docs: integration overview

Group integrations into high-level types

api/types.go

@@ -749,7 +749,7 @@ type ShowResponse struct {
 	Messages      []Message          `json:"messages,omitempty"`
 	RemoteModel   string             `json:"remote_model,omitempty"`
 	RemoteHost    string             `json:"remote_host,omitempty"`
-	ModelInfo     map[string]any     `json:"model_info,omitempty"`
+	ModelInfo     map[string]any     `json:"model_info"`
 	ProjectorInfo map[string]any     `json:"projector_info,omitempty"`
 	Tensors       []Tensor           `json:"tensors,omitempty"`
 	Capabilities  []model.Capability `json:"capabilities,omitempty"`

cmd/cmd.go

@@ -899,11 +899,11 @@ func DeleteHandler(cmd *cobra.Command, args []string) error {
 	for _, arg := range args {
 		// Unload the model if it's running before deletion
 		if err := loadOrUnloadModel(cmd, &runOptions{
-			Model:     args[0],
+			Model:     arg,
 			KeepAlive: &api.Duration{Duration: 0},
 		}); err != nil {
 			if !strings.Contains(strings.ToLower(err.Error()), "not found") {
-				fmt.Fprintf(os.Stderr, "Warning: unable to stop model '%s'\n", args[0])
+				fmt.Fprintf(os.Stderr, "Warning: unable to stop model '%s'\n", arg)
 			}
 		}
 

convert/convert.go

@@ -311,6 +311,10 @@ func LoadModelMetadata(fsys fs.FS) (ModelKV, *Tokenizer, error) {
 		conv = &deepseekocr{}
 	case "DeepseekV3ForCausalLM":
 		conv = &deepseek2Model{}
+	case "Glm4MoeLiteForCausalLM":
+		conv = &glm4MoeLiteModel{}
+	case "Lfm2ForCausalLM":
+		conv = &lfm2Model{}
 	default:
 		return nil, nil, fmt.Errorf("unsupported architecture %q", p.Architectures[0])
 	}

convert/convert_glm4moelite.go

@@ -0,0 +1,150 @@
+package convert
+
+import (
+	"cmp"
+	"fmt"
+	"log/slog"
+	"regexp"
+	"strconv"
+
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+type glm4MoeLiteModel struct {
+	ModelParameters
+	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
+	HiddenSize            uint32  `json:"hidden_size"`
+	HiddenLayers          uint32  `json:"num_hidden_layers"`
+	IntermediateSize      uint32  `json:"intermediate_size"`
+	NumAttentionHeads     uint32  `json:"num_attention_heads"`
+	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
+	RMSNormEPS            float32 `json:"rms_norm_eps"`
+
+	RopeTheta     float32 `json:"rope_theta"`
+	QKNopeHeadDim uint32  `json:"qk_nope_head_dim"`
+	QKRopeHeadDim uint32  `json:"qk_rope_head_dim"`
+	KVLoraRank    uint32  `json:"kv_lora_rank"`
+	QLoraRank     uint32  `json:"q_lora_rank"`
+	VHeadDim      uint32  `json:"v_head_dim"`
+
+	ExpertCount            uint32  `json:"n_routed_experts"`
+	ExpertSharedCount      uint32  `json:"n_shared_experts"`
+	ExpertIntermediateSize uint32  `json:"moe_intermediate_size"`
+	ExpertUsedCount        uint32  `json:"num_experts_per_tok"`
+	ExpertWeightsNorm      bool    `json:"norm_topk_prob"`
+	ExpertWeightsScale     float32 `json:"routed_scaling_factor"`
+
+	LeadingDenseBlockCount uint32 `json:"first_k_dense_replace"`
+}
+
+func (p *glm4MoeLiteModel) KV(t *Tokenizer) KV {
+	kv := p.ModelParameters.KV(t)
+	kv["general.architecture"] = "glm4moelite"
+	kv["general.type"] = "model"
+	kv["glm4moelite.block_count"] = p.HiddenLayers
+
+	numHeads := p.NumAttentionHeads
+	numKVHeads := p.NumKeyValueHeads
+
+	kv["glm4moelite.attention.head_count"] = numHeads
+	kv["glm4moelite.attention.head_count_kv"] = numKVHeads
+	kv["glm4moelite.attention.key_length"] = p.QKNopeHeadDim + p.QKRopeHeadDim
+	kv["glm4moelite.attention.kv_lora_rank"] = p.KVLoraRank
+	kv["glm4moelite.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
+	kv["glm4moelite.attention.q_lora_rank"] = p.QLoraRank
+	kv["glm4moelite.attention.value_length"] = p.VHeadDim
+	kv["glm4moelite.context_length"] = p.MaxPositionEmbeddings
+	kv["glm4moelite.embedding_length"] = p.HiddenSize
+	kv["glm4moelite.expert_count"] = p.ExpertCount
+	kv["glm4moelite.expert_feed_forward_length"] = p.ExpertIntermediateSize
+	kv["glm4moelite.expert_shared_count"] = p.ExpertSharedCount
+
+	kv["glm4moelite.expert_gating_func"] = uint32(2)
+	kv["glm4moelite.expert_used_count"] = p.ExpertUsedCount
+	kv["glm4moelite.expert_weights_norm"] = p.ExpertWeightsNorm
+	kv["glm4moelite.expert_weights_scale"] = p.ExpertWeightsScale
+	kv["glm4moelite.feed_forward_length"] = p.IntermediateSize
+	kv["glm4moelite.leading_dense_block_count"] = p.LeadingDenseBlockCount
+
+	kv["glm4moelite.rope.dimension_count"] = p.QKRopeHeadDim
+	kv["glm4moelite.rope.freq_base"] = cmp.Or(p.RopeTheta, float32(1000000.0))
+
+	kv["tokenizer.ggml.pre"] = "glm4"
+
+	return kv
+}
+
+func (p *glm4MoeLiteModel) Replacements() []string {
+	return []string{
+		"lm_head", "output",
+		"model.embed_tokens", "token_embd",
+		"model.norm", "output_norm",
+		"model.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"self_attn.kv_a_proj_with_mqa", "attn_kv_a_mqa",
+		"self_attn.kv_a_layernorm", "attn_kv_a_norm",
+		"self_attn.kv_b_proj", "attn_kv_b",
+		"self_attn.q_a_proj", "attn_q_a",
+		"self_attn.q_a_layernorm", "attn_q_a_norm",
+		"self_attn.q_b_proj", "attn_q_b",
+		"self_attn.o_proj", "attn_output",
+		"post_attention_layernorm", "ffn_norm",
+		"mlp.shared_experts.down_proj", "ffn_down_shexp",
+		"mlp.shared_experts.gate_proj", "ffn_gate_shexp",
+		"mlp.shared_experts.up_proj", "ffn_up_shexp",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.down_proj", "ffn_down",
+		"mlp.up_proj", "ffn_up",
+		"mlp.gate.e_score_correction_bias", "exp_probs_b.bias",
+		"mlp.gate", "ffn_gate_inp",
+	}
+}
+
+func (p *glm4MoeLiteModel) Tensors(s []Tensor) (out []*ggml.Tensor) {
+	merges := make([]merge, p.HiddenLayers*3)
+	for i := range p.HiddenLayers {
+		merges[i*3+0] = merge{
+			fmt.Sprintf("blk.%d.mlp.experts.*.gate_proj.weight", i),
+			fmt.Sprintf("blk.%d.ffn_gate_exps.weight", i),
+		}
+		merges[i*3+1] = merge{
+			fmt.Sprintf("blk.%d.mlp.experts.*.up_proj.weight", i),
+			fmt.Sprintf("blk.%d.ffn_up_exps.weight", i),
+		}
+		merges[i*3+2] = merge{
+			fmt.Sprintf("blk.%d.mlp.experts.*.down_proj.weight", i),
+			fmt.Sprintf("blk.%d.ffn_down_exps.weight", i),
+		}
+	}
+
+	skipLayer := func(n string, minValue uint32) bool {
+		re := regexp.MustCompile(`^blk\.(\d+)`)
+		matches := re.FindStringSubmatch(n)
+		if matches == nil {
+			return false
+		}
+
+		blkNum, err := strconv.Atoi(matches[1])
+		if err != nil {
+			return false
+		}
+
+		return uint32(blkNum) >= minValue
+	}
+
+	out, s = mergeTensors(s, merges...)
+	for _, t := range s {
+		// skip any additional layers (such as the Multi-Token Prediction layer)
+		if skipLayer(t.Name(), p.HiddenLayers) {
+			slog.Debug("skipping layer", "name", t.Name())
+			continue
+		}
+		out = append(out, &ggml.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+	return out
+}

convert/convert_lfm2.go

@@ -0,0 +1,100 @@
+package convert
+
+import (
+	"slices"
+	"strings"
+
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+type lfm2Model struct {
+	ModelParameters
+	HiddenSize            uint32   `json:"hidden_size"`
+	NumHiddenLayers       uint32   `json:"num_hidden_layers"`
+	MaxPositionEmbeddings uint32   `json:"max_position_embeddings"`
+	IntermediateSize      uint32   `json:"intermediate_size"`
+	NumAttentionHeads     uint32   `json:"num_attention_heads"`
+	NumKeyValueHeads      uint32   `json:"num_key_value_heads"`
+	RopeTheta             float32  `json:"rope_theta"`
+	NormEps               float32  `json:"norm_eps"`
+	ConvLCache            uint32   `json:"conv_L_cache"`
+	LayerTypes            []string `json:"layer_types"`
+	TieEmbedding          bool     `json:"tie_embedding"`
+}
+
+var _ ModelConverter = (*lfm2Model)(nil)
+
+func (p *lfm2Model) KV(t *Tokenizer) KV {
+	kv := p.ModelParameters.KV(t)
+	kv["general.architecture"] = "lfm2"
+	kv["lfm2.vocab_size"] = p.VocabSize
+	kv["lfm2.block_count"] = p.NumHiddenLayers
+	kv["lfm2.embedding_length"] = p.HiddenSize
+	kv["lfm2.feed_forward_length"] = p.IntermediateSize
+	kv["lfm2.context_length"] = p.MaxPositionEmbeddings
+
+	// Build per-layer KV head count array based on layer_types
+	// (0 = shortconv layer, non-zero = attention layer with that many KV heads)
+	kvHeadCounts := make([]uint32, p.NumHiddenLayers)
+	for i := range p.NumHiddenLayers {
+		if int(i) < len(p.LayerTypes) && p.LayerTypes[i] == "full_attention" {
+			kvHeadCounts[i] = p.NumKeyValueHeads
+		}
+	}
+
+	kv["lfm2.attention.head_count"] = p.NumAttentionHeads
+	kv["lfm2.attention.head_count_kv"] = kvHeadCounts
+	kv["lfm2.attention.key_length"] = p.HiddenSize / p.NumAttentionHeads
+	kv["lfm2.attention.value_length"] = p.HiddenSize / p.NumAttentionHeads
+	kv["lfm2.attention.layer_norm_rms_epsilon"] = p.NormEps
+	kv["lfm2.rope.freq_base"] = p.RopeTheta
+	kv["lfm2.shortconv.l_cache"] = p.ConvLCache
+
+	return kv
+}
+
+func (p *lfm2Model) Tensors(ts []Tensor) []*ggml.Tensor {
+	var out []*ggml.Tensor
+
+	for _, t := range ts {
+		shape := t.Shape()
+
+		// Squeeze conv weights: [D, 1, K] -> [D, K]
+		if strings.HasSuffix(t.Name(), "shortconv.conv.weight") {
+			if len(shape) == 3 && shape[1] == 1 {
+				shape = []uint64{shape[0], shape[2]}
+			}
+		}
+
+		out = append(out, &ggml.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    slices.Clone(shape),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *lfm2Model) Replacements() []string {
+	return []string{
+		"model.embed_tokens", "token_embd",
+		"model.embedding_norm", "output_norm",
+		"model.layers", "blk",
+		"operator_norm", "attn_norm",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.out_proj", "attn_output",
+		"self_attn.q_layernorm", "attn_q_norm",
+		"self_attn.k_layernorm", "attn_k_norm",
+		"conv.conv", "shortconv.conv",
+		"conv.in_proj", "shortconv.in_proj",
+		"conv.out_proj", "shortconv.out_proj",
+		"feed_forward.w1", "ffn_gate",
+		"feed_forward.w2", "ffn_down",
+		"feed_forward.w3", "ffn_up",
+		"ffn_norm", "ffn_norm",
+	}
+}

convert/reader.go

@@ -40,6 +40,7 @@ const (
 func (t tensorBase) Kind() uint32 {
 	if strings.HasSuffix(t.name, ".ffn_gate_inp.weight") ||
 		strings.HasSuffix(t.name, ".bias") ||
+		strings.HasSuffix(t.name, ".shortconv.conv.weight") ||
 		t.name == "token_types.weight" ||
 		t.name == "v.positional_embedding_vlm" ||
 		t.name == "v.tile_position_embd.weight" ||

docs/integrations/index.mdx

@@ -0,0 +1,43 @@
+---
+title: Integrations
+---
+
+Ollama integrates with a wide range of tools, including:
+
+## Coding Agents
+
+Coding assistants that can read, modify, and execute code in your projects.
+
+- [Claude Code](/integrations/claude-code)
+- [Codex](/integrations/codex)
+- [Droid](/integrations/droid)
+- [Goose](/integrations/goose)
+
+## IDEs & Editors
+
+Native integrations for popular development environments.
+
+- [VS Code](/integrations/vscode)
+- [Cline](/integrations/cline)
+- [Roo Code](/integrations/roo-code)
+- [JetBrains](/integrations/jetbrains)
+- [Xcode](/integrations/xcode)
+- [Zed](/integrations/zed)
+
+## Chat & RAG
+
+Chat interfaces and retrieval-augmented generation platforms.
+
+- [Onyx](/integrations/onyx)
+
+## Automation
+
+Workflow automation platforms with AI integration.
+
+- [n8n](/integrations/n8n)
+
+## Notebooks
+
+Interactive computing environments with AI capabilities.
+
+- [marimo](/integrations/marimo)

fs/ggml/ggml.go

@@ -269,6 +269,8 @@ func (kv KV) OllamaEngineRequired() bool {
 		"qwen25vl",
 		"qwen3", "qwen3moe",
 		"qwen3vl", "qwen3vlmoe",
+		"glm4moelite",
+		"lfm2",
 	}, kv.Architecture())
 }
 
@@ -856,7 +858,9 @@ func (f GGML) FlashAttention() bool {
 	return slices.Contains([]string{
 		"bert",
 		"gemma3",
+		"glm4moelite",
 		"gptoss", "gpt-oss",
+		"lfm2",
 		"mistral3",
 		"olmo3",
 		"qwen3", "qwen3moe",

integration/imagegen_test.go

@@ -0,0 +1,148 @@
+//go:build integration
+
+package integration
+
+import (
+	"context"
+	"encoding/base64"
+	"fmt"
+	"strings"
+	"testing"
+	"time"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestImageGeneration(t *testing.T) {
+	skipUnderMinVRAM(t, 8)
+
+	type testCase struct {
+		imageGenModel string
+		visionModel   string
+		prompt        string
+		expectedWords []string
+	}
+
+	testCases := []testCase{
+		{
+			imageGenModel: "jmorgan/z-image-turbo",
+			visionModel:   "llama3.2-vision",
+			prompt:        "A cartoon style llama flying like a superhero through the air with clouds in the background",
+			expectedWords: []string{"llama", "flying", "cartoon", "cloud", "sky", "superhero", "air", "animal", "camelid"},
+		},
+	}
+
+	for _, tc := range testCases {
+		t.Run(fmt.Sprintf("%s->%s", tc.imageGenModel, tc.visionModel), func(t *testing.T) {
+			ctx, cancel := context.WithTimeout(context.Background(), 10*time.Minute)
+			defer cancel()
+
+			client, _, cleanup := InitServerConnection(ctx, t)
+			defer cleanup()
+
+			// Pull both models
+			if err := PullIfMissing(ctx, client, tc.imageGenModel); err != nil {
+				t.Fatalf("failed to pull image gen model: %v", err)
+			}
+			if err := PullIfMissing(ctx, client, tc.visionModel); err != nil {
+				t.Fatalf("failed to pull vision model: %v", err)
+			}
+
+			// Generate the image
+			t.Logf("Generating image with prompt: %s", tc.prompt)
+			imageBase64, err := generateImage(ctx, client, tc.imageGenModel, tc.prompt)
+			if err != nil {
+				if strings.Contains(err.Error(), "image generation not available") {
+					t.Skip("Target system does not support image generation")
+				} else if strings.Contains(err.Error(), "executable file not found in") { // Windows pattern, not yet supported
+					t.Skip("Windows does not support image generation yet")
+				} else if strings.Contains(err.Error(), "CUDA driver version is insufficient") {
+					t.Skip("Driver is too old")
+				} else if strings.Contains(err.Error(), "insufficient memory for image generation") {
+					t.Skip("insufficient memory for image generation")
+				} else if strings.Contains(err.Error(), "error while loading shared libraries: libcuda.so.1") { // AMD GPU or CPU
+					t.Skip("CUDA GPU is not available")
+				} else if strings.Contains(err.Error(), "ollama-mlx: no such file or directory") {
+					// most likely linux arm - not supported yet
+					t.Skip("unsupported architecture")
+				}
+				t.Fatalf("failed to generate image: %v", err)
+			}
+
+			imageData, err := base64.StdEncoding.DecodeString(imageBase64)
+			if err != nil {
+				t.Fatalf("failed to decode image: %v", err)
+			}
+			t.Logf("Generated image: %d bytes", len(imageData))
+
+			// Preload vision model and check GPU loading
+			err = client.Generate(ctx, &api.GenerateRequest{Model: tc.visionModel}, func(response api.GenerateResponse) error { return nil })
+			if err != nil {
+				t.Fatalf("failed to load vision model: %v", err)
+			}
+
+			// Use vision model to describe the image
+			chatReq := api.ChatRequest{
+				Model: tc.visionModel,
+				Messages: []api.Message{
+					{
+						Role:    "user",
+						Content: "Describe this image in detail. What is shown? What style is it? What is the main subject doing?",
+						Images:  []api.ImageData{imageData},
+					},
+				},
+				Stream: &stream,
+				Options: map[string]any{
+					"seed":        42,
+					"temperature": 0.0,
+				},
+			}
+
+			// Verify the vision model's response contains expected keywords
+			response := DoChat(ctx, t, client, chatReq, tc.expectedWords, 240*time.Second, 30*time.Second)
+			if response != nil {
+				t.Logf("Vision model response: %s", response.Content)
+
+				// Additional detailed check for keywords
+				content := strings.ToLower(response.Content)
+				foundWords := []string{}
+				missingWords := []string{}
+				for _, word := range tc.expectedWords {
+					if strings.Contains(content, word) {
+						foundWords = append(foundWords, word)
+					} else {
+						missingWords = append(missingWords, word)
+					}
+				}
+				t.Logf("Found keywords: %v", foundWords)
+				if len(missingWords) > 0 {
+					t.Logf("Missing keywords (at least one was found so test passed): %v", missingWords)
+				}
+			}
+		})
+	}
+}
+
+// generateImage calls the Ollama API to generate an image and returns the base64 image data
+func generateImage(ctx context.Context, client *api.Client, model, prompt string) (string, error) {
+	var imageBase64 string
+
+	err := client.Generate(ctx, &api.GenerateRequest{
+		Model:  model,
+		Prompt: prompt,
+	}, func(resp api.GenerateResponse) error {
+		if resp.Image != "" {
+			imageBase64 = resp.Image
+		}
+		return nil
+	})
+	if err != nil {
+		return "", fmt.Errorf("failed to generate image: %w", err)
+	}
+
+	if imageBase64 == "" {
+		return "", fmt.Errorf("no image data in response")
+	}
+
+	return imageBase64, nil
+}

integration/utils_test.go

@@ -38,6 +38,7 @@ var (
 
 	// Note: add newer models at the top of the list to test them first
 	ollamaEngineChatModels = []string{
+		"lfm2.5-thinking",
 		"ministral-3",
 		"qwen3-coder:30b",
 		"gpt-oss:20b",
@@ -143,6 +144,7 @@ var (
 		"granite3.3",
 		"hermes3",
 		"internlm2",
+		"lfm2.5-thinking",
 		"llama-guard3",
 		"llama-pro",
 		"llama2-chinese",
@@ -263,6 +265,7 @@ var (
 		"snowflake-arctic-embed2",
 	}
 	libraryToolsModels = []string{
+		"lfm2.5-thinking",
 		"qwen3-vl",
 		"gpt-oss:20b",
 		"gpt-oss:120b",

manifest/layer.go

@@ -1,4 +1,4 @@
-package server
+package manifest
 
 import (
 	"crypto/sha256"
@@ -14,7 +14,7 @@ type Layer struct {
 	Size      int64  `json:"size"`
 	From      string `json:"from,omitempty"`
 	Name      string `json:"name,omitempty"` // tensor name, e.g., "text_encoder/model.embed_tokens.weight"
-	status    string
+	Status    string `json:"-"`
 }
 
 const (
@@ -22,7 +22,7 @@ const (
 )
 
 func NewLayer(r io.Reader, mediatype string) (Layer, error) {
-	blobs, err := GetBlobsPath("")
+	blobs, err := BlobsPath("")
 	if err != nil {
 		return Layer{}, err
 	}
@@ -45,7 +45,7 @@ func NewLayer(r io.Reader, mediatype string) (Layer, error) {
 	}
 
 	digest := fmt.Sprintf("sha256:%x", sha256sum.Sum(nil))
-	blob, err := GetBlobsPath(digest)
+	blob, err := BlobsPath(digest)
 	if err != nil {
 		return Layer{}, err
 	}
@@ -65,7 +65,7 @@ func NewLayer(r io.Reader, mediatype string) (Layer, error) {
 		MediaType: mediatype,
 		Digest:    digest,
 		Size:      n,
-		status:    fmt.Sprintf("%s %s", status, digest),
+		Status:    fmt.Sprintf("%s %s", status, digest),
 	}, nil
 }
 
@@ -74,7 +74,7 @@ func NewLayerFromLayer(digest, mediatype, from string) (Layer, error) {
 		return Layer{}, errors.New("creating new layer from layer with empty digest")
 	}
 
-	blob, err := GetBlobsPath(digest)
+	blob, err := BlobsPath(digest)
 	if err != nil {
 		return Layer{}, err
 	}
@@ -89,7 +89,7 @@ func NewLayerFromLayer(digest, mediatype, from string) (Layer, error) {
 		Digest:    digest,
 		Size:      fi.Size(),
 		From:      from,
-		status:    fmt.Sprintf("using existing layer %s", digest),
+		Status:    fmt.Sprintf("using existing layer %s", digest),
 	}, nil
 }
 
@@ -98,7 +98,7 @@ func (l *Layer) Open() (io.ReadSeekCloser, error) {
 		return nil, errors.New("opening layer with empty digest")
 	}
 
-	blob, err := GetBlobsPath(l.Digest)
+	blob, err := BlobsPath(l.Digest)
 	if err != nil {
 		return nil, err
 	}
@@ -126,7 +126,7 @@ func (l *Layer) Remove() error {
 		}
 	}
 
-	blob, err := GetBlobsPath(l.Digest)
+	blob, err := BlobsPath(l.Digest)
 	if err != nil {
 		return err
 	}

manifest/manifest.go

@@ -1,10 +1,9 @@
-package server
+package manifest
 
 import (
 	"crypto/sha256"
 	"encoding/hex"
 	"encoding/json"
-	"errors"
 	"fmt"
 	"io"
 	"log/slog"
@@ -33,12 +32,38 @@ func (m *Manifest) Size() (size int64) {
 	return
 }
 
+func (m *Manifest) Digest() string {
+	return m.digest
+}
+
+func (m *Manifest) FileInfo() os.FileInfo {
+	return m.fi
+}
+
+// ReadConfigJSON reads and unmarshals a config layer as JSON.
+func (m *Manifest) ReadConfigJSON(configPath string, v any) error {
+	for _, layer := range m.Layers {
+		if layer.MediaType == "application/vnd.ollama.image.json" && layer.Name == configPath {
+			blobPath, err := BlobsPath(layer.Digest)
+			if err != nil {
+				return err
+			}
+			data, err := os.ReadFile(blobPath)
+			if err != nil {
+				return err
+			}
+			return json.Unmarshal(data, v)
+		}
+	}
+	return fmt.Errorf("config %q not found in manifest", configPath)
+}
+
 func (m *Manifest) Remove() error {
 	if err := os.Remove(m.filepath); err != nil {
 		return err
 	}
 
-	manifests, err := GetManifestPath()
+	manifests, err := Path()
 	if err != nil {
 		return err
 	}
@@ -70,11 +95,11 @@ func (m *Manifest) RemoveLayers() error {
 		if _, used := inUse[layer.Digest]; used {
 			continue
 		}
-		blob, err := GetBlobsPath(layer.Digest)
+		blob, err := BlobsPath(layer.Digest)
 		if err != nil {
 			return err
 		}
-		if err := os.Remove(blob); errors.Is(err, os.ErrNotExist) {
+		if err := os.Remove(blob); os.IsNotExist(err) {
 			slog.Debug("layer does not exist", "digest", layer.Digest)
 		} else if err != nil {
 			return err
@@ -89,7 +114,7 @@ func ParseNamedManifest(n model.Name) (*Manifest, error) {
 		return nil, model.Unqualified(n)
 	}
 
-	manifests, err := GetManifestPath()
+	manifests, err := Path()
 	if err != nil {
 		return nil, err
 	}
@@ -121,7 +146,7 @@ func ParseNamedManifest(n model.Name) (*Manifest, error) {
 }
 
 func WriteManifest(name model.Name, config Layer, layers []Layer) error {
-	manifests, err := GetManifestPath()
+	manifests, err := Path()
 	if err != nil {
 		return err
 	}
@@ -148,7 +173,7 @@ func WriteManifest(name model.Name, config Layer, layers []Layer) error {
 }
 
 func Manifests(continueOnError bool) (map[model.Name]*Manifest, error) {
-	manifests, err := GetManifestPath()
+	manifests, err := Path()
 	if err != nil {
 		return nil, err
 	}

manifest/manifest_test.go

@@ -1,4 +1,4 @@
-package server
+package manifest
 
 import (
 	"encoding/json"

manifest/paths.go

@@ -0,0 +1,95 @@
+package manifest
+
+import (
+	"errors"
+	"fmt"
+	"os"
+	"path/filepath"
+	"regexp"
+	"strings"
+
+	"github.com/ollama/ollama/envconfig"
+	"github.com/ollama/ollama/types/model"
+)
+
+var ErrInvalidDigestFormat = errors.New("invalid digest format")
+
+func Path() (string, error) {
+	path := filepath.Join(envconfig.Models(), "manifests")
+	if err := os.MkdirAll(path, 0o755); err != nil {
+		return "", fmt.Errorf("%w: ensure path elements are traversable", err)
+	}
+
+	return path, nil
+}
+
+// PathForName returns the path to the manifest file for a specific model name.
+func PathForName(n model.Name) (string, error) {
+	if !n.IsValid() {
+		return "", os.ErrNotExist
+	}
+
+	manifests, err := Path()
+	if err != nil {
+		return "", err
+	}
+
+	return filepath.Join(manifests, n.Filepath()), nil
+}
+
+func BlobsPath(digest string) (string, error) {
+	// only accept actual sha256 digests
+	pattern := "^sha256[:-][0-9a-fA-F]{64}$"
+	re := regexp.MustCompile(pattern)
+
+	if digest != "" && !re.MatchString(digest) {
+		return "", ErrInvalidDigestFormat
+	}
+
+	digest = strings.ReplaceAll(digest, ":", "-")
+	path := filepath.Join(envconfig.Models(), "blobs", digest)
+	dirPath := filepath.Dir(path)
+	if digest == "" {
+		dirPath = path
+	}
+
+	if err := os.MkdirAll(dirPath, 0o755); err != nil {
+		return "", fmt.Errorf("%w: ensure path elements are traversable", err)
+	}
+
+	return path, nil
+}
+
+// PruneDirectory removes empty directories recursively.
+func PruneDirectory(path string) error {
+	info, err := os.Lstat(path)
+	if err != nil {
+		return err
+	}
+
+	if info.IsDir() && info.Mode()&os.ModeSymlink == 0 {
+		entries, err := os.ReadDir(path)
+		if err != nil {
+			return err
+		}
+
+		for _, entry := range entries {
+			if err := PruneDirectory(filepath.Join(path, entry.Name())); err != nil {
+				return err
+			}
+		}
+
+		entries, err = os.ReadDir(path)
+		if err != nil {
+			return err
+		}
+
+		if len(entries) > 0 {
+			return nil
+		}
+
+		return os.Remove(path)
+	}
+
+	return nil
+}

ml/backend.go

@@ -162,6 +162,7 @@ type Tensor interface {
 	AvgPool2D(ctx Context, k, s int, p float32) Tensor
 	Conv2D(ctx Context, weight Tensor, s0, s1, p0, p1, d0, d1 int) Tensor
 	Conv3D(ctx Context, weight Tensor, c, s0, s1, s2, p0, p1, p2, d0, d1, d2 int) Tensor
+	SSMConv(ctx Context, kernel Tensor) Tensor
 
 	IM2Col(ctx Context, weight Tensor, s0, s1, p0, p1, d0, d1 int) Tensor
 

ml/backend/ggml/ggml.go

@@ -1641,6 +1641,13 @@ func (t *Tensor) Conv3D(ctx ml.Context, t2 ml.Tensor, c, s0, s1, s2, p0, p1, p2,
 	return tt
 }
 
+func (t *Tensor) SSMConv(ctx ml.Context, kernel ml.Tensor) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_ssm_conv(ctx.(*Context).ctx, t.t, kernel.(*Tensor).t),
+	}
+}
+
 func (t *Tensor) AvgPool2D(ctx ml.Context, k, s int, p float32) ml.Tensor {
 	return &Tensor{
 		b: t.b,

model/models/glm4moelite/model.go

@@ -0,0 +1,304 @@
+package glm4moelite
+
+import (
+	"math"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type Options struct {
+	numExpertsUsed      int
+	numExperts          int
+	normTopKProb        bool
+	routedScalingFactor float32
+
+	kvLoraRank,
+	qkNopeHeadDim,
+	qkRopeHeadDim,
+	kqNopeHeadDim,
+	qkHeadDim int
+	qLoraRank int
+	vHeadDim  int
+
+	hiddenSize,
+	numHeads,
+	numKVHeads int
+
+	eps,
+	ropeBase float32
+	kqScale float64
+}
+
+func (o Options) applyRotaryPositionEmbeddings(ctx ml.Context, t, p ml.Tensor) ml.Tensor {
+	return nn.RoPE(ctx, t, p, o.qkRopeHeadDim, o.ropeBase, 1.0)
+}
+
+type Attention struct {
+	Q *nn.Linear `gguf:"attn_q"`
+
+	QA     *nn.Linear  `gguf:"attn_q_a"`
+	QANorm *nn.RMSNorm `gguf:"attn_q_a_norm"`
+	QB     *nn.Linear  `gguf:"attn_q_b"`
+
+	KVA     *nn.Linear  `gguf:"attn_kv_a_mqa"`
+	KVANorm *nn.RMSNorm `gguf:"attn_kv_a_norm"`
+	KVB     *nn.Linear  `gguf:"attn_kv_b"`
+
+	Output *nn.Linear `gguf:"attn_out,alt:attn_output"`
+}
+
+func (attn *Attention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
+	seqLength := hiddenStates.Dim(1)
+
+	var query ml.Tensor
+	if opts.qLoraRank == 0 {
+		query = attn.Q.Forward(ctx, hiddenStates)
+	} else {
+		query = attn.QA.Forward(ctx, hiddenStates)
+		query = attn.QANorm.Forward(ctx, query, opts.eps)
+		query = attn.QB.Forward(ctx, query)
+	}
+
+	query = query.Reshape(ctx, query.Dim(0)/opts.numHeads, opts.numHeads, seqLength)
+	queryChunks := query.ChunkSections(ctx, 0, opts.qkNopeHeadDim, opts.qkRopeHeadDim)
+
+	compressedKV := attn.KVA.Forward(ctx, hiddenStates)
+	kPass := compressedKV.Slice(ctx, 0, 0, opts.kvLoraRank, 1)
+	kRot := compressedKV.View(ctx,
+		opts.kvLoraRank*compressedKV.Stride(0), opts.qkRopeHeadDim,
+		compressedKV.Stride(1), 1,
+		compressedKV.Stride(1), compressedKV.Dim(1),
+	)
+
+	qRot := opts.applyRotaryPositionEmbeddings(ctx, queryChunks[1], positions)
+	kRot = opts.applyRotaryPositionEmbeddings(ctx, kRot, positions)
+	kPass = attn.KVANorm.Forward(ctx, kPass, opts.eps)
+	kPass = attn.KVB.Forward(ctx, kPass)
+
+	kv := kPass.Reshape(ctx, kPass.Dim(0)/opts.numKVHeads, opts.numKVHeads, seqLength)
+	kvChunks := kv.ChunkSections(ctx, 0, opts.kqNopeHeadDim, opts.vHeadDim)
+
+	kRot = kRot.Repeat(ctx, 1, queryChunks[0].Dim(1))
+	query = qRot.Concat(ctx, queryChunks[0], 0)
+	key := kRot.Concat(ctx, kvChunks[0], 0)
+	attention := nn.Attention(ctx, query, key, kvChunks[1], opts.kqScale, cache)
+
+	attention = attention.Reshape(ctx, attention.Dim(0)*attention.Dim(1), seqLength)
+	return attn.Output.Forward(ctx, attention)
+}
+
+type MLP interface {
+	Forward(ml.Context, ml.Tensor, *Options) ml.Tensor
+}
+
+type sparse struct {
+	Router       *nn.Linear `gguf:"ffn_gate_inp"`
+	Gate         *nn.Linear `gguf:"ffn_gate_exps"`
+	Up           *nn.Linear `gguf:"ffn_up_exps"`
+	Down         *nn.Linear `gguf:"ffn_down_exps"`
+	SharedExpert *dense     `gguf:",suf:_shexp"`
+	ExpProbsBias ml.Tensor  `gguf:"exp_probs_b.bias,alt:exp_probs_b"`
+}
+
+func (moe *sparse) Moe(ctx ml.Context, hiddenStates, topKIndices, topKWeights ml.Tensor, opts *Options) ml.Tensor {
+	hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0), 1, hiddenStates.Dim(1))
+
+	upStates := moe.Up.Weight.MulmatID(ctx, hiddenStates, topKIndices)
+	hiddenStates = moe.Gate.Weight.MulmatID(ctx, hiddenStates, topKIndices)
+	hiddenStates = hiddenStates.SILU(ctx, upStates)
+
+	experts := moe.Down.Weight.MulmatID(ctx, hiddenStates, topKIndices)
+	experts = experts.Mul(ctx, topKWeights)
+
+	nextStates := experts.View(ctx, 0, experts.Dim(0), experts.Stride(2), experts.Dim(2))
+	for i := 1; i < opts.numExpertsUsed; i++ {
+		nextStates = nextStates.Add(ctx, experts.View(ctx, i*experts.Stride(1), experts.Dim(0), experts.Stride(2), experts.Dim(2)))
+	}
+	return nextStates
+}
+
+func (moe *sparse) topKIndices(ctx ml.Context, scores ml.Tensor, opts *Options) ml.Tensor {
+	if moe.ExpProbsBias != nil {
+		scores = scores.Add(ctx, moe.ExpProbsBias)
+	}
+	topKIndices := scores.TopK(ctx, opts.numExpertsUsed)
+	return topKIndices
+}
+
+func (moe *sparse) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
+	residuals := hiddenStates
+
+	routerLogits := moe.Router.Forward(ctx, hiddenStates)
+	scores := routerLogits.Sigmoid(ctx)
+	topKIndices := moe.topKIndices(ctx, scores, opts)
+	topKWeights := scores.Reshape(ctx, 1, opts.numExperts, hiddenStates.Dim(1)).Rows(ctx, topKIndices)
+
+	if opts.normTopKProb {
+		topKWeights = topKWeights.Reshape(ctx, opts.numExpertsUsed, hiddenStates.Dim(1))
+		topKWeights = topKWeights.Div(ctx, topKWeights.SumRows(ctx))
+		topKWeights = topKWeights.Reshape(ctx, 1, opts.numExpertsUsed, hiddenStates.Dim(1))
+	}
+
+	topKWeights = topKWeights.Scale(ctx, float64(opts.routedScalingFactor))
+	hiddenStates = moe.Moe(ctx, hiddenStates, topKIndices, topKWeights, opts)
+	sharedExpertResult := moe.SharedExpert.Forward(ctx, residuals, opts)
+
+	hiddenStates = hiddenStates.Add(ctx, sharedExpertResult)
+	return hiddenStates
+}
+
+type dense struct {
+	Gate *nn.Linear `gguf:"ffn_gate"`
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+}
+
+func (mlp *dense) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
+	return mlp.Down.Forward(ctx, hiddenStates)
+}
+
+type Layer struct {
+	AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
+	Attention     *Attention
+
+	MLPNorm *nn.RMSNorm `gguf:"ffn_norm"`
+	MLP     MLP
+}
+
+func (t *Layer) Forward(ctx ml.Context, hiddenStates, positions, outputs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
+	residual := hiddenStates
+	hiddenStates = t.AttentionNorm.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = t.Attention.Forward(ctx, hiddenStates, positions, cache, opts)
+
+	if outputs != nil {
+		hiddenStates = hiddenStates.Rows(ctx, outputs)
+		residual = residual.Rows(ctx, outputs)
+	}
+
+	hiddenStates = hiddenStates.Add(ctx, residual)
+	residual = hiddenStates
+
+	hiddenStates = t.MLPNorm.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = t.MLP.Forward(ctx, hiddenStates, opts)
+	hiddenStates = hiddenStates.Add(ctx, residual)
+	return hiddenStates
+}
+
+type Model struct {
+	model.Base
+	model.BytePairEncoding
+
+	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
+	Layers         []Layer       `gguf:"blk"`
+
+	OutputNorm *nn.RMSNorm `gguf:"output_norm"`
+	Output     *nn.Linear  `gguf:"output,alt:token_embd"`
+
+	*Options
+}
+
+func New(c fs.Config) (model.Model, error) {
+	layers := make([]Layer, c.Uint("block_count"))
+
+	firstDenseLayerIndex := int(c.Uint("leading_dense_block_count"))
+	for i := range layers {
+		if i < firstDenseLayerIndex {
+			layers[i].MLP = &dense{}
+		} else {
+			layers[i].MLP = &sparse{}
+		}
+	}
+
+	keyLength := int(c.Uint("attention.key_length"))
+	valueLength := int(c.Uint("attention.value_length"))
+
+	kqScale := 1.0 / math.Sqrt(float64(keyLength))
+
+	var pre []string
+	switch c.String("tokenizer.ggml.pre") {
+	case "glm4":
+		pre = []string{
+			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
+		}
+	default:
+		return nil, model.ErrUnsupportedTokenizer
+	}
+
+	m := Model{
+		BytePairEncoding: model.NewBytePairEncoding(
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
+				Merges: c.Strings("tokenizer.ggml.merges"),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+				BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+				EOS: append(
+					[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
+					c.Ints("tokenizer.ggml.eos_token_ids")...,
+				),
+			},
+			pre...,
+		),
+		Layers: layers,
+		Options: &Options{
+			hiddenSize:     int(c.Uint("embedding_length")),
+			numHeads:       int(c.Uint("attention.head_count")),
+			numKVHeads:     int(c.Uint("attention.head_count_kv")),
+			eps:            c.Float("attention.layer_norm_rms_epsilon"),
+			ropeBase:       c.Float("rope.freq_base"),
+			numExperts:     int(c.Uint("expert_count")),
+			numExpertsUsed: int(c.Uint("expert_used_count")),
+			normTopKProb:   c.Bool("expert_weights_norm", true),
+
+			qLoraRank:     int(c.Uint("attention.q_lora_rank")),
+			kvLoraRank:    int(c.Uint("attention.kv_lora_rank")),
+			qkHeadDim:     keyLength,
+			vHeadDim:      valueLength,
+			qkRopeHeadDim: int(c.Uint("rope.dimension_count")),
+			qkNopeHeadDim: keyLength - int(c.Uint("rope.dimension_count")),
+			kqNopeHeadDim: keyLength - int(c.Uint("rope.dimension_count")),
+
+			routedScalingFactor: c.Float("expert_weights_scale"),
+
+			kqScale: kqScale,
+		},
+	}
+
+	m.Cache = kvcache.NewCausalCache(m.Shift)
+	return &m, nil
+}
+
+func (m Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	return m.applyRotaryPositionEmbeddings(ctx, key, shift), nil
+}
+
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
+
+	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
+
+	for i, layer := range m.Layers {
+		m.Cache.SetLayer(i)
+
+		var outputs ml.Tensor
+		if i == len(m.Layers)-1 {
+			outputs = batch.Outputs
+		}
+
+		hiddenStates = layer.Forward(ctx, hiddenStates, positions, outputs, m.Cache, m.Options)
+	}
+
+	hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.eps)
+	return m.Output.Forward(ctx, hiddenStates), nil
+}
+
+func init() {
+	model.Register("glm4moelite", New)
+}

model/models/lfm2/cache.go

@@ -0,0 +1,410 @@
+package lfm2
+
+import (
+	"slices"
+
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model/input"
+)
+
+var _ kvcache.Cache = (*HybridCache)(nil)
+
+// HybridCache stores:
+// - a standard causal KV cache for attention layers
+// - a per-sequence recurrent conv state for shortconv layers
+//
+// Conv state shape (per layer, per sequence): [dConv, hiddenSize] where dConv = L_cache - 1.
+// Stored internally as a tensor of shape [dConv * hiddenSize, maxSlots].
+type HybridCache struct {
+	kv *kvcache.Causal
+
+	backend      ml.Backend
+	dtype        ml.DType
+	maxSequences int
+
+	hiddenSize int
+	dConv      int
+
+	// slot mapping for recurrent state
+	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 // [dConv*hiddenSize, maxSlots]
+
+	// 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
+	// track any error from EnsureWritable to propagate later
+	writableError error
+}
+
+func NewHybridCache(shift func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error), hiddenSize, dConv int) *HybridCache {
+	return &HybridCache{
+		kv:         kvcache.NewCausalCache(shift),
+		hiddenSize: hiddenSize,
+		dConv:      dConv,
+		slotForSeq: make(map[int]int),
+		convCtxs:   make(map[int]ml.Context),
+		convStates: make(map[int]ml.Tensor),
+	}
+}
+
+func (c *HybridCache) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int) {
+	c.backend = backend
+	c.dtype = dtype
+	c.maxSequences = maxSequences
+
+	// 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()
+	}
+	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 shortconv.
+	// LFM2 shortconv assumes tokens form a [seq_tokens, seqs] grid.
+	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
+	// without modifying permanent state (slotForSeq, refCount)
+	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))
+		return nil
+	}
+
+	// Ensure slots exist for sequences in this batch
+	c.curSlots = c.curSlots[:0]
+	var newSlots []int // track newly allocated slots that need zeroing
+	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 conv state for newly allocated slots to clear stale data from previous sequences
+	if len(newSlots) > 0 {
+		c.zeroConvSlots(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
+
+	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) {
+	// Bounds check before freeing
+	if slot >= 0 && slot < c.maxSequences {
+		c.freeSlots = append(c.freeSlots, slot)
+	}
+}
+
+// zeroConvSlots zeros the conv state for the given slots across all layers.
+// This must be called when recycling slots to prevent stale state from affecting new sequences.
+func (c *HybridCache) zeroConvSlots(ctx ml.Context, slots []int) {
+	if len(slots) == 0 || len(c.convStates) == 0 {
+		return
+	}
+
+	// Use input context for creating tensors
+	inputCtx := ctx.Input()
+
+	// Create slot indices tensor
+	slotIndices := make([]int32, len(slots))
+	for i, s := range slots {
+		slotIndices[i] = int32(s)
+	}
+	slotsTensor := inputCtx.FromInts(slotIndices, len(slotIndices))
+
+	// Create zero tensor for the slots (SetRows requires F32 source)
+	zeros := inputCtx.Zeros(ml.DTypeF32, c.dConv*c.hiddenSize, len(slots))
+
+	// Zero each layer's conv state for these slots
+	for _, buf := range c.convStates {
+		ctx.Forward(buf.SetRows(ctx, zeros, slotsTensor))
+	}
+}
+
+// EnsureWritable ensures that sequences in the current batch have private (non-shared) conv slots.
+// Returns an error if slot allocation fails.
+func (c *HybridCache) EnsureWritable(ctx ml.Context) error {
+	for i, seq := range c.curSeqs {
+		slot, ok := c.slotForSeq[seq]
+		if !ok {
+			continue
+		}
+
+		// Bounds check
+		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
+
+		// Copy existing conv state for all initialized layers
+		for _, buf := range c.convStates {
+			// buf: [dConv*hiddenSize, maxSlots]
+			src := buf.Rows(ctx, ctx.Input().FromInts([]int32{int32(slot)}, 1))
+			// SetRows requires F32 source
+			srcF32 := src.Cast(ctx, ml.DTypeF32)
+			ctx.Forward(buf.SetRows(ctx, srcF32, ctx.Input().FromInts([]int32{int32(newSlot)}, 1)))
+		}
+	}
+
+	// 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) CopyPrefix(srcSeq, dstSeq int, prefixLen int32) {
+	// KV cache shares prefix metadata (no copy) which is correct for prefix reuse.
+	c.kv.CopyPrefix(srcSeq, dstSeq, prefixLen)
+
+	// For shortconv state we implement copy-on-write: dst shares the same slot as src.
+	// On the first write to dst, EnsureWritable will create a private slot.
+	if dstSlot, ok := c.slotForSeq[dstSeq]; ok {
+		// Bounds check before decrementing
+		if dstSlot >= 0 && dstSlot < len(c.refCount) {
+			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 {
+		// src may not have a slot yet; dst will allocate on demand
+		return
+	}
+
+	// Bounds check before incrementing
+	if srcSlot >= 0 && srcSlot < len(c.refCount) {
+		c.slotForSeq[dstSeq] = srcSlot
+		c.refCount[srcSlot]++
+	}
+}
+
+func (c *HybridCache) CanResume(seq int, pos int32) bool {
+	return c.kv.CanResume(seq, pos)
+}
+
+func (c *HybridCache) Remove(seq int, beginIndex, endIndex int32) error {
+	if err := c.kv.Remove(seq, beginIndex, endIndex); err != nil {
+		return err
+	}
+
+	// For recurrent state, any removal invalidates the state because
+	// the state at position N depends on all previous positions.
+	// Drop the slot mapping so it resets on next use.
+	slot, ok := c.slotForSeq[seq]
+	if !ok {
+		return nil
+	}
+
+	// Bounds check
+	if slot < 0 || slot >= len(c.refCount) {
+		delete(c.slotForSeq, seq)
+		return nil
+	}
+
+	c.refCount[slot]--
+	if c.refCount[slot] <= 0 {
+		c.refCount[slot] = 0
+		c.freeSlot(slot)
+	}
+	delete(c.slotForSeq, seq)
+
+	return nil
+}
+
+func (c *HybridCache) slotsTensor() ml.Tensor {
+	return c.curSlotsInput
+}
+
+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)
+	}
+
+	buf := c.convCtxs[layer].Zeros(c.dtype, c.dConv*c.hiddenSize, c.maxSequences)
+	c.convStates[layer] = buf
+	return buf
+}
+
+// ConvState returns the conv state for current batch sequences as shape [dConv, hiddenSize, nSeqs].
+// Returns an error if copy-on-write allocation fails.
+func (c *HybridCache) ConvState(ctx ml.Context, layer int) (ml.Tensor, error) {
+	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
+	}
+
+	if c.writableError != nil {
+		return nil, c.writableError
+	}
+
+	buf := c.convBuffer(ctx, layer)
+	cur := buf.Rows(ctx, c.slotsTensor())
+	return cur.Reshape(ctx, c.dConv, c.hiddenSize, c.numSeqs()), nil
+}
+
+// UpdateConvState writes a new conv state for current batch sequences.
+// newState must have shape [dConv, hiddenSize, nSeqs].
+func (c *HybridCache) UpdateConvState(ctx ml.Context, layer int, newState ml.Tensor) {
+	buf := c.convBuffer(ctx, layer)
+	src := newState.Reshape(ctx, c.dConv*c.hiddenSize, c.numSeqs())
+	// SetRows requires F32 source
+	srcF32 := src.Cast(ctx, ml.DTypeF32)
+	ctx.Forward(buf.SetRows(ctx, srcF32, c.slotsTensor()))
+}
+
+// IsSupportedForBatch returns true if the current batch layout supports shortconv.
+func (c *HybridCache) IsSupportedForBatch() bool {
+	return c.curSeqTokens > 0 && len(c.curSeqs) > 0
+}
+
+// Seqs returns the ordered unique sequences for the current forward pass.
+func (c *HybridCache) Seqs() []int {
+	return slices.Clone(c.curSeqs)
+}

model/models/lfm2/cache_test.go

@@ -0,0 +1,444 @@
+package lfm2
+
+import (
+	"testing"
+
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+)
+
+// TestHybridCache tests verify the slot management logic of HybridCache.
+// These tests focus on the recurrent state slot allocation, reference counting,
+// and copy-on-write semantics without requiring a full ML backend.
+
+// createSlotOnlyCache creates a HybridCache with only the slot management
+// fields initialized. Used to test slot logic in isolation.
+func createSlotOnlyCache(maxSequences int) *HybridCache {
+	return &HybridCache{
+		hiddenSize:   256,
+		dConv:        3,
+		maxSequences: maxSequences,
+		refCount:     make([]int, maxSequences),
+		freeSlots:    initFreeSlots(maxSequences),
+		slotForSeq:   make(map[int]int),
+		convCtxs:     make(map[int]ml.Context),
+		convStates:   make(map[int]ml.Tensor),
+	}
+}
+
+func initFreeSlots(n int) []int {
+	slots := make([]int, 0, n)
+	for i := n - 1; i >= 0; i-- {
+		slots = append(slots, i)
+	}
+	return slots
+}
+
+func TestHybridCache_SlotAllocation(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Verify initial state
+	if len(cache.freeSlots) != 4 {
+		t.Errorf("expected 4 free slots, got %d", len(cache.freeSlots))
+	}
+
+	// Allocate all slots
+	for range 4 {
+		slot, err := cache.allocSlot()
+		if err != nil {
+			t.Fatalf("allocSlot failed: %v", err)
+		}
+		cache.refCount[slot] = 1
+	}
+
+	// Should be full now
+	if len(cache.freeSlots) != 0 {
+		t.Errorf("expected 0 free slots, got %d", len(cache.freeSlots))
+	}
+
+	// Trying to allocate another should fail
+	_, err := cache.allocSlot()
+	if err != kvcache.ErrKvCacheFull {
+		t.Errorf("expected ErrKvCacheFull, got %v", err)
+	}
+}
+
+func TestHybridCache_SlotReuse(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Allocate a slot
+	slot1, _ := cache.allocSlot()
+	cache.refCount[slot1] = 1
+
+	// Free it
+	cache.refCount[slot1] = 0
+	cache.freeSlot(slot1)
+
+	// Allocate again - should get the same slot back (LIFO)
+	slot2, _ := cache.allocSlot()
+	if slot2 != slot1 {
+		t.Errorf("expected slot %d to be reused, got %d", slot1, slot2)
+	}
+}
+
+func TestHybridCache_SlotRefCounting_ShareSlot(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Allocate slot for seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Simulate sharing slot with seq 2 (copy-on-write style)
+	cache.slotForSeq[2] = slot1
+	cache.refCount[slot1]++
+
+	// Should share the same slot
+	if cache.slotForSeq[2] != slot1 {
+		t.Errorf("expected seq 2 to share slot %d, got %d", slot1, cache.slotForSeq[2])
+	}
+
+	// Ref count should be 2
+	if cache.refCount[slot1] != 2 {
+		t.Errorf("expected refCount 2, got %d", cache.refCount[slot1])
+	}
+}
+
+func TestHybridCache_SlotRefCounting_DecRef(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Allocate slot for seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Share with seq 2
+	cache.slotForSeq[2] = slot1
+	cache.refCount[slot1]++
+
+	// Unshare seq 2
+	cache.refCount[slot1]--
+	delete(cache.slotForSeq, 2)
+
+	// Ref count should be back to 1
+	if cache.refCount[slot1] != 1 {
+		t.Errorf("expected refCount 1 after unshare, got %d", cache.refCount[slot1])
+	}
+
+	// Seq 2 should no longer have a slot
+	if _, ok := cache.slotForSeq[2]; ok {
+		t.Error("seq 2 should not have a slot after unshare")
+	}
+}
+
+func TestHybridCache_SlotFreeWhenUnused(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	initialFreeSlots := len(cache.freeSlots)
+
+	// Allocate slot for seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Free the slot when refCount drops to 0
+	cache.refCount[slot1]--
+	if cache.refCount[slot1] <= 0 {
+		cache.refCount[slot1] = 0
+		cache.freeSlot(slot1)
+	}
+	delete(cache.slotForSeq, 1)
+
+	// Slot should be freed
+	if len(cache.freeSlots) != initialFreeSlots {
+		t.Errorf("expected %d free slots, got %d", initialFreeSlots, len(cache.freeSlots))
+	}
+
+	// Ref count should be 0
+	if cache.refCount[slot1] != 0 {
+		t.Errorf("expected refCount 0, got %d", cache.refCount[slot1])
+	}
+}
+
+func TestHybridCache_SlotOverwrite(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Allocate slots for seq 1 and seq 2
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	slot2, _ := cache.allocSlot()
+	cache.slotForSeq[2] = slot2
+	cache.refCount[slot2] = 1
+
+	initialFreeSlots := len(cache.freeSlots)
+
+	// Simulate overwriting seq 2's slot with slot1 (sharing)
+	// First free the old slot
+	cache.refCount[slot2]--
+	if cache.refCount[slot2] <= 0 {
+		cache.refCount[slot2] = 0
+		cache.freeSlot(slot2)
+	}
+	// Then share slot1
+	cache.slotForSeq[2] = slot1
+	cache.refCount[slot1]++
+
+	// Seq 2 should now share slot1
+	if cache.slotForSeq[2] != slot1 {
+		t.Errorf("expected seq 2 to share slot %d, got %d", slot1, cache.slotForSeq[2])
+	}
+
+	// Old slot2 should be freed
+	if len(cache.freeSlots) != initialFreeSlots+1 {
+		t.Errorf("expected %d free slots, got %d", initialFreeSlots+1, len(cache.freeSlots))
+	}
+}
+
+func TestHybridCache_BoundsChecking(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Test freeing invalid slot (should not panic)
+	cache.freeSlot(-1)
+	cache.freeSlot(100) // out of bounds
+
+	// freeSlot does bounds checking, so invalid slots should be ignored
+	if len(cache.freeSlots) != 4 {
+		t.Errorf("invalid slots should not affect free list, got %d slots", len(cache.freeSlots))
+	}
+}
+
+func TestHybridCache_MultipleSequences_RefCounting(t *testing.T) {
+	cache := createSlotOnlyCache(8)
+
+	// Allocate slot for seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Fork to seq 2, 3, 4 (all share slot1)
+	for _, seq := range []int{2, 3, 4} {
+		cache.slotForSeq[seq] = slot1
+		cache.refCount[slot1]++
+	}
+
+	// Ref count should be 4
+	if cache.refCount[slot1] != 4 {
+		t.Errorf("expected refCount 4, got %d", cache.refCount[slot1])
+	}
+
+	// Remove seq 2, 3
+	for _, seq := range []int{2, 3} {
+		delete(cache.slotForSeq, seq)
+		cache.refCount[slot1]--
+	}
+
+	if cache.refCount[slot1] != 2 {
+		t.Errorf("expected refCount 2, got %d", cache.refCount[slot1])
+	}
+
+	// Slot should still be allocated (not in free list)
+	found := false
+	for _, s := range cache.freeSlots {
+		if s == slot1 {
+			found = true
+			break
+		}
+	}
+	if found {
+		t.Error("slot1 should not be in free list yet")
+	}
+
+	// Remove remaining sequences
+	for _, seq := range []int{1, 4} {
+		delete(cache.slotForSeq, seq)
+		cache.refCount[slot1]--
+	}
+
+	if cache.refCount[slot1] != 0 {
+		t.Errorf("expected refCount 0, got %d", cache.refCount[slot1])
+	}
+}
+
+func TestHybridCache_ChainedSharing(t *testing.T) {
+	cache := createSlotOnlyCache(8)
+
+	// Create seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Share 1 -> 2
+	cache.slotForSeq[2] = slot1
+	cache.refCount[slot1]++
+
+	// Share 2 -> 3 (should still share slot1)
+	cache.slotForSeq[3] = cache.slotForSeq[2] // which is slot1
+	cache.refCount[slot1]++
+
+	// All should share slot1
+	if cache.slotForSeq[1] != slot1 || cache.slotForSeq[2] != slot1 || cache.slotForSeq[3] != slot1 {
+		t.Error("all sequences should share slot1")
+	}
+
+	if cache.refCount[slot1] != 3 {
+		t.Errorf("expected refCount 3, got %d", cache.refCount[slot1])
+	}
+}
+
+func TestHybridCache_CacheParameters(t *testing.T) {
+	cache := NewHybridCache(nil, 512, 5) // hiddenSize=512, dConv=5
+
+	if cache.hiddenSize != 512 {
+		t.Errorf("expected hiddenSize 512, got %d", cache.hiddenSize)
+	}
+	if cache.dConv != 5 {
+		t.Errorf("expected dConv 5, got %d", cache.dConv)
+	}
+}
+
+func TestHybridCache_NumSeqs(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Initially no sequences
+	if cache.numSeqs() != 0 {
+		t.Errorf("expected 0 seqs, got %d", cache.numSeqs())
+	}
+
+	// Manually set up current batch state
+	cache.curSeqs = []int{1, 2, 3}
+
+	if cache.numSeqs() != 3 {
+		t.Errorf("expected 3 seqs, got %d", cache.numSeqs())
+	}
+}
+
+func TestHybridCache_SeqTokens(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Initially 0
+	if cache.seqTokens() != 0 {
+		t.Errorf("expected 0 seqTokens, got %d", cache.seqTokens())
+	}
+
+	// Manually set up current batch state
+	cache.curSeqTokens = 16
+
+	if cache.seqTokens() != 16 {
+		t.Errorf("expected 16 seqTokens, got %d", cache.seqTokens())
+	}
+}
+
+// Test that Seqs returns a clone of curSeqs
+func TestHybridCache_Seqs_ReturnsClone(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	cache.curSeqs = []int{1, 2, 3}
+
+	seqs := cache.Seqs()
+
+	// Modify returned slice
+	seqs[0] = 999
+
+	// Original should be unchanged
+	if cache.curSeqs[0] != 1 {
+		t.Error("Seqs should return a clone, not the original slice")
+	}
+}
+
+func TestHybridCache_IsSupportedForBatch(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Initially not supported (no batch set up)
+	if cache.IsSupportedForBatch() {
+		t.Error("expected IsSupportedForBatch to be false initially")
+	}
+
+	// Set up a valid batch
+	cache.curSeqTokens = 1
+	cache.curSeqs = []int{1}
+
+	if !cache.IsSupportedForBatch() {
+		t.Error("expected IsSupportedForBatch to be true with valid batch")
+	}
+}
+
+func TestHybridCache_ZeroConvSlots_EmptyInputs(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// zeroConvSlots should handle empty slots without panicking
+	cache.zeroConvSlots(nil, nil)
+	cache.zeroConvSlots(nil, []int{})
+
+	// zeroConvSlots should handle empty convStates without panicking
+	cache.zeroConvSlots(nil, []int{0, 1, 2})
+}
+
+func TestHybridCache_SlotRecycling_TracksNewSlots(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Allocate slot for seq 1
+	slot1, _ := cache.allocSlot()
+	cache.slotForSeq[1] = slot1
+	cache.refCount[slot1] = 1
+
+	// Free the slot (simulating sequence removal)
+	cache.refCount[slot1]--
+	cache.freeSlot(slot1)
+	delete(cache.slotForSeq, 1)
+
+	// Verify slot is in free list
+	if len(cache.freeSlots) != 4 {
+		t.Errorf("expected 4 free slots after freeing, got %d", len(cache.freeSlots))
+	}
+
+	// Allocate for new seq 2 - should get recycled slot
+	slot2, _ := cache.allocSlot()
+	if slot2 != slot1 {
+		t.Errorf("expected recycled slot %d, got %d", slot1, slot2)
+	}
+
+	// This recycled slot would need zeroing in the real implementation
+	// The actual zeroing is tested via integration tests since it requires ML context
+}
+
+func TestHybridCache_NewSequence_GetsTrackedForZeroing(t *testing.T) {
+	cache := createSlotOnlyCache(4)
+
+	// Simulate the slot allocation flow from StartForward
+	// When a sequence doesn't have a slot, it gets allocated and tracked as "new"
+
+	newSlots := []int{}
+
+	// Seq 1 doesn't have a slot - allocate and track
+	seq := 1
+	if _, ok := cache.slotForSeq[seq]; !ok {
+		slot, err := cache.allocSlot()
+		if err != nil {
+			t.Fatalf("allocSlot failed: %v", err)
+		}
+		cache.slotForSeq[seq] = slot
+		cache.refCount[slot] = 1
+		newSlots = append(newSlots, slot)
+	}
+
+	// Verify newSlots contains the allocated slot
+	if len(newSlots) != 1 {
+		t.Errorf("expected 1 new slot, got %d", len(newSlots))
+	}
+
+	// Seq 1 already has a slot - should NOT be tracked as new
+	newSlots2 := []int{}
+	if _, ok := cache.slotForSeq[seq]; !ok {
+		slot, _ := cache.allocSlot()
+		cache.slotForSeq[seq] = slot
+		cache.refCount[slot] = 1
+		newSlots2 = append(newSlots2, slot)
+	}
+
+	// Verify no new slots for existing sequence
+	if len(newSlots2) != 0 {
+		t.Errorf("expected 0 new slots for existing sequence, got %d", len(newSlots2))
+	}
+}

