fix: lazy init MLX for quantization and improve library discovery

- Add lazy MLX initialization in quantizeTensor to ensure the library
  is loaded when quantization is requested
- Add exe-relative build path search for dev mode on macOS, so the
  ollama binary can find libmlxc.dylib in build/lib/ollama/ when
  running from the repo root

cmd/cmd.go

@@ -101,6 +101,67 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		return fmt.Errorf("invalid model name: %s", modelName)
 	}
 
+	// Check for --experimental flag for safetensors model creation
+	experimental, _ := cmd.Flags().GetBool("experimental")
+	if experimental {
+		// Get Modelfile content - either from -f flag or default to "FROM ."
+		var reader io.Reader
+		filename, err := getModelfileName(cmd)
+		if os.IsNotExist(err) || filename == "" {
+			// No Modelfile specified or found - use default
+			reader = strings.NewReader("FROM .\n")
+		} else if err != nil {
+			return err
+		} else {
+			f, err := os.Open(filename)
+			if err != nil {
+				return err
+			}
+			defer f.Close()
+			reader = f
+		}
+
+		// Parse the Modelfile
+		modelfile, err := parser.ParseFile(reader)
+		if err != nil {
+			return fmt.Errorf("failed to parse Modelfile: %w", err)
+		}
+
+		// Extract FROM path and configuration
+		var modelDir string
+		mfConfig := &xcreateclient.ModelfileConfig{}
+
+		for _, cmd := range modelfile.Commands {
+			switch cmd.Name {
+			case "model":
+				modelDir = cmd.Args
+			case "template":
+				mfConfig.Template = cmd.Args
+			case "system":
+				mfConfig.System = cmd.Args
+			case "license":
+				mfConfig.License = cmd.Args
+			}
+		}
+
+		if modelDir == "" {
+			modelDir = "."
+		}
+
+		// Resolve relative paths based on Modelfile location
+		if !filepath.IsAbs(modelDir) && filename != "" {
+			modelDir = filepath.Join(filepath.Dir(filename), modelDir)
+		}
+
+		quantize, _ := cmd.Flags().GetString("quantize")
+		return xcreateclient.CreateModel(xcreateclient.CreateOptions{
+			ModelName: modelName,
+			ModelDir:  modelDir,
+			Quantize:  quantize,
+			Modelfile: mfConfig,
+		}, p)
+	}
+
 	var reader io.Reader
 
 	filename, err := getModelfileName(cmd)
@@ -136,28 +197,6 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
-	// Check if this is a tensor model (image generation) and handle it directly
-	quantize, _ := cmd.Flags().GetString("quantize")
-	modelDir := filepath.Dir(filename)
-	for _, cmd := range modelfile.Commands {
-		if cmd.Name == "model" {
-			if filepath.IsAbs(cmd.Args) {
-				modelDir = cmd.Args
-			} else {
-				modelDir = filepath.Join(filepath.Dir(filename), cmd.Args)
-			}
-			break
-		}
-	}
-	if create.IsTensorModelDir(modelDir) {
-		return xcreateclient.CreateModel(xcreateclient.CreateOptions{
-			ModelName: modelName,
-			ModelDir:  modelDir,
-			Quantize:  quantize,
-			Modelfile: xcreateclient.ExtractModelfileConfig(modelfile),
-		}, p)
-	}
-
 	status := "gathering model components"
 	spinner := progress.NewSpinner(status)
 	p.Add(status, spinner)
@@ -169,6 +208,7 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 	spinner.Stop()
 
 	req.Model = modelName
+	quantize, _ := cmd.Flags().GetString("quantize")
 	if quantize != "" {
 		req.Quantize = quantize
 	}
@@ -859,11 +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:     arg,
+			Model:     args[0],
 			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", arg)
+				fmt.Fprintf(os.Stderr, "Warning: unable to stop model '%s'\n", args[0])
 			}
 		}
 
@@ -1775,15 +1815,22 @@ func NewCLI() *cobra.Command {
 	rootCmd.Flags().BoolP("version", "v", false, "Show version information")
 
 	createCmd := &cobra.Command{
-		Use:     "create MODEL",
-		Short:   "Create a model",
-		Args:    cobra.ExactArgs(1),
-		PreRunE: checkServerHeartbeat,
-		RunE:    CreateHandler,
+		Use:   "create MODEL",
+		Short: "Create a model",
+		Args:  cobra.ExactArgs(1),
+		PreRunE: func(cmd *cobra.Command, args []string) error {
+			// Skip server check for experimental mode (writes directly to disk)
+			if experimental, _ := cmd.Flags().GetBool("experimental"); experimental {
+				return nil
+			}
+			return checkServerHeartbeat(cmd, args)
+		},
+		RunE: CreateHandler,
 	}
 
 	createCmd.Flags().StringP("file", "f", "", "Name of the Modelfile (default \"Modelfile\")")
 	createCmd.Flags().StringP("quantize", "q", "", "Quantize model to this level (e.g. q4_K_M)")
+	createCmd.Flags().Bool("experimental", false, "Enable experimental safetensors model creation")
 
 	showCmd := &cobra.Command{
 		Use:     "show MODEL",

convert/convert.go

@@ -311,8 +311,6 @@ func LoadModelMetadata(fsys fs.FS) (ModelKV, *Tokenizer, error) {
 		conv = &deepseekocr{}
 	case "DeepseekV3ForCausalLM":
 		conv = &deepseek2Model{}
-	case "Glm4MoeLiteForCausalLM":
-		conv = &glm4MoeLiteModel{}
 	default:
 		return nil, nil, fmt.Errorf("unsupported architecture %q", p.Architectures[0])
 	}

convert/convert_glm4moelite.go

@@ -1,150 +0,0 @@
-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
-}

fs/ggml/ggml.go

@@ -269,7 +269,6 @@ func (kv KV) OllamaEngineRequired() bool {
 		"qwen25vl",
 		"qwen3", "qwen3moe",
 		"qwen3vl", "qwen3vlmoe",
-		"glm4moelite",
 	}, kv.Architecture())
 }
 
@@ -857,7 +856,6 @@ func (f GGML) FlashAttention() bool {
 	return slices.Contains([]string{
 		"bert",
 		"gemma3",
-		"glm4moelite",
 		"gptoss", "gpt-oss",
 		"mistral3",
 		"olmo3",

model/models/glm4moelite/model.go

@@ -1,304 +0,0 @@
-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/models.go

@@ -7,7 +7,6 @@ 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/llama"
 	_ "github.com/ollama/ollama/model/models/llama4"

model/parsers/glm46.go

@@ -1,410 +0,0 @@
-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

@@ -1,862 +0,0 @@
-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

@@ -1,20 +0,0 @@
-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

@@ -1,99 +0,0 @@
-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/parsers.go

@@ -68,8 +68,6 @@ func ParserForName(name string) Parser {
 		return &Nemotron3NanoParser{}
 	case "functiongemma":
 		return &FunctionGemmaParser{}
-	case "glm-4.7":
-		return &GLM47Parser{}
 	default:
 		return nil
 	}

model/parsers/testhelpers_test.go

@@ -96,11 +96,3 @@ 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

@@ -1,110 +0,0 @@
-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

@@ -1,223 +0,0 @@
-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

@@ -1,170 +0,0 @@
-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

@@ -1,191 +0,0 @@
-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/renderer.go

