ImageTokenID
+	}
+	if q.VisionStartTokenID > 0 {
+		kv["vision_start_token_id"] = q.VisionStartTokenID
+	}
+	if q.VisionEndTokenID > 0 {
+		kv["vision_end_token_id"] = q.VisionEndTokenID
 	}
 
 	return kv
@@ -168,7 +375,6 @@ func (q *qwen3NextModel) KV(t *Tokenizer) KV {
 func (q *qwen3NextModel) Tensors(ts []Tensor) []*ggml.Tensor {
 	var out []*ggml.Tensor
 
-	// Create merges for expert tensors - stack individual experts into batched tensors
 	merges := make([]merge, q.NumHiddenLayers*3)
 	for i := range q.NumHiddenLayers {
 		merges[i*3+0] = merge{
@@ -185,16 +391,13 @@ func (q *qwen3NextModel) Tensors(ts []Tensor) []*ggml.Tensor {
 		}
 	}
 
-	// Merge expert tensors
 	merged, remaining := mergeTensors(ts, merges...)
 	out = append(out, merged...)
 
-	// Process remaining tensors
 	for _, t := range remaining {
 		name := t.Name()
 		shape := t.Shape()
 
-		// Split linear_attn.in_proj_qkvz (ssm_in) into attn_qkv + attn_gate when possible
 		if strings.HasSuffix(name, ".ssm_in.weight") {
 			if qkv, gate, ok := q.splitQKVZTensor(t); ok {
 				out = append(out, qkv, gate)
@@ -204,84 +407,299 @@ func (q *qwen3NextModel) Tensors(ts []Tensor) []*ggml.Tensor {
 		}
 
 		switch {
-		// Add 1 to norm weights (except ssm_norm which is linear_attn.norm)
-		// This matches the Python converter behavior for qwen3next
+		case strings.Contains(name, ".mlp.experts.gate_up_proj"):
+			out = append(out, slices.Collect(splitDim(t, 1,
+				split{Replacer: strings.NewReplacer(".mlp.experts.gate_up_proj", ".ffn_gate_exps.weight")},
+				split{Replacer: strings.NewReplacer(".mlp.experts.gate_up_proj", ".ffn_up_exps.weight")},
+			))...)
+
+		case strings.Contains(name, ".mlp.experts.down_proj"):
+			out = append(out, &ggml.Tensor{
+				Name:     strings.NewReplacer(".mlp.experts.down_proj", ".ffn_down_exps.weight").Replace(name),
+				Kind:     t.Kind(),
+				Shape:    slices.Clone(shape),
+				WriterTo: t,
+			})
+
+		case strings.HasPrefix(name, "v.blk.") && strings.Contains(name, ".attn_qkv"):
+			out = append(out, slices.Collect(splitDim(t, 0,
+				split{Replacer: strings.NewReplacer("attn_qkv", "attn_q")},
+				split{Replacer: strings.NewReplacer("attn_qkv", "attn_k")},
+				split{Replacer: strings.NewReplacer("attn_qkv", "attn_v")},
+			))...)
+
+		case strings.Contains(name, "patch_embed") && strings.HasSuffix(name, "weight"):
+			out = append(out, &ggml.Tensor{
+				Name:     name,
+				Kind:     t.Kind(),
+				Shape:    append([]uint64{shape[0] * shape[1]}, shape[2:]...),
+				WriterTo: t,
+			})
+
 		case strings.HasSuffix(name, "_norm.weight") && !strings.HasSuffix(name, ".ssm_norm.weight"):
 			t.SetRepacker(q.addOne)
-			out = append(out, &ggml.Tensor{
-				Name:     name,
-				Kind:     t.Kind(),
-				Shape:    slices.Clone(shape),
-				WriterTo: t,
-			})
+			out = append(out, &ggml.Tensor{Name: name, Kind: t.Kind(), Shape: slices.Clone(shape), WriterTo: t})
 
-		// Handle linear attention A_log -> ssm_a (negate and exp)
-		// Note: name has already been transformed by Replacements at this point
 		case strings.HasSuffix(name, ".ssm_a"):
-			t.SetRepacker(func(_ string, data []float32, shape []uint64) ([]float32, error) {
-				// Compute -exp(A_log)
-				result := make([]float32, len(data))
-				for i, v := range data {
-					// -exp(v)
-					result[i] = -float32(math.Exp(float64(v)))
-				}
-				return result, nil
-			})
-			out = append(out, &ggml.Tensor{
-				Name:     name,
-				Kind:     t.Kind(),
-				Shape:    slices.Clone(shape),
-				WriterTo: t,
-			})
+			t.SetRepacker(q.repackSSMA())
+			out = append(out, &ggml.Tensor{Name: name, Kind: t.Kind(), Shape: slices.Clone(shape), WriterTo: t})
+
+		case strings.HasSuffix(name, ".attn_qkv.weight"):
+			if q.shouldReorderVHeads() {
+				t.SetRepacker(q.repackAttnQKV())
+			}
+			out = append(out, &ggml.Tensor{Name: name, Kind: t.Kind(), Shape: slices.Clone(shape), WriterTo: t})
+
+		case strings.HasSuffix(name, ".attn_gate.weight"):
+			if q.shouldReorderVHeads() {
+				// HF tensor layout is [out_features, in_features]; reorder rows.
+				t.SetRepacker(q.repackReorderDim(0, int(q.LinearValueHeadDim)))
+			}
+			out = append(out, &ggml.Tensor{Name: name, Kind: t.Kind(), Shape: slices.Clone(shape), WriterTo: t})
+
+		case strings.HasSuffix(name, ".ssm_beta.weight"), strings.HasSuffix(name, ".ssm_alpha.weight"):
+			if q.shouldReorderVHeads() {
+				// HF tensor layout is [out_features, in_features]; reorder rows.
+				t.SetRepacker(q.repackReorderDim(0, 1))
+			}
+			out = append(out, &ggml.Tensor{Name: name, Kind: t.Kind(), Shape: slices.Clone(shape), WriterTo: t})
+
+		case strings.HasSuffix(name, ".ssm_dt"):
+			if q.shouldReorderVHeads() {
+				t.SetRepacker(q.repackReorderDim(0, 1))
+			}
+			out = append(out, &ggml.Tensor{Name: name, Kind: t.Kind(), Shape: slices.Clone(shape), WriterTo: t})
+
+		case strings.HasSuffix(name, ".ssm_out.weight"):
+			if q.shouldReorderVHeads() {
+				// HF out_proj layout is [out_features, in_features]; reorder columns.
+				t.SetRepacker(q.repackReorderDim(1, int(q.LinearValueHeadDim)))
+			}
+			out = append(out, &ggml.Tensor{Name: name, Kind: t.Kind(), Shape: slices.Clone(shape), WriterTo: t})
 
-		// Squeeze conv1d weights: [1, D, K] or [D, 1, K] -> [D, K]
 		case strings.HasSuffix(name, ".ssm_conv1d.weight"):
 			newShape := slices.Clone(shape)
 			if len(shape) == 3 {
 				if shape[0] == 1 {
-					// [1, D, K] -> [D, K]
 					newShape = []uint64{shape[1], shape[2]}
 				} else if shape[1] == 1 {
-					// [D, 1, K] -> [D, K]
 					newShape = []uint64{shape[0], shape[2]}
 				}
 			}
-			out = append(out, &ggml.Tensor{
-				Name:     name,
-				Kind:     t.Kind(),
-				Shape:    newShape,
-				WriterTo: t,
-			})
-		// Squeeze shared expert gate: [D, 1] or [1, D] -> [D]
-		case strings.HasSuffix(name, ".ffn_gate_inp_shexp.weight"):
-			newShape := slices.Clone(shape)
-			if len(shape) == 2 {
-				if shape[0] == 1 && shape[1] > 1 {
-					newShape = []uint64{shape[1]}
-				} else if shape[1] == 1 && shape[0] > 1 {
-					newShape = []uint64{shape[0]}
-				}
+			if q.shouldReorderVHeads() {
+				t.SetRepacker(q.repackConv1D())
 			}
-			out = append(out, &ggml.Tensor{
-				Name:     name,
-				Kind:     t.Kind(),
-				Shape:    newShape,
-				WriterTo: t,
-			})
+			out = append(out, &ggml.Tensor{Name: name, Kind: t.Kind(), Shape: newShape, WriterTo: t})
 
 		default:
-			out = append(out, &ggml.Tensor{
-				Name:     name,
-				Kind:     t.Kind(),
-				Shape:    slices.Clone(shape),
-				WriterTo: t,
-			})
+			out = append(out, &ggml.Tensor{Name: name, Kind: t.Kind(), Shape: slices.Clone(shape), WriterTo: t})
 		}
 	}
 