model/models/lfm2/model.go

@@ -0,0 +1,253 @@
+package lfm2
+
+import (
+	"cmp"
+	"math"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/ml/nn/rope"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type Options struct {
+	hiddenSize       int
+	headDim, ropeDim int
+
+	eps, ropeBase, ropeScale float32
+
+	ropeType              string
+	originalContextLength int
+
+	// per-layer head counts (LFM2 alternates attention and recurrent layers)
+	numHeadsByLayer   []int
+	numKVHeadsByLayer []int
+}
+
+func (o Options) headDimValue() int {
+	// Head dim is shared across layers; fall back to first attention layer head count.
+	for _, h := range o.numHeadsByLayer {
+		if h > 0 {
+			return cmp.Or(o.headDim, o.hiddenSize/h)
+		}
+	}
+	return cmp.Or(o.headDim, o.hiddenSize)
+}
+
+func (o Options) applyRotaryPositionEmbeddings(ctx ml.Context, states, positions ml.Tensor) ml.Tensor {
+	opts := []func(*rope.Options){rope.WithTypeNeoX()}
+	if o.ropeType == "yarn" {
+		attnFactor := float32(1.0 / (1.0 + 0.1*math.Log(float64(o.ropeScale))))
+		opts = append(opts,
+			rope.WithOriginalContextLength(o.originalContextLength),
+			rope.WithExtrapolationFactor(1.),
+			rope.WithAttentionFactor(attnFactor),
+		)
+	}
+
+	headCount := 1
+	for _, h := range o.numHeadsByLayer {
+		if h > 0 {
+			headCount = h
+			break
+		}
+	}
+	return nn.RoPE(ctx, states, positions, cmp.Or(o.ropeDim, o.headDim, o.hiddenSize/headCount), o.ropeBase, 1./o.ropeScale, opts...)
+}
+
+type Model struct {
+	model.Base
+	model.TextProcessor
+
+	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
+	Layers         []Layer       `gguf:"blk"`
+	OutputNorm     *nn.RMSNorm   `gguf:"output_norm,alt:token_embd_norm"`
+	Output         *nn.Linear    `gguf:"output,alt:token_embd"`
+
+	Options
+}
+
+func New(c fs.Config) (model.Model, error) {
+	if c.Uint("expert_count") > 0 {
+		return nil, model.ErrUnsupportedModel
+	}
+
+	if c.String("tokenizer.ggml.model") != "gpt2" {
+		return nil, model.ErrUnsupportedTokenizer
+	}
+
+	vocabulary := model.Vocabulary{
+		Values: c.Strings("tokenizer.ggml.tokens"),
+		Scores: c.Floats("tokenizer.ggml.scores"),
+		Types:  c.Ints("tokenizer.ggml.token_type"),
+		Merges: c.Strings("tokenizer.ggml.merges"),
+		AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+		BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
+		AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+		EOS: append(
+			[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
+			c.Ints("tokenizer.ggml.eos_token_ids")...,
+		),
+	}
+
+	var pretokenizers []string
+	switch c.String("tokenizer.ggml.pre") {
+	case "default":
+		// use default BPE pretokenizer
+	default:
+		// llama-bpe style (default for LFM2)
+		pretokenizers = []string{
+			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
+		}
+	}
+
+	m := Model{
+		TextProcessor: model.NewBytePairEncoding(&vocabulary, pretokenizers...),
+		Layers:        make([]Layer, c.Uint("block_count")),
+		Options: Options{
+			hiddenSize:            int(c.Uint("embedding_length")),
+			headDim:               int(c.Uint("attention.key_length")),
+			ropeDim:               int(c.Uint("rope.dimension_count")),
+			eps:                   c.Float("attention.layer_norm_rms_epsilon"),
+			ropeType:              c.String("rope.scaling.type"),
+			ropeBase:              c.Float("rope.freq_base"),
+			ropeScale:             c.Float("rope.scaling.factor", 1),
+			originalContextLength: int(c.Uint("rope.scaling.original_context_length")),
+		},
+	}
+
+	type headCounts interface {
+		HeadCount() []uint64
+		HeadCountKV() []uint64
+	}
+	hc, ok := c.(headCounts)
+	if !ok {
+		return nil, model.ErrUnsupportedModel
+	}
+
+	headCount := hc.HeadCount()
+	headCountKV := hc.HeadCountKV()
+
+	m.numHeadsByLayer = make([]int, len(m.Layers))
+	m.numKVHeadsByLayer = make([]int, len(m.Layers))
+	for i := range m.Layers {
+		m.numHeadsByLayer[i] = int(headCount[i])
+		m.numKVHeadsByLayer[i] = int(headCountKV[i])
+
+		if m.numKVHeadsByLayer[i] == 0 {
+			m.Layers[i].Operator = &ShortConv{}
+		} else {
+			m.Layers[i].Operator = &Attention{}
+		}
+	}
+
+	lCache := int(c.Uint("shortconv.l_cache"))
+	dConv := max(0, lCache-1)
+	m.Cache = NewHybridCache(m.Shift, m.hiddenSize, dConv)
+	return &m, nil
+}
+
+type Operator interface {
+	Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache *HybridCache, layer int, opts *Options) ml.Tensor
+}
+
+type Attention struct {
+	Query     *nn.Linear  `gguf:"attn_q"`
+	QueryNorm *nn.RMSNorm `gguf:"attn_q_norm"`
+	Key       *nn.Linear  `gguf:"attn_k"`
+	KeyNorm   *nn.RMSNorm `gguf:"attn_k_norm"`
+	Value     *nn.Linear  `gguf:"attn_v"`
+	Output    *nn.Linear  `gguf:"attn_output,alt:attn_out"`
+}
+
+func (sa *Attention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache *HybridCache, layer int, opts *Options) ml.Tensor {
+	batchSize := hiddenStates.Dim(1)
+	headDim := opts.headDimValue()
+	numHeads := opts.numHeadsByLayer[layer]
+	numKVHeads := opts.numKVHeadsByLayer[layer]
+
+	query := sa.Query.Forward(ctx, hiddenStates)
+	key := sa.Key.Forward(ctx, hiddenStates)
+	value := sa.Value.Forward(ctx, hiddenStates)
+
+	query = query.Reshape(ctx, headDim, numHeads, batchSize)
+	key = key.Reshape(ctx, headDim, numKVHeads, batchSize)
+	value = value.Reshape(ctx, headDim, numKVHeads, batchSize)
+
+	query = sa.QueryNorm.Forward(ctx, query, opts.eps)
+	key = sa.KeyNorm.Forward(ctx, key, opts.eps)
+
+	query = opts.applyRotaryPositionEmbeddings(ctx, query, positions)
+	key = opts.applyRotaryPositionEmbeddings(ctx, key, positions)
+
+	attention := nn.Attention(ctx, query, key, value, 1./math.Sqrt(float64(headDim)), cache)
+	attention = attention.Reshape(ctx, attention.Dim(0)*attention.Dim(1), batchSize)
+	return sa.Output.Forward(ctx, attention)
+}
+
+type MLP struct {
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+	Gate *nn.Linear `gguf:"ffn_gate"`
+}
+
+func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *Options) ml.Tensor {
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx, mlp.Up.Forward(ctx, hiddenState))
+	return mlp.Down.Forward(ctx, hiddenState)
+}
+
+type Layer struct {
+	AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
+	Operator      Operator
+	MLPNorm       *nn.RMSNorm `gguf:"ffn_norm"`
+	MLP           *MLP
+}
+
+func (l *Layer) Forward(ctx ml.Context, layer int, hiddenState, positions, outputs ml.Tensor, cache *HybridCache, opts *Options) ml.Tensor {
+	residual := hiddenState
+
+	hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.Operator.Forward(ctx, hiddenState, positions, cache, layer, opts)
+
+	if outputs != nil {
+		hiddenState = hiddenState.Rows(ctx, outputs)
+		residual = residual.Rows(ctx, outputs)
+	}
+
+	hiddenState = hiddenState.Add(ctx, residual)
+	residual = hiddenState
+
+	hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.MLP.Forward(ctx, hiddenState, opts)
+	return hiddenState.Add(ctx, residual)
+}
+
+func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	return m.applyRotaryPositionEmbeddings(ctx, key, shift), nil
+}
+
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
+
+	hiddenState := m.TokenEmbedding.Forward(ctx, batch.Inputs)
+
+	for i, layer := range m.Layers {
+		m.Cache.SetLayer(i)
+
+		var outputs ml.Tensor
+		if i == len(m.Layers)-1 {
+			outputs = batch.Outputs
+		}
+
+		hiddenState = layer.Forward(ctx, i, hiddenState, positions, outputs, m.Cache.(*HybridCache), &m.Options)
+	}
+
+	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
+	return m.Output.Forward(ctx, hiddenState), nil
+}
+
+func init() {
+	model.Register("lfm2", New)
+}

model/models/lfm2/shortconv.go

@@ -0,0 +1,50 @@
+package lfm2
+
+import (
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+)
+
+type shortConvKernel struct {
+	Weight ml.Tensor `gguf:"weight"`
+}
+
+// ShortConv implements the LFM2 short-convolution block (GGML_OP_SSM_CONV) with a recurrent
+// state stored in the HybridCache.
+type ShortConv struct {
+	Conv    *shortConvKernel `gguf:"shortconv.conv"`
+	InProj  *nn.Linear       `gguf:"shortconv.in_proj"`
+	OutProj *nn.Linear       `gguf:"shortconv.out_proj"`
+}
+
+func (sc *ShortConv) Forward(ctx ml.Context, hiddenStates ml.Tensor, _ ml.Tensor, cache *HybridCache, layer int, opts *Options) ml.Tensor {
+	nSeqs := cache.numSeqs()
+	seqTokens := cache.seqTokens()
+	hiddenSize := hiddenStates.Dim(0)
+	if nSeqs <= 0 || seqTokens <= 0 || hiddenStates.Dim(1) != nSeqs*seqTokens {
+		panic("lfm2: unsupported batch layout for shortconv")
+	}
+
+	bcx := sc.InProj.Forward(ctx, hiddenStates).Reshape(ctx, 3*hiddenSize, seqTokens, nSeqs)
+
+	elementSize := bcx.Stride(0)
+	b := bcx.View(ctx, 0*hiddenSize*elementSize, hiddenSize, bcx.Stride(1), seqTokens, bcx.Stride(2), nSeqs)
+	c := bcx.View(ctx, 1*hiddenSize*elementSize, hiddenSize, bcx.Stride(1), seqTokens, bcx.Stride(2), nSeqs)
+	x := bcx.View(ctx, 2*hiddenSize*elementSize, hiddenSize, bcx.Stride(1), seqTokens, bcx.Stride(2), nSeqs)
+
+	bx := b.Mul(ctx, x).Permute(ctx, 1, 0, 2, 3)
+
+	state, err := cache.ConvState(ctx, layer)
+	if err != nil {
+		panic("lfm2: failed to get conv state: " + err.Error())
+	}
+	sx := state.Concat(ctx, bx, 0)
+
+	convOut := sx.SSMConv(ctx, sc.Conv.Weight)
+	y := c.Mul(ctx, convOut)
+
+	dConv := sx.Dim(0) - seqTokens
+	cache.UpdateConvState(ctx, layer, sx.Slice(ctx, 0, sx.Dim(0)-dConv, sx.Dim(0), 1))
+
+	return sc.OutProj.Forward(ctx, y.Reshape(ctx, hiddenSize, seqTokens*nSeqs))
+}

model/models/models.go

@@ -7,7 +7,9 @@ import (
 	_ "github.com/ollama/ollama/model/models/gemma2"
 	_ "github.com/ollama/ollama/model/models/gemma3"
 	_ "github.com/ollama/ollama/model/models/gemma3n"
+	_ "github.com/ollama/ollama/model/models/glm4moelite"
 	_ "github.com/ollama/ollama/model/models/gptoss"
+	_ "github.com/ollama/ollama/model/models/lfm2"
 	_ "github.com/ollama/ollama/model/models/llama"
 	_ "github.com/ollama/ollama/model/models/llama4"
 	_ "github.com/ollama/ollama/model/models/mistral3"

model/parsers/glm46.go

@@ -0,0 +1,410 @@
+package parsers
+
+import (
+	"context"
+	"encoding/xml"
+	"fmt"
+	"log/slog"
+	"strings"
+	"unicode"
+
+	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/logutil"
+)
+
+type glm46ParserState int
+
+const (
+	glm46ParserState_LookingForThinkingOpen glm46ParserState = iota
+	glm46ParserState_ThinkingStartedEatingWhitespace
+	glm46ParserState_CollectingThinking
+	glm46ParserState_ThinkingDoneEatingWhitespace
+	glm46ParserState_CollectingContent
+	glm46ParserState_ToolStartedEatingWhitespace
+	glm46ParserState_CollectingToolContent
+)
+
+const (
+	glm46ThinkingOpenTag  = "<think>"
+	glm46ThinkingCloseTag = "</think>"
+	glm46ToolOpenTag      = "<tool_call>"
+	glm46ToolCloseTag     = "</tool_call>"
+)
+
+type GLM46Parser struct {
+	state  glm46ParserState
+	buffer strings.Builder
+	tools  []api.Tool
+}
+
+func (p *GLM46Parser) HasToolSupport() bool {
+	return true
+}
+
+func (p *GLM46Parser) HasThinkingSupport() bool {
+	return true
+}
+
+// func (p *GLM46Parser) Init(tools []api.Tool, lastMessage *api.Message) []api.Tool {
+func (p *GLM46Parser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
+	p.tools = tools
+	return tools
+}
+
+type glm46Event interface {
+	isGLM46Event()
+}
+
+type glm46EventContent struct {
+	content string
+}
+
+func (glm46EventContent) isGLM46Event() {}
+
+type glm46EventRawToolCall struct {
+	raw string
+}
+
+func (glm46EventRawToolCall) isGLM46Event() {}
+
+type glm46EventThinkingContent struct {
+	content string
+}
+
+func (glm46EventThinkingContent) isGLM46Event() {}
+
+func (p *GLM46Parser) Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error) {
+	p.buffer.WriteString(s)
+	events := p.parseEvents()
+
+	var toolCalls []api.ToolCall
+	var contentSb strings.Builder
+	var thinkingSb strings.Builder
+
+	for _, event := range events {
+		switch event := event.(type) {
+		case glm46EventRawToolCall:
+			toolCall, err := parseGLM46ToolCall(event, p.tools)
+			if err != nil {
+				slog.Warn("glm-4.6 tool call parsing failed", "error", err)
+				return "", "", nil, err
+			}
+			toolCalls = append(toolCalls, toolCall)
+		case glm46EventThinkingContent:
+			thinkingSb.WriteString(event.content)
+		case glm46EventContent:
+			// TODO(drifkin): if the same turn contains multiple interleaved content
+			// events, we naively append them together here.
+			contentSb.WriteString(event.content)
+		}
+	}
+
+	return contentSb.String(), thinkingSb.String(), toolCalls, nil
+}
+
+func (p *GLM46Parser) parseEvents() []glm46Event {
+	var all []glm46Event
+
+	keepLooping := true
+	for keepLooping {
+		var events []glm46Event
+		events, keepLooping = p.eat()
+		if len(events) > 0 {
+			all = append(all, events...)
+		}
+	}
+
+	if len(all) > 0 {
+		slog.Log(context.TODO(), logutil.LevelTrace, "glm-4.6 events parsed", "events", all, "state", p.state, "buffer", p.buffer.String())
+	}
+
+	return all
+}
+
+// eatLeadingWhitespaceAndTransitionTo consumes leading whitespace from the buffer
+// and transitions to the next state. Returns (nil, false) if only whitespace remains
+// in the buffer (needs more input), or (nil, true) if we successfully transitioned.
+func (p *GLM46Parser) eatLeadingWhitespaceAndTransitionTo(nextState glm46ParserState) ([]glm46Event, bool) {
+	trimmed := strings.TrimLeftFunc(p.buffer.String(), unicode.IsSpace)
+	p.buffer.Reset()
+	if trimmed == "" {
+		return nil, false // Still only whitespace, keep waiting for more input
+	}
+	p.state = nextState
+	p.buffer.WriteString(trimmed)
+	return nil, true // Successfully transitioned
+}
+
+// glm46SplitAtTag splits the buffer at the given tag, returns the content before (trimmed of trailing whitespace),
+// the content after (optionally trimmed of leading whitespace), and updates the buffer
+func glm46SplitAtTag(p *GLM46Parser, tag string, trimAfter bool) (string, string) {
+	split := strings.SplitN(p.buffer.String(), tag, 2)
+	before := split[0]
+	before = strings.TrimRightFunc(before, unicode.IsSpace)
+	after := split[1]
+	if trimAfter {
+		after = strings.TrimLeftFunc(after, unicode.IsSpace)
+	}
+	p.buffer.Reset()
+	p.buffer.WriteString(after)
+	return before, after
+}
+
+func (p *GLM46Parser) eat() ([]glm46Event, bool) {
+	var events []glm46Event
+
+	switch p.state {
+	case glm46ParserState_LookingForThinkingOpen:
+		trimmed := strings.TrimLeftFunc(p.buffer.String(), unicode.IsSpace)
+		if strings.HasPrefix(trimmed, glm46ThinkingOpenTag) {
+			// Found <think> opening tag
+			after := strings.TrimPrefix(trimmed, glm46ThinkingOpenTag)
+			after = strings.TrimLeftFunc(after, unicode.IsSpace)
+			p.buffer.Reset()
+			p.buffer.WriteString(after)
+			if after == "" {
+				p.state = glm46ParserState_ThinkingStartedEatingWhitespace
+			} else {
+				p.state = glm46ParserState_CollectingThinking
+			}
+			return events, true
+		} else if strings.HasPrefix(glm46ThinkingOpenTag, trimmed) {
+			// Partial opening tag seen, keep accumulating
+			return events, false
+		} else if trimmed == "" {
+			// Only whitespace, keep accumulating
+			return events, false
+		} else {
+			// No thinking tag found, skip to content collection
+			p.state = glm46ParserState_CollectingContent
+			// Don't trim - we want to keep the original content
+			return events, true
+		}
+
+	case glm46ParserState_ThinkingStartedEatingWhitespace:
+		return p.eatLeadingWhitespaceAndTransitionTo(glm46ParserState_CollectingThinking)
+
+	case glm46ParserState_CollectingThinking:
+		acc := p.buffer.String()
+		if strings.Contains(acc, glm46ThinkingCloseTag) {
+			thinking, remaining := glm46SplitAtTag(p, glm46ThinkingCloseTag, true)
+			if len(thinking) > 0 {
+				events = append(events, glm46EventThinkingContent{content: thinking})
+			}
+			if remaining == "" {
+				p.state = glm46ParserState_ThinkingDoneEatingWhitespace
+			} else {
+				p.state = glm46ParserState_CollectingContent
+			}
+			return events, true
+		} else if overlapLen := overlap(acc, glm46ThinkingCloseTag); overlapLen > 0 {
+			// Partial closing tag - withhold it along with any trailing whitespace before it
+			beforePartialTag := acc[:len(acc)-overlapLen]
+			trailingWhitespaceLen := trailingWhitespaceLen(beforePartialTag)
+			ambiguousStart := len(beforePartialTag) - trailingWhitespaceLen
+
+			unambiguous := acc[:ambiguousStart]
+			ambiguous := acc[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, glm46EventThinkingContent{content: unambiguous})
+			}
+			return events, false
+		} else {
+			// Pure thinking content - withhold trailing whitespace (might precede closing tag)
+			whitespaceLen := trailingWhitespaceLen(acc)
+			ambiguousStart := len(acc) - whitespaceLen
+
+			unambiguous := acc[:ambiguousStart]
+			ambiguous := acc[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, glm46EventThinkingContent{content: unambiguous})
+			}
+			return events, false
+		}
+
+	case glm46ParserState_ThinkingDoneEatingWhitespace:
+		return p.eatLeadingWhitespaceAndTransitionTo(glm46ParserState_CollectingContent)
+
+	case glm46ParserState_CollectingContent:
+		if strings.Contains(p.buffer.String(), glm46ToolOpenTag) {
+			before, after := glm46SplitAtTag(p, glm46ToolOpenTag, true)
+			if len(before) > 0 {
+				events = append(events, glm46EventContent{content: before})
+			}
+			if after == "" {
+				p.state = glm46ParserState_ToolStartedEatingWhitespace
+			} else {
+				p.state = glm46ParserState_CollectingToolContent
+			}
+			return events, true
+		} else if overlapLen := overlap(p.buffer.String(), glm46ToolOpenTag); overlapLen > 0 {
+			beforePartialTag := p.buffer.String()[:len(p.buffer.String())-overlapLen]
+			trailingWhitespaceLen := trailingWhitespaceLen(beforePartialTag)
+			ambiguousStart := len(beforePartialTag) - trailingWhitespaceLen
+
+			unambiguous := p.buffer.String()[:ambiguousStart]
+			ambiguous := p.buffer.String()[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, glm46EventContent{content: unambiguous})
+			}
+			return events, false
+		} else {
+			whitespaceLen := trailingWhitespaceLen(p.buffer.String())
+			ambiguousStart := len(p.buffer.String()) - whitespaceLen
+
+			unambiguous := p.buffer.String()[:ambiguousStart]
+			ambiguous := p.buffer.String()[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, glm46EventContent{content: unambiguous})
+			}
+			return events, false
+		}
+
+	case glm46ParserState_ToolStartedEatingWhitespace:
+		return p.eatLeadingWhitespaceAndTransitionTo(glm46ParserState_CollectingToolContent)
+
+	case glm46ParserState_CollectingToolContent:
+		acc := p.buffer.String()
+		if strings.Contains(acc, glm46ToolCloseTag) {
+			toolContent, _ := glm46SplitAtTag(p, glm46ToolCloseTag, true)
+			if len(toolContent) == 0 {
+				slog.Warn("glm46 tool call closing tag found but no content before it")
+			}
+			events = append(events, glm46EventRawToolCall{raw: toolContent})
+			p.state = glm46ParserState_CollectingContent
+			return events, true
+		} else {
+			// Keep accumulating - tool calls are not streamed
+			// We just wait for the closing tag
+			return events, false
+		}
+
+	default:
+		panic("unreachable")
+	}
+}
+
+// GLMToolCallXML represents the structure of a GLM-4.6 tool call for XML parsing
+type GLMToolCallXML struct {
+	XMLName xml.Name `xml:"tool_call"`
+	Content string   `xml:",chardata"` // Function name (text nodes between tags)
+	Keys    []string `xml:"arg_key"`   // All arg_key elements in document order
+	Values  []string `xml:"arg_value"` // All arg_value elements in document order
+}
+
+// escapeGLM46Content escapes XML entities in text content while preserving arg_key/arg_value tags
+func escapeGLM46Content(s string) string {
+	var result strings.Builder
+	inTag := false
+
+	for i := range len(s) {
+		ch := s[i]
+
+		if ch == '<' {
+			// Check if this is a known tag
+			if strings.HasPrefix(s[i:], "<arg_key>") ||
+				strings.HasPrefix(s[i:], "</arg_key>") ||
+				strings.HasPrefix(s[i:], "<arg_value>") ||
+				strings.HasPrefix(s[i:], "</arg_value>") {
+				inTag = true
+			}
+		}
+
+		if inTag {
+			result.WriteByte(ch)
+			if ch == '>' {
+				inTag = false
+			}
+		} else {
+			// Escape special characters in text content
+			switch ch {
+			case '&':
+				result.WriteString("&amp;")
+			case '<':
+				result.WriteString("&lt;")
+			case '>':
+				result.WriteString("&gt;")
+			default:
+				result.WriteByte(ch)
+			}
+		}
+	}
+
+	return result.String()
+}
+
+func parseGLM46ToolCall(raw glm46EventRawToolCall, tools []api.Tool) (api.ToolCall, error) {
+	// Escape any unescaped entities in text content
+	// We need to escape text between tags, but not the tags themselves
+	escaped := escapeGLM46Content(raw.raw)
+
+	// Wrap the content in a root element to make it valid XML
+	xmlString := "<tool_call>" + escaped + "</tool_call>"
+
+	// Parse XML into struct
+	var parsed GLMToolCallXML
+	if err := xml.Unmarshal([]byte(xmlString), &parsed); err != nil {
+		return api.ToolCall{}, fmt.Errorf("failed to parse XML: %w", err)
+	}
+
+	// Extract and trim function name
+	functionName := strings.TrimSpace(parsed.Content)
+	if functionName == "" {
+		return api.ToolCall{}, fmt.Errorf("empty function name")
+	}
+
+	// Verify keys and values are paired correctly
+	if len(parsed.Keys) != len(parsed.Values) {
+		return api.ToolCall{}, fmt.Errorf("mismatched arg_key and arg_value counts: %d keys, %d values", len(parsed.Keys), len(parsed.Values))
+	}
+
+	// Find the matching tool to get parameter types
+	var matchedTool *api.Tool
+	for i := range tools {
+		if tools[i].Function.Name == functionName {
+			matchedTool = &tools[i]
+			break
+		}
+	}
+
+	// Build arguments map by pairing keys and values
+	toolCall := api.ToolCall{
+		Function: api.ToolCallFunction{
+			Name:      functionName,
+			Arguments: api.NewToolCallFunctionArguments(),
+		},
+	}
+
+	for i := range parsed.Keys {
+		key := strings.TrimSpace(parsed.Keys[i])
+		value := parsed.Values[i] // Don't trim here - parseValue handles it
+
+		// Look up parameter type
+		var paramType api.PropertyType
+		if matchedTool != nil && matchedTool.Function.Parameters.Properties != nil {
+			if prop, ok := matchedTool.Function.Parameters.Properties.Get(key); ok {
+				// Handle anyOf by collecting all types from the union
+				if len(prop.AnyOf) > 0 {
+					for _, anyOfProp := range prop.AnyOf {
+						paramType = append(paramType, anyOfProp.Type...)
+					}
+				} else {
+					paramType = prop.Type
+				}
+			}
+		}
+
+		// Parse value with type coercion
+		toolCall.Function.Arguments.Set(key, parseValue(value, paramType))
+	}
+
+	return toolCall, nil
+}

model/parsers/glm46_test.go

@@ -0,0 +1,862 @@
+package parsers
+
+import (
+	"encoding/xml"
+	"reflect"
+	"testing"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestGLM46ParserStreaming(t *testing.T) {
+	type step struct {
+		input      string
+		wantEvents []glm46Event
+	}
+
+	cases := []struct {
+		desc  string
+		steps []step
+		only  bool
+	}{
+		{
+			desc: "leading whitespace before think tag",
+			steps: []step{
+				{
+					input:      "   \n\t  ",
+					wantEvents: []glm46Event{},
+				},
+				{
+					input:      "<think>thinking</think>",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: "thinking"}},
+				},
+			},
+		},
+		{
+			desc: "think tag with whitespace inside",
+			steps: []step{
+				{
+					input: "<think>  \n  thinking content  \n  </think>regular content",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking content"},
+						glm46EventContent{content: "regular content"},
+					},
+				},
+			},
+		},
+		{
+			desc: "tool call with leading whitespace after opening tag",
+			steps: []step{
+				{
+					input: "<think></think><tool_call>  \n  test  \n  </tool_call>",
+					wantEvents: []glm46Event{
+						glm46EventRawToolCall{raw: "test"},
+					},
+				},
+			},
+		},
+		{
+			desc: "simple thinking then content",
+			steps: []step{
+				{
+					input: "<think>I am thinking</think>Now I respond",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "I am thinking"},
+						glm46EventContent{content: "Now I respond"},
+					},
+				},
+			},
+		},
+		{
+			desc: "streamed thinking content",
+			steps: []step{
+				{
+					input:      "<think>hello",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: "hello"}},
+				},
+				{
+					input:      " world",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: " world"}},
+				},
+				{
+					input: "</think>content",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "content"},
+					},
+				},
+			},
+		},
+		{
+			desc: "content before tool call",
+			steps: []step{
+				{
+					input: "<think>Let me call a tool</think>here is text<tool_call>",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "Let me call a tool"},
+						glm46EventContent{content: "here is text"},
+					},
+				},
+				{
+					input: "function_name\n<arg_key>param</arg_key>\n<arg_value>value</arg_value>\n</tool_call>",
+					wantEvents: []glm46Event{
+						glm46EventRawToolCall{raw: "function_name\n<arg_key>param</arg_key>\n<arg_value>value</arg_value>"},
+					},
+				},
+			},
+		},
+		{
+			desc: "tool call with content after",
+			steps: []step{
+				{
+					input: "<think>thinking</think><tool_call>test</tool_call>after tool",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking"},
+						glm46EventRawToolCall{raw: "test"},
+						glm46EventContent{content: "after tool"},
+					},
+				},
+			},
+		},
+		{
+			desc: "trailing whitespace between content and tool call is trimmed",
+			steps: []step{
+				{
+					input: "<think>thinking</think>content\n  \t  <tool_call>test</tool_call>",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking"},
+						glm46EventContent{content: "content"},
+						glm46EventRawToolCall{raw: "test"},
+					},
+				},
+			},
+		},
+		{
+			desc: "trailing whitespace between tool call and content is trimmed",
+			steps: []step{
+				{
+					input: "<think>think</think><tool_call>test</tool_call>\n\t  after",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "think"},
+						glm46EventRawToolCall{raw: "test"},
+						glm46EventContent{content: "after"},
+					},
+				},
+			},
+		},
+		{
+			desc: "split thinking close tag",
+			steps: []step{
+				{
+					input:      "<think>thinking content</th",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: "thinking content"}},
+				},
+				{
+					input: "ink>after",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "after"},
+					},
+				},
+			},
+		},
+		{
+			desc: "split thinking open tag",
+			steps: []step{
+				{
+					input:      "  <thi",
+					wantEvents: []glm46Event{},
+				},
+				{
+					input:      "nk>content</think>",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: "content"}},
+				},
+			},
+		},
+		{
+			desc: "split tool open tag",
+			steps: []step{
+				{
+					input:      "<think>think</think>content<tool",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: "think"}, glm46EventContent{content: "content"}},
+				},
+				{
+					input:      "_call>inside",
+					wantEvents: []glm46Event{},
+				},
+				{
+					input: "</tool_call>",
+					wantEvents: []glm46Event{
+						glm46EventRawToolCall{raw: "inside"},
+					},
+				},
+			},
+		},
+		{
+			desc: "partial thinking close tag fakeout",
+			steps: []step{
+				{
+					input:      "<think>content</th",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: "content"}},
+				},
+				{
+					input:      "ought more",
+					wantEvents: []glm46Event{glm46EventThinkingContent{content: "</thought more"}},
+				},
+			},
+		},
+		{
+			desc: "partial thinking open tag fakeout",
+			steps: []step{
+				{
+					input:      "  <thi",
+					wantEvents: []glm46Event{},
+				},
+				{
+					input: "nking is fun",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "  <thinking is fun"},
+					},
+				},
+			},
+		},
+		{
+			desc: "partial tool open tag fakeout",
+			steps: []step{
+				{
+					input: "<think></think>content\n<tool",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "content"},
+					},
+				},
+				{
+					input: " fakeout",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "\n<tool fakeout"},
+					},
+				},
+			},
+		},
+		{
+			desc: "partial tool close tag fakeout",
+			steps: []step{
+				{
+					input:      "<think></think><tool_call>content</tool",
+					wantEvents: []glm46Event{},
+				},
+				{
+					input:      " fakeout",
+					wantEvents: []glm46Event{},
+				},
+				{
+					input: "</tool_call>",
+					wantEvents: []glm46Event{
+						glm46EventRawToolCall{raw: "content</tool fakeout"},
+					},
+				},
+			},
+		},
+		{
+			desc: "empty thinking tag",
+			steps: []step{
+				{
+					input: "<think></think>content here",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "content here"},
+					},
+				},
+			},
+		},
+		{
+			desc: "multiple tool calls in sequence",
+			steps: []step{
+				{
+					input: "<think>think</think><tool_call>first</tool_call>between<tool_call>second</tool_call>end",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "think"},
+						glm46EventRawToolCall{raw: "first"},
+						glm46EventContent{content: "between"},
+						glm46EventRawToolCall{raw: "second"},
+						glm46EventContent{content: "end"},
+					},
+				},
+			},
+		},
+		{
+			desc: "no thinking tag - direct to content",
+			steps: []step{
+				{
+					input: "just content here",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "just content here"},
+					},
+				},
+			},
+		},
+		{
+			desc: "no thinking tag - skip to content then tool call",
+			steps: []step{
+				{
+					input: "Here's the answer:<tool_call>test</tool_call>done",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "Here's the answer:"},
+						glm46EventRawToolCall{raw: "test"},
+						glm46EventContent{content: "done"},
+					},
+				},
+			},
+		},
+		{
+			desc: "no thinking tag - whitespace preserved when no tags",
+			steps: []step{
+				{
+					input: "  \n  content with leading whitespace",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "  \n  content with leading whitespace"},
+					},
+				},
+			},
+		},
+		{
+			desc: "whitespace after think close tag gets eaten",
+			steps: []step{
+				{
+					input: "<think>thinking</think>  \n\t  content",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking"},
+						glm46EventContent{content: "content"},
+					},
+				},
+			},
+		},
+		{
+			desc: "whitespace after tool_call close tag gets eaten",
+			steps: []step{
+				{
+					input: "<think></think><tool_call>test</tool_call>  \n\t  content",
+					wantEvents: []glm46Event{
+						glm46EventRawToolCall{raw: "test"},
+						glm46EventContent{content: "content"},
+					},
+				},
+			},
+		},
+		{
+			desc: "thinking content withholds trailing whitespace (single chunk)",
+			steps: []step{
+				{
+					input: "<think>thinking content   ",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking content"},
+					},
+				},
+				{
+					input: "</think>after",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "after"},
+					},
+				},
+			},
+		},
+		{
+			desc: "thinking content withholds trailing whitespace with newlines",
+			steps: []step{
+				{
+					input: "<think>thinking\n\n  ",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking"},
+					},
+				},
+				{
+					input: "</think>content",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "content"},
+					},
+				},
+			},
+		},
+		{
+			desc: "thinking content trailing whitespace emitted when more content arrives",
+			steps: []step{
+				{
+					input: "<think>thinking   ",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking"},
+					},
+				},
+				{
+					input: "more thinking",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "   more thinking"},
+					},
+				},
+				{
+					input:      "</think>",
+					wantEvents: []glm46Event{},
+				},
+			},
+		},
+		{
+			desc: "thinking content withholds trailing whitespace before partial close tag",
+			steps: []step{
+				{
+					input: "<think>thinking   </th",
+					wantEvents: []glm46Event{
+						glm46EventThinkingContent{content: "thinking"},
+					},
+				},
+				{
+					input: "ink>content",
+					wantEvents: []glm46Event{
+						glm46EventContent{content: "content"},
+					},
+				},
+			},
+		},
+	}
+
+	anyOnlies := false
+	for _, tc := range cases {
+		if tc.only {
+			anyOnlies = true
+		}
+	}
+
+	for _, tc := range cases {
+		if anyOnlies && !tc.only {
+			continue
+		}
+
+		t.Run(tc.desc, func(t *testing.T) {
+			parser := GLM46Parser{}
+
+			for i, step := range tc.steps {
+				parser.buffer.WriteString(step.input)
+				gotEvents := parser.parseEvents()
+
+				if len(gotEvents) == 0 && len(step.wantEvents) == 0 {
+					// avoid deep equal on empty vs. nil slices
+					continue
+				}
+
+				if !reflect.DeepEqual(gotEvents, step.wantEvents) {
+					t.Errorf("step %d: input %q: got events %#v, want %#v", i, step.input, gotEvents, step.wantEvents)
+				}
+			}
+		})
+	}
+}
+
+// TestGLMToolCallXMLOrderPreservation verifies that xml.Unmarshal preserves
+// document order when collecting multiple elements with the same tag name into slices.
+// This is a critical assumption for the GLM-4.6 parser's struct-based approach.
+func TestGLMToolCallXMLOrderPreservation(t *testing.T) {
+	testCases := []struct {
+		name       string
+		xml        string
+		wantKeys   []string
+		wantValues []string
+	}{
+		{
+			name: "alternating keys and values",
+			xml: `<tool_call>
+function_name
+<arg_key>first</arg_key>
+<arg_value>A</arg_value>
+<arg_key>second</arg_key>
+<arg_value>B</arg_value>
+<arg_key>third</arg_key>
+<arg_value>C</arg_value>
+</tool_call>`,
+			wantKeys:   []string{"first", "second", "third"},
+			wantValues: []string{"A", "B", "C"},
+		},
+		{
+			name: "all keys then all values",
+			xml: `<tool_call>
+function_name
+<arg_key>key1</arg_key>
+<arg_key>key2</arg_key>
+<arg_key>key3</arg_key>
+<arg_value>val1</arg_value>
+<arg_value>val2</arg_value>
+<arg_value>val3</arg_value>
+</tool_call>`,
+			wantKeys:   []string{"key1", "key2", "key3"},
+			wantValues: []string{"val1", "val2", "val3"},
+		},
+		{
+			name: "mixed grouping",
+			xml: `<tool_call>
+function_name
+<arg_key>a</arg_key>
+<arg_value>1</arg_value>
+<arg_key>b</arg_key>
+<arg_key>c</arg_key>
+<arg_value>2</arg_value>
+<arg_value>3</arg_value>
+</tool_call>`,
+			wantKeys:   []string{"a", "b", "c"},
+			wantValues: []string{"1", "2", "3"},
+		},
+		{
+			name: "reverse order - all values then all keys",
+			xml: `<tool_call>
+function_name
+<arg_value>X</arg_value>
+<arg_value>Y</arg_value>
+<arg_value>Z</arg_value>
+<arg_key>x</arg_key>
+<arg_key>y</arg_key>
+<arg_key>z</arg_key>
+</tool_call>`,
+			wantKeys:   []string{"x", "y", "z"},
+			wantValues: []string{"X", "Y", "Z"},
+		},
+	}
+
+	for _, tc := range testCases {
+		t.Run(tc.name, func(t *testing.T) {
+			var parsed GLMToolCallXML
+			err := xml.Unmarshal([]byte(tc.xml), &parsed)
+			if err != nil {
+				t.Fatalf("failed to unmarshal XML: %v", err)
+			}
+
+			if !reflect.DeepEqual(parsed.Keys, tc.wantKeys) {
+				t.Errorf("Keys order mismatch:\ngot:  %v\nwant: %v", parsed.Keys, tc.wantKeys)
+			}
+
+			if !reflect.DeepEqual(parsed.Values, tc.wantValues) {
+				t.Errorf("Values order mismatch:\ngot:  %v\nwant: %v", parsed.Values, tc.wantValues)
+			}
+		})
+	}
+}
+
+func TestGLM46ToolCallParsing(t *testing.T) {
+	type testCase struct {
+		name         string
+		rawToolCall  string
+		tools        []api.Tool
+		wantToolCall api.ToolCall
+	}
+
+	cases := []testCase{
+		{
+			name:  "simple tool call",
+			tools: []api.Tool{},
+			rawToolCall: `get-current-weather
+<arg_key>location</arg_key>
+<arg_value>New York, NY</arg_value>
+<arg_key>unit</arg_key>
+<arg_value>celsius</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "get-current-weather",
+					Arguments: args(`{"location": "New York, NY", "unit": "celsius"}`),
+				},
+			},
+		},
+		{
+			name: "tool call with typed parameters",
+			tools: []api.Tool{
+				tool("calculate", map[string]api.ToolProperty{
+					"x":       {Type: api.PropertyType{"number"}},
+					"y":       {Type: api.PropertyType{"integer"}},
+					"enabled": {Type: api.PropertyType{"boolean"}},
+					"items":   {Type: api.PropertyType{"array"}},
+				}),
+			},
+			rawToolCall: `calculate
+<arg_key>x</arg_key>
+<arg_value>3.14</arg_value>
+<arg_key>y</arg_key>
+<arg_value>42</arg_value>
+<arg_key>enabled</arg_key>
+<arg_value>true</arg_value>
+<arg_key>items</arg_key>
+<arg_value>["a", "b", "c"]</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "calculate",
+					Arguments: args(`{"enabled": true, "items": ["a", "b", "c"], "x": 3.14, "y": 42}`),
+				},
+			},
+		},
+		{
+			name:  "function name with whitespace",
+			tools: []api.Tool{},
+			rawToolCall: `  get-weather
+<arg_key>city</arg_key>
+<arg_value>Paris</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "get-weather",
+					Arguments: args(`{"city": "Paris"}`),
+				},
+			},
+		},
+		{
+			name:  "values with special characters",
+			tools: []api.Tool{},
+			rawToolCall: `execute-command
+<arg_key>command</arg_key>
+<arg_value>ls && echo "done"</arg_value>
+<arg_key>message</arg_key>
+<arg_value>a < b and c > d</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "execute-command",
+					Arguments: args(`{"command": "ls && echo \"done\"", "message": "a < b and c > d"}`),
+				},
+			},
+		},
+		{
+			name:  "unicode in function names and values",
+			tools: []api.Tool{},
+			rawToolCall: `获取天气
+<arg_key>城市</arg_key>
+<arg_value>北京</arg_value>
+<arg_key>message</arg_key>
+<arg_value>Hello! 你好! 🌟</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "获取天气",
+					Arguments: args(`{"message": "Hello! 你好! 🌟", "城市": "北京"}`),
+				},
+			},
+		},
+		{
+			name:  "empty value",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>param1</arg_key>
+<arg_value></arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"param1": ""}`),
+				},
+			},
+		},
+		{
+			name:  "special chars in arg_key names",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>param<1></arg_key>
+<arg_value>value1</arg_value>
+<arg_key>a&b</arg_key>
+<arg_value>value2</arg_value>
+<arg_key>x>y</arg_key>
+<arg_value>value3</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"a&b": "value2", "param<1>": "value1", "x>y": "value3"}`),
+				},
+			},
+		},
+		{
+			name:  "multiple consecutive ampersands",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>param</arg_key>
+<arg_value>test &&&& more</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"param": "test &&&& more"}`),
+				},
+			},
+		},
+		{
+			name:  "mixed special chars together",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>param</arg_key>
+<arg_value><>&<>&</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"param": "<>&<>&"}`),
+				},
+			},
+		},
+		{
+			name:  "newlines and tabs in parameter values",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>multiline</arg_key>
+<arg_value>line1
+	indented line2
+line3</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"multiline": "line1\n\tindented line2\nline3"}`),
+				},
+			},
+		},
+		{
+			name:  "single and double quotes in values",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>quotes</arg_key>
+<arg_value>She said "Hello's there!"</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"quotes": "She said \"Hello's there!\""}`),
+				},
+			},
+		},
+		{
+			name:  "CDATA-like content that should be treated as text",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>cdata</arg_key>
+<arg_value><![CDATA[not actual cdata]]></arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"cdata": "<![CDATA[not actual cdata]]>"}`),
+				},
+			},
+		},
+		{
+			name:  "all special XML entities",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>entities</arg_key>
+<arg_value>&lt;&gt;&amp;&apos;&quot;</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"entities": "&lt;&gt;&amp;&apos;&quot;"}`),
+				},
+			},
+		},
+		{
+			name:  "order preservation with multiple parameters",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>first</arg_key>
+<arg_value>value1</arg_value>
+<arg_key>second</arg_key>
+<arg_value>value2</arg_value>
+<arg_key>third</arg_key>
+<arg_value>value3</arg_value>
+<arg_key>fourth</arg_key>
+<arg_value>value4</arg_value>
+<arg_key>fifth</arg_key>
+<arg_value>value5</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test-function",
+					Arguments: args(`{"fifth": "value5", "first": "value1", "fourth": "value4", "second": "value2", "third": "value3"}`),
+				},
+			},
+		},
+		{
+			name:  "order preservation with identical key names but different positions",
+			tools: []api.Tool{},
+			rawToolCall: `test-function
+<arg_key>param</arg_key>
+<arg_value>first occurrence</arg_value>
+<arg_key>other</arg_key>
+<arg_value>middle</arg_value>
+<arg_key>param</arg_key>
+<arg_value>second occurrence</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name: "test-function",
+					// Later occurrence should overwrite earlier one
+					Arguments: args(`{"other": "middle", "param": "second occurrence"}`),
+				},
+			},
+		},
+		{
+			name: "array with mixed types",
+			tools: []api.Tool{
+				tool("process", map[string]api.ToolProperty{
+					"items": {Type: api.PropertyType{"array"}},
+				}),
+			},
+			rawToolCall: `process
+<arg_key>items</arg_key>
+<arg_value>[1, "hello", true, null]</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "process",
+					Arguments: args(`{"items": [1, "hello", true, null]}`),
+				},
+			},
+		},
+		{
+			name: "empty array",
+			tools: []api.Tool{
+				tool("test", map[string]api.ToolProperty{
+					"tags": {Type: api.PropertyType{"array"}},
+				}),
+			},
+			rawToolCall: `test
+<arg_key>tags</arg_key>
+<arg_value>[]</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "test",
+					Arguments: args(`{"tags": []}`),
+				},
+			},
+		},
+		{
+			name: "anyOf array or string - with array of objects",
+			tools: []api.Tool{
+				tool("TodoWrite", map[string]api.ToolProperty{
+					"todos": {AnyOf: []api.ToolProperty{{Type: api.PropertyType{"array"}}, {Type: api.PropertyType{"string"}}}},
+				}),
+			},
+			// <tool_call>TodoWrite
+			// <arg_key>todos</arg_key>
+			// <arg_value>[{"content": "Set up HTML file and basic structure", "id": "1", "priority": "high", "status": "pending"}, {"content": "Create 3D scene with Three.js", "id": "2", "priority": "high", "status": "pending"}, {"content": "Implement terrain generation with blocks", "id": "3", "priority": "high", "status": "pending"}, {"content": "Add player controls (movement, camera)", "id": "4", "priority": "high", "status": "pending"}, {"content": "Implement block placement/destruction", "id": "5", "priority": "medium", "status": "pending"}, {"content": "Add lighting and textures", "id": "6", "priority": "medium", "status": "pending"}, {"content": "Test and optimize performance", "id": "7", "priority": "low", "status": "pending"}]</arg_value>
+			// </tool_call>
+			rawToolCall: `TodoWrite
+<arg_key>todos</arg_key>
+<arg_value>[{"content": "task 1", "status": "pending", "priority": "high", "id": "1"}, {"content": "task 2", "status": "completed", "priority": "low", "id": "2"}]</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "TodoWrite",
+					Arguments: args(`{"todos": [{"content": "task 1", "id": "1", "priority": "high", "status": "pending"}, {"content": "task 2", "id": "2", "priority": "low", "status": "completed"}]}`),
+				},
+			},
+		},
+		{
+			name: "anyOf array or string - with plain string",
+			tools: []api.Tool{
+				tool("TodoWrite", map[string]api.ToolProperty{
+					"todos": {Type: api.PropertyType{"array", "string"}},
+				}),
+			},
+			rawToolCall: `TodoWrite
+<arg_key>todos</arg_key>
+<arg_value>Error: could not load todos</arg_value>`,
+			wantToolCall: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "TodoWrite",
+					Arguments: args(`{"todos": "Error: could not load todos"}`),
+				},
+			},
+		},
+	}
+
+	for i, tc := range cases {
+		t.Run(tc.name, func(t *testing.T) {
+			gotToolCall, err := parseGLM46ToolCall(glm46EventRawToolCall{raw: tc.rawToolCall}, tc.tools)
+			if err != nil {
+				t.Errorf("case %d (%s): %v", i, tc.name, err)
+			}
+			if !toolCallEqual(gotToolCall, tc.wantToolCall) {
+				t.Errorf("case %d (%s): got tool call %#v, want %#v", i, tc.name, gotToolCall, tc.wantToolCall)
+			}
+		})
+	}
+}

model/parsers/glm47.go

@@ -0,0 +1,20 @@
+package parsers
+
+import "github.com/ollama/ollama/api"
+
+// GLM47Parser extends GLM46Parser with thinking-aware initialization.
+// GLM-4.7's prompt ends with <think> when thinking is enabled, so the parser
+// must start in CollectingThinking state (the model outputs thinking content directly).
+type GLM47Parser struct {
+	GLM46Parser
+}
+
+func (p *GLM47Parser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
+	p.tools = tools
+	// When thinking is enabled (nil or true), the prompt ends with <think>,
+	// so model output starts directly with thinking content (no opening tag).
+	if thinkValue == nil || thinkValue.Bool() {
+		p.state = glm46ParserState_CollectingThinking
+	}
+	return tools
+}

model/parsers/glm47_test.go

@@ -0,0 +1,99 @@
+package parsers
+
+import (
+	"reflect"
+	"testing"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestGLM47ParserAdd(t *testing.T) {
+	parser := GLM47Parser{}
+	parser.Init([]api.Tool{
+		tool("calculate", map[string]api.ToolProperty{
+			"count":   {Type: api.PropertyType{"integer"}},
+			"enabled": {Type: api.PropertyType{"boolean"}},
+		}),
+	}, nil, nil)
+
+	// When thinking is enabled (thinkValue nil), the prompt ends with <think>,
+	// so the model output does NOT include the opening <think> tag.
+	content, thinking, calls, err := parser.Add("plan</think>Answer<tool_call>calculate<arg_key>count</arg_key><arg_value>3</arg_value><arg_key>enabled</arg_key><arg_value>true</arg_value></tool_call>", true)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+	if thinking != "plan" {
+		t.Fatalf("expected thinking 'plan', got %q", thinking)
+	}
+	if content != "Answer" {
+		t.Fatalf("expected content 'Answer', got %q", content)
+	}
+	if len(calls) != 1 {
+		t.Fatalf("expected 1 tool call, got %d", len(calls))
+	}
+	expectedArgs := args(`{"count": 3, "enabled": true}`)
+	if !toolCallEqual(api.ToolCall{Function: api.ToolCallFunction{Arguments: calls[0].Function.Arguments}}, api.ToolCall{Function: api.ToolCallFunction{Arguments: expectedArgs}}) {
+		t.Fatalf("expected args %#v, got %#v", expectedArgs.ToMap(), calls[0].Function.Arguments.ToMap())
+	}
+}
+
+func TestGLM47ParserNoThinkingContent(t *testing.T) {
+	parser := GLM47Parser{}
+	parser.Init(nil, nil, nil)
+
+	// When thinking is enabled but model has no thinking to output,
+	// it should output </think> immediately followed by content.
+	content, thinking, calls, err := parser.Add("</think>Plain answer", true)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+	if thinking != "" {
+		t.Fatalf("expected empty thinking, got %q", thinking)
+	}
+	if content != "Plain answer" {
+		t.Fatalf("expected content 'Plain answer', got %q", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+}
+
+func TestGLM47ParserThinkingDisabled(t *testing.T) {
+	parser := GLM47Parser{}
+	// When thinking is disabled, parser stays in LookingForThinkingOpen state
+	parser.Init(nil, nil, &api.ThinkValue{Value: false})
+
+	// Model outputs plain content (prompt ended with </think>)
+	content, thinking, calls, err := parser.Add("Plain answer", true)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+	if thinking != "" {
+		t.Fatalf("expected empty thinking, got %q", thinking)
+	}
+	if content != "Plain answer" {
+		t.Fatalf("expected content 'Plain answer', got %q", content)
+	}
+	if len(calls) != 0 {
+		t.Fatalf("expected no tool calls, got %d", len(calls))
+	}
+}
+
+func TestGLM47ParserToolCallEscaping(t *testing.T) {
+	toolCall, err := parseGLM46ToolCall(glm46EventRawToolCall{raw: `exec
+<arg_key>expr</arg_key>
+<arg_value>a < b && c > d</arg_value>`}, nil)
+	if err != nil {
+		t.Fatalf("parse failed: %v", err)
+	}
+
+	expected := api.ToolCall{
+		Function: api.ToolCallFunction{
+			Name:      "exec",
+			Arguments: args(`{"expr": "a < b && c > d"}`),
+		},
+	}
+	if !reflect.DeepEqual(toolCall, expected) {
+		t.Fatalf("expected %#v, got %#v", expected, toolCall)
+	}
+}