@@ -80,8 +80,6 @@ func rendererForName(name string) Renderer {
 		return &Nemotron3NanoRenderer{}
 	case "functiongemma":
 		return &FunctionGemmaRenderer{}
-	case "glm-4.7":
-		return &GLM47Renderer{}
 	default:
 		return nil
 	}

model/renderers/testhelpers_test.go

@@ -1,26 +1,6 @@
 package renderers
 
-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
-}
+import "github.com/ollama/ollama/api"
 
 // testPropsMap creates a ToolPropertiesMap from a map (convenience function for tests, order not preserved)
 func testPropsMap(m map[string]api.ToolProperty) *api.ToolPropertiesMap {

server/routes.go

@@ -220,6 +220,12 @@ 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.TopLogprobs < 0 || req.TopLogprobs > 20 {
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "top_logprobs must be between 0 and 20"})
 		return
@@ -315,7 +321,7 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 		return
 	}
 
-	// expire the runner if unload is requested (empty prompt, keep alive is 0)
+	// expire the runner
 	if req.Prompt == "" && req.KeepAlive != nil && req.KeepAlive.Duration == 0 {
 		s.sched.expireRunner(m)
 
@@ -329,12 +335,6 @@ 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.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

server/routes_generate_test.go

@@ -2101,95 +2101,3 @@ 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/sched.go

@@ -571,7 +571,6 @@ func (s *Scheduler) loadImageGen(req *LlmRequest) bool {
 		model:           req.model,
 		modelPath:       req.model.ModelPath,
 		llama:           server,
-		Options:         &req.opts,
 		loading:         false,
 		sessionDuration: sessionDuration,
 		totalSize:       server.TotalSize(),

x/create/client/create.go

@@ -12,7 +12,6 @@ import (
 	"fmt"
 	"io"
 
-	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/progress"
 	"github.com/ollama/ollama/server"
 	"github.com/ollama/ollama/types/model"
@@ -281,19 +280,3 @@ func createModelfileLayers(mf *ModelfileConfig) ([]server.Layer, error) {
 
 	return layers, nil
 }
-
-// ExtractModelfileConfig extracts template, system, and license from a parsed Modelfile.
-func ExtractModelfileConfig(modelfile *parser.Modelfile) *ModelfileConfig {
-	mfConfig := &ModelfileConfig{}
-	for _, cmd := range modelfile.Commands {
-		switch cmd.Name {
-		case "template":
-			mfConfig.Template = cmd.Args
-		case "system":
-			mfConfig.System = cmd.Args
-		case "license":
-			mfConfig.License = cmd.Args
-		}
-	}
-	return mfConfig
-}

x/create/client/quantize.go

@@ -16,6 +16,11 @@ import (
 // Supported quantization types: "fp8" (affine 8-bit)
 // Uses MLX's native SaveSafetensors to ensure correct dtype handling (especially uint32 for quantized weights).
 func quantizeTensor(r io.Reader, name, dtype string, shape []int32, quantize string) (qweightData, scalesData, qbiasData []byte, qweightShape, scalesShape, qbiasShape []int32, err error) {
+	// Lazy init MLX when needed for quantization
+	if err := mlx.InitMLX(); err != nil {
+		return nil, nil, nil, nil, nil, nil, fmt.Errorf("MLX initialization failed: %w", err)
+	}
+
 	tmpDir := ensureTempDir()
 
 	// Read safetensors data to a temp file (LoadSafetensorsNative needs a path)
@@ -54,9 +59,6 @@ func quantizeTensor(r io.Reader, name, dtype string, shape []int32, quantize str
 	// Quantize based on quantization type
 	var qweight, scales, qbiases *mlx.Array
 	switch quantize {
-	case "fp4":
-		// affine mode: group_size=32, bits=4
-		qweight, scales, qbiases = mlx.Quantize(arr, 32, 4, "affine")
 	case "fp8":
 		// affine mode: group_size=32, bits=8
 		qweight, scales, qbiases = mlx.Quantize(arr, 32, 8, "affine")

x/create/imagegen.go

@@ -20,10 +20,10 @@ import (
 func CreateImageGenModel(modelName, modelDir, quantize string, createLayer LayerCreator, createTensorLayer QuantizingTensorLayerCreator, writeManifest ManifestWriter, fn func(status string)) error {
 	// Validate quantization type
 	switch quantize {
-	case "", "fp4", "fp8":
+	case "", "fp8":
 		// valid
 	default:
-		return fmt.Errorf("unsupported quantization type %q: supported types are fp4, fp8", quantize)
+		return fmt.Errorf("unsupported quantization type %q: supported types are fp8", quantize)
 	}
 
 	var layers []LayerInfo

x/imagegen/cmd/engine/main.go

@@ -7,17 +7,12 @@ import (
 	"encoding/json"
 	"flag"
 	"fmt"
-	"image"
-	_ "image/jpeg"
-	_ "image/png"
 	"log"
 	"os"
 	"path/filepath"
 	"runtime/pprof"
 
-	"github.com/ollama/ollama/x/imagegen"
 	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/models/flux2"
 	"github.com/ollama/ollama/x/imagegen/models/gemma3"
 	"github.com/ollama/ollama/x/imagegen/models/gpt_oss"
 	"github.com/ollama/ollama/x/imagegen/models/llama"
@@ -51,8 +46,8 @@ func main() {
 	imagePath := flag.String("image", "", "Image path for multimodal models")
 
 	// Image generation params
-	width := flag.Int("width", 0, "Image width (0 = auto from input or 1024)")
-	height := flag.Int("height", 0, "Image height (0 = auto from input or 1024)")
+	width := flag.Int("width", 1024, "Image width")
+	height := flag.Int("height", 1024, "Image height")
 	steps := flag.Int("steps", 0, "Denoising steps (0 = model default)")
 	seed := flag.Int64("seed", 42, "Random seed")
 	out := flag.String("output", "output.png", "Output path")
@@ -66,7 +61,6 @@ func main() {
 
 	// 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
@@ -128,44 +122,6 @@ func main() {
 		if err == nil {
 			err = saveImageArray(img, *out)
 		}
-	case *flux2Flag:
-		m := &flux2.Model{}
-		if loadErr := m.Load(*modelPath); loadErr != nil {
-			log.Fatal(loadErr)
-		}
-		// Load input images with EXIF orientation correction
-		var loadedImages []image.Image
-		for _, path := range inputImages {
-			img, loadErr := loadImageWithEXIF(path)
-			if loadErr != nil {
-				log.Fatalf("Failed to load image %s: %v", path, loadErr)
-			}
-			loadedImages = append(loadedImages, img)
-		}
-		// When input images provided and user didn't override dimensions, use 0 to match input
-		fluxWidth := int32(*width)
-		fluxHeight := int32(*height)
-		if len(loadedImages) > 0 && *width == 0 && *height == 0 {
-			// Both unset, will auto-detect from input
-		} else if len(loadedImages) > 0 && *width == 0 {
-			fluxWidth = 0 // Compute from height + aspect ratio
-		} else if len(loadedImages) > 0 && *height == 0 {
-			fluxHeight = 0 // Compute from width + aspect ratio
-		}
-		var img *mlx.Array
-		img, err = m.GenerateFromConfig(context.Background(), &flux2.GenerateConfig{
-			Prompt:        *prompt,
-			Width:         fluxWidth,
-			Height:        fluxHeight,
-			Steps:         *steps,
-			GuidanceScale: float32(*cfgScale),
-			Seed:          *seed,
-			CapturePath:   *gpuCapture,
-			InputImages:   loadedImages,
-		})
-		if err == nil {
-			err = saveImageArray(img, *out)
-		}
 	case *qwenImage:
 		m, loadErr := qwen_image.LoadPersistent(*modelPath)
 		if loadErr != nil {
@@ -320,8 +276,6 @@ func detectModelKind(modelPath string) (string, error) {
 			switch index.ClassName {
 			case "FluxPipeline", "ZImagePipeline":
 				return "zimage", nil
-			case "Flux2KleinPipeline":
-				return "flux2", nil
 			}
 		}
 		return "zimage", nil
@@ -342,12 +296,3 @@ func detectModelKind(modelPath string) (string, error) {
 