 	return out
 }
 
+func (q *qwen3NextModel) repackReorderDim(dim, headDim int) Repacker {
+	return func(_ string, data []float32, shape []uint64) ([]float32, error) {
+		if !q.shouldReorderVHeads() {
+			return data, nil
+		}
+		numK := int(q.LinearNumKeyHeads)
+		numVPerK := int(q.LinearNumValueHeads / q.LinearNumKeyHeads)
+		return reorderHeadLayout(data, shape, dim, numK, numVPerK, headDim)
+	}
+}
+
+func (q *qwen3NextModel) repackAttnQKV() Repacker {
+	return func(_ string, data []float32, shape []uint64) ([]float32, error) {
+		if !q.shouldReorderVHeads() || len(shape) != 2 {
+			return data, nil
+		}
+
+		rows := int(shape[0])
+		cols := int(shape[1])
+		numK := int(q.LinearNumKeyHeads)
+		numV := int(q.LinearNumValueHeads)
+		headK := int(q.LinearKeyHeadDim)
+		headV := int(q.LinearValueHeadDim)
+		qDim := headK * numK
+		kDim := headK * numK
+		vDim := headV * numV
+		qkvDim := qDim + kDim + vDim
+
+		switch {
+		case rows == qkvDim:
+			// HF layout: [out_features, in_features]. Keep Q/K rows unchanged and
+			// reorder only V rows from grouped -> tiled head layout.
+			out := make([]float32, len(data))
+			qkRows := qDim + kDim
+			qkSize := qkRows * cols
+			copy(out[:qkSize], data[:qkSize])
+
+			vStart := qkSize
+			vEnd := vStart + vDim*cols
+			reorderedV, err := reorderHeadLayout(data[vStart:vEnd], []uint64{uint64(vDim), uint64(cols)}, 0, numK, numV/numK, headV)
+			if err != nil {
+				return nil, err
+			}
+			copy(out[vStart:vEnd], reorderedV)
+			copy(out[vEnd:], data[vEnd:])
+			return out, nil
+
+		case cols == qkvDim:
+			// Fallback for already-transposed [in_features, out_features] tensors.
+			out := make([]float32, len(data))
+			copy(out, data)
+			for r := range rows {
+				base := r * cols
+				vStart := base + qDim + kDim
+				vEnd := vStart + vDim
+				reorderedV, err := reorderHeadLayout(out[vStart:vEnd], []uint64{uint64(vDim)}, 0, numK, numV/numK, headV)
+				if err != nil {
+					return nil, err
+				}
+				copy(out[vStart:vEnd], reorderedV)
+			}
+			return out, nil
+
+		default:
+			return data, nil
+		}
+	}
+}
+
+func (q *qwen3NextModel) repackConv1D() Repacker {
+	return func(_ string, data []float32, shape []uint64) ([]float32, error) {
+		if !q.shouldReorderVHeads() {
+			return data, nil
+		}
+
+		normShape := slices.Clone(shape)
+		if len(shape) == 3 {
+			if shape[0] == 1 {
+				normShape = []uint64{shape[1], shape[2]}
+			} else if shape[1] == 1 {
+				normShape = []uint64{shape[0], shape[2]}
+			}
+		}
+		if len(normShape) != 2 {
+			return data, nil
+		}
+
+		rows := int(normShape[0])
+		cols := int(normShape[1])
+		numK := int(q.LinearNumKeyHeads)
+		numV := int(q.LinearNumValueHeads)
+		headK := int(q.LinearKeyHeadDim)
+		headV := int(q.LinearValueHeadDim)
+		qkChannels := 2 * headK * numK
+		totalChannels := qkChannels + headV*numV
+		if qkChannels <= 0 {
+			return data, nil
+		}
+
+		switch {
+		case rows == totalChannels:
+			// HF layout after squeeze: [channels, kernel]
+			out := make([]float32, len(data))
+			prefix := qkChannels * cols
+			copy(out[:prefix], data[:prefix])
+			reorderedV, err := reorderHeadLayout(data[prefix:], []uint64{uint64(totalChannels - qkChannels), uint64(cols)}, 0, numK, numV/numK, headV)
+			if err != nil {
+				return nil, err
+			}
+			copy(out[prefix:], reorderedV)
+			return out, nil
+		case cols == totalChannels:
+			// Fallback for transposed [kernel, channels]
+			out := make([]float32, len(data))
+			copy(out, data)
+			vChannels := totalChannels - qkChannels
+			for r := range rows {
+				base := r * cols
+				vStart := base + qkChannels
+				vEnd := vStart + vChannels
+				reorderedV, err := reorderHeadLayout(out[vStart:vEnd], []uint64{uint64(vChannels)}, 0, numK, numV/numK, headV)
+				if err != nil {
+					return nil, err
+				}
+				copy(out[vStart:vEnd], reorderedV)
+			}
+			return out, nil
+		default:
+			return data, nil
+		}
+	}
+}
+
+func (q *qwen3NextModel) repackSSMA() Repacker {
+	return func(_ string, data []float32, shape []uint64) ([]float32, error) {
+		result := make([]float32, len(data))
+		for i, v := range data {
+			result[i] = -float32(math.Exp(float64(v)))
+		}
+		if !q.shouldReorderVHeads() {
+			return result, nil
+		}
+		numK := int(q.LinearNumKeyHeads)
+		numVPerK := int(q.LinearNumValueHeads / q.LinearNumKeyHeads)
+		return reorderHeadLayout(result, shape, 0, numK, numVPerK, 1)
+	}
+}
+
+func reorderHeadLayout(data []float32, shape []uint64, dim int, numKHeads, numVPerK, headDim int) ([]float32, error) {
+	if len(shape) == 0 || numKHeads <= 0 || numVPerK <= 0 || headDim <= 0 {
+		return data, nil
+	}
+
+	dims := make([]int, len(shape))
+	for i := range shape {
+		dims[i] = int(shape[i])
+	}
+	if dim < 0 {
+		dim += len(dims)
+	}
+	if dim < 0 || dim >= len(dims) {
+		return data, nil
+	}
+
+	expected := numKHeads * numVPerK * headDim
+	if dims[dim] != expected {
+		return data, nil
+	}
+
+	newShape := make([]int, 0, len(dims)+2)
+	newShape = append(newShape, dims[:dim]...)
+	newShape = append(newShape, numKHeads, numVPerK, headDim)
+	newShape = append(newShape, dims[dim+1:]...)
+
+	var tt tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
+	if err := tt.Reshape(newShape...); err != nil {
+		return nil, err
+	}
+
+	perm := make([]int, len(newShape))
+	for i := range perm {
+		perm[i] = i
+	}
+	perm[dim], perm[dim+1] = perm[dim+1], perm[dim]
+
+	tt, err := tensor.Transpose(tt, perm...)
+	if err != nil {
+		return nil, err
+	}
+	tt = tensor.Materialize(tt)
+
+	total := 1
+	for _, d := range dims {
+		total *= d
+	}
+	if err := tt.Reshape(total); err != nil {
+		return nil, err
+	}
+	return native.VectorF32(tt.(*tensor.Dense))
+}
+
 type qkvzSplitSpec struct {
 	hidden    int
 	headKDim  int
@@ -369,7 +787,6 @@ func (q *qwen3NextModel) repackQKVZ(spec qkvzSplitSpec, extractGate bool) Repack
 		var tt tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
 		var err error
 
-		// Convert to [hidden, out_features] layout for slicing
 		tt, err = tensor.Transpose(tt, 1, 0)
 		if err != nil {
 			return nil, err
@@ -444,7 +861,6 @@ func (q *qwen3NextModel) repackQKVZ(spec qkvzSplitSpec, extractGate bool) Repack
 	}
 }
 
-// addOne adds 1.0 to all elements in the tensor (for norm weights)
 func (*qwen3NextModel) addOne(_ string, data []float32, shape []uint64) ([]float32, error) {
 	n := tensor.New(tensor.WithShape(int(shape[0])), tensor.WithBacking(data))
 	ones := tensor.Ones(tensor.Float32, int(shape[0]))
@@ -471,10 +887,21 @@ func (q *qwen3NextModel) Replacements() []string {
 	return []string{
 		// Embeddings and output
 		"lm_head", "output",
+		"model.language_model.embed_tokens", "token_embd",
+		"model.language_model.norm", "output_norm",
+		"model.language_model.layers", "blk",
 		"model.embed_tokens", "token_embd",
 		"model.norm", "output_norm",
 		"model.layers", "blk",
 
+		// Vision
+		"model.visual", "v",
+		"patch_embed.proj", "patch_embed",
+		"blocks", "blk",
+		"attn.qkv", "attn_qkv",
+		"attn.proj", "attn_out",
+		"deepstack_merger_list", "deepstack_merger",
+
 		// Layer norms
 		"input_layernorm", "attn_norm",
 		"post_attention_layernorm", "post_attention_norm",
@@ -487,9 +914,16 @@ func (q *qwen3NextModel) Replacements() []string {
 		"self_attn.v_proj", "attn_v",
 		"self_attn.o_proj", "attn_output",
 
-		// Linear attention (Gated Delta Net)
+		// Linear attention (legacy qwen3next)
 		"linear_attn.in_proj_qkvz", "ssm_in",
 		"linear_attn.in_proj_ba", "ssm_ba",
+
+		// Linear attention (qwen35)
+		"linear_attn.in_proj_qkv", "attn_qkv",
+		"linear_attn.in_proj_z", "attn_gate",
+		"linear_attn.in_proj_a", "ssm_alpha",
+		"linear_attn.in_proj_b", "ssm_beta",
+
 		"linear_attn.conv1d", "ssm_conv1d",
 		"linear_attn.dt_bias", "ssm_dt",
 		"linear_attn.dt_proj", "ssm_dt",
@@ -497,14 +931,14 @@ func (q *qwen3NextModel) Replacements() []string {
 		"linear_attn.norm", "ssm_norm",
 		"linear_attn.out_proj", "ssm_out",
 
-		// MoE (experts are stacked via mergeTensors, not replaced here)
+		// MoE
 		"mlp.gate.weight", "ffn_gate_inp.weight",
 		"mlp.shared_expert.down_proj", "ffn_down_shexp",
 		"mlp.shared_expert.gate_proj", "ffn_gate_shexp",
 		"mlp.shared_expert.up_proj", "ffn_up_shexp",
 		"mlp.shared_expert_gate", "ffn_gate_inp_shexp",
 
-		// Dense FFN (if any layers use it)
+		// Dense FFN
 		"mlp.down_proj", "ffn_down",
 		"mlp.gate_proj", "ffn_gate",
 		"mlp.up_proj", "ffn_up",

convert/convert_qwen3next_test.go

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

convert/tokenizer.go

@@ -101,6 +101,8 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
 			t.Pre = "deepseek-coder"
 		case "1ff7f41064896984db5d1bb6ff64fa4bc29007d08c1b439e505b7392777a319e":
 			t.Pre = "qwen2"
+		case "00431aed57e696b747435f734d1e3b9b1bfd931a121fb5cac7129e97c181e9ba":
+			t.Pre = "qwen35"
 		case "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855":
 			// noop, empty pretokenizer
 		default:
@@ -171,6 +173,13 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
 		}
 	}
 
+	// Match upstream behavior: prefer chat_template.jinja when present.
+	if bts, err := fs.ReadFile(fsys, "chat_template.jinja"); err == nil {
+		t.Template = string(bts)
+	} else if err != nil && !errors.Is(err, os.ErrNotExist) {
+		return nil, err
+	}
+
 	if f, err := fsys.Open("generation_config.json"); errors.Is(err, os.ErrNotExist) {
 	} else if err != nil {
 		return nil, err

convert/tokenizer_test.go

@@ -79,6 +79,21 @@ func TestParseTokenizer(t *testing.T) {
 				Template:   "<default template>",
 			},
 		},
+		{
+			name: "chat template jinja overrides tokenizer config",
+			fsys: createTokenizerFS(t, t.TempDir(), map[string]io.Reader{
+				"tokenizer.json": strings.NewReader(`{}`),
+				"tokenizer_config.json": strings.NewReader(`{
+					"chat_template": "<template from tokenizer config>"
+				}`),
+				"chat_template.jinja": strings.NewReader("<template from jinja>"),
+			}),
+			want: &Tokenizer{
+				Vocabulary: &Vocabulary{Model: "gpt2"},
+				Pre:        "default",
+				Template:   "<template from jinja>",
+			},
+		},
 		{
 			name: "added tokens",
 			fsys: createTokenizerFS(t, t.TempDir(), map[string]io.Reader{
@@ -386,6 +401,28 @@ func TestParseTokenizer(t *testing.T) {
 				Pre: "default",
 			},
 		},
+		{
+			name: "qwen35 pretokenizer",
+			fsys: createTokenizerFS(t, t.TempDir(), map[string]io.Reader{
+				"tokenizer.json": strings.NewReader(`{
+					"pre_tokenizer": {
+						"type": "Sequence",
+						"pretokenizers": [
+							{
+								"type": "Split",
+								"pattern": {
+									"Regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
+								}
+							}
+						]
+					}
+				}`),
+			}),
+			want: &Tokenizer{
+				Vocabulary: &Vocabulary{Model: "gpt2"},
+				Pre:        "qwen35",
+			},
+		},
 	}
 
 	for _, tt := range cases {

fs/ggml/ggml.go

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

kvcache/recurrent.go

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

llm/server.go

@@ -211,6 +211,7 @@ func NewLlamaServer(systemInfo ml.SystemInfo, gpus []ml.DeviceInfo, modelPath st
 	}
 
 	kvct := strings.ToLower(envconfig.KvCacheType())
+	arch := f.KV().Architecture()
 
 	if tok == nil {
 		flashAttention := ml.FlashAttentionAuto
@@ -220,6 +221,9 @@ func NewLlamaServer(systemInfo ml.SystemInfo, gpus []ml.DeviceInfo, modelPath st
 			} else {
 				flashAttention = ml.FlashAttentionDisabled
 			}
+		} else if fa && slices.Contains([]string{"qwen35", "qwen35moe", "qwen3next"}, arch) {
+			// Keep FA explicit for qwen35-family models to avoid architecture fallback to Auto.
+			flashAttention = ml.FlashAttentionEnabled
 		}
 
 		if kvct != "" {

model/models/qwen3next/cache.go

@@ -2,595 +2,58 @@ package qwen3next
 
 import (
 	"math"
-	"slices"
 
 	"github.com/ollama/ollama/kvcache"
 	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/model/input"
 )
 