model/parsers/lfm2.go

@@ -0,0 +1,498 @@
+package parsers
+
+import (
+	"encoding/json"
+	"errors"
+	"log/slog"
+	"strconv"
+	"strings"
+	"unicode"
+
+	"github.com/ollama/ollama/api"
+)
+
+type LFM2ParserState int
+
+const (
+	LFM2CollectingThinking LFM2ParserState = iota
+	LFM2CollectingContent
+	LFM2CollectingToolCalls
+)
+
+const (
+	lfm2ThinkingOpenTag  = "<think>"
+	lfm2ThinkingCloseTag = "</think>"
+	lfm2ToolCallStartTag = "<|tool_call_start|>"
+	lfm2ToolCallEndTag   = "<|tool_call_end|>"
+)
+
+type LFM2Parser struct {
+	state                    LFM2ParserState
+	buffer                   strings.Builder
+	hasThinkingSupport       bool
+	needsThinkingLeadingTrim bool // trim leading whitespace after <think> tag
+	needsContentLeadingTrim  bool // trim leading whitespace after </think> tag
+}
+
+func (p *LFM2Parser) HasToolSupport() bool {
+	return true
+}
+
+func (p *LFM2Parser) HasThinkingSupport() bool {
+	return p.hasThinkingSupport
+}
+
+func (p *LFM2Parser) setInitialState(lastMessage *api.Message, thinkValue *api.ThinkValue) {
+	prefill := lastMessage != nil && lastMessage.Role == "assistant"
+
+	// Check both model capability AND request preference
+	thinkingEnabled := p.HasThinkingSupport() && (thinkValue != nil && thinkValue.Bool())
+
+	if !thinkingEnabled {
+		p.state = LFM2CollectingContent
+		return
+	}
+
+	if prefill && lastMessage.Content != "" {
+		p.state = LFM2CollectingContent
+		return
+	}
+
+	p.state = LFM2CollectingThinking
+	p.needsThinkingLeadingTrim = true
+}
+
+func (p *LFM2Parser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
+	p.setInitialState(lastMessage, thinkValue)
+	return tools
+}
+
+type lfm2Event interface {
+	isLFM2Event()
+}
+
+type lfm2EventThinkingContent struct {
+	content string
+}
+
+type lfm2EventContent struct {
+	content string
+}
+
+type lfm2EventToolCall struct {
+	toolCall api.ToolCall
+}
+
+func (lfm2EventThinkingContent) isLFM2Event() {}
+func (lfm2EventContent) isLFM2Event()         {}
+func (lfm2EventToolCall) isLFM2Event()        {}
+
+func (p *LFM2Parser) Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error) {
+	p.buffer.WriteString(s)
+	events := p.parseEvents()
+
+	var toolCalls []api.ToolCall
+	var contentSb strings.Builder
+	var thinkingSb strings.Builder
+	for _, event := range events {
+		switch event := event.(type) {
+		case lfm2EventToolCall:
+			toolCalls = append(toolCalls, event.toolCall)
+		case lfm2EventThinkingContent:
+			thinkingSb.WriteString(event.content)
+		case lfm2EventContent:
+			contentSb.WriteString(event.content)
+		}
+	}
+
+	return contentSb.String(), thinkingSb.String(), toolCalls, nil
+}
+
+func (p *LFM2Parser) parseEvents() []lfm2Event {
+	var all []lfm2Event
+
+	keepLooping := true
+	for keepLooping {
+		var events []lfm2Event
+		events, keepLooping = p.eat()
+		if len(events) > 0 {
+			all = append(all, events...)
+		}
+	}
+
+	return all
+}
+
+func (p *LFM2Parser) eat() ([]lfm2Event, bool) {
+	var events []lfm2Event
+	bufStr := p.buffer.String()
+	if bufStr == "" {
+		return events, false
+	}
+
+	switch p.state {
+	case LFM2CollectingThinking:
+		// Strip opening <think> tag if present
+		if strings.HasPrefix(bufStr, lfm2ThinkingOpenTag) {
+			bufStr = bufStr[len(lfm2ThinkingOpenTag):]
+			p.needsThinkingLeadingTrim = true
+			p.buffer.Reset()
+			p.buffer.WriteString(bufStr)
+		}
+
+		// Trim leading whitespace after <think> tag (may span multiple chunks)
+		if p.needsThinkingLeadingTrim {
+			if trimmed := strings.TrimLeftFunc(bufStr, unicode.IsSpace); trimmed != bufStr {
+				bufStr = trimmed
+				p.buffer.Reset()
+				p.buffer.WriteString(bufStr)
+			}
+			// Clear flag once we have non-whitespace content or buffer is empty
+			if len(bufStr) > 0 {
+				p.needsThinkingLeadingTrim = false
+			}
+		}
+
+		if strings.Contains(bufStr, lfm2ThinkingCloseTag) { // thinking[</think>] -> content
+			split := strings.SplitN(bufStr, lfm2ThinkingCloseTag, 2)
+			thinking := split[0]
+			thinking = strings.TrimRightFunc(thinking, unicode.IsSpace)
+
+			remaining := split[1]
+			remaining = strings.TrimLeftFunc(remaining, unicode.IsSpace)
+
+			p.buffer.Reset()
+			p.buffer.WriteString(remaining)
+			p.state = LFM2CollectingContent
+			p.needsThinkingLeadingTrim = false
+			// Set flag to trim any additional whitespace that may arrive in later chunks
+			p.needsContentLeadingTrim = len(remaining) == 0
+
+			if len(thinking) > 0 {
+				events = append(events, lfm2EventThinkingContent{content: thinking})
+			}
+			return events, true
+		} else if overlapLen := overlap(bufStr, lfm2ThinkingCloseTag); overlapLen > 0 { // partial </think>
+			beforePartialTag := bufStr[:len(bufStr)-overlapLen]
+			trailingLen := trailingWhitespaceLen(beforePartialTag)
+			ambiguousStart := len(beforePartialTag) - trailingLen
+
+			unambiguous := bufStr[:ambiguousStart]
+			ambiguous := bufStr[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, lfm2EventThinkingContent{content: unambiguous})
+			}
+			return events, false
+		} else { // otherwise its thinking content
+			whitespaceLen := trailingWhitespaceLen(bufStr)
+			ambiguousStart := len(bufStr) - whitespaceLen
+
+			unambiguous := bufStr[:ambiguousStart]
+			ambiguous := bufStr[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, lfm2EventThinkingContent{content: unambiguous})
+			}
+			return events, false
+		}
+
+	case LFM2CollectingContent:
+		// Trim leading whitespace after </think> tag (may span multiple chunks)
+		if p.needsContentLeadingTrim {
+			if trimmed := strings.TrimLeftFunc(bufStr, unicode.IsSpace); trimmed != bufStr {
+				bufStr = trimmed
+				p.buffer.Reset()
+				p.buffer.WriteString(bufStr)
+			}
+			// Clear flag once we have non-whitespace content
+			if len(bufStr) > 0 {
+				p.needsContentLeadingTrim = false
+			}
+		}
+
+		if strings.Contains(bufStr, lfm2ToolCallStartTag) { // content[<|tool_call_start|>] -> tool calls
+			split := strings.SplitN(bufStr, lfm2ToolCallStartTag, 2)
+			contentBefore := strings.TrimRightFunc(split[0], unicode.IsSpace)
+			remaining := split[1]
+
+			p.buffer.Reset()
+			p.buffer.WriteString(remaining)
+			p.state = LFM2CollectingToolCalls
+
+			if len(contentBefore) > 0 {
+				events = append(events, lfm2EventContent{content: contentBefore})
+			}
+			return events, true
+		} else { // otherwise its content
+			p.buffer.Reset()
+			if len(bufStr) > 0 {
+				events = append(events, lfm2EventContent{content: bufStr})
+			}
+			return events, false
+		}
+
+	case LFM2CollectingToolCalls:
+		// Look for complete tool call JSON between tags
+		if idx := strings.Index(bufStr, lfm2ToolCallEndTag); idx != -1 {
+			toolCallContent := bufStr[:idx]
+
+			if toolCalls, err := p.parseToolCallsContent(toolCallContent); err == nil && len(toolCalls) > 0 {
+				remaining := bufStr[idx+len(lfm2ToolCallEndTag):]
+
+				// Check if there's another tool call
+				if strings.HasPrefix(remaining, lfm2ToolCallStartTag) {
+					remaining = remaining[len(lfm2ToolCallStartTag):]
+				} else {
+					// No more tool calls, go back to content
+					remaining = strings.TrimLeftFunc(remaining, unicode.IsSpace)
+					p.state = LFM2CollectingContent
+				}
+
+				p.buffer.Reset()
+				p.buffer.WriteString(remaining)
+
+				for _, tc := range toolCalls {
+					events = append(events, lfm2EventToolCall{toolCall: tc})
+				}
+				return events, true
+			} else if err != nil {
+				slog.Warn("lfm2 tool call parsing failed", "error", err, "content", toolCallContent)
+			}
+		}
+
+		return events, false
+	}
+
+	return events, false
+}
+
+// parseToolCallsContent parses one or more tool calls from content
+// Supports JSON format and Python-style format including multiple calls: [func1(...),func2(...)]
+func (p *LFM2Parser) parseToolCallsContent(content string) ([]api.ToolCall, error) {
+	content = strings.TrimSpace(content)
+
+	// Try JSON format first: {"name": "func", "arguments": {...}}
+	var parsed struct {
+		Name      string          `json:"name"`
+		Arguments json.RawMessage `json:"arguments"`
+	}
+
+	if err := json.Unmarshal([]byte(content), &parsed); err == nil && parsed.Name != "" {
+		var args api.ToolCallFunctionArguments
+		if len(parsed.Arguments) > 0 {
+			if err := json.Unmarshal(parsed.Arguments, &args); err != nil {
+				return nil, err
+			}
+		} else {
+			args = api.NewToolCallFunctionArguments()
+		}
+
+		return []api.ToolCall{{
+			Function: api.ToolCallFunction{
+				Name:      parsed.Name,
+				Arguments: args,
+			},
+		}}, nil
+	}
+
+	// Try Python-style format: [func(arg1='val1'),func2(arg2='val2')] or func(arg1='val1')
+	return p.parsePythonStyleToolCalls(content)
+}
+
+// parsePythonStyleToolCalls parses one or more Python-style tool calls
+// Examples: [bash(command='ls'),bash(command='pwd')] or bash(command='ls')
+func (p *LFM2Parser) parsePythonStyleToolCalls(content string) ([]api.ToolCall, error) {
+	content = strings.TrimSpace(content)
+
+	// Strip outer brackets if present: [func(...)] -> func(...)
+	if strings.HasPrefix(content, "[") && strings.HasSuffix(content, "]") {
+		content = content[1 : len(content)-1]
+	}
+
+	var toolCalls []api.ToolCall
+
+	// Parse multiple function calls separated by commas at the top level
+	for len(content) > 0 {
+		content = strings.TrimSpace(content)
+		if content == "" {
+			break
+		}
+
+		// Skip leading comma from previous iteration
+		if strings.HasPrefix(content, ",") {
+			content = strings.TrimSpace(content[1:])
+			if content == "" {
+				break
+			}
+		}
+
+		// Find function name
+		parenIdx := strings.Index(content, "(")
+		if parenIdx == -1 {
+			return nil, errors.New("invalid tool call: no opening parenthesis")
+		}
+
+		funcName := strings.TrimSpace(content[:parenIdx])
+		if funcName == "" {
+			return nil, errors.New("invalid tool call: empty function name")
+		}
+
+		// Find matching closing parenthesis
+		closeIdx := findMatchingParen(content, parenIdx)
+		if closeIdx == -1 {
+			return nil, errors.New("invalid tool call: no matching closing parenthesis")
+		}
+
+		argsStr := content[parenIdx+1 : closeIdx]
+		args := api.NewToolCallFunctionArguments()
+
+		if argsStr != "" {
+			if err := parsePythonArgs(argsStr, &args); err != nil {
+				return nil, err
+			}
+		}
+
+		toolCalls = append(toolCalls, api.ToolCall{
+			Function: api.ToolCallFunction{
+				Name:      funcName,
+				Arguments: args,
+			},
+		})
+
+		// Move past this function call
+		content = content[closeIdx+1:]
+	}
+
+	if len(toolCalls) == 0 {
+		return nil, errors.New("no tool calls found")
+	}
+
+	return toolCalls, nil
+}
+
+// findMatchingParen finds the index of the closing parenthesis matching the one at openIdx
+// Returns -1 if not found. Handles nested parentheses and quoted strings.
+func findMatchingParen(s string, openIdx int) int {
+	depth := 1
+	i := openIdx + 1
+	for i < len(s) && depth > 0 {
+		switch s[i] {
+		case '(':
+			depth++
+		case ')':
+			depth--
+			if depth == 0 {
+				return i
+			}
+		case '\'', '"':
+			// Skip quoted string
+			quote := s[i]
+			i++
+			for i < len(s) && s[i] != quote {
+				if s[i] == '\\' && i+1 < len(s) {
+					i++ // skip escaped char
+				}
+				i++
+			}
+		}
+		i++
+	}
+	return -1
+}
+
+// parseToolCallContent parses a single tool call (for backward compatibility with tests)
+func (p *LFM2Parser) parseToolCallContent(content string) (api.ToolCall, error) {
+	calls, err := p.parseToolCallsContent(content)
+	if err != nil {
+		return api.ToolCall{}, err
+	}
+	if len(calls) == 0 {
+		return api.ToolCall{}, errors.New("no tool call found")
+	}
+	return calls[0], nil
+}
+
+// parsePythonArgs parses Python-style keyword arguments: key='value', key2="value2"
+func parsePythonArgs(argsStr string, args *api.ToolCallFunctionArguments) error {
+	// Simple state machine to parse key='value' pairs
+	// Handles: command='ls', flag="-la", count=42, enabled=true
+	var key string
+	i := 0
+
+	for i < len(argsStr) {
+		// Skip whitespace
+		for i < len(argsStr) && (argsStr[i] == ' ' || argsStr[i] == '\t' || argsStr[i] == '\n') {
+			i++
+		}
+		if i >= len(argsStr) {
+			break
+		}
+
+		// Parse key
+		keyStart := i
+		for i < len(argsStr) && argsStr[i] != '=' && argsStr[i] != ',' {
+			i++
+		}
+		if i >= len(argsStr) || argsStr[i] != '=' {
+			return errors.New("invalid argument: expected '='")
+		}
+		key = strings.TrimSpace(argsStr[keyStart:i])
+		i++ // skip '='
+
+		// Skip whitespace after =
+		for i < len(argsStr) && (argsStr[i] == ' ' || argsStr[i] == '\t') {
+			i++
+		}
+
+		// Parse value
+		var value string
+		if i < len(argsStr) && (argsStr[i] == '\'' || argsStr[i] == '"') {
+			// Quoted string
+			quote := argsStr[i]
+			i++
+			valueStart := i
+			for i < len(argsStr) && argsStr[i] != quote {
+				if argsStr[i] == '\\' && i+1 < len(argsStr) {
+					i += 2 // skip escaped char
+				} else {
+					i++
+				}
+			}
+			value = argsStr[valueStart:i]
+			if i < len(argsStr) {
+				i++ // skip closing quote
+			}
+			args.Set(key, value)
+		} else {
+			// Unquoted value (number, bool, etc)
+			valueStart := i
+			for i < len(argsStr) && argsStr[i] != ',' {
+				i++
+			}
+			value = strings.TrimSpace(argsStr[valueStart:i])
+
+			// Try to parse as number or bool
+			if v, err := strconv.ParseInt(value, 10, 64); err == nil {
+				args.Set(key, v)
+			} else if v, err := strconv.ParseFloat(value, 64); err == nil {
+				args.Set(key, v)
+			} else if value == "true" {
+				args.Set(key, true)
+			} else if value == "false" {
+				args.Set(key, false)
+			} else {
+				args.Set(key, value)
+			}
+		}
+
+		// Skip comma and whitespace
+		for i < len(argsStr) && (argsStr[i] == ',' || argsStr[i] == ' ' || argsStr[i] == '\t' || argsStr[i] == '\n') {
+			i++
+		}
+	}
+
+	return nil
+}

model/parsers/parsers.go

@@ -68,6 +68,12 @@ func ParserForName(name string) Parser {
 		return &Nemotron3NanoParser{}
 	case "functiongemma":
 		return &FunctionGemmaParser{}
+	case "glm-4.7":
+		return &GLM47Parser{}
+	case "lfm2":
+		return &LFM2Parser{hasThinkingSupport: false}
+	case "lfm2-thinking":
+		return &LFM2Parser{hasThinkingSupport: true}
 	default:
 		return nil
 	}

model/parsers/testhelpers_test.go

@@ -96,3 +96,11 @@ func testArgs(m map[string]any) api.ToolCallFunctionArguments {
 	}
 	return args
 }
+
+func args(s string) api.ToolCallFunctionArguments {
+	var result api.ToolCallFunctionArguments
+	if err := json.Unmarshal([]byte(s), &result); err != nil {
+		panic("invalid JSON in args(): " + err.Error())
+	}
+	return result
+}

model/renderers/glm46.go

@@ -0,0 +1,110 @@
+package renderers
+
+import (
+	"encoding/json"
+	"fmt"
+	"strings"
+
+	"github.com/ollama/ollama/api"
+)
+
+type GLM46Renderer struct{}
+
+func (r *GLM46Renderer) Render(messages []api.Message, tools []api.Tool, thinkValue *api.ThinkValue) (string, error) {
+	var sb strings.Builder
+
+	sb.WriteString("[gMASK]<sop>")
+
+	var lastUserIndex int
+	for i, message := range messages {
+		if message.Role == "user" {
+			lastUserIndex = i
+		}
+	}
+
+	if len(tools) > 0 {
+		sb.WriteString("<|system|>\n")
+		sb.WriteString("# Tools\n\n")
+		sb.WriteString("You may call one or more functions to assist with the user query.\n\n")
+		sb.WriteString("You are provided with function signatures within <tools></tools> XML tags:\n")
+		sb.WriteString("<tools>\n")
+		for _, tool := range tools {
+			d, _ := json.Marshal(tool)
+			sb.WriteString(string(d) + "\n")
+		}
+		sb.WriteString("</tools>\n\n")
+		sb.WriteString("For each function call, output the function name and arguments within the following XML format:\n")
+		sb.WriteString("<tool_call>{function-name}\n")
+		sb.WriteString("<arg_key>{arg-key-1}</arg_key>\n")
+		sb.WriteString("<arg_value>{arg-value-1}</arg_value>\n")
+		sb.WriteString("<arg_key>{arg-key-2}</arg_key>\n")
+		sb.WriteString("<arg_value>{arg-value-2}</arg_value>\n")
+		sb.WriteString("...\n")
+		sb.WriteString("</tool_call>")
+	}
+
+	for i, message := range messages {
+		switch message.Role {
+		case "user":
+			sb.WriteString("<|user|>\n")
+			sb.WriteString(message.Content)
+			if thinkValue != nil && !thinkValue.Bool() && !strings.HasSuffix(message.Content, "/nothink") {
+				sb.WriteString("/nothink")
+			}
+		case "assistant":
+			sb.WriteString("<|assistant|>")
+			if i > lastUserIndex {
+				if message.Thinking != "" {
+					sb.WriteString("\n<think>" + message.Thinking + "</think>")
+				} else {
+					sb.WriteString("\n<think></think>")
+				}
+			}
+			if message.Content != "" {
+				sb.WriteString("\n" + message.Content)
+			}
+			if len(message.ToolCalls) > 0 {
+				for _, toolCall := range message.ToolCalls {
+					sb.WriteString("\n<tool_call>" + toolCall.Function.Name + "\n")
+					for key, value := range toolCall.Function.Arguments.All() {
+						sb.WriteString("<arg_key>" + key + "</arg_key>\n")
+
+						var valueStr string
+						if str, ok := value.(string); ok {
+							valueStr = str
+						} else {
+							jsonBytes, err := json.Marshal(value)
+							if err != nil {
+								valueStr = fmt.Sprintf("%v", value)
+							} else {
+								valueStr = string(jsonBytes)
+							}
+						}
+
+						sb.WriteString("<arg_value>" + valueStr + "</arg_value>\n")
+					}
+
+					sb.WriteString("</tool_call>")
+				}
+			}
+		case "tool":
+			if i == 0 || messages[i-1].Role != "tool" {
+				sb.WriteString("<|observation|>")
+			}
+			sb.WriteString("\n<tool_response>\n")
+			sb.WriteString(message.Content)
+			sb.WriteString("\n</tool_response>")
+		case "system":
+			sb.WriteString("<|system|>\n")
+			sb.WriteString(message.Content)
+		}
+	}
+
+	// Add generation prompt
+	sb.WriteString("<|assistant|>")
+	if thinkValue != nil && !thinkValue.Bool() {
+		sb.WriteString("\n<think></think>\n")
+	}
+
+	return sb.String(), nil
+}

model/renderers/glm46_test.go

@@ -0,0 +1,223 @@
+package renderers
+
+import (
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+	"github.com/ollama/ollama/api"
+)
+
+func TestGLM46Renderer(t *testing.T) {
+	tests := []struct {
+		name       string
+		messages   []api.Message
+		tools      []api.Tool
+		thinkValue *api.ThinkValue
+		expected   string
+		skip       string
+	}{
+		{
+			name: "basic",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello, how are you?"},
+			},
+			expected: `[gMASK]<sop><|user|>
+Hello, how are you?<|assistant|>`,
+		},
+		{
+			name: "basic with system message",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant."},
+				{Role: "user", Content: "Hello, how are you?"},
+			},
+			expected: `[gMASK]<sop><|system|>
+You are a helpful assistant.<|user|>
+Hello, how are you?<|assistant|>`,
+		},
+		{
+			name: "basic with user assistant user",
+			messages: []api.Message{
+				{Role: "user", Content: "What is the capital of France?"},
+				{Role: "assistant", Thinking: "Let me analyze the request...", Content: "The capital of France is Paris."},
+				{Role: "user", Content: "Fantastic!"},
+			},
+			expected: `[gMASK]<sop><|user|>
+What is the capital of France?<|assistant|>
+The capital of France is Paris.<|user|>
+Fantastic!<|assistant|>`,
+		},
+		{
+			skip: "tool call ordering not guaranteed yet",
+			name: "tools",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant with access to tools."},
+				{Role: "user", Content: "What is the weather like in Tokyo?"},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get the current weather in a given location",
+						Parameters: api.ToolFunctionParameters{
+							Type:       "object",
+							Required:   []string{"location"},
+							Properties: propsMap(`{"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}}`),
+						},
+					},
+				},
+			},
+			expected: `[gMASK]<sop><|system|>
+# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{"type":"function","function":{"name":"get_weather","description":"Get the current weather in a given location","parameters":{"type":"object","required":["location"],"properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","description":"","enum":["celsius","fahrenheit"]}}}}}
+</tools>
+
+For each function call, output the function name and arguments within the following XML format:
+<tool_call>{function-name}
+<arg_key>{arg-key-1}</arg_key>
+<arg_value>{arg-value-1}</arg_value>
+<arg_key>{arg-key-2}</arg_key>
+<arg_value>{arg-value-2}</arg_value>
+...
+</tool_call><|system|>
+You are a helpful assistant with access to tools.<|user|>
+What is the weather like in Tokyo?<|assistant|>`,
+		},
+		{
+			skip: "tool call ordering not guaranteed yet",
+			name: "tool calls",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant with access to tools."},
+				{Role: "user", Content: "What is the weather like in Tokyo?"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name:      "get_weather",
+								Arguments: args(`{"location": "Tokyo, Japan", "unit": "celsius"}`),
+							},
+						},
+						{
+							Function: api.ToolCallFunction{
+								Name:      "get_weather",
+								Arguments: args(`{"location": "Japan", "unit": "fahrenheit"}`),
+							},
+						},
+					},
+				},
+				{
+					Role:     "tool",
+					Content:  "{\"temperature\": 22, \"weather\": \"partly cloudy\", \"humidity\": 65}",
+					ToolName: "get_weather",
+				},
+				{
+					Role:     "tool",
+					Content:  "{\"temperature\": 68, \"weather\": \"sunny\", \"humidity\": 75}",
+					ToolName: "get_weather",
+				},
+				{
+					Role:    "assistant",
+					Content: "The weather in Tokyo is currently partly cloudy with a temperature of 22°C and 65% humidity. It's a pleasant day with moderate temperatures.",
+				},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get the current weather in a given location",
+						Parameters: api.ToolFunctionParameters{
+							Type:       "object",
+							Required:   []string{"location"},
+							Properties: propsMap(`{"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}}`),
+						},
+					},
+				},
+			},
+			expected: `[gMASK]<sop><|system|>
+# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{"type":"function","function":{"name":"get_weather","description":"Get the current weather in a given location","parameters":{"type":"object","required":["location"],"properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","description":"","enum":["celsius","fahrenheit"]}}}}}
+</tools>
+
+For each function call, output the function name and arguments within the following XML format:
+<tool_call>{function-name}
+<arg_key>{arg-key-1}</arg_key>
+<arg_value>{arg-value-1}</arg_value>
+<arg_key>{arg-key-2}</arg_key>
+<arg_value>{arg-value-2}</arg_value>
+...
+</tool_call><|system|>
+You are a helpful assistant with access to tools.<|user|>
+What is the weather like in Tokyo?<|assistant|>
+<think></think>
+<tool_call>get_weather
+<arg_key>location</arg_key>
+<arg_value>Tokyo, Japan</arg_value>
+<arg_key>unit</arg_key>
+<arg_value>celsius</arg_value>
+</tool_call>
+<tool_call>get_weather
+<arg_key>location</arg_key>
+<arg_value>Japan</arg_value>
+<arg_key>unit</arg_key>
+<arg_value>fahrenheit</arg_value>
+</tool_call><|observation|>
+<tool_response>
+{"temperature": 22, "weather": "partly cloudy", "humidity": 65}
+</tool_response>
+<tool_response>
+{"temperature": 68, "weather": "sunny", "humidity": 75}
+</tool_response><|assistant|>
+<think></think>
+The weather in Tokyo is currently partly cloudy with a temperature of 22°C and 65% humidity. It's a pleasant day with moderate temperatures.<|assistant|>`,
+		},
+		{
+			name: "think true",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello, how are you?"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected: `[gMASK]<sop><|user|>
+Hello, how are you?<|assistant|>`,
+		},
+		{
+			name: "think false",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello, how are you?"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected: `[gMASK]<sop><|user|>
+Hello, how are you?/nothink<|assistant|>
+<think></think>
+`,
+		},
+	}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			if tt.skip != "" {
+				t.Skip(tt.skip)
+			}
+			renderer := &GLM46Renderer{}
+			rendered, err := renderer.Render(tt.messages, tt.tools, tt.thinkValue)
+			if err != nil {
+				t.Fatal(err)
+			}
+			if diff := cmp.Diff(rendered, tt.expected); diff != "" {
+				t.Errorf("mismatch (-got +want):\n%s", diff)
+				t.Logf("Got:\n%s", rendered)
+				t.Logf("Expected:\n%s", tt.expected)
+			}
+		})
+	}
+}

model/renderers/glm47.go

@@ -0,0 +1,170 @@
+package renderers
+
+import (
+	"encoding/json"
+	"fmt"
+	"strings"
+
+	"github.com/ollama/ollama/api"
+)
+
+// GLM47Renderer renders messages for GLM-4.7 models.
+//
+// GLM-4.7 Thinking Modes (ref: https://docs.z.ai/guides/capabilities/thinking-mode):
+//
+//  1. INTERLEAVED THINKING
+//     The model thinks between tool calls and after receiving tool results.
+//     This enables complex step-by-step reasoning: interpreting each tool output
+//     before deciding what to do next. Thinking blocks are preserved and returned
+//     with tool results to maintain reasoning continuity.
+//
+//  2. PRESERVED THINKING
+//     The model retains reasoning content from previous assistant turns in context.
+//     This preserves reasoning continuity across multi-turn conversations. The
+//     upstream API has a "clear_thinking" parameter to control this:
+//     - clear_thinking=true:  clears reasoning from previous turns (outputs </think>)
+//     - clear_thinking=false: preserves <think>...</think> blocks from previous turns
+//
+//  3. TURN-LEVEL THINKING
+//     Controls whether the model should reason on each turn. The upstream API
+//     uses "enable_thinking" parameter:
+//     - enable_thinking=true:  outputs <think> to start reasoning
+//     - enable_thinking=false: outputs </think> to skip reasoning
+//
+// OLLAMA DEFAULTS:
+//   - Thinking is ENABLED by default (thinkValue=nil or true outputs <think>)
+//   - Thinking is PRESERVED by default (reasoning content from previous turns is always
+//     included in <think>...</think> blocks, equivalent to clear_thinking=false)
+//   - Users can disable thinking per-turn via thinkValue=false
+type GLM47Renderer struct{}
+
+func (r *GLM47Renderer) Render(messages []api.Message, tools []api.Tool, thinkValue *api.ThinkValue) (string, error) {
+	var sb strings.Builder
+
+	sb.WriteString("[gMASK]<sop>")
+
+	if len(tools) > 0 {
+		sb.WriteString("<|system|>\n")
+		sb.WriteString("# Tools\n\n")
+		sb.WriteString("You may call one or more functions to assist with the user query.\n\n")
+		sb.WriteString("You are provided with function signatures within <tools></tools> XML tags:\n")
+		sb.WriteString("<tools>\n")
+		for _, tool := range tools {
+			d, _ := json.Marshal(tool)
+			sb.WriteString(formatGLM47ToolJSON(d))
+			sb.WriteString("\n")
+		}
+		sb.WriteString("</tools>\n\n")
+		sb.WriteString("For each function call, output the function name and arguments within the following XML format:\n")
+		sb.WriteString("<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call>")
+	}
+
+	think := true
+	if thinkValue != nil && !thinkValue.Bool() {
+		think = false
+	}
+
+	for i, message := range messages {
+		switch message.Role {
+		case "user":
+			sb.WriteString("<|user|>")
+			sb.WriteString(message.Content)
+		case "assistant":
+			sb.WriteString("<|assistant|>")
+			if message.Thinking != "" {
+				sb.WriteString("<think>" + message.Thinking + "</think>")
+			} else {
+				sb.WriteString("</think>")
+			}
+			if message.Content != "" {
+				sb.WriteString(message.Content)
+			}
+			if len(message.ToolCalls) > 0 {
+				for _, toolCall := range message.ToolCalls {
+					sb.WriteString("<tool_call>" + toolCall.Function.Name)
+					sb.WriteString(renderGLM47ToolArguments(toolCall.Function.Arguments))
+					sb.WriteString("</tool_call>")
+				}
+			}
+		case "tool":
+			if i == 0 || messages[i-1].Role != "tool" {
+				sb.WriteString("<|observation|>")
+			}
+			sb.WriteString("<tool_response>")
+			sb.WriteString(message.Content)
+			sb.WriteString("</tool_response>")
+		case "system":
+			sb.WriteString("<|system|>")
+			sb.WriteString(message.Content)
+		}
+	}
+
+	sb.WriteString("<|assistant|>")
+	if think {
+		sb.WriteString("<think>")
+	} else {
+		sb.WriteString("</think>")
+	}
+
+	return sb.String(), nil
+}
+
+func renderGLM47ToolArguments(args api.ToolCallFunctionArguments) string {
+	var sb strings.Builder
+	for key, value := range args.All() {
+		sb.WriteString("<arg_key>" + key + "</arg_key>")
+		var valueStr string
+		if str, ok := value.(string); ok {
+			valueStr = str
+		} else {
+			jsonBytes, err := json.Marshal(value)
+			if err != nil {
+				valueStr = fmt.Sprintf("%v", value)
+			} else {
+				valueStr = string(jsonBytes)
+			}
+		}
+
+		sb.WriteString("<arg_value>" + valueStr + "</arg_value>")
+	}
+
+	return sb.String()
+}
+
+func formatGLM47ToolJSON(raw []byte) string {
+	var sb strings.Builder
+	sb.Grow(len(raw) + len(raw)/10)
+
+	inString := false
+	escaped := false
+	for i := range raw {
+		ch := raw[i]
+		sb.WriteByte(ch)
+
+		if inString {
+			if escaped {
+				escaped = false
+				continue
+			}
+			if ch == '\\' {
+				escaped = true
+				continue
+			}
+			if ch == '"' {
+				inString = false
+			}
+			continue
+		}
+
+		if ch == '"' {
+			inString = true
+			continue
+		}
+
+		if ch == ':' || ch == ',' {
+			sb.WriteByte(' ')
+		}
+	}
+
+	return sb.String()
+}

model/renderers/glm47_test.go

@@ -0,0 +1,191 @@
+package renderers
+
+import (
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+	"github.com/ollama/ollama/api"
+)
+
+func TestGLM47Renderer(t *testing.T) {
+	tests := []struct {
+		name       string
+		messages   []api.Message
+		tools      []api.Tool
+		thinkValue *api.ThinkValue
+		expected   string
+	}{
+		{
+			name: "basic user message",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello"},
+			},
+			expected: "[gMASK]<sop><|user|>Hello<|assistant|><think>",
+		},
+		{
+			name: "thinking disabled",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "[gMASK]<sop><|user|>Hello<|assistant|></think>",
+		},
+		{
+			name: "system and user",
+			messages: []api.Message{
+				{Role: "system", Content: "You are helpful."},
+				{Role: "user", Content: "Hello"},
+			},
+			expected: "[gMASK]<sop><|system|>You are helpful.<|user|>Hello<|assistant|><think>",
+		},
+		{
+			name: "multi-turn conversation",
+			messages: []api.Message{
+				{Role: "user", Content: "Hi"},
+				{Role: "assistant", Content: "Hello there"},
+				{Role: "user", Content: "How are you?"},
+			},
+			expected: "[gMASK]<sop><|user|>Hi<|assistant|></think>Hello there<|user|>How are you?<|assistant|><think>",
+		},
+		{
+			name: "assistant with reasoning_content",
+			messages: []api.Message{
+				{Role: "user", Content: "Answer with reasoning."},
+				{Role: "assistant", Thinking: "Plan.", Content: "Done."},
+			},
+			expected: "[gMASK]<sop><|user|>Answer with reasoning.<|assistant|><think>Plan.</think>Done.<|assistant|><think>",
+		},
+		{
+			name: "tool call with empty content",
+			messages: []api.Message{
+				{Role: "user", Content: "Weather?"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name:      "get_weather",
+								Arguments: args(`{"location": "Tokyo", "unit": "celsius"}`),
+							},
+						},
+					},
+				},
+				{Role: "tool", Content: `{"temperature":22}`},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get weather",
+						Parameters: api.ToolFunctionParameters{
+							Type:       "object",
+							Required:   []string{"location"},
+							Properties: propsMap(`{"location": {"type": "string"}}`),
+						},
+					},
+				},
+			},
+			expected: "[gMASK]<sop><|system|>\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>\n{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"description\": \"Get weather\", \"parameters\": {\"type\": \"object\", \"required\": [\"location\"], \"properties\": {\"location\": {\"type\": \"string\"}}}}}\n</tools>\n\nFor each function call, output the function name and arguments within the following XML format:\n<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call><|user|>Weather?<|assistant|></think><tool_call>get_weather<arg_key>location</arg_key><arg_value>Tokyo</arg_value><arg_key>unit</arg_key><arg_value>celsius</arg_value></tool_call><|observation|><tool_response>{\"temperature\":22}</tool_response><|assistant|><think>",
+		},
+		{
+			name: "tool call with content",
+			messages: []api.Message{
+				{Role: "user", Content: "Weather?"},
+				{
+					Role:    "assistant",
+					Content: "Let me check",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name:      "get_weather",
+								Arguments: args(`{"location": "Tokyo"}`),
+							},
+						},
+					},
+				},
+				{Role: "tool", Content: `{"temperature":22}`},
+				{Role: "assistant", Content: "It is 22C."},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get weather",
+						Parameters: api.ToolFunctionParameters{
+							Type:       "object",
+							Required:   []string{"location"},
+							Properties: propsMap(`{"location": {"type": "string"}}`),
+						},
+					},
+				},
+			},
+			expected: "[gMASK]<sop><|system|>\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>\n{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"description\": \"Get weather\", \"parameters\": {\"type\": \"object\", \"required\": [\"location\"], \"properties\": {\"location\": {\"type\": \"string\"}}}}}\n</tools>\n\nFor each function call, output the function name and arguments within the following XML format:\n<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call><|user|>Weather?<|assistant|></think>Let me check<tool_call>get_weather<arg_key>location</arg_key><arg_value>Tokyo</arg_value></tool_call><|observation|><tool_response>{\"temperature\":22}</tool_response><|assistant|></think>It is 22C.<|assistant|><think>",
+		},
+		{
+			name: "multiple tool calls and responses",
+			messages: []api.Message{
+				{Role: "user", Content: "Compare weather"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name:      "get_weather",
+								Arguments: args(`{"location": "Tokyo"}`),
+							},
+						},
+						{
+							Function: api.ToolCallFunction{
+								Name:      "get_weather",
+								Arguments: args(`{"location": "Paris"}`),
+							},
+						},
+					},
+				},
+				{Role: "tool", Content: `{"temperature":22}`},
+				{Role: "tool", Content: `{"temperature":18}`},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get weather",
+						Parameters: api.ToolFunctionParameters{
+							Type:       "object",
+							Required:   []string{"location"},
+							Properties: propsMap(`{"location": {"type": "string"}}`),
+						},
+					},
+				},
+			},
+			expected: "[gMASK]<sop><|system|>\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>\n{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"description\": \"Get weather\", \"parameters\": {\"type\": \"object\", \"required\": [\"location\"], \"properties\": {\"location\": {\"type\": \"string\"}}}}}\n</tools>\n\nFor each function call, output the function name and arguments within the following XML format:\n<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call><|user|>Compare weather<|assistant|></think><tool_call>get_weather<arg_key>location</arg_key><arg_value>Tokyo</arg_value></tool_call><tool_call>get_weather<arg_key>location</arg_key><arg_value>Paris</arg_value></tool_call><|observation|><tool_response>{\"temperature\":22}</tool_response><tool_response>{\"temperature\":18}</tool_response><|assistant|><think>",
+		},
+		{
+			name: "preserved thinking in multi-turn",
+			messages: []api.Message{
+				{Role: "user", Content: "Think step by step"},
+				{Role: "assistant", Thinking: "Let me think...", Content: "Here's my answer."},
+				{Role: "user", Content: "Continue"},
+			},
+			expected: "[gMASK]<sop><|user|>Think step by step<|assistant|><think>Let me think...</think>Here's my answer.<|user|>Continue<|assistant|><think>",
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			renderer := &GLM47Renderer{}
+			rendered, err := renderer.Render(tt.messages, tt.tools, tt.thinkValue)
+			if err != nil {
+				t.Fatal(err)
+			}
+			if diff := cmp.Diff(rendered, tt.expected); diff != "" {
+				t.Errorf("mismatch (-got +want):\n%s", diff)
+				t.Logf("Got:\n%s", rendered)
+				t.Logf("Expected:\n%s", tt.expected)
+			}
+		})
+	}
+}

model/renderers/lfm2.go

@@ -0,0 +1,144 @@
+package renderers
+
+import (
+	"encoding/json"
+	"strings"
+
+	"github.com/ollama/ollama/api"
+)
+
+type LFM2Renderer struct {
+	IsThinking bool
+}
+
+func (r *LFM2Renderer) Render(messages []api.Message, tools []api.Tool, thinkValue *api.ThinkValue) (string, error) {
+	var sb strings.Builder
+
+	// Note: BOS token is added by the tokenizer (add_bos_token: true), not the renderer
+
+	// Extract first system message if present (to combine with tools)
+	var firstSystemContent string
+	startIdx := 0
+	if len(messages) > 0 && messages[0].Role == "system" {
+		firstSystemContent = messages[0].Content
+		startIdx = 1
+	}
+
+	// Append tools to first system content
+	if len(tools) > 0 {
+		if firstSystemContent != "" {
+			firstSystemContent += "\n"
+		}
+		firstSystemContent += "List of tools: ["
+		for i, tool := range tools {
+			toolJSON, err := json.Marshal(tool)
+			if err != nil {
+				return "", err
+			}
+			firstSystemContent += string(toolJSON)
+			if i < len(tools)-1 {
+				firstSystemContent += ", "
+			}
+		}
+		firstSystemContent += "]"
+	}
+
+	// Output first system block if it has content
+	if firstSystemContent != "" {
+		sb.WriteString("<|im_start|>system\n")
+		sb.WriteString(firstSystemContent)
+		sb.WriteString("<|im_end|>\n")
+	}
+
+	// Find the index of the last assistant message for thinking stripping
+	lastAssistantIndex := -1
+	for i := len(messages) - 1; i >= startIdx; i-- {
+		if messages[i].Role == "assistant" {
+			lastAssistantIndex = i
+			break
+		}
+	}
+
+	// Track whether we need to add generation prompt
+	needsGenerationPrompt := len(messages) > 0
+
+	for i := startIdx; i < len(messages); i++ {
+		message := messages[i]
+		switch message.Role {
+		case "system":
+			// Additional system messages (after the first) are rendered normally
+			sb.WriteString("<|im_start|>system\n")
+			sb.WriteString(message.Content)
+			sb.WriteString("<|im_end|>\n")
+
+		case "user":
+			sb.WriteString("<|im_start|>user\n")
+			sb.WriteString(message.Content)
+			sb.WriteString("<|im_end|>\n")
+			needsGenerationPrompt = true
+
+		case "assistant":
+			sb.WriteString("<|im_start|>assistant\n")
+
+			// Check if this is the last assistant message
+			isLastAssistant := i == lastAssistantIndex
+
+			// Process content (may need thinking stripped)
+			content := message.Content
+
+			// Handle thinking tags in assistant content
+			keepPastThinking := r.IsThinking && (thinkValue != nil && thinkValue.Bool())
+			if strings.Contains(content, "</think>") {
+				parts := strings.SplitN(content, "</think>", 2)
+				if len(parts) > 1 {
+					if !isLastAssistant && !keepPastThinking {
+						// Strip thinking entirely for past assistant messages
+						content = strings.TrimSpace(parts[1])
+					} else {
+						// Preserve thinking but trim whitespace after </think>
+						content = parts[0] + "</think>" + strings.TrimLeft(parts[1], " \t\n\r")
+					}
+				}
+			}
+
+			if len(message.ToolCalls) > 0 {
+				// Assistant with tool calls - write content first (if any after stripping)
+				if content != "" {
+					sb.WriteString(content)
+				}
+
+				for _, toolCall := range message.ToolCalls {
+					sb.WriteString("<|tool_call_start|>")
+					toolCallJSON := map[string]any{
+						"name":      toolCall.Function.Name,
+						"arguments": toolCall.Function.Arguments,
+					}
+					callJSON, _ := json.Marshal(toolCallJSON)
+					sb.WriteString(string(callJSON))
+					sb.WriteString("<|tool_call_end|>")
+				}
+			} else {
+				sb.WriteString(content)
+			}
+
+			sb.WriteString("<|im_end|>\n")
+			needsGenerationPrompt = true // Always add gen prompt after assistant when add_generation_prompt=true
+
+		case "tool":
+			// Tool responses are rendered as plain messages per the chat template
+			sb.WriteString("<|im_start|>tool\n")
+			sb.WriteString(message.Content)
+			sb.WriteString("<|im_end|>\n")
+			needsGenerationPrompt = true
+		}
+	}
+
+	// Add generation prompt
+	if needsGenerationPrompt {
+		sb.WriteString("<|im_start|>assistant\n")
+		// Note: Model is a "thinking-only" model - it will output <think> itself
+		// We don't add <think> tag to the prompt
+	}
+
+	return sb.String(), nil
+}

model/renderers/lfm2_test.go

@@ -0,0 +1,427 @@
+package renderers
+
+import (
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestLFM2Renderer(t *testing.T) {
+	tests := []struct {
+		name       string
+		messages   []api.Message
+		tools      []api.Tool
+		thinkValue *api.ThinkValue
+		expected   string
+	}{
+		{
+			name: "basic user message",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "basic with system message",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant."},
+				{Role: "user", Content: "Hello!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "multiple system messages rendered separately",
+			messages: []api.Message{
+				{Role: "system", Content: "First instruction."},
+				{Role: "system", Content: "Second instruction."},
+				{Role: "user", Content: "Hello!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>system\nFirst instruction.<|im_end|>\n<|im_start|>system\nSecond instruction.<|im_end|>\n<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "multi-turn conversation",
+			messages: []api.Message{
+				{Role: "user", Content: "What is 2+2?"},
+				{Role: "assistant", Content: "The answer is 4."},
+				{Role: "user", Content: "Thanks!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nWhat is 2+2?<|im_end|>\n<|im_start|>assistant\nThe answer is 4.<|im_end|>\n<|im_start|>user\nThanks!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "only system message",
+			messages: []api.Message{
+				{Role: "system", Content: "You are helpful."},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>system\nYou are helpful.<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// When assistant is the LAST assistant, thinking is preserved (even with keep_past_thinking=false)
+			name: "user-assistant-user: last assistant preserves thinking",
+			messages: []api.Message{
+				{Role: "user", Content: "Q1"},
+				{Role: "assistant", Content: "<think>reasoning</think>A1"},
+				{Role: "user", Content: "Q2"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nQ1<|im_end|>\n<|im_start|>assistant\n<think>reasoning</think>A1<|im_end|>\n<|im_start|>user\nQ2<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// With two assistants, first is stripped (not last), second preserved (is last)
+			name: "multi-turn thinking: first stripped, second preserved",
+			messages: []api.Message{
+				{Role: "user", Content: "Q1"},
+				{Role: "assistant", Content: "<think>reason1</think>A1"},
+				{Role: "user", Content: "Q2"},
+				{Role: "assistant", Content: "<think>reason2</think>A2"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nQ1<|im_end|>\n<|im_start|>assistant\nA1<|im_end|>\n<|im_start|>user\nQ2<|im_end|>\n<|im_start|>assistant\n<think>reason2</think>A2<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// With thinking enabled (keep_past_thinking=true), both preserved
+			name: "multi-turn thinking: both preserved when thinking enabled",
+			messages: []api.Message{
+				{Role: "user", Content: "Q1"},
+				{Role: "assistant", Content: "<think>reason1</think>A1"},
+				{Role: "user", Content: "Q2"},
+				{Role: "assistant", Content: "<think>reason2</think>A2"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected:   "<|im_start|>user\nQ1<|im_end|>\n<|im_start|>assistant\n<think>reason1</think>A1<|im_end|>\n<|im_start|>user\nQ2<|im_end|>\n<|im_start|>assistant\n<think>reason2</think>A2<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "assistant with tool calls",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<|im_start|>user` + "\n" + `What's the weather?<|im_end|>` + "\n" + `<|im_start|>assistant` + "\n" + `<|tool_call_start|>{"arguments":{"location":"Paris"},"name":"get_weather"}<|tool_call_end|><|im_end|>` + "\n" + `<|im_start|>assistant` + "\n",
+		},
+		{
+			name: "assistant with content and tool calls",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather in Paris?"},
+				{
+					Role:    "assistant",
+					Content: "Let me check.",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<|im_start|>user` + "\n" + `What's the weather in Paris?<|im_end|>` + "\n" + `<|im_start|>assistant` + "\n" + `Let me check.<|tool_call_start|>{"arguments":{"location":"Paris"},"name":"get_weather"}<|tool_call_end|><|im_end|>` + "\n" + `<|im_start|>assistant` + "\n",
+		},
+		{
+			name: "tool response",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{Role: "assistant", Content: "Let me check."},
+				{Role: "tool", Content: "22C, Sunny"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nWhat's the weather?<|im_end|>\n<|im_start|>assistant\nLet me check.<|im_end|>\n<|im_start|>tool\n22C, Sunny<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "multiple tool calls",
+			messages: []api.Message{
+				{Role: "user", Content: "Get weather for Paris and London"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "London",
+								}),
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<|im_start|>user` + "\n" + `Get weather for Paris and London<|im_end|>` + "\n" + `<|im_start|>assistant` + "\n" + `<|tool_call_start|>{"arguments":{"location":"Paris"},"name":"get_weather"}<|tool_call_end|><|tool_call_start|>{"arguments":{"location":"London"},"name":"get_weather"}<|tool_call_end|><|im_end|>` + "\n" + `<|im_start|>assistant` + "\n",
+		},
+		{
+			name: "tools definitions with system message",
+			messages: []api.Message{
+				{Role: "system", Content: "You are helpful."},
+				{Role: "user", Content: "What's the weather?"},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get current weather",
+						Parameters: api.ToolFunctionParameters{
+							Type: "object",
+							Properties: testPropsMap(map[string]api.ToolProperty{
+								"location": {
+									Type:        api.PropertyType{"string"},
+									Description: "City name",
+								},
+							}),
+							Required: []string{"location"},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<|im_start|>system` + "\n" + `You are helpful.` + "\n" + `List of tools: [{"type":"function","function":{"name":"get_weather","description":"Get current weather","parameters":{"type":"object","required":["location"],"properties":{"location":{"type":"string","description":"City name"}}}}}]<|im_end|>` + "\n" + `<|im_start|>user` + "\n" + `What's the weather?<|im_end|>` + "\n" + `<|im_start|>assistant` + "\n",
+		},
+		{
+			name: "tools definitions without system message",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get current weather",
+						Parameters: api.ToolFunctionParameters{
+							Type: "object",
+							Properties: testPropsMap(map[string]api.ToolProperty{
+								"location": {
+									Type:        api.PropertyType{"string"},
+									Description: "City name",
+								},
+							}),
+							Required: []string{"location"},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<|im_start|>system` + "\n" + `List of tools: [{"type":"function","function":{"name":"get_weather","description":"Get current weather","parameters":{"type":"object","required":["location"],"properties":{"location":{"type":"string","description":"City name"}}}}}]<|im_end|>` + "\n" + `<|im_start|>user` + "\n" + `What's the weather?<|im_end|>` + "\n" + `<|im_start|>assistant` + "\n",
+		},
+		{
+			name: "multiple tools without system message",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello"},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get weather",
+					},
+				},
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_time",
+						Description: "Get time",
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>system\nList of tools: [{\"type\":\"function\",\"function\":{\"name\":\"get_weather\",\"description\":\"Get weather\",\"parameters\":{\"type\":\"\",\"properties\":null}}}, {\"type\":\"function\",\"function\":{\"name\":\"get_time\",\"description\":\"Get time\",\"parameters\":{\"type\":\"\",\"properties\":null}}}]<|im_end|>\n<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "user-tool sequence",
+			messages: []api.Message{
+				{Role: "user", Content: "Check weather"},
+				{Role: "tool", Content: "22C"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nCheck weather<|im_end|>\n<|im_start|>tool\n22C<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "full tool call cycle",
+			messages: []api.Message{
+				{Role: "user", Content: "Check weather"},
+				{Role: "assistant", Content: "Let me check"},
+				{Role: "tool", Content: "22C"},
+				{Role: "assistant", Content: "It's 22C"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nCheck weather<|im_end|>\n<|im_start|>assistant\nLet me check<|im_end|>\n<|im_start|>tool\n22C<|im_end|>\n<|im_start|>assistant\nIt's 22C<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "unicode content",
+			messages: []api.Message{
+				{Role: "user", Content: "你好世界! مرحبا 🌍"},
+				{Role: "assistant", Content: "Hello! 👋"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\n你好世界! مرحبا 🌍<|im_end|>\n<|im_start|>assistant\nHello! 👋<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "newlines in content",
+			messages: []api.Message{
+				{Role: "user", Content: "Line 1\nLine 2\n\nLine 4"},
+				{Role: "assistant", Content: "Response with\nmultiple\nlines"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nLine 1\nLine 2\n\nLine 4<|im_end|>\n<|im_start|>assistant\nResponse with\nmultiple\nlines<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			name: "empty assistant content",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello"},
+				{Role: "assistant", Content: ""},
+				{Role: "user", Content: "OK"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\n<|im_end|>\n<|im_start|>user\nOK<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// Generation prompt does NOT include <think> - model outputs it
+			name: "generation prompt has no think tag",
+			messages: []api.Message{
+				{Role: "user", Content: "Think hard"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected:   "<|im_start|>user\nThink hard<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// Interleaved: thinking before tool call - last assistant preserves thinking
+			name: "thinking before tool call (last assistant)",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{
+					Role:    "assistant",
+					Content: "<think>I need to check the weather</think>",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nWhat's the weather?<|im_end|>\n<|im_start|>assistant\n<think>I need to check the weather</think><|tool_call_start|>{\"arguments\":{\"location\":\"Paris\"},\"name\":\"get_weather\"}<|tool_call_end|><|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// Two assistants with tool calls - first has thinking stripped
+			name: "two assistants with tools: first thinking stripped",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{
+					Role:    "assistant",
+					Content: "<think>checking</think>",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+				{Role: "tool", Content: "22C"},
+				{Role: "assistant", Content: "<think>got result</think>It's 22C!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nWhat's the weather?<|im_end|>\n<|im_start|>assistant\n<|tool_call_start|>{\"arguments\":{\"location\":\"Paris\"},\"name\":\"get_weather\"}<|tool_call_end|><|im_end|>\n<|im_start|>tool\n22C<|im_end|>\n<|im_start|>assistant\n<think>got result</think>It's 22C!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// Two assistants with tools - both preserved when thinking enabled
+			name: "two assistants with tools: both preserved when thinking enabled",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{
+					Role:    "assistant",
+					Content: "<think>checking</think>",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+				{Role: "tool", Content: "22C"},
+				{Role: "assistant", Content: "<think>got result</think>It's 22C!"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected:   "<|im_start|>user\nWhat's the weather?<|im_end|>\n<|im_start|>assistant\n<think>checking</think><|tool_call_start|>{\"arguments\":{\"location\":\"Paris\"},\"name\":\"get_weather\"}<|tool_call_end|><|im_end|>\n<|im_start|>tool\n22C<|im_end|>\n<|im_start|>assistant\n<think>got result</think>It's 22C!<|im_end|>\n<|im_start|>assistant\n",
+		},
+		{
+			// Content before thinking before tool call
+			name: "content then thinking then tool call",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather?"},
+				{
+					Role:    "assistant",
+					Content: "Let me check.<think>Using weather API</think>",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: testArgs(map[string]any{
+									"location": "Paris",
+								}),
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   "<|im_start|>user\nWhat's the weather?<|im_end|>\n<|im_start|>assistant\nLet me check.<think>Using weather API</think><|tool_call_start|>{\"arguments\":{\"location\":\"Paris\"},\"name\":\"get_weather\"}<|tool_call_end|><|im_end|>\n<|im_start|>assistant\n",
+		},
+	}
+
+	renderer := &LFM2Renderer{IsThinking: true}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			rendered, err := renderer.Render(tt.messages, tt.tools, tt.thinkValue)
+			if err != nil {
+				t.Fatalf("Render() error = %v", err)
+			}
+			if diff := cmp.Diff(tt.expected, rendered); diff != "" {
+				t.Errorf("Render() mismatch (-want +got):\n%s", diff)
+			}
+		})
+	}
+}

model/renderers/renderer.go

@@ -80,6 +80,12 @@ func rendererForName(name string) Renderer {
 		return &Nemotron3NanoRenderer{}
 	case "functiongemma":
 		return &FunctionGemmaRenderer{}
+	case "glm-4.7":
+		return &GLM47Renderer{}
+	case "lfm2":
+		return &LFM2Renderer{IsThinking: false}
+	case "lfm2-thinking":
+		return &LFM2Renderer{IsThinking: true}
 	default:
 		return nil
 	}

model/renderers/testhelpers_test.go

@@ -1,6 +1,26 @@
 package renderers
 
-import "github.com/ollama/ollama/api"
+import (
+	"encoding/json"
+
+	"github.com/ollama/ollama/api"
+)
+
+func args(s string) api.ToolCallFunctionArguments {
+	var result api.ToolCallFunctionArguments
+	if err := json.Unmarshal([]byte(s), &result); err != nil {
+		panic("invalid JSON in args(): " + err.Error())
+	}
+	return result
+}
+
+func propsMap(s string) *api.ToolPropertiesMap {
+	var result api.ToolPropertiesMap
+	if err := json.Unmarshal([]byte(s), &result); err != nil {
+		panic("invalid JSON in propsMap(): " + err.Error())
+	}
+	return &result
+}
 
 // testPropsMap creates a ToolPropertiesMap from a map (convenience function for tests, order not preserved)
 func testPropsMap(m map[string]api.ToolProperty) *api.ToolPropertiesMap {

server/create.go

@@ -28,6 +28,7 @@ import (
 	"github.com/ollama/ollama/format"
 	ofs "github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/manifest"
 	"github.com/ollama/ollama/template"
 	"github.com/ollama/ollama/types/errtypes"
 	"github.com/ollama/ollama/types/model"
@@ -90,7 +91,7 @@ func (s *Server) CreateHandler(c *gin.Context) {
 			ch <- resp
 		}
 
-		oldManifest, _ := ParseNamedManifest(name)
+		oldManifest, _ := manifest.ParseNamedManifest(name)
 
 		var baseLayers []*layerGGML
 		var err error
@@ -123,9 +124,9 @@ func (s *Server) CreateHandler(c *gin.Context) {
 				}
 
 				if err == nil && !remote && (config.Renderer == "" || config.Parser == "" || config.Requires == "") {
-					manifest, mErr := ParseNamedManifest(fromName)
-					if mErr == nil && manifest.Config.Digest != "" {
-						configPath, pErr := GetBlobsPath(manifest.Config.Digest)
+					mf, mErr := manifest.ParseNamedManifest(fromName)
+					if mErr == nil && mf.Config.Digest != "" {
+						configPath, pErr := manifest.BlobsPath(mf.Config.Digest)
 						if pErr == nil {
 							if cfgFile, fErr := os.Open(configPath); fErr == nil {
 								var baseConfig model.ConfigV2
@@ -342,7 +343,7 @@ func detectModelTypeFromFiles(files map[string]string) string {
 			return "gguf"
 		} else {
 			// try to see if we can find a gguf file even without the file extension
-			blobPath, err := GetBlobsPath(files[fn])
+			blobPath, err := manifest.BlobsPath(files[fn])
 			if err != nil {
 				slog.Error("error getting blobs path", "file", fn)
 				return ""
@@ -394,7 +395,7 @@ func convertFromSafetensors(files map[string]string, baseLayers []*layerGGML, is
 			return nil, fmt.Errorf("%w: %s: %s", errFilePath, err, fp)
 		}
 
-		blobPath, err := GetBlobsPath(digest)
+		blobPath, err := manifest.BlobsPath(digest)
 		if err != nil {
 			return nil, err
 		}
@@ -432,7 +433,7 @@ func convertFromSafetensors(files map[string]string, baseLayers []*layerGGML, is
 		return nil, err
 	}
 
-	layer, err := NewLayer(t, mediaType)
+	layer, err := manifest.NewLayer(t, mediaType)
 	if err != nil {
 		return nil, err
 	}
@@ -465,7 +466,7 @@ func kvFromLayers(baseLayers []*layerGGML) (ofs.Config, error) {
 }
 
 func createModel(r api.CreateRequest, name model.Name, baseLayers []*layerGGML, config *model.ConfigV2, fn func(resp api.ProgressResponse)) (err error) {
-	var layers []Layer
+	var layers []manifest.Layer
 	for _, layer := range baseLayers {
 		if layer.GGML != nil {
 			quantType := strings.ToUpper(cmp.Or(r.Quantize, r.Quantization))
@@ -550,13 +551,13 @@ func createModel(r api.CreateRequest, name model.Name, baseLayers []*layerGGML,
 	}
 
 	for _, layer := range layers {
-		if layer.status != "" {
-			fn(api.ProgressResponse{Status: layer.status})
+		if layer.Status != "" {
+			fn(api.ProgressResponse{Status: layer.Status})
 		}
 	}
 
 	fn(api.ProgressResponse{Status: "writing manifest"})
-	if err := WriteManifest(name, *configLayer, layers); err != nil {
+	if err := manifest.WriteManifest(name, *configLayer, layers); err != nil {
 		return err
 	}
 
@@ -577,7 +578,7 @@ func quantizeLayer(layer *layerGGML, quantizeType string, fn func(resp api.Progr
 		return nil, err
 	}
 
-	blob, err := GetBlobsPath(layer.Digest)
+	blob, err := manifest.BlobsPath(layer.Digest)
 	if err != nil {
 		return nil, err
 	}
@@ -599,7 +600,7 @@ func quantizeLayer(layer *layerGGML, quantizeType string, fn func(resp api.Progr
 	}
 	temp.Seek(0, io.SeekStart)
 	fn(api.ProgressResponse{Status: "verifying conversion"})
-	newLayer, err := NewLayer(temp, layer.MediaType)
+	newLayer, err := manifest.NewLayer(temp, layer.MediaType)
 	if err != nil {
 		return nil, err
 	}
@@ -619,7 +620,7 @@ func ggufLayers(digest string, fn func(resp api.ProgressResponse)) ([]*layerGGML
 	var layers []*layerGGML
 
 	fn(api.ProgressResponse{Status: "parsing GGUF"})
-	blobPath, err := GetBlobsPath(digest)
+	blobPath, err := manifest.BlobsPath(digest)
 	if err != nil {
 		return nil, err
 	}
@@ -654,7 +655,7 @@ func ggufLayers(digest string, fn func(resp api.ProgressResponse)) ([]*layerGGML
 		mediatype = "application/vnd.ollama.image.projector"
 	}
 
-	layer, err := NewLayerFromLayer(digest, mediatype, blob.Name())
+	layer, err := manifest.NewLayerFromLayer(digest, mediatype, blob.Name())
 	if err != nil {
 		slog.Debug("could not create new layer from layer", "error", err)
 		return nil, err
@@ -665,8 +666,8 @@ func ggufLayers(digest string, fn func(resp api.ProgressResponse)) ([]*layerGGML
 	return detectChatTemplate(layers)
 }
 
-func removeLayer(layers []Layer, mediatype string) []Layer {
-	return slices.DeleteFunc(layers, func(layer Layer) bool {
+func removeLayer(layers []manifest.Layer, mediatype string) []manifest.Layer {
+	return slices.DeleteFunc(layers, func(layer manifest.Layer) bool {
 		if layer.MediaType != mediatype {
 			return false
 		}
@@ -680,7 +681,7 @@ func removeLayer(layers []Layer, mediatype string) []Layer {
 	})
 }
 
-func setTemplate(layers []Layer, t string) ([]Layer, error) {
+func setTemplate(layers []manifest.Layer, t string) ([]manifest.Layer, error) {
 	layers = removeLayer(layers, "application/vnd.ollama.image.template")
 	if _, err := template.Parse(t); err != nil {
 		return nil, fmt.Errorf("%w: %s", errBadTemplate, err)
@@ -690,7 +691,7 @@ func setTemplate(layers []Layer, t string) ([]Layer, error) {
 	}
 
 	blob := strings.NewReader(t)
-	layer, err := NewLayer(blob, "application/vnd.ollama.image.template")
+	layer, err := manifest.NewLayer(blob, "application/vnd.ollama.image.template")
 	if err != nil {
 		return nil, err
 	}
@@ -699,11 +700,11 @@ func setTemplate(layers []Layer, t string) ([]Layer, error) {
 	return layers, nil
 }
 
-func setSystem(layers []Layer, s string) ([]Layer, error) {
+func setSystem(layers []manifest.Layer, s string) ([]manifest.Layer, error) {
 	layers = removeLayer(layers, "application/vnd.ollama.image.system")
 	if s != "" {
 		blob := strings.NewReader(s)
-		layer, err := NewLayer(blob, "application/vnd.ollama.image.system")
+		layer, err := manifest.NewLayer(blob, "application/vnd.ollama.image.system")
 		if err != nil {
 			return nil, err
 		}
@@ -712,9 +713,9 @@ func setSystem(layers []Layer, s string) ([]Layer, error) {
 	return layers, nil
 }
 
-func setLicense(layers []Layer, l string) ([]Layer, error) {
+func setLicense(layers []manifest.Layer, l string) ([]manifest.Layer, error) {
 	blob := strings.NewReader(l)
-	layer, err := NewLayer(blob, "application/vnd.ollama.image.license")
+	layer, err := manifest.NewLayer(blob, "application/vnd.ollama.image.license")
 	if err != nil {
 		return nil, err
 	}
@@ -722,7 +723,7 @@ func setLicense(layers []Layer, l string) ([]Layer, error) {
 	return layers, nil
 }
 
-func setParameters(layers []Layer, p map[string]any) ([]Layer, error) {
+func setParameters(layers []manifest.Layer, p map[string]any) ([]manifest.Layer, error) {
 	if p == nil {
 		p = make(map[string]any)
 	}
@@ -731,7 +732,7 @@ func setParameters(layers []Layer, p map[string]any) ([]Layer, error) {
 			continue
 		}
 
-		digestPath, err := GetBlobsPath(layer.Digest)
+		digestPath, err := manifest.BlobsPath(layer.Digest)
 		if err != nil {
 			return nil, err
 		}
@@ -765,7 +766,7 @@ func setParameters(layers []Layer, p map[string]any) ([]Layer, error) {
 	if err := json.NewEncoder(&b).Encode(p); err != nil {
 		return nil, err
 	}
-	layer, err := NewLayer(&b, "application/vnd.ollama.image.params")
+	layer, err := manifest.NewLayer(&b, "application/vnd.ollama.image.params")
 	if err != nil {
 		return nil, err
 	}
@@ -773,7 +774,7 @@ func setParameters(layers []Layer, p map[string]any) ([]Layer, error) {
 	return layers, nil
 }
 
-func setMessages(layers []Layer, m []api.Message) ([]Layer, error) {
+func setMessages(layers []manifest.Layer, m []api.Message) ([]manifest.Layer, error) {
 	// this leaves the old messages intact if no new messages were specified
 	// which may not be the correct behaviour
 	if len(m) == 0 {
@@ -786,7 +787,7 @@ func setMessages(layers []Layer, m []api.Message) ([]Layer, error) {
 	if err := json.NewEncoder(&b).Encode(m); err != nil {
 		return nil, err
 	}
-	layer, err := NewLayer(&b, "application/vnd.ollama.image.messages")
+	layer, err := manifest.NewLayer(&b, "application/vnd.ollama.image.messages")
 	if err != nil {
 		return nil, err
 	}
@@ -794,7 +795,7 @@ func setMessages(layers []Layer, m []api.Message) ([]Layer, error) {
 	return layers, nil
 }
 
-func createConfigLayer(layers []Layer, config model.ConfigV2) (*Layer, error) {
+func createConfigLayer(layers []manifest.Layer, config model.ConfigV2) (*manifest.Layer, error) {
 	digests := make([]string, len(layers))
 	for i, layer := range layers {
 		digests[i] = layer.Digest
@@ -805,7 +806,7 @@ func createConfigLayer(layers []Layer, config model.ConfigV2) (*Layer, error) {
 	if err := json.NewEncoder(&b).Encode(config); err != nil {
 		return nil, err
 	}
-	layer, err := NewLayer(&b, "application/vnd.docker.container.image.v1+json")
+	layer, err := manifest.NewLayer(&b, "application/vnd.docker.container.image.v1+json")
 	if err != nil {
 		return nil, err
 	}

server/create_test.go

@@ -10,6 +10,7 @@ import (
 	"testing"
 
 	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/manifest"
 )
 
 func TestConvertFromSafetensors(t *testing.T) {
@@ -17,7 +18,7 @@ func TestConvertFromSafetensors(t *testing.T) {
 