 	return cfg.ModelType, nil
 }
-
-// loadImageWithEXIF loads an image from a file path with EXIF orientation correction.
-func loadImageWithEXIF(path string) (image.Image, error) {
-	data, err := os.ReadFile(path)
-	if err != nil {
-		return nil, fmt.Errorf("read file: %w", err)
-	}
-	return imagegen.DecodeImage(data)
-}

x/imagegen/image.go

@@ -7,7 +7,6 @@ import (
 	"encoding/base64"
 	"fmt"
 	"image"
-	_ "image/jpeg"
 	"image/png"
 	"os"
 	"path/filepath"
@@ -109,160 +108,3 @@ func clampF(v, min, max float32) float32 {
 	}
 	return v
 }
-
-// DecodeImage decodes image bytes with EXIF orientation applied.
-func DecodeImage(data []byte) (image.Image, error) {
-	orientation := readJPEGOrientation(data)
-
-	img, _, err := image.Decode(bytes.NewReader(data))
-	if err != nil {
-		return nil, err
-	}
-
-	return applyOrientation(img, orientation), nil
-}
-
-// readJPEGOrientation extracts EXIF orientation from JPEG bytes.
-// Returns 1 (normal) for non-JPEG or if orientation not found.
-func readJPEGOrientation(data []byte) int {
-	if len(data) < 2 || data[0] != 0xFF || data[1] != 0xD8 {
-		return 1 // Not JPEG
-	}
-
-	r := bytes.NewReader(data[2:])
-	for {
-		var marker [2]byte
-		if _, err := r.Read(marker[:]); err != nil || marker[0] != 0xFF {
-			return 1
-		}
-
-		if marker[1] == 0xE1 { // APP1 (EXIF)
-			var lenBytes [2]byte
-			if _, err := r.Read(lenBytes[:]); err != nil {
-				return 1
-			}
-			segLen := int(uint16(lenBytes[0])<<8|uint16(lenBytes[1])) - 2
-			if segLen < 14 {
-				r.Seek(int64(segLen), 1)
-				continue
-			}
-			seg := make([]byte, segLen)
-			if _, err := r.Read(seg); err != nil {
-				return 1
-			}
-			if string(seg[:4]) == "Exif" && seg[4] == 0 && seg[5] == 0 {
-				return parseTIFFOrientation(seg[6:])
-			}
-			continue
-		}
-
-		if marker[1] == 0xD9 || marker[1] == 0xDA {
-			return 1 // EOI or SOS
-		}
-		if marker[1] >= 0xD0 && marker[1] <= 0xD7 {
-			continue // RST markers
-		}
-
-		var lenBytes [2]byte
-		if _, err := r.Read(lenBytes[:]); err != nil {
-			return 1
-		}
-		segLen := int(uint16(lenBytes[0])<<8|uint16(lenBytes[1])) - 2
-		if segLen > 0 {
-			r.Seek(int64(segLen), 1)
-		}
-	}
-}
-
-func parseTIFFOrientation(tiff []byte) int {
-	if len(tiff) < 8 {
-		return 1
-	}
-
-	var big bool
-	switch string(tiff[:2]) {
-	case "MM":
-		big = true
-	case "II":
-		big = false
-	default:
-		return 1
-	}
-
-	u16 := func(b []byte) uint16 {
-		if big {
-			return uint16(b[0])<<8 | uint16(b[1])
-		}
-		return uint16(b[1])<<8 | uint16(b[0])
-	}
-	u32 := func(b []byte) uint32 {
-		if big {
-			return uint32(b[0])<<24 | uint32(b[1])<<16 | uint32(b[2])<<8 | uint32(b[3])
-		}
-		return uint32(b[3])<<24 | uint32(b[2])<<16 | uint32(b[1])<<8 | uint32(b[0])
-	}
-
-	if u16(tiff[2:4]) != 42 {
-		return 1
-	}
-
-	ifdOffset := u32(tiff[4:8])
-	if int(ifdOffset)+2 > len(tiff) {
-		return 1
-	}
-
-	numEntries := u16(tiff[ifdOffset : ifdOffset+2])
-	for i := range int(numEntries) {
-		offset := ifdOffset + 2 + uint32(i)*12
-		if int(offset)+12 > len(tiff) {
-			break
-		}
-		if u16(tiff[offset:offset+2]) == 0x0112 { // Orientation tag
-			o := int(u16(tiff[offset+8 : offset+10]))
-			if o >= 1 && o <= 8 {
-				return o
-			}
-			return 1
-		}
-	}
-	return 1
-}
-
-func applyOrientation(img image.Image, orientation int) image.Image {
-	if orientation <= 1 || orientation > 8 {
-		return img
-	}
-
-	bounds := img.Bounds()
-	w, h := bounds.Dx(), bounds.Dy()
-
-	outW, outH := w, h
-	if orientation >= 5 {
-		outW, outH = h, w
-	}
-
-	out := image.NewRGBA(image.Rect(0, 0, outW, outH))
-	for y := range h {
-		for x := range w {
-			var dx, dy int
-			switch orientation {
-			case 2:
-				dx, dy = w-1-x, y
-			case 3:
-				dx, dy = w-1-x, h-1-y
-			case 4:
-				dx, dy = x, h-1-y
-			case 5:
-				dx, dy = y, x
-			case 6:
-				dx, dy = h-1-y, x
-			case 7:
-				dx, dy = h-1-y, w-1-x
-			case 8:
-				dx, dy = y, w-1-x
-			}
-			out.Set(dx, dy, img.At(x+bounds.Min.X, y+bounds.Min.Y))
-		}
-	}
-	return out
-}

x/imagegen/memory.go

@@ -24,8 +24,9 @@ var SupportedBackends = []string{"metal", "cuda", "cpu"}
 
 // modelVRAMEstimates maps pipeline class names to their estimated VRAM requirements.
 var modelVRAMEstimates = map[string]uint64{
-	"ZImagePipeline": 21 * GB, // ~21GB for Z-Image (text encoder + transformer + VAE)
-	"FluxPipeline":   20 * GB, // ~20GB for Flux
+	"ZImagePipeline":    21 * GB, // ~21GB for Z-Image (text encoder + transformer + VAE)
+	"FluxPipeline":      21 * GB, // ~21GB for Flux (same architecture)
+	"QwenImagePipeline": 80 * GB, // TODO: verify actual requirements, using conservative estimate for now
 }
 