-var _ kvcache.Cache = (*HybridCache)(nil)
+var (
+	_ kvcache.Cache           = (*HybridCache)(nil)
+	_ kvcache.CheckpointCache = (*HybridCache)(nil)
+)
 
-// HybridCache stores:
-// - a standard causal KV cache for full attention layers
-// - per-sequence conv state for linear attention layers
-// - per-sequence delta state for linear attention layers
-//
-// Conv state shape (per layer, per sequence): [convKernelSize-1, convChannels]
-// Delta state shape (per layer, per sequence): [headVDim, headVDim * numVHeads]
+// HybridCache adapts the shared recurrent cache base for Qwen3-Next naming.
 type HybridCache struct {
-	kv *kvcache.Causal
-
-	backend      ml.Backend
-	dtype        ml.DType
-	maxSequences int
-
-	// Conv state dimensions
-	convDim      int // convKernelSize - 1
-	convChannels int // d_inner + 2 * num_k_heads * head_k_dim
-
-	// Delta state dimensions
-	deltaStateSize int // headVDim * headVDim * numVHeads
-
-	// slot mapping for recurrent state (copy-on-write)
-	slotForSeq map[int]int
-	refCount   []int
-	freeSlots  []int
-
-	// per-layer conv state buffers (allocated lazily)
-	convCtxs   map[int]ml.Context
-	convStates map[int]ml.Tensor // [convDim*convChannels, maxSlots]
-
-	// per-layer delta state buffers (allocated lazily)
-	deltaCtxs   map[int]ml.Context
-	deltaStates map[int]ml.Tensor // [deltaStateSize, maxSlots]
-
-	// recurrent checkpoints (per slot)
-	checkpointCount     int
-	checkpointMinPos    int32
-	checkpointInterval  int32
-	checkpointCtxSize   int
-	checkpoints         map[int]*slotCheckpointStore
-	pendingRestore      map[int]checkpointRestore
-	curCheckpointPos    []int32
-	curCheckpointSlots  map[int]int
-	reserveCheckpoints  bool
-	checkpointConvCtxs  map[int]ml.Context
-	checkpointDeltaCtxs map[int]ml.Context
-	checkpointReserved  map[int]struct{}
-
-	// current forward batch (derived in StartForward)
-	curSeqs       []int
-	curSlots      []int
-	curSlotsInput ml.Tensor
-	curSeqTokens  int
-
-	// track if EnsureWritable has been called for this forward pass
-	writableEnsured bool
-	writableError   error
+	*kvcache.Recurrent
 }
 
 func NewHybridCache(
 	shift func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error),
 	convDim, convChannels, deltaStateSize int,
 ) *HybridCache {
-	return &HybridCache{
-		kv:                  kvcache.NewCausalCache(shift),
-		convDim:             convDim,
-		convChannels:        convChannels,
-		deltaStateSize:      deltaStateSize,
-		slotForSeq:          make(map[int]int),
-		convCtxs:            make(map[int]ml.Context),
-		convStates:          make(map[int]ml.Tensor),
-		deltaCtxs:           make(map[int]ml.Context),
-		deltaStates:         make(map[int]ml.Tensor),
-		checkpointCount:     checkpointCountDefault,
-		checkpointMinPos:    checkpointMinPosDefault,
-		checkpointInterval:  checkpointIntervalDefault,
-		checkpoints:         make(map[int]*slotCheckpointStore),
-		pendingRestore:      make(map[int]checkpointRestore),
-		curCheckpointSlots:  make(map[int]int),
-		checkpointConvCtxs:  make(map[int]ml.Context),
-		checkpointDeltaCtxs: make(map[int]ml.Context),
-		checkpointReserved:  make(map[int]struct{}),
-	}
+	base := kvcache.NewRecurrentCache(kvcache.RecurrentConfig{
+		Shift:               shift,
+		ConvDim:             convDim,
+		ConvChannels:        convChannels,
+		RecurrentStateSize:  deltaStateSize,
+		CheckpointLogPrefix: "qwen3next",
+	})
+	return &HybridCache{Recurrent: base}
 }
 