 	// Helper function to create a new layer and return its digest
 	makeTemp := func(content string) string {
-		l, err := NewLayer(strings.NewReader(content), "application/octet-stream")
+		l, err := manifest.NewLayer(strings.NewReader(content), "application/octet-stream")
 		if err != nil {
 			t.Fatalf("Failed to create layer: %v", err)
 		}

server/download.go

@@ -24,6 +24,8 @@ import (
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/format"
+	"github.com/ollama/ollama/manifest"
+	"github.com/ollama/ollama/types/model"
 )
 
 const maxRetries = 6
@@ -456,7 +458,7 @@ func (b *blobDownload) Wait(ctx context.Context, fn func(api.ProgressResponse))
 }
 
 type downloadOpts struct {
-	mp      ModelPath
+	n       model.Name
 	digest  string
 	regOpts *registryOptions
 	fn      func(api.ProgressResponse)
@@ -465,10 +467,10 @@ type downloadOpts struct {
 // downloadBlob downloads a blob from the registry and stores it in the blobs directory
 func downloadBlob(ctx context.Context, opts downloadOpts) (cacheHit bool, _ error) {
 	if opts.digest == "" {
-		return false, fmt.Errorf(("%s: %s"), opts.mp.GetNamespaceRepository(), "digest is empty")
+		return false, fmt.Errorf(("%s: %s"), opts.n.DisplayNamespaceModel(), "digest is empty")
 	}
 
-	fp, err := GetBlobsPath(opts.digest)
+	fp, err := manifest.BlobsPath(opts.digest)
 	if err != nil {
 		return false, err
 	}
@@ -492,8 +494,8 @@ func downloadBlob(ctx context.Context, opts downloadOpts) (cacheHit bool, _ erro
 	data, ok := blobDownloadManager.LoadOrStore(opts.digest, &blobDownload{Name: fp, Digest: opts.digest})
 	download := data.(*blobDownload)
 	if !ok {
-		requestURL := opts.mp.BaseURL()
-		requestURL = requestURL.JoinPath("v2", opts.mp.GetNamespaceRepository(), "blobs", opts.digest)
+		requestURL := opts.n.BaseURL()
+		requestURL = requestURL.JoinPath("v2", opts.n.DisplayNamespaceModel(), "blobs", opts.digest)
 		if err := download.Prepare(ctx, requestURL, opts.regOpts); err != nil {
 			blobDownloadManager.Delete(opts.digest)
 			return false, err

server/images.go

@@ -4,7 +4,6 @@ import (
 	"bytes"
 	"context"
 	"crypto/sha256"
-	"encoding/hex"
 	"encoding/json"
 	"errors"
 	"fmt"
@@ -24,6 +23,7 @@ import (
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/fs/gguf"
+	"github.com/ollama/ollama/manifest"
 	"github.com/ollama/ollama/model/parsers"
 	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/template"
@@ -274,44 +274,22 @@ func (m *Model) String() string {
 	return modelfile.String()
 }
 
-func GetManifest(mp ModelPath) (*Manifest, string, error) {
-	fp, err := mp.GetManifestPath()
-	if err != nil {
-		return nil, "", err
-	}
-
-	f, err := os.Open(fp)
-	if err != nil {
-		return nil, "", err
-	}
-	defer f.Close()
-
-	sha256sum := sha256.New()
-
-	var manifest Manifest
-	if err := json.NewDecoder(io.TeeReader(f, sha256sum)).Decode(&manifest); err != nil {
-		return nil, "", err
-	}
-
-	return &manifest, hex.EncodeToString(sha256sum.Sum(nil)), nil
-}
-
 func GetModel(name string) (*Model, error) {
-	mp := ParseModelPath(name)
-	manifest, digest, err := GetManifest(mp)
+	n := model.ParseName(name)
+	mf, err := manifest.ParseNamedManifest(n)
 	if err != nil {
 		return nil, err
 	}
 
-	model := &Model{
-		Name:      mp.GetFullTagname(),
-		ShortName: mp.GetShortTagname(),
-		Digest:    digest,
+	m := &Model{
+		Name:      n.String(),
+		ShortName: n.DisplayShortest(),
+		Digest:    mf.Digest(),
 		Template:  template.DefaultTemplate,
 	}
 
-	if manifest.Config.Digest != "" {
-		filename, err := GetBlobsPath(manifest.Config.Digest)
+	if mf.Config.Digest != "" {
+		filename, err := manifest.BlobsPath(mf.Config.Digest)
 		if err != nil {
 			return nil, err
 		}
@@ -322,29 +300,29 @@ func GetModel(name string) (*Model, error) {
 		}
 		defer configFile.Close()
 
-		if err := json.NewDecoder(configFile).Decode(&model.Config); err != nil {
+		if err := json.NewDecoder(configFile).Decode(&m.Config); err != nil {
 			return nil, err
 		}
 	}
 
-	for _, layer := range manifest.Layers {
-		filename, err := GetBlobsPath(layer.Digest)
+	for _, layer := range mf.Layers {
+		filename, err := manifest.BlobsPath(layer.Digest)
 		if err != nil {
 			return nil, err
 		}
 
 		switch layer.MediaType {
 		case "application/vnd.ollama.image.model":
-			model.ModelPath = filename
-			model.ParentModel = layer.From
+			m.ModelPath = filename
+			m.ParentModel = layer.From
 		case "application/vnd.ollama.image.embed":
 			// Deprecated in versions  > 0.1.2
 			// TODO: remove this warning in a future version
 			slog.Info("WARNING: model contains embeddings, but embeddings in modelfiles have been deprecated and will be ignored.")
 		case "application/vnd.ollama.image.adapter":
-			model.AdapterPaths = append(model.AdapterPaths, filename)
+			m.AdapterPaths = append(m.AdapterPaths, filename)
 		case "application/vnd.ollama.image.projector":
-			model.ProjectorPaths = append(model.ProjectorPaths, filename)
+			m.ProjectorPaths = append(m.ProjectorPaths, filename)
 		case "application/vnd.ollama.image.prompt",
 			"application/vnd.ollama.image.template":
 			bts, err := os.ReadFile(filename)
@@ -352,7 +330,7 @@ func GetModel(name string) (*Model, error) {
 				return nil, err
 			}
 
-			model.Template, err = template.Parse(string(bts))
+			m.Template, err = template.Parse(string(bts))
 			if err != nil {
 				return nil, err
 			}
@@ -362,7 +340,7 @@ func GetModel(name string) (*Model, error) {
 				return nil, err
 			}
 
-			model.System = string(bts)
+			m.System = string(bts)
 		case "application/vnd.ollama.image.params":
 			params, err := os.Open(filename)
 			if err != nil {
@@ -371,7 +349,7 @@ func GetModel(name string) (*Model, error) {
 			defer params.Close()
 
 			// parse model options parameters into a map so that we can see which fields have been specified explicitly
-			if err = json.NewDecoder(params).Decode(&model.Options); err != nil {
+			if err = json.NewDecoder(params).Decode(&m.Options); err != nil {
 				return nil, err
 			}
 		case "application/vnd.ollama.image.messages":
@@ -381,7 +359,7 @@ func GetModel(name string) (*Model, error) {
 			}
 			defer msgs.Close()
 
-			if err = json.NewDecoder(msgs).Decode(&model.Messages); err != nil {
+			if err = json.NewDecoder(msgs).Decode(&m.Messages); err != nil {
 				return nil, err
 			}
 		case "application/vnd.ollama.image.license":
@@ -389,11 +367,11 @@ func GetModel(name string) (*Model, error) {
 			if err != nil {
 				return nil, err
 			}
-			model.License = append(model.License, string(bts))
+			m.License = append(m.License, string(bts))
 		}
 	}
 
-	return model, nil
+	return m, nil
 }
 
 func CopyModel(src, dst model.Name) error {
@@ -408,7 +386,7 @@ func CopyModel(src, dst model.Name) error {
 		return nil
 	}
 
-	manifests, err := GetManifestPath()
+	manifests, err := manifest.Path()
 	if err != nil {
 		return err
 	}
@@ -437,7 +415,7 @@ func CopyModel(src, dst model.Name) error {
 
 func deleteUnusedLayers(deleteMap map[string]struct{}) error {
 	// Ignore corrupt manifests to avoid blocking deletion of layers that are freshly orphaned
-	manifests, err := Manifests(true)
+	manifests, err := manifest.Manifests(true)
 	if err != nil {
 		return err
 	}
@@ -452,7 +430,7 @@ func deleteUnusedLayers(deleteMap map[string]struct{}) error {
 
 	// only delete the files which are still in the deleteMap
 	for k := range deleteMap {
-		fp, err := GetBlobsPath(k)
+		fp, err := manifest.BlobsPath(k)
 		if err != nil {
 			slog.Info(fmt.Sprintf("couldn't get file path for '%s': %v", k, err))
 			continue
@@ -468,7 +446,7 @@ func deleteUnusedLayers(deleteMap map[string]struct{}) error {
 
 func PruneLayers() error {
 	deleteMap := make(map[string]struct{})
-	p, err := GetBlobsPath("")
+	p, err := manifest.BlobsPath("")
 	if err != nil {
 		return err
 	}
@@ -483,9 +461,9 @@ func PruneLayers() error {
 		name := blob.Name()
 		name = strings.ReplaceAll(name, "-", ":")
 
-		_, err := GetBlobsPath(name)
+		_, err := manifest.BlobsPath(name)
 		if err != nil {
-			if errors.Is(err, ErrInvalidDigestFormat) {
+			if errors.Is(err, manifest.ErrInvalidDigestFormat) {
 				// remove invalid blobs (e.g. partial downloads)
 				if err := os.Remove(filepath.Join(p, blob.Name())); err != nil {
 					slog.Error("couldn't remove blob", "blob", blob.Name(), "error", err)
@@ -510,63 +488,30 @@ func PruneLayers() error {
 	return nil
 }
 
-func PruneDirectory(path string) error {
-	info, err := os.Lstat(path)
-	if err != nil {
-		return err
-	}
-
-	if info.IsDir() && info.Mode()&os.ModeSymlink == 0 {
-		entries, err := os.ReadDir(path)
-		if err != nil {
-			return err
-		}
-
-		for _, entry := range entries {
-			if err := PruneDirectory(filepath.Join(path, entry.Name())); err != nil {
-				return err
-			}
-		}
-
-		entries, err = os.ReadDir(path)
-		if err != nil {
-			return err
-		}
-
-		if len(entries) > 0 {
-			return nil
-		}
-
-		return os.Remove(path)
-	}
-
-	return nil
-}
-
 func PushModel(ctx context.Context, name string, regOpts *registryOptions, fn func(api.ProgressResponse)) error {
-	mp := ParseModelPath(name)
+	n := model.ParseName(name)
 	fn(api.ProgressResponse{Status: "retrieving manifest"})
 
-	if mp.ProtocolScheme == "http" && !regOpts.Insecure {
+	if n.ProtocolScheme == "http" && !regOpts.Insecure {
 		return errInsecureProtocol
 	}
 
-	manifest, _, err := GetManifest(mp)
+	mf, err := manifest.ParseNamedManifest(n)
 	if err != nil {
 		fn(api.ProgressResponse{Status: "couldn't retrieve manifest"})
 		return err
 	}
 
-	var layers []Layer
-	layers = append(layers, manifest.Layers...)
-	if manifest.Config.Digest != "" {
-		layers = append(layers, manifest.Config)
+	var layers []manifest.Layer
+	layers = append(layers, mf.Layers...)
+	if mf.Config.Digest != "" {
+		layers = append(layers, mf.Config)
 	}
 
 	// Use fast transfer for models with tensor layers (many small blobs)
 	if hasTensorLayers(layers) {
 		// Read raw manifest JSON to preserve tensor metadata fields
-		manifestPath, err := mp.GetManifestPath()
+		manifestPath, err := manifest.PathForName(n)
 		if err != nil {
 			return err
 		}
@@ -574,7 +519,7 @@ func PushModel(ctx context.Context, name string, regOpts *registryOptions, fn fu
 		if err != nil {
 			return err
 		}
-		if err := pushWithTransfer(ctx, mp, layers, manifestJSON, regOpts, fn); err != nil {
+		if err := pushWithTransfer(ctx, n, layers, manifestJSON, regOpts, fn); err != nil {
 			return err
 		}
 		fn(api.ProgressResponse{Status: "success"})
@@ -582,17 +527,17 @@ func PushModel(ctx context.Context, name string, regOpts *registryOptions, fn fu
 	}
 
 	for _, layer := range layers {
-		if err := uploadBlob(ctx, mp, layer, regOpts, fn); err != nil {
+		if err := uploadBlob(ctx, n, layer, regOpts, fn); err != nil {
 			slog.Info(fmt.Sprintf("error uploading blob: %v", err))
 			return err
 		}
 	}
 
 	fn(api.ProgressResponse{Status: "pushing manifest"})
-	requestURL := mp.BaseURL()
-	requestURL = requestURL.JoinPath("v2", mp.GetNamespaceRepository(), "manifests", mp.Tag)
+	requestURL := n.BaseURL()
+	requestURL = requestURL.JoinPath("v2", n.DisplayNamespaceModel(), "manifests", n.Tag)
 
-	manifestJSON, err := json.Marshal(manifest)
+	manifestJSON, err := json.Marshal(mf)
 	if err != nil {
 		return err
 	}
@@ -611,44 +556,44 @@ func PushModel(ctx context.Context, name string, regOpts *registryOptions, fn fu
 }
 
 func PullModel(ctx context.Context, name string, regOpts *registryOptions, fn func(api.ProgressResponse)) error {
-	mp := ParseModelPath(name)
+	n := model.ParseName(name)
 
 	// build deleteMap to prune unused layers
 	deleteMap := make(map[string]struct{})
-	manifest, _, err := GetManifest(mp)
+	existingMf, err := manifest.ParseNamedManifest(n)
 	if errors.Is(err, os.ErrNotExist) {
 		// noop
 	} else if err != nil {
 		slog.Warn("pulling model with bad existing manifest", "name", name, "error", err)
 	} else {
-		for _, l := range manifest.Layers {
+		for _, l := range existingMf.Layers {
 			deleteMap[l.Digest] = struct{}{}
 		}
-		if manifest.Config.Digest != "" {
-			deleteMap[manifest.Config.Digest] = struct{}{}
+		if existingMf.Config.Digest != "" {
+			deleteMap[existingMf.Config.Digest] = struct{}{}
 		}
 	}
 
-	if mp.ProtocolScheme == "http" && !regOpts.Insecure {
+	if n.ProtocolScheme == "http" && !regOpts.Insecure {
 		return errInsecureProtocol
 	}
 
 	fn(api.ProgressResponse{Status: "pulling manifest"})
 
-	manifest, err = pullModelManifest(ctx, mp, regOpts)
+	mf, err := pullModelManifest(ctx, n, regOpts)
 	if err != nil {
 		return fmt.Errorf("pull model manifest: %s", err)
 	}
 
-	var layers []Layer
-	layers = append(layers, manifest.Layers...)
-	if manifest.Config.Digest != "" {
-		layers = append(layers, manifest.Config)
+	var layers []manifest.Layer
+	layers = append(layers, mf.Layers...)
+	if mf.Config.Digest != "" {
+		layers = append(layers, mf.Config)
 	}
 
 	// Use fast transfer for models with tensor layers (many small blobs)
 	if hasTensorLayers(layers) {
-		if err := pullWithTransfer(ctx, mp, layers, manifest, regOpts, fn); err != nil {
+		if err := pullWithTransfer(ctx, n, layers, mf, regOpts, fn); err != nil {
 			return err
 		}
 		fn(api.ProgressResponse{Status: "success"})
@@ -658,7 +603,7 @@ func PullModel(ctx context.Context, name string, regOpts *registryOptions, fn fu
 	skipVerify := make(map[string]bool)
 	for _, layer := range layers {
 		cacheHit, err := downloadBlob(ctx, downloadOpts{
-			mp:      mp,
+			n:       n,
 			digest:  layer.Digest,
 			regOpts: regOpts,
 			fn:      fn,
@@ -677,7 +622,7 @@ func PullModel(ctx context.Context, name string, regOpts *registryOptions, fn fu
 		}
 		if err := verifyBlob(layer.Digest); err != nil {
 			if errors.Is(err, errDigestMismatch) {
-				fp, err := GetBlobsPath(layer.Digest)
+				fp, err := manifest.BlobsPath(layer.Digest)
 				if err != nil {
 					return err
 				}
@@ -692,16 +637,16 @@ func PullModel(ctx context.Context, name string, regOpts *registryOptions, fn fu
 	for _, layer := range layers {
 		delete(deleteMap, layer.Digest)
 	}
-	delete(deleteMap, manifest.Config.Digest)
+	delete(deleteMap, mf.Config.Digest)
 
 	fn(api.ProgressResponse{Status: "writing manifest"})
 
-	manifestJSON, err := json.Marshal(manifest)
+	manifestJSON, err := json.Marshal(mf)
 	if err != nil {
 		return err
 	}
 
-	fp, err := mp.GetManifestPath()
+	fp, err := manifest.PathForName(n)
 	if err != nil {
 		return err
 	}
@@ -728,9 +673,9 @@ func PullModel(ctx context.Context, name string, regOpts *registryOptions, fn fu
 }
 
 // hasTensorLayers checks if any layer has tensor media type.
-func hasTensorLayers(layers []Layer) bool {
+func hasTensorLayers(layers []manifest.Layer) bool {
 	for _, layer := range layers {
-		if layer.MediaType == MediaTypeImageTensor {
+		if layer.MediaType == manifest.MediaTypeImageTensor {
 			return true
 		}
 	}
@@ -738,7 +683,7 @@ func hasTensorLayers(layers []Layer) bool {
 }
 
 // pullWithTransfer uses the simplified x/transfer package for downloading blobs.
-func pullWithTransfer(ctx context.Context, mp ModelPath, layers []Layer, manifest *Manifest, regOpts *registryOptions, fn func(api.ProgressResponse)) error {
+func pullWithTransfer(ctx context.Context, n model.Name, layers []manifest.Layer, mf *manifest.Manifest, regOpts *registryOptions, fn func(api.ProgressResponse)) error {
 	blobs := make([]transfer.Blob, len(layers))
 	for i, layer := range layers {
 		blobs[i] = transfer.Blob{
@@ -747,12 +692,12 @@ func pullWithTransfer(ctx context.Context, mp ModelPath, layers []Layer, manifes
 		}
 	}
 
-	destDir, err := GetBlobsPath("")
+	destDir, err := manifest.BlobsPath("")
 	if err != nil {
 		return err
 	}
 
-	base := mp.BaseURL()
+	base := n.BaseURL()
 	if base.Scheme != "http" && regOpts != nil && regOpts.Insecure {
 		base.Scheme = "http"
 	}
@@ -784,7 +729,7 @@ func pullWithTransfer(ctx context.Context, mp ModelPath, layers []Layer, manifes
 		Blobs:      blobs,
 		BaseURL:    baseURL,
 		DestDir:    destDir,
-		Repository: mp.GetNamespaceRepository(),
+		Repository: n.DisplayNamespaceModel(),
 		Progress:   progress,
 		Token:      regOpts.Token,
 		GetToken:   getToken,
@@ -795,12 +740,12 @@ func pullWithTransfer(ctx context.Context, mp ModelPath, layers []Layer, manifes
 
 	// Write manifest
 	fn(api.ProgressResponse{Status: "writing manifest"})
-	manifestJSON, err := json.Marshal(manifest)
+	manifestJSON, err := json.Marshal(mf)
 	if err != nil {
 		return err
 	}
 
-	fp, err := mp.GetManifestPath()
+	fp, err := manifest.PathForName(n)
 	if err != nil {
 		return err
 	}
@@ -812,7 +757,7 @@ func pullWithTransfer(ctx context.Context, mp ModelPath, layers []Layer, manifes
 }
 
 // pushWithTransfer uses the simplified x/transfer package for uploading blobs and manifest.
-func pushWithTransfer(ctx context.Context, mp ModelPath, layers []Layer, manifestJSON []byte, regOpts *registryOptions, fn func(api.ProgressResponse)) error {
+func pushWithTransfer(ctx context.Context, n model.Name, layers []manifest.Layer, manifestJSON []byte, regOpts *registryOptions, fn func(api.ProgressResponse)) error {
 	blobs := make([]transfer.Blob, len(layers))
 	for i, layer := range layers {
 		blobs[i] = transfer.Blob{
@@ -822,12 +767,12 @@ func pushWithTransfer(ctx context.Context, mp ModelPath, layers []Layer, manifes
 		}
 	}
 
-	srcDir, err := GetBlobsPath("")
+	srcDir, err := manifest.BlobsPath("")
 	if err != nil {
 		return err
 	}
 
-	base := mp.BaseURL()
+	base := n.BaseURL()
 	if base.Scheme != "http" && regOpts != nil && regOpts.Insecure {
 		base.Scheme = "http"
 	}
@@ -864,13 +809,13 @@ func pushWithTransfer(ctx context.Context, mp ModelPath, layers []Layer, manifes
 		GetToken:    getToken,
 		Logger:      slog.Default(),
 		Manifest:    manifestJSON,
-		ManifestRef: mp.Tag,
-		Repository:  mp.GetNamespaceRepository(),
+		ManifestRef: n.Tag,
+		Repository:  n.DisplayNamespaceModel(),
 	})
 }
 
-func pullModelManifest(ctx context.Context, mp ModelPath, regOpts *registryOptions) (*Manifest, error) {
-	requestURL := mp.BaseURL().JoinPath("v2", mp.GetNamespaceRepository(), "manifests", mp.Tag)
+func pullModelManifest(ctx context.Context, n model.Name, regOpts *registryOptions) (*manifest.Manifest, error) {
+	requestURL := n.BaseURL().JoinPath("v2", n.DisplayNamespaceModel(), "manifests", n.Tag)
 
 	headers := make(http.Header)
 	headers.Set("Accept", "application/vnd.docker.distribution.manifest.v2+json")
@@ -880,7 +825,7 @@ func pullModelManifest(ctx context.Context, mp ModelPath, regOpts *registryOptio
 	}
 	defer resp.Body.Close()
 
-	var m Manifest
+	var m manifest.Manifest
 	if err := json.NewDecoder(resp.Body).Decode(&m); err != nil {
 		return nil, err
 	}
@@ -1042,7 +987,7 @@ func parseRegistryChallenge(authStr string) registryChallenge {
 var errDigestMismatch = errors.New("digest mismatch, file must be downloaded again")
 
 func verifyBlob(digest string) error {
-	fp, err := GetBlobsPath(digest)
+	fp, err := manifest.BlobsPath(digest)
 	if err != nil {
 		return err
 	}

server/model.go

@@ -13,6 +13,7 @@ import (
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/manifest"
 	"github.com/ollama/ollama/template"
 	"github.com/ollama/ollama/types/model"
 )
@@ -20,19 +21,19 @@ import (
 var intermediateBlobs map[string]string = make(map[string]string)
 
 type layerGGML struct {
-	Layer
+	manifest.Layer
 	*ggml.GGML
 }
 
 func parseFromModel(ctx context.Context, name model.Name, fn func(api.ProgressResponse)) (layers []*layerGGML, err error) {
-	m, err := ParseNamedManifest(name)
+	m, err := manifest.ParseNamedManifest(name)
 	switch {
 	case errors.Is(err, os.ErrNotExist):
 		if err := PullModel(ctx, name.String(), &registryOptions{}, fn); err != nil {
 			return nil, err
 		}
 
-		m, err = ParseNamedManifest(name)
+		m, err = manifest.ParseNamedManifest(name)
 		if err != nil {
 			return nil, err
 		}
@@ -41,7 +42,7 @@ func parseFromModel(ctx context.Context, name model.Name, fn func(api.ProgressRe
 	}
 
 	for _, layer := range m.Layers {
-		layer, err := NewLayerFromLayer(layer.Digest, layer.MediaType, name.DisplayShortest())
+		layer, err := manifest.NewLayerFromLayer(layer.Digest, layer.MediaType, name.DisplayShortest())
 		if err != nil {
 			return nil, err
 		}
@@ -50,7 +51,7 @@ func parseFromModel(ctx context.Context, name model.Name, fn func(api.ProgressRe
 		case "application/vnd.ollama.image.model",
 			"application/vnd.ollama.image.projector",
 			"application/vnd.ollama.image.adapter":
-			blobpath, err := GetBlobsPath(layer.Digest)
+			blobpath, err := manifest.BlobsPath(layer.Digest)
 			if err != nil {
 				return nil, err
 			}
@@ -81,12 +82,12 @@ func detectChatTemplate(layers []*layerGGML) ([]*layerGGML, error) {
 			if t, err := template.Named(s); err != nil {
 				slog.Debug("template detection", "error", err, "template", s)
 			} else {
-				layer, err := NewLayer(t.Reader(), "application/vnd.ollama.image.template")
+				layer, err := manifest.NewLayer(t.Reader(), "application/vnd.ollama.image.template")
 				if err != nil {
 					return nil, err
 				}
 
-				layer.status = fmt.Sprintf("using autodetected template %s", t.Name)
+				layer.Status = fmt.Sprintf("using autodetected template %s", t.Name)
 				layers = append(layers, &layerGGML{layer, nil})
 
 				if t.Parameters != nil {
@@ -95,7 +96,7 @@ func detectChatTemplate(layers []*layerGGML) ([]*layerGGML, error) {
 						return nil, err
 					}
 
-					layer, err := NewLayer(&b, "application/vnd.ollama.image.params")
+					layer, err := manifest.NewLayer(&b, "application/vnd.ollama.image.params")
 					if err != nil {
 						return nil, err
 					}

server/modelpath.go

@@ -1,146 +0,0 @@
-package server
-
-import (
-	"errors"
-	"fmt"
-	"io/fs"
-	"net/url"
-	"os"
-	"path/filepath"
-	"regexp"
-	"strings"
-
-	"github.com/ollama/ollama/envconfig"
-	"github.com/ollama/ollama/types/model"
-)
-
-type ModelPath struct {
-	ProtocolScheme string
-	Registry       string
-	Namespace      string
-	Repository     string
-	Tag            string
-}
-
-const (
-	DefaultRegistry       = "registry.ollama.ai"
-	DefaultNamespace      = "library"
-	DefaultTag            = "latest"
-	DefaultProtocolScheme = "https"
-)
-
-var (
-	ErrInvalidImageFormat  = errors.New("invalid image format")
-	ErrInvalidDigestFormat = errors.New("invalid digest format")
-	ErrInvalidProtocol     = errors.New("invalid protocol scheme")
-	ErrInsecureProtocol    = errors.New("insecure protocol http")
-	ErrModelPathInvalid    = errors.New("invalid model path")
-)
-
-func ParseModelPath(name string) ModelPath {
-	mp := ModelPath{
-		ProtocolScheme: DefaultProtocolScheme,
-		Registry:       DefaultRegistry,
-		Namespace:      DefaultNamespace,
-		Repository:     "",
-		Tag:            DefaultTag,
-	}
-
-	before, after, found := strings.Cut(name, "://")
-	if found {
-		mp.ProtocolScheme = before
-		name = after
-	}
-
-	name = strings.ReplaceAll(name, string(os.PathSeparator), "/")
-	parts := strings.Split(name, "/")
-	switch len(parts) {
-	case 3:
-		mp.Registry = parts[0]
-		mp.Namespace = parts[1]
-		mp.Repository = parts[2]
-	case 2:
-		mp.Namespace = parts[0]
-		mp.Repository = parts[1]
-	case 1:
-		mp.Repository = parts[0]
-	}
-
-	if repo, tag, found := strings.Cut(mp.Repository, ":"); found {
-		mp.Repository = repo
-		mp.Tag = tag
-	}
-
-	return mp
-}
-
-func (mp ModelPath) GetNamespaceRepository() string {
-	return fmt.Sprintf("%s/%s", mp.Namespace, mp.Repository)
-}
-
-func (mp ModelPath) GetFullTagname() string {
-	return fmt.Sprintf("%s/%s/%s:%s", mp.Registry, mp.Namespace, mp.Repository, mp.Tag)
-}
-
-func (mp ModelPath) GetShortTagname() string {
-	if mp.Registry == DefaultRegistry {
-		if mp.Namespace == DefaultNamespace {
-			return fmt.Sprintf("%s:%s", mp.Repository, mp.Tag)
-		}
-		return fmt.Sprintf("%s/%s:%s", mp.Namespace, mp.Repository, mp.Tag)
-	}
-	return fmt.Sprintf("%s/%s/%s:%s", mp.Registry, mp.Namespace, mp.Repository, mp.Tag)
-}
-
-// GetManifestPath returns the path to the manifest file for the given model path, it is up to the caller to create the directory if it does not exist.
-func (mp ModelPath) GetManifestPath() (string, error) {
-	name := model.Name{
-		Host:      mp.Registry,
-		Namespace: mp.Namespace,
-		Model:     mp.Repository,
-		Tag:       mp.Tag,
-	}
-	if !name.IsValid() {
-		return "", fs.ErrNotExist
-	}
-	return filepath.Join(envconfig.Models(), "manifests", name.Filepath()), nil
-}
-
-func (mp ModelPath) BaseURL() *url.URL {
-	return &url.URL{
-		Scheme: mp.ProtocolScheme,
-		Host:   mp.Registry,
-	}
-}
-
-func GetManifestPath() (string, error) {
-	path := filepath.Join(envconfig.Models(), "manifests")
-	if err := os.MkdirAll(path, 0o755); err != nil {
-		return "", fmt.Errorf("%w: ensure path elements are traversable", err)
-	}
-
-	return path, nil
-}
-
-func GetBlobsPath(digest string) (string, error) {
-	// only accept actual sha256 digests
-	pattern := "^sha256[:-][0-9a-fA-F]{64}$"
-	re := regexp.MustCompile(pattern)
-
-	if digest != "" && !re.MatchString(digest) {
-		return "", ErrInvalidDigestFormat
-	}
-
-	digest = strings.ReplaceAll(digest, ":", "-")
-	path := filepath.Join(envconfig.Models(), "blobs", digest)
-	dirPath := filepath.Dir(path)
-	if digest == "" {
-		dirPath = path
-	}
-
-	if err := os.MkdirAll(dirPath, 0o755); err != nil {
-		return "", fmt.Errorf("%w: ensure path elements are traversable", err)
-	}
-
-	return path, nil
-}

server/modelpath_test.go

@@ -1,153 +0,0 @@
-package server
-
-import (
-	"path/filepath"
-	"testing"
-
-	"github.com/stretchr/testify/assert"
-	"github.com/stretchr/testify/require"
-)
-
-func TestGetBlobsPath(t *testing.T) {
-	// GetBlobsPath expects an actual directory to exist
-	tempDir := t.TempDir()
-
-	tests := []struct {
-		name     string
-		digest   string
-		expected string
-		err      error
-	}{
-		{
-			"empty digest",
-			"",
-			filepath.Join(tempDir, "blobs"),
-			nil,
-		},
-		{
-			"valid with colon",
-			"sha256:456402914e838a953e0cf80caa6adbe75383d9e63584a964f504a7bbb8f7aad9",
-			filepath.Join(tempDir, "blobs", "sha256-456402914e838a953e0cf80caa6adbe75383d9e63584a964f504a7bbb8f7aad9"),
-			nil,
-		},
-		{
-			"valid with dash",
-			"sha256-456402914e838a953e0cf80caa6adbe75383d9e63584a964f504a7bbb8f7aad9",
-			filepath.Join(tempDir, "blobs", "sha256-456402914e838a953e0cf80caa6adbe75383d9e63584a964f504a7bbb8f7aad9"),
-			nil,
-		},
-		{
-			"digest too short",
-			"sha256-45640291",
-			"",
-			ErrInvalidDigestFormat,
-		},
-		{
-			"digest too long",
-			"sha256-456402914e838a953e0cf80caa6adbe75383d9e63584a964f504a7bbb8f7aad9aaaaaaaaaa",
-			"",
-			ErrInvalidDigestFormat,
-		},
-		{
-			"digest invalid chars",
-			"../sha256-456402914e838a953e0cf80caa6adbe75383d9e63584a964f504a7bbb8f7a",
-			"",
-			ErrInvalidDigestFormat,
-		},
-	}
-	for _, tc := range tests {
-		t.Run(tc.name, func(t *testing.T) {
-			t.Setenv("OLLAMA_MODELS", tempDir)
-
-			got, err := GetBlobsPath(tc.digest)
-
-			require.ErrorIs(t, tc.err, err, tc.name)
-			assert.Equal(t, tc.expected, got, tc.name)
-		})
-	}
-}
-
-func TestParseModelPath(t *testing.T) {
-	tests := []struct {
-		name string
-		arg  string
-		want ModelPath
-	}{
-		{
-			"full path https",
-			"https://example.com/ns/repo:tag",
-			ModelPath{
-				ProtocolScheme: "https",
-				Registry:       "example.com",
-				Namespace:      "ns",
-				Repository:     "repo",
-				Tag:            "tag",
-			},
-		},
-		{
-			"full path http",
-			"http://example.com/ns/repo:tag",
-			ModelPath{
-				ProtocolScheme: "http",
-				Registry:       "example.com",
-				Namespace:      "ns",
-				Repository:     "repo",
-				Tag:            "tag",
-			},
-		},
-		{
-			"no protocol",
-			"example.com/ns/repo:tag",
-			ModelPath{
-				ProtocolScheme: "https",
-				Registry:       "example.com",
-				Namespace:      "ns",
-				Repository:     "repo",
-				Tag:            "tag",
-			},
-		},
-		{
-			"no registry",
-			"ns/repo:tag",
-			ModelPath{
-				ProtocolScheme: "https",
-				Registry:       DefaultRegistry,
-				Namespace:      "ns",
-				Repository:     "repo",
-				Tag:            "tag",
-			},
-		},
-		{
-			"no namespace",
-			"repo:tag",
-			ModelPath{
-				ProtocolScheme: "https",
-				Registry:       DefaultRegistry,
-				Namespace:      DefaultNamespace,
-				Repository:     "repo",
-				Tag:            "tag",
-			},
-		},
-		{
-			"no tag",
-			"repo",
-			ModelPath{
-				ProtocolScheme: "https",
-				Registry:       DefaultRegistry,
-				Namespace:      DefaultNamespace,
-				Repository:     "repo",
-				Tag:            DefaultTag,
-			},
-		},
-	}
-
-	for _, tc := range tests {
-		t.Run(tc.name, func(t *testing.T) {
-			got := ParseModelPath(tc.arg)
-
-			if got != tc.want {
-				t.Errorf("got: %q want: %q", got, tc.want)
-			}
-		})
-	}
-}

server/quantization.go

@@ -198,8 +198,8 @@ func newType(t *fsggml.Tensor, kv fsggml.KV, qs *quantizeState, ftype fsggml.Fil
 	name := t.Name
 	quantize := strings.HasSuffix(name, "weight")
 
-	// don't quantize vision stuff
-	quantize = quantize && (!strings.Contains(name, "v.") || strings.Contains(name, "_v."))
+	// don't quantize vision encoder tensors (named with "v." prefix)
+	quantize = quantize && !strings.HasPrefix(name, "v.")
 	quantize = quantize && !strings.Contains(name, "mm.")
 
 	// quantize only 2D and 3D tensors (experts)
@@ -219,6 +219,9 @@ func newType(t *fsggml.Tensor, kv fsggml.KV, qs *quantizeState, ftype fsggml.Fil
 	// NOTE: can't use LLM_TN here because the layer number is not known
 	quantize = quantize && !strings.Contains(name, "ssm_conv1d.weight")
 
+	// do not quantize LFM2's shortconv kernel weights
+	quantize = quantize && !strings.Contains(name, "shortconv.conv.weight")
+
 	// do not quantize RWKV's time_mix_first tensors
 	quantize = quantize && !strings.Contains(name, "time_mix_first.weight")
 	quantize = quantize && !strings.Contains(name, "time_mix_w1.weight")

server/routes.go

@@ -39,6 +39,7 @@ import (
 	"github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/llm"
 	"github.com/ollama/ollama/logutil"
+	"github.com/ollama/ollama/manifest"
 	"github.com/ollama/ollama/middleware"
 	"github.com/ollama/ollama/model/parsers"
 	"github.com/ollama/ollama/model/renderers"
@@ -220,12 +221,6 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 		return
 	}
 
-	// Handle image generation models
-	if slices.Contains(m.Capabilities(), model.CapabilityImage) {
-		s.handleImageGenerate(c, req, name.String(), checkpointStart)
-		return
-	}
-
 	if req.TopLogprobs < 0 || req.TopLogprobs > 20 {
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "top_logprobs must be between 0 and 20"})
 		return
@@ -321,7 +316,7 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 		return
 	}
 
-	// expire the runner
+	// expire the runner if unload is requested (empty prompt, keep alive is 0)
 	if req.Prompt == "" && req.KeepAlive != nil && req.KeepAlive.Duration == 0 {
 		s.sched.expireRunner(m)
 
@@ -335,6 +330,12 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 		return
 	}
 
+	// Handle image generation models
+	if slices.Contains(m.Capabilities(), model.CapabilityImage) {
+		s.handleImageGenerate(c, req, name.String(), checkpointStart)
+		return
+	}
+
 	if req.Raw && (req.Template != "" || req.System != "" || len(req.Context) > 0) {
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "raw mode does not support template, system, or context"})
 		return
@@ -974,7 +975,7 @@ func (s *Server) PushHandler(c *gin.Context) {
 // is.
 func getExistingName(n model.Name) (model.Name, error) {
 	var zero model.Name
-	existing, err := Manifests(true)
+	existing, err := manifest.Manifests(true)
 	if err != nil {
 		return zero, err
 	}
@@ -1018,7 +1019,7 @@ func (s *Server) DeleteHandler(c *gin.Context) {
 		return
 	}
 
-	m, err := ParseNamedManifest(n)
+	m, err := manifest.ParseNamedManifest(n)
 	if err != nil {
 		switch {
 		case os.IsNotExist(err):
@@ -1080,7 +1081,7 @@ func (s *Server) ShowHandler(c *gin.Context) {
 func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 	name := model.ParseName(req.Model)
 	if !name.IsValid() {
-		return nil, ErrModelPathInvalid
+		return nil, model.Unqualified(name)
 	}
 	name, err := getExistingName(name)
 	if err != nil {
@@ -1112,7 +1113,7 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 
 	// For safetensors LLM models (experimental), populate details from config.json
 	if m.Config.ModelFormat == "safetensors" && slices.Contains(m.Config.Capabilities, "completion") {
-		if info, err := xserver.GetSafetensorsLLMInfo(name.String()); err == nil {
+		if info, err := xserver.GetSafetensorsLLMInfo(name); err == nil {
 			if arch, ok := info["general.architecture"].(string); ok && arch != "" {
 				modelDetails.Family = arch
 			}
@@ -1121,7 +1122,7 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 			}
 		}
 		// Get torch_dtype directly from config.json for quantization level
-		if dtype, err := xserver.GetSafetensorsDtype(name.String()); err == nil && dtype != "" {
+		if dtype, err := xserver.GetSafetensorsDtype(name); err == nil && dtype != "" {
 			modelDetails.QuantizationLevel = dtype
 		}
 	}
@@ -1135,7 +1136,7 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 		msgs[i] = api.Message{Role: msg.Role, Content: msg.Content}
 	}
 
-	manifest, err := ParseNamedManifest(name)
+	mf, err := manifest.ParseNamedManifest(name)
 	if err != nil {
 		return nil, err
 	}
@@ -1147,8 +1148,11 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 		Details:      modelDetails,
 		Messages:     msgs,
 		Capabilities: m.Capabilities(),
-		ModifiedAt:   manifest.fi.ModTime(),
+		ModifiedAt:   mf.FileInfo().ModTime(),
 		Requires:     m.Config.Requires,
+		// Several integrations crash on a nil/omitempty+empty ModelInfo, so by
+		// default we return an empty map.
+		ModelInfo: make(map[string]any),
 	}
 
 	if m.Config.RemoteHost != "" {
@@ -1211,7 +1215,7 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 	if slices.Contains(m.Capabilities(), model.CapabilityImage) {
 		// Populate tensor info if verbose
 		if req.Verbose {
-			if tensors, err := xserver.GetSafetensorsTensorInfo(name.String()); err == nil {
+			if tensors, err := xserver.GetSafetensorsTensorInfo(name); err == nil {
 				resp.Tensors = tensors
 			}
 		}
@@ -1220,12 +1224,12 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 
 	// For safetensors LLM models (experimental), populate ModelInfo from config.json
 	if m.Config.ModelFormat == "safetensors" && slices.Contains(m.Config.Capabilities, "completion") {
-		if info, err := xserver.GetSafetensorsLLMInfo(name.String()); err == nil {
+		if info, err := xserver.GetSafetensorsLLMInfo(name); err == nil {
 			resp.ModelInfo = info
 		}
 		// Populate tensor info if verbose
 		if req.Verbose {
-			if tensors, err := xserver.GetSafetensorsTensorInfo(name.String()); err == nil {
+			if tensors, err := xserver.GetSafetensorsTensorInfo(name); err == nil {
 				resp.Tensors = tensors
 			}
 		}
@@ -1282,7 +1286,7 @@ func getModelData(digest string, verbose bool) (ggml.KV, ggml.Tensors, error) {
 }
 
 func (s *Server) ListHandler(c *gin.Context) {
-	ms, err := Manifests(true)
+	ms, err := manifest.Manifests(true)
 	if err != nil {
 		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 		return
@@ -1313,8 +1317,8 @@ func (s *Server) ListHandler(c *gin.Context) {
 			RemoteModel: cf.RemoteModel,
 			RemoteHost:  cf.RemoteHost,
 			Size:        m.Size(),
-			Digest:      m.digest,
-			ModifiedAt:  m.fi.ModTime(),
+			Digest:      m.Digest(),
+			ModifiedAt:  m.FileInfo().ModTime(),
 			Details: api.ModelDetails{
 				Format:            cf.ModelFormat,
 				Family:            cf.ModelFamily,
@@ -1373,7 +1377,7 @@ func (s *Server) CopyHandler(c *gin.Context) {
 }
 
 func (s *Server) HeadBlobHandler(c *gin.Context) {
-	path, err := GetBlobsPath(c.Param("digest"))
+	path, err := manifest.BlobsPath(c.Param("digest"))
 	if err != nil {
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
 		return
@@ -1389,7 +1393,7 @@ func (s *Server) HeadBlobHandler(c *gin.Context) {
 
 func (s *Server) CreateBlobHandler(c *gin.Context) {
 	if ib, ok := intermediateBlobs[c.Param("digest")]; ok {
-		p, err := GetBlobsPath(ib)
+		p, err := manifest.BlobsPath(ib)
 		if err != nil {
 			c.AbortWithStatusJSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 			return
@@ -1407,7 +1411,7 @@ func (s *Server) CreateBlobHandler(c *gin.Context) {
 		}
 	}
 
-	path, err := GetBlobsPath(c.Param("digest"))
+	path, err := manifest.BlobsPath(c.Param("digest"))
 	if err != nil {
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": err.Error()})
 		return
@@ -1425,7 +1429,7 @@ func (s *Server) CreateBlobHandler(c *gin.Context) {
 		return
 	}
 
-	layer, err := NewLayer(c.Request.Body, "")
+	layer, err := manifest.NewLayer(c.Request.Body, "")
 	if err != nil {
 		c.AbortWithStatusJSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
 		return
@@ -1625,7 +1629,7 @@ func Serve(ln net.Listener) error {
 	slog.SetDefault(logutil.NewLogger(os.Stderr, envconfig.LogLevel()))
 	slog.Info("server config", "env", envconfig.Values())
 
-	blobsDir, err := GetBlobsPath("")
+	blobsDir, err := manifest.BlobsPath("")
 	if err != nil {
 		return err
 	}
@@ -1634,7 +1638,7 @@ func Serve(ln net.Listener) error {
 	}
 
 	if !envconfig.NoPrune() {
-		if _, err := Manifests(false); err != nil {
+		if _, err := manifest.Manifests(false); err != nil {
 			slog.Warn("corrupt manifests detected, skipping prune operation.  Re-pull or delete to clear", "error", err)
 		} else {
 			// clean up unused layers and manifests
@@ -1642,12 +1646,12 @@ func Serve(ln net.Listener) error {
 				return err
 			}
 
-			manifestsPath, err := GetManifestPath()
+			manifestsPath, err := manifest.Path()
 			if err != nil {
 				return err
 			}
 
-			if err := PruneDirectory(manifestsPath); err != nil {
+			if err := manifest.PruneDirectory(manifestsPath); err != nil {
 				return err
 			}
 		}

server/routes_create_test.go

@@ -25,6 +25,7 @@ import (
 	"github.com/ollama/ollama/convert"
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/manifest"
 	"github.com/ollama/ollama/types/model"
 )
 
@@ -223,15 +224,15 @@ func TestCreateFromModelInheritsRendererParser(t *testing.T) {
 		t.Fatalf("expected status code 200, actual %d", w.Code)
 	}
 
-	manifest, err := ParseNamedManifest(model.ParseName("child"))
+	mf, err := manifest.ParseNamedManifest(model.ParseName("child"))
 	if err != nil {
 		t.Fatalf("parse manifest: %v", err)
 	}
-	if manifest.Config.Digest == "" {
+	if mf.Config.Digest == "" {
 		t.Fatalf("unexpected empty config digest for child manifest")
 	}
 
-	configPath, err := GetBlobsPath(manifest.Config.Digest)
+	configPath, err := manifest.BlobsPath(mf.Config.Digest)
 	if err != nil {
 		t.Fatalf("config blob path: %v", err)
 	}

server/routes_delete_test.go

@@ -10,6 +10,7 @@ import (
 	"github.com/gin-gonic/gin"
 
 	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/manifest"
 	"github.com/ollama/ollama/types/model"
 )
 
@@ -93,13 +94,13 @@ func TestDeleteDuplicateLayers(t *testing.T) {
 		t.Fatal(err)
 	}
 
-	config, err := NewLayer(&b, "application/vnd.docker.container.image.v1+json")
+	config, err := manifest.NewLayer(&b, "application/vnd.docker.container.image.v1+json")
 	if err != nil {
 		t.Fatal(err)
 	}
 
 	// create a manifest with duplicate layers
-	if err := WriteManifest(n, config, []Layer{config}); err != nil {
+	if err := manifest.WriteManifest(n, config, []manifest.Layer{config}); err != nil {
 		t.Fatal(err)
 	}
 

server/routes_generate_test.go

@@ -2101,3 +2101,95 @@ func TestChatWithPromptEndingInThinkTag(t *testing.T) {
 		}
 	})
 }
+
+func TestGenerateUnload(t *testing.T) {
+	gin.SetMode(gin.TestMode)
+
+	var loadFnCalled bool
+
+	s := Server{
+		sched: &Scheduler{
+			pendingReqCh:    make(chan *LlmRequest, 1),
+			finishedReqCh:   make(chan *LlmRequest, 1),
+			expiredCh:       make(chan *runnerRef, 1),
+			unloadedCh:      make(chan any, 1),
+			loaded:          make(map[string]*runnerRef),
+			newServerFn:     newMockServer(&mockRunner{}),
+			getGpuFn:        getGpuFn,
+			getSystemInfoFn: getSystemInfoFn,
+			loadFn: func(req *LlmRequest, _ *ggml.GGML, _ ml.SystemInfo, _ []ml.DeviceInfo, _ bool) bool {
+				loadFnCalled = true
+				req.successCh <- &runnerRef{llama: &mockRunner{}}
+				return false
+			},
+		},
+	}
+
+	go s.sched.Run(t.Context())
+
+	_, digest := createBinFile(t, ggml.KV{
+		"general.architecture":          "llama",
+		"llama.block_count":             uint32(1),
+		"llama.context_length":          uint32(8192),
+		"llama.embedding_length":        uint32(4096),
+		"llama.attention.head_count":    uint32(32),
+		"llama.attention.head_count_kv": uint32(8),
+		"tokenizer.ggml.tokens":         []string{""},
+		"tokenizer.ggml.scores":         []float32{0},
+		"tokenizer.ggml.token_type":     []int32{0},
+	}, []*ggml.Tensor{
+		{Name: "token_embd.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
+		{Name: "blk.0.attn_norm.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
+		{Name: "blk.0.ffn_down.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
+		{Name: "blk.0.ffn_gate.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
+		{Name: "blk.0.ffn_up.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
+		{Name: "blk.0.ffn_norm.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
+		{Name: "blk.0.attn_k.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
+		{Name: "blk.0.attn_output.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
+		{Name: "blk.0.attn_q.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
+		{Name: "blk.0.attn_v.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
+		{Name: "output.weight", Shape: []uint64{1}, WriterTo: bytes.NewReader(make([]byte, 4))},
+	})
+
+	w := createRequest(t, s.CreateHandler, api.CreateRequest{
+		Model:  "test",
+		Files:  map[string]string{"file.gguf": digest},
+		Stream: &stream,
+	})
+
+	if w.Code != http.StatusOK {
+		t.Fatalf("expected status 200, got %d", w.Code)
+	}
+
+	t.Run("unload with empty prompt and keepalive 0", func(t *testing.T) {
+		loadFnCalled = false
+
+		w := createRequest(t, s.GenerateHandler, api.GenerateRequest{
+			Model:     "test",
+			Prompt:    "",
+			KeepAlive: &api.Duration{Duration: 0},
+			Stream:    &stream,
+		})
+
+		if w.Code != http.StatusOK {
+			t.Errorf("expected status 200, got %d", w.Code)
+		}
+
+		var resp api.GenerateResponse
+		if err := json.Unmarshal(w.Body.Bytes(), &resp); err != nil {
+			t.Fatalf("failed to unmarshal response: %v", err)
+		}
+
+		if resp.DoneReason != "unload" {
+			t.Errorf("expected done_reason 'unload', got %q", resp.DoneReason)
+		}
+
+		if !resp.Done {
+			t.Error("expected done to be true")
+		}
+
+		if loadFnCalled {
+			t.Error("expected model NOT to be loaded for unload request, but loadFn was called")
+		}
+	})
+}

server/upload.go

@@ -21,12 +21,14 @@ import (
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/format"
+	"github.com/ollama/ollama/manifest"
+	"github.com/ollama/ollama/types/model"
 )
 
 var blobUploadManager sync.Map
 
 type blobUpload struct {
-	Layer
+	manifest.Layer
 
 	Total     int64
 	Completed atomic.Int64
@@ -51,7 +53,7 @@ const (
 )
 
 func (b *blobUpload) Prepare(ctx context.Context, requestURL *url.URL, opts *registryOptions) error {
-	p, err := GetBlobsPath(b.Digest)
+	p, err := manifest.BlobsPath(b.Digest)
 	if err != nil {
 		return err
 	}
@@ -59,7 +61,7 @@ func (b *blobUpload) Prepare(ctx context.Context, requestURL *url.URL, opts *reg
 	if b.From != "" {
 		values := requestURL.Query()
 		values.Add("mount", b.Digest)
-		values.Add("from", ParseModelPath(b.From).GetNamespaceRepository())
+		values.Add("from", model.ParseName(b.From).DisplayNamespaceModel())
 		requestURL.RawQuery = values.Encode()
 	}
 
@@ -128,7 +130,7 @@ func (b *blobUpload) Run(ctx context.Context, opts *registryOptions) {
 	defer blobUploadManager.Delete(b.Digest)
 	ctx, b.CancelFunc = context.WithCancel(ctx)
 
-	p, err := GetBlobsPath(b.Digest)
+	p, err := manifest.BlobsPath(b.Digest)
 	if err != nil {
 		b.err = err
 		return
@@ -364,9 +366,9 @@ func (p *progressWriter) Rollback() {
 	p.written = 0
 }
 
-func uploadBlob(ctx context.Context, mp ModelPath, layer Layer, opts *registryOptions, fn func(api.ProgressResponse)) error {
-	requestURL := mp.BaseURL()
-	requestURL = requestURL.JoinPath("v2", mp.GetNamespaceRepository(), "blobs", layer.Digest)
+func uploadBlob(ctx context.Context, n model.Name, layer manifest.Layer, opts *registryOptions, fn func(api.ProgressResponse)) error {
+	requestURL := n.BaseURL()
+	requestURL = requestURL.JoinPath("v2", n.DisplayNamespaceModel(), "blobs", layer.Digest)
 
 	resp, err := makeRequestWithRetry(ctx, http.MethodHead, requestURL, nil, nil, opts)
 	switch {
@@ -388,8 +390,8 @@ func uploadBlob(ctx context.Context, mp ModelPath, layer Layer, opts *registryOp
 	data, ok := blobUploadManager.LoadOrStore(layer.Digest, &blobUpload{Layer: layer})
 	upload := data.(*blobUpload)
 	if !ok {
-		requestURL := mp.BaseURL()
-		requestURL = requestURL.JoinPath("v2", mp.GetNamespaceRepository(), "blobs/uploads/")
+		requestURL := n.BaseURL()
+		requestURL = requestURL.JoinPath("v2", n.DisplayNamespaceModel(), "blobs/uploads/")
 		if err := upload.Prepare(ctx, requestURL, opts); err != nil {
 			blobUploadManager.Delete(layer.Digest)
 			return err

types/model/name.go

@@ -7,6 +7,7 @@ import (
 	"errors"
 	"fmt"
 	"log/slog"
+	"net/url"
 	"path/filepath"
 	"strings"
 )
@@ -35,22 +36,25 @@ func Unqualified(n Name) error {
 const MissingPart = "!MISSING!"
 
 const (
-	defaultHost      = "registry.ollama.ai"
-	defaultNamespace = "library"
-	defaultTag       = "latest"
+	defaultHost           = "registry.ollama.ai"
+	defaultNamespace      = "library"
+	defaultTag            = "latest"
+	defaultProtocolScheme = "https"
 )
 
 // DefaultName returns a name with the default values for the host, namespace,
-// and tag parts. The model and digest parts are empty.
+// tag, and protocol scheme parts. The model and digest parts are empty.
 //
 //   - The default host is ("registry.ollama.ai")
 //   - The default namespace is ("library")
 //   - The default tag is ("latest")
+//   - The default protocol scheme is ("https")
 func DefaultName() Name {
 	return Name{
-		Host:      defaultHost,
-		Namespace: defaultNamespace,
-		Tag:       defaultTag,
+		Host:           defaultHost,
+		Namespace:      defaultNamespace,
+		Tag:            defaultTag,
+		ProtocolScheme: defaultProtocolScheme,
 	}
 }
 
@@ -87,10 +91,11 @@ func (k partKind) String() string {
 // It is not guaranteed to be valid. Use [Name.IsValid] to check if the name
 // is valid.
 type Name struct {
-	Host      string
-	Namespace string
-	Model     string
-	Tag       string
+	Host           string
+	Namespace      string
+	Model          string
+	Tag            string
+	ProtocolScheme string
 }
 
 // ParseName parses and assembles a Name from a name string. The
@@ -160,7 +165,9 @@ func ParseNameBare(s string) Name {
 	}
 
 	scheme, host, ok := strings.Cut(s, "://")
-	if !ok {
+	if ok {
+		n.ProtocolScheme = scheme
+	} else {
 		host = scheme
 	}
 	n.Host = host
@@ -189,12 +196,13 @@ func ParseNameFromFilepath(s string) (n Name) {
 	return n
 }
 
-// Merge merges the host, namespace, and tag parts of the two names,
+// Merge merges the host, namespace, tag, and protocol scheme parts of the two names,
 // preferring the non-empty parts of a.
 func Merge(a, b Name) Name {
 	a.Host = cmp.Or(a.Host, b.Host)
 	a.Namespace = cmp.Or(a.Namespace, b.Namespace)
 	a.Tag = cmp.Or(a.Tag, b.Tag)
+	a.ProtocolScheme = cmp.Or(a.ProtocolScheme, b.ProtocolScheme)
 	return a
 }
 
@@ -305,6 +313,23 @@ func (n Name) EqualFold(o Name) bool {
 		strings.EqualFold(n.Tag, o.Tag)
 }
 
+// BaseURL returns the base URL for the registry.
+func (n Name) BaseURL() *url.URL {
+	return &url.URL{
+		Scheme: n.ProtocolScheme,
+		Host:   n.Host,
+	}
+}
+
+// DisplayNamespaceModel returns the namespace and model joined by "/".
+func (n Name) DisplayNamespaceModel() string {
+	var b strings.Builder
+	b.WriteString(n.Namespace)
+	b.WriteByte('/')
+	b.WriteString(n.Model)
+	return b.String()
+}
+
 func isValidLen(kind partKind, s string) bool {
 	switch kind {
 	case kindHost:

types/model/name_test.go

@@ -32,10 +32,11 @@ func TestParseNameParts(t *testing.T) {
 		{
 			in: "scheme://host:port/namespace/model:tag",
 			want: Name{
-				Host:      "host:port",
-				Namespace: "namespace",
-				Model:     "model",
-				Tag:       "tag",
+				Host:           "host:port",
+				Namespace:      "namespace",
+				Model:          "model",
+				Tag:            "tag",
+				ProtocolScheme: "scheme",
 			},
 			wantFilepath: filepath.Join("host:port", "namespace", "model", "tag"),
 		},

x/create/client/create.go

@@ -12,8 +12,8 @@ import (
 	"fmt"
 	"io"
 
+	"github.com/ollama/ollama/manifest"
 	"github.com/ollama/ollama/progress"
-	"github.com/ollama/ollama/server"
 	"github.com/ollama/ollama/types/model"
 	"github.com/ollama/ollama/x/create"
 )
@@ -103,7 +103,7 @@ func CreateModel(opts CreateOptions, p *progress.Progress) error {
 // newLayerCreator returns a LayerCreator callback for creating config/JSON layers.
 func newLayerCreator() create.LayerCreator {
 	return func(r io.Reader, mediaType, name string) (create.LayerInfo, error) {
-		layer, err := server.NewLayer(r, mediaType)
+		layer, err := manifest.NewLayer(r, mediaType)
 		if err != nil {
 			return create.LayerInfo{}, err
 		}
@@ -141,13 +141,13 @@ func createQuantizedLayers(r io.Reader, name, dtype string, shape []int32, quant
 	}
 
 	// Create layer for quantized weight
-	weightLayer, err := server.NewLayer(bytes.NewReader(qweightData), server.MediaTypeImageTensor)
+	weightLayer, err := manifest.NewLayer(bytes.NewReader(qweightData), manifest.MediaTypeImageTensor)
 	if err != nil {
 		return nil, err
 	}
 
 	// Create layer for scales
-	scalesLayer, err := server.NewLayer(bytes.NewReader(scalesData), server.MediaTypeImageTensor)
+	scalesLayer, err := manifest.NewLayer(bytes.NewReader(scalesData), manifest.MediaTypeImageTensor)
 	if err != nil {
 		return nil, err
 	}
@@ -169,7 +169,7 @@ func createQuantizedLayers(r io.Reader, name, dtype string, shape []int32, quant
 
 	// Add qbiases layer if present (affine mode)
 	if qbiasData != nil {
-		qbiasLayer, err := server.NewLayer(bytes.NewReader(qbiasData), server.MediaTypeImageTensor)
+		qbiasLayer, err := manifest.NewLayer(bytes.NewReader(qbiasData), manifest.MediaTypeImageTensor)
 		if err != nil {
 			return nil, err
 		}
@@ -186,7 +186,7 @@ func createQuantizedLayers(r io.Reader, name, dtype string, shape []int32, quant
 
 // createUnquantizedLayer creates a single tensor layer without quantization.
 func createUnquantizedLayer(r io.Reader, name string) ([]create.LayerInfo, error) {
-	layer, err := server.NewLayer(r, server.MediaTypeImageTensor)
+	layer, err := manifest.NewLayer(r, manifest.MediaTypeImageTensor)
 	if err != nil {
 		return nil, err
 	}
@@ -221,15 +221,15 @@ func newManifestWriter(opts CreateOptions, capabilities []string) create.Manifes
 		}
 
 		// Create config layer blob
-		configLayer, err := server.NewLayer(bytes.NewReader(configJSON), "application/vnd.docker.container.image.v1+json")
+		configLayer, err := manifest.NewLayer(bytes.NewReader(configJSON), "application/vnd.docker.container.image.v1+json")
 		if err != nil {
 			return fmt.Errorf("failed to create config layer: %w", err)
 		}
 