 // CheckPlatformSupport validates that image generation is supported on the current platform.
@@ -71,38 +72,26 @@ func ResolveModelName(modelName string) string {
 // EstimateVRAM returns the estimated VRAM needed for an image generation model.
 // Returns a conservative default of 21GB if the model type cannot be determined.
 func EstimateVRAM(modelName string) uint64 {
-	className := DetectModelType(modelName)
-	if estimate, ok := modelVRAMEstimates[className]; ok {
-		return estimate
-	}
-	return 21 * GB
-}
-
-// DetectModelType reads model_index.json and returns the model type.
-// Checks both "architecture" (Ollama format) and "_class_name" (diffusers format).
-// Returns empty string if detection fails.
-func DetectModelType(modelName string) string {
 	manifest, err := LoadManifest(modelName)
 	if err != nil {
-		return ""
+		return 21 * GB
 	}
 
 	data, err := manifest.ReadConfig("model_index.json")
 	if err != nil {
-		return ""
+		return 21 * GB
 	}
 
+	// Parse just the class name
 	var index struct {
-		Architecture string `json:"architecture"`
-		ClassName    string `json:"_class_name"`
+		ClassName string `json:"_class_name"`
 	}
 	if err := json.Unmarshal(data, &index); err != nil {
-		return ""
+		return 21 * GB
 	}
 
-	// Prefer architecture (Ollama format), fall back to _class_name (diffusers)
-	if index.Architecture != "" {
-		return index.Architecture
+	if estimate, ok := modelVRAMEstimates[index.ClassName]; ok {
+		return estimate
 	}
-	return index.ClassName
+	return 21 * GB
 }

x/imagegen/memory_test.go

@@ -72,8 +72,9 @@ func TestCheckMemoryRequirements(t *testing.T) {
 func TestModelVRAMEstimates(t *testing.T) {
 	// Verify the VRAM estimates map has expected entries
 	expected := map[string]uint64{
-		"ZImagePipeline": 21 * GB,
-		"FluxPipeline":   20 * GB,
+		"ZImagePipeline":    21 * GB,
+		"FluxPipeline":      21 * GB,
+		"QwenImagePipeline": 80 * GB,
 	}
 
 	for name, expectedVRAM := range expected {

x/imagegen/mlx/mlx.go

@@ -1137,27 +1137,6 @@ func RMSNormNoWeight(x *Array, eps float32) *Array {
 	return RMSNorm(x, ones, eps)
 }
 
-// LayerNorm applies layer normalization without learnable params
-// (x - mean) / sqrt(var + eps)
-func LayerNorm(x *Array, eps float32) *Array {
-	return LayerNormWithWeightBias(x, nil, nil, eps)
-}
-
-// LayerNormWithWeightBias computes layer normalization using mlx.fast
-// weight and bias can be nil for elementwise_affine=False
-func LayerNormWithWeightBias(x, weight, bias *Array, eps float32) *Array {
-	res := C.mlx_array_new()
-	var wc, bc C.mlx_array
-	if weight != nil {
-		wc = weight.c
-	}
-	if bias != nil {
-		bc = bias.c
-	}
-	C.mlx_fast_layer_norm(&res, x.c, wc, bc, C.float(eps), C.default_stream())
-	return newArray(res)
-}
-
 // RoPE applies rotary position embeddings using mlx.fast
 func RoPE(x *Array, dims int, traditional bool, base, scale float32, offset int) *Array {
 	res := C.mlx_array_new()