-func (c *HybridCache) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int) {
-	c.backend = backend
-	c.dtype = dtype
-	c.maxSequences = maxSequences
-	c.checkpoints = make(map[int]*slotCheckpointStore)
-	c.pendingRestore = make(map[int]checkpointRestore)
-	c.curCheckpointPos = c.curCheckpointPos[:0]
-	c.curCheckpointSlots = make(map[int]int)
-	c.checkpointReserved = make(map[int]struct{})
-	c.checkpointCtxSize = c.checkpointCount * c.maxSequences
-	if c.checkpointCtxSize < 8 {
-		c.checkpointCtxSize = 8
-	}
-
-	// initialize slot allocator
-	c.refCount = make([]int, maxSequences)
-	c.freeSlots = c.freeSlots[:0]
-	for i := maxSequences - 1; i >= 0; i-- {
-		c.freeSlots = append(c.freeSlots, i)
-	}
-
-	c.kv.Init(backend, dtype, maxSequences, capacity, maxBatch)
-}
-
-func (c *HybridCache) Close() {
-	for _, ctx := range c.convCtxs {
-		ctx.Close()
-	}
-	for _, ctx := range c.deltaCtxs {
-		ctx.Close()
-	}
-	for _, ctx := range c.checkpointConvCtxs {
-		ctx.Close()
-	}
-	for _, ctx := range c.checkpointDeltaCtxs {
-		ctx.Close()
-	}
-	c.kv.Close()
-}
-
-func (c *HybridCache) SetConfig(config ml.CacheConfig) {
-	c.kv.SetConfig(config)
-}
-
-func (c *HybridCache) SetLayer(layer int) {
-	c.kv.SetLayer(layer)
-}
-
-func (c *HybridCache) Get(ctx ml.Context) (ml.Tensor, ml.Tensor, ml.Tensor) {
-	return c.kv.Get(ctx)
-}
-
-func (c *HybridCache) Put(ctx ml.Context, key, value ml.Tensor) {
-	c.kv.Put(ctx, key, value)
-}
-
-func (c *HybridCache) StartForward(ctx ml.Context, batch input.Batch, reserve bool) error {
-	if err := c.kv.StartForward(ctx, batch, reserve); err != nil {
-		return err
-	}
-
-	// Derive equal-length sequence layout for recurrent layers
-	seqCounts := make(map[int]int)
-	c.curSeqs = c.curSeqs[:0]
-	for _, s := range batch.Sequences {
-		if _, ok := seqCounts[s]; !ok {
-			c.curSeqs = append(c.curSeqs, s)
-		}
-		seqCounts[s]++
-	}
-
-	if len(c.curSeqs) == 0 {
-		return nil
-	}
-
-	nTokens := len(batch.Sequences)
-	nSeqs := len(c.curSeqs)
-	want := nTokens / nSeqs
-	for _, s := range c.curSeqs {
-		if seqCounts[s] != want {
-			return kvcache.ErrNotSupported
-		}
-	}
-
-	c.curSeqTokens = want
-
-	// When reserving memory for estimation, use fake slot assignments
-	if reserve {
-		c.curSlots = c.curSlots[:0]
-		slots := make([]int32, nSeqs)
-		for i := range nSeqs {
-			c.curSlots = append(c.curSlots, i)
-			slots[i] = int32(i)
-		}
-		c.curSlotsInput = ctx.Input().FromInts(slots, len(slots))
-		c.reserveCheckpoints = true
-		c.planCheckpoints(batch)
-		return nil
-	}
-
-	// Ensure slots exist for sequences in this batch
-	c.curSlots = c.curSlots[:0]
-	var newSlots []int
-	for _, s := range c.curSeqs {
-		slot, ok := c.slotForSeq[s]
-		if !ok {
-			var err error
-			slot, err = c.allocSlot()
-			if err != nil {
-				return err
-			}
-			c.slotForSeq[s] = slot
-			c.refCount[slot] = 1
-			newSlots = append(newSlots, slot)
-		}
-		c.curSlots = append(c.curSlots, slot)
-	}
-
-	// Zero state for newly allocated slots
-	if len(newSlots) > 0 {
-		c.zeroSlots(ctx, newSlots)
-	}
-
-	// Create a tensor for the current slots
-	slots := make([]int32, len(c.curSlots))
-	for i, v := range c.curSlots {
-		slots[i] = int32(v)
-	}
-	c.curSlotsInput = ctx.Input().FromInts(slots, len(slots))
-
-	// Reset writable state for new forward pass
-	c.writableEnsured = false
-	c.writableError = nil
-	c.reserveCheckpoints = false
-	c.planCheckpoints(batch)
-
-	return nil
-}
-
-func (c *HybridCache) allocSlot() (int, error) {
-	if len(c.freeSlots) == 0 {
-		return 0, kvcache.ErrKvCacheFull
-	}
-	slot := c.freeSlots[len(c.freeSlots)-1]
-	c.freeSlots = c.freeSlots[:len(c.freeSlots)-1]
-	return slot, nil
-}
-
-func (c *HybridCache) freeSlot(slot int) {
-	if slot >= 0 && slot < c.maxSequences {
-		c.freeSlots = append(c.freeSlots, slot)
-	}
-}
-
-// zeroSlots zeros the recurrent state for the given slots across all layers.
-func (c *HybridCache) zeroSlots(ctx ml.Context, slots []int) {
-	if len(slots) == 0 {
-		return
-	}
-
-	inputCtx := ctx.Input()
-
-	slotIndices := make([]int32, len(slots))
-	for i, s := range slots {
-		slotIndices[i] = int32(s)
-	}
-	slotsTensor := inputCtx.FromInts(slotIndices, len(slotIndices))
-
-	// Zero conv states
-	if len(c.convStates) > 0 {
-		zeros := inputCtx.Zeros(ml.DTypeF32, c.convDim*c.convChannels, len(slots))
-		for _, buf := range c.convStates {
-			ctx.Forward(buf.SetRows(ctx, zeros, slotsTensor))
-		}
-	}
-
-	// Zero delta states
-	if len(c.deltaStates) > 0 {
-		zeros := inputCtx.Zeros(ml.DTypeF32, c.deltaStateSize, len(slots))
-		for _, buf := range c.deltaStates {
-			ctx.Forward(buf.SetRows(ctx, zeros, slotsTensor))
-		}
-	}
-}
-
-// EnsureWritable ensures sequences have private slots (copy-on-write).
-func (c *HybridCache) EnsureWritable(ctx ml.Context) error {
-	for i, seq := range c.curSeqs {
-		slot, ok := c.slotForSeq[seq]
-		if !ok {
-			continue
-		}
-
-		if slot < 0 || slot >= len(c.refCount) {
-			continue
-		}
-
-		if c.refCount[slot] <= 1 {
-			continue
-		}
-
-		newSlot, err := c.allocSlot()
-		if err != nil {
-			return err
-		}
-		c.refCount[slot]--
-		c.refCount[newSlot] = 1
-		c.slotForSeq[seq] = newSlot
-		c.curSlots[i] = newSlot
-
-		c.copyRecurrentState(ctx, slot, newSlot)
-		c.copyCheckpoints(ctx, slot, newSlot)
-	}
-
-	// Rebuild current slots tensor
-	slots := make([]int32, len(c.curSlots))
-	for i, v := range c.curSlots {
-		slots[i] = int32(v)
-	}
-	c.curSlotsInput = ctx.Input().FromInts(slots, len(slots))
-
-	return nil
-}
-
-func (c *HybridCache) copyRecurrentState(ctx ml.Context, srcSlot, dstSlot int) {
-	src := ctx.Input().FromInts([]int32{int32(srcSlot)}, 1)
-	dst := ctx.Input().FromInts([]int32{int32(dstSlot)}, 1)
-
-	for _, buf := range c.convStates {
-		rows := buf.Rows(ctx, src)
-		rowsF32 := rows.Cast(ctx, ml.DTypeF32)
-		ctx.Forward(buf.SetRows(ctx, rowsF32, dst))
-	}
-
-	for _, buf := range c.deltaStates {
-		rows := buf.Rows(ctx, src)
-		rowsF32 := rows.Cast(ctx, ml.DTypeF32)
-		ctx.Forward(buf.SetRows(ctx, rowsF32, dst))
-	}
-}
-
-func (c *HybridCache) CopyPrefix(srcSeq, dstSeq int, prefixLen int32) {
-	c.kv.CopyPrefix(srcSeq, dstSeq, prefixLen)
-
-	// Copy-on-write for recurrent state
-	if dstSlot, ok := c.slotForSeq[dstSeq]; ok {
-		if c.validSlot(dstSlot) {
-			c.refCount[dstSlot]--
-			if c.refCount[dstSlot] <= 0 {
-				c.refCount[dstSlot] = 0
-				c.freeSlot(dstSlot)
-			}
-		}
-		delete(c.slotForSeq, dstSeq)
-	}
-
-	srcSlot, ok := c.slotForSeq[srcSeq]
-	if !ok {
-		return
-	}
-
-	if c.validSlot(srcSlot) {
-		c.slotForSeq[dstSeq] = srcSlot
-		c.refCount[srcSlot]++
-	}
-}
-
-func (c *HybridCache) CanResume(seq int, pos int32) bool {
-	if !c.kv.CanResume(seq, pos) {
-		return false
-	}
-	if pos == 0 {
-		return true
-	}
-	return c.hasCheckpoint(seq, pos)
-}
-
-func (c *HybridCache) Remove(seq int, beginIndex, endIndex int32) error {
-	if beginIndex > 0 && endIndex != math.MaxInt32 {
-		return kvcache.ErrNotSupported
-	}
-
-	if beginIndex > 0 {
-		restore, ok := c.pendingRestore[seq]
-		if !ok || restore.pos+1 != beginIndex {
-			return kvcache.ErrNotSupported
-		}
-		if !c.restoreComplete(restore) {
-			return kvcache.ErrNotSupported
-		}
-		// If the recurrent slot is shared, detach it before applying a restore.
-		if slot, ok := c.slotForSeq[seq]; ok && c.validSlot(slot) && c.refCount[slot] > 1 {
-			newSlot, err := c.allocSlot()
-			if err != nil {
-				return err
-			}
-			ctx := c.backend.NewContext()
-			c.copyRecurrentState(ctx, slot, newSlot)
-			c.copyCheckpoints(ctx, slot, newSlot)
-			if len(c.convStates) > 0 || len(c.deltaStates) > 0 {
-				ctx.Compute()
-			}
-			ctx.Close()
-
-			c.refCount[slot]--
-			c.refCount[newSlot] = 1
-			c.slotForSeq[seq] = newSlot
-
-			restore.slot = newSlot
-			c.pendingRestore[seq] = restore
-		}
-	}
-
-	if err := c.kv.Remove(seq, beginIndex, endIndex); err != nil {
-		return err
-	}
-
-	if beginIndex > 0 {
-		restore := c.pendingRestore[seq]
-		delete(c.pendingRestore, seq)
-		return c.applyCheckpointRestore(restore)
-	}
-
-	// Removal invalidates recurrent state
-	slot, ok := c.slotForSeq[seq]
-	delete(c.pendingRestore, seq)
-	if !ok {
-		return nil
-	}
-
-	if !c.validSlot(slot) {
-		delete(c.slotForSeq, seq)
-		return nil
-	}
-
-	c.refCount[slot]--
-	if c.refCount[slot] <= 0 {
-		c.refCount[slot] = 0
-		c.clearCheckpoints(slot)
-		c.freeSlot(slot)
-	}
-	delete(c.slotForSeq, seq)
-
-	return nil
-}
-
-func (c *HybridCache) validSlot(slot int) bool {
-	return slot >= 0 && slot < len(c.refCount)
-}
-
-func (c *HybridCache) slotsTensor() ml.Tensor {
-	return c.curSlotsInput
-}
-
-// contiguousSlots returns the starting slot if current slots are contiguous and ordered.
-func (c *HybridCache) contiguousSlots() (int, bool) {
-	if len(c.curSlots) == 0 {
-		return 0, false
-	}
-	start := c.curSlots[0]
-	for i, s := range c.curSlots {
-		if s != start+i {
-			return 0, false
-		}
-	}
-	return start, true
-}
-
-func (c *HybridCache) seqTokens() int {
-	return c.curSeqTokens
-}
-
-func (c *HybridCache) numSeqs() int {
-	return len(c.curSeqs)
-}
-
-func (c *HybridCache) convBuffer(ctx ml.Context, layer int) ml.Tensor {
-	if buf, ok := c.convStates[layer]; ok {
-		return buf
-	}
-
-	if _, ok := c.convCtxs[layer]; !ok {
-		c.convCtxs[layer] = c.backend.NewContextSize(1).Layer(layer)
-	}
-
-	// Recurrent state must stay in F32 (ssm_conv kernels are F32-only).
-	buf := c.convCtxs[layer].Zeros(ml.DTypeF32, c.convDim*c.convChannels, c.maxSequences)
-	c.convStates[layer] = buf
-	return buf
-}
-
-func (c *HybridCache) deltaBuffer(ctx ml.Context, layer int) ml.Tensor {
-	if buf, ok := c.deltaStates[layer]; ok {
-		return buf
-	}
-
-	if _, ok := c.deltaCtxs[layer]; !ok {
-		c.deltaCtxs[layer] = c.backend.NewContextSize(1).Layer(layer)
-	}
-
-	// Recurrent delta state must stay in F32.
-	buf := c.deltaCtxs[layer].Zeros(ml.DTypeF32, c.deltaStateSize, c.maxSequences)
-	c.deltaStates[layer] = buf
-	return buf
-}
-
-func (c *HybridCache) ensureWritableOnce(ctx ml.Context) {
-	if !c.writableEnsured {
-		needsWritable := false
-		for _, seq := range c.curSeqs {
-			slot, ok := c.slotForSeq[seq]
-			if !ok {
-				continue
-			}
-			if slot >= 0 && slot < len(c.refCount) && c.refCount[slot] > 1 {
-				needsWritable = true
-				break
-			}
-		}
-
-		if needsWritable {
-			if err := c.EnsureWritable(ctx); err != nil {
-				c.writableError = err
-			}
-		}
-		c.writableEnsured = true
-	}
-}
-
-// ConvState returns the conv state for current batch sequences as [convDim, convChannels, nSeqs].
-func (c *HybridCache) ConvState(ctx ml.Context, layer int) (ml.Tensor, error) {
-	c.ensureWritableOnce(ctx)
-
-	if c.writableError != nil {
-		return nil, c.writableError
-	}
-
-	buf := c.convBuffer(ctx, layer)
-	cur := buf.Rows(ctx, c.slotsTensor())
-	return cur.Reshape(ctx, c.convDim, c.convChannels, c.numSeqs()), nil
-}
-
-// UpdateConvState writes a new conv state for current batch sequences.
-func (c *HybridCache) UpdateConvState(ctx ml.Context, layer int, newState ml.Tensor) {
-	buf := c.convBuffer(ctx, layer)
-	src := newState.Reshape(ctx, c.convDim*c.convChannels, c.numSeqs())
-	srcF32 := src.Cast(ctx, ml.DTypeF32)
-	if start, ok := c.contiguousSlots(); ok {
-		// Fast path: contiguous slots allow a single view + copy
-		offset := start * buf.Stride(1)
-		view := buf.View(ctx, offset, c.convDim*c.convChannels, buf.Stride(1), c.numSeqs())
-		ctx.Forward(srcF32.Copy(ctx, view))
-	} else {
-		ctx.Forward(buf.SetRows(ctx, srcF32, c.slotsTensor()))
-	}
-
-	c.captureConvCheckpoint(ctx, layer, srcF32)
-}
-
-// DeltaState returns the delta state for current batch sequences as [headVDim, headVDim*numVHeads, nSeqs].
+// DeltaState returns the delta state for current batch sequences as
+// [headVDim, headVDim*numVHeads, nSeqs].
 func (c *HybridCache) DeltaState(ctx ml.Context, layer int, headVDim, numVHeads int) (ml.Tensor, error) {
-	c.ensureWritableOnce(ctx)
-
-	if c.writableError != nil {
-		return nil, c.writableError
-	}
-
-	buf := c.deltaBuffer(ctx, layer)
-	cur := buf.Rows(ctx, c.slotsTensor())
-	return cur.Reshape(ctx, headVDim, headVDim*numVHeads, c.numSeqs()), nil
+	return c.RecurrentState(ctx, layer, headVDim, headVDim*numVHeads)
 }
 
 // UpdateDeltaState writes a new delta state for current batch sequences.
 func (c *HybridCache) UpdateDeltaState(ctx ml.Context, layer int, newState ml.Tensor) {
-	buf := c.deltaBuffer(ctx, layer)
-	src := newState.Reshape(ctx, c.deltaStateSize, c.numSeqs())
-	srcF32 := src.Cast(ctx, ml.DTypeF32)
-	if start, ok := c.contiguousSlots(); ok {
-		// Fast path: contiguous slots allow a single view + copy
-		offset := start * buf.Stride(1)
-		view := buf.View(ctx, offset, c.deltaStateSize, buf.Stride(1), c.numSeqs())
-		ctx.Forward(srcF32.Copy(ctx, view))
-	} else {
-		ctx.Forward(buf.SetRows(ctx, srcF32, c.slotsTensor()))
+	c.UpdateRecurrentState(ctx, layer, newState)
+}
+
+func (c *HybridCache) seqTokens() int {
+	return c.SeqTokens()
+}
+
+func (c *HybridCache) numSeqs() int {
+	return c.NumSeqs()
+}
+
+// Keep qwen3next behavior for partial mid-sequence removals.
+func (c *HybridCache) Remove(seq int, beginIndex, endIndex int32) error {
+	if beginIndex > 0 && endIndex != math.MaxInt32 {
+		return kvcache.ErrNotSupported
 	}
-
-	c.captureDeltaCheckpoint(ctx, layer, srcF32)
-}
-
-// IsSupportedForBatch returns true if the current batch layout supports recurrent layers.
-func (c *HybridCache) IsSupportedForBatch() bool {
-	return c.curSeqTokens > 0 && len(c.curSeqs) > 0
-}
-
-// Seqs returns the ordered unique sequences for the current forward pass.
-func (c *HybridCache) Seqs() []int {
-	return slices.Clone(c.curSeqs)
+	return c.Recurrent.Remove(seq, beginIndex, endIndex)
 }

model/models/qwen3next/checkpoints.go

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

model/models/qwen3next/checkpoints_test.go

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

model/models/qwen3next/deltanet.go

@@ -37,7 +37,9 @@ type GatedDeltaNet struct {
 	// Optimized path: pre-split QKV and gate
 	SSMQKV       *nn.Linear  `gguf:"attn_qkv"`  // -> Q, K, V (concatenated)
 	SSMQKVGate   *nn.Linear  `gguf:"attn_gate"` // -> Z gate
-	SSMBetaAlpha *nn.Linear  `gguf:"ssm_ba"`    // -> beta, alpha
+	SSMBetaAlpha *nn.Linear  `gguf:"ssm_ba"`    // -> beta, alpha (legacy qwen3next)
+	SSMBeta      *nn.Linear  `gguf:"ssm_beta"`  // -> beta (qwen35)
+	SSMAlpha     *nn.Linear  `gguf:"ssm_alpha"` // -> alpha (qwen35)
 	SSMConv1D    *convKernel `gguf:"ssm_conv1d"`
 	SSMDT        ml.Tensor   `gguf:"ssm_dt"` // alpha bias
 	SSMA         ml.Tensor   `gguf:"ssm_a"`  // -A_log.exp()
@@ -96,7 +98,6 @@ func (gdn *GatedDeltaNet) Forward(ctx ml.Context, hiddenStates, _ ml.Tensor, cac
 	headVDim := opts.ssmDInner / numVHeads
 	convKernelSize := opts.convKernelSize
 