-		// Convert LayerInfo to server.Layer
-		serverLayers := make([]server.Layer, 0, len(layers))
+		// Convert LayerInfo to manifest.Layer
+		manifestLayers := make([]manifest.Layer, 0, len(layers))
 		for _, l := range layers {
-			serverLayers = append(serverLayers, server.Layer{
+			manifestLayers = append(manifestLayers, manifest.Layer{
 				MediaType: l.MediaType,
 				Digest:    l.Digest,
 				Size:      l.Size,
@@ -243,19 +243,19 @@ func newManifestWriter(opts CreateOptions, capabilities []string) create.Manifes
 			if err != nil {
 				return err
 			}
-			serverLayers = append(serverLayers, modelfileLayers...)
+			manifestLayers = append(manifestLayers, modelfileLayers...)
 		}
 
-		return server.WriteManifest(name, configLayer, serverLayers)
+		return manifest.WriteManifest(name, configLayer, manifestLayers)
 	}
 }
 
 // createModelfileLayers creates layers for template, system, and license from Modelfile config.
-func createModelfileLayers(mf *ModelfileConfig) ([]server.Layer, error) {
-	var layers []server.Layer
+func createModelfileLayers(mf *ModelfileConfig) ([]manifest.Layer, error) {
+	var layers []manifest.Layer
 
 	if mf.Template != "" {
-		layer, err := server.NewLayer(bytes.NewReader([]byte(mf.Template)), "application/vnd.ollama.image.template")
+		layer, err := manifest.NewLayer(bytes.NewReader([]byte(mf.Template)), "application/vnd.ollama.image.template")
 		if err != nil {
 			return nil, fmt.Errorf("failed to create template layer: %w", err)
 		}
@@ -263,7 +263,7 @@ func createModelfileLayers(mf *ModelfileConfig) ([]server.Layer, error) {
 	}
 
 	if mf.System != "" {
-		layer, err := server.NewLayer(bytes.NewReader([]byte(mf.System)), "application/vnd.ollama.image.system")
+		layer, err := manifest.NewLayer(bytes.NewReader([]byte(mf.System)), "application/vnd.ollama.image.system")
 		if err != nil {
 			return nil, fmt.Errorf("failed to create system layer: %w", err)
 		}
@@ -271,7 +271,7 @@ func createModelfileLayers(mf *ModelfileConfig) ([]server.Layer, error) {
 	}
 
 	if mf.License != "" {
-		layer, err := server.NewLayer(bytes.NewReader([]byte(mf.License)), "application/vnd.ollama.image.license")
+		layer, err := manifest.NewLayer(bytes.NewReader([]byte(mf.License)), "application/vnd.ollama.image.license")
 		if err != nil {
 			return nil, fmt.Errorf("failed to create license layer: %w", err)
 		}

x/imagegen/cache/step.go

@@ -9,7 +9,7 @@ import "github.com/ollama/ollama/x/imagegen/mlx"
 // shallow layers change little between consecutive steps, so we can
 // cache their outputs and skip recomputation on non-refresh steps.
 //
-// Supports both single-stream (Z-Image) and dual-stream (Qwen-Image) architectures:
+// Supports both single-stream and dual-stream architectures:
 //   - Single-stream: use Get/Set for the single output per layer
 //   - Dual-stream: use Get/Set for stream 1 (imgH), Get2/Set2 for stream 2 (txtH)
 //
@@ -87,7 +87,7 @@ func (c *StepCache) Set(layer int, arr *mlx.Array) {
 }
 
 // Get2 returns the cached output for a layer (stream 2), or nil if not cached.
-// Used for dual-stream architectures like Qwen-Image.
+// Used for dual-stream architectures.
 func (c *StepCache) Get2(layer int) *mlx.Array {
 	if layer < len(c.layers2) {
 		return c.layers2[layer]
@@ -96,7 +96,7 @@ func (c *StepCache) Get2(layer int) *mlx.Array {
 }
 
 // Set2 stores a layer output (stream 2), freeing any previous value.
-// Used for dual-stream architectures like Qwen-Image.
+// Used for dual-stream architectures.
 func (c *StepCache) Set2(layer int, arr *mlx.Array) {
 	if layer < len(c.layers2) {
 		if c.layers2[layer] != nil {

x/imagegen/cmd/engine/main.go

@@ -21,8 +21,6 @@ import (
 	"github.com/ollama/ollama/x/imagegen/models/gemma3"
 	"github.com/ollama/ollama/x/imagegen/models/gpt_oss"
 	"github.com/ollama/ollama/x/imagegen/models/llama"
-	"github.com/ollama/ollama/x/imagegen/models/qwen_image"
-	"github.com/ollama/ollama/x/imagegen/models/qwen_image_edit"
 	"github.com/ollama/ollama/x/imagegen/models/zimage"
 	"github.com/ollama/ollama/x/imagegen/safetensors"
 )
@@ -61,14 +59,11 @@ func main() {
 	listTensors := flag.Bool("list", false, "List tensors only")
 	cpuProfile := flag.String("cpuprofile", "", "Write CPU profile to file")
 	gpuCapture := flag.String("gpu-capture", "", "Capture GPU trace to .gputrace file (run with MTL_CAPTURE_ENABLED=1)")
-	layerCache := flag.Bool("layer-cache", false, "Enable layer caching for faster diffusion (Z-Image, Qwen-Image). Not compatible with CFG/negative prompts.")
 	wiredLimitGB := flag.Int("wired-limit", 32, "Metal wired memory limit in GB")
 
 	// Legacy mode flags
 	zimageFlag := flag.Bool("zimage", false, "Z-Image generation")
 	flux2Flag := flag.Bool("flux2", false, "FLUX.2 Klein generation")
-	qwenImage := flag.Bool("qwen-image", false, "Qwen-Image text-to-image generation")
-	qwenImageEdit := flag.Bool("qwen-image-edit", false, "Qwen-Image-Edit image editing")
 	var inputImages stringSlice
 	flag.Var(&inputImages, "input-image", "Input image for image editing (can be specified multiple times)")
 	negativePrompt := flag.String("negative-prompt", "", "Negative prompt for CFG (empty = no CFG, matching Python)")
@@ -166,60 +161,6 @@ func main() {
 		if err == nil {
 			err = saveImageArray(img, *out)
 		}
-	case *qwenImage:
-		m, loadErr := qwen_image.LoadPersistent(*modelPath)
-		if loadErr != nil {
-			log.Fatal(loadErr)
-		}
-		var img *mlx.Array
-		img, err = m.GenerateFromConfig(&qwen_image.GenerateConfig{
-			Prompt:         *prompt,
-			NegativePrompt: *negativePrompt,
-			CFGScale:       float32(*cfgScale),
-			Width:          int32(*width),
-			Height:         int32(*height),
-			Steps:          *steps,
-			Seed:           *seed,
-			LayerCache:     *layerCache,
-		})
-		if err == nil {
-			err = saveImageArray(img, *out)
-		}
-	case *qwenImageEdit:
-		if len(inputImages) == 0 {
-			log.Fatal("qwen-image-edit requires at least one -input-image")
-		}
-
-		m, loadErr := qwen_image_edit.LoadPersistent(*modelPath)
-		if loadErr != nil {
-			log.Fatal(loadErr)
-		}
-		// For image editing, use 0 for dimensions to auto-detect from input image
-		// unless explicitly overridden from defaults
-		editWidth := int32(0)
-		editHeight := int32(0)
-		if *width != 1024 {
-			editWidth = int32(*width)
-		}
-		if *height != 1024 {
-			editHeight = int32(*height)
-		}
-
-		cfg := &qwen_image_edit.GenerateConfig{
-			Prompt:         *prompt,
-			NegativePrompt: *negativePrompt,
-			CFGScale:       float32(*cfgScale),
-			Width:          editWidth,
-			Height:         editHeight,
-			Steps:          *steps,
-			Seed:           *seed,
-		}
-
-		var img *mlx.Array
-		img, err = m.EditFromConfig(inputImages, cfg)
-		if err == nil {
-			err = saveImageArray(img, *out)
-		}
 	case *listTensors:
 		err = listModelTensors(*modelPath)
 	default:

x/imagegen/manifest.go

@@ -6,8 +6,9 @@ import (
 	"io"
 	"os"
 	"path/filepath"
-	"runtime"
 	"strings"
+
+	"github.com/ollama/ollama/envconfig"
 )
 
 // ManifestLayer represents a layer in the manifest.
@@ -32,31 +33,15 @@ type ModelManifest struct {
 	BlobDir  string
 }
 
-// DefaultBlobDir returns the default blob storage directory.
 func DefaultBlobDir() string {
-	home, err := os.UserHomeDir()
-	if err != nil {
-		home = "."
-	}
-	switch runtime.GOOS {
-	case "darwin":
-		return filepath.Join(home, ".ollama", "models", "blobs")
-	case "linux":
-		return filepath.Join(home, ".ollama", "models", "blobs")
-	case "windows":
-		return filepath.Join(home, ".ollama", "models", "blobs")
-	default:
-		return filepath.Join(home, ".ollama", "models", "blobs")
-	}
+	return filepath.Join(envconfig.Models(), "blobs")
 }
 
-// DefaultManifestDir returns the default manifest storage directory.
+// DefaultManifestDir returns the manifest storage directory.
+// Respects OLLAMA_MODELS.
+
 func DefaultManifestDir() string {
-	home, err := os.UserHomeDir()
-	if err != nil {
-		home = "."
-	}
-	return filepath.Join(home, ".ollama", "models", "manifests")
+	return filepath.Join(envconfig.Models(), "manifests")
 }
 
 // LoadManifest loads a manifest for the given model name.

x/imagegen/manifest_test.go

@@ -0,0 +1,26 @@
+package imagegen
+
+import (
+	"path/filepath"
+	"testing"
+)
+
+func TestManifestAndBlobDirsRespectOLLAMAModels(t *testing.T) {
+	modelsDir := filepath.Join(t.TempDir(), "models")
+
+	// Simulate packaged/systemd environment
+	t.Setenv("OLLAMA_MODELS", modelsDir)
+	t.Setenv("HOME", "/usr/share/ollama")
+
+	// Manifest dir must respect OLLAMA_MODELS
+	wantManifest := filepath.Join(modelsDir, "manifests")
+	if got := DefaultManifestDir(); got != wantManifest {
+		t.Fatalf("DefaultManifestDir() = %q, want %q", got, wantManifest)
+	}
+
+	// Blob dir must respect OLLAMA_MODELS
+	wantBlobs := filepath.Join(modelsDir, "blobs")
+	if got := DefaultBlobDir(); got != wantBlobs {
+		t.Fatalf("DefaultBlobDir() = %q, want %q", got, wantBlobs)
+	}
+}

x/imagegen/models/qwen_image/pipeline_test.go

@@ -1,87 +0,0 @@
-//go:build mlx
-
-package qwen_image
-
-import (
-	"fmt"
-	"os"
-	"path/filepath"
-	"runtime"
-	"testing"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-)
-
-// TestMain initializes MLX before running tests.
-// If MLX libraries are not available, tests are skipped.
-func TestMain(m *testing.M) {
-	// Change to repo root so ./build/lib/ollama/ path works
-	_, thisFile, _, _ := runtime.Caller(0)
-	repoRoot := filepath.Join(filepath.Dir(thisFile), "..", "..", "..", "..")
-	if err := os.Chdir(repoRoot); err != nil {
-		fmt.Printf("Failed to change to repo root: %v\n", err)
-		os.Exit(1)
-	}
-
-	if err := mlx.InitMLX(); err != nil {
-		fmt.Printf("Skipping qwen_image tests: %v\n", err)
-		os.Exit(0)
-	}
-	os.Exit(m.Run())
-}
-
-// TestPipelineOutput runs the full pipeline (integration test).
-// Skips if model weights not found. Requires ~50GB VRAM.
-func TestPipelineOutput(t *testing.T) {
-	modelPath := "../../../weights/Qwen-Image-2512"
-	if _, err := os.Stat(modelPath); os.IsNotExist(err) {
-		t.Skip("Skipping: model weights not found at " + modelPath)
-	}
-
-	// Load model
-	pm, err := LoadPersistent(modelPath)
-	if err != nil {
-		t.Skipf("Skipping: failed to load model: %v", err)
-	}
-
-	// Run 2-step pipeline (minimum for stable scheduler)
-	cfg := &GenerateConfig{
-		Prompt: "a cat",
-		Width:  256,
-		Height: 256,
-		Steps:  2,
-		Seed:   42,
-	}
-
-	output, err := pm.GenerateFromConfig(cfg)
-	if err != nil {
-		t.Fatalf("Pipeline failed: %v", err)
-	}
-	mlx.Eval(output)
-
-	// Verify output shape [1, C, H, W]
-	shape := output.Shape()
-	if len(shape) != 4 {
-		t.Errorf("Expected 4D output, got %v", shape)
-	}
-	if shape[0] != 1 || shape[1] != 3 || shape[2] != cfg.Height || shape[3] != cfg.Width {
-		t.Errorf("Shape mismatch: got %v, expected [1, 3, %d, %d]", shape, cfg.Height, cfg.Width)
-	}
-
-	// Verify values in expected range [0, 1]
-	data := output.Data()
-	minVal, maxVal := float32(1.0), float32(0.0)
-	for _, v := range data {
-		if v < minVal {
-			minVal = v
-		}
-		if v > maxVal {
-			maxVal = v
-		}
-	}
-	t.Logf("Output range: [%.4f, %.4f]", minVal, maxVal)
-
-	if minVal < -0.1 || maxVal > 1.1 {
-		t.Errorf("Output values out of range: [%.4f, %.4f]", minVal, maxVal)
-	}
-}

x/imagegen/models/qwen_image/qwen_image.go

@@ -1,367 +0,0 @@
-//go:build mlx
-
-// Package qwen_image implements the Qwen-Image diffusion transformer model.
-package qwen_image
-
-import (
-	"context"
-	"fmt"
-	"path/filepath"
-	"time"
-
-	"github.com/ollama/ollama/x/imagegen/cache"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/tokenizer"
-)
-
-// GenerateConfig holds all options for image generation.
-type GenerateConfig struct {
-	Prompt         string
-	NegativePrompt string                // Empty = no CFG
-	CFGScale       float32               // Only used if NegativePrompt is set (default: 4.0)
-	Width          int32                 // Image width (default: 1024)
-	Height         int32                 // Image height (default: 1024)
-	Steps          int                   // Denoising steps (default: 30)
-	Seed           int64                 // Random seed
-	Progress       func(step, totalSteps int) // Optional progress callback
-
-	// Layer caching (DeepCache/Learning-to-Cache speedup)
-	LayerCache    bool // Enable layer caching (default: false)
-	CacheInterval int  // Refresh cache every N steps (default: 3)
-	CacheLayers   int  // Number of shallow layers to cache (default: 25)
-}
-
-// Model represents a Qwen-Image diffusion model.
-type Model struct {
-	ModelPath   string
-	Tokenizer   *tokenizer.Tokenizer
-	TextEncoder *Qwen25VL
-	Transformer *Transformer
-	VAEDecoder  *VAEDecoder
-}
-
-// Load loads the Qwen-Image model from a directory.
-func (m *Model) Load(modelPath string) error {
-	fmt.Println("Loading Qwen-Image model...")
-	start := time.Now()
-
-	if mlx.GPUIsAvailable() {
-		mlx.SetDefaultDeviceGPU()
-		mlx.EnableCompile()
-	}
-
-	m.ModelPath = modelPath
-
-	// Load tokenizer
-	fmt.Print("  Loading tokenizer... ")
-	tokenizerPath := filepath.Join(modelPath, "tokenizer")
-	tok, err := tokenizer.Load(tokenizerPath)
-	if err != nil {
-		return fmt.Errorf("tokenizer: %w", err)
-	}
-	m.Tokenizer = tok
-	fmt.Println("✓")
-
-	// Load text encoder (Qwen2.5-VL in text-only mode - skip vision tower for efficiency)
-	m.TextEncoder = &Qwen25VL{}
-	if err := m.TextEncoder.LoadTextOnly(filepath.Join(modelPath, "text_encoder")); err != nil {
-		return fmt.Errorf("text encoder: %w", err)
-	}
-	mlx.Eval(mlx.Collect(m.TextEncoder)...)
-	fmt.Printf("  (%.1f GB, peak %.1f GB)\n",
-		float64(mlx.MetalGetActiveMemory())/(1024*1024*1024),
-		float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	// Load transformer
-	m.Transformer = &Transformer{}
-	if err := m.Transformer.Load(filepath.Join(modelPath, "transformer")); err != nil {
-		return fmt.Errorf("transformer: %w", err)
-	}
-	mlx.Eval(mlx.Collect(m.Transformer)...)
-	fmt.Printf("  (%.1f GB, peak %.1f GB)\n",
-		float64(mlx.MetalGetActiveMemory())/(1024*1024*1024),
-		float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	// Load VAE decoder
-	m.VAEDecoder = &VAEDecoder{}
-	if err := m.VAEDecoder.Load(filepath.Join(modelPath, "vae")); err != nil {
-		return fmt.Errorf("VAE decoder: %w", err)
-	}
-	mlx.Eval(mlx.Collect(m.VAEDecoder)...)
-	fmt.Printf("  (%.1f GB, peak %.1f GB)\n",
-		float64(mlx.MetalGetActiveMemory())/(1024*1024*1024),
-		float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	mem := mlx.MetalGetActiveMemory()
-	peak := mlx.MetalGetPeakMemory()
-	fmt.Printf("  Loaded in %.2fs (%.1f GB active, %.1f GB peak)\n",
-		time.Since(start).Seconds(),
-		float64(mem)/(1024*1024*1024),
-		float64(peak)/(1024*1024*1024))
-
-	return nil
-}
-
-// Generate creates an image from a prompt.
-func (m *Model) Generate(prompt string, width, height int32, steps int, seed int64) (*mlx.Array, error) {
-	return m.GenerateFromConfig(&GenerateConfig{
-		Prompt: prompt,
-		Width:  width,
-		Height: height,
-		Steps:  steps,
-		Seed:   seed,
-	})
-}
-
-// GenerateWithProgress creates an image with progress callback.
-func (m *Model) GenerateWithProgress(prompt string, width, height int32, steps int, seed int64, progress func(step, totalSteps int)) (*mlx.Array, error) {
-	return m.GenerateFromConfig(&GenerateConfig{
-		Prompt:   prompt,
-		Width:    width,
-		Height:   height,
-		Steps:    steps,
-		Seed:     seed,
-		Progress: progress,
-	})
-}
-
-// GenerateWithCFG creates an image with classifier-free guidance.
-func (m *Model) GenerateWithCFG(prompt, negativePrompt string, width, height int32, steps int, seed int64, cfgScale float32, progress func(step, totalSteps int)) (*mlx.Array, error) {
-	return m.GenerateFromConfig(&GenerateConfig{
-		Prompt:         prompt,
-		NegativePrompt: negativePrompt,
-		CFGScale:       cfgScale,
-		Width:          width,
-		Height:         height,
-		Steps:          steps,
-		Seed:           seed,
-		Progress:       progress,
-	})
-}
-
-// GenerateFromConfig generates an image using the unified config struct.
-func (m *Model) GenerateFromConfig(cfg *GenerateConfig) (*mlx.Array, error) {
-	start := time.Now()
-	result, err := m.generate(cfg)
-	if err != nil {
-		return nil, err
-	}
-	if cfg.NegativePrompt != "" {
-		fmt.Printf("Generated with CFG (scale=%.1f) in %.2fs (%d steps)\n", cfg.CFGScale, time.Since(start).Seconds(), cfg.Steps)
-	} else {
-		fmt.Printf("Generated in %.2fs (%d steps)\n", time.Since(start).Seconds(), cfg.Steps)
-	}
-	return result, nil
-}
-
-// GenerateImage implements model.ImageModel interface.
-func (m *Model) GenerateImage(ctx context.Context, prompt string, width, height int32, steps int, seed int64) (*mlx.Array, error) {
-	return m.Generate(prompt, width, height, steps, seed)
-}
-
-// generate is the internal denoising pipeline.
-func (m *Model) generate(cfg *GenerateConfig) (*mlx.Array, error) {
-	// Apply defaults
-	if cfg.Width <= 0 {
-		cfg.Width = 1024
-	}
-	if cfg.Height <= 0 {
-		cfg.Height = 1024
-	}
-	if cfg.Steps <= 0 {
-		cfg.Steps = 50
-	}
-	if cfg.CFGScale <= 0 {
-		cfg.CFGScale = 4.0
-	}
-	if cfg.CacheInterval <= 0 {
-		cfg.CacheInterval = 3
-	}
-	if cfg.CacheLayers <= 0 {
-		cfg.CacheLayers = 25 // ~42% of 60 layers (similar ratio to Z-Image's 15/38)
-	}
-
-	useCFG := cfg.NegativePrompt != ""
-	tcfg := m.Transformer.Config
-	latentH := cfg.Height / 8
-	latentW := cfg.Width / 8
-	pH := latentH / tcfg.PatchSize
-	pW := latentW / tcfg.PatchSize
-	imgSeqLen := pH * pW
-
-	// Text encoding
-	var posEmb, negEmb *mlx.Array
-	{
-		posEmb = m.TextEncoder.EncodePrompt(m.Tokenizer, cfg.Prompt)
-		if useCFG {
-			negEmb = m.TextEncoder.EncodePrompt(m.Tokenizer, cfg.NegativePrompt)
-			mlx.Keep(posEmb, negEmb)
-			mlx.Eval(posEmb, negEmb)
-		} else {
-			mlx.Keep(posEmb)
-			mlx.Eval(posEmb)
-		}
-	}
-
-	// Pad sequences to same length for CFG
-	txtLen := posEmb.Shape()[1]
-	if useCFG {
-		negLen := negEmb.Shape()[1]
-		if negLen > txtLen {
-			txtLen = negLen
-		}
-		if posEmb.Shape()[1] < txtLen {
-			posEmb = padSequence(posEmb, txtLen)
-		}
-		if negEmb.Shape()[1] < txtLen {
-			negEmb = padSequence(negEmb, txtLen)
-		}
-		mlx.Keep(posEmb, negEmb)
-	}
-
-	// Pre-compute batched embeddings for CFG (single forward pass optimization)
-	var batchedEmb *mlx.Array
-	if useCFG {
-		batchedEmb = mlx.Concatenate([]*mlx.Array{posEmb, negEmb}, 0)
-		mlx.Keep(batchedEmb)
-		mlx.Eval(batchedEmb)
-	}
-
-	// Scheduler
-	scheduler := NewFlowMatchScheduler(DefaultSchedulerConfig())
-	scheduler.SetTimesteps(cfg.Steps, imgSeqLen)
-
-	// Init latents [B, C, T, H, W]
-	var latents *mlx.Array
-	{
-		latents = scheduler.InitNoise([]int32{1, tcfg.OutChannels, 1, latentH, latentW}, cfg.Seed)
-		mlx.Eval(latents)
-	}
-
-	// RoPE cache
-	var ropeCache *RoPECache
-	{
-		ropeCache = PrepareRoPE(pH, pW, txtLen, tcfg.AxesDimsRope)
-		mlx.Keep(ropeCache.ImgFreqs, ropeCache.TxtFreqs)
-		mlx.Eval(ropeCache.ImgFreqs)
-	}
-
-	// Layer cache for DeepCache/Learning-to-Cache speedup
-	var stepCache *cache.StepCache
-	if cfg.LayerCache {
-		stepCache = cache.NewStepCache(cfg.CacheLayers)
-		fmt.Printf("  Layer caching: %d layers, refresh every %d steps\n", cfg.CacheLayers, cfg.CacheInterval)
-	}
-
-	// Denoising loop
-	for i := 0; i < cfg.Steps; i++ {
-		stepStart := time.Now()
-		if cfg.Progress != nil {
-			cfg.Progress(i+1, cfg.Steps)
-		}
-
-		t := scheduler.Timesteps[i]
-		timestep := mlx.ToBFloat16(mlx.NewArray([]float32{t}, []int32{1}))
-
-		// Squeeze temporal dim: [B, C, T, H, W] -> [B, C, H, W]
-		latents2D := mlx.Squeeze(latents, 2)
-		patches := PackLatents(latents2D, tcfg.PatchSize)
-
-		var output *mlx.Array
-		if useCFG {
-			// CFG Batching: single forward pass with batch=2
-			// Note: layer caching with CFG is not supported yet (would need 2 caches)
-			batchedPatches := mlx.Tile(patches, []int32{2, 1, 1})
-			batchedTimestep := mlx.Tile(timestep, []int32{2})
-
-			// Single batched forward pass
-			batchedOutput := m.Transformer.Forward(batchedPatches, batchedEmb, batchedTimestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs)
-
-			// Split output: [2, L, D] -> pos [1, L, D], neg [1, L, D]
-			L := batchedOutput.Shape()[1]
-			D := batchedOutput.Shape()[2]
-			posOutput := mlx.Slice(batchedOutput, []int32{0, 0, 0}, []int32{1, L, D})
-			negOutput := mlx.Slice(batchedOutput, []int32{1, 0, 0}, []int32{2, L, D})
-
-			diff := mlx.Sub(posOutput, negOutput)
-			scaledDiff := mlx.MulScalar(diff, cfg.CFGScale)
-			combPred := mlx.Add(negOutput, scaledDiff)
-
-			// Norm rescaling: rescale combined prediction to match conditional prediction's norm
-			condNorm := mlx.Sqrt(mlx.Sum(mlx.Square(posOutput), -1, true))
-			combNorm := mlx.Sqrt(mlx.Sum(mlx.Square(combPred), -1, true))
-			output = mlx.Mul(combPred, mlx.Div(condNorm, combNorm))
-		} else if stepCache != nil {
-			output = m.Transformer.ForwardWithCache(patches, posEmb, timestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs,
-				stepCache, i, cfg.CacheInterval, cfg.CacheLayers)
-		} else {
-			output = m.Transformer.Forward(patches, posEmb, timestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs)
-		}
-
-		noisePred := UnpackLatents(output, latentH, latentW, tcfg.PatchSize)
-		oldLatents := latents
-		latents = scheduler.Step(noisePred, latents, i)
-
-		// Keep cached arrays alive across cleanup
-		if stepCache != nil {
-			mlx.Keep(stepCache.Arrays()...)
-		}
-		mlx.Eval(latents)
-		oldLatents.Free()
-
-		activeMem := float64(mlx.MetalGetActiveMemory()) / (1024 * 1024 * 1024)
-		peakMem := float64(mlx.MetalGetPeakMemory()) / (1024 * 1024 * 1024)
-		fmt.Printf("  Step %d/%d: t=%.4f (%.2fs) [%.1f GB active, %.1f GB peak]\n", i+1, cfg.Steps, t, time.Since(stepStart).Seconds(), activeMem, peakMem)
-	}
-
-	// Free denoising temporaries before VAE decode
-	posEmb.Free()
-	if negEmb != nil {
-		negEmb.Free()
-	}
-	if batchedEmb != nil {
-		batchedEmb.Free()
-	}
-	ropeCache.ImgFreqs.Free()
-	ropeCache.TxtFreqs.Free()
-	if stepCache != nil {
-		stepCache.Free()
-	}
-
-	// VAE decode (Decode manages its own pools for staged memory)
-	decoded := m.VAEDecoder.Decode(latents)
-	latents.Free()
-	// Post-process: squeeze temporal dim and rescale to [0, 1]
-	{
-		decoded = mlx.Squeeze(decoded, 2)
-		decoded = mlx.AddScalar(decoded, 1.0)
-		decoded = mlx.DivScalar(decoded, 2.0)
-		mlx.Eval(decoded)
-	}
-
-	fmt.Printf("  Peak memory: %.2f GB\n", float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	return decoded, nil
-}
-
-// padSequence pads a sequence tensor to the target length with zeros
-func padSequence(x *mlx.Array, targetLen int32) *mlx.Array {
-	shape := x.Shape()
-	currentLen := shape[1]
-	if currentLen >= targetLen {
-		return x
-	}
-	padLen := targetLen - currentLen
-	// Pad on sequence dimension (axis 1)
-	return mlx.Pad(x, []int32{0, 0, 0, padLen, 0, 0})
-}
-
-// LoadPersistent is an alias for backward compatibility.
-// Use m := &Model{}; m.Load(path) instead.
-func LoadPersistent(modelPath string) (*Model, error) {
-	m := &Model{}
-	if err := m.Load(modelPath); err != nil {
-		return nil, err
-	}
-	return m, nil
-}

x/imagegen/models/qwen_image/scheduler.go

@@ -1,218 +0,0 @@
-//go:build mlx
-
-package qwen_image
-
-import (
-	"math"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-)
-
-// SchedulerConfig holds FlowMatchEulerDiscreteScheduler configuration
-type SchedulerConfig struct {
-	NumTrainTimesteps int32   `json:"num_train_timesteps"` // 1000
-	BaseShift         float32 `json:"base_shift"`          // 0.5
-	MaxShift          float32 `json:"max_shift"`           // 0.9
-	BaseImageSeqLen   int32   `json:"base_image_seq_len"`  // 256
-	MaxImageSeqLen    int32   `json:"max_image_seq_len"`   // 8192
-	ShiftTerminal     float32 `json:"shift_terminal"`      // 0.02
-	UseDynamicShift   bool    `json:"use_dynamic_shifting"` // true
-}
-
-// DefaultSchedulerConfig returns config for FlowMatchEulerDiscreteScheduler
-func DefaultSchedulerConfig() *SchedulerConfig {
-	return &SchedulerConfig{
-		NumTrainTimesteps: 1000,
-		BaseShift:         0.5,
-		MaxShift:          0.9, // Matches scheduler_config.json
-		BaseImageSeqLen:   256,
-		MaxImageSeqLen:    8192,
-		ShiftTerminal:     0.02,
-		UseDynamicShift:   true,
-	}
-}
-
-// FlowMatchScheduler implements the Flow Match Euler discrete scheduler
-type FlowMatchScheduler struct {
-	Config    *SchedulerConfig
-	Timesteps []float32
-	Sigmas    []float32
-	NumSteps  int
-}
-
-// NewFlowMatchScheduler creates a new scheduler
-func NewFlowMatchScheduler(cfg *SchedulerConfig) *FlowMatchScheduler {
-	return &FlowMatchScheduler{
-		Config: cfg,
-	}
-}
-
-// CalculateShift computes the dynamic shift based on image sequence length
-// This matches Python's calculate_shift function
-func CalculateShift(imageSeqLen int32, baseSeqLen int32, maxSeqLen int32, baseShift float32, maxShift float32) float32 {
-	m := (maxShift - baseShift) / float32(maxSeqLen-baseSeqLen)
-	b := baseShift - m*float32(baseSeqLen)
-	mu := float32(imageSeqLen)*m + b
-	return mu
-}
-
-// SetTimesteps sets up the scheduler for the given number of inference steps
-// Matches Python diffusers FlowMatchEulerDiscreteScheduler behavior:
-// 1. Create sigmas from sigma_max to sigma_min (linspace)
-// 2. Apply time_shift with mu (if dynamic shifting)
-// 3. Apply stretch_shift_to_terminal to make final value = shift_terminal
-func (s *FlowMatchScheduler) SetTimesteps(numSteps int, imageSeqLen int32) {
-	s.NumSteps = numSteps
-
-	// Calculate mu for dynamic shifting
-	var mu float32
-	if s.Config.UseDynamicShift {
-		mu = CalculateShift(
-			imageSeqLen,
-			s.Config.BaseImageSeqLen,
-			s.Config.MaxImageSeqLen,
-			s.Config.BaseShift,
-			s.Config.MaxShift,
-		)
-	}
-
-	// Step 1: Create sigmas from 1.0 to 1/num_steps
-	// Python (pipeline_qwenimage.py:639):
-	//   sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
-	// This gives sigmas from 1.0 to 1/30 = 0.033 for 30 steps
-	sigmas := make([]float32, numSteps)
-	sigmaMax := float32(1.0)
-	sigmaMin := 1.0 / float32(numSteps) // 1/30 = 0.033 for 30 steps
-	if numSteps == 1 {
-		sigmas[0] = sigmaMax
-	} else {
-		for i := 0; i < numSteps; i++ {
-			sigmas[i] = sigmaMax + float32(i)*(sigmaMin-sigmaMax)/float32(numSteps-1)
-		}
-	}
-
-	// Step 2: Apply time shift if using dynamic shifting
-	if s.Config.UseDynamicShift && mu != 0 {
-		for i := range sigmas {
-			sigmas[i] = s.timeShift(mu, sigmas[i])
-		}
-	}
-
-	// Step 3: Apply stretch_shift_to_terminal
-	if s.Config.ShiftTerminal > 0 {
-		sigmas = s.stretchShiftToTerminal(sigmas)
-	}
-
-	// Step 4: Append terminal sigma (0) and store
-	// Note: Python's scheduler.timesteps are sigmas*1000, but the pipeline divides by 1000
-	// before passing to transformer. We skip both steps and just use sigmas directly.
-	s.Sigmas = make([]float32, numSteps+1)
-	s.Timesteps = make([]float32, numSteps+1)
-	for i := 0; i < numSteps; i++ {
-		s.Sigmas[i] = sigmas[i]
-		s.Timesteps[i] = sigmas[i]
-	}
-	s.Sigmas[numSteps] = 0.0
-	s.Timesteps[numSteps] = 0.0
-}
-
-// stretchShiftToTerminal stretches and shifts the timestep schedule
-// so the final value equals shift_terminal (matches Python behavior)
-func (s *FlowMatchScheduler) stretchShiftToTerminal(sigmas []float32) []float32 {
-	if len(sigmas) == 0 {
-		return sigmas
-	}
-
-	// one_minus_z = 1 - t
-	// scale_factor = one_minus_z[-1] / (1 - shift_terminal)
-	// stretched_t = 1 - (one_minus_z / scale_factor)
-	lastSigma := sigmas[len(sigmas)-1]
-	scaleFactor := (1.0 - lastSigma) / (1.0 - s.Config.ShiftTerminal)
-
-	// Handle edge case: if scaleFactor is 0 or near 0, skip stretch
-	// This happens when lastSigma ≈ 1.0 (e.g., single step with timeshift)
-	if scaleFactor < 1e-6 {
-		return sigmas
-	}
-
-	result := make([]float32, len(sigmas))
-	for i, t := range sigmas {
-		oneMinusZ := 1.0 - t
-		result[i] = 1.0 - (oneMinusZ / scaleFactor)
-	}
-	return result
-}
-
-// timeShift applies the dynamic time shift (exponential)
-// exp(mu) / (exp(mu) + (1/t - 1))
-func (s *FlowMatchScheduler) timeShift(mu float32, t float32) float32 {
-	if t <= 0 {
-		return 0
-	}
-	expMu := float32(math.Exp(float64(mu)))
-	return expMu / (expMu + (1.0/t - 1.0))
-}
-
-// Step performs one denoising step
-// modelOutput: predicted velocity from the transformer
-// sample: current noisy sample
-// timestepIdx: current timestep index
-func (s *FlowMatchScheduler) Step(modelOutput, sample *mlx.Array, timestepIdx int) *mlx.Array {
-	// Get current and next sigma
-	sigma := s.Sigmas[timestepIdx]
-	sigmaNext := s.Sigmas[timestepIdx+1]
-
-	// Euler step: x_{t-dt} = x_t + (sigma_next - sigma) * v_t
-	dt := sigmaNext - sigma
-
-	// Upcast to float32 to avoid precision issues (matches Python diffusers)
-	sampleF32 := mlx.AsType(sample, mlx.DtypeFloat32)
-	modelOutputF32 := mlx.AsType(modelOutput, mlx.DtypeFloat32)
-
-	scaledOutput := mlx.MulScalar(modelOutputF32, dt)
-	result := mlx.Add(sampleF32, scaledOutput)
-
-	// Cast back to original dtype
-	return mlx.ToBFloat16(result)
-}
-
-// GetTimestep returns the timestep value at the given index
-func (s *FlowMatchScheduler) GetTimestep(idx int) float32 {
-	if idx < len(s.Timesteps) {
-		return s.Timesteps[idx]
-	}
-	return 0.0
-}
-
-// InitNoise creates initial noise for sampling in unpacked format [B, C, T, H, W]
-func (s *FlowMatchScheduler) InitNoise(shape []int32, seed int64) *mlx.Array {
-	return mlx.RandomNormal(shape, uint64(seed))
-}
-
-// InitNoisePacked creates initial noise directly in packed format [B, L, C*4]
-// This matches how Python diffusers generates noise - directly in packed space.
-// Generating in unpacked format and then packing produces different spatial
-// correlation structure, which affects model output quality.
-func (s *FlowMatchScheduler) InitNoisePacked(batchSize, seqLen, channels int32, seed int64) *mlx.Array {
-	shape := []int32{batchSize, seqLen, channels}
-	return mlx.RandomNormal(shape, uint64(seed))
-}
-
-// GetLatentShape returns the latent shape for a given image size
-// For qwen_image: VAE downscale is 8x (spatial), latent has 16 channels
-func GetLatentShape(batchSize, height, width int32) []int32 {
-	latentH := height / 8
-	latentW := width / 8
-	return []int32{batchSize, 16, 1, latentH, latentW} // [B, C, T, H, W]
-}
-
-// GetPatchedLatentShape returns the patchified latent shape
-// After patchification: [B, L, C*patch_size^2] where L = H/2 * W/2
-func GetPatchedLatentShape(batchSize, height, width, patchSize int32) []int32 {
-	latentH := height / 8
-	latentW := width / 8
-	pH := latentH / patchSize
-	pW := latentW / patchSize
-	inChannels := int32(64) // 16 * patch_size^2
-	return []int32{batchSize, pH * pW, inChannels}
-}

x/imagegen/models/qwen_image/scheduler_test.go

@@ -1,135 +0,0 @@
-//go:build mlx
-
-package qwen_image
-
-import (
-	"math"
-	"testing"
-)
-
-// TestSchedulerSetTimesteps verifies scheduler sigmas match Python diffusers reference.
-// Golden values generated via:
-//
-//	python3 -c "
-//	from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
-//	import numpy as np
-//	s = FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, base_shift=0.5, max_shift=0.9,
-//	    base_image_seq_len=256, max_image_seq_len=8192, shift_terminal=0.02, use_dynamic_shifting=True)
-//	mu = 4096 * (0.9-0.5)/(8192-256) + 0.5 - (0.9-0.5)/(8192-256)*256
-//	sigmas = np.linspace(1.0, 1.0/30, 30)
-//	s.set_timesteps(sigmas=sigmas, mu=mu)
-//	print(s.sigmas.numpy())"
-func TestSchedulerSetTimesteps(t *testing.T) {
-	cfg := DefaultSchedulerConfig()
-	scheduler := NewFlowMatchScheduler(cfg)
-	scheduler.SetTimesteps(30, 4096)
-
-	// Golden values from Python diffusers (first 3, last 3 before terminal)
-	wantFirst := []float32{1.000000, 0.982251, 0.963889}
-	wantLast := []float32{0.142924, 0.083384, 0.020000}
-
-	// Check first 3
-	for i, want := range wantFirst {
-		got := scheduler.Sigmas[i]
-		if abs32(got-want) > 1e-4 {
-			t.Errorf("sigma[%d]: got %v, want %v", i, got, want)
-		}
-	}
-
-	// Check last 3 (indices 27, 28, 29)
-	for i, want := range wantLast {
-		idx := 27 + i
-		got := scheduler.Sigmas[idx]
-		if abs32(got-want) > 1e-4 {
-			t.Errorf("sigma[%d]: got %v, want %v", idx, got, want)
-		}
-	}
-
-	// Check terminal is 0
-	if scheduler.Sigmas[30] != 0.0 {
-		t.Errorf("terminal sigma: got %v, want 0", scheduler.Sigmas[30])
-	}
-
-	// Check length
-	if len(scheduler.Sigmas) != 31 {
-		t.Errorf("sigmas length: got %d, want 31", len(scheduler.Sigmas))
-	}
-}
-
-// TestSchedulerProperties tests mathematical invariants of the scheduler.
-func TestSchedulerProperties(t *testing.T) {
-	cfg := DefaultSchedulerConfig()
-	scheduler := NewFlowMatchScheduler(cfg)
-	scheduler.SetTimesteps(30, 4096)
-
-	// Property: sigmas monotonically decreasing
-	for i := 1; i < len(scheduler.Sigmas); i++ {
-		if scheduler.Sigmas[i] > scheduler.Sigmas[i-1] {
-			t.Errorf("sigmas not monotonically decreasing at %d: %v > %v",
-				i, scheduler.Sigmas[i], scheduler.Sigmas[i-1])
-		}
-	}
-
-	// Property: first sigma should be ~1.0 (with time shift)
-	if scheduler.Sigmas[0] < 0.9 || scheduler.Sigmas[0] > 1.01 {
-		t.Errorf("first sigma out of expected range [0.9, 1.01]: %v", scheduler.Sigmas[0])
-	}
-
-	// Property: terminal sigma should be exactly 0
-	if scheduler.Sigmas[len(scheduler.Sigmas)-1] != 0.0 {
-		t.Errorf("terminal sigma should be 0, got %v", scheduler.Sigmas[len(scheduler.Sigmas)-1])
-	}
-
-	// Property: last non-terminal sigma should be shift_terminal (0.02)
-	lastNonTerminal := scheduler.Sigmas[len(scheduler.Sigmas)-2]
-	if abs32(lastNonTerminal-0.02) > 1e-5 {
-		t.Errorf("last non-terminal sigma should be 0.02, got %v", lastNonTerminal)
-	}
-
-	// Property: length = steps + 1
-	if len(scheduler.Sigmas) != scheduler.NumSteps+1 {
-		t.Errorf("sigmas length should be steps+1: got %d, want %d",
-			len(scheduler.Sigmas), scheduler.NumSteps+1)
-	}
-}
-
-// TestCalculateShift verifies the mu calculation against Python reference.
-// Golden values from: mu = img_seq_len * m + b where m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
-func TestCalculateShift(t *testing.T) {
-	cases := []struct {
-		imgSeqLen int32
-		want      float32
-	}{
-		{256, 0.5},     // base case
-		{8192, 0.9},    // max case
-		{4096, 0.6935}, // middle case (rounded)
-	}
-
-	for _, c := range cases {
-		got := CalculateShift(c.imgSeqLen, 256, 8192, 0.5, 0.9)
-		if abs32(got-c.want) > 0.001 {
-			t.Errorf("CalculateShift(%d): got %v, want %v", c.imgSeqLen, got, c.want)
-		}
-	}
-}
-
-// TestSchedulerStep verifies the Euler step formula.
-func TestSchedulerStep(t *testing.T) {
-	cfg := DefaultSchedulerConfig()
-	scheduler := NewFlowMatchScheduler(cfg)
-	scheduler.SetTimesteps(30, 4096)
-
-	// Verify dt calculation for first step
-	sigma0 := scheduler.Sigmas[0]
-	sigma1 := scheduler.Sigmas[1]
-	expectedDt := sigma1 - sigma0
-
-	// dt should be negative (sigmas decrease)
-	if expectedDt >= 0 {
-		t.Errorf("expected negative dt, got %v (sigma0=%v, sigma1=%v)", expectedDt, sigma0, sigma1)
-	}
-}
-
-func abs32(x float32) float32 {
-	return float32(math.Abs(float64(x)))
-}

x/imagegen/models/qwen_image/text_encoder_test.go

@@ -1,174 +0,0 @@
-//go:build mlx
-
-package qwen_image
-
-import (
-	"encoding/json"
-	"math"
-	"os"
-	"path/filepath"
-	"slices"
-	"testing"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-)
-
-// TinyTextEncoderConfig holds config for the tiny test text encoder
-type TinyTextEncoderConfig struct {
-	HiddenSize        int32   `json:"hidden_size"`
-	NumHiddenLayers   int32   `json:"num_hidden_layers"`
-	IntermediateSize  int32   `json:"intermediate_size"`
-	NumAttentionHeads int32   `json:"num_attention_heads"`
-	NumKeyValueHeads  int32   `json:"num_key_value_heads"`
-	VocabSize         int32   `json:"vocab_size"`
-	RMSNormEps        float32 `json:"rms_norm_eps"`
-	RopeTheta         float32 `json:"rope_theta"`
-	HeadDim           int32   `json:"head_dim"`
-	MRoPESection      []int32 `json:"mrope_section"`
-}
-
-// loadTinyTextEncoder loads the tiny text encoder from testdata
-func loadTinyTextEncoder(t *testing.T) (*Qwen25VL, *TinyTextEncoderConfig) {
-	t.Helper()
-
-	testdataDir := filepath.Join("testdata", "tiny_text_encoder")
-
-	// Load config
-	configData, err := os.ReadFile(filepath.Join(testdataDir, "config.json"))
-	if err != nil {
-		t.Skipf("Skipping: tiny weights not found. Regenerate with Python (see models/CLAUDE.md)")
-	}
-
-	var tinyCfg TinyTextEncoderConfig
-	if err := json.Unmarshal(configData, &tinyCfg); err != nil {
-		t.Fatalf("Failed to parse config: %v", err)
-	}
-
-	// Create encoder config (using Qwen25VLConfig)
-	cfg := &Qwen25VLConfig{
-		HiddenSize:        tinyCfg.HiddenSize,
-		NumHiddenLayers:   tinyCfg.NumHiddenLayers,
-		IntermediateSize:  tinyCfg.IntermediateSize,
-		NumAttentionHeads: tinyCfg.NumAttentionHeads,
-		NumKeyValueHeads:  tinyCfg.NumKeyValueHeads,
-		VocabSize:         tinyCfg.VocabSize,
-		RMSNormEps:        tinyCfg.RMSNormEps,
-		RopeTheta:         tinyCfg.RopeTheta,
-		HeadDim:           tinyCfg.HeadDim,
-		MRoPESection:      tinyCfg.MRoPESection,
-	}
-
-	// Load weights
-	weights, err := safetensors.LoadModelWeights(testdataDir)
-	if err != nil {
-		t.Fatalf("Failed to load weights: %v", err)
-	}
-
-	if err := weights.Load(mlx.DtypeBFloat16); err != nil {
-		t.Fatalf("Failed to bulk load weights: %v", err)
-	}
-
-	// Build encoder
-	embedding, err := weights.Get("model.embed_tokens.weight")
-	if err != nil {
-		t.Fatalf("Failed to get embedding: %v", err)
-	}
-
-	blocks := make([]*VLTextBlock, cfg.NumHiddenLayers)
-	for i := int32(0); i < cfg.NumHiddenLayers; i++ {
-		block, err := newVLTextBlock(weights, int(i), cfg)
-		if err != nil {
-			t.Fatalf("Failed to load block %d: %v", i, err)
-		}
-		blocks[i] = block
-	}
-
-	finalNorm, err := weights.Get("model.norm.weight")
-	if err != nil {
-		t.Fatalf("Failed to get final norm: %v", err)
-	}
-
-	encoder := &Qwen25VL{
-		Config:    cfg,
-		Embedding: embedding,
-		Blocks:    blocks,
-		FinalNorm: finalNorm,
-		HasVision: false, // Text-only mode
-	}
-
-	return encoder, &tinyCfg
-}
-
-// TestTextEncoderForward verifies the text encoder forward pass with tiny weights.
-func TestTextEncoderForward(t *testing.T) {
-	encoder, cfg := loadTinyTextEncoder(t)
-
-	// Create test tokens (within vocab range)
-	tokens := []int32{1, 2, 3, 4, 5}
-
-	// Forward pass using EncodeTextOnly
-	out := encoder.EncodeTextOnly(tokens)
-	mlx.Eval(out)
-
-	// Verify output shape: [batch, seq_len, hidden_size]
-	wantShape := []int32{1, 5, cfg.HiddenSize}
-	if !slices.Equal(out.Shape(), wantShape) {
-		t.Errorf("output shape: got %v, want %v", out.Shape(), wantShape)
-	}
-
-	// Verify output is finite (not NaN or Inf)
-	data := out.Data()
-	for i, v := range data {
-		if math.IsNaN(float64(v)) || math.IsInf(float64(v), 0) {
-			t.Errorf("output[%d] is not finite: %v", i, v)
-			break
-		}
-	}
-}
-
-// TestTextEncoderBatch tests batch processing.
-func TestTextEncoderBatch(t *testing.T) {
-	encoder, cfg := loadTinyTextEncoder(t)
-
-	// For batch test, we'll use EncodeTextOnly with a single sequence
-	// (EncodeTextOnly doesn't support batch, but we can verify single sequence works)
-	tokens := []int32{1, 2, 3}
-
-	out := encoder.EncodeTextOnly(tokens)
-	mlx.Eval(out)
-
-	wantShape := []int32{1, 3, cfg.HiddenSize}
-	if !slices.Equal(out.Shape(), wantShape) {
-		t.Errorf("shape: got %v, want %v", out.Shape(), wantShape)
-	}
-}
-
-// TestMRoPEComputation verifies M-RoPE frequency computation produces valid values.
-func TestMRoPEComputation(t *testing.T) {
-	encoder, cfg := loadTinyTextEncoder(t)
-
-	cossin := encoder.computeTextRoPE(10, 1)
-	mlx.Eval(cossin[0], cossin[1])
-
-	// Verify shapes: [3, B, L, head_dim]
-	wantShape := []int32{3, 1, 10, cfg.HeadDim}
-	if !slices.Equal(cossin[0].Shape(), wantShape) {
-		t.Errorf("cos shape: got %v, want %v", cossin[0].Shape(), wantShape)
-	}
-	if !slices.Equal(cossin[1].Shape(), wantShape) {
-		t.Errorf("sin shape: got %v, want %v", cossin[1].Shape(), wantShape)
-	}
-
-	// Verify cos/sin values are in valid range [-1, 1]
-	cosData := cossin[0].Data()
-	sinData := cossin[1].Data()
-	for i := 0; i < min(100, len(cosData)); i++ {
-		if cosData[i] < -1.01 || cosData[i] > 1.01 {
-			t.Errorf("cos[%d] out of range: %v", i, cosData[i])
-		}
-		if sinData[i] < -1.01 || sinData[i] > 1.01 {
-			t.Errorf("sin[%d] out of range: %v", i, sinData[i])
-		}
-	}
-}