x/imagegen/models/flux2/flux2.go

@@ -1,539 +0,0 @@
-//go:build mlx
-
-// Package flux2 implements the FLUX.2 Klein diffusion transformer model.
-// Klein is a 4B parameter distilled model that supports sub-second inference.
-package flux2
-
-import (
-	"context"
-	"encoding/json"
-	"fmt"
-	"image"
-	"math"
-	"time"
-
-	"github.com/ollama/ollama/x/imagegen"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/models/qwen3"
-	"github.com/ollama/ollama/x/imagegen/tokenizer"
-	"golang.org/x/image/draw"
-)
-
-// GenerateConfig holds all options for image generation.
-type GenerateConfig struct {
-	Prompt        string
-	Width         int32                 // Image width (default: 1024)
-	Height        int32                 // Image height (default: 1024)
-	Steps         int                   // Denoising steps (default: 4 for Klein)
-	GuidanceScale float32               // Guidance scale (default: 1.0, Klein doesn't need CFG)
-	Seed          int64                 // Random seed
-	Progress      func(step, totalSteps int) // Optional progress callback
-	CapturePath   string                // GPU capture path (debug)
-	InputImages   []image.Image         // Reference images for image conditioning (already loaded)
-}
-
-// Model represents a FLUX.2 Klein model.
-type Model struct {
-	ModelName       string
-	Tokenizer       *tokenizer.Tokenizer
-	TextEncoder     *qwen3.TextEncoder
-	Transformer     *Flux2Transformer2DModel
-	VAE             *AutoencoderKLFlux2
-	SchedulerConfig *SchedulerConfig
-}
-
-// TextEncoderLayerIndices are the layers from which to extract text embeddings.
-// Diffusers uses hidden_states[9, 18, 27]. In Python, hidden_states[0] is the embedding
-// output before any layers, so hidden_states[9] = after layer 8 (0-indexed).
-// Go's ForwardWithLayerOutputs captures after layer i runs, so we use [8, 17, 26].
-var TextEncoderLayerIndices = []int{8, 17, 26}
-
-// Load loads the FLUX.2 Klein model from ollama blob storage.
-func (m *Model) Load(modelName string) error {
-	fmt.Printf("Loading FLUX.2 Klein model from manifest: %s...\n", modelName)
-	start := time.Now()
-
-	if mlx.GPUIsAvailable() {
-		mlx.SetDefaultDeviceGPU()
-		mlx.EnableCompile()
-	}
-
-	m.ModelName = modelName
-
-	// Load manifest
-	manifest, err := imagegen.LoadManifest(modelName)
-	if err != nil {
-		return fmt.Errorf("load manifest: %w", err)
-	}
-
-	// Load tokenizer
-	fmt.Print("  Loading tokenizer... ")
-	tokData, err := manifest.ReadConfig("tokenizer/tokenizer.json")
-	if err != nil {
-		return fmt.Errorf("tokenizer: %w", err)
-	}
-
-	tokConfig := &tokenizer.TokenizerConfig{}
-	if data, err := manifest.ReadConfig("tokenizer/tokenizer_config.json"); err == nil {
-		tokConfig.TokenizerConfigJSON = data
-	}
-	if data, err := manifest.ReadConfig("tokenizer/generation_config.json"); err == nil {
-		tokConfig.GenerationConfigJSON = data
-	}
-	if data, err := manifest.ReadConfig("tokenizer/special_tokens_map.json"); err == nil {
-		tokConfig.SpecialTokensMapJSON = data
-	}
-
-	tok, err := tokenizer.LoadFromBytesWithConfig(tokData, tokConfig)
-	if err != nil {
-		return fmt.Errorf("tokenizer: %w", err)
-	}
-	m.Tokenizer = tok
-	fmt.Println("✓")
-
-	// Load text encoder
-	m.TextEncoder = &qwen3.TextEncoder{}
-	if err := m.TextEncoder.Load(manifest, "text_encoder/config.json"); err != nil {
-		return fmt.Errorf("text encoder: %w", err)
-	}
-
-	// Load transformer
-	m.Transformer = &Flux2Transformer2DModel{}
-	if err := m.Transformer.Load(manifest); err != nil {
-		return fmt.Errorf("transformer: %w", err)
-	}
-
-	// Load VAE
-	m.VAE = &AutoencoderKLFlux2{}
-	if err := m.VAE.Load(manifest); err != nil {
-		return fmt.Errorf("VAE: %w", err)
-	}
-
-	// Evaluate all weights in a single batch (reduces GPU sync overhead)
-	fmt.Print("  Evaluating weights... ")
-	allWeights := mlx.Collect(m.TextEncoder)
-	allWeights = append(allWeights, mlx.Collect(m.Transformer)...)
-	allWeights = append(allWeights, mlx.Collect(m.VAE)...)
-	mlx.Eval(allWeights...)
-	fmt.Println("✓")
-
-	// Load scheduler config
-	m.SchedulerConfig = DefaultSchedulerConfig()
-	if schedData, err := manifest.ReadConfig("scheduler/scheduler_config.json"); err == nil {
-		if err := json.Unmarshal(schedData, m.SchedulerConfig); err != nil {
-			fmt.Printf("  Warning: failed to parse scheduler config: %v\n", err)
-		}
-	}
-
-	mem := mlx.MetalGetActiveMemory()
-	fmt.Printf("  Loaded in %.2fs (%.1f GB VRAM)\n", time.Since(start).Seconds(), float64(mem)/(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(context.Background(), &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(context.Background(), &GenerateConfig{
-		Prompt:   prompt,
-		Width:    width,
-		Height:   height,
-		Steps:    steps,
-		Seed:     seed,
-		Progress: progress,
-	})
-}
-
-// GenerateFromConfig generates an image using the unified config struct.
-func (m *Model) GenerateFromConfig(ctx context.Context, cfg *GenerateConfig) (*mlx.Array, error) {
-	start := time.Now()
-	result, err := m.generate(ctx, cfg)
-	if err != nil {
-		return nil, err
-	}
-	fmt.Printf("Generated in %.2fs (%d steps)\n", time.Since(start).Seconds(), cfg.Steps)
-	return result, nil
-}
-
-// GenerateImage implements runner.ImageModel interface.
-func (m *Model) GenerateImage(ctx context.Context, prompt string, width, height int32, steps int, seed int64, progress func(step, total int)) (*mlx.Array, error) {
-	return m.GenerateFromConfig(ctx, &GenerateConfig{
-		Prompt:   prompt,
-		Width:    width,
-		Height:   height,
-		Steps:    steps,
-		Seed:     seed,
-		Progress: progress,
-	})
-}
-
-// MaxOutputPixels is the maximum output resolution (4 megapixels, ~2048x2048)
-const MaxOutputPixels = 2048 * 2048
-
-// MaxRefPixels is the maximum resolution for reference images (smaller to reduce attention memory)