-	mixedBA := gdn.SSMBetaAlpha.Forward(ctx, hiddenStates)
 	qkvDim := headKDim*numKHeads*2 + headVDim*numVHeads
 
 	if gdn.SSMQKV == nil || gdn.SSMQKVGate == nil {
@@ -106,24 +107,42 @@ func (gdn *GatedDeltaNet) Forward(ctx ml.Context, hiddenStates, _ ml.Tensor, cac
 	qkvMixed := gdn.SSMQKV.Forward(ctx, hiddenStates).Reshape(ctx, qkvDim, nSeqTokens, nSeqs)
 	z := gdn.SSMQKVGate.Forward(ctx, hiddenStates)
 
-	baNewDim := 2 * numVHeads / numKHeads
-	mixedBAReshaped := mixedBA.Reshape(ctx, baNewDim, numKHeads, nSeqTokens, nSeqs)
+	var beta ml.Tensor
+	var alpha ml.Tensor
+	qwen35BetaGateLayout := false
+	switch {
+	case gdn.SSMBetaAlpha != nil:
+		// Legacy qwen3next path: in_proj_ba packs beta/alpha grouped by K-head.
+		mixedBA := gdn.SSMBetaAlpha.Forward(ctx, hiddenStates)
+		baNewDim := 2 * numVHeads / numKHeads
+		mixedBAReshaped := mixedBA.Reshape(ctx, baNewDim, numKHeads, nSeqTokens, nSeqs)
 
-	// Split beta and alpha
-	betaSize := numVHeads / numKHeads
-	alphaSize := numVHeads / numKHeads
+		betaSize := numVHeads / numKHeads
+		alphaSize := numVHeads / numKHeads
 
-	b := mixedBAReshaped.Slice(ctx, 0, 0, betaSize, 1)
-	a := mixedBAReshaped.Slice(ctx, 0, betaSize, betaSize+alphaSize, 1)
+		b := mixedBAReshaped.Slice(ctx, 0, 0, betaSize, 1)
+		a := mixedBAReshaped.Slice(ctx, 0, betaSize, betaSize+alphaSize, 1)
 
-	// Reshape to merge head dimensions
-	beta := b.Contiguous(ctx, numVHeads, 1, nSeqTokens, nSeqs)
-	alpha := a.Contiguous(ctx, numVHeads, nSeqTokens, nSeqs)
+		beta = b.Contiguous(ctx, numVHeads, 1, nSeqTokens, nSeqs)
+		alpha = a.Contiguous(ctx, numVHeads, nSeqTokens, nSeqs)
+
+	case gdn.SSMBeta != nil && gdn.SSMAlpha != nil:
+		// qwen35 path: beta/alpha are separate projections.
+		beta = gdn.SSMBeta.Forward(ctx, hiddenStates).Reshape(ctx, 1, numVHeads, nSeqTokens, nSeqs)
+		alpha = gdn.SSMAlpha.Forward(ctx, hiddenStates).Reshape(ctx, numVHeads, nSeqTokens, nSeqs)
+		qwen35BetaGateLayout = true
+
+	default:
+		return nil, errors.New("qwen3next: missing linear attention beta/alpha projections")
+	}
 
 	// Compute gate: softplus(alpha + dt_bias) * -A
 	alphaBiased := alpha.Add(ctx, gdn.SSMDT)
 	alphaSoftplus := alphaBiased.Softplus(ctx)
 	gate := alphaSoftplus.Mul(ctx, gdn.SSMA)
+	if qwen35BetaGateLayout {
+		gate = gate.Reshape(ctx, 1, numVHeads, nSeqTokens, nSeqs)
+	}
 	qkvMixed = qkvMixed.Permute(ctx, 1, 0, 2, 3)
 
 	// Get conv state from cache
@@ -172,16 +191,20 @@ func (gdn *GatedDeltaNet) Forward(ctx ml.Context, hiddenStates, _ ml.Tensor, cac
 
 	// Repeat interleave Q and K if numKHeads != numVHeads
 	if numKHeads != numVHeads {
-		repeatFactor := numVHeads / numKHeads
+		if opts.vHeadReordered {
+			qConv = qConv.Repeat4D(ctx, headKDim, numVHeads, nSeqTokens, nSeqs)
+			kConv = kConv.Repeat4D(ctx, headKDim, numVHeads, nSeqTokens, nSeqs)
+		} else {
+			repeatFactor := numVHeads / numKHeads
+			qReshaped := qConv.Reshape(ctx, headKDim, 1, numKHeads*nSeqTokens*nSeqs)
+			kReshaped := kConv.Reshape(ctx, headKDim, 1, numKHeads*nSeqTokens*nSeqs)
 
-		qReshaped := qConv.Reshape(ctx, headKDim, 1, numKHeads*nSeqTokens*nSeqs)
-		kReshaped := kConv.Reshape(ctx, headKDim, 1, numKHeads*nSeqTokens*nSeqs)
+			qRepeated := qReshaped.Repeat4D(ctx, headKDim, repeatFactor, numKHeads*nSeqTokens*nSeqs, 1)
+			kRepeated := kReshaped.Repeat4D(ctx, headKDim, repeatFactor, numKHeads*nSeqTokens*nSeqs, 1)
 
-		qRepeated := qReshaped.Repeat4D(ctx, headKDim, repeatFactor, numKHeads*nSeqTokens*nSeqs, 1)
-		kRepeated := kReshaped.Repeat4D(ctx, headKDim, repeatFactor, numKHeads*nSeqTokens*nSeqs, 1)
-
-		qConv = qRepeated.Reshape(ctx, headKDim, numKHeads*repeatFactor, nSeqTokens, nSeqs)
-		kConv = kRepeated.Reshape(ctx, headKDim, numKHeads*repeatFactor, nSeqTokens, nSeqs)
+			qConv = qRepeated.Reshape(ctx, headKDim, numKHeads*repeatFactor, nSeqTokens, nSeqs)
+			kConv = kRepeated.Reshape(ctx, headKDim, numKHeads*repeatFactor, nSeqTokens, nSeqs)
+		}
 	}
 
 	// Choose computation mode based on sequence length
@@ -189,7 +212,9 @@ func (gdn *GatedDeltaNet) Forward(ctx ml.Context, hiddenStates, _ ml.Tensor, cac
 	if nSeqTokens == 1 {
 		attnOut = gdn.deltaNetAutoregressive(ctx, qConv, kConv, vConv, gate, beta, state, opts, layer, cache)
 	} else {
-		// Use pre-computed masks from opts (created once in Model.Forward)
+		if opts.masks == nil {
+			opts.masks = createMasks(ctx)
+		}
 		attnOut = gdn.deltaNetChunked(ctx, qConv, kConv, vConv, gate, beta, state, opts.masks, opts, layer, cache)
 	}
 

model/models/qwen3next/model.go

@@ -1,9 +1,12 @@
 package qwen3next
 
 import (
+	"bytes"
 	"cmp"
 	"fmt"
+	"image"
 	"math"
+	"slices"
 
 	"github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/ml"
@@ -11,6 +14,7 @@ import (
 	"github.com/ollama/ollama/ml/nn/rope"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/model/models/qwen3vl"
 	"github.com/ollama/ollama/tokenizer"
 )
 
@@ -41,10 +45,15 @@ type Options struct {
 	ssmNGroup      int // num_k_heads
 	ssmDtRank      int // num_v_heads
 	convKernelSize int // SSM conv kernel size
+	vHeadReordered bool
 
 	// Per-layer type from GGUF metadata
 	isRecurrent []bool
 
+	// RoPE mode config (used by qwen35/qwen35moe)
+	mropeSections    []int
+	mropeInterleaved bool
+
 	// Pre-computed masks for chunked attention (created once per forward pass)
 	masks *Masks
 }
@@ -54,7 +63,17 @@ func (o Options) headDim() int {
 }
 
 func (o Options) applyRotaryPositionEmbeddings(ctx ml.Context, states, positions ml.Tensor) ml.Tensor {
-	opts := []func(*rope.Options){rope.WithTypeNeoX()}
+	var opts []func(*rope.Options)
+	if len(o.mropeSections) > 0 {
+		if o.mropeInterleaved {
+			opts = append(opts, rope.WithInterleaveMRoPE(o.mropeSections))
+		} else {
+			opts = append(opts, rope.WithMRoPE(o.mropeSections))
+		}
+	} else {
+		opts = append(opts, rope.WithTypeNeoX())
+	}
+
 	if o.ropeType == "yarn" {
 		attnFactor := float32(1.0 / (1.0 + 0.1*math.Log(float64(o.ropeScale))))
 		opts = append(opts,
@@ -214,20 +233,193 @@ type Model struct {
 	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
 	Output         *nn.Linear    `gguf:"output,alt:token_embd"`
 
-	Layers []Layer `gguf:"blk"`
+	Layers []Layer              `gguf:"blk"`
+	Vision *qwen3vl.VisionModel `gguf:"v"`
+
+	ImageProcessor *qwen3vl.ImageProcessor
 