x/imagegen/models/qwen_image/transformer.go

@@ -1,868 +0,0 @@
-//go:build mlx
-
-package qwen_image
-
-import (
-	"fmt"
-	"math"
-	"path/filepath"
-
-	"github.com/ollama/ollama/x/imagegen/cache"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-)
-
-// TransformerConfig holds Qwen-Image transformer configuration
-type TransformerConfig struct {
-	HiddenDim         int32   `json:"hidden_dim"`          // 3072 (24 * 128)
-	NHeads            int32   `json:"num_attention_heads"` // 24
-	HeadDim           int32   `json:"attention_head_dim"`  // 128
-	NLayers           int32   `json:"num_layers"`          // 60
-	InChannels        int32   `json:"in_channels"`         // 64
-	OutChannels       int32   `json:"out_channels"`        // 16
-	PatchSize         int32   `json:"patch_size"`          // 2
-	JointAttentionDim int32   `json:"joint_attention_dim"` // 3584 (text encoder dim)
-	NormEps           float32 `json:"norm_eps"`            // 1e-6
-	AxesDimsRope      []int32 `json:"axes_dims_rope"`      // [16, 56, 56]
-	GuidanceEmbeds    bool    `json:"guidance_embeds"`     // false
-}
-
-// defaultTransformerConfig returns config for Qwen-Image transformer
-func defaultTransformerConfig() *TransformerConfig {
-	return &TransformerConfig{
-		HiddenDim:         3072, // 24 * 128
-		NHeads:            24,
-		HeadDim:           128,
-		NLayers:           60,
-		InChannels:        64,
-		OutChannels:       16,
-		PatchSize:         2,
-		JointAttentionDim: 3584,
-		NormEps:           1e-6,
-		AxesDimsRope:      []int32{16, 56, 56},
-		GuidanceEmbeds:    false,
-	}
-}
-
-// TimestepEmbedder creates timestep embeddings
-type TimestepEmbedder struct {
-	Linear1Weight *mlx.Array // [256, hidden_dim]
-	Linear1Bias   *mlx.Array
-	Linear2Weight *mlx.Array // [hidden_dim, hidden_dim]
-	Linear2Bias   *mlx.Array
-}
-
-// newTimestepEmbedder creates a timestep embedder from weights
-func newTimestepEmbedder(weights *safetensors.ModelWeights) (*TimestepEmbedder, error) {
-	linear1Weight, err := weights.Get("time_text_embed.timestep_embedder.linear_1.weight")
-	if err != nil {
-		return nil, err
-	}
-	linear1Bias, err := weights.Get("time_text_embed.timestep_embedder.linear_1.bias")
-	if err != nil {
-		return nil, err
-	}
-	linear2Weight, err := weights.Get("time_text_embed.timestep_embedder.linear_2.weight")
-	if err != nil {
-		return nil, err
-	}
-	linear2Bias, err := weights.Get("time_text_embed.timestep_embedder.linear_2.bias")
-	if err != nil {
-		return nil, err
-	}
-
-	return &TimestepEmbedder{
-		Linear1Weight: mlx.Transpose(linear1Weight, 1, 0),
-		Linear1Bias:   linear1Bias,
-		Linear2Weight: mlx.Transpose(linear2Weight, 1, 0),
-		Linear2Bias:   linear2Bias,
-	}, nil
-}
-
-// Forward computes timestep embeddings
-// t: [B] timesteps (normalized 0-1, will be scaled by 1000 internally)
-func (te *TimestepEmbedder) Forward(t *mlx.Array) *mlx.Array {
-	half := int32(128) // embedding_dim / 2
-
-	// Sinusoidal embedding with flip_sin_to_cos=True, scale=1000
-	freqs := make([]float32, half)
-	for i := int32(0); i < half; i++ {
-		freqs[i] = float32(math.Exp(-math.Log(10000.0) * float64(i) / float64(half)))
-	}
-	freqsArr := mlx.NewArray(freqs, []int32{1, half})
-
-	tExpanded := mlx.ExpandDims(t, 1)
-	args := mlx.Mul(tExpanded, freqsArr)
-	args = mlx.MulScalar(args, 1000.0) // scale
-
-	// [cos, sin] (flip_sin_to_cos=True)
-	sinArgs := mlx.Sin(args)
-	cosArgs := mlx.Cos(args)
-	embedding := mlx.Concatenate([]*mlx.Array{cosArgs, sinArgs}, 1) // [B, 256]
-
-	// MLP: linear1 -> silu -> linear2
-	h := mlx.Linear(embedding, te.Linear1Weight)
-	h = mlx.Add(h, te.Linear1Bias)
-	h = mlx.SiLU(h)
-	h = mlx.Linear(h, te.Linear2Weight)
-	h = mlx.Add(h, te.Linear2Bias)
-
-	return h
-}
-
-// JointAttention implements dual-stream joint attention
-type JointAttention struct {
-	// Image projections
-	ToQ    *mlx.Array
-	ToQB   *mlx.Array
-	ToK    *mlx.Array
-	ToKB   *mlx.Array
-	ToV    *mlx.Array
-	ToVB   *mlx.Array
-	ToOut  *mlx.Array
-	ToOutB *mlx.Array
-	NormQ  *mlx.Array
-	NormK  *mlx.Array
-
-	// Text (added) projections
-	AddQProj  *mlx.Array
-	AddQProjB *mlx.Array
-	AddKProj  *mlx.Array
-	AddKProjB *mlx.Array
-	AddVProj  *mlx.Array
-	AddVProjB *mlx.Array
-	ToAddOut  *mlx.Array
-	ToAddOutB *mlx.Array
-	NormAddQ  *mlx.Array
-	NormAddK  *mlx.Array
-
-	NHeads  int32
-	HeadDim int32
-	Scale   float32
-}
-
-// newJointAttention creates a joint attention layer
-func newJointAttention(weights *safetensors.ModelWeights, prefix string, cfg *TransformerConfig) (*JointAttention, error) {
-	toQ, _ := weights.Get(prefix + ".attn.to_q.weight")
-	toQB, _ := weights.Get(prefix + ".attn.to_q.bias")
-	toK, _ := weights.Get(prefix + ".attn.to_k.weight")
-	toKB, _ := weights.Get(prefix + ".attn.to_k.bias")
-	toV, _ := weights.Get(prefix + ".attn.to_v.weight")
-	toVB, _ := weights.Get(prefix + ".attn.to_v.bias")
-	toOut, _ := weights.Get(prefix + ".attn.to_out.0.weight")
-	toOutB, _ := weights.Get(prefix + ".attn.to_out.0.bias")
-	normQ, _ := weights.Get(prefix + ".attn.norm_q.weight")
-	normK, _ := weights.Get(prefix + ".attn.norm_k.weight")
-
-	addQProj, _ := weights.Get(prefix + ".attn.add_q_proj.weight")
-	addQProjB, _ := weights.Get(prefix + ".attn.add_q_proj.bias")
-	addKProj, _ := weights.Get(prefix + ".attn.add_k_proj.weight")
-	addKProjB, _ := weights.Get(prefix + ".attn.add_k_proj.bias")
-	addVProj, _ := weights.Get(prefix + ".attn.add_v_proj.weight")
-	addVProjB, _ := weights.Get(prefix + ".attn.add_v_proj.bias")
-	toAddOut, _ := weights.Get(prefix + ".attn.to_add_out.weight")
-	toAddOutB, _ := weights.Get(prefix + ".attn.to_add_out.bias")
-	normAddQ, _ := weights.Get(prefix + ".attn.norm_added_q.weight")
-	normAddK, _ := weights.Get(prefix + ".attn.norm_added_k.weight")
-
-	return &JointAttention{
-		ToQ:       mlx.Transpose(toQ, 1, 0),
-		ToQB:      toQB,
-		ToK:       mlx.Transpose(toK, 1, 0),
-		ToKB:      toKB,
-		ToV:       mlx.Transpose(toV, 1, 0),
-		ToVB:      toVB,
-		ToOut:     mlx.Transpose(toOut, 1, 0),
-		ToOutB:    toOutB,
-		NormQ:     normQ,
-		NormK:     normK,
-		AddQProj:  mlx.Transpose(addQProj, 1, 0),
-		AddQProjB: addQProjB,
-		AddKProj:  mlx.Transpose(addKProj, 1, 0),
-		AddKProjB: addKProjB,
-		AddVProj:  mlx.Transpose(addVProj, 1, 0),
-		AddVProjB: addVProjB,
-		ToAddOut:  mlx.Transpose(toAddOut, 1, 0),
-		ToAddOutB: toAddOutB,
-		NormAddQ:  normAddQ,
-		NormAddK:  normAddK,
-		NHeads:    cfg.NHeads,
-		HeadDim:   cfg.HeadDim,
-		Scale:     float32(1.0 / math.Sqrt(float64(cfg.HeadDim))),
-	}, nil
-}
-
-// Forward computes joint attention
-// img: [B, L_img, D], txt: [B, L_txt, D]
-// imgFreqs, txtFreqs: complex RoPE frequencies [L, head_dim/2] as interleaved real/imag
-func (attn *JointAttention) Forward(img, txt *mlx.Array, imgFreqs, txtFreqs *mlx.Array) (*mlx.Array, *mlx.Array) {
-	imgShape := img.Shape()
-	B := imgShape[0]
-	Limg := imgShape[1]
-	D := imgShape[2]
-
-	txtShape := txt.Shape()
-	Ltxt := txtShape[1]
-
-	// === Image Q/K/V ===
-	imgFlat := mlx.Reshape(img, B*Limg, D)
-	qImg := mlx.Add(mlx.Linear(imgFlat, attn.ToQ), attn.ToQB)
-	kImg := mlx.Add(mlx.Linear(imgFlat, attn.ToK), attn.ToKB)
-	vImg := mlx.Add(mlx.Linear(imgFlat, attn.ToV), attn.ToVB)
-
-	qImg = mlx.Reshape(qImg, B, Limg, attn.NHeads, attn.HeadDim)
-	kImg = mlx.Reshape(kImg, B, Limg, attn.NHeads, attn.HeadDim)
-	vImg = mlx.Reshape(vImg, B, Limg, attn.NHeads, attn.HeadDim)
-
-	// QK norm (RMSNorm per head)
-	qImg = mlx.RMSNorm(qImg, attn.NormQ, 1e-6)
-	kImg = mlx.RMSNorm(kImg, attn.NormK, 1e-6)
-
-	// Apply RoPE
-	if imgFreqs != nil {
-		qImg = applyRoPE(qImg, imgFreqs)
-		kImg = applyRoPE(kImg, imgFreqs)
-	}
-
-	// === Text Q/K/V ===
-	txtFlat := mlx.Reshape(txt, B*Ltxt, D)
-	qTxt := mlx.Add(mlx.Linear(txtFlat, attn.AddQProj), attn.AddQProjB)
-	kTxt := mlx.Add(mlx.Linear(txtFlat, attn.AddKProj), attn.AddKProjB)
-	vTxt := mlx.Add(mlx.Linear(txtFlat, attn.AddVProj), attn.AddVProjB)
-
-	qTxt = mlx.Reshape(qTxt, B, Ltxt, attn.NHeads, attn.HeadDim)
-	kTxt = mlx.Reshape(kTxt, B, Ltxt, attn.NHeads, attn.HeadDim)
-	vTxt = mlx.Reshape(vTxt, B, Ltxt, attn.NHeads, attn.HeadDim)
-
-	qTxt = mlx.RMSNorm(qTxt, attn.NormAddQ, 1e-6)
-	kTxt = mlx.RMSNorm(kTxt, attn.NormAddK, 1e-6)
-
-	if txtFreqs != nil {
-		qTxt = applyRoPE(qTxt, txtFreqs)
-		kTxt = applyRoPE(kTxt, txtFreqs)
-	}
-
-	// Concatenate for joint attention: [txt, img] order
-	qJoint := mlx.Concatenate([]*mlx.Array{qTxt, qImg}, 1)
-	kJoint := mlx.Concatenate([]*mlx.Array{kTxt, kImg}, 1)
-	vJoint := mlx.Concatenate([]*mlx.Array{vTxt, vImg}, 1)
-
-	// Transpose to [B, nheads, L, head_dim]
-	qJoint = mlx.Transpose(qJoint, 0, 2, 1, 3)
-	kJoint = mlx.Transpose(kJoint, 0, 2, 1, 3)
-	vJoint = mlx.Transpose(vJoint, 0, 2, 1, 3)
-
-	// SDPA
-	outJoint := mlx.ScaledDotProductAttention(qJoint, kJoint, vJoint, attn.Scale, false)
-
-	// Transpose back and split
-	outJoint = mlx.Transpose(outJoint, 0, 2, 1, 3) // [B, L, nheads, head_dim]
-	outJoint = mlx.Reshape(outJoint, B, Ltxt+Limg, D)
-
-	outTxt := mlx.Slice(outJoint, []int32{0, 0, 0}, []int32{B, Ltxt, D})
-	outImg := mlx.Slice(outJoint, []int32{0, Ltxt, 0}, []int32{B, Ltxt + Limg, D})
-
-	// Output projections
-	outImg = mlx.Reshape(outImg, B*Limg, D)
-	outImg = mlx.Add(mlx.Linear(outImg, attn.ToOut), attn.ToOutB)
-	outImg = mlx.Reshape(outImg, B, Limg, D)
-
-	outTxt = mlx.Reshape(outTxt, B*Ltxt, D)
-	outTxt = mlx.Add(mlx.Linear(outTxt, attn.ToAddOut), attn.ToAddOutB)
-	outTxt = mlx.Reshape(outTxt, B, Ltxt, D)
-
-	return outImg, outTxt
-}
-
-// applyRoPE applies rotary embeddings using complex multiplication
-// x: [B, L, nheads, head_dim]
-// freqs: [L, head_dim] as complex (interleaved real/imag pairs)
-func applyRoPE(x *mlx.Array, freqs *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	L := shape[1]
-	nheads := shape[2]
-	headDim := shape[3]
-	halfDim := headDim / 2
-
-	// Reshape x to pairs: [B, L, nheads, half, 2]
-	xPairs := mlx.Reshape(x, B, L, nheads, halfDim, 2)
-
-	// freqs: [L, head_dim] -> [1, L, 1, half, 2]
-	freqsExp := mlx.Reshape(freqs, 1, L, 1, halfDim, 2)
-
-	// Extract real/imag parts
-	xReal := mlx.SliceStride(xPairs, []int32{0, 0, 0, 0, 0}, []int32{B, L, nheads, halfDim, 1}, []int32{1, 1, 1, 1, 1})
-	xImag := mlx.SliceStride(xPairs, []int32{0, 0, 0, 0, 1}, []int32{B, L, nheads, halfDim, 2}, []int32{1, 1, 1, 1, 1})
-	xReal = mlx.Squeeze(xReal, 4)
-	xImag = mlx.Squeeze(xImag, 4)
-
-	freqReal := mlx.SliceStride(freqsExp, []int32{0, 0, 0, 0, 0}, []int32{1, L, 1, halfDim, 1}, []int32{1, 1, 1, 1, 1})
-	freqImag := mlx.SliceStride(freqsExp, []int32{0, 0, 0, 0, 1}, []int32{1, L, 1, halfDim, 2}, []int32{1, 1, 1, 1, 1})
-	freqReal = mlx.Squeeze(freqReal, 4)
-	freqImag = mlx.Squeeze(freqImag, 4)
-
-	// Complex multiplication: (a + bi) * (c + di) = (ac - bd) + (ad + bc)i
-	outReal := mlx.Sub(mlx.Mul(xReal, freqReal), mlx.Mul(xImag, freqImag))
-	outImag := mlx.Add(mlx.Mul(xReal, freqImag), mlx.Mul(xImag, freqReal))
-
-	// Interleave back
-	outReal = mlx.ExpandDims(outReal, 4)
-	outImag = mlx.ExpandDims(outImag, 4)
-	out := mlx.Concatenate([]*mlx.Array{outReal, outImag}, 4)
-
-	return mlx.Reshape(out, B, L, nheads, headDim)
-}
-
-// MLP implements GELU MLP (not GEGLU)
-type MLP struct {
-	ProjWeight *mlx.Array
-	ProjBias   *mlx.Array
-	OutWeight  *mlx.Array
-	OutBias    *mlx.Array
-}
-
-// newMLP creates a GELU MLP
-func newMLP(weights *safetensors.ModelWeights, prefix string) (*MLP, error) {
-	projWeight, _ := weights.Get(prefix + ".net.0.proj.weight")
-	projBias, _ := weights.Get(prefix + ".net.0.proj.bias")
-	outWeight, _ := weights.Get(prefix + ".net.2.weight")
-	outBias, _ := weights.Get(prefix + ".net.2.bias")
-
-	return &MLP{
-		ProjWeight: mlx.Transpose(projWeight, 1, 0),
-		ProjBias:   projBias,
-		OutWeight:  mlx.Transpose(outWeight, 1, 0),
-		OutBias:    outBias,
-	}, nil
-}
-
-// Forward applies GELU MLP
-func (m *MLP) Forward(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	L := shape[1]
-	D := shape[2]
-
-	xFlat := mlx.Reshape(x, B*L, D)
-	h := mlx.Add(mlx.Linear(xFlat, m.ProjWeight), m.ProjBias)
-	h = geluApprox(h)
-	h = mlx.Add(mlx.Linear(h, m.OutWeight), m.OutBias)
-	return mlx.Reshape(h, B, L, m.OutBias.Dim(0))
-}
-
-// geluApprox implements approximate GELU
-func geluApprox(x *mlx.Array) *mlx.Array {
-	sqrt2OverPi := float32(math.Sqrt(2.0 / math.Pi))
-	x3 := mlx.Mul(mlx.Mul(x, x), x)
-	inner := mlx.Add(x, mlx.MulScalar(x3, 0.044715))
-	inner = mlx.MulScalar(inner, sqrt2OverPi)
-	return mlx.Mul(mlx.MulScalar(x, 0.5), mlx.AddScalar(mlx.Tanh(inner), 1.0))
-}
-
-// TransformerBlock is a single dual-stream transformer block
-type TransformerBlock struct {
-	Attention *JointAttention
-	ImgMLP    *MLP
-	TxtMLP    *MLP
-
-	ImgModWeight *mlx.Array
-	ImgModBias   *mlx.Array
-	TxtModWeight *mlx.Array
-	TxtModBias   *mlx.Array
-
-	HiddenDim int32
-	NormEps   float32
-}
-
-// newTransformerBlock creates a transformer block
-func newTransformerBlock(weights *safetensors.ModelWeights, prefix string, cfg *TransformerConfig) (*TransformerBlock, error) {
-	attn, err := newJointAttention(weights, prefix, cfg)
-	if err != nil {
-		return nil, err
-	}
-
-	imgMLP, _ := newMLP(weights, prefix+".img_mlp")
-	txtMLP, _ := newMLP(weights, prefix+".txt_mlp")
-
-	imgModWeight, _ := weights.Get(prefix + ".img_mod.1.weight")
-	imgModBias, _ := weights.Get(prefix + ".img_mod.1.bias")
-	txtModWeight, _ := weights.Get(prefix + ".txt_mod.1.weight")
-	txtModBias, _ := weights.Get(prefix + ".txt_mod.1.bias")
-
-	return &TransformerBlock{
-		Attention:    attn,
-		ImgMLP:       imgMLP,
-		TxtMLP:       txtMLP,
-		ImgModWeight: mlx.Transpose(imgModWeight, 1, 0),
-		ImgModBias:   imgModBias,
-		TxtModWeight: mlx.Transpose(txtModWeight, 1, 0),
-		TxtModBias:   txtModBias,
-		HiddenDim:    cfg.HiddenDim,
-		NormEps:      cfg.NormEps,
-	}, nil
-}
-
-// Forward applies the transformer block
-func (tb *TransformerBlock) Forward(img, txt, temb *mlx.Array, imgFreqs, txtFreqs *mlx.Array) (*mlx.Array, *mlx.Array) {
-	// Compute modulation: silu(temb) -> linear -> [B, 6*D]
-	siluT := mlx.SiLU(temb)
-	imgMod := mlx.Add(mlx.Linear(siluT, tb.ImgModWeight), tb.ImgModBias)
-	txtMod := mlx.Add(mlx.Linear(siluT, tb.TxtModWeight), tb.TxtModBias)
-
-	// Split into 6 parts: shift1, scale1, gate1, shift2, scale2, gate2
-	imgModParts := splitMod6(imgMod, tb.HiddenDim)
-	txtModParts := splitMod6(txtMod, tb.HiddenDim)
-
-	// Pre-attention: norm + modulate
-	imgNorm := layerNormNoAffine(img, tb.NormEps)
-	imgNorm = mlx.Add(mlx.Mul(imgNorm, mlx.AddScalar(imgModParts[1], 1.0)), imgModParts[0])
-
-	txtNorm := layerNormNoAffine(txt, tb.NormEps)
-	txtNorm = mlx.Add(mlx.Mul(txtNorm, mlx.AddScalar(txtModParts[1], 1.0)), txtModParts[0])
-
-	// Joint attention
-	attnImg, attnTxt := tb.Attention.Forward(imgNorm, txtNorm, imgFreqs, txtFreqs)
-
-	// Residual with gate
-	img = mlx.Add(img, mlx.Mul(imgModParts[2], attnImg))
-	txt = mlx.Add(txt, mlx.Mul(txtModParts[2], attnTxt))
-
-	// Pre-MLP: norm + modulate
-	imgNorm2 := layerNormNoAffine(img, tb.NormEps)
-	imgNorm2 = mlx.Add(mlx.Mul(imgNorm2, mlx.AddScalar(imgModParts[4], 1.0)), imgModParts[3])
-
-	txtNorm2 := layerNormNoAffine(txt, tb.NormEps)
-	txtNorm2 = mlx.Add(mlx.Mul(txtNorm2, mlx.AddScalar(txtModParts[4], 1.0)), txtModParts[3])
-
-	// MLP
-	mlpImg := tb.ImgMLP.Forward(imgNorm2)
-	mlpTxt := tb.TxtMLP.Forward(txtNorm2)
-
-	// Residual with gate
-	img = mlx.Add(img, mlx.Mul(imgModParts[5], mlpImg))
-	txt = mlx.Add(txt, mlx.Mul(txtModParts[5], mlpTxt))
-
-	return img, txt
-}
-
-// splitMod6 splits modulation into 6 parts each [B, 1, D]
-func splitMod6(mod *mlx.Array, hiddenDim int32) []*mlx.Array {
-	shape := mod.Shape()
-	B := shape[0]
-	parts := make([]*mlx.Array, 6)
-	for i := int32(0); i < 6; i++ {
-		part := mlx.Slice(mod, []int32{0, i * hiddenDim}, []int32{B, (i + 1) * hiddenDim})
-		parts[i] = mlx.ExpandDims(part, 1)
-	}
-	return parts
-}
-
-// layerNormNoAffine applies layer norm without learnable parameters
-func layerNormNoAffine(x *mlx.Array, eps float32) *mlx.Array {
-	ndim := x.Ndim()
-	lastAxis := ndim - 1
-	mean := mlx.Mean(x, lastAxis, true)
-	xCentered := mlx.Sub(x, mean)
-	variance := mlx.Mean(mlx.Square(xCentered), lastAxis, true)
-	return mlx.Div(xCentered, mlx.Sqrt(mlx.AddScalar(variance, eps)))
-}
-
-// Transformer is the full Qwen-Image transformer model
-type Transformer struct {
-	Config *TransformerConfig
-
-	ImgIn     *mlx.Array
-	ImgInBias *mlx.Array
-	TxtIn     *mlx.Array
-	TxtInBias *mlx.Array
-	TxtNorm   *mlx.Array
-
-	TEmbed *TimestepEmbedder
-	Layers []*TransformerBlock
-
-	NormOutWeight *mlx.Array
-	NormOutBias   *mlx.Array
-	ProjOut       *mlx.Array
-	ProjOutBias   *mlx.Array
-}
-
-// Load loads the transformer from a directory
-func (m *Transformer) Load(path string) error {
-	fmt.Println("Loading Qwen-Image transformer...")
-
-	cfg := defaultTransformerConfig()
-	m.Config = cfg
-
-	weights, err := safetensors.LoadModelWeights(path)
-	if err != nil {
-		return fmt.Errorf("weights: %w", err)
-	}
-
-	// Bulk load all weights as bf16
-	fmt.Print("  Loading weights as bf16... ")
-	if err := weights.Load(mlx.DtypeBFloat16); err != nil {
-		return fmt.Errorf("load weights: %w", err)
-	}
-	fmt.Printf("✓ (%.1f GB)\n", float64(mlx.MetalGetActiveMemory())/(1024*1024*1024))
-
-	fmt.Print("  Loading input projections... ")
-	imgIn, _ := weights.Get("img_in.weight")
-	imgInBias, _ := weights.Get("img_in.bias")
-	txtIn, _ := weights.Get("txt_in.weight")
-	txtInBias, _ := weights.Get("txt_in.bias")
-	txtNorm, _ := weights.Get("txt_norm.weight")
-	m.ImgIn = mlx.Transpose(imgIn, 1, 0)
-	m.ImgInBias = imgInBias
-	m.TxtIn = mlx.Transpose(txtIn, 1, 0)
-	m.TxtInBias = txtInBias
-	m.TxtNorm = txtNorm
-	fmt.Println("✓")
-
-	fmt.Print("  Loading timestep embedder... ")
-	m.TEmbed, err = newTimestepEmbedder(weights)
-	if err != nil {
-		return fmt.Errorf("timestep embedder: %w", err)
-	}
-	fmt.Println("✓")
-
-	m.Layers = make([]*TransformerBlock, cfg.NLayers)
-	for i := int32(0); i < cfg.NLayers; i++ {
-		fmt.Printf("\r  Loading transformer layers... %d/%d", i+1, cfg.NLayers)
-		prefix := fmt.Sprintf("transformer_blocks.%d", i)
-		m.Layers[i], err = newTransformerBlock(weights, prefix, cfg)
-		if err != nil {
-			return fmt.Errorf("layer %d: %w", i, err)
-		}
-	}
-	fmt.Printf("\r  Loading transformer layers... ✓ [%d blocks]          \n", cfg.NLayers)
-
-	fmt.Print("  Loading output layers... ")
-	normOutWeight, _ := weights.Get("norm_out.linear.weight")
-	normOutBias, _ := weights.Get("norm_out.linear.bias")
-	projOut, _ := weights.Get("proj_out.weight")
-	projOutBias, _ := weights.Get("proj_out.bias")
-	m.NormOutWeight = mlx.Transpose(normOutWeight, 1, 0)
-	m.NormOutBias = normOutBias
-	m.ProjOut = mlx.Transpose(projOut, 1, 0)
-	m.ProjOutBias = projOutBias
-	fmt.Println("✓")
-
-	weights.ReleaseAll()
-	return nil
-}
-
-// LoadFromPath is a convenience function to load transformer from path
-func LoadTransformerFromPath(path string) (*Transformer, error) {
-	m := &Transformer{}
-	if err := m.Load(filepath.Join(path, "transformer")); err != nil {
-		return nil, err
-	}
-	return m, nil
-}
-
-// Forward runs the transformer
-// img: [B, L_img, in_channels] patchified latents
-// txt: [B, L_txt, joint_attention_dim] text embeddings
-// t: [B] timesteps (0-1)
-// imgFreqs, txtFreqs: RoPE frequencies
-func (tr *Transformer) Forward(img, txt, t *mlx.Array, imgFreqs, txtFreqs *mlx.Array) *mlx.Array {
-	imgShape := img.Shape()
-	B := imgShape[0]
-	Limg := imgShape[1]
-
-	txtShape := txt.Shape()
-	Ltxt := txtShape[1]
-
-	// Timestep embedding
-	temb := tr.TEmbed.Forward(t)
-
-	// Project image: [B, L, in_channels] -> [B, L, hidden_dim]
-	imgFlat := mlx.Reshape(img, B*Limg, tr.Config.InChannels)
-	imgH := mlx.Add(mlx.Linear(imgFlat, tr.ImgIn), tr.ImgInBias)
-	imgH = mlx.Reshape(imgH, B, Limg, tr.Config.HiddenDim)
-
-	// Project text: RMSNorm then linear
-	txtFlat := mlx.Reshape(txt, B*Ltxt, tr.Config.JointAttentionDim)
-	txtNormed := mlx.RMSNorm(txtFlat, tr.TxtNorm, 1e-6)
-	txtH := mlx.Add(mlx.Linear(txtNormed, tr.TxtIn), tr.TxtInBias)
-	txtH = mlx.Reshape(txtH, B, Ltxt, tr.Config.HiddenDim)
-
-	for _, layer := range tr.Layers {
-		imgH, txtH = layer.Forward(imgH, txtH, temb, imgFreqs, txtFreqs)
-	}
-
-	// Final norm with modulation (AdaLayerNormContinuous)
-	// Python: scale, shift = torch.chunk(emb, 2, dim=1)
-	finalMod := mlx.Add(mlx.Linear(mlx.SiLU(temb), tr.NormOutWeight), tr.NormOutBias)
-	modShape := finalMod.Shape()
-	halfDim := modShape[1] / 2
-	scale := mlx.ExpandDims(mlx.Slice(finalMod, []int32{0, 0}, []int32{B, halfDim}), 1)
-	shift := mlx.ExpandDims(mlx.Slice(finalMod, []int32{0, halfDim}, []int32{B, modShape[1]}), 1)
-
-	imgH = layerNormNoAffine(imgH, tr.Config.NormEps)
-	imgH = mlx.Add(mlx.Mul(imgH, mlx.AddScalar(scale, 1.0)), shift)
-
-	// Final projection: [B, L, hidden_dim] -> [B, L, patch_size^2 * out_channels]
-	imgFlat = mlx.Reshape(imgH, B*Limg, tr.Config.HiddenDim)
-	out := mlx.Add(mlx.Linear(imgFlat, tr.ProjOut), tr.ProjOutBias)
-
-	outChannels := tr.Config.PatchSize * tr.Config.PatchSize * tr.Config.OutChannels
-	return mlx.Reshape(out, B, Limg, outChannels)
-}
-
-// ForwardWithCache runs the transformer with layer caching for speedup.
-// Based on DeepCache (CVPR 2024) / Learning-to-Cache (NeurIPS 2024):
-// shallow layers change little between denoising steps, so we cache their
-// outputs and reuse them on non-refresh steps.
-//
-// stepCache: cache for layer outputs (use cache.NewStepCache(cacheLayers))
-// step: current denoising step (0-indexed)
-// cacheInterval: refresh cache every N steps (e.g., 3)
-// cacheLayers: number of shallow layers to cache (e.g., 15)
-func (tr *Transformer) ForwardWithCache(
-	img, txt, t *mlx.Array,
-	imgFreqs, txtFreqs *mlx.Array,
-	stepCache *cache.StepCache,
-	step, cacheInterval, cacheLayers int,
-) *mlx.Array {
-	imgShape := img.Shape()
-	B := imgShape[0]
-	Limg := imgShape[1]
-
-	txtShape := txt.Shape()
-	Ltxt := txtShape[1]
-
-	// Timestep embedding
-	temb := tr.TEmbed.Forward(t)
-
-	// Project image: [B, L, in_channels] -> [B, L, hidden_dim]
-	imgFlat := mlx.Reshape(img, B*Limg, tr.Config.InChannels)
-	imgH := mlx.Add(mlx.Linear(imgFlat, tr.ImgIn), tr.ImgInBias)
-	imgH = mlx.Reshape(imgH, B, Limg, tr.Config.HiddenDim)
-
-	// Project text: RMSNorm then linear
-	txtFlat := mlx.Reshape(txt, B*Ltxt, tr.Config.JointAttentionDim)
-	txtNormed := mlx.RMSNorm(txtFlat, tr.TxtNorm, 1e-6)
-	txtH := mlx.Add(mlx.Linear(txtNormed, tr.TxtIn), tr.TxtInBias)
-	txtH = mlx.Reshape(txtH, B, Ltxt, tr.Config.HiddenDim)
-
-	// Check if we should refresh the cache
-	refreshCache := stepCache.ShouldRefresh(step, cacheInterval)
-
-	for i, layer := range tr.Layers {
-		if i < cacheLayers && !refreshCache && stepCache.Get(i) != nil {
-			// Use cached outputs for shallow layers
-			imgH = stepCache.Get(i)
-			txtH = stepCache.Get2(i)
-		} else {
-			// Compute layer
-			imgH, txtH = layer.Forward(imgH, txtH, temb, imgFreqs, txtFreqs)
-			// Cache shallow layers on refresh steps
-			if i < cacheLayers && refreshCache {
-				stepCache.Set(i, imgH)
-				stepCache.Set2(i, txtH)
-			}
-		}
-	}
-
-	// Final norm with modulation (AdaLayerNormContinuous)
-	finalMod := mlx.Add(mlx.Linear(mlx.SiLU(temb), tr.NormOutWeight), tr.NormOutBias)
-	modShape := finalMod.Shape()
-	halfDim := modShape[1] / 2
-	scale := mlx.ExpandDims(mlx.Slice(finalMod, []int32{0, 0}, []int32{B, halfDim}), 1)
-	shift := mlx.ExpandDims(mlx.Slice(finalMod, []int32{0, halfDim}, []int32{B, modShape[1]}), 1)
-
-	imgH = layerNormNoAffine(imgH, tr.Config.NormEps)
-	imgH = mlx.Add(mlx.Mul(imgH, mlx.AddScalar(scale, 1.0)), shift)
-
-	// Final projection: [B, L, hidden_dim] -> [B, L, patch_size^2 * out_channels]
-	imgFlat = mlx.Reshape(imgH, B*Limg, tr.Config.HiddenDim)
-	out := mlx.Add(mlx.Linear(imgFlat, tr.ProjOut), tr.ProjOutBias)
-
-	outChannels := tr.Config.PatchSize * tr.Config.PatchSize * tr.Config.OutChannels
-	return mlx.Reshape(out, B, Limg, outChannels)
-}
-
-// RoPECache holds precomputed RoPE frequencies
-type RoPECache struct {
-	ImgFreqs *mlx.Array // [L_img, head_dim]
-	TxtFreqs *mlx.Array // [L_txt, head_dim]
-}
-
-// PrepareRoPE computes RoPE for image and text sequences
-// This matches Python's QwenEmbedRope with scale_rope=True
-func PrepareRoPE(imgH, imgW int32, txtLen int32, axesDims []int32) *RoPECache {
-	theta := float64(10000)
-	maxIdx := int32(4096)
-
-	// Compute base frequencies for each axis dimension
-	freqsT := ComputeAxisFreqs(axesDims[0], theta)
-	freqsH := ComputeAxisFreqs(axesDims[1], theta)
-	freqsW := ComputeAxisFreqs(axesDims[2], theta)
-
-	// Build frequency lookup tables
-	posFreqsT := MakeFreqTable(maxIdx, freqsT, false)
-	posFreqsH := MakeFreqTable(maxIdx, freqsH, false)
-	posFreqsW := MakeFreqTable(maxIdx, freqsW, false)
-	negFreqsH := MakeFreqTable(maxIdx, freqsH, true)
-	negFreqsW := MakeFreqTable(maxIdx, freqsW, true)
-
-	// Image frequencies with scale_rope=True
-	imgLen := imgH * imgW
-	headDim := int32(len(freqsT)+len(freqsH)+len(freqsW)) * 2
-	imgFreqsData := make([]float32, imgLen*headDim)
-
-	hHalf := imgH / 2
-	wHalf := imgW / 2
-
-	idx := int32(0)
-	for y := int32(0); y < imgH; y++ {
-		for x := int32(0); x < imgW; x++ {
-			// Frame = 0
-			for i := 0; i < len(freqsT)*2; i++ {
-				imgFreqsData[idx+int32(i)] = posFreqsT[0][i]
-			}
-			idx += int32(len(freqsT) * 2)
-
-			// Height: scale_rope pattern
-			hNegCount := imgH - hHalf
-			if y < hNegCount {
-				negTableIdx := maxIdx - hNegCount + y
-				for i := 0; i < len(freqsH)*2; i++ {
-					imgFreqsData[idx+int32(i)] = negFreqsH[negTableIdx][i]
-				}
-			} else {
-				posIdx := y - hNegCount
-				for i := 0; i < len(freqsH)*2; i++ {
-					imgFreqsData[idx+int32(i)] = posFreqsH[posIdx][i]
-				}
-			}
-			idx += int32(len(freqsH) * 2)
-
-			// Width: scale_rope pattern
-			wNegCount := imgW - wHalf
-			if x < wNegCount {
-				negTableIdx := maxIdx - wNegCount + x
-				for i := 0; i < len(freqsW)*2; i++ {
-					imgFreqsData[idx+int32(i)] = negFreqsW[negTableIdx][i]
-				}
-			} else {
-				posIdx := x - wNegCount
-				for i := 0; i < len(freqsW)*2; i++ {
-					imgFreqsData[idx+int32(i)] = posFreqsW[posIdx][i]
-				}
-			}
-			idx += int32(len(freqsW) * 2)
-		}
-	}
-
-	imgFreqs := mlx.NewArray(imgFreqsData, []int32{imgLen, headDim})
-	imgFreqs = mlx.ToBFloat16(imgFreqs)
-
-	// Text frequencies
-	maxVidIdx := max(hHalf, wHalf)
-	txtFreqsData := make([]float32, txtLen*headDim)
-
-	idx = 0
-	for t := int32(0); t < txtLen; t++ {
-		pos := maxVidIdx + t
-		for i := 0; i < len(freqsT)*2; i++ {
-			txtFreqsData[idx+int32(i)] = posFreqsT[pos][i]
-		}
-		idx += int32(len(freqsT) * 2)
-		for i := 0; i < len(freqsH)*2; i++ {
-			txtFreqsData[idx+int32(i)] = posFreqsH[pos][i]
-		}
-		idx += int32(len(freqsH) * 2)
-		for i := 0; i < len(freqsW)*2; i++ {
-			txtFreqsData[idx+int32(i)] = posFreqsW[pos][i]
-		}
-		idx += int32(len(freqsW) * 2)
-	}
-
-	txtFreqs := mlx.NewArray(txtFreqsData, []int32{txtLen, headDim})
-	txtFreqs = mlx.ToBFloat16(txtFreqs)
-
-	return &RoPECache{
-		ImgFreqs: imgFreqs,
-		TxtFreqs: txtFreqs,
-	}
-}
-
-// ComputeAxisFreqs computes RoPE base frequencies for a given dimension.
-func ComputeAxisFreqs(dim int32, theta float64) []float64 {
-	halfDim := dim / 2
-	freqs := make([]float64, halfDim)
-	for i := int32(0); i < halfDim; i++ {
-		freqs[i] = 1.0 / math.Pow(theta, float64(i)/float64(halfDim))
-	}
-	return freqs
-}
-
-// MakeFreqTable builds a table of cos/sin values for RoPE positions.
-func MakeFreqTable(maxIdx int32, baseFreqs []float64, negative bool) [][]float32 {
-	table := make([][]float32, maxIdx)
-	for idx := int32(0); idx < maxIdx; idx++ {
-		var pos float64
-		if negative {
-			pos = float64(-maxIdx + int32(idx))
-		} else {
-			pos = float64(idx)
-		}
-
-		row := make([]float32, len(baseFreqs)*2)
-		for i, f := range baseFreqs {
-			angle := pos * f
-			row[i*2] = float32(math.Cos(angle))
-			row[i*2+1] = float32(math.Sin(angle))
-		}
-		table[idx] = row
-	}
-	return table
-}
-
-func max(a, b int32) int32 {
-	if a > b {
-		return a
-	}
-	return b
-}
-
-// PackLatents converts [B, C, H, W] to [B, L, C*4] patches
-func PackLatents(latents *mlx.Array, patchSize int32) *mlx.Array {
-	shape := latents.Shape()
-	B := shape[0]
-	C := shape[1]
-	H := shape[2]
-	W := shape[3]
-
-	pH := H / patchSize
-	pW := W / patchSize
-
-	// [B, C, H, W] -> [B, C, pH, 2, pW, 2]
-	x := mlx.Reshape(latents, B, C, pH, patchSize, pW, patchSize)
-	// -> [B, pH, pW, C, 2, 2]
-	x = mlx.Transpose(x, 0, 2, 4, 1, 3, 5)
-	// -> [B, pH*pW, C*4]
-	return mlx.Reshape(x, B, pH*pW, C*patchSize*patchSize)
-}
-
-// UnpackLatents converts [B, L, C*4] back to [B, C, 1, H, W] (5D for VAE)
-func UnpackLatents(patches *mlx.Array, H, W, patchSize int32) *mlx.Array {
-	shape := patches.Shape()
-	B := shape[0]
-	channels := shape[2] / (patchSize * patchSize)
-
-	pH := H / patchSize
-	pW := W / patchSize
-
-	// [B, L, C*4] -> [B, pH, pW, C, 2, 2]
-	x := mlx.Reshape(patches, B, pH, pW, channels, patchSize, patchSize)
-	// -> [B, C, pH, 2, pW, 2]
-	x = mlx.Transpose(x, 0, 3, 1, 4, 2, 5)
-	// -> [B, C, H, W]
-	x = mlx.Reshape(x, B, channels, pH*patchSize, pW*patchSize)
-	// Add temporal dimension for VAE: [B, C, 1, H, W]
-	return mlx.ExpandDims(x, 2)
-}

x/imagegen/models/qwen_image/transformer_test.go

@@ -1,119 +0,0 @@
-//go:build mlx
-
-package qwen_image
-
-import (
-	"math"
-	"os"
-	"testing"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-)
-
-// TestTransformerConfig tests configuration invariants.
-func TestTransformerConfig(t *testing.T) {
-	cfg := defaultTransformerConfig()
-
-	// Property: hidden_dim = n_heads * head_dim
-	if cfg.HiddenDim != cfg.NHeads*cfg.HeadDim {
-		t.Errorf("hidden_dim != n_heads * head_dim: %d != %d * %d",
-			cfg.HiddenDim, cfg.NHeads, cfg.HeadDim)
-	}
-
-	// Property: axes_dims_rope sums to head_dim
-	var ropeSum int32
-	for _, d := range cfg.AxesDimsRope {
-		ropeSum += d
-	}
-	if ropeSum != cfg.HeadDim {
-		t.Errorf("axes_dims_rope sum != head_dim: %d != %d", ropeSum, cfg.HeadDim)
-	}
-
-	// Property: in_channels = out_channels * patch_size^2
-	expectedIn := cfg.OutChannels * cfg.PatchSize * cfg.PatchSize
-	if cfg.InChannels != expectedIn {
-		t.Errorf("in_channels != out_channels * patch_size^2: %d != %d", cfg.InChannels, expectedIn)
-	}
-}
-
-// TestTransformerRoPE tests RoPE frequency computation produces valid values.
-func TestTransformerRoPE(t *testing.T) {
-	cfg := defaultTransformerConfig()
-
-	// Test with small image dimensions
-	imgH, imgW := int32(4), int32(4) // 4x4 latent = 16 patches
-	txtLen := int32(5)
-
-	ropeCache := PrepareRoPE(imgH, imgW, txtLen, cfg.AxesDimsRope)
-	mlx.Eval(ropeCache.ImgFreqs, ropeCache.TxtFreqs)
-
-	// Verify shapes: [seq_len, head_dim]
-	imgSeqLen := imgH * imgW
-	if ropeCache.ImgFreqs.Shape()[0] != imgSeqLen {
-		t.Errorf("ImgFreqs seq_len: got %d, want %d", ropeCache.ImgFreqs.Shape()[0], imgSeqLen)
-	}
-	if ropeCache.ImgFreqs.Shape()[1] != cfg.HeadDim {
-		t.Errorf("ImgFreqs head_dim: got %d, want %d", ropeCache.ImgFreqs.Shape()[1], cfg.HeadDim)
-	}
-
-	if ropeCache.TxtFreqs.Shape()[0] != txtLen {
-		t.Errorf("TxtFreqs seq_len: got %d, want %d", ropeCache.TxtFreqs.Shape()[0], txtLen)
-	}
-
-	// Verify values are finite
-	imgData := ropeCache.ImgFreqs.Data()
-	for i := 0; i < min(100, len(imgData)); i++ {
-		if math.IsNaN(float64(imgData[i])) || math.IsInf(float64(imgData[i]), 0) {
-			t.Errorf("ImgFreqs[%d] not finite: %v", i, imgData[i])
-			break
-		}
-	}
-}
-
-// TestTransformerForward tests full forward pass (integration test).
-// Skips if model weights are not available.
-func TestTransformerForward(t *testing.T) {
-	weightsPath := "../../../weights/Qwen-Image-2512/transformer"
-	if _, err := os.Stat(weightsPath); os.IsNotExist(err) {
-		t.Skip("Skipping: model weights not found at " + weightsPath)
-	}
-
-	transformer := &Transformer{}
-	if err := transformer.Load(weightsPath); err != nil {
-		t.Fatalf("Failed to load transformer: %v", err)
-	}
-	mlx.Keep(mlx.Collect(transformer)...)
-	cfg := transformer.Config
-
-	// Small test inputs
-	batchSize := int32(1)
-	imgH, imgW := int32(4), int32(4)
-	imgSeqLen := imgH * imgW
-	txtSeqLen := int32(5)
-
-	hiddenStates := mlx.RandomNormal([]int32{batchSize, imgSeqLen, cfg.InChannels}, 0)
-	encoderHiddenStates := mlx.RandomNormal([]int32{batchSize, txtSeqLen, cfg.JointAttentionDim}, 0)
-	timestep := mlx.NewArray([]float32{0.5}, []int32{batchSize})
-
-	ropeCache := PrepareRoPE(imgH, imgW, txtSeqLen, cfg.AxesDimsRope)
-
-	// Forward pass
-	out := transformer.Forward(hiddenStates, encoderHiddenStates, timestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs)
-	mlx.Eval(out)
-
-	// Verify output shape: [batch, img_seq_len, in_channels]
-	wantShape := []int32{batchSize, imgSeqLen, cfg.InChannels}
-	gotShape := out.Shape()
-	if gotShape[0] != wantShape[0] || gotShape[1] != wantShape[1] || gotShape[2] != wantShape[2] {
-		t.Errorf("output shape: got %v, want %v", gotShape, wantShape)
-	}
-
-	// Verify output is finite
-	outData := out.Data()
-	for i := 0; i < min(100, len(outData)); i++ {
-		if math.IsNaN(float64(outData[i])) || math.IsInf(float64(outData[i]), 0) {
-			t.Errorf("output[%d] not finite: %v", i, outData[i])
-			break
-		}
-	}
-}

x/imagegen/models/qwen_image/vae.go

@@ -1,854 +0,0 @@
-//go:build mlx
-
-package qwen_image
-
-import (
-	"fmt"
-	"math"
-	"path/filepath"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-)
-
-// VAEConfig holds Qwen-Image VAE configuration
-type VAEConfig struct {
-	ZDim               int32     `json:"z_dim"`               // 16
-	BaseDim            int32     `json:"base_dim"`            // 96
-	DimMult            []int32   `json:"dim_mult"`            // [1, 2, 4, 4]
-	NumResBlocks       int32     `json:"num_res_blocks"`      // 2
-	LatentsMean        []float32 `json:"latents_mean"`        // 16 values
-	LatentsStd         []float32 `json:"latents_std"`         // 16 values
-	TemperalDownsample []bool    `json:"temperal_downsample"` // [false, true, true]
-}
-
-// defaultVAEConfig returns config for Qwen-Image VAE
-func defaultVAEConfig() *VAEConfig {
-	return &VAEConfig{
-		ZDim:         16,
-		BaseDim:      96,
-		DimMult:      []int32{1, 2, 4, 4},
-		NumResBlocks: 2,
-		LatentsMean: []float32{
-			-0.7571, -0.7089, -0.9113, 0.1075,
-			-0.1745, 0.9653, -0.1517, 1.5508,
-			0.4134, -0.0715, 0.5517, -0.3632,
-			-0.1922, -0.9497, 0.2503, -0.2921,
-		},
-		LatentsStd: []float32{
-			2.8184, 1.4541, 2.3275, 2.6558,
-			1.2196, 1.7708, 2.6052, 2.0743,
-			3.2687, 2.1526, 2.8652, 1.5579,
-			1.6382, 1.1253, 2.8251, 1.916,
-		},
-		TemperalDownsample: []bool{false, true, true},
-	}
-}
-
-// CausalConv3d is a causal 3D convolution (for temporal causality)
-type CausalConv3d struct {
-	Weight       *mlx.Array
-	Bias         *mlx.Array
-	BiasReshaped *mlx.Array // [1, C, 1, 1, 1]
-	KernelT      int32
-}
-
-// newCausalConv3d creates a 3D causal conv
-func newCausalConv3d(weights *safetensors.ModelWeights, prefix string) (*CausalConv3d, error) {
-	weight, err := weights.Get(prefix + ".weight")
-	if err != nil {
-		return nil, fmt.Errorf("weight not found: %s", prefix)
-	}
-	bias, _ := weights.Get(prefix + ".bias")
-
-	kernelT := weight.Shape()[2]
-	outC := weight.Shape()[0]
-
-	var biasReshaped *mlx.Array
-	if bias != nil {
-		biasReshaped = mlx.Reshape(bias, 1, outC, 1, 1, 1)
-	}
-
-	return &CausalConv3d{
-		Weight:       weight,
-		Bias:         bias,
-		BiasReshaped: biasReshaped,
-		KernelT:      kernelT,
-	}, nil
-}
-
-// Forward applies causal 3D convolution
-// x: [B, T, H, W, C] (channels-last, MLX format)
-func (c *CausalConv3d) Forward(x *mlx.Array) *mlx.Array {
-	shape := c.Weight.Shape() // PyTorch format: [O, I, kT, kH, kW]
-	kernelT := shape[2]
-	kernelH := shape[3]
-	kernelW := shape[4]
-
-	// Causal temporal padding, same spatial padding
-	// Input is channels-last: [B, T, H, W, C]
-	padT := kernelT - 1
-	padH := kernelH / 2
-	padW := kernelW / 2
-
-	// Stage 1: Pad
-	{
-			x = pad3DChannelsLast(x, padT, 0, padH, padH, padW, padW)
-		mlx.Eval(x)
-	}
-
-	// Stage 2: Conv + bias
-	var out *mlx.Array
-	{
-			prev := x
-		weight := mlx.Transpose(c.Weight, 0, 2, 3, 4, 1)
-		out = mlx.Conv3d(x, weight, 1, 1, 1, 0, 0, 0)
-		if c.Bias != nil {
-			bias := mlx.Reshape(c.Bias, 1, 1, 1, 1, c.Bias.Dim(0))
-			out = mlx.Add(out, bias)
-		}
-		prev.Free()
-		mlx.Eval(out)
-	}
-
-	return out
-}
-
-// RMSNorm3D applies RMS normalization over channels
-// Works with channels-last [B, T, H, W, C] format
-type RMSNorm3D struct {
-	Gamma *mlx.Array // [1, 1, 1, 1, C] for broadcasting
-}
-
-// newRMSNorm3D creates an RMS norm
-func newRMSNorm3D(weights *safetensors.ModelWeights, prefix string, dim int32) (*RMSNorm3D, error) {
-	gamma, err := weights.Get(prefix + ".gamma")
-	if err != nil {
-		return nil, err
-	}
-	// Reshape for channels-last broadcasting: [1, 1, 1, 1, C]
-	gamma = mlx.Reshape(gamma, 1, 1, 1, 1, gamma.Dim(0))
-	return &RMSNorm3D{Gamma: gamma}, nil
-}
-
-// Forward applies RMS norm to channels-last input [B, T, H, W, C]
-func (n *RMSNorm3D) Forward(x *mlx.Array) *mlx.Array {
-	// RMSNorm: x * rsqrt(mean(x^2) + eps) * gamma
-	normalized := mlx.RMSNormNoWeight(x, 1e-6)
-	return mlx.Mul(normalized, n.Gamma)
-}
-
-// ResBlock is a residual block with RMS norm and causal convs
-type ResBlock struct {
-	Norm1    *RMSNorm3D
-	Conv1    *CausalConv3d
-	Norm2    *RMSNorm3D
-	Conv2    *CausalConv3d
-	Shortcut *CausalConv3d
-}
-
-// newResBlock creates a residual block
-func newResBlock(weights *safetensors.ModelWeights, prefix string, inDim, outDim int32) (*ResBlock, error) {
-	norm1, err := newRMSNorm3D(weights, prefix+".norm1", inDim)
-	if err != nil {
-		return nil, err
-	}
-	conv1, err := newCausalConv3d(weights, prefix+".conv1")
-	if err != nil {
-		return nil, err
-	}
-	norm2, err := newRMSNorm3D(weights, prefix+".norm2", outDim)
-	if err != nil {
-		return nil, err
-	}
-	conv2, err := newCausalConv3d(weights, prefix+".conv2")
-	if err != nil {
-		return nil, err
-	}
-
-	var shortcut *CausalConv3d
-	if inDim != outDim {
-		shortcut, err = newCausalConv3d(weights, prefix+".conv_shortcut")
-		if err != nil {
-			return nil, err
-		}
-	}
-
-	return &ResBlock{
-		Norm1:    norm1,
-		Conv1:    conv1,
-		Norm2:    norm2,
-		Conv2:    conv2,
-		Shortcut: shortcut,
-	}, nil
-}
-
-// Forward applies the residual block
-func (r *ResBlock) Forward(x *mlx.Array) *mlx.Array {
-	// Use h as working variable, keep x intact for residual (caller will free x)
-	// Conv handles its own pools, so we just need pools for non-conv operations
-	var h *mlx.Array
-
-	// Keep x so it survives Eval() cleanup - needed for residual connection
-	mlx.Keep(x)
-
-	// Stage 1: norm1 + silu
-	{
-			h = r.Norm1.Forward(x)
-		h = silu3D(h)
-		mlx.Eval(h)
-	}
-
-	// Stage 2: conv1 (handles its own pools)
-	{
-		prev := h
-		h = r.Conv1.Forward(h)
-		prev.Free()
-	}
-
-	// Stage 3: norm2 + silu
-	{
-			prev := h
-		h = r.Norm2.Forward(h)
-		h = silu3D(h)
-		prev.Free()
-		mlx.Eval(h)
-	}
-
-	// Stage 4: conv2 (handles its own pools)
-	{
-		prev := h
-		h = r.Conv2.Forward(h)
-		prev.Free()
-	}
-
-	// Residual connection (shortcut handles its own pools if present)
-	if r.Shortcut != nil {
-		shortcut := r.Shortcut.Forward(x)
-			h = mlx.Add(h, shortcut)
-		mlx.Eval(h)
-	} else {
-			h = mlx.Add(h, x)
-		mlx.Eval(h)
-	}
-
-	return h
-}
-
-// AttentionBlock is a 2D attention block
-type AttentionBlock struct {
-	Norm      *RMSNorm3D
-	ToQKV     *mlx.Array
-	ToQKVBias *mlx.Array
-	Proj      *mlx.Array
-	ProjBias  *mlx.Array
-	Dim       int32
-}
-
-// newAttentionBlock creates an attention block
-func newAttentionBlock(weights *safetensors.ModelWeights, prefix string, dim int32) (*AttentionBlock, error) {
-	norm, err := newRMSNorm3D(weights, prefix+".norm", dim)
-	if err != nil {
-		return nil, err
-	}
-	toQKV, _ := weights.Get(prefix + ".to_qkv.weight")
-	toQKVBias, _ := weights.Get(prefix + ".to_qkv.bias")
-	proj, _ := weights.Get(prefix + ".proj.weight")
-	projBias, _ := weights.Get(prefix + ".proj.bias")
-
-	return &AttentionBlock{
-		Norm:      norm,
-		ToQKV:     toQKV,
-		ToQKVBias: toQKVBias,
-		Proj:      proj,
-		ProjBias:  projBias,
-		Dim:       dim,
-	}, nil
-}
-
-// Forward applies 2D attention
-// Input: [B, T, H, W, C] (channels-last)
-func (a *AttentionBlock) Forward(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	T := shape[1]
-	H := shape[2]
-	W := shape[3]
-	C := shape[4]
-
-	identity := x
-
-	// Flatten to [B*T, 1, H, W, C] for norm
-	x = mlx.Reshape(x, B*T, 1, H, W, C)
-	x = a.Norm.Forward(x)
-	x = mlx.Reshape(x, B*T, H, W, C)
-
-	// Flatten spatial to [B*T, H*W, C]
-	x = mlx.Reshape(x, B*T, H*W, C)
-
-	// Linear to get Q, K, V: [B*T, H*W, 3*C]
-	// Weight is [outC, inC] or [outC, inC, 1, 1]
-	wShape := a.ToQKV.Shape()
-	var w *mlx.Array
-	if len(wShape) == 4 {
-		w = mlx.Reshape(a.ToQKV, wShape[0], wShape[1])
-	} else {
-		w = a.ToQKV
-	}
-	w = mlx.Transpose(w, 1, 0) // [inC, outC]
-
-	qkv := mlx.Linear(x, w) // [B*T, H*W, 3*C]
-	if a.ToQKVBias != nil {
-		qkv = mlx.Add(qkv, a.ToQKVBias)
-	}
-	qkv = mlx.Reshape(qkv, B*T, 1, H*W, 3*C)
-
-	q := mlx.Slice(qkv, []int32{0, 0, 0, 0}, []int32{B * T, 1, H * W, C})
-	k := mlx.Slice(qkv, []int32{0, 0, 0, C}, []int32{B * T, 1, H * W, 2 * C})
-	v := mlx.Slice(qkv, []int32{0, 0, 0, 2 * C}, []int32{B * T, 1, H * W, 3 * C})
-
-	scale := float32(1.0 / math.Sqrt(float64(C)))
-	out := mlx.ScaledDotProductAttention(q, k, v, scale, false)
-
-	// out: [B*T, 1, H*W, C]
-	out = mlx.Reshape(out, B*T, H*W, C)
-
-	// Project back
-	pShape := a.Proj.Shape()
-	var p *mlx.Array
-	if len(pShape) == 4 {
-		p = mlx.Reshape(a.Proj, pShape[0], pShape[1])
-	} else {
-		p = a.Proj
-	}
-	p = mlx.Transpose(p, 1, 0) // [inC, outC]
-	out = mlx.Linear(out, p) // [B*T, H*W, C]
-	if a.ProjBias != nil {
-		out = mlx.Add(out, a.ProjBias)
-	}
-
-	out = mlx.Reshape(out, B, T, H, W, C)
-	return mlx.Add(out, identity)
-}
-
-// UpBlock handles upsampling in decoder
-type UpBlock struct {
-	ResBlocks []*ResBlock
-	Upsampler *Upsample
-}
-
-// newUpBlock creates an up block
-func newUpBlock(weights *safetensors.ModelWeights, prefix string, inDim, outDim int32, numBlocks int32, upsampleMode string) (*UpBlock, error) {
-	resBlocks := make([]*ResBlock, numBlocks+1)
-
-	currentDim := inDim
-	for i := int32(0); i <= numBlocks; i++ {
-		resPrefix := fmt.Sprintf("%s.resnets.%d", prefix, i)
-		block, err := newResBlock(weights, resPrefix, currentDim, outDim)
-		if err != nil {
-			return nil, err
-		}
-		resBlocks[i] = block
-		currentDim = outDim
-	}
-
-	var upsampler *Upsample
-	if upsampleMode != "" {
-		upsampler = newUpsample(weights, prefix+".upsamplers.0", outDim, upsampleMode)
-	}
-
-	return &UpBlock{
-		ResBlocks: resBlocks,
-		Upsampler: upsampler,
-	}, nil
-}
-
-// Forward applies up block with staged memory management
-func (u *UpBlock) Forward(x *mlx.Array) *mlx.Array {
-	// ResBlocks handle their own pools
-	for _, block := range u.ResBlocks {
-		prev := x
-		x = block.Forward(x)
-		prev.Free()
-	}
-
-	// Upsampler handles its own pools
-	if u.Upsampler != nil {
-		prev := x
-		x = u.Upsampler.Forward(x)
-		prev.Free()
-	}
-	return x
-}
-
-// Upsample handles spatial upsampling
-type Upsample struct {
-	Conv *mlx.Array
-	Bias *mlx.Array
-	Mode string
-}
-
-// newUpsample creates an upsampler
-func newUpsample(weights *safetensors.ModelWeights, prefix string, dim int32, mode string) *Upsample {
-	conv, _ := weights.Get(prefix + ".resample.1.weight")
-	bias, _ := weights.Get(prefix + ".resample.1.bias")
-	return &Upsample{
-		Conv: conv,
-		Bias: bias,
-		Mode: mode,
-	}
-}
-
-// Forward applies upsampling to channels-last input [B, T, H, W, C]
-// Uses staged pools to reduce peak memory during 2x upsampling
-func (u *Upsample) Forward(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	T := shape[1]
-	H := shape[2]
-	W := shape[3]
-	C := shape[4]
-	outC := u.Conv.Shape()[0]
-
-	// Stage 1: 2x nearest neighbor upsample
-	{
-			x = mlx.Reshape(x, B*T, H, W, C)
-		x = upsample2xChannelsLast(x)
-		mlx.Eval(x)
-	}
-
-	// Stage 2: Conv + bias
-	{
-			prev := x
-		weight := mlx.Transpose(u.Conv, 0, 2, 3, 1)
-		x = conv2D3x3PaddedChannelsLast(x, weight)
-		if u.Bias != nil {
-			bias := mlx.Reshape(u.Bias, 1, 1, 1, outC)
-			x = mlx.Add(x, bias)
-		}
-		x = mlx.Reshape(x, B, T, H*2, W*2, outC)
-		prev.Free()
-		mlx.Eval(x)
-	}
-
-	return x
-}
-
-// MidBlock is the middle block of decoder
-type MidBlock struct {
-	ResBlock1 *ResBlock
-	Attention *AttentionBlock
-	ResBlock2 *ResBlock
-}
-
-// newMidBlock creates a mid block
-func newMidBlock(weights *safetensors.ModelWeights, prefix string, dim int32) (*MidBlock, error) {
-	res1, err := newResBlock(weights, prefix+".resnets.0", dim, dim)
-	if err != nil {
-		return nil, err
-	}
-	attn, err := newAttentionBlock(weights, prefix+".attentions.0", dim)
-	if err != nil {
-		return nil, err
-	}
-	res2, err := newResBlock(weights, prefix+".resnets.1", dim, dim)
-	if err != nil {
-		return nil, err
-	}
-
-	return &MidBlock{
-		ResBlock1: res1,
-		Attention: attn,
-		ResBlock2: res2,
-	}, nil
-}
-
-// Forward applies mid block
-func (m *MidBlock) Forward(x *mlx.Array) *mlx.Array {
-	// Each component handles its own pools; we just free inputs
-	prev := x
-	x = m.ResBlock1.Forward(x)
-	prev.Free()
-
-	prev = x
-	x = m.Attention.Forward(x)
-	prev.Free()
-
-	prev = x
-	x = m.ResBlock2.Forward(x)
-	prev.Free()
-
-	return x
-}
-
-// VAEDecoder is the full VAE decoder
-type VAEDecoder struct {
-	Config *VAEConfig
-
-	PostQuantConv *CausalConv3d
-	ConvIn        *CausalConv3d
-	MidBlock      *MidBlock
-	UpBlocks      []*UpBlock
-	NormOut       *RMSNorm3D
-	ConvOut       *CausalConv3d
-}
-
-// Load loads the VAE decoder from a directory
-func (m *VAEDecoder) Load(path string) error {
-	fmt.Println("Loading Qwen-Image VAE decoder...")
-
-	cfg := defaultVAEConfig()
-	m.Config = cfg
-
-	weights, err := safetensors.LoadModelWeights(path)
-	if err != nil {
-		return fmt.Errorf("weights: %w", err)
-	}
-
-	// Bulk load all weights as bf16
-	fmt.Print("  Loading weights as bf16... ")
-	if err := weights.Load(mlx.DtypeBFloat16); err != nil {
-		return fmt.Errorf("failed to load weights: %w", err)
-	}
-	fmt.Printf("✓ (%.1f GB)\n", float64(mlx.MetalGetActiveMemory())/(1024*1024*1024))
-
-	fmt.Print("  Loading post_quant_conv... ")
-	postQuantConv, err := newCausalConv3d(weights, "post_quant_conv")
-	if err != nil {
-		return err
-	}
-	m.PostQuantConv = postQuantConv
-	fmt.Println("✓")
-
-	fmt.Print("  Loading conv_in... ")
-	convIn, err := newCausalConv3d(weights, "decoder.conv_in")
-	if err != nil {
-		return err
-	}
-	m.ConvIn = convIn
-	fmt.Println("✓")
-
-	// Mid block (dim = base_dim * dim_mult[-1] = 96 * 4 = 384)
-	fmt.Print("  Loading mid_block... ")
-	midDim := cfg.BaseDim * cfg.DimMult[len(cfg.DimMult)-1]
-	midBlock, err := newMidBlock(weights, "decoder.mid_block", midDim)
-	if err != nil {
-		return err
-	}
-	m.MidBlock = midBlock
-	fmt.Println("✓")
-
-	// Up blocks (reversed dim_mult)
-	fmt.Print("  Loading up_blocks... ")
-	numUpBlocks := len(cfg.DimMult)
-	m.UpBlocks = make([]*UpBlock, numUpBlocks)
-
-	dimsMult := make([]int32, numUpBlocks+1)
-	dimsMult[0] = cfg.DimMult[numUpBlocks-1]
-	for i := 0; i < numUpBlocks; i++ {
-		dimsMult[i+1] = cfg.DimMult[numUpBlocks-1-i]
-	}
-
-	temporalUpsample := make([]bool, len(cfg.TemperalDownsample))
-	for i := range cfg.TemperalDownsample {
-		temporalUpsample[i] = cfg.TemperalDownsample[len(cfg.TemperalDownsample)-1-i]
-	}
-
-	for i := 0; i < numUpBlocks; i++ {
-		inDim := cfg.BaseDim * dimsMult[i]
-		outDim := cfg.BaseDim * dimsMult[i+1]
-
-		if i > 0 {
-			inDim = inDim / 2
-		}
-
-		upsampleMode := ""
-		if i < numUpBlocks-1 {
-			if temporalUpsample[i] {
-				upsampleMode = "upsample3d"
-			} else {
-				upsampleMode = "upsample2d"
-			}
-		}
-
-		prefix := fmt.Sprintf("decoder.up_blocks.%d", i)
-		upBlock, err := newUpBlock(weights, prefix, inDim, outDim, cfg.NumResBlocks, upsampleMode)
-		if err != nil {
-			return err
-		}
-		m.UpBlocks[i] = upBlock
-	}
-	fmt.Printf("✓ [%d blocks]\n", numUpBlocks)
-
-	fmt.Print("  Loading output layers... ")
-	normOut, err := newRMSNorm3D(weights, "decoder.norm_out", cfg.BaseDim)
-	if err != nil {
-		return err
-	}
-	m.NormOut = normOut
-	convOut, err := newCausalConv3d(weights, "decoder.conv_out")
-	if err != nil {
-		return err
-	}
-	m.ConvOut = convOut
-	fmt.Println("✓")
-
-	weights.ReleaseAll()
-	return nil
-}
-
-// LoadVAEDecoderFromPath is a convenience function to load VAE from path
-func LoadVAEDecoderFromPath(path string) (*VAEDecoder, error) {
-	m := &VAEDecoder{}
-	if err := m.Load(filepath.Join(path, "vae")); err != nil {
-		return nil, err
-	}
-	return m, nil
-}
-
-// Decode converts latents to image
-// z: [B, C, T, H, W] normalized latents
-// Uses staged pools to free intermediate arrays and reduce peak memory.
-func (vae *VAEDecoder) Decode(z *mlx.Array) *mlx.Array {
-	var x *mlx.Array
-
-	// Stage 1a: Denormalize and transpose
-	{
-			z = vae.Denormalize(z)
-		// Convert from channels-first [N, C, T, H, W] to channels-last [N, T, H, W, C]
-		z = mlx.Contiguous(mlx.Transpose(z, 0, 2, 3, 4, 1))
-		mlx.Eval(z)
-	}
-
-	// Stage 1b: PostQuantConv (handles its own pools)
-	x = vae.PostQuantConv.Forward(z)
-	z.Free()
-
-	// Stage 1c: ConvIn (handles its own pools)
-	{
-		prev := x
-		x = vae.ConvIn.Forward(x)
-		prev.Free()
-	}
-
-	// Stage 2: Mid block (handles its own pools)
-	x = vae.MidBlock.Forward(x)
-
-	// Stage 3: Up blocks (each handles its own pools)
-	for _, upBlock := range vae.UpBlocks {
-		x = upBlock.Forward(x)
-	}
-
-	// Stage 4a: NormOut + silu
-	{
-			prev := x
-		x = vae.NormOut.Forward(x)
-		x = silu3D(x)
-		prev.Free()
-		mlx.Eval(x)
-	}
-
-	// Stage 4b: ConvOut (handles its own pools)
-	{
-		prev := x
-		x = vae.ConvOut.Forward(x)
-		prev.Free()
-	}
-
-	// Stage 4c: Post-processing
-	{
-			prev := x
-		// Clamp to [-1, 1]
-		x = mlx.ClipScalar(x, -1.0, 1.0, true, true)
-		// Convert back from channels-last to channels-first
-		x = mlx.Contiguous(mlx.Transpose(x, 0, 4, 1, 2, 3))
-		prev.Free()
-		mlx.Eval(x)
-	}
-
-	return x
-}
-
-// Denormalize reverses the normalization applied during encoding
-func (vae *VAEDecoder) Denormalize(z *mlx.Array) *mlx.Array {
-	shape := z.Shape()
-	C := shape[1]
-
-	mean := mlx.NewArray(vae.Config.LatentsMean[:C], []int32{1, C, 1, 1, 1})
-	std := mlx.NewArray(vae.Config.LatentsStd[:C], []int32{1, C, 1, 1, 1})
-
-	mean = mlx.ToBFloat16(mean)
-	std = mlx.ToBFloat16(std)
-
-	return mlx.Add(mlx.Mul(z, std), mean)
-}
-
-// Helper functions
-
-func silu3D(x *mlx.Array) *mlx.Array {
-	return mlx.Mul(x, mlx.Sigmoid(x))
-}
-
-// pad3DChannelsLast pads a channels-last [B, T, H, W, C] tensor
-func pad3DChannelsLast(x *mlx.Array, tBefore, tAfter, hBefore, hAfter, wBefore, wAfter int32) *mlx.Array {
-	if tBefore == 0 && tAfter == 0 && hBefore == 0 && hAfter == 0 && wBefore == 0 && wAfter == 0 {
-		return x
-	}
-	// Pad dims: [B before, B after, T before, T after, H before, H after, W before, W after, C before, C after]
-	return mlx.Pad(x, []int32{0, 0, tBefore, tAfter, hBefore, hAfter, wBefore, wAfter, 0, 0})
-}
-
-func pad2D(x *mlx.Array, hBefore, hAfter, wBefore, wAfter int32) *mlx.Array {
-	if hBefore == 0 && hAfter == 0 && wBefore == 0 && wAfter == 0 {
-		return x
-	}
-	return mlx.Pad(x, []int32{0, 0, 0, 0, hBefore, hAfter, wBefore, wAfter})
-}
-
-func conv2D1x1(x, weight *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	H := shape[2]
-	W := shape[3]
-
-	x = mlx.Transpose(x, 0, 2, 3, 1)
-	x = mlx.Reshape(x, B*H*W, shape[1])
-
-	wShape := weight.Shape()
-	var w *mlx.Array
-	if len(wShape) == 4 {
-		w = mlx.Reshape(weight, wShape[0], wShape[1])
-	} else {
-		w = weight
-	}
-	w = mlx.Transpose(w, 1, 0)
-
-	out := mlx.Linear(x, w)
-	outC := w.Dim(1)
-	out = mlx.Reshape(out, B, H, W, outC)
-	return mlx.Transpose(out, 0, 3, 1, 2)
-}
-
-func conv2D3x3Padded(x, weight *mlx.Array) *mlx.Array {
-	x = pad2D(x, 1, 1, 1, 1)
-	return conv2D(x, weight, 1, 1)
-}
-
-func conv2D(x, w *mlx.Array, strideH, strideW int32) *mlx.Array {
-	x = mlx.Transpose(x, 0, 2, 3, 1)
-	w = mlx.Transpose(w, 0, 2, 3, 1)
-
-	shape := x.Shape()
-	B := shape[0]
-	H := shape[1]
-	W := shape[2]
-
-	wShape := w.Shape()
-	Cout := wShape[0]
-	kH := wShape[1]
-	kW := wShape[2]
-
-	outH := (H-kH)/strideH + 1
-	outW := (W-kW)/strideW + 1
-
-	patches := extractPatches2D(x, kH, kW, strideH, strideW)
-	wFlat := mlx.Reshape(w, Cout, -1)
-	patches = mlx.Reshape(patches, B*outH*outW, -1)
-	out := mlx.Linear(patches, mlx.Transpose(wFlat, 1, 0))
-	out = mlx.Reshape(out, B, outH, outW, Cout)
-	return mlx.Transpose(out, 0, 3, 1, 2)
-}
-
-func extractPatches2D(x *mlx.Array, kH, kW, strideH, strideW int32) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	H := shape[1]
-	W := shape[2]
-	C := shape[3]
-
-	outH := (H-kH)/strideH + 1
-	outW := (W-kW)/strideW + 1
-
-	patches := make([]*mlx.Array, outH*outW)
-	idx := 0
-	for i := int32(0); i < outH; i++ {
-		for j := int32(0); j < outW; j++ {
-			startH := i * strideH
-			startW := j * strideW
-			patch := mlx.Slice(x, []int32{0, startH, startW, 0}, []int32{B, startH + kH, startW + kW, C})
-			patch = mlx.Reshape(patch, B, kH*kW*C)
-			patches[idx] = patch
-			idx++
-		}
-	}
-
-	for i := range patches {
-		patches[i] = mlx.ExpandDims(patches[i], 1)
-	}
-	stacked := mlx.Concatenate(patches, 1)
-	return mlx.Reshape(stacked, B, outH, outW, kH*kW*C)
-}
-
-func upsample2x(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	H := shape[2]
-	W := shape[3]
-
-	rowIdxData := make([]int32, H*2)
-	for i := int32(0); i < H; i++ {
-		rowIdxData[i*2] = i
-		rowIdxData[i*2+1] = i
-	}
-	rowIdx := mlx.NewArrayInt32(rowIdxData, []int32{H * 2})
-
-	colIdxData := make([]int32, W*2)
-	for i := int32(0); i < W; i++ {
-		colIdxData[i*2] = i
-		colIdxData[i*2+1] = i
-	}
-	colIdx := mlx.NewArrayInt32(colIdxData, []int32{W * 2})
-
-	x = mlx.Take(x, rowIdx, 2)
-	x = mlx.Take(x, colIdx, 3)
-
-	return x
-}
-
-// upsample2xChannelsLast upsamples channels-last input [B, H, W, C] by 2x
-func upsample2xChannelsLast(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	H := shape[1]
-	W := shape[2]
-
-	// Create repeat indices for rows
-	rowIdxData := make([]int32, H*2)
-	for i := int32(0); i < H; i++ {
-		rowIdxData[i*2] = i
-		rowIdxData[i*2+1] = i
-	}
-	rowIdx := mlx.NewArrayInt32(rowIdxData, []int32{H * 2})
-
-	// Create repeat indices for columns
-	colIdxData := make([]int32, W*2)
-	for i := int32(0); i < W; i++ {
-		colIdxData[i*2] = i
-		colIdxData[i*2+1] = i
-	}
-	colIdx := mlx.NewArrayInt32(colIdxData, []int32{W * 2})
-
-	// Take along H (axis 1) then W (axis 2)
-	x = mlx.Take(x, rowIdx, 1)
-	x = mlx.Take(x, colIdx, 2)
-
-	return x
-}
-
-// conv2D3x3PaddedChannelsLast applies 3x3 conv with padding to channels-last input [B, H, W, C]
-// weight: [outC, kH, kW, inC] (MLX channels-last format)
-func conv2D3x3PaddedChannelsLast(x, weight *mlx.Array) *mlx.Array {
-	// Pad spatial dims: [B, H, W, C] -> pad H and W by 1 each side
-	x = mlx.Pad(x, []int32{0, 0, 1, 1, 1, 1, 0, 0})
-	// Conv2d expects: input [B, H, W, inC], weight [outC, kH, kW, inC]
-	// stride=1, padding=0 (we already padded manually)
-	return mlx.Conv2d(x, weight, 1, 0)
-}