-const MaxRefPixels = 728 * 728
-
-// generate is the internal denoising pipeline.
-func (m *Model) generate(ctx context.Context, cfg *GenerateConfig) (*mlx.Array, error) {
-	// Enable MLX compilation for fused kernels
-	mlx.EnableCompile()
-
-	// Apply defaults
-	if cfg.Steps <= 0 {
-		cfg.Steps = 4 // Klein default: 4 steps for distilled model
-	}
-	if cfg.GuidanceScale <= 0 {
-		cfg.GuidanceScale = 1.0 // Klein doesn't need guidance
-	}
-
-	// Determine output dimensions
-	if len(cfg.InputImages) > 0 {
-		// With input images, compute missing dimension from aspect ratio
-		// Images are already EXIF-rotated by the caller
-		bounds := cfg.InputImages[0].Bounds()
-		imgW, imgH := bounds.Dx(), bounds.Dy()
-		aspectRatio := float64(imgH) / float64(imgW)
-		if cfg.Width > 0 && cfg.Height <= 0 {
-			// Width specified, compute height
-			cfg.Height = int32(math.Round(float64(cfg.Width)*aspectRatio/16) * 16)
-		} else if cfg.Height > 0 && cfg.Width <= 0 {
-			// Height specified, compute width
-			cfg.Width = int32(math.Round(float64(cfg.Height)/aspectRatio/16) * 16)
-		} else if cfg.Width <= 0 && cfg.Height <= 0 {
-			// Neither specified, use input dimensions
-			cfg.Width = int32(imgW)
-			cfg.Height = int32(imgH)
-		}
-	}
-	if cfg.Width <= 0 {
-		cfg.Width = 1024
-	}
-	if cfg.Height <= 0 {
-		cfg.Height = 1024
-	}
-
-	// Cap to max pixels, preserve aspect ratio, round to multiple of 16
-	pixels := int(cfg.Width) * int(cfg.Height)
-	if pixels > MaxOutputPixels {
-		scale := math.Sqrt(float64(MaxOutputPixels) / float64(pixels))
-		cfg.Width = int32(math.Round(float64(cfg.Width) * scale / 16) * 16)
-		cfg.Height = int32(math.Round(float64(cfg.Height) * scale / 16) * 16)
-	}
-	cfg.Height = int32((cfg.Height + 8) / 16 * 16) // round to nearest 16
-	cfg.Width = int32((cfg.Width + 8) / 16 * 16)
-	fmt.Printf("  Output: %dx%d\n", cfg.Width, cfg.Height)
-
-	tcfg := m.Transformer.TransformerConfig
-	patchSize := m.VAE.Config.PatchSize
-
-	// Latent dimensions: image / 8 (VAE downscale) / patch_size
-	latentH := cfg.Height / 8
-	latentW := cfg.Width / 8
-	patchH := latentH / patchSize[0]
-	patchW := latentW / patchSize[1]
-	imgSeqLen := patchH * patchW
-
-	// Text encoding with multi-layer extraction (no padding, use true sequence length)
-	fmt.Print("  Encoding prompt... ")
-	promptEmbeds, textLen := m.TextEncoder.EncodePromptWithLayers(m.Tokenizer, cfg.Prompt, 512, TextEncoderLayerIndices, false)
-	fmt.Println("✓")
-
-	// Encode reference images if provided
-	var refTokens *ImageCondTokens
-	var refHeights, refWidths []int32
-	if len(cfg.InputImages) > 0 {
-		fmt.Printf("  Encoding %d reference image(s):\n", len(cfg.InputImages))
-
-		var err error
-		refTokens, err = m.EncodeImageRefs(cfg.InputImages)
-		if err != nil {
-			return nil, fmt.Errorf("encode reference images: %w", err)
-		}
-
-		// Extract heights/widths for RoPE computation (same limits as EncodeImageRefs)
-		limitPixels := MaxRefPixels
-		if len(cfg.InputImages) > 1 {
-			limitPixels = MaxRefPixels / 2
-		}
-		for _, img := range cfg.InputImages {
-			_, w, h := PrepareImage(img, limitPixels)
-			refHeights = append(refHeights, int32(h/16))
-			refWidths = append(refWidths, int32(w/16))
-		}
-	}
-
-	// Scheduler
-	scheduler := NewFlowMatchScheduler(m.SchedulerConfig)
-	scheduler.SetTimestepsWithMu(cfg.Steps, CalculateShift(imgSeqLen, cfg.Steps))
-
-	// Init latents in packed form [B, C*4, H/2, W/2] like diffusers
-	// diffusers creates noise in [B, 128, 64, 64] and packs to [B, 4096, 128]
-	latentChannels := m.VAE.Config.LatentChannels
-	packedChannels := latentChannels * 4 // 32 * 4 = 128
-	latents := scheduler.InitNoise([]int32{1, packedChannels, patchH, patchW}, cfg.Seed)
-
-	// Pack latents (transpose): [B, C, H, W] -> [B, H*W, C]
-	// This matches diffusers' _pack_latents
-	patches := packLatents(latents)
-	noiseSeqLen := patches.Shape()[1]
-
-	// RoPE cache - includes reference images if present
-	rope := PrepareRoPECache(textLen, patchH, patchW, tcfg.AxesDimsRoPE, tcfg.RopeTheta, refHeights, refWidths, ImageRefScale)
-
-	// Cleanup setup arrays when done
-	defer func() {
-		rope.Cos.Free()
-		rope.Sin.Free()
-		promptEmbeds.Free()
-		if refTokens != nil {
-			refTokens.Tokens.Free()
-		}
-	}()
-
-	// Pre-compute all timesteps before the loop to avoid per-step tensor creation
-	timesteps := make([]*mlx.Array, cfg.Steps)
-	for i := 0; i < cfg.Steps; i++ {
-		tCurr := scheduler.Timesteps[i] / float32(m.SchedulerConfig.NumTrainTimesteps)
-		timesteps[i] = mlx.ToBFloat16(mlx.NewArray([]float32{tCurr}, []int32{1}))
-	}
-
-	// Evaluate setup arrays
-	fmt.Print("  Evaluating setup... ")
-	setupStart := time.Now()
-	toEval := []*mlx.Array{promptEmbeds, patches, rope.Cos, rope.Sin}
-	toEval = append(toEval, timesteps...)
-	if refTokens != nil {
-		toEval = append(toEval, refTokens.Tokens)
-	}
-	mlx.Eval(toEval...)
-	mlx.MetalResetPeakMemory() // Reset peak to measure generation separately
-	fmt.Printf("✓ (%.2fs, %.1f GB)\n", time.Since(setupStart).Seconds(),
-		float64(mlx.MetalGetActiveMemory())/(1024*1024*1024))
-
-	if cfg.Progress != nil {
-		cfg.Progress(0, cfg.Steps)
-	}
-
-	loopStart := time.Now()
-	stepStart := time.Now()
-
-	// Denoising loop
-	for i := 0; i < cfg.Steps; i++ {
-		// Check for cancellation
-		if ctx != nil {
-			select {
-			case <-ctx.Done():
-				return nil, ctx.Err()
-			default:
-			}
-		}
-
-		// GPU capture on step 2 if requested
-		if cfg.CapturePath != "" && i == 1 {
-			mlx.MetalStartCapture(cfg.CapturePath)
-		}
-
-		timestep := timesteps[i]
-
-		// Prepare input - concatenate noise patches with reference tokens if present
-		imgInput := patches
-		if refTokens != nil {
-			imgInput = mlx.Concatenate([]*mlx.Array{patches, refTokens.Tokens}, 1)