 	*Options
+
+	positionCache    []int32
+	imageToken       int32
+	visionStart      int32
+	visionEnd        int32
+	spatialMergeSize uint32
+}
+
+func (m *Model) mapPosition(id int32) int32 {
+	if id < int32(len(m.positionCache)) {
+		return m.positionCache[id]
+	}
+	if len(m.positionCache) > 0 {
+		return id - int32(len(m.positionCache)) + m.positionCache[len(m.positionCache)-1] + 1
+	}
+	return id
+}
+
+func (m *Model) buildPositions(ctx ml.Context, batch input.Batch) ml.Tensor {
+	if len(m.mropeSections) == 0 {
+		return ctx.Input().FromInts(batch.Positions, len(batch.Positions))
+	}
+
+	// ggml MRoPE expects [time, height, width, extra] for each token.
+	positionSlice := [][]int32{
+		make([]int32, len(batch.Positions)),
+		make([]int32, len(batch.Positions)),
+		make([]int32, len(batch.Positions)),
+		make([]int32, len(batch.Positions)),
+	}
+
+	for i, id := range batch.Positions {
+		p := m.mapPosition(id)
+		positionSlice[0][i] = p
+		positionSlice[1][i] = p
+		positionSlice[2][i] = p
+	}
+
+	if m.Vision != nil {
+		for _, mi := range batch.Multimodal {
+			grid, ok := mi.Multimodal[0].Data.(*qwen3vl.Grid)
+			if !ok {
+				continue
+			}
+			w := max(1, grid.Width/int(m.spatialMergeSize))
+			for i := range mi.Multimodal[0].Tensor.Dim(1) {
+				positionSlice[1][mi.Index+i] += int32(i / w)
+				positionSlice[2][mi.Index+i] += int32(i % w)
+			}
+		}
+	}
+
+	return ctx.Input().FromInts(slices.Concat(positionSlice...), len(positionSlice[0])*len(positionSlice))
+}
+
+func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input.Multimodal, error) {
+	if m.Vision == nil || m.ImageProcessor == nil || len(m.Vision.Layers) == 0 {
+		return nil, model.ErrNoVisionModel
+	}
+
+	img, _, err := image.Decode(bytes.NewReader(multimodalData))
+	if err != nil {
+		return nil, err
+	}
+
+	pixelValues, grid, err := m.ImageProcessor.ProcessImage(ctx, img)
+	if err != nil {
+		return nil, err
+	}
+
+	visionOutputs, deepstackVisualEmbeds := m.Vision.Forward(ctx, pixelValues, grid)
+	mm := []input.Multimodal{{Tensor: visionOutputs, Data: grid}}
+	for i := range deepstackVisualEmbeds {
+		mm = append(mm, input.Multimodal{Tensor: deepstackVisualEmbeds[i]})
+	}
+
+	return mm, nil
+}
+
+type modelInput struct {
+	*input.Input
+	position int32
+}
+
+func (m *Model) PostTokenize(inputs []*input.Input) ([]*input.Input, error) {
+	m.positionCache = m.positionCache[:0]
+	return slices.Collect(func(yield func(*input.Input) bool) {
+		for i := range inputs {
+			s := []modelInput{{Input: inputs[i]}}
+			if mm := inputs[i].Multimodal; mm != nil {
+				t := mm[0].Tensor
+				s = slices.Repeat([]modelInput{
+					{
+						position: int32(i + 1),
+						Input:    &input.Input{Token: m.imageToken},
+					},
+				}, t.Dim(1)+2)
+
+				s[0] = modelInput{
+					Input:    &input.Input{Token: m.visionStart},
+					position: int32(i),
+				}
+
+				s[len(s)-1] = modelInput{
+					Input:    &input.Input{Token: m.visionEnd},
+					position: int32(i + mm[0].Data.(*qwen3vl.Grid).Width/int(m.spatialMergeSize) + 1),
+				}
+
+				s[1] = modelInput{
+					Input: &input.Input{
+						Token:          m.imageToken,
+						Multimodal:     inputs[i].Multimodal,
+						MultimodalHash: inputs[i].MultimodalHash,
+						SameBatch:      t.Dim(1),
+					},
+					position: int32(i + 1),
+				}
+			}
+
+			for _, e := range s {
+				position := e.position
+				if position == 0 && len(m.positionCache) > 0 {
+					position = m.positionCache[len(m.positionCache)-1] + 1
+				}
+
+				m.positionCache = append(m.positionCache, position)
+				if !yield(e.Input) {
+					return
+				}
+			}
+		}
+	}), nil
 }
 
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
-	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
+	positions := m.buildPositions(ctx, batch)
 
 	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
+	if len(batch.Multimodal) > 0 {
+		hiddenStates = hiddenStates.Duplicate(ctx)
+
+		var deepstackVisualEmbeds []ml.Tensor
+		for _, mi := range batch.Multimodal {
+			visionOutputs := mi.Multimodal[0].Tensor
+			ctx.Forward(visionOutputs.Copy(ctx, hiddenStates.View(ctx, mi.Index*hiddenStates.Stride(1), visionOutputs.Dim(0)*visionOutputs.Dim(1))))
+
+			deepstackVisualEmbeds = make([]ml.Tensor, len(mi.Multimodal[1:]))
+			for i, mm := range mi.Multimodal[1:] {
+				deepstackVisualEmbeds[i] = ctx.Input().Zeros(mm.Tensor.DType(), hiddenStates.Shape()...)
+				ctx.Forward(mm.Tensor.Copy(ctx, deepstackVisualEmbeds[i].View(ctx, mi.Index*deepstackVisualEmbeds[i].Stride(1), mm.Tensor.Dim(0)*mm.Tensor.Dim(1))))
+			}
+		}
+
+		cache := m.Cache.(*HybridCache)
+		m.Options.masks = nil
+		for i, layer := range m.Layers {
+			cache.SetLayer(i)
+
+			var outputs ml.Tensor
+			if i == len(m.Layers)-1 {
+				outputs = batch.Outputs
+			}
+
+			var err error
+			hiddenStates, err = layer.Forward(ctx, i, hiddenStates, positions, outputs, cache, m.Options)
+			if err != nil {
+				return nil, err
+			}
+			if i < len(deepstackVisualEmbeds) {
+				hiddenStates = hiddenStates.Add(ctx, deepstackVisualEmbeds[i])
+			}
+		}
+
+		hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.eps)
+		return m.Output.Forward(ctx, hiddenStates), nil
+	}
 
 	cache := m.Cache.(*HybridCache)
 
-	// Create masks once per forward pass
-	m.Options.masks = createMasks(ctx)
+	// Masks are allocated lazily only for chunked recurrent prefill.
+	m.Options.masks = nil
 
 	for i, layer := range m.Layers {
 		cache.SetLayer(i)
@@ -249,10 +441,17 @@ func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 }
 
 func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	m.positionCache = nil
+	if len(m.mropeSections) > 0 {
+		shift = shift.Repeat(ctx, 1, 4).Reshape(ctx, -1)
+	}
 	return m.applyRotaryPositionEmbeddings(ctx, key, shift), nil
 }
 
-var _ model.Model = (*Model)(nil)
+var (
+	_ model.Model               = (*Model)(nil)
+	_ model.MultimodalProcessor = (*Model)(nil)
+)
 
 func New(c fs.Config) (model.Model, error) {
 	numLayers := int(c.Uint("block_count"))
@@ -303,6 +502,22 @@ func New(c fs.Config) (model.Model, error) {
 		}
 	}
 
+	mropeSections := c.Ints("mrope_sections", nil)
+	if len(mropeSections) == 0 {
+		mropeSections = c.Ints("rope.mrope_section", nil)
+	}
+	if len(mropeSections) == 0 {
+		mropeSections = c.Ints("rope.dimension_sections", nil)
+	}
+	if len(mropeSections) > 4 {
+		mropeSections = mropeSections[:4]
+	}
+
+	ropeType := c.String("rope.scaling.type")
+	if ropeType == "" {
+		ropeType = c.String("rope.type")
+	}
+
 	opts := &Options{
 		hiddenSize: int(c.Uint("embedding_length")),
 		numHeads:   int(c.Uint("attention.head_count")),
@@ -318,7 +533,7 @@ func New(c fs.Config) (model.Model, error) {
 		valueLength:           int(c.Uint("attention.value_length")),
 		ropeDim:               int(c.Uint("rope.dimension_count")),
 		eps:                   c.Float("attention.layer_norm_rms_epsilon"),
-		ropeType:              c.String("rope.scaling.type"),
+		ropeType:              ropeType,
 		ropeBase:              c.Float("rope.freq_base"),
 		ropeScale:             c.Float("rope.scaling.factor", 1),
 		originalContextLength: int(c.Uint("rope.scaling.original_context_length")),
@@ -331,7 +546,16 @@ func New(c fs.Config) (model.Model, error) {
 		ssmNGroup:             int(c.Uint("ssm.group_count")),
 		ssmDtRank:             int(c.Uint("ssm.time_step_rank")),
 		convKernelSize:        int(c.Uint("ssm.conv_kernel")),
+		vHeadReordered:        c.Bool("ssm.v_head_reordered", false),
 		isRecurrent:           isRecurrent,
+		mropeSections: slices.Collect(func(yield func(int) bool) {
+			for _, section := range mropeSections {
+				if !yield(int(section)) {
+					return
+				}
+			}
+		}),
+		mropeInterleaved: c.Bool("rope.mrope_interleaved", c.Bool("mrope_interleaved", false)),
 	}
 	if opts.numKVHeads == 0 {
 		return nil, fmt.Errorf("qwen3next: attention.head_count_kv array must include at least one non-zero value")
@@ -353,6 +577,19 @@ func New(c fs.Config) (model.Model, error) {
 		return nil, fmt.Errorf("qwen3next: headKDim (%d) != headVDim (%d) not supported; state computations require equal dimensions", headKDim, headVDim)
 	}
 
+	var vision *qwen3vl.VisionModel
+	var imageProcessor *qwen3vl.ImageProcessor
+	if c.Uint("vision.block_count", 0) > 0 {
+		vision = qwen3vl.NewVisionModel(c)
+		processor := qwen3vl.NewImageProcessor(c)
+		imageProcessor = &processor
+	}
+
+	spatialMergeSize := c.Uint("vision.spatial_merge_size", 2)
+	if spatialMergeSize == 0 {
+		spatialMergeSize = 2
+	}
+
 	m := Model{
 		Tokenizer: tokenizer.NewBytePairEncoding(
 			&tokenizer.Vocabulary{
@@ -371,8 +608,14 @@ func New(c fs.Config) (model.Model, error) {
 			},
 			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
 		),
-		Layers:  layers,
-		Options: opts,
+		Layers:           layers,
+		Vision:           vision,
+		ImageProcessor:   imageProcessor,
+		Options:          opts,
+		imageToken:       int32(c.Uint("image_token_id", 151655)),
+		visionStart:      int32(c.Uint("vision_start_token_id", 151652)),
+		visionEnd:        int32(c.Uint("vision_end_token_id", 151653)),
+		spatialMergeSize: spatialMergeSize,
 	}
 
 	m.Cache = NewHybridCache(m.Shift, convDim, convChannels, deltaStateSize)
@@ -380,5 +623,7 @@ func New(c fs.Config) (model.Model, error) {
 }
 
 func init() {
+	model.Register("qwen35", New)
+	model.Register("qwen35moe", New)
 	model.Register("qwen3next", New)
 }

model/models/qwen3vl/imageprocessor.go

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

model/models/qwen3vl/model.go

@@ -189,8 +189,8 @@ func New(c fs.Config) (model.Model, error) {
 			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
 		),
 		TextModel:      newTextModel(c),
-		VisionModel:    newVisionModel(c),
-		ImageProcessor: newImageProcessor(c),
+		VisionModel:    NewVisionModel(c),
+		ImageProcessor: NewImageProcessor(c),
 	}
 
 	m.Cache = kvcache.NewCausalCache(func(ctx ml.Context, layer int, key, positions ml.Tensor) (ml.Tensor, error) {

model/models/qwen3vl/model_vision.go

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

model/parsers/parsers.go

@@ -52,9 +52,13 @@ func ParserForName(name string) Parser {
 	case "qwen3-coder":
 		p = &Qwen3CoderParser{}
 	case "qwen3-vl-instruct":
-		p = &Qwen3VLParser{hasThinkingSupport: false}
+		p = &Qwen3VLParser{hasThinkingSupport: false, defaultThinking: false}
 	case "qwen3-vl-thinking":
-		p = &Qwen3VLParser{hasThinkingSupport: true}
+		p = &Qwen3VLParser{hasThinkingSupport: true, defaultThinking: true}
+	case "qwen3.5", "qwen3-vl":
+		// qwen3.5 and qwen3-vl share one parser with runtime thinking toggle.
+		// Default is non-thinking unless think=true is requested.
+		p = &Qwen3VLParser{hasThinkingSupport: true, defaultThinking: false}
 	case "ministral":
 		p = &MinistralParser{hasThinkingSupport: false}
 	case "passthrough":

model/parsers/parsers_test.go

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

model/parsers/qwen3vl.go

@@ -29,6 +29,7 @@ type Qwen3VLParser struct {
 	buffer             strings.Builder
 	tools              []api.Tool
 	hasThinkingSupport bool
+	defaultThinking    bool
 }
 
 func (p *Qwen3VLParser) HasToolSupport() bool {
@@ -39,9 +40,18 @@ func (p *Qwen3VLParser) HasThinkingSupport() bool {
 	return p.hasThinkingSupport
 }
 