x/imagegen/models/qwen_image/vae_test.go

@@ -1,114 +0,0 @@
-//go:build mlx
-
-package qwen_image
-
-import (
-	"math"
-	"os"
-	"testing"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-)
-
-// TestVAEConfig tests configuration invariants.
-func TestVAEConfig(t *testing.T) {
-	cfg := defaultVAEConfig()
-
-	// Property: latents_mean and latents_std have z_dim elements
-	if int32(len(cfg.LatentsMean)) != cfg.ZDim {
-		t.Errorf("latents_mean length != z_dim: %d != %d", len(cfg.LatentsMean), cfg.ZDim)
-	}
-	if int32(len(cfg.LatentsStd)) != cfg.ZDim {
-		t.Errorf("latents_std length != z_dim: %d != %d", len(cfg.LatentsStd), cfg.ZDim)
-	}
-
-	// Property: dim_mult defines 4 stages
-	if len(cfg.DimMult) != 4 {
-		t.Errorf("dim_mult should have 4 stages: got %d", len(cfg.DimMult))
-	}
-
-	// Property: temperal_downsample has 3 elements (for 3 transitions)
-	if len(cfg.TemperalDownsample) != 3 {
-		t.Errorf("temperal_downsample should have 3 elements: got %d", len(cfg.TemperalDownsample))
-	}
-}
-
-// TestVAELatentsNormalization tests the latent denormalization values.
-func TestVAELatentsNormalization(t *testing.T) {
-	cfg := defaultVAEConfig()
-
-	// Verify latents_std values are all positive
-	for i, std := range cfg.LatentsStd {
-		if std <= 0 {
-			t.Errorf("latents_std[%d] should be positive: %v", i, std)
-		}
-	}
-
-	// Verify values are in reasonable range (from actual model)
-	for i, mean := range cfg.LatentsMean {
-		if math.Abs(float64(mean)) > 5 {
-			t.Errorf("latents_mean[%d] seems too large: %v", i, mean)
-		}
-	}
-	for i, std := range cfg.LatentsStd {
-		if std > 10 {
-			t.Errorf("latents_std[%d] seems too large: %v", i, std)
-		}
-	}
-}
-
-// TestVAEDecoderForward tests full forward pass (integration test).
-// Skips if model weights are not available.
-func TestVAEDecoderForward(t *testing.T) {
-	weightsPath := "../../../weights/Qwen-Image-2512/vae"
-	if _, err := os.Stat(weightsPath); os.IsNotExist(err) {
-		t.Skip("Skipping: model weights not found at " + weightsPath)
-	}
-
-	vae := &VAEDecoder{}
-	if err := vae.Load(weightsPath); err != nil {
-		t.Fatalf("Failed to load VAE decoder: %v", err)
-	}
-	mlx.Keep(mlx.Collect(vae)...)
-
-	// Small test input: [B, C, T, H, W]
-	// After 4 upsampling stages (2x each), H/W multiply by 16
-	batchSize := int32(1)
-	channels := int32(16)
-	frames := int32(1)
-	latentH := int32(4)
-	latentW := int32(4)
-
-	latents := mlx.RandomNormal([]int32{batchSize, channels, frames, latentH, latentW}, 0)
-
-	// Decode
-	out := vae.Decode(latents)
-	mlx.Eval(out)
-
-	// Verify output shape: [B, 3, T, H*16, W*16]
-	outShape := out.Shape()
-	if outShape[0] != batchSize {
-		t.Errorf("batch size: got %d, want %d", outShape[0], batchSize)
-	}
-	if outShape[1] != 3 {
-		t.Errorf("channels: got %d, want 3", outShape[1])
-	}
-	if outShape[2] != frames {
-		t.Errorf("frames: got %d, want %d", outShape[2], frames)
-	}
-	expectedH := latentH * 16 // 4 stages of 2x upsampling
-	expectedW := latentW * 16
-	if outShape[3] != expectedH || outShape[4] != expectedW {
-		t.Errorf("spatial dims: got [%d, %d], want [%d, %d]",
-			outShape[3], outShape[4], expectedH, expectedW)
-	}
-
-	// Verify output is in valid range (should be clamped to [0, 1] by decode)
-	outData := out.Data()
-	for i := 0; i < min(100, len(outData)); i++ {
-		if math.IsNaN(float64(outData[i])) || math.IsInf(float64(outData[i]), 0) {
-			t.Errorf("output[%d] not finite: %v", i, outData[i])
-			break
-		}
-	}
-}

x/imagegen/models/qwen_image_edit/layers.go

@@ -1,682 +0,0 @@
-//go:build mlx
-
-package qwen_image_edit
-
-import (
-	"fmt"
-	"math"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-)
-
-// CausalConv3d is a causal 3D convolution (for temporal causality)
-type CausalConv3d struct {
-	Weight       *mlx.Array
-	Bias         *mlx.Array
-	BiasReshaped *mlx.Array // [1, C, 1, 1, 1]
-	KernelT      int32
-}
-
-// newCausalConv3d creates a 3D causal conv
-func newCausalConv3d(weights *safetensors.ModelWeights, prefix string) (*CausalConv3d, error) {
-	weight, err := weights.Get(prefix + ".weight")
-	if err != nil {
-		return nil, fmt.Errorf("weight not found: %s", prefix)
-	}
-	bias, _ := weights.Get(prefix + ".bias")
-
-	kernelT := weight.Shape()[2]
-	outC := weight.Shape()[0]
-
-	var biasReshaped *mlx.Array
-	if bias != nil {
-		biasReshaped = mlx.Reshape(bias, 1, outC, 1, 1, 1)
-	}
-
-	return &CausalConv3d{
-		Weight:       weight,
-		Bias:         bias,
-		BiasReshaped: biasReshaped,
-		KernelT:      kernelT,
-	}, nil
-}
-
-// Forward applies causal 3D convolution (or 2D if weight is 4D)
-// x: [B, T, H, W, C] (channels-last, MLX format)
-func (c *CausalConv3d) Forward(x *mlx.Array) *mlx.Array {
-	shape := c.Weight.Shape()
-
-	// Handle both 5D (3D conv) and 4D (2D conv) weights
-	if len(shape) == 4 {
-		// 2D conv: [O, I, kH, kW] - need to apply per-frame
-		return c.forward2D(x)
-	}
-
-	// 3D conv: [O, I, kT, kH, kW]
-	kernelT := shape[2]
-	kernelH := shape[3]
-	kernelW := shape[4]
-
-	// Causal temporal padding, same spatial padding
-	padT := kernelT - 1
-	padH := kernelH / 2
-	padW := kernelW / 2
-
-	// Stage 1: Pad
-	{
-		x = pad3DChannelsLast(x, padT, 0, padH, padH, padW, padW)
-		mlx.Eval(x)
-	}
-
-	// Stage 2: Conv + bias
-	var out *mlx.Array
-	{
-		prev := x
-		weight := mlx.Transpose(c.Weight, 0, 2, 3, 4, 1)
-		out = mlx.Conv3d(x, weight, 1, 1, 1, 0, 0, 0)
-		if c.Bias != nil {
-			bias := mlx.Reshape(c.Bias, 1, 1, 1, 1, c.Bias.Dim(0))
-			out = mlx.Add(out, bias)
-		}
-		prev.Free()
-		mlx.Eval(out)
-	}
-
-	return out
-}
-
-// forward2D applies 2D conv per-frame for [B, T, H, W, C] input
-func (c *CausalConv3d) forward2D(x *mlx.Array) *mlx.Array {
-	xShape := x.Shape()
-	B := xShape[0]
-	T := xShape[1]
-	H := xShape[2]
-	W := xShape[3]
-	C := xShape[4]
-
-	wShape := c.Weight.Shape() // [O, I, kH, kW]
-	kernelH := wShape[2]
-	kernelW := wShape[3]
-	outC := wShape[0]
-
-	padH := kernelH / 2
-	padW := kernelW / 2
-
-	// Reshape to [B*T, H, W, C] for 2D conv
-	x = mlx.Reshape(x, B*T, H, W, C)
-
-	// Pad spatially
-	x = mlx.Pad(x, []int32{0, 0, padH, padH, padW, padW, 0, 0})
-
-	// Apply 2D conv
-	weight := mlx.Transpose(c.Weight, 0, 2, 3, 1) // [O, I, kH, kW] -> [O, kH, kW, I]
-	x = mlx.Conv2d(x, weight, 1, 0)
-
-	if c.Bias != nil {
-		bias := mlx.Reshape(c.Bias, 1, 1, 1, outC)
-		x = mlx.Add(x, bias)
-	}
-
-	// Get output spatial dims
-	outH := H
-	outW := W
-
-	// Reshape back to [B, T, H, W, C]
-	x = mlx.Reshape(x, B, T, outH, outW, outC)
-	mlx.Eval(x)
-
-	return x
-}
-
-// RMSNorm3D applies RMS normalization over channels
-type RMSNorm3D struct {
-	Gamma *mlx.Array // [1, 1, 1, 1, C] for broadcasting
-}
-
-// newRMSNorm3D creates an RMS norm
-func newRMSNorm3D(weights *safetensors.ModelWeights, prefix string, dim int32) (*RMSNorm3D, error) {
-	gamma, err := weights.Get(prefix + ".gamma")
-	if err != nil {
-		return nil, err
-	}
-	gamma = mlx.Reshape(gamma, 1, 1, 1, 1, gamma.Dim(0))
-	return &RMSNorm3D{Gamma: gamma}, nil
-}
-
-// Forward applies RMS norm to channels-last input [B, T, H, W, C]
-func (n *RMSNorm3D) Forward(x *mlx.Array) *mlx.Array {
-	normalized := mlx.RMSNormNoWeight(x, 1e-6)
-	return mlx.Mul(normalized, n.Gamma)
-}
-
-// ResBlock is a residual block with RMS norm and causal convs
-type ResBlock struct {
-	Norm1    *RMSNorm3D
-	Conv1    *CausalConv3d
-	Norm2    *RMSNorm3D
-	Conv2    *CausalConv3d
-	Shortcut *CausalConv3d
-}
-
-// newResBlock creates a residual block
-func newResBlock(weights *safetensors.ModelWeights, prefix string, inDim, outDim int32) (*ResBlock, error) {
-	norm1, err := newRMSNorm3D(weights, prefix+".norm1", inDim)
-	if err != nil {
-		return nil, err
-	}
-	conv1, err := newCausalConv3d(weights, prefix+".conv1")
-	if err != nil {
-		return nil, err
-	}
-	norm2, err := newRMSNorm3D(weights, prefix+".norm2", outDim)
-	if err != nil {
-		return nil, err
-	}
-	conv2, err := newCausalConv3d(weights, prefix+".conv2")
-	if err != nil {
-		return nil, err
-	}
-
-	var shortcut *CausalConv3d
-	if inDim != outDim {
-		shortcut, err = newCausalConv3d(weights, prefix+".conv_shortcut")
-		if err != nil {
-			return nil, err
-		}
-	}
-
-	return &ResBlock{
-		Norm1:    norm1,
-		Conv1:    conv1,
-		Norm2:    norm2,
-		Conv2:    conv2,
-		Shortcut: shortcut,
-	}, nil
-}
-
-// Forward applies the residual block
-func (r *ResBlock) Forward(x *mlx.Array) *mlx.Array {
-	var h *mlx.Array
-
-	mlx.Keep(x)
-
-	// Stage 1: norm1 + silu
-	{
-		h = r.Norm1.Forward(x)
-		h = silu3D(h)
-		mlx.Eval(h)
-	}
-
-	// Stage 2: conv1
-	{
-		prev := h
-		h = r.Conv1.Forward(h)
-		prev.Free()
-	}
-
-	// Stage 3: norm2 + silu
-	{
-		prev := h
-		h = r.Norm2.Forward(h)
-		h = silu3D(h)
-		prev.Free()
-		mlx.Eval(h)
-	}
-
-	// Stage 4: conv2
-	{
-		prev := h
-		h = r.Conv2.Forward(h)
-		prev.Free()
-	}
-
-	// Residual connection
-	if r.Shortcut != nil {
-		shortcut := r.Shortcut.Forward(x)
-		h = mlx.Add(h, shortcut)
-		mlx.Eval(h)
-	} else {
-		h = mlx.Add(h, x)
-		mlx.Eval(h)
-	}
-
-	return h
-}
-
-// AttentionBlock is a 2D attention block
-type AttentionBlock struct {
-	Norm      *RMSNorm3D
-	ToQKV     *mlx.Array
-	ToQKVBias *mlx.Array
-	Proj      *mlx.Array
-	ProjBias  *mlx.Array
-	Dim       int32
-}
-
-// newAttentionBlock creates an attention block
-func newAttentionBlock(weights *safetensors.ModelWeights, prefix string, dim int32) (*AttentionBlock, error) {
-	norm, err := newRMSNorm3D(weights, prefix+".norm", dim)
-	if err != nil {
-		return nil, err
-	}
-	toQKV, _ := weights.Get(prefix + ".to_qkv.weight")
-	toQKVBias, _ := weights.Get(prefix + ".to_qkv.bias")
-	proj, _ := weights.Get(prefix + ".proj.weight")
-	projBias, _ := weights.Get(prefix + ".proj.bias")
-
-	return &AttentionBlock{
-		Norm:      norm,
-		ToQKV:     toQKV,
-		ToQKVBias: toQKVBias,
-		Proj:      proj,
-		ProjBias:  projBias,
-		Dim:       dim,
-	}, nil
-}
-
-// Forward applies 2D attention
-// Input: [B, T, H, W, C] (channels-last)
-func (a *AttentionBlock) Forward(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	T := shape[1]
-	H := shape[2]
-	W := shape[3]
-	C := shape[4]
-
-	identity := x
-
-	// Flatten to [B*T, 1, H, W, C] for norm
-	x = mlx.Reshape(x, B*T, 1, H, W, C)
-	x = a.Norm.Forward(x)
-	x = mlx.Reshape(x, B*T, H, W, C)
-
-	// Flatten spatial to [B*T, H*W, C]
-	x = mlx.Reshape(x, B*T, H*W, C)
-
-	// Linear to get Q, K, V
-	wShape := a.ToQKV.Shape()
-	var w *mlx.Array
-	if len(wShape) == 4 {
-		w = mlx.Reshape(a.ToQKV, wShape[0], wShape[1])
-	} else {
-		w = a.ToQKV
-	}
-	w = mlx.Transpose(w, 1, 0)
-
-	qkv := mlx.Linear(x, w)
-	if a.ToQKVBias != nil {
-		qkv = mlx.Add(qkv, a.ToQKVBias)
-	}
-	qkv = mlx.Reshape(qkv, B*T, 1, H*W, 3*C)
-
-	q := mlx.Slice(qkv, []int32{0, 0, 0, 0}, []int32{B * T, 1, H * W, C})
-	k := mlx.Slice(qkv, []int32{0, 0, 0, C}, []int32{B * T, 1, H * W, 2 * C})
-	v := mlx.Slice(qkv, []int32{0, 0, 0, 2 * C}, []int32{B * T, 1, H * W, 3 * C})
-
-	scale := float32(1.0 / math.Sqrt(float64(C)))
-	out := mlx.ScaledDotProductAttention(q, k, v, scale, false)
-
-	out = mlx.Reshape(out, B*T, H*W, C)
-
-	// Project back
-	pShape := a.Proj.Shape()
-	var p *mlx.Array
-	if len(pShape) == 4 {
-		p = mlx.Reshape(a.Proj, pShape[0], pShape[1])
-	} else {
-		p = a.Proj
-	}
-	p = mlx.Transpose(p, 1, 0)
-	out = mlx.Linear(out, p)
-	if a.ProjBias != nil {
-		out = mlx.Add(out, a.ProjBias)
-	}
-
-	out = mlx.Reshape(out, B, T, H, W, C)
-	return mlx.Add(out, identity)
-}
-
-// UpBlock handles upsampling in decoder
-type UpBlock struct {
-	ResBlocks []*ResBlock
-	Upsampler *Upsample
-}
-
-// newUpBlock creates an up block
-func newUpBlock(weights *safetensors.ModelWeights, prefix string, inDim, outDim int32, numBlocks int32, upsampleMode string) (*UpBlock, error) {
-	resBlocks := make([]*ResBlock, numBlocks+1)
-
-	currentDim := inDim
-	for i := int32(0); i <= numBlocks; i++ {
-		resPrefix := fmt.Sprintf("%s.resnets.%d", prefix, i)
-		block, err := newResBlock(weights, resPrefix, currentDim, outDim)
-		if err != nil {
-			return nil, err
-		}
-		resBlocks[i] = block
-		currentDim = outDim
-	}
-
-	var upsampler *Upsample
-	if upsampleMode != "" {
-		upsampler = newUpsample(weights, prefix+".upsamplers.0", outDim, upsampleMode)
-	}
-
-	return &UpBlock{
-		ResBlocks: resBlocks,
-		Upsampler: upsampler,
-	}, nil
-}
-
-// Forward applies up block
-func (u *UpBlock) Forward(x *mlx.Array) *mlx.Array {
-	for _, block := range u.ResBlocks {
-		prev := x
-		x = block.Forward(x)
-		prev.Free()
-	}
-
-	if u.Upsampler != nil {
-		prev := x
-		x = u.Upsampler.Forward(x)
-		prev.Free()
-	}
-	return x
-}
-
-// Upsample handles spatial upsampling
-type Upsample struct {
-	Conv *mlx.Array
-	Bias *mlx.Array
-	Mode string
-}
-
-// newUpsample creates an upsampler
-func newUpsample(weights *safetensors.ModelWeights, prefix string, dim int32, mode string) *Upsample {
-	conv, _ := weights.Get(prefix + ".resample.1.weight")
-	bias, _ := weights.Get(prefix + ".resample.1.bias")
-	return &Upsample{
-		Conv: conv,
-		Bias: bias,
-		Mode: mode,
-	}
-}
-
-// Forward applies upsampling to channels-last input [B, T, H, W, C]
-func (u *Upsample) Forward(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	T := shape[1]
-	H := shape[2]
-	W := shape[3]
-	C := shape[4]
-	outC := u.Conv.Shape()[0]
-
-	// Stage 1: 2x nearest neighbor upsample
-	{
-		x = mlx.Reshape(x, B*T, H, W, C)
-		x = upsample2xChannelsLast(x)
-		mlx.Eval(x)
-	}
-
-	// Stage 2: Conv + bias
-	{
-		prev := x
-		weight := mlx.Transpose(u.Conv, 0, 2, 3, 1)
-		x = conv2D3x3PaddedChannelsLast(x, weight)
-		if u.Bias != nil {
-			bias := mlx.Reshape(u.Bias, 1, 1, 1, outC)
-			x = mlx.Add(x, bias)
-		}
-		x = mlx.Reshape(x, B, T, H*2, W*2, outC)
-		prev.Free()
-		mlx.Eval(x)
-	}
-
-	return x
-}
-
-// MidBlock is the middle block
-type MidBlock struct {
-	ResBlock1 *ResBlock
-	Attention *AttentionBlock
-	ResBlock2 *ResBlock
-}
-
-// newMidBlock creates a mid block
-func newMidBlock(weights *safetensors.ModelWeights, prefix string, dim int32) (*MidBlock, error) {
-	res1, err := newResBlock(weights, prefix+".resnets.0", dim, dim)
-	if err != nil {
-		return nil, err
-	}
-	attn, err := newAttentionBlock(weights, prefix+".attentions.0", dim)
-	if err != nil {
-		return nil, err
-	}
-	res2, err := newResBlock(weights, prefix+".resnets.1", dim, dim)
-	if err != nil {
-		return nil, err
-	}
-
-	return &MidBlock{
-		ResBlock1: res1,
-		Attention: attn,
-		ResBlock2: res2,
-	}, nil
-}
-
-// Forward applies mid block
-func (m *MidBlock) Forward(x *mlx.Array) *mlx.Array {
-	prev := x
-	x = m.ResBlock1.Forward(x)
-	prev.Free()
-
-	prev = x
-	x = m.Attention.Forward(x)
-	prev.Free()
-
-	prev = x
-	x = m.ResBlock2.Forward(x)
-	prev.Free()
-
-	return x
-}
-
-// Helper functions
-
-func silu3D(x *mlx.Array) *mlx.Array {
-	return mlx.Mul(x, mlx.Sigmoid(x))
-}
-
-// pad3DChannelsLast pads a channels-last [B, T, H, W, C] tensor
-func pad3DChannelsLast(x *mlx.Array, tBefore, tAfter, hBefore, hAfter, wBefore, wAfter int32) *mlx.Array {
-	if tBefore == 0 && tAfter == 0 && hBefore == 0 && hAfter == 0 && wBefore == 0 && wAfter == 0 {
-		return x
-	}
-	return mlx.Pad(x, []int32{0, 0, tBefore, tAfter, hBefore, hAfter, wBefore, wAfter, 0, 0})
-}
-
-// upsample2xChannelsLast upsamples channels-last input [B, H, W, C] by 2x
-func upsample2xChannelsLast(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	H := shape[1]
-	W := shape[2]
-
-	rowIdxData := make([]int32, H*2)
-	for i := int32(0); i < H; i++ {
-		rowIdxData[i*2] = i
-		rowIdxData[i*2+1] = i
-	}
-	rowIdx := mlx.NewArrayInt32(rowIdxData, []int32{H * 2})
-
-	colIdxData := make([]int32, W*2)
-	for i := int32(0); i < W; i++ {
-		colIdxData[i*2] = i
-		colIdxData[i*2+1] = i
-	}
-	colIdx := mlx.NewArrayInt32(colIdxData, []int32{W * 2})
-
-	x = mlx.Take(x, rowIdx, 1)
-	x = mlx.Take(x, colIdx, 2)
-
-	return x
-}
-
-// conv2D3x3PaddedChannelsLast applies 3x3 conv with padding to channels-last input [B, H, W, C]
-func conv2D3x3PaddedChannelsLast(x, weight *mlx.Array) *mlx.Array {
-	x = mlx.Pad(x, []int32{0, 0, 1, 1, 1, 1, 0, 0})
-	return mlx.Conv2d(x, weight, 1, 0)
-}
-
-// conv2DStrided applies conv with stride > 1 using manual patch extraction
-// x: [B, H, W, C] (channels-last), weight: [O, kH, kW, I]
-func conv2DStrided(x, weight *mlx.Array, stride int32) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	H := shape[1]
-	W := shape[2]
-
-	wShape := weight.Shape()
-	Cout := wShape[0]
-	kH := wShape[1]
-	kW := wShape[2]
-
-	outH := (H - kH) / stride + 1
-	outW := (W - kW) / stride + 1
-
-	patches := extractPatches2DStrided(x, kH, kW, stride)
-	wFlat := mlx.Reshape(weight, Cout, -1)
-	patches = mlx.Reshape(patches, B*outH*outW, -1)
-	out := mlx.Linear(patches, mlx.Transpose(wFlat, 1, 0))
-	return mlx.Reshape(out, B, outH, outW, Cout)
-}
-
-// conv3DStrided applies 3D conv with strides using manual patch extraction
-// x: [B, T, H, W, C] (channels-last), weight: [O, I, kT, kH, kW] (PyTorch format)
-// strideT, strideH, strideW are the strides for each dimension
-// Patches are extracted in [C, T, H, W] order to match Python's preprocessing
-func conv3DStrided(x, weight *mlx.Array, strideT, strideH, strideW int32) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	T := shape[1]
-	H := shape[2]
-	W := shape[3]
-	C := shape[4]
-
-	wShape := weight.Shape()
-	Cout := wShape[0]
-	// I := wShape[1]
-	kT := wShape[2]
-	kH := wShape[3]
-	kW := wShape[4]
-
-	// For temporal: if T < kT, we need to repeat frames temporally
-	// For single image with T=1 and kT=2, we duplicate the frame to T=kT
-	// Python Qwen2.5-VL duplicates the frame, not zero-pads
-	if T < kT {
-		// Tile along T dimension: [B, T, H, W, C] -> [B, kT, H, W, C]
-		x = mlx.Tile(x, []int32{1, kT, 1, 1, 1})
-		T = kT
-	}
-
-	outT := (T - kT) / strideT + 1
-	outH := (H - kH) / strideH + 1
-	outW := (W - kW) / strideW + 1
-
-	// Extract 3D patches in [C, T, H, W] order to match Python
-	patches := extractPatches3DStrided(x, kT, kH, kW, strideT, strideH, strideW)
-	// patches shape: [B, outT, outH, outW, C*kT*kH*kW]
-
-	// Weight is [O, I, kT, kH, kW] - flatten to [O, I*kT*kH*kW] to match patch order [C, T, H, W]
-	wFlat := mlx.Reshape(weight, Cout, -1) // [Cout, I*kT*kH*kW]
-	patches = mlx.Reshape(patches, B*outT*outH*outW, C*kT*kH*kW)
-	out := mlx.Linear(patches, mlx.Transpose(wFlat, 1, 0))
-	return mlx.Reshape(out, B, outT, outH, outW, Cout)
-}
-
-// extractPatches3DStrided extracts 3D patches with given strides
-// Returns patches with values in [C, T, H, W] order to match Python's preprocessing
-func extractPatches3DStrided(x *mlx.Array, kT, kH, kW, strideT, strideH, strideW int32) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	T := shape[1]
-	H := shape[2]
-	W := shape[3]
-	C := shape[4]
-
-	outT := (T - kT) / strideT + 1
-	outH := (H - kH) / strideH + 1
-	outW := (W - kW) / strideW + 1
-
-	numPatches := outT * outH * outW
-	patches := make([]*mlx.Array, numPatches)
-	idx := 0
-	for t := int32(0); t < outT; t++ {
-		for i := int32(0); i < outH; i++ {
-			for j := int32(0); j < outW; j++ {
-				startT := t * strideT
-				startH := i * strideH
-				startW := j * strideW
-				// Extract patch: [B, kT, kH, kW, C]
-				patch := mlx.Slice(x,
-					[]int32{0, startT, startH, startW, 0},
-					[]int32{B, startT + kT, startH + kH, startW + kW, C})
-				// Transpose from [B, T, H, W, C] to [B, C, T, H, W] to match Python's order
-				patch = mlx.Transpose(patch, 0, 4, 1, 2, 3)
-				// Flatten to [B, C*T*H*W]
-				patch = mlx.Reshape(patch, B, C*kT*kH*kW)
-				patches[idx] = patch
-				idx++
-			}
-		}
-	}
-
-	for i := range patches {
-		patches[i] = mlx.ExpandDims(patches[i], 1)
-	}
-	stacked := mlx.Concatenate(patches, 1)
-	return mlx.Reshape(stacked, B, outT, outH, outW, C*kT*kH*kW)
-}
-
-// extractPatches2DStrided extracts patches with given stride
-func extractPatches2DStrided(x *mlx.Array, kH, kW, stride int32) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	H := shape[1]
-	W := shape[2]
-	C := shape[3]
-
-	outH := (H - kH) / stride + 1
-	outW := (W - kW) / stride + 1
-
-	patches := make([]*mlx.Array, outH*outW)
-	idx := 0
-	for i := int32(0); i < outH; i++ {
-		for j := int32(0); j < outW; j++ {
-			startH := i * stride
-			startW := j * stride
-			patch := mlx.Slice(x, []int32{0, startH, startW, 0}, []int32{B, startH + kH, startW + kW, C})
-			patch = mlx.Reshape(patch, B, kH*kW*C)
-			patches[idx] = patch
-			idx++
-		}
-	}
-
-	for i := range patches {
-		patches[i] = mlx.ExpandDims(patches[i], 1)
-	}
-	stacked := mlx.Concatenate(patches, 1)
-	return mlx.Reshape(stacked, B, outH, outW, kH*kW*C)
-}
-
-// layerNormNoAffine applies layer norm without learnable parameters
-func layerNormNoAffine(x *mlx.Array, eps float32) *mlx.Array {
-	ndim := x.Ndim()
-	lastAxis := ndim - 1
-	mean := mlx.Mean(x, lastAxis, true)
-	xCentered := mlx.Sub(x, mean)
-	variance := mlx.Mean(mlx.Square(xCentered), lastAxis, true)
-	return mlx.Div(xCentered, mlx.Sqrt(mlx.AddScalar(variance, eps)))
-}

x/imagegen/models/qwen_image_edit/processor.go

@@ -1,475 +0,0 @@
-//go:build mlx
-
-package qwen_image_edit
-
-import (
-	"fmt"
-	"image"
-	"image/color"
-	_ "image/jpeg"
-	_ "image/png"
-	"math"
-	"os"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"golang.org/x/image/draw"
-	_ "golang.org/x/image/webp"
-)
-
-// loadImageFile loads an image from disk
-func loadImageFile(path string) (image.Image, error) {
-	f, err := os.Open(path)
-	if err != nil {
-		return nil, fmt.Errorf("open image: %w", err)
-	}
-	defer f.Close()
-
-	img, _, err := image.Decode(f)
-	if err != nil {
-		return nil, fmt.Errorf("decode image: %w", err)
-	}
-	return img, nil
-}
-
-// imageToFloat32Pixels converts an image to a float32 pixel array [H, W, C] in [0, 1] range
-func imageToFloat32Pixels(img image.Image, width, height int) []float32 {
-	pixels := make([]float32, width*height*3)
-	idx := 0
-	for y := 0; y < height; y++ {
-		for x := 0; x < width; x++ {
-			r, g, b, _ := img.At(x, y).RGBA()
-			pixels[idx] = float32(r) / 65535.0
-			pixels[idx+1] = float32(g) / 65535.0
-			pixels[idx+2] = float32(b) / 65535.0
-			idx += 3
-		}
-	}
-	return pixels
-}
-
-// normalizeImageNet applies ImageNet normalization to an image tensor
-func (p *Processor) normalizeImageNet(arr *mlx.Array) *mlx.Array {
-	mean := mlx.NewArray(p.Config.ImageMean, []int32{1, 1, 3})
-	std := mlx.NewArray(p.Config.ImageStd, []int32{1, 1, 3})
-	return mlx.Div(mlx.Sub(arr, mean), std)
-}
-
-// prepareImageTensor transforms [H, W, C] to [B, C, H, W] and converts to bf16
-func prepareImageTensor(arr *mlx.Array) *mlx.Array {
-	// Transpose to [C, H, W] and make contiguous
-	arr = mlx.Contiguous(mlx.Transpose(arr, 2, 0, 1))
-	// Add batch dimension [1, C, H, W]
-	arr = mlx.ExpandDims(arr, 0)
-	// Convert to bf16
-	arr = mlx.ToBFloat16(arr)
-	mlx.Eval(arr)
-	return arr
-}
-
-// clampFloat clamps a value to [0, 255] and returns uint8
-func clampFloat(v, weightSum float64) uint8 {
-	v /= weightSum
-	if v < 0 {
-		v = 0
-	}
-	if v > 255 {
-		v = 255
-	}
-	return uint8(math.Round(v))
-}
-
-// ImageDims holds dimensions for a preprocessed image
-type ImageDims struct {
-	// Original image dimensions
-	OrigW, OrigH int32
-	// Condition image dimensions (for vision encoder)
-	CondW, CondH int32
-	// VAE image dimensions
-	VaeW, VaeH int32
-	// Latent dimensions (VAE dims / vae_scale_factor)
-	LatentW, LatentH int32
-	// Patch dimensions (latent dims / patch_size)
-	PatchW, PatchH int32
-}
-
-// ProcessorConfig holds image processor configuration
-type ProcessorConfig struct {
-	// Condition image size (target pixel area for vision encoder input)
-	// Python: CONDITION_IMAGE_SIZE = 384 * 384 = 147456
-	// Pipeline resizes image to this area before passing to encode_prompt
-	ConditionImageSize int32
-
-	// VAE image size (target pixel area)
-	// Python: VAE_IMAGE_SIZE = 1024 * 1024 = 1048576
-	VAEImageSize int32
-
-	// Image normalization (ImageNet stats for vision encoder)
-	ImageMean []float32
-	ImageStd  []float32
-}
-
-// defaultProcessorConfig returns default processor config
-func defaultProcessorConfig() *ProcessorConfig {
-	return &ProcessorConfig{
-		ConditionImageSize: 384 * 384,   // 147456 - matches Python CONDITION_IMAGE_SIZE
-		VAEImageSize:       1024 * 1024, // 1048576 - matches Python VAE_IMAGE_SIZE
-		ImageMean:          []float32{0.48145466, 0.4578275, 0.40821073},
-		ImageStd:           []float32{0.26862954, 0.26130258, 0.27577711},
-	}
-}
-
-// Processor handles image preprocessing for Qwen-Image-Edit
-type Processor struct {
-	Config *ProcessorConfig
-}
-
-// Load loads the processor config
-func (p *Processor) Load(path string) error {
-	p.Config = defaultProcessorConfig()
-	return nil
-}
-
-// LoadAndPreprocess loads an image and preprocesses it for both paths
-// Returns: condImage (for vision encoder), vaeImage (for VAE encoding)
-func (p *Processor) LoadAndPreprocess(imagePath string) (*mlx.Array, *mlx.Array, error) {
-	img, err := loadImageFile(imagePath)
-	if err != nil {
-		return nil, nil, err
-	}
-
-	bounds := img.Bounds()
-	origW := bounds.Dx()
-	origH := bounds.Dy()
-	ratio := float64(origW) / float64(origH)
-
-	// Calculate dimensions for condition image (vision encoder)
-	// Python pipeline does TWO resizes:
-	// 1. VaeImageProcessor.resize with Lanczos to CONDITION_IMAGE_SIZE (384x384 area)
-	// 2. Qwen2VLProcessor's smart_resize with Bicubic to multiple of 28
-	intermediateW, intermediateH := calculateDimensions(p.Config.ConditionImageSize, ratio, 32)
-	finalH, finalW := smartResize(intermediateH, intermediateW, 28, 56*56, 28*28*1280)
-
-	// Calculate dimensions for VAE image (1024x1024 area)
-	// Use multiple of 32 (vae_scale_factor * patch_size * 2 = 8 * 2 * 2 = 32)
-	vaeW, vaeH := calculateDimensions(p.Config.VAEImageSize, ratio, 32)
-
-	// Preprocess for condition (vision encoder) - two-step resize
-	condImage := p.preprocessImageTwoStep(img, intermediateW, intermediateH, finalW, finalH)
-
-	// Preprocess for VAE ([-1, 1] range, 5D tensor)
-	vaeImage := p.preprocessImageForVAE(img, vaeW, vaeH)
-
-	return condImage, vaeImage, nil
-}
-
-// preprocessImageLanczos does single-step Lanczos resize for vision encoder
-// Matches Python VaeImageProcessor.resize with resample='lanczos' (the default)
-// Used by edit_plus pipeline for multi-image input
-// Returns: [B, C, H, W] normalized tensor
-func (p *Processor) preprocessImageLanczos(img image.Image, width, height int32) *mlx.Array {
-	resized := resizeImageLanczos(img, int(width), int(height))
-	pixels := imageToFloat32Pixels(resized, int(width), int(height))
-	arr := mlx.NewArray(pixels, []int32{height, width, 3})
-	arr = p.normalizeImageNet(arr)
-	return prepareImageTensor(arr)
-}
-
-// preprocessImageTwoStep does two-step resize for vision encoder to match Python pipeline
-// Step 1: Lanczos resize from original to intermediate size (VaeImageProcessor.resize)
-// Step 2: Bicubic resize from intermediate to final size (Qwen2VLProcessor smart_resize)
-// Returns: [B, C, H, W] normalized tensor
-func (p *Processor) preprocessImageTwoStep(img image.Image, intermediateW, intermediateH, finalW, finalH int32) *mlx.Array {
-	intermediate := resizeImageLanczos(img, int(intermediateW), int(intermediateH))
-	resized := resizeImageBicubic(intermediate, int(finalW), int(finalH))
-	pixels := imageToFloat32Pixels(resized, int(finalW), int(finalH))
-	arr := mlx.NewArray(pixels, []int32{finalH, finalW, 3})
-	arr = p.normalizeImageNet(arr)
-	return prepareImageTensor(arr)
-}
-
-// preprocessImage converts image to tensor for vision encoder
-// Returns: [B, C, H, W] normalized tensor
-func (p *Processor) preprocessImage(img image.Image, width, height int32, normalize bool) *mlx.Array {
-	resized := resizeImageBicubic(img, int(width), int(height))
-	pixels := imageToFloat32Pixels(resized, int(width), int(height))
-	arr := mlx.NewArray(pixels, []int32{height, width, 3})
-	if normalize {
-		arr = p.normalizeImageNet(arr)
-	}
-	return prepareImageTensor(arr)
-}
-
-// preprocessImageForVAE converts image to tensor for VAE encoding
-// Returns: [B, C, T, H, W] tensor in [-1, 1] range
-func (p *Processor) preprocessImageForVAE(img image.Image, width, height int32) *mlx.Array {
-	resized := resizeImageLanczos(img, int(width), int(height))
-	pixels := imageToFloat32Pixels(resized, int(width), int(height))
-	arr := mlx.NewArray(pixels, []int32{height, width, 3})
-
-	// Scale to [-1, 1]: arr * 2 - 1
-	arr = mlx.MulScalar(arr, 2.0)
-	arr = mlx.AddScalar(arr, -1.0)
-
-	// Transpose to [C, H, W] and make contiguous
-	arr = mlx.Contiguous(mlx.Transpose(arr, 2, 0, 1))
-
-	// Add batch and temporal dimensions [1, C, 1, H, W]
-	arr = mlx.ExpandDims(arr, 0) // [1, C, H, W]
-	arr = mlx.ExpandDims(arr, 2) // [1, C, 1, H, W]
-
-	arr = mlx.ToBFloat16(arr)
-	mlx.Eval(arr)
-	return arr
-}
-
-// smartResize implements Python Qwen2VL processor's smart_resize logic
-// Returns (resizedHeight, resizedWidth) that fit within min/max pixel constraints
-func smartResize(height, width, factor, minPixels, maxPixels int32) (int32, int32) {
-	// Round to factor
-	hBar := int32(math.Round(float64(height)/float64(factor))) * factor
-	wBar := int32(math.Round(float64(width)/float64(factor))) * factor
-
-	// Ensure minimum factor size
-	if hBar < factor {
-		hBar = factor
-	}
-	if wBar < factor {
-		wBar = factor
-	}
-
-	// Check pixel constraints
-	total := hBar * wBar
-	if total > maxPixels {
-		// Scale down
-		beta := math.Sqrt(float64(maxPixels) / float64(total))
-		hBar = int32(math.Floor(float64(height)*beta/float64(factor))) * factor
-		wBar = int32(math.Floor(float64(width)*beta/float64(factor))) * factor
-	} else if total < minPixels {
-		// Scale up
-		beta := math.Sqrt(float64(minPixels) / float64(total))
-		hBar = int32(math.Ceil(float64(height)*beta/float64(factor))) * factor
-		wBar = int32(math.Ceil(float64(width)*beta/float64(factor))) * factor
-	}
-
-	return hBar, wBar
-}
-
-// calculateDimensions calculates width and height for a target area while maintaining ratio
-// multiple: the value to round dimensions to (e.g., 28 for vision encoder with patch 14 and 2x2 merge)
-func calculateDimensions(targetArea int32, ratio float64, multiple int32) (int32, int32) {
-	width := math.Sqrt(float64(targetArea) * ratio)
-	height := width / ratio
-
-	m := float64(multiple)
-	width = math.Round(width/m) * m
-	height = math.Round(height/m) * m
-
-	// Ensure minimum dimensions
-	if width < m {
-		width = m
-	}
-	if height < m {
-		height = m
-	}
-
-	return int32(width), int32(height)
-}
-
-// resizeImageLanczos resizes an image using Lanczos3 interpolation (matches PIL.LANCZOS)
-func resizeImageLanczos(img image.Image, width, height int) image.Image {
-	bounds := img.Bounds()
-	dst := image.NewRGBA(image.Rect(0, 0, width, height))
-
-	// Lanczos3 kernel (a=3) to match PIL.LANCZOS
-	lanczos3 := &draw.Kernel{
-		Support: 3.0,
-		At: func(t float64) float64 {
-			if t == 0 {
-				return 1.0
-			}
-			if t < 0 {
-				t = -t
-			}
-			if t >= 3.0 {
-				return 0.0
-			}
-			// sinc(t) * sinc(t/3)
-			piT := math.Pi * t
-			return (math.Sin(piT) / piT) * (math.Sin(piT/3) / (piT / 3))
-		},
-	}
-	lanczos3.Scale(dst, dst.Bounds(), img, bounds, draw.Over, nil)
-
-	return dst
-}
-
-// resizeImageBicubic resizes an image using bicubic interpolation (matches PIL.BICUBIC)
-// Uses separable interpolation with PIL's coordinate mapping for exact match
-func resizeImageBicubic(img image.Image, width, height int) image.Image {
-	bounds := img.Bounds()
-	srcW := bounds.Dx()
-	srcH := bounds.Dy()
-
-	// Convert to RGBA if needed
-	var src *image.RGBA
-	if rgba, ok := img.(*image.RGBA); ok {
-		src = rgba
-	} else {
-		src = image.NewRGBA(bounds)
-		for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
-			for x := bounds.Min.X; x < bounds.Max.X; x++ {
-				src.Set(x, y, img.At(x, y))
-			}
-		}
-	}
-
-	// Keys cubic with a=-0.5 (PIL BICUBIC)
-	cubic := func(x float64) float64 {
-		if x < 0 {
-			x = -x
-		}
-		if x < 1 {
-			return 1.5*x*x*x - 2.5*x*x + 1
-		}
-		if x < 2 {
-			return -0.5*x*x*x + 2.5*x*x - 4*x + 2
-		}
-		return 0
-	}
-
-	// Horizontal pass: srcW -> width, keep srcH rows
-	temp := image.NewRGBA(image.Rect(0, 0, width, srcH))
-	for y := 0; y < srcH; y++ {
-		for dstX := 0; dstX < width; dstX++ {
-			// PIL coordinate mapping: center-to-center
-			srcXf := (float64(dstX)+0.5)*(float64(srcW)/float64(width)) - 0.5
-			baseX := int(math.Floor(srcXf))
-
-			var sumR, sumG, sumB, sumA, weightSum float64
-			for i := -1; i <= 2; i++ {
-				sx := baseX + i
-				if sx < 0 {
-					sx = 0
-				}
-				if sx >= srcW {
-					sx = srcW - 1
-				}
-
-				w := cubic(math.Abs(srcXf - float64(baseX+i)))
-				c := src.RGBAAt(sx, y)
-				sumR += float64(c.R) * w
-				sumG += float64(c.G) * w
-				sumB += float64(c.B) * w
-				sumA += float64(c.A) * w
-				weightSum += w
-			}
-
-			temp.SetRGBA(dstX, y, color.RGBA{
-				clampFloat(sumR, weightSum),
-				clampFloat(sumG, weightSum),
-				clampFloat(sumB, weightSum),
-				clampFloat(sumA, weightSum),
-			})
-		}
-	}
-
-	// Vertical pass: srcH -> height
-	dst := image.NewRGBA(image.Rect(0, 0, width, height))
-	for x := 0; x < width; x++ {
-		for dstY := 0; dstY < height; dstY++ {
-			srcYf := (float64(dstY)+0.5)*(float64(srcH)/float64(height)) - 0.5
-			baseY := int(math.Floor(srcYf))
-
-			var sumR, sumG, sumB, sumA, weightSum float64
-			for j := -1; j <= 2; j++ {
-				sy := baseY + j
-				if sy < 0 {
-					sy = 0
-				}
-				if sy >= srcH {
-					sy = srcH - 1
-				}
-
-				w := cubic(math.Abs(srcYf - float64(baseY+j)))
-				c := temp.RGBAAt(x, sy)
-				sumR += float64(c.R) * w
-				sumG += float64(c.G) * w
-				sumB += float64(c.B) * w
-				sumA += float64(c.A) * w
-				weightSum += w
-			}
-
-			dst.SetRGBA(x, dstY, color.RGBA{
-				clampFloat(sumR, weightSum),
-				clampFloat(sumG, weightSum),
-				clampFloat(sumB, weightSum),
-				clampFloat(sumA, weightSum),
-			})
-		}
-	}
-
-	return dst
-}
-
-// LoadAndPreprocessMultiple loads multiple images and preprocesses them
-// Returns: condImages (for vision encoder), vaeImages (for VAE encoding), dims (per-image dimensions)
-func (p *Processor) LoadAndPreprocessMultiple(imagePaths []string) ([]*mlx.Array, []*mlx.Array, []ImageDims, error) {
-	const vaeScaleFactor int32 = 8
-	const patchSize int32 = 2
-
-	condImages := make([]*mlx.Array, len(imagePaths))
-	vaeImages := make([]*mlx.Array, len(imagePaths))
-	dims := make([]ImageDims, len(imagePaths))
-
-	for i, imagePath := range imagePaths {
-		img, err := loadImageFile(imagePath)
-		if err != nil {
-			return nil, nil, nil, fmt.Errorf("image %d: %w", i, err)
-		}
-
-		bounds := img.Bounds()
-		origW := int32(bounds.Dx())
-		origH := int32(bounds.Dy())
-		ratio := float64(origW) / float64(origH)
-
-		// Calculate dimensions for condition image (vision encoder)
-		// Python pipeline does TWO resizes:
-		// 1. VaeImageProcessor.resize with Lanczos to CONDITION_IMAGE_SIZE (384x384 area)
-		// 2. Qwen2VLProcessor's smart_resize with Bicubic to multiple of 28
-		intermediateW, intermediateH := calculateDimensions(p.Config.ConditionImageSize, ratio, 32)
-		condH, condW := smartResize(intermediateH, intermediateW, 28, 56*56, 28*28*1280)
-
-		// Calculate dimensions for VAE image (1024x1024 area)
-		vaeW, vaeH := calculateDimensions(p.Config.VAEImageSize, ratio, 32)
-
-		// Calculate derived dimensions
-		latentW := vaeW / vaeScaleFactor
-		latentH := vaeH / vaeScaleFactor
-		patchW := latentW / patchSize
-		patchH := latentH / patchSize
-
-		dims[i] = ImageDims{
-			OrigW:   origW,
-			OrigH:   origH,
-			CondW:   condW,
-			CondH:   condH,
-			VaeW:    vaeW,
-			VaeH:    vaeH,
-			LatentW: latentW,
-			LatentH: latentH,
-			PatchW:  patchW,
-			PatchH:  patchH,
-		}
-
-		fmt.Printf("  Image %d: orig=%dx%d, cond=%dx%d, vae=%dx%d, latent=%dx%d, patch=%dx%d\n",
-			i+1, origW, origH, condW, condH, vaeW, vaeH, latentW, latentH, patchW, patchH)
-
-		// Preprocess for condition (vision encoder) - two-step resize to match Python pipeline
-		condImages[i] = p.preprocessImageTwoStep(img, intermediateW, intermediateH, condW, condH)
-
-		// Preprocess for VAE ([-1, 1] range, 5D tensor)
-		vaeImages[i] = p.preprocessImageForVAE(img, vaeW, vaeH)
-	}
-
-	return condImages, vaeImages, dims, nil
-}