-		}
-
-		// Transformer forward pass
-		output := m.Transformer.Forward(imgInput, promptEmbeds, timestep, rope)
-
-		// If we concatenated reference tokens, slice to only get noise portion
-		if refTokens != nil {
-			output = mlx.Slice(output, []int32{0, 0, 0}, []int32{1, noiseSeqLen, output.Shape()[2]})
-		}
-
-		// Scheduler step (keep reference to old patches for the computation graph)
-		newPatches := scheduler.Step(output, patches, i)
-
-		if cfg.CapturePath != "" && i == 1 {
-			mlx.MetalStopCapture()
-		}
-
-		mlx.Eval(newPatches)
-		patches = newPatches
-
-		elapsed := time.Since(stepStart).Seconds()
-		peakGB := float64(mlx.MetalGetPeakMemory()) / (1024 * 1024 * 1024)
-		if i == 0 {
-			fmt.Printf("    step %d: %.2fs (JIT warmup), peak %.1f GB\n", i+1, elapsed, peakGB)
-		} else {
-			fmt.Printf("    step %d: %.2fs, peak %.1f GB\n", i+1, elapsed, peakGB)
-		}
-		stepStart = time.Now()
-		if cfg.Progress != nil {
-			cfg.Progress(i+1, cfg.Steps)
-		}
-	}
-
-	loopTime := time.Since(loopStart).Seconds()
-	peakMem := float64(mlx.MetalGetPeakMemory()) / (1024 * 1024 * 1024)
-	fmt.Printf("  Denoised %d steps in %.2fs (%.2fs/step), peak %.1f GB\n",
-		cfg.Steps, loopTime, loopTime/float64(cfg.Steps), peakMem)
-
-	// Free timesteps now that denoising is done
-	for _, ts := range timesteps {
-		ts.Free()
-	}
-
-	// VAE decode with tiling for larger images
-	fmt.Print("  Decoding VAE... ")
-	vaeStart := time.Now()
-	// Enable tiling for images > 512x512 (latent > 64x64)
-	// VAE attention is O(n²) on latent pixels, tiling reduces memory significantly
-	if patchH*2 > 64 || patchW*2 > 64 {
-		m.VAE.Tiling = DefaultTilingConfig()
-	}
-	decoded := m.VAE.Decode(patches, patchH, patchW)
-	mlx.Eval(decoded)
-
-	// Free patches now that decode is done
-	patches.Free()
-
-	fmt.Printf("✓ (%.2fs, peak %.1f GB)\n", time.Since(vaeStart).Seconds(),
-		float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	return decoded, nil
-}
-
-// packLatents converts [B, C, H, W] to [B, H*W, C] (matches diffusers _pack_latents)
-func packLatents(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	C := shape[1]
-	H := shape[2]
-	W := shape[3]
-	// [B, C, H, W] -> [B, C, H*W] -> [B, H*W, C]
-	x = mlx.Reshape(x, B, C, H*W)
-	return mlx.Transpose(x, 0, 2, 1)
-}
-
-// LoadPersistent loads the model and keeps it in memory for repeated use.
-func LoadPersistent(modelName string) (*Model, error) {
-	m := &Model{}
-	if err := m.Load(modelName); err != nil {
-		return nil, err
-	}
-	return m, nil
-}
-
-// ImageRefScale is the time coordinate offset between reference images (matches diffusers scale=10)
-const ImageRefScale = 10
-
-// PrepareImage resizes and crops an image to be a multiple of 16, with optional pixel limit.
-// Returns the processed image and its dimensions.
-func PrepareImage(img image.Image, limitPixels int) (image.Image, int, int) {
-	bounds := img.Bounds()
-	w, h := bounds.Dx(), bounds.Dy()
-
-	// Cap pixels if needed (like diffusers cap_pixels)
-	if limitPixels > 0 && w*h > limitPixels {
-		scale := math.Sqrt(float64(limitPixels) / float64(w*h))
-		w = int(float64(w) * scale)
-		h = int(float64(h) * scale)
-	}
-
-	// Round down to multiple of 16
-	w = (w / 16) * 16
-	h = (h / 16) * 16
-
-	if w < 16 {
-		w = 16
-	}
-	if h < 16 {
-		h = 16
-	}
-
-	// Resize using high-quality bicubic interpolation (matches diffusers' default lanczos)
-	resized := image.NewRGBA(image.Rect(0, 0, w, h))
-	draw.CatmullRom.Scale(resized, resized.Bounds(), img, img.Bounds(), draw.Over, nil)
-
-	return resized, w, h
-}
-
-// ImageToTensor converts an image to a tensor in [-1, 1] range with shape [1, C, H, W].
-func ImageToTensor(img image.Image) *mlx.Array {
-	bounds := img.Bounds()
-	w, h := bounds.Dx(), bounds.Dy()
-
-	// Convert to float32 array in NCHW format [1, 3, H, W] with values in [-1, 1]
-	data := make([]float32, 3*h*w)
-
-	for y := 0; y < h; y++ {
-		for x := 0; x < w; x++ {
-			r, g, b, _ := img.At(x+bounds.Min.X, y+bounds.Min.Y).RGBA()
-			// RGBA returns 16-bit values, convert to [-1, 1]
-			data[0*h*w+y*w+x] = float32(r>>8)/127.5 - 1.0
-			data[1*h*w+y*w+x] = float32(g>>8)/127.5 - 1.0
-			data[2*h*w+y*w+x] = float32(b>>8)/127.5 - 1.0
-		}
-	}
-
-	arr := mlx.NewArrayFloat32(data, []int32{1, 3, int32(h), int32(w)})
-	return arr
-}
-
-// ImageCondTokens holds encoded reference image tokens.
-type ImageCondTokens struct {
-	Tokens *mlx.Array // [1, total_tokens, C] - concatenated reference tokens
-}
-
-// EncodeImageRefs encodes reference images using the VAE.
-func (m *Model) EncodeImageRefs(images []image.Image) (*ImageCondTokens, error) {
-	if len(images) == 0 {
-		return nil, nil
-	}
-
-	// Limit reference images to reduce attention memory
-	limitPixels := MaxRefPixels
-	if len(images) > 1 {
-		limitPixels = MaxRefPixels / 2
-	}
-
-	var allTokens []*mlx.Array
-
-	for _, img := range images {
-		// Prepare image (resize, crop to multiple of 16)
-		prepared, prepW, prepH := PrepareImage(img, limitPixels)
-		fmt.Printf("    Encoding %dx%d image... ", prepW, prepH)
-
-		// Convert to tensor [-1, 1]
-		tensor := ImageToTensor(prepared)
-
-		// Encode with VAE - returns [1, L, 128]
-		encoded := m.VAE.EncodeImage(tensor)
-		squeezed := mlx.Squeeze(encoded, 0) // [L, C]
-
-		// Defer eval - will be done with other setup arrays
-		allTokens = append(allTokens, squeezed)
-		fmt.Println("✓")
-	}
-
-	// For single image, just add batch dimension directly
-	// For multiple images, concatenate first
-	var tokens *mlx.Array
-	if len(allTokens) == 1 {
-		tokens = mlx.ExpandDims(allTokens[0], 0) // [1, L, C]
-	} else {
-		tokens = mlx.Concatenate(allTokens, 0) // [total_L, C]
-		tokens = mlx.ExpandDims(tokens, 0)     // [1, total_L, C]
-	}
-
-	return &ImageCondTokens{Tokens: tokens}, nil
-}