-func (p *Qwen3VLParser) setInitialState(lastMessage *api.Message) {
+func (p *Qwen3VLParser) setInitialState(lastMessage *api.Message, thinkValue *api.ThinkValue) {
 	prefill := lastMessage != nil && lastMessage.Role == "assistant"
-	if !p.HasThinkingSupport() {
+
+	thinkingEnabled := false
+	if p.HasThinkingSupport() {
+		if thinkValue != nil {
+			thinkingEnabled = thinkValue.Bool()
+		} else {
+			thinkingEnabled = p.defaultThinking
+		}
+	}
+	if !thinkingEnabled {
 		p.state = CollectingContent
 		return
 	}
@@ -56,7 +66,7 @@ func (p *Qwen3VLParser) setInitialState(lastMessage *api.Message) {
 
 func (p *Qwen3VLParser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
 	p.tools = tools
-	p.setInitialState(lastMessage)
+	p.setInitialState(lastMessage, thinkValue)
 	return tools
 }
 

model/parsers/qwen3vl_thinking_test.go

@@ -204,7 +204,7 @@ func TestQwen3VLThinkingParserStreaming(t *testing.T) {
 		}
 
 		t.Run(tc.desc, func(t *testing.T) {
-			parser := Qwen3VLParser{hasThinkingSupport: true}
+			parser := Qwen3VLParser{hasThinkingSupport: true, defaultThinking: true}
 			parser.Init([]api.Tool{}, nil, nil)
 			// parser.state = CollectingThinkingContent
 
@@ -385,7 +385,7 @@ func TestQwen3VLParserState(t *testing.T) {
 	}
 
 	for _, tc := range cases {
-		parser := Qwen3VLParser{hasThinkingSupport: tc.hasThinking}
+		parser := Qwen3VLParser{hasThinkingSupport: tc.hasThinking, defaultThinking: tc.hasThinking}
 		parser.Init(nil, tc.last, nil)
 		if parser.state != tc.wantState {
 			t.Errorf("%s: got state %v, want %v", tc.desc, parser.state, tc.wantState)
@@ -436,7 +436,7 @@ func TestQwen3VLThinkingParserWithThinkingPrefill(t *testing.T) {
 
 	for _, tc := range cases {
 		t.Run(tc.desc, func(t *testing.T) {
-			parser := Qwen3VLParser{hasThinkingSupport: true}
+			parser := Qwen3VLParser{hasThinkingSupport: true, defaultThinking: true}
 			parser.Init([]api.Tool{}, last, nil)
 
 			for i, step := range tc.steps {
@@ -499,7 +499,7 @@ func TestQwen3VLThinkingParserWithNonThinkingPrefill(t *testing.T) {
 
 	for _, tc := range cases {
 		t.Run(tc.desc, func(t *testing.T) {
-			parser := Qwen3VLParser{hasThinkingSupport: true}
+			parser := Qwen3VLParser{hasThinkingSupport: true, defaultThinking: true}
 			parser.Init([]api.Tool{}, last, nil)
 
 			for i, step := range tc.steps {
@@ -522,7 +522,7 @@ func TestQwen3VLThinkingParserWithNonThinkingPrefill(t *testing.T) {
 func TestQwen3VLThinkingParserStreamingAssistantPrefillContent(t *testing.T) {
 	// last message is assistant with content ⇒ start in CollectingContent
 	last := &api.Message{Role: "assistant", Content: "has content"}
-	parser := Qwen3VLParser{hasThinkingSupport: true}
+	parser := Qwen3VLParser{hasThinkingSupport: true, defaultThinking: true}
 	parser.Init([]api.Tool{}, last, nil)
 
 	type step struct {
@@ -749,7 +749,7 @@ func TestQwen3VLThinkingWhitespaceHandling(t *testing.T) {
 		}
 
 		t.Run(tc.desc, func(t *testing.T) {
-			parser := Qwen3VLParser{hasThinkingSupport: true}
+			parser := Qwen3VLParser{hasThinkingSupport: true, defaultThinking: true}
 			parser.Init([]api.Tool{}, nil, nil)
 
 			for i, step := range tc.steps {
@@ -858,7 +858,7 @@ func TestQwen3VLToolCallWhitespaceHandling(t *testing.T) {
 		}
 
 		t.Run(tc.desc, func(t *testing.T) {
-			parser := Qwen3VLParser{hasThinkingSupport: true}
+			parser := Qwen3VLParser{hasThinkingSupport: true, defaultThinking: true}
 			parser.Init([]api.Tool{}, tc.prefillMsg, nil)
 
 			for i, step := range tc.steps {

model/parsers/qwen3vl_toggle_test.go

@@ -0,0 +1,47 @@
+package parsers
+
+import (
+	"testing"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestQwen3VLParserThinkToggle(t *testing.T) {
+	t.Run("thinking enabled by default when configured", func(t *testing.T) {
+		p := &Qwen3VLParser{hasThinkingSupport: true, defaultThinking: true}
+		p.Init(nil, nil, nil)
+		if p.state != CollectingThinkingContent {
+			t.Fatalf("state = %v, want %v", p.state, CollectingThinkingContent)
+		}
+	})
+
+	t.Run("thinking disabled by default", func(t *testing.T) {
+		p := &Qwen3VLParser{hasThinkingSupport: true, defaultThinking: false}
+		p.Init(nil, nil, nil)
+		if p.state != CollectingContent {
+			t.Fatalf("state = %v, want %v", p.state, CollectingContent)
+		}
+	})
+
+	t.Run("thinking disabled at runtime", func(t *testing.T) {
+		p := &Qwen3VLParser{hasThinkingSupport: true, defaultThinking: true}
+		p.Init(nil, nil, &api.ThinkValue{Value: false})
+		if p.state != CollectingContent {
+			t.Fatalf("state = %v, want %v", p.state, CollectingContent)
+		}
+
+		content, thinking, calls, err := p.Add("plan</think>answer", true)
+		if err != nil {
+			t.Fatal(err)
+		}
+		if content != "plan</think>answer" {
+			t.Fatalf("content = %q, want %q", content, "plan</think>answer")
+		}
+		if thinking != "" {
+			t.Fatalf("thinking = %q, want empty", thinking)
+		}
+		if len(calls) != 0 {
+			t.Fatalf("calls = %d, want 0", len(calls))
+		}
+	})
+}

model/renderers/qwen3vl.go

@@ -7,7 +7,8 @@ import (
 )
 
 type Qwen3VLRenderer struct {
-	isThinking bool
+	hasThinkingSupport bool
+	defaultThinking    bool
 
 	useImgTags bool
 }
@@ -31,8 +32,16 @@ func (r *Qwen3VLRenderer) renderContent(content api.Message) string {
 	return subSb.String()
 }
 
-func (r *Qwen3VLRenderer) Render(messages []api.Message, tools []api.Tool, _ *api.ThinkValue) (string, error) {
+func (r *Qwen3VLRenderer) Render(messages []api.Message, tools []api.Tool, thinkValue *api.ThinkValue) (string, error) {
 	var sb strings.Builder
+	thinking := false
+	if r.hasThinkingSupport {
+		if thinkValue != nil {
+			thinking = thinkValue.Bool()
+		} else {
+			thinking = r.defaultThinking
+		}
+	}
 
 	if len(tools) > 0 {
 		sb.WriteString(imStartTag + "system\n")
@@ -76,13 +85,13 @@ func (r *Qwen3VLRenderer) Render(messages []api.Message, tools []api.Tool, _ *ap
 		} else if message.Role == "assistant" {
 			contentReasoning := ""
 
-			if r.isThinking {
+			if r.hasThinkingSupport {
 				if message.Thinking != "" {
 					contentReasoning = message.Thinking
 				}
 			}
 
-			if r.isThinking && i > lastQueryIndex {
+			if thinking && i > lastQueryIndex {
 				if i == len(messages)-1 || contentReasoning != "" {
 					sb.WriteString("<|im_start|>" + message.Role + "\n<think>\n" + strings.Trim(contentReasoning, "\n")) // do we want to add a new line here?
 					if content != "" {
@@ -125,8 +134,12 @@ func (r *Qwen3VLRenderer) Render(messages []api.Message, tools []api.Tool, _ *ap
 		// prefill at the end
 		if lastMessage && !prefill {
 			sb.WriteString("<|im_start|>assistant\n")
-			if r.isThinking {
+			if thinking {
 				sb.WriteString("<think>\n")
+			} else if r.hasThinkingSupport {
+				// In nothink mode, explicitly close any latent think block so
+				// checkpoints that default to thinking start directly in content.
+				sb.WriteString("</think>\n")
 			}
 		}
 	}

model/renderers/qwen3vl_nonthinking_test.go

@@ -509,7 +509,7 @@ I'll check.
 	}
 	for _, tt := range tests {
 		t.Run(tt.name, func(t *testing.T) {
-			rendered, err := (&Qwen3VLRenderer{isThinking: false, useImgTags: tt.useImgTags}).Render(tt.msgs, tt.tools, nil)
+			rendered, err := (&Qwen3VLRenderer{hasThinkingSupport: false, useImgTags: tt.useImgTags}).Render(tt.msgs, tt.tools, nil)
 			if err != nil {
 				t.Fatal(err)
 			}

model/renderers/qwen3vl_thinking_test.go

@@ -1,6 +1,7 @@
 package renderers
 
 import (
+	"strings"
 	"testing"
 
 	"github.com/google/go-cmp/cmp"
@@ -323,7 +324,7 @@ Speak poetry after the first sentence.</think><think>Speak poetry after the seco
 	}
 	for _, tt := range tests {
 		t.Run(tt.name, func(t *testing.T) {
-			rendered, err := (&Qwen3VLRenderer{isThinking: true}).Render(tt.msgs, tt.tools, nil)
+			rendered, err := (&Qwen3VLRenderer{hasThinkingSupport: true, defaultThinking: true}).Render(tt.msgs, tt.tools, nil)
 			if err != nil {
 				t.Fatal(err)
 			}
@@ -334,6 +335,51 @@ Speak poetry after the first sentence.</think><think>Speak poetry after the seco
 	}
 }
 