x/imagegen/models/qwen_image_edit/qwen_image_edit.go

@@ -1,625 +0,0 @@
-//go:build mlx
-
-// Package qwen_image_edit implements the Qwen-Image-Edit diffusion model for image editing.
-// It reuses components from qwen_image where possible.
-package qwen_image_edit
-
-import (
-	"context"
-	"fmt"
-	"path/filepath"
-	"time"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/models/qwen_image"
-	"github.com/ollama/ollama/x/imagegen/tokenizer"
-)
-
-// GenerateConfig holds all options for image editing.
-type GenerateConfig struct {
-	Prompt         string
-	NegativePrompt string                // Unconditional prompt for CFG (empty string "" is valid)
-	CFGScale       float32               // CFG enabled when > 1.0 (default: 4.0)
-	Width          int32                 // Output width (default: from input image)
-	Height         int32                 // Output height (default: from input image)
-	Steps          int                   // Denoising steps (default: 50)
-	Seed           int64                 // Random seed
-	Progress       func(step, totalSteps int) // Optional progress callback
-}
-
-// Model represents a Qwen-Image-Edit diffusion model.
-type Model struct {
-	ModelPath     string
-	Tokenizer     *tokenizer.Tokenizer
-	Processor     *Processor                // Image processor for vision encoder
-	TextEncoder   *qwen_image.Qwen25VL      // Qwen2.5-VL vision-language encoder (from qwen_image)
-	Transformer   *qwen_image.Transformer   // Reuse qwen_image transformer
-	VAE           *VAE                      // Combined encoder + decoder
-}
-
-// Load loads the Qwen-Image-Edit model from a directory.
-func (m *Model) Load(modelPath string) error {
-	fmt.Println("Loading Qwen-Image-Edit model...")
-	start := time.Now()
-
-	if mlx.GPUIsAvailable() {
-		mlx.SetDefaultDeviceGPU()
-		mlx.EnableCompile()
-	}
-
-	m.ModelPath = modelPath
-
-	// Load tokenizer from processor directory
-	fmt.Print("  Loading tokenizer... ")
-	processorPath := filepath.Join(modelPath, "processor")
-	tok, err := tokenizer.Load(processorPath)
-	if err != nil {
-		// Fallback to tokenizer directory
-		tokenizerPath := filepath.Join(modelPath, "tokenizer")
-		tok, err = tokenizer.Load(tokenizerPath)
-		if err != nil {
-			return fmt.Errorf("tokenizer: %w", err)
-		}
-	}
-	m.Tokenizer = tok
-	fmt.Println("✓")
-
-	// Load processor (image preprocessing config)
-	fmt.Print("  Loading processor... ")
-	m.Processor = &Processor{}
-	if err := m.Processor.Load(processorPath); err != nil {
-		return fmt.Errorf("processor: %w", err)
-	}
-	fmt.Println("✓")
-
-	// Load vision-language text encoder (Qwen2.5-VL from qwen_image package)
-	m.TextEncoder = &qwen_image.Qwen25VL{}
-	if err := m.TextEncoder.Load(filepath.Join(modelPath, "text_encoder")); err != nil {
-		return fmt.Errorf("text encoder: %w", err)
-	}
-	mlx.Eval(mlx.Collect(m.TextEncoder)...)
-	fmt.Printf("  (%.1f GB, peak %.1f GB)\n",
-		float64(mlx.MetalGetActiveMemory())/(1024*1024*1024),
-		float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	// Load transformer (reuse qwen_image)
-	m.Transformer = &qwen_image.Transformer{}
-	if err := m.Transformer.Load(filepath.Join(modelPath, "transformer")); err != nil {
-		return fmt.Errorf("transformer: %w", err)
-	}
-	mlx.Eval(mlx.Collect(m.Transformer)...)
-	fmt.Printf("  (%.1f GB, peak %.1f GB)\n",
-		float64(mlx.MetalGetActiveMemory())/(1024*1024*1024),
-		float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	// Load VAE (encoder + decoder)
-	m.VAE = &VAE{}
-	if err := m.VAE.Load(filepath.Join(modelPath, "vae")); err != nil {
-		return fmt.Errorf("VAE: %w", err)
-	}
-	mlx.Eval(mlx.Collect(m.VAE)...)
-	fmt.Printf("  (%.1f GB, peak %.1f GB)\n",
-		float64(mlx.MetalGetActiveMemory())/(1024*1024*1024),
-		float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	mem := mlx.MetalGetActiveMemory()
-	peak := mlx.MetalGetPeakMemory()
-	fmt.Printf("  Loaded in %.2fs (%.1f GB active, %.1f GB peak)\n",
-		time.Since(start).Seconds(),
-		float64(mem)/(1024*1024*1024),
-		float64(peak)/(1024*1024*1024))
-
-	return nil
-}
-
-// Edit edits an image based on a text prompt.
-// inputImagePath: path to input image
-// prompt: text description of desired edit
-func (m *Model) Edit(inputImagePath string, prompt string, width, height int32, steps int, seed int64) (*mlx.Array, error) {
-	return m.EditFromConfig([]string{inputImagePath}, &GenerateConfig{
-		Prompt: prompt,
-		Width:  width,
-		Height: height,
-		Steps:  steps,
-		Seed:   seed,
-	})
-}
-
-// EditFromConfig edits images using the unified config struct.
-// Accepts one or more input images.
-func (m *Model) EditFromConfig(inputImagePaths []string, cfg *GenerateConfig) (*mlx.Array, error) {
-	if len(inputImagePaths) == 0 {
-		return nil, fmt.Errorf("no input images provided")
-	}
-
-	start := time.Now()
-	result, err := m.edit(inputImagePaths, cfg)
-	if err != nil {
-		return nil, err
-	}
-
-	if cfg.NegativePrompt != "" {
-		fmt.Printf("Edited %d image(s) with CFG (scale=%.1f) in %.2fs (%d steps)\n",
-			len(inputImagePaths), cfg.CFGScale, time.Since(start).Seconds(), cfg.Steps)
-	} else {
-		fmt.Printf("Edited %d image(s) in %.2fs (%d steps)\n",
-			len(inputImagePaths), time.Since(start).Seconds(), cfg.Steps)
-	}
-	return result, nil
-}
-
-// EditImage implements model.ImageEditModel interface.
-func (m *Model) EditImage(ctx context.Context, inputImagePath, prompt string, width, height int32, steps int, seed int64) (*mlx.Array, error) {
-	return m.Edit(inputImagePath, prompt, width, height, steps, seed)
-}
-
-// EditMultiImage edits using multiple source images.
-// This matches diffusers' QwenImageEditPlusPipeline behavior.
-func (m *Model) EditMultiImage(inputImagePaths []string, cfg *GenerateConfig) (*mlx.Array, error) {
-	return m.EditFromConfig(inputImagePaths, cfg)
-}
-
-// edit is the internal editing pipeline that handles one or more images.
-func (m *Model) edit(inputImagePaths []string, cfg *GenerateConfig) (*mlx.Array, error) {
-	// Apply defaults
-	if cfg.Steps <= 0 {
-		cfg.Steps = 50
-	}
-	if cfg.CFGScale <= 0 {
-		cfg.CFGScale = 4.0
-	}
-
-	// Load and preprocess all input images
-	fmt.Printf("Loading %d image(s)...\n", len(inputImagePaths))
-	condImages, vaeImages, inputDims, err := m.Processor.LoadAndPreprocessMultiple(inputImagePaths)
-	if err != nil {
-		return nil, fmt.Errorf("preprocess images: %w", err)
-	}
-	for _, img := range condImages {
-		mlx.Keep(img)
-	}
-	for _, img := range vaeImages {
-		mlx.Keep(img)
-	}
-	mlx.Eval(append(condImages, vaeImages...)...)
-
-	useCFG := cfg.NegativePrompt != ""
-	tcfg := m.Transformer.Config
-	vaeScaleFactor := int32(8)
-
-	// Output dimensions - if not specified, use first input image dimensions
-	if cfg.Width <= 0 {
-		cfg.Width = inputDims[0].VaeW
-	}
-	if cfg.Height <= 0 {
-		cfg.Height = inputDims[0].VaeH
-	}
-
-	// Output (noise) latent dimensions
-	outLatentH := cfg.Height / vaeScaleFactor
-	outLatentW := cfg.Width / vaeScaleFactor
-	outPH := outLatentH / tcfg.PatchSize
-	outPW := outLatentW / tcfg.PatchSize
-	noiseSeqLen := outPH * outPW
-	imgSeqLen := noiseSeqLen
-
-	// Encode prompt with all images for conditioning
-	posEmb, _, _, err := m.TextEncoder.EncodePromptWithImages(m.Tokenizer, cfg.Prompt, condImages)
-	if err != nil {
-		return nil, fmt.Errorf("encoding prompt: %w", err)
-	}
-	mlx.Keep(posEmb)
-	mlx.Eval(posEmb)
-
-	var negEmb *mlx.Array
-	if useCFG {
-		negEmb, _, _, err = m.TextEncoder.EncodePromptWithImages(m.Tokenizer, cfg.NegativePrompt, condImages)
-		if err != nil {
-			return nil, fmt.Errorf("encoding negative prompt: %w", err)
-		}
-		mlx.Keep(negEmb)
-		mlx.Eval(negEmb)
-	}
-
-	// Pad sequences to same length for CFG
-	txtLen := posEmb.Shape()[1]
-	if useCFG {
-		negLen := negEmb.Shape()[1]
-		if negLen > txtLen {
-			txtLen = negLen
-		}
-		if posEmb.Shape()[1] < txtLen {
-			posEmb = padSequence(posEmb, txtLen)
-		}
-		if negEmb.Shape()[1] < txtLen {
-			negEmb = padSequence(negEmb, txtLen)
-		}
-		mlx.Keep(posEmb, negEmb)
-		mlx.Eval(posEmb, negEmb)
-	}
-
-	// Pre-compute batched embeddings for CFG (single forward pass optimization)
-	var batchedEmb *mlx.Array
-	if useCFG {
-		batchedEmb = mlx.Concatenate([]*mlx.Array{posEmb, negEmb}, 0)
-		mlx.Keep(batchedEmb)
-		mlx.Eval(batchedEmb)
-	}
-
-	// Encode all input images to latents and concatenate
-	fmt.Println("Encoding images to latents...")
-	allImageLatentsPacked := make([]*mlx.Array, len(vaeImages))
-	for i, vaeImage := range vaeImages {
-		imageLatents := m.VAE.Encode(vaeImage)
-		imageLatents = m.VAE.Normalize(imageLatents)
-		imageLatents2D := mlx.Squeeze(imageLatents, 2)
-		packed := qwen_image.PackLatents(imageLatents2D, tcfg.PatchSize)
-		mlx.Keep(packed)
-		mlx.Eval(packed)
-		allImageLatentsPacked[i] = packed
-	}
-
-	imageLatentsPacked := mlx.Concatenate(allImageLatentsPacked, 1)
-	mlx.Keep(imageLatentsPacked)
-	mlx.Eval(imageLatentsPacked)
-
-	// Scheduler
-	scheduler := qwen_image.NewFlowMatchScheduler(qwen_image.DefaultSchedulerConfig())
-	scheduler.SetTimesteps(cfg.Steps, noiseSeqLen)
-
-	// Init noise latents in packed format
-	packedChannels := tcfg.OutChannels * tcfg.PatchSize * tcfg.PatchSize
-	packedNoise := scheduler.InitNoisePacked(1, noiseSeqLen, packedChannels, cfg.Seed)
-	latents := qwen_image.UnpackLatents(packedNoise, outLatentH, outLatentW, tcfg.PatchSize)
-	mlx.Eval(latents)
-
-	// RoPE cache
-	ropeCache := PrepareRoPEMultiImage(outPH, outPW, inputDims, txtLen, tcfg.AxesDimsRope)
-	mlx.Keep(ropeCache.ImgFreqs, ropeCache.TxtFreqs)
-	mlx.Eval(ropeCache.ImgFreqs, ropeCache.TxtFreqs)
-
-	// Denoising loop
-	fmt.Printf("Running denoising (%d steps)...\n", cfg.Steps)
-	for i := 0; i < cfg.Steps; i++ {
-		stepStart := time.Now()
-		if cfg.Progress != nil {
-			cfg.Progress(i+1, cfg.Steps)
-		}
-
-		t := scheduler.Timesteps[i]
-		timestep := mlx.ToBFloat16(mlx.NewArray([]float32{t}, []int32{1}))
-		mlx.Eval(timestep)
-
-		latents2D := mlx.Squeeze(latents, 2)
-		patches := qwen_image.PackLatents(latents2D, tcfg.PatchSize)
-		latentInput := mlx.Concatenate([]*mlx.Array{patches, imageLatentsPacked}, 1)
-
-		var output *mlx.Array
-		if useCFG {
-			// CFG Batching: single forward pass with batch=2
-			// Tile inputs: [1, L, D] -> [2, L, D]
-			batchedLatentInput := mlx.Tile(latentInput, []int32{2, 1, 1})
-			batchedTimestep := mlx.Tile(timestep, []int32{2})
-
-			// Single batched forward pass
-			batchedOutput := m.Transformer.Forward(batchedLatentInput, batchedEmb, batchedTimestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs)
-
-			// Split output: [2, L, D] -> pos [1, L, D], neg [1, L, D]
-			D := batchedOutput.Shape()[2]
-			posOutput := mlx.Slice(batchedOutput, []int32{0, 0, 0}, []int32{1, imgSeqLen, D})
-			negOutput := mlx.Slice(batchedOutput, []int32{1, 0, 0}, []int32{2, imgSeqLen, D})
-
-			output = applyCFGWithNormRescale(posOutput, negOutput, cfg.CFGScale)
-		} else {
-			output = m.Transformer.Forward(latentInput, posEmb, timestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs)
-			output = mlx.Slice(output, []int32{0, 0, 0}, []int32{1, imgSeqLen, output.Shape()[2]})
-		}
-
-		noisePred := qwen_image.UnpackLatents(output, outLatentH, outLatentW, tcfg.PatchSize)
-		oldLatents := latents
-		latents = scheduler.Step(noisePred, latents, i)
-		mlx.Eval(latents)
-		oldLatents.Free()
-
-		fmt.Printf("  Step %d/%d: t=%.4f (%.2fs)\n", i+1, cfg.Steps, t, time.Since(stepStart).Seconds())
-	}
-
-	// Free denoising temporaries
-	posEmb.Free()
-	if negEmb != nil {
-		negEmb.Free()
-	}
-	if batchedEmb != nil {
-		batchedEmb.Free()
-	}
-	ropeCache.ImgFreqs.Free()
-	ropeCache.TxtFreqs.Free()
-	imageLatentsPacked.Free()
-
-	// Decode latents
-	decoded := m.decodeAndPostprocess(latents)
-	latents.Free()
-
-	fmt.Printf("  Peak memory: %.2f GB\n", float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-	return decoded, nil
-}
-
-// applyCFGWithNormRescale applies classifier-free guidance with norm rescaling.
-// This prevents CFG from inflating magnitude too much.
-func applyCFGWithNormRescale(posOutput, negOutput *mlx.Array, scale float32) *mlx.Array {
-	// Upcast to float32 for precision
-	posF32 := mlx.AsType(posOutput, mlx.DtypeFloat32)
-	negF32 := mlx.AsType(negOutput, mlx.DtypeFloat32)
-
-	// CFG: pred = neg + scale * (pos - neg)
-	diff := mlx.Sub(posF32, negF32)
-	scaledDiff := mlx.MulScalar(diff, scale)
-	combPred := mlx.Add(negF32, scaledDiff)
-
-	// Norm rescaling: rescale combined prediction to match conditional norm
-	condNorm := mlx.Sqrt(mlx.Sum(mlx.Square(posF32), -1, true))
-	combNorm := mlx.Sqrt(mlx.Sum(mlx.Square(combPred), -1, true))
-	output := mlx.Mul(combPred, mlx.Div(condNorm, combNorm))
-
-	mlx.Eval(output)
-	return mlx.ToBFloat16(output)
-}
-
-// decodeAndPostprocess denormalizes latents, decodes through VAE, and scales to [0,1].
-func (m *Model) decodeAndPostprocess(latents *mlx.Array) *mlx.Array {
-	latents = m.VAE.Denormalize(latents)
-	decoded := m.VAE.Decode(latents)
-
-	// Post-process: squeeze temporal dim and rescale to [0, 1]
-	decoded = mlx.Squeeze(decoded, 2)
-	decoded = mlx.AddScalar(decoded, 1.0)
-	decoded = mlx.DivScalar(decoded, 2.0)
-	decoded = mlx.ClipScalar(decoded, 0.0, 1.0, true, true)
-	mlx.Eval(decoded)
-	return decoded
-}
-
-// padSequence pads a sequence tensor to the target length with zeros
-func padSequence(x *mlx.Array, targetLen int32) *mlx.Array {
-	shape := x.Shape()
-	currentLen := shape[1]
-	if currentLen >= targetLen {
-		return x
-	}
-	padLen := targetLen - currentLen
-	// Pad on sequence dimension (axis 1)
-	return mlx.Pad(x, []int32{0, 0, 0, padLen, 0, 0})
-}
-
-// LoadPersistent is an alias for backward compatibility.
-func LoadPersistent(modelPath string) (*Model, error) {
-	m := &Model{}
-	if err := m.Load(modelPath); err != nil {
-		return nil, err
-	}
-	return m, nil
-}
-
-// PrepareRoPEMultiImage computes RoPE with interpolation for image editing.
-// Handles single or multiple input images with different resolutions.
-//
-// Parameters:
-//   - outPH, outPW: output patch dimensions (noise latent resolution)
-//   - inputDims: patch dimensions for each input image [(pH1, pW1), (pH2, pW2), ...]
-//   - txtLen: text sequence length
-//   - axesDims: RoPE axis dimensions [16, 56, 56]
-//
-// Returns RoPE cache where:
-//   - ImgFreqs has (outPH*outPW + sum(inPH*inPW for each image)) positions
-//   - First outPH*outPW positions are for noise latents (standard RoPE at output res)
-//   - Following positions are for each input image (interpolated from output res)
-func PrepareRoPEMultiImage(outPH, outPW int32, inputDims []ImageDims, txtLen int32, axesDims []int32) *qwen_image.RoPECache {
-	theta := float64(10000)
-	maxIdx := int32(4096)
-
-	// Compute base frequencies for each axis dimension
-	freqsT := qwen_image.ComputeAxisFreqs(axesDims[0], theta)
-	freqsH := qwen_image.ComputeAxisFreqs(axesDims[1], theta)
-	freqsW := qwen_image.ComputeAxisFreqs(axesDims[2], theta)
-
-	// Build frequency lookup tables
-	posFreqsT := qwen_image.MakeFreqTable(maxIdx, freqsT, false)
-	posFreqsH := qwen_image.MakeFreqTable(maxIdx, freqsH, false)
-	posFreqsW := qwen_image.MakeFreqTable(maxIdx, freqsW, false)
-	negFreqsT := qwen_image.MakeFreqTable(maxIdx, freqsT, true) // For frame -1 on last condition image
-	negFreqsH := qwen_image.MakeFreqTable(maxIdx, freqsH, true)
-	negFreqsW := qwen_image.MakeFreqTable(maxIdx, freqsW, true)
-
-	headDim := int32(len(freqsT)+len(freqsH)+len(freqsW)) * 2
-
-	// Helper to compute RoPE for a single position at output resolution with scale_rope
-	computePosFreqs := func(framePos, y, x int32) []float32 {
-		row := make([]float32, headDim)
-		idx := 0
-
-		// Frame position
-		for i := 0; i < len(freqsT)*2; i++ {
-			row[idx+i] = posFreqsT[framePos][i]
-		}
-		idx += len(freqsT) * 2
-
-		// Height with scale_rope centering (using OUTPUT dimensions)
-		outHHalf := outPH / 2
-		hNegCount := outPH - outHHalf
-		if y < hNegCount {
-			negTableIdx := maxIdx - hNegCount + y
-			for i := 0; i < len(freqsH)*2; i++ {
-				row[idx+i] = negFreqsH[negTableIdx][i]
-			}
-		} else {
-			posIdx := y - hNegCount
-			for i := 0; i < len(freqsH)*2; i++ {
-				row[idx+i] = posFreqsH[posIdx][i]
-			}
-		}
-		idx += len(freqsH) * 2
-
-		// Width with scale_rope centering (using OUTPUT dimensions)
-		outWHalf := outPW / 2
-		wNegCount := outPW - outWHalf
-		if x < wNegCount {
-			negTableIdx := maxIdx - wNegCount + x
-			for i := 0; i < len(freqsW)*2; i++ {
-				row[idx+i] = negFreqsW[negTableIdx][i]
-			}
-		} else {
-			posIdx := x - wNegCount
-			for i := 0; i < len(freqsW)*2; i++ {
-				row[idx+i] = posFreqsW[posIdx][i]
-			}
-		}
-
-		return row
-	}
-
-	// Helper to compute RoPE for frame -1 (used for last condition image)
-	// This matches Python's _compute_condition_freqs which uses freqs_neg[0][-1:]
-	computeNegFrameFreqs := func(y, x int32) []float32 {
-		row := make([]float32, headDim)
-		idx := 0
-
-		// Frame -1: use last row of negative frame frequencies
-		negFrameIdx := maxIdx - 1
-		for i := 0; i < len(freqsT)*2; i++ {
-			row[idx+i] = negFreqsT[negFrameIdx][i]
-		}
-		idx += len(freqsT) * 2
-
-		// Height with scale_rope centering (using OUTPUT dimensions)
-		outHHalf := outPH / 2
-		hNegCount := outPH - outHHalf
-		if y < hNegCount {
-			negTableIdx := maxIdx - hNegCount + y
-			for i := 0; i < len(freqsH)*2; i++ {
-				row[idx+i] = negFreqsH[negTableIdx][i]
-			}
-		} else {
-			posIdx := y - hNegCount
-			for i := 0; i < len(freqsH)*2; i++ {
-				row[idx+i] = posFreqsH[posIdx][i]
-			}
-		}
-		idx += len(freqsH) * 2
-
-		// Width with scale_rope centering (using OUTPUT dimensions)
-		outWHalf := outPW / 2
-		wNegCount := outPW - outWHalf
-		if x < wNegCount {
-			negTableIdx := maxIdx - wNegCount + x
-			for i := 0; i < len(freqsW)*2; i++ {
-				row[idx+i] = negFreqsW[negTableIdx][i]
-			}
-		} else {
-			posIdx := x - wNegCount
-			for i := 0; i < len(freqsW)*2; i++ {
-				row[idx+i] = posFreqsW[posIdx][i]
-			}
-		}
-
-		return row
-	}
-
-	// Total image sequence length: noise + all input images
-	noiseSeqLen := outPH * outPW
-	totalImgLen := noiseSeqLen
-	for _, dims := range inputDims {
-		totalImgLen += dims.PatchH * dims.PatchW
-	}
-
-	imgFreqsData := make([]float32, totalImgLen*headDim)
-	idx := int32(0)
-
-	// Segment 0: Noise latents - standard RoPE at output resolution (frame 0)
-	for y := int32(0); y < outPH; y++ {
-		for x := int32(0); x < outPW; x++ {
-			row := computePosFreqs(0, y, x)
-			copy(imgFreqsData[idx:], row)
-			idx += headDim
-		}
-	}
-
-	// Segments 1..N: Edit image latents - INTERPOLATED RoPE
-	// For single image: use frame 1 (matches original PrepareRoPEInterpolated)
-	// For multiple images: Python uses frame -1 for the LAST condition image
-	// (_compute_condition_freqs), positive indices for others.
-	numImages := len(inputDims)
-	lastImgIdx := numImages - 1
-	for imgIdx, dims := range inputDims {
-		inPH := dims.PatchH
-		inPW := dims.PatchW
-
-		// Determine frame index for this image
-		// Single image case: use frame 1 (like original PrepareRoPEInterpolated)
-		// Multi-image case: last image uses frame -1, others use frame 1, 2, etc.
-		useNegFrame := numImages > 1 && imgIdx == lastImgIdx
-
-		// Map each input position to an output position using linear interpolation
-		for y := int32(0); y < inPH; y++ {
-			for x := int32(0); x < inPW; x++ {
-				// Interpolate: map input (y, x) to output grid position
-				// This is the key fix from DiffSynth's forward_sampling
-				var yOut, xOut int32
-				if inPH == 1 {
-					yOut = 0
-				} else {
-					// Linear interpolation: y_out = y * (outPH - 1) / (inPH - 1)
-					yOut = y * (outPH - 1) / (inPH - 1)
-				}
-				if inPW == 1 {
-					xOut = 0
-				} else {
-					xOut = x * (outPW - 1) / (inPW - 1)
-				}
-
-				var row []float32
-				if useNegFrame {
-					// Last image in multi-image uses frame -1
-					row = computeNegFrameFreqs(yOut, xOut)
-				} else {
-					// Single image uses frame 1, multi-image uses frame 1, 2, etc.
-					frameIdx := int32(imgIdx + 1)
-					row = computePosFreqs(frameIdx, yOut, xOut)
-				}
-				copy(imgFreqsData[idx:], row)
-				idx += headDim
-			}
-		}
-	}
-
-	imgFreqs := mlx.NewArray(imgFreqsData, []int32{totalImgLen, headDim})
-	imgFreqs = mlx.ToBFloat16(imgFreqs)
-
-	// Text frequencies - start after max video index
-	maxVidIdx := max(outPH/2, outPW/2)
-
-	txtFreqsData := make([]float32, txtLen*headDim)
-	idx = 0
-	for t := int32(0); t < txtLen; t++ {
-		pos := maxVidIdx + t
-		for i := 0; i < len(freqsT)*2; i++ {
-			txtFreqsData[idx+int32(i)] = posFreqsT[pos][i]
-		}
-		idx += int32(len(freqsT) * 2)
-		for i := 0; i < len(freqsH)*2; i++ {
-			txtFreqsData[idx+int32(i)] = posFreqsH[pos][i]
-		}
-		idx += int32(len(freqsH) * 2)
-		for i := 0; i < len(freqsW)*2; i++ {
-			txtFreqsData[idx+int32(i)] = posFreqsW[pos][i]
-		}
-		idx += int32(len(freqsW) * 2)
-	}
-
-	txtFreqs := mlx.NewArray(txtFreqsData, []int32{txtLen, headDim})
-	txtFreqs = mlx.ToBFloat16(txtFreqs)
-
-	return &qwen_image.RoPECache{
-		ImgFreqs: imgFreqs,
-		TxtFreqs: txtFreqs,
-	}
-}

x/imagegen/models/qwen_image_edit/rope_test.go

@@ -1,249 +0,0 @@
-//go:build mlx
-
-package qwen_image_edit
-
-import (
-	"fmt"
-	"math"
-	"os"
-	"path/filepath"
-	"runtime"
-	"testing"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/models/qwen_image"
-)
-
-// TestMain initializes MLX before running tests.
-// If MLX libraries are not available, tests are skipped.
-func TestMain(m *testing.M) {
-	// Change to repo root so ./build/lib/ollama/ path works
-	_, thisFile, _, _ := runtime.Caller(0)
-	repoRoot := filepath.Join(filepath.Dir(thisFile), "..", "..", "..", "..")
-	if err := os.Chdir(repoRoot); err != nil {
-		fmt.Printf("Failed to change to repo root: %v\n", err)
-		os.Exit(1)
-	}
-
-	if err := mlx.InitMLX(); err != nil {
-		fmt.Printf("Skipping qwen_image_edit tests: %v\n", err)
-		os.Exit(0)
-	}
-	os.Exit(m.Run())
-}
-
-// TestComputeAxisFreqs verifies frequency computation matches Python reference
-func TestComputeAxisFreqs(t *testing.T) {
-	theta := float64(10000)
-
-	// Expected values from Python:
-	// freqs = 1.0 / (theta ** (np.arange(0, half_dim) / half_dim))
-	expectedFreqsT := []float64{
-		1.000000000000000, 0.316227766016838, 0.100000000000000, 0.031622776601684,
-		0.010000000000000, 0.003162277660168, 0.001000000000000, 0.000316227766017,
-	}
-
-	expectedFreqsH_first4 := []float64{
-		1.000000000000000, 0.719685673001152, 0.517947467923121, 0.372759372031494,
-	}
-
-	expectedFreqsH_last4 := []float64{
-		0.000372759372031, 0.000268269579528, 0.000193069772888, 0.000138949549437,
-	}
-
-	// Test temporal frequencies (dim=16)
-	freqsT := qwen_image.ComputeAxisFreqs(16, theta)
-	if len(freqsT) != 8 {
-		t.Fatalf("expected 8 temporal frequencies, got %d", len(freqsT))
-	}
-	for i, expected := range expectedFreqsT {
-		if diff := math.Abs(freqsT[i] - expected); diff > 1e-10 {
-			t.Errorf("freqsT[%d]: expected %.15f, got %.15f, diff %.2e", i, expected, freqsT[i], diff)
-		}
-	}
-
-	// Test height/width frequencies (dim=56)
-	freqsH := qwen_image.ComputeAxisFreqs(56, theta)
-	if len(freqsH) != 28 {
-		t.Fatalf("expected 28 height frequencies, got %d", len(freqsH))
-	}
-	for i, expected := range expectedFreqsH_first4 {
-		if diff := math.Abs(freqsH[i] - expected); diff > 1e-10 {
-			t.Errorf("freqsH[%d]: expected %.15f, got %.15f, diff %.2e", i, expected, freqsH[i], diff)
-		}
-	}
-	for i, expected := range expectedFreqsH_last4 {
-		idx := 24 + i // last 4 of 28
-		if diff := math.Abs(freqsH[idx] - expected); diff > 1e-10 {
-			t.Errorf("freqsH[%d]: expected %.15f, got %.15f, diff %.2e", idx, expected, freqsH[idx], diff)
-		}
-	}
-}
-
-// TestMakeFreqTable verifies the frequency lookup table for both positive and negative positions
-func TestMakeFreqTable(t *testing.T) {
-	theta := float64(10000)
-	freqsT := qwen_image.ComputeAxisFreqs(16, theta)
-	maxIdx := int32(4096)
-
-	// Test positive table
-	posTable := qwen_image.MakeFreqTable(maxIdx, freqsT, false)
-
-	// Position 0 should give cos=1, sin=0 for all frequencies
-	for i := 0; i < len(freqsT)*2; i += 2 {
-		if posTable[0][i] != 1.0 {
-			t.Errorf("posTable[0][%d] (cos): expected 1.0, got %f", i, posTable[0][i])
-		}
-		if posTable[0][i+1] != 0.0 {
-			t.Errorf("posTable[0][%d] (sin): expected 0.0, got %f", i+1, posTable[0][i+1])
-		}
-	}
-
-	// Position 1, first frequency (1.0): angle = 1*1 = 1
-	// cos(1) = 0.5403, sin(1) = 0.8415
-	if diff := math.Abs(float64(posTable[1][0]) - 0.5403023058681398); diff > 1e-6 {
-		t.Errorf("posTable[1][0] (cos): expected 0.5403, got %f", posTable[1][0])
-	}
-	if diff := math.Abs(float64(posTable[1][1]) - 0.8414709848078965); diff > 1e-6 {
-		t.Errorf("posTable[1][1] (sin): expected 0.8415, got %f", posTable[1][1])
-	}
-
-	// Test negative table
-	negTable := qwen_image.MakeFreqTable(maxIdx, freqsT, true)
-
-	// negTable[4095] corresponds to position -1
-	// cos(-1) = cos(1), sin(-1) = -sin(1)
-	if diff := math.Abs(float64(negTable[4095][0]) - 0.5403023058681398); diff > 1e-6 {
-		t.Errorf("negTable[4095][0] (cos(-1)): expected 0.5403, got %f", negTable[4095][0])
-	}
-	if diff := math.Abs(float64(negTable[4095][1]) - (-0.8414709848078965)); diff > 1e-6 {
-		t.Errorf("negTable[4095][1] (sin(-1)): expected -0.8415, got %f", negTable[4095][1])
-	}
-
-	// negTable[4094] corresponds to position -2
-	// cos(-2) = cos(2), sin(-2) = -sin(2)
-	cos2 := math.Cos(2.0)
-	sin2 := math.Sin(2.0)
-	if diff := math.Abs(float64(negTable[4094][0]) - cos2); diff > 1e-6 {
-		t.Errorf("negTable[4094][0] (cos(-2)): expected %f, got %f", cos2, negTable[4094][0])
-	}
-	if diff := math.Abs(float64(negTable[4094][1]) - (-sin2)); diff > 1e-6 {
-		t.Errorf("negTable[4094][1] (sin(-2)): expected %f, got %f", -sin2, negTable[4094][1])
-	}
-}
-
-// TestPrepareRoPE_QwenImage verifies qwen_image.PrepareRoPE for single-segment case
-func TestPrepareRoPE_QwenImage(t *testing.T) {
-	if !mlx.GPUIsAvailable() {
-		t.Skip("GPU not available")
-	}
-
-	mlx.SetDefaultDeviceCPU()
-
-	// 4x4 patch grid, single image
-	imgH, imgW := int32(4), int32(4)
-	txtLen := int32(5)
-	axesDims := []int32{16, 56, 56}
-
-	cache := qwen_image.PrepareRoPE(imgH, imgW, txtLen, axesDims)
-	mlx.Eval(cache.ImgFreqs, cache.TxtFreqs)
-
-	// Check shapes
-	imgShape := cache.ImgFreqs.Shape()
-	if imgShape[0] != 16 { // 4*4 patches
-		t.Errorf("ImgFreqs seq len: expected 16, got %d", imgShape[0])
-	}
-
-	// For single image (frame=0), all temporal values should be cos=1, sin=0
-	imgFreqsCPU := mlx.AsType(cache.ImgFreqs, mlx.DtypeFloat32)
-	mlx.Eval(imgFreqsCPU)
-	imgData := imgFreqsCPU.Data()
-
-	// Check first 16 values of patch 0 (temporal cos/sin pairs)
-	for i := 0; i < 16; i += 2 {
-		cosVal := imgData[i]
-		sinVal := imgData[i+1]
-		if diff := math.Abs(float64(cosVal - 1.0)); diff > 1e-5 {
-			t.Errorf("ImgFreqs[0][%d] (cos): expected 1.0, got %f", i, cosVal)
-		}
-		if diff := math.Abs(float64(sinVal - 0.0)); diff > 1e-5 {
-			t.Errorf("ImgFreqs[0][%d] (sin): expected 0.0, got %f", i+1, sinVal)
-		}
-	}
-
-	cache.ImgFreqs.Free()
-	cache.TxtFreqs.Free()
-}
-
-// TestScaleRopePositions verifies the centered position calculation for scale_rope=True
-func TestScaleRopePositions(t *testing.T) {
-	// For a 4x4 grid with scale_rope=True:
-	// hHalf = 2, wHalf = 2
-	// hNegCount = 4 - 2 = 2 (positions 0,1 are negative)
-	// wNegCount = 4 - 2 = 2 (positions 0,1 are negative)
-	//
-	// Height positions:
-	//   y=0: -(4-2) + 0 = -2
-	//   y=1: -(4-2) + 1 = -1
-	//   y=2: 2 - 2 = 0
-	//   y=3: 3 - 2 = 1
-	//
-	// Same for width
-
-	pH, pW := int32(4), int32(4)
-	hHalf := pH / 2
-	wHalf := pW / 2
-	hNegCount := pH - hHalf
-	wNegCount := pW - wHalf
-
-	expectedH := []int32{-2, -1, 0, 1}
-	expectedW := []int32{-2, -1, 0, 1}
-
-	for y := int32(0); y < pH; y++ {
-		var hPos int32
-		if y < hNegCount {
-			hPos = -(pH - hHalf) + y
-		} else {
-			hPos = y - hNegCount
-		}
-		if hPos != expectedH[y] {
-			t.Errorf("y=%d: expected h_pos=%d, got %d", y, expectedH[y], hPos)
-		}
-	}
-
-	for x := int32(0); x < pW; x++ {
-		var wPos int32
-		if x < wNegCount {
-			wPos = -(pW - wHalf) + x
-		} else {
-			wPos = x - wNegCount
-		}
-		if wPos != expectedW[x] {
-			t.Errorf("x=%d: expected w_pos=%d, got %d", x, expectedW[x], wPos)
-		}
-	}
-}
-
-// TestRoPEHeadDimensions verifies the head dimension breakdown
-func TestRoPEHeadDimensions(t *testing.T) {
-	// axes_dims_rope = [16, 56, 56]
-	// Each dimension uses half the values for frequencies
-	// So we get: 8 + 28 + 28 = 64 frequency values
-	// Each frequency produces cos + sin, so: 64 * 2 = 128 total values per position
-
-	axesDims := []int32{16, 56, 56}
-	expectedFreqs := (axesDims[0]/2 + axesDims[1]/2 + axesDims[2]/2)
-	expectedHeadDim := expectedFreqs * 2
-
-	if expectedFreqs != 64 {
-		t.Errorf("expected 64 frequency values, got %d", expectedFreqs)
-	}
-	if expectedHeadDim != 128 {
-		t.Errorf("expected head_dim=128, got %d", expectedHeadDim)
-	}
-
-	// This should match the transformer's attention head dimension
-	// hidden_size = 3072, num_heads = 24
-	// head_dim = 3072 / 24 = 128
-}
-

x/imagegen/models/qwen_image_edit/vae.go

@@ -1,642 +0,0 @@
-//go:build mlx
-
-package qwen_image_edit
-
-import (
-	"fmt"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-)
-
-// VAEConfig holds Qwen-Image VAE configuration
-type VAEConfig struct {
-	ZDim               int32     `json:"z_dim"`               // 16
-	BaseDim            int32     `json:"base_dim"`            // 96
-	DimMult            []int32   `json:"dim_mult"`            // [1, 2, 4, 4]
-	NumResBlocks       int32     `json:"num_res_blocks"`      // 2
-	LatentsMean        []float32 `json:"latents_mean"`        // 16 values
-	LatentsStd         []float32 `json:"latents_std"`         // 16 values
-	TemperalDownsample []bool    `json:"temperal_downsample"` // [false, true, true]
-}
-
-// defaultVAEConfig returns config for Qwen-Image VAE
-func defaultVAEConfig() *VAEConfig {
-	return &VAEConfig{
-		ZDim:         16,
-		BaseDim:      96,
-		DimMult:      []int32{1, 2, 4, 4},
-		NumResBlocks: 2,
-		LatentsMean: []float32{
-			-0.7571, -0.7089, -0.9113, 0.1075,
-			-0.1745, 0.9653, -0.1517, 1.5508,
-			0.4134, -0.0715, 0.5517, -0.3632,
-			-0.1922, -0.9497, 0.2503, -0.2921,
-		},
-		LatentsStd: []float32{
-			2.8184, 1.4541, 2.3275, 2.6558,
-			1.2196, 1.7708, 2.6052, 2.0743,
-			3.2687, 2.1526, 2.8652, 1.5579,
-			1.6382, 1.1253, 2.8251, 1.916,
-		},
-		TemperalDownsample: []bool{false, true, true},
-	}
-}
-
-// VAE is the full VAE with encoder and decoder
-type VAE struct {
-	Config  *VAEConfig
-	Encoder *VAEEncoder
-	Decoder *VAEDecoder
-}
-
-// Load loads the VAE from a directory
-func (m *VAE) Load(path string) error {
-	fmt.Println("Loading Qwen-Image-Edit VAE (encoder + decoder)...")
-
-	cfg := defaultVAEConfig()
-	m.Config = cfg
-
-	weights, err := safetensors.LoadModelWeights(path)
-	if err != nil {
-		return fmt.Errorf("weights: %w", err)
-	}
-
-	// Load weights as f32 for quality (matches Python default behavior)
-	// VAE decoder precision is critical for final image quality
-	fmt.Print("  Loading weights as f32... ")
-	if err := weights.Load(mlx.DtypeFloat32); err != nil {
-		return fmt.Errorf("failed to load weights: %w", err)
-	}
-	fmt.Printf("✓ (%.1f GB)\n", float64(mlx.MetalGetActiveMemory())/(1024*1024*1024))
-
-	// Load encoder
-	fmt.Print("  Loading encoder... ")
-	m.Encoder = &VAEEncoder{}
-	if err := m.Encoder.loadFromWeights(weights, cfg); err != nil {
-		return fmt.Errorf("encoder: %w", err)
-	}
-	fmt.Println("✓")
-
-	// Load decoder
-	fmt.Print("  Loading decoder... ")
-	m.Decoder = &VAEDecoder{}
-	if err := m.Decoder.loadFromWeights(weights, cfg); err != nil {
-		return fmt.Errorf("decoder: %w", err)
-	}
-	fmt.Println("✓")
-
-	weights.ReleaseAll()
-	return nil
-}
-
-// Encode encodes an image to latents
-// x: [B, C, T, H, W] image tensor in [-1, 1] range
-// Returns: [B, C, T, H/8, W/8] latents (unnormalized)
-func (m *VAE) Encode(x *mlx.Array) *mlx.Array {
-	return m.Encoder.Encode(x)
-}
-
-// Decode decodes latents to image
-// z: [B, C, T, H, W] latents (denormalized)
-// Returns: [B, C, T, H*8, W*8] image in [-1, 1]
-func (m *VAE) Decode(z *mlx.Array) *mlx.Array {
-	return m.Decoder.Decode(z)
-}
-
-// Normalize applies latent normalization
-// Input z should be f32 (from VAE encoder), output is f32 for transformer
-func (m *VAE) Normalize(z *mlx.Array) *mlx.Array {
-	shape := z.Shape()
-	C := shape[1]
-
-	mean := mlx.NewArray(m.Config.LatentsMean[:C], []int32{1, C, 1, 1, 1})
-	std := mlx.NewArray(m.Config.LatentsStd[:C], []int32{1, C, 1, 1, 1})
-
-	// Mean/std are f32, will match z dtype through broadcasting
-	return mlx.Div(mlx.Sub(z, mean), std)
-}
-
-// Denormalize reverses latent normalization
-// Input z is bf16 (from transformer), output converted to f32 for VAE decoder
-func (m *VAE) Denormalize(z *mlx.Array) *mlx.Array {
-	shape := z.Shape()
-	C := shape[1]
-
-	// Convert latents to f32 for VAE decoder quality
-	z = mlx.AsType(z, mlx.DtypeFloat32)
-
-	mean := mlx.NewArray(m.Config.LatentsMean[:C], []int32{1, C, 1, 1, 1})
-	std := mlx.NewArray(m.Config.LatentsStd[:C], []int32{1, C, 1, 1, 1})
-
-	return mlx.Add(mlx.Mul(z, std), mean)
-}
-
-// VAEEncoder is the encoder part of the VAE
-// The encoder uses a flat structure where down_blocks contains a mix of ResBlocks and Downsamplers:
-// - Blocks 0,1: ResBlocks (base_dim)
-// - Block 2: Downsample
-// - Blocks 3,4: ResBlocks (base_dim*2)
-// - Block 5: Downsample + temporal
-// - Blocks 6,7: ResBlocks (base_dim*4)
-// - Block 8: Downsample + temporal
-// - Blocks 9,10: ResBlocks (base_dim*4)
-type VAEEncoder struct {
-	Config *VAEConfig
-
-	ConvIn     *CausalConv3d
-	Blocks     []EncoderBlock // Flat list of ResBlocks and Downsamplers
-	MidBlock   *MidBlock
-	NormOut    *RMSNorm3D
-	ConvOut    *CausalConv3d
-	QuantConv  *CausalConv3d
-}
-
-// EncoderBlock is either a ResBlock or a Downsample
-type EncoderBlock interface {
-	Forward(x *mlx.Array) *mlx.Array
-	IsDownsample() bool
-}
-
-// EncoderResBlock wraps ResBlock
-type EncoderResBlock struct {
-	*ResBlock
-}
-
-func (b *EncoderResBlock) IsDownsample() bool { return false }
-
-// EncoderDownsample is a downsample layer
-type EncoderDownsample struct {
-	Resample *CausalConv3d
-	TimeConv *CausalConv3d // Optional temporal downsample
-}
-
-func (d *EncoderDownsample) IsDownsample() bool { return true }
-
-func (d *EncoderDownsample) Forward(x *mlx.Array) *mlx.Array {
-	// Spatial downsample with stride 2
-	// WAN VAE uses: ZeroPad2d(0,1,0,1) + Conv2d(3x3, stride=2)
-	x = d.forwardSpatialDownsample(x)
-
-	// NOTE: In WAN VAE, time_conv is ONLY used in streaming/chunked mode
-	// with feat_cache. For single-frame encoding (T=1), time_conv is skipped.
-	// The Python forward checks: if feat_cache is not None ... then use time_conv
-	// Since we don't support streaming, we skip time_conv entirely.
-	return x
-}
-
-// forwardSpatialDownsample applies 2D conv with stride 2 for spatial downsampling
-func (d *EncoderDownsample) forwardSpatialDownsample(x *mlx.Array) *mlx.Array {
-	xShape := x.Shape()
-	B := xShape[0]
-	T := xShape[1]
-	H := xShape[2]
-	W := xShape[3]
-	C := xShape[4]
-
-	wShape := d.Resample.Weight.Shape()
-	outC := wShape[0]
-
-	// Reshape to [B*T, H, W, C] for 2D conv
-	x = mlx.Reshape(x, B*T, H, W, C)
-
-	// Asymmetric padding: pad right and bottom by 1 (WAN VAE style)
-	// ZeroPad2d(0, 1, 0, 1) means (left=0, right=1, top=0, bottom=1)
-	x = mlx.Pad(x, []int32{0, 0, 0, 1, 0, 1, 0, 0}) // [B, H, W, C] -> pad H and W
-
-	// Apply 2D conv with stride 2
-	weight := mlx.Transpose(d.Resample.Weight, 0, 2, 3, 1) // [O, I, kH, kW] -> [O, kH, kW, I]
-	x = conv2DStrided(x, weight, 2)
-
-	if d.Resample.Bias != nil {
-		bias := mlx.Reshape(d.Resample.Bias, 1, 1, 1, outC)
-		x = mlx.Add(x, bias)
-	}
-
-	// Output dims after stride 2: (H+1)/2, (W+1)/2
-	outH := (H + 1) / 2
-	outW := (W + 1) / 2
-
-	// Reshape back to [B, T, H', W', C]
-	x = mlx.Reshape(x, B, T, outH, outW, outC)
-	mlx.Eval(x)
-
-	return x
-}
-
-// loadFromWeights loads the encoder from pre-loaded weights
-func (e *VAEEncoder) loadFromWeights(weights *safetensors.ModelWeights, cfg *VAEConfig) error {
-	e.Config = cfg
-
-	// Conv in
-	convIn, err := newCausalConv3d(weights, "encoder.conv_in")
-	if err != nil {
-		return err
-	}
-	e.ConvIn = convIn
-
-	// Encoder uses flat block structure:
-	// dim_mult = [1, 2, 4, 4], num_res_blocks = 2, temporal_downsample = [false, true, true]
-	// Block layout: res,res,down, res,res,down+t, res,res,down+t, res,res
-	// That's 11 blocks: 0,1=res, 2=down, 3,4=res, 5=down+t, 6,7=res, 8=down+t, 9,10=res
-	e.Blocks = make([]EncoderBlock, 0, 11)
-
-	// Track dimensions
-	dims := []int32{cfg.BaseDim, cfg.BaseDim * 2, cfg.BaseDim * 4, cfg.BaseDim * 4}
-	blockIdx := 0
-
-	for stage := 0; stage < len(cfg.DimMult); stage++ {
-		inDim := cfg.BaseDim
-		if stage > 0 {
-			inDim = dims[stage-1]
-		}
-		outDim := dims[stage]
-
-		// ResBlocks for this stage (num_res_blocks per stage)
-		for r := int32(0); r < cfg.NumResBlocks; r++ {
-			prefix := fmt.Sprintf("encoder.down_blocks.%d", blockIdx)
-			currentInDim := inDim
-			if r > 0 {
-				currentInDim = outDim
-			}
-			block, err := newEncoderResBlock(weights, prefix, currentInDim, outDim)
-			if err != nil {
-				return fmt.Errorf("encoder res block %d: %w", blockIdx, err)
-			}
-			e.Blocks = append(e.Blocks, block)
-			blockIdx++
-		}
-
-		// Downsample after each stage except the last
-		if stage < len(cfg.DimMult)-1 {
-			prefix := fmt.Sprintf("encoder.down_blocks.%d", blockIdx)
-			down, err := newEncoderDownsample(weights, prefix, cfg.TemperalDownsample[stage])
-			if err != nil {
-				return fmt.Errorf("encoder downsample %d: %w", blockIdx, err)
-			}
-			e.Blocks = append(e.Blocks, down)
-			blockIdx++
-		}
-	}
-
-	// Mid block
-	midDim := cfg.BaseDim * cfg.DimMult[len(cfg.DimMult)-1]
-	midBlock, err := newMidBlock(weights, "encoder.mid_block", midDim)
-	if err != nil {
-		return err
-	}
-	e.MidBlock = midBlock
-
-	// Norm out
-	normOut, err := newRMSNorm3D(weights, "encoder.norm_out", midDim)
-	if err != nil {
-		return err
-	}
-	e.NormOut = normOut
-
-	// Conv out
-	convOut, err := newCausalConv3d(weights, "encoder.conv_out")
-	if err != nil {
-		return err
-	}
-	e.ConvOut = convOut
-
-	// Quant conv
-	quantConv, err := newCausalConv3d(weights, "quant_conv")
-	if err != nil {
-		return err
-	}
-	e.QuantConv = quantConv
-
-	return nil
-}
-
-// newEncoderResBlock creates a ResBlock for the encoder (flat structure)
-func newEncoderResBlock(weights *safetensors.ModelWeights, prefix string, inDim, outDim int32) (*EncoderResBlock, error) {
-	block, err := newResBlock(weights, prefix, inDim, outDim)
-	if err != nil {
-		return nil, err
-	}
-	return &EncoderResBlock{block}, nil
-}
-
-// newEncoderDownsample creates a downsample layer for the encoder
-func newEncoderDownsample(weights *safetensors.ModelWeights, prefix string, temporal bool) (*EncoderDownsample, error) {
-	resample, err := newCausalConv3d(weights, prefix+".resample.1")
-	if err != nil {
-		return nil, err
-	}
-
-	var timeConv *CausalConv3d
-	if temporal {
-		timeConv, _ = newCausalConv3d(weights, prefix+".time_conv")
-	}
-
-	return &EncoderDownsample{
-		Resample: resample,
-		TimeConv: timeConv,
-	}, nil
-}
-
-// Encode encodes an image to latents
-// x: [B, C, T, H, W] image tensor (channels-first)
-// Returns: [B, latent_C, T, H/8, W/8] latent distribution mode
-func (e *VAEEncoder) Encode(x *mlx.Array) *mlx.Array {
-	// Convert from channels-first [N, C, T, H, W] to channels-last [N, T, H, W, C]
-	x = mlx.Contiguous(mlx.Transpose(x, 0, 2, 3, 4, 1))
-	mlx.Eval(x)
-
-	// Conv in
-	x = e.ConvIn.Forward(x)
-
-	// Encoder blocks (mix of ResBlocks and Downsamplers)
-	for _, block := range e.Blocks {
-		prev := x
-		x = block.Forward(x)
-		prev.Free()
-	}
-
-	// Mid block
-	x = e.MidBlock.Forward(x)
-
-	// Norm + silu
-	{
-		prev := x
-		x = e.NormOut.Forward(x)
-		x = silu3D(x)
-		prev.Free()
-		mlx.Eval(x)
-	}
-
-	// Conv out
-	{
-		prev := x
-		x = e.ConvOut.Forward(x)
-		prev.Free()
-	}
-
-	// Quant conv
-	{
-		prev := x
-		x = e.QuantConv.Forward(x)
-		prev.Free()
-	}
-
-	// Get mode from distribution (first half of channels = mean)
-	// Output is [B, T, H, W, 2*latent_C], we take first latent_C channels
-	shape := x.Shape()
-	latentC := shape[4] / 2
-	x = mlx.Slice(x, []int32{0, 0, 0, 0, 0}, []int32{shape[0], shape[1], shape[2], shape[3], latentC})
-
-	// Convert back to channels-first [N, C, T, H, W]
-	x = mlx.Contiguous(mlx.Transpose(x, 0, 4, 1, 2, 3))
-	mlx.Eval(x)
-
-	return x
-}
-
-// VAEDecoder is the decoder part of the VAE
-type VAEDecoder struct {
-	Config *VAEConfig
-
-	PostQuantConv *CausalConv3d
-	ConvIn        *CausalConv3d
-	MidBlock      *MidBlock
-	UpBlocks      []*UpBlock
-	NormOut       *RMSNorm3D
-	ConvOut       *CausalConv3d
-}
-
-// loadFromWeights loads the decoder from pre-loaded weights
-func (d *VAEDecoder) loadFromWeights(weights *safetensors.ModelWeights, cfg *VAEConfig) error {
-	d.Config = cfg
-
-	postQuantConv, err := newCausalConv3d(weights, "post_quant_conv")
-	if err != nil {
-		return err
-	}
-	d.PostQuantConv = postQuantConv
-
-	convIn, err := newCausalConv3d(weights, "decoder.conv_in")
-	if err != nil {
-		return err
-	}
-	d.ConvIn = convIn
-
-	// Mid block
-	midDim := cfg.BaseDim * cfg.DimMult[len(cfg.DimMult)-1]
-	midBlock, err := newMidBlock(weights, "decoder.mid_block", midDim)
-	if err != nil {
-		return err
-	}
-	d.MidBlock = midBlock
-
-	// Up blocks (reversed dim_mult)
-	numUpBlocks := len(cfg.DimMult)
-	d.UpBlocks = make([]*UpBlock, numUpBlocks)
-
-	dimsMult := make([]int32, numUpBlocks+1)
-	dimsMult[0] = cfg.DimMult[numUpBlocks-1]
-	for i := 0; i < numUpBlocks; i++ {
-		dimsMult[i+1] = cfg.DimMult[numUpBlocks-1-i]
-	}
-
-	temporalUpsample := make([]bool, len(cfg.TemperalDownsample))
-	for i := range cfg.TemperalDownsample {
-		temporalUpsample[i] = cfg.TemperalDownsample[len(cfg.TemperalDownsample)-1-i]
-	}
-
-	for i := 0; i < numUpBlocks; i++ {
-		inDim := cfg.BaseDim * dimsMult[i]
-		outDim := cfg.BaseDim * dimsMult[i+1]
-
-		if i > 0 {
-			inDim = inDim / 2
-		}
-
-		upsampleMode := ""
-		if i < numUpBlocks-1 {
-			if temporalUpsample[i] {
-				upsampleMode = "upsample3d"
-			} else {
-				upsampleMode = "upsample2d"
-			}
-		}
-
-		prefix := fmt.Sprintf("decoder.up_blocks.%d", i)
-		upBlock, err := newUpBlock(weights, prefix, inDim, outDim, cfg.NumResBlocks, upsampleMode)
-		if err != nil {
-			return err
-		}
-		d.UpBlocks[i] = upBlock
-	}
-
-	normOut, err := newRMSNorm3D(weights, "decoder.norm_out", cfg.BaseDim)
-	if err != nil {
-		return err
-	}
-	d.NormOut = normOut
-
-	convOut, err := newCausalConv3d(weights, "decoder.conv_out")
-	if err != nil {
-		return err
-	}
-	d.ConvOut = convOut
-
-	return nil
-}
-
-// Decode converts latents to image
-// z: [B, C, T, H, W] denormalized latents
-func (d *VAEDecoder) Decode(z *mlx.Array) *mlx.Array {
-	var x *mlx.Array
-
-	// Convert from channels-first to channels-last
-	{
-		z = mlx.Contiguous(mlx.Transpose(z, 0, 2, 3, 4, 1))
-		mlx.Eval(z)
-	}
-
-	// PostQuantConv
-	x = d.PostQuantConv.Forward(z)
-	z.Free()
-
-	// ConvIn
-	{
-		prev := x
-		x = d.ConvIn.Forward(x)
-		prev.Free()
-	}
-
-	// Mid block
-	x = d.MidBlock.Forward(x)
-
-	// Up blocks
-	for _, upBlock := range d.UpBlocks {
-		x = upBlock.Forward(x)
-	}
-
-	// NormOut + silu
-	{
-		prev := x
-		x = d.NormOut.Forward(x)
-		x = silu3D(x)
-		prev.Free()
-		mlx.Eval(x)
-	}
-
-	// ConvOut
-	{
-		prev := x
-		x = d.ConvOut.Forward(x)
-		prev.Free()
-	}
-
-	// Post-processing: clamp and convert back to channels-first
-	{
-		prev := x
-		x = mlx.ClipScalar(x, -1.0, 1.0, true, true)
-		x = mlx.Contiguous(mlx.Transpose(x, 0, 4, 1, 2, 3))
-		prev.Free()
-		mlx.Eval(x)
-	}
-
-	return x
-}
-
-// DownBlock handles downsampling in encoder
-type DownBlock struct {
-	ResBlocks   []*ResBlock
-	Downsampler *Downsample
-}
-
-// newDownBlock creates a down block
-func newDownBlock(weights *safetensors.ModelWeights, prefix string, inDim, outDim int32, numBlocks int32, downsampleMode string) (*DownBlock, error) {
-	resBlocks := make([]*ResBlock, numBlocks+1)
-
-	currentDim := inDim
-	for i := int32(0); i <= numBlocks; i++ {
-		resPrefix := fmt.Sprintf("%s.resnets.%d", prefix, i)
-		block, err := newResBlock(weights, resPrefix, currentDim, outDim)
-		if err != nil {
-			return nil, err
-		}
-		resBlocks[i] = block
-		currentDim = outDim
-	}
-
-	var downsampler *Downsample
-	if downsampleMode != "" {
-		downsampler = newDownsample(weights, prefix+".downsamplers.0", outDim, downsampleMode)
-	}
-
-	return &DownBlock{
-		ResBlocks:   resBlocks,
-		Downsampler: downsampler,
-	}, nil
-}
-
-// Forward applies down block
-func (d *DownBlock) Forward(x *mlx.Array) *mlx.Array {
-	for _, block := range d.ResBlocks {
-		prev := x
-		x = block.Forward(x)
-		prev.Free()
-	}
-
-	if d.Downsampler != nil {
-		prev := x
-		x = d.Downsampler.Forward(x)
-		prev.Free()
-	}
-	return x
-}
-
-// Downsample handles spatial downsampling
-type Downsample struct {
-	Conv *mlx.Array
-	Bias *mlx.Array
-	Mode string
-}
-
-// newDownsample creates a downsampler
-func newDownsample(weights *safetensors.ModelWeights, prefix string, dim int32, mode string) *Downsample {
-	conv, _ := weights.Get(prefix + ".resample.1.weight")
-	bias, _ := weights.Get(prefix + ".resample.1.bias")
-	return &Downsample{
-		Conv: conv,
-		Bias: bias,
-		Mode: mode,
-	}
-}
-
-// Forward applies downsampling to channels-last input [B, T, H, W, C]
-func (d *Downsample) Forward(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	T := shape[1]
-	H := shape[2]
-	W := shape[3]
-	C := shape[4]
-	outC := d.Conv.Shape()[0]
-
-	// Reshape to [B*T, H, W, C] for 2D conv
-	x = mlx.Reshape(x, B*T, H, W, C)
-
-	// Pad for stride-2 conv: need (3-1)/2 = 1 on each side, but for stride 2 we need specific padding
-	// For 3x3 stride 2: pad 1 on all sides
-	x = mlx.Pad(x, []int32{0, 0, 1, 1, 1, 1, 0, 0})
-
-	// Conv with stride 2 using manual strided patching
-	weight := mlx.Transpose(d.Conv, 0, 2, 3, 1)
-	x = conv2DStrided(x, weight, 2)
-	if d.Bias != nil {
-		bias := mlx.Reshape(d.Bias, 1, 1, 1, outC)
-		x = mlx.Add(x, bias)
-	}
-
-	x = mlx.Reshape(x, B, T, H/2, W/2, outC)
-	mlx.Eval(x)
-
-	return x
-}

x/server/show.go

@@ -9,7 +9,8 @@ import (
 	"strings"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/x/imagegen"
+	"github.com/ollama/ollama/manifest"
+	"github.com/ollama/ollama/types/model"
 )
 
 // modelConfig represents the HuggingFace config.json structure
@@ -35,22 +36,22 @@ type modelConfig struct {
 
 // GetSafetensorsLLMInfo extracts model information from safetensors LLM models.
 // It reads the config.json layer and returns a map compatible with GGML's KV format.
-func GetSafetensorsLLMInfo(modelName string) (map[string]any, error) {
-	manifest, err := imagegen.LoadManifest(modelName)
+func GetSafetensorsLLMInfo(name model.Name) (map[string]any, error) {
+	mf, err := manifest.ParseNamedManifest(name)
 	if err != nil {
 		return nil, fmt.Errorf("failed to load manifest: %w", err)
 	}
 
 	var config modelConfig
-	if err := manifest.ReadConfigJSON("config.json", &config); err != nil {
+	if err := mf.ReadConfigJSON("config.json", &config); err != nil {
 		return nil, fmt.Errorf("failed to read config.json: %w", err)
 	}
 
 	// Calculate total tensor bytes from manifest layers
 	var totalBytes int64
 	var tensorCount int64
-	for _, layer := range manifest.Manifest.Layers {
-		if layer.MediaType == "application/vnd.ollama.image.tensor" {
+	for _, layer := range mf.Layers {
+		if layer.MediaType == manifest.MediaTypeImageTensor {
 			totalBytes += layer.Size
 			tensorCount++
 		}
@@ -151,27 +152,30 @@ func buildModelInfo(config modelConfig, totalTensorBytes, tensorCount int64) map
 
 // GetSafetensorsTensorInfo extracts tensor information from safetensors model layers.
 // Each tensor is stored as a minimal safetensors file with an 88-byte header containing metadata.
-func GetSafetensorsTensorInfo(modelName string) ([]api.Tensor, error) {
-	manifest, err := imagegen.LoadManifest(modelName)
+func GetSafetensorsTensorInfo(name model.Name) ([]api.Tensor, error) {
+	mf, err := manifest.ParseNamedManifest(name)
 	if err != nil {
 		return nil, fmt.Errorf("failed to load manifest: %w", err)
 	}
 
-	return getTensorInfoFromManifest(manifest)
+	return getTensorInfoFromManifest(mf)
 }
 
 // getTensorInfoFromManifest extracts tensor info from a manifest.
 // This is separated for testability.
-func getTensorInfoFromManifest(manifest *imagegen.ModelManifest) ([]api.Tensor, error) {
+func getTensorInfoFromManifest(mf *manifest.Manifest) ([]api.Tensor, error) {
 	var tensors []api.Tensor
 
-	for _, layer := range manifest.Manifest.Layers {
-		if layer.MediaType != "application/vnd.ollama.image.tensor" {
+	for _, layer := range mf.Layers {
+		if layer.MediaType != manifest.MediaTypeImageTensor {
 			continue
 		}
 
 		// Read the safetensors header from the blob
-		blobPath := manifest.BlobPath(layer.Digest)
+		blobPath, err := manifest.BlobsPath(layer.Digest)
+		if err != nil {
+			continue
+		}
 		info, err := readSafetensorsHeader(blobPath)
 		if err != nil {
 			// Skip tensors we can't read
@@ -197,15 +201,15 @@ func getTensorInfoFromManifest(manifest *imagegen.ModelManifest) ([]api.Tensor,
 // GetSafetensorsDtype returns the quantization type for a safetensors model.
 // If the model is quantized (has _scale tensors), returns the quantization type (e.g., "FP8").
 // Otherwise returns the torch_dtype from config.json.
-func GetSafetensorsDtype(modelName string) (string, error) {
-	manifest, err := imagegen.LoadManifest(modelName)
+func GetSafetensorsDtype(name model.Name) (string, error) {
+	mf, err := manifest.ParseNamedManifest(name)
 	if err != nil {
 		return "", fmt.Errorf("failed to load manifest: %w", err)
 	}
 
 	// Check if model is quantized by looking for _scale tensors
-	for _, layer := range manifest.Manifest.Layers {
-		if layer.MediaType == "application/vnd.ollama.image.tensor" {
+	for _, layer := range mf.Layers {
+		if layer.MediaType == manifest.MediaTypeImageTensor {
 			if strings.HasSuffix(layer.Name, "_scale") {
 				// Model is quantized - return FP8 (affine quantization)
 				return "FP8", nil
@@ -217,7 +221,7 @@ func GetSafetensorsDtype(modelName string) (string, error) {
 	var cfg struct {
 		TorchDtype string `json:"torch_dtype"`
 	}
-	if err := manifest.ReadConfigJSON("config.json", &cfg); err != nil {
+	if err := mf.ReadConfigJSON("config.json", &cfg); err != nil {
 		return "", fmt.Errorf("failed to read config.json: %w", err)
 	}
 

x/server/show_test.go

@@ -8,7 +8,7 @@ import (
 	"path/filepath"
 	"testing"
 
-	"github.com/ollama/ollama/x/imagegen"
+	"github.com/ollama/ollama/manifest"
 )
 
 func TestBuildModelInfo(t *testing.T) {
@@ -451,8 +451,14 @@ func TestParseSafetensorsHeader_Errors(t *testing.T) {
 }
 
 func TestGetTensorInfoFromManifest(t *testing.T) {
-	// Create a temp directory for blobs
+	// Create a temp directory for blobs and set OLLAMA_MODELS
 	tempDir := t.TempDir()
+	t.Setenv("OLLAMA_MODELS", tempDir)
+
+	blobDir := filepath.Join(tempDir, "blobs")
+	if err := os.MkdirAll(blobDir, 0o755); err != nil {
+		t.Fatalf("failed to create blobs dir: %v", err)
+	}
 
 	// Create test tensor blobs
 	tensors := []struct {
@@ -463,26 +469,26 @@ func TestGetTensorInfoFromManifest(t *testing.T) {
 	}{
 		{
 			name:   "model.embed_tokens.weight",
-			digest: "sha256:abc123",
+			digest: "sha256:abc123abc123abc123abc123abc123abc123abc123abc123abc123abc123abc0",
 			dtype:  "BF16",
 			shape:  []int64{262144, 2560},
 		},
 		{
 			name:   "model.layers.0.self_attn.q_proj.weight",
-			digest: "sha256:def456",
+			digest: "sha256:def456def456def456def456def456def456def456def456def456def456def0",
 			dtype:  "BF16",
 			shape:  []int64{2560, 2560},
 		},
 		{
 			name:   "model.norm.weight",
-			digest: "sha256:ghi789",
+			digest: "sha256:789789789789789789789789789789789789789789789789789789789789abc0",
 			dtype:  "F32",
 			shape:  []int64{2560},
 		},
 	}
 
 	// Create blob files
-	var layers []imagegen.ManifestLayer
+	var layers []manifest.Layer
 	for _, tensor := range tensors {
 		// Create safetensors blob
 		header := map[string]any{
@@ -498,15 +504,17 @@ func TestGetTensorInfoFromManifest(t *testing.T) {
 		binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON)))
 		buf.Write(headerJSON)
 
-		// Write blob file
-		blobName := "sha256-" + tensor.digest[7:]
-		blobPath := filepath.Join(tempDir, blobName)
+		// Write blob file using the digest format expected by GetBlobsPath
+		blobPath, err := manifest.BlobsPath(tensor.digest)
+		if err != nil {
+			t.Fatalf("failed to get blob path: %v", err)
+		}
 		if err := os.WriteFile(blobPath, buf.Bytes(), 0o644); err != nil {
 			t.Fatalf("failed to write blob: %v", err)
 		}
 
-		layers = append(layers, imagegen.ManifestLayer{
-			MediaType: "application/vnd.ollama.image.tensor",
+		layers = append(layers, manifest.Layer{
+			MediaType: manifest.MediaTypeImageTensor,
 			Digest:    tensor.digest,
 			Size:      int64(buf.Len() + 1000), // header + fake data
 			Name:      tensor.name,
@@ -514,21 +522,20 @@ func TestGetTensorInfoFromManifest(t *testing.T) {
 	}
 
 	// Add a non-tensor layer (should be skipped)
-	layers = append(layers, imagegen.ManifestLayer{
+	layers = append(layers, manifest.Layer{
 		MediaType: "application/vnd.ollama.image.json",
-		Digest:    "sha256:config",
+		Digest:    "sha256:0000000000000000000000000000000000000000000000000000000000000000",
 		Size:      100,
 		Name:      "config.json",
 	})
 
-	manifest := &imagegen.ModelManifest{
-		Manifest: &imagegen.Manifest{
-			Layers: layers,
-		},
-		BlobDir: tempDir,
+	mf := &manifest.Manifest{
+		SchemaVersion: 2,
+		MediaType:     "application/vnd.docker.distribution.manifest.v2+json",
+		Layers:        layers,
 	}
 
-	result, err := getTensorInfoFromManifest(manifest)
+	result, err := getTensorInfoFromManifest(mf)
 	if err != nil {
 		t.Fatalf("getTensorInfoFromManifest() error = %v", err)
 	}