+func TestQwen3VLRendererThinkToggle(t *testing.T) {
+	msgs := []api.Message{
+		{Role: "user", Content: "hello"},
+	}
+
+	t.Run("thinking enabled by default when configured", func(t *testing.T) {
+		r := &Qwen3VLRenderer{hasThinkingSupport: true, defaultThinking: true}
+		out, err := r.Render(msgs, nil, nil)
+		if err != nil {
+			t.Fatal(err)
+		}
+		if !strings.Contains(out, "<think>\n") {
+			t.Fatalf("expected thinking prefill in output, got: %q", out)
+		}
+	})
+
+	t.Run("thinking disabled by default", func(t *testing.T) {
+		r := &Qwen3VLRenderer{hasThinkingSupport: true, defaultThinking: false}
+		out, err := r.Render(msgs, nil, nil)
+		if err != nil {
+			t.Fatal(err)
+		}
+		if strings.Contains(out, "<think>\n") {
+			t.Fatalf("did not expect thinking prefill in output, got: %q", out)
+		}
+		if !strings.HasSuffix(out, "<|im_start|>assistant\n</think>\n") {
+			t.Fatalf("unexpected assistant prefill: %q", out)
+		}
+	})
+
+	t.Run("thinking disabled at runtime", func(t *testing.T) {
+		r := &Qwen3VLRenderer{hasThinkingSupport: true, defaultThinking: true}
+		out, err := r.Render(msgs, nil, &api.ThinkValue{Value: false})
+		if err != nil {
+			t.Fatal(err)
+		}
+		if strings.Contains(out, "<think>\n") {
+			t.Fatalf("did not expect thinking prefill in output, got: %q", out)
+		}
+		if !strings.HasSuffix(out, "<|im_start|>assistant\n</think>\n") {
+			t.Fatalf("unexpected assistant prefill: %q", out)
+		}
+	})
+}
+
 func TestFormatToolCallArgumentThinkingVL(t *testing.T) {
 	tests := []struct {
 		name     string

model/renderers/renderer.go

@@ -51,10 +51,15 @@ func rendererForName(name string) Renderer {
 		renderer := &Qwen3CoderRenderer{}
 		return renderer
 	case "qwen3-vl-instruct":
-		renderer := &Qwen3VLRenderer{isThinking: false, useImgTags: RenderImgTags}
+		renderer := &Qwen3VLRenderer{hasThinkingSupport: false, defaultThinking: false, useImgTags: RenderImgTags}
 		return renderer
 	case "qwen3-vl-thinking":
-		renderer := &Qwen3VLRenderer{isThinking: true, useImgTags: RenderImgTags}
+		renderer := &Qwen3VLRenderer{hasThinkingSupport: true, defaultThinking: true, useImgTags: RenderImgTags}
+		return renderer
+	case "qwen3.5", "qwen3-vl":
+		// qwen3.5 and qwen3-vl share one renderer with runtime think toggle.
+		// Default is non-thinking unless think=true is requested.
+		renderer := &Qwen3VLRenderer{hasThinkingSupport: true, defaultThinking: false, useImgTags: RenderImgTags}
 		return renderer
 	case "cogito":
 		renderer := &CogitoRenderer{isThinking: true}

model/renderers/renderer_test.go

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

server/quantization.go

@@ -6,6 +6,7 @@ import (
 	"log/slog"
 	"maps"
 	"os"
+	"slices"
 	"strings"
 	"unsafe"
 
@@ -58,7 +59,7 @@ func useMoreBits(iLayer, nLayers int) bool {
 	return iLayer < (nLayers/8) || iLayer >= 7*nLayers/8 || (iLayer-nLayers/8)%3 == 2
 }
 
-func qwen3nextQuantType(name string) (fsggml.TensorType, bool) {
+func qwen3LinearAttnQuantType(name string) (fsggml.TensorType, bool) {
 	switch {
 	// Full attention
 	case strings.HasSuffix(name, ".attn_q.weight"):
@@ -79,6 +80,10 @@ func qwen3nextQuantType(name string) (fsggml.TensorType, bool) {
 	// SSM
 	case strings.HasSuffix(name, ".ssm_ba.weight"):
 		return fsggml.TensorTypeQ4_K, true
+	case strings.HasSuffix(name, ".ssm_beta.weight"):
+		return fsggml.TensorTypeQ4_K, true
+	case strings.HasSuffix(name, ".ssm_alpha.weight"):
+		return fsggml.TensorTypeQ4_K, true
 	case strings.HasSuffix(name, ".ssm_out.weight"):
 		return fsggml.TensorTypeQ4_K, true
 
@@ -287,8 +292,8 @@ func newType(t *fsggml.Tensor, kv fsggml.KV, qs *quantizeState, ftype fsggml.Fil
 
 	newType := fsggml.TensorType(t.Kind)
 	if quantize {
-		if kv.Architecture() == "qwen3next" && (ftype == fsggml.FileTypeQ4_K_M || ftype == fsggml.FileTypeQ4_K_S) {
-			if qt, ok := qwen3nextQuantType(name); ok {
+		if slices.Contains([]string{"qwen3next", "qwen35", "qwen35moe"}, kv.Architecture()) && (ftype == fsggml.FileTypeQ4_K_M || ftype == fsggml.FileTypeQ4_K_S) {
+			if qt, ok := qwen3LinearAttnQuantType(name); ok {
 				return qt
 			}
 		}

server/quantization_test.go

@@ -166,6 +166,60 @@ func TestGetTensorNewType(t *testing.T) {
 	}
 }
 
+func TestQwen3LinearAttentionQuantOverride(t *testing.T) {
+	cases := []struct {
+		name     string
+		arch     string
+		tensor   string
+		fileType fsggml.FileType
+		expected fsggml.TensorType
+	}{
+		{
+			name:     "qwen35_beta",
+			arch:     "qwen35",
+			tensor:   "blk.0.ssm_beta.weight",
+			fileType: fsggml.FileTypeQ4_K_M,
+			expected: fsggml.TensorTypeQ4_K,
+		},
+		{
+			name:     "qwen35_alpha",
+			arch:     "qwen35",
+			tensor:   "blk.0.ssm_alpha.weight",
+			fileType: fsggml.FileTypeQ4_K_M,
+			expected: fsggml.TensorTypeQ4_K,
+		},
+		{
+			name:     "qwen35moe_attn_qkv",
+			arch:     "qwen35moe",
+			tensor:   "blk.0.attn_qkv.weight",
+			fileType: fsggml.FileTypeQ4_K_M,
+			expected: fsggml.TensorTypeQ4_K,
+		},
+		{
+			name:     "non_qwen35_falls_back",
+			arch:     "foo",
+			tensor:   "blk.0.attn_qkv.weight",
+			fileType: fsggml.FileTypeQ4_K_M,
+			expected: fsggml.TensorTypeQ5_K,
+		},
+	}
+
+	for _, tt := range cases {
+		t.Run(tt.name, func(t *testing.T) {
+			kv := fsggml.KV{"general.architecture": tt.arch}
+			got := newType(&fsggml.Tensor{
+				Name:  tt.tensor,
+				Shape: []uint64{256, 256},
+				Kind:  uint32(fsggml.TensorTypeF16),
+			}, kv, &quantizeState{}, tt.fileType)
+
+			if got != tt.expected {
+				t.Fatalf("unexpected tensor type for %s (%s): got %s want %s", tt.tensor, tt.arch, got, tt.expected)
+			}
+		})
+	}
+}
+
 func TestQuantizeModel(t *testing.T) {
 	cases := []struct {
 		name                string

server/routes.go

@@ -76,6 +76,22 @@ func shouldUseHarmony(model *Model) bool {
 	return false
 }
 
+func defaultThinkingForModel(model *Model) bool {
+	// qwen3.5/qwen3-vl are runtime-toggle models that should default to
+	// non-thinking unless explicitly requested.
+	if slices.Contains([]string{"qwen3.5", "qwen3-vl"}, model.Config.Parser) ||
+		slices.Contains([]string{"qwen3.5", "qwen3-vl"}, model.Config.Renderer) {
+		return false
+	}
+
+	// Preserve legacy default-on behavior for explicit thinking variants.
+	if model.Config.Parser == "qwen3-vl-thinking" || model.Config.Renderer == "qwen3-vl-thinking" {
+		return true
+	}
+
+	return true
+}
+
 func experimentEnabled(name string) bool {
 	return slices.Contains(strings.Split(os.Getenv("OLLAMA_EXPERIMENT"), ","), name)
 }
@@ -385,7 +401,7 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 	if slices.Contains(modelCaps, model.CapabilityThinking) {
 		caps = append(caps, model.CapabilityThinking)
 		if req.Think == nil {
-			req.Think = &api.ThinkValue{Value: true}
+			req.Think = &api.ThinkValue{Value: defaultThinkingForModel(m)}
 		}
 	} else {
 		if req.Think != nil && req.Think.Bool() {
@@ -2149,7 +2165,7 @@ func (s *Server) ChatHandler(c *gin.Context) {
 	if slices.Contains(modelCaps, model.CapabilityThinking) {
 		caps = append(caps, model.CapabilityThinking)
 		if req.Think == nil {
-			req.Think = &api.ThinkValue{Value: true}
+			req.Think = &api.ThinkValue{Value: defaultThinkingForModel(m)}
 		}
 	} else {
 		if req.Think != nil && req.Think.Bool() {

server/sched.go

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

x/create/client/create.go

@@ -264,13 +264,16 @@ func newManifestWriter(opts CreateOptions, capabilities []string, parserName, re
 			}
 		}
 
+		resolvedParser := resolveParserName(opts.Modelfile, parserName)
+		resolvedRenderer := resolveRendererName(opts.Modelfile, rendererName)
+
 		// Create config blob with version requirement
 		configData := model.ConfigV2{
 			ModelFormat:  "safetensors",
 			Capabilities: caps,
 			Requires:     MinOllamaVersion,
-			Parser:       resolveParserName(opts.Modelfile, parserName),
-			Renderer:     resolveRendererName(opts.Modelfile, rendererName),
+			Parser:       resolvedParser,
+			Renderer:     resolvedRenderer,
 		}
 		configJSON, err := json.Marshal(configData)
 		if err != nil {
@@ -427,6 +430,9 @@ func getParserName(modelDir string) string {
 		if strings.Contains(archLower, "deepseek") {
 			return "deepseek3"
 		}
+		if strings.Contains(archLower, "qwen3_5") {
+			return "qwen3.5"
+		}
 		if strings.Contains(archLower, "qwen3") {
 			return "qwen3"
 		}
@@ -441,6 +447,9 @@ func getParserName(modelDir string) string {
 		if strings.Contains(typeLower, "deepseek") {
 			return "deepseek3"
 		}
+		if strings.Contains(typeLower, "qwen3_5") {
+			return "qwen3.5"
+		}
 		if strings.Contains(typeLower, "qwen3") {
 			return "qwen3"
 		}
@@ -475,6 +484,9 @@ func getRendererName(modelDir string) string {
 		if strings.Contains(archLower, "deepseek") {
 			return "deepseek3"
 		}
+		if strings.Contains(archLower, "qwen3_5") {
+			return "qwen3.5"
+		}
 		if strings.Contains(archLower, "qwen3") {
 			return "qwen3-coder"
 		}
@@ -489,6 +501,9 @@ func getRendererName(modelDir string) string {
 		if strings.Contains(typeLower, "deepseek") {
 			return "deepseek3"
 		}
+		if strings.Contains(typeLower, "qwen3_5") {
+			return "qwen3.5"
+		}
 		if strings.Contains(typeLower, "qwen3") {
 			return "qwen3-coder"
 		}

x/create/client/create_qwen35_test.go

@@ -0,0 +1,40 @@
+package client
+
+import (
+	"os"
+	"path/filepath"
+	"testing"
+)
+
+func writeConfig(t *testing.T, dir, cfg string) {
+	t.Helper()
+	if err := os.WriteFile(filepath.Join(dir, "config.json"), []byte(cfg), 0o644); err != nil {
+		t.Fatal(err)
+	}
+}
+
+func TestParserAndRendererInferenceQwen35(t *testing.T) {
+	t.Run("qwen3.5 architecture uses qwen3.5 runtime-toggle stack", func(t *testing.T) {
+		dir := t.TempDir()
+		writeConfig(t, dir, `{"architectures":["Qwen3_5ForConditionalGeneration"],"model_type":"qwen3_5"}`)
+
+		if got, want := getParserName(dir), "qwen3.5"; got != want {
+			t.Fatalf("getParserName() = %q, want %q", got, want)
+		}
+		if got, want := getRendererName(dir), "qwen3.5"; got != want {
+			t.Fatalf("getRendererName() = %q, want %q", got, want)
+		}
+	})
+
+	t.Run("qwen3 legacy inference unchanged", func(t *testing.T) {
+		dir := t.TempDir()
+		writeConfig(t, dir, `{"architectures":["Qwen3ForCausalLM"],"model_type":"qwen3"}`)
+
+		if got, want := getParserName(dir), "qwen3"; got != want {
+			t.Fatalf("getParserName() = %q, want %q", got, want)
+		}
+		if got, want := getRendererName(dir), "qwen3-coder"; got != want {
+			t.Fatalf("getRendererName() = %q, want %q", got, want)
+		}
+	})
+}