x/imagegen/models/flux2/rope.go

@@ -1,224 +0,0 @@
-//go:build mlx
-
-package flux2
-
-import (
-	"math"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-)
-
-// RoPEConfig holds 4D RoPE configuration for Flux2
-type RoPEConfig struct {
-	Theta    int32   // 2000 for Klein
-	AxesDims []int32 // [32, 32, 32, 32] - dimensions for T, H, W, L axes
-}
-
-// RoPECache holds precomputed RoPE cos/sin values
-type RoPECache struct {
-	Cos      *mlx.Array // [1, TotalSeqLen, 1, head_dim/2]
-	Sin      *mlx.Array // [1, TotalSeqLen, 1, head_dim/2]
-	TextLen  int32      // Length of text sequence
-	ImageLen int32      // Length of image sequence
-}
-
-// PrepareTextIDs creates position IDs for text tokens.
-// Text tokens use: T=0, H=0, W=0, L=0..seqLen-1
-// Returns: [seqLen, 4]
-func PrepareTextIDs(seqLen int32) *mlx.Array {
-	ids := make([]float32, seqLen*4)
-	for i := int32(0); i < seqLen; i++ {
-		idx := i * 4
-		ids[idx+0] = 0             // T = 0
-		ids[idx+1] = 0             // H = 0
-		ids[idx+2] = 0             // W = 0
-		ids[idx+3] = float32(i)    // L = sequence position
-	}
-	return mlx.NewArray(ids, []int32{seqLen, 4})
-}
-
-// PrepareLatentIDs creates position IDs for image latent tokens.
-// Latent tokens use: T=0, H=0..height-1, W=0..width-1, L=0
-// The latents are in row-major order (H then W).
-// Returns: [height*width, 4]
-func PrepareLatentIDs(height, width int32) *mlx.Array {
-	seqLen := height * width
-	ids := make([]float32, seqLen*4)
-	idx := 0
-	for h := int32(0); h < height; h++ {
-		for w := int32(0); w < width; w++ {
-			ids[idx*4+0] = 0           // T = 0
-			ids[idx*4+1] = float32(h)  // H = row
-			ids[idx*4+2] = float32(w)  // W = column
-			ids[idx*4+3] = 0           // L = 0
-			idx++
-		}
-	}
-	return mlx.NewArray(ids, []int32{seqLen, 4})
-}
-
-// PrepareImageIDs creates position IDs for reference image tokens (used in editing).
-// Reference images use: T=scale*(i+1), H=0..h-1, W=0..w-1, L=0
-// where i is the image index (0, 1, 2, ...) and scale separates images in T dimension.
-// Returns: [total_tokens, 4]
-func PrepareImageIDs(imageHeights, imageWidths []int32, scale int32) *mlx.Array {
-	// Calculate total tokens
-	totalTokens := int32(0)
-	for i := range imageHeights {
-		totalTokens += imageHeights[i] * imageWidths[i]
-	}
-
-	ids := make([]float32, totalTokens*4)
-	idx := int32(0)
-	for imgIdx, h := range imageHeights {
-		w := imageWidths[imgIdx]
-		tValue := float32(scale * int32(imgIdx+1))
-		for hi := int32(0); hi < h; hi++ {
-			for wi := int32(0); wi < w; wi++ {
-				ids[idx*4+0] = tValue       // T = scale * (imgIdx + 1)
-				ids[idx*4+1] = float32(hi)  // H = row
-				ids[idx*4+2] = float32(wi)  // W = column
-				ids[idx*4+3] = 0            // L = 0
-				idx++
-			}
-		}
-	}
-	return mlx.NewArray(ids, []int32{totalTokens, 4})
-}
-
-// ComputeRoPE computes cos and sin for 4D rotary position embeddings.
-// ids: [L, 4] with (T, H, W, L) coordinates
-// axesDims: [32, 32, 32, 32] - each axis has this many dimensions (total = head_dim = 128)
-// theta: base frequency (2000 for Klein)
-// Returns: cos, sin each [1, L, 1, head_dim] with repeat_interleave applied
-func ComputeRoPE(ids *mlx.Array, axesDims []int32, theta int32) (*mlx.Array, *mlx.Array) {
-	shape := ids.Shape()
-	seqLen := shape[0]
-
-	// Compute total head dim (sum of all axes dims)
-	headDim := int32(0)
-	for _, d := range axesDims {
-		headDim += d
-	}
-
-	// Extract each coordinate dimension
-	// ids[:, 0] = T, ids[:, 1] = H, ids[:, 2] = W, ids[:, 3] = L
-	posT := mlx.Slice(ids, []int32{0, 0}, []int32{seqLen, 1}) // [L, 1]
-	posH := mlx.Slice(ids, []int32{0, 1}, []int32{seqLen, 2}) // [L, 1]
-	posW := mlx.Slice(ids, []int32{0, 2}, []int32{seqLen, 3}) // [L, 1]
-	posL := mlx.Slice(ids, []int32{0, 3}, []int32{seqLen, 4}) // [L, 1]
-
-	// Compute frequencies for each axis
-	logTheta := float32(math.Log(float64(theta)))
-	cosArrs := make([]*mlx.Array, 4)
-	sinArrs := make([]*mlx.Array, 4)
-	positions := []*mlx.Array{posT, posH, posW, posL}
-
-	for i, axisDim := range axesDims {
-		half := axisDim / 2
-
-		// Create frequency array for this axis: theta^(-2j/dim) for j=0..half-1
-		// This matches diffusers: 1.0 / (theta ** (torch.arange(0, dim, 2) / dim))
-		freqs := make([]float32, half)
-		for j := int32(0); j < half; j++ {
-			freqs[j] = float32(math.Exp(float64(-logTheta * float32(2*j) / float32(axisDim))))
-		}
-		freqArr := mlx.NewArray(freqs, []int32{1, half})
-
-		// Compute pos * freq -> [L, half]
-		posExpanded := positions[i] // [L, 1]
-		args := mlx.Mul(posExpanded, freqArr) // [L, half]
-
-		// Compute cos and sin for this axis
-		cosAxis := mlx.Cos(args) // [L, half]
-		sinAxis := mlx.Sin(args) // [L, half]
-
-		// repeat_interleave(2): [c0, c1, ...] -> [c0, c0, c1, c1, ...]
-		// Reshape [L, half] -> [L, half, 1], tile to [L, half, 2], reshape to [L, axisDim]
-		cosAxis = mlx.ExpandDims(cosAxis, 2)                        // [L, half, 1]
-		cosAxis = mlx.Tile(cosAxis, []int32{1, 1, 2})               // [L, half, 2]
-		cosAxis = mlx.Reshape(cosAxis, seqLen, axisDim)             // [L, axisDim]
-
-		sinAxis = mlx.ExpandDims(sinAxis, 2)
-		sinAxis = mlx.Tile(sinAxis, []int32{1, 1, 2})
-		sinAxis = mlx.Reshape(sinAxis, seqLen, axisDim)
-
-		cosArrs[i] = cosAxis
-		sinArrs[i] = sinAxis
-	}
-
-	// Concatenate all axes: [L, headDim]
-	cos := mlx.Concatenate(cosArrs, 1)
-	sin := mlx.Concatenate(sinArrs, 1)
-
-	// Reshape to [1, L, 1, headDim] for broadcasting with attention
-	cos = mlx.Reshape(cos, 1, seqLen, 1, headDim)
-	sin = mlx.Reshape(sin, 1, seqLen, 1, headDim)
-
-	return cos, sin
-}
-
-// ApplyRoPE4D applies 4D rotary position embeddings to queries and keys.
-// x: [B, L, nheads, head_dim]
-// cos, sin: [1, L, 1, head_dim] (with repeat_interleave applied)
-// Returns: x with RoPE applied
-// Matches diffusers apply_rotary_emb with use_real=True, use_real_unbind_dim=-1
-func ApplyRoPE4D(x *mlx.Array, cos, sin *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	L := shape[1]
-	nheads := shape[2]
-	headDim := shape[3]
-	half := headDim / 2
-
-	// Reshape x to [B, L, nheads, half, 2] and split into real/imag
-	xReshaped := mlx.Reshape(x, B, L, nheads, half, 2)
-
-	// Extract real (index 0) and imag (index 1) parts
-	xReal := mlx.Slice(xReshaped, []int32{0, 0, 0, 0, 0}, []int32{B, L, nheads, half, 1})
-	xImag := mlx.Slice(xReshaped, []int32{0, 0, 0, 0, 1}, []int32{B, L, nheads, half, 2})
-	xReal = mlx.Squeeze(xReal, 4) // [B, L, nheads, half]
-	xImag = mlx.Squeeze(xImag, 4) // [B, L, nheads, half]
-
-	// x_rotated = stack([-x_imag, x_real], dim=-1).flatten(-2)
-	// This creates [-x_imag[0], x_real[0], -x_imag[1], x_real[1], ...]
-	negXImag := mlx.Neg(xImag)
-	negXImag = mlx.ExpandDims(negXImag, 4) // [B, L, nheads, half, 1]
-	xReal = mlx.ExpandDims(xReal, 4)       // [B, L, nheads, half, 1]
-	xRotated := mlx.Concatenate([]*mlx.Array{negXImag, xReal}, 4) // [B, L, nheads, half, 2]
-	xRotated = mlx.Reshape(xRotated, B, L, nheads, headDim)       // [B, L, nheads, headDim]
-
-	// out = x * cos + x_rotated * sin
-	return mlx.Add(mlx.Mul(x, cos), mlx.Mul(xRotated, sin))
-}
-
-// PrepareRoPECache creates RoPE cache for text + noise, optionally with reference images.
-// textLen: number of text tokens
-// noiseH, noiseW: dimensions of the noise latent in patch tokens
-// axesDims: [32, 32, 32, 32]
-// theta: 2000
-// refHeights, refWidths: optional reference image dimensions (pass nil/empty for no images)
-// scale: time coordinate offset between reference images (e.g., 10)
-func PrepareRoPECache(textLen, noiseH, noiseW int32, axesDims []int32, theta int32, refHeights, refWidths []int32, scale int32) *RoPECache {
-	textIDs := PrepareTextIDs(textLen)
-	noiseIDs := PrepareLatentIDs(noiseH, noiseW)
-
-	var allIDs *mlx.Array
-	imageLen := noiseH * noiseW
-
-	if len(refHeights) > 0 {
-		refIDs := PrepareImageIDs(refHeights, refWidths, scale)
-		allIDs = mlx.Concatenate([]*mlx.Array{textIDs, noiseIDs, refIDs}, 0)
-		for i := range refHeights {
-			imageLen += refHeights[i] * refWidths[i]
-		}
-	} else {
-		allIDs = mlx.Concatenate([]*mlx.Array{textIDs, noiseIDs}, 0)
-	}
-
-	cos, sin := ComputeRoPE(allIDs, axesDims, theta)
-	cos = mlx.ToBFloat16(cos)
-	sin = mlx.ToBFloat16(sin)
-
-	return &RoPECache{Cos: cos, Sin: sin, TextLen: textLen, ImageLen: imageLen}
-}

x/imagegen/models/flux2/scheduler.go

@@ -1,149 +0,0 @@
-//go:build mlx
-
-package flux2
-
-import (
-	"math"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-)
-
-// SchedulerConfig holds Flow-Match scheduler configuration
-type SchedulerConfig struct {
-	NumTrainTimesteps  int32   `json:"num_train_timesteps"`  // 1000
-	Shift              float32 `json:"shift"`                // 3.0 for Klein
-	UseDynamicShifting bool    `json:"use_dynamic_shifting"` // true
-	TimeShiftType      string  `json:"time_shift_type"`      // "exponential" or "linear"
-}
-
-// DefaultSchedulerConfig returns default config for Klein
-func DefaultSchedulerConfig() *SchedulerConfig {
-	return &SchedulerConfig{
-		NumTrainTimesteps:  1000,
-		Shift:              3.0, // Klein uses 3.0
-		UseDynamicShifting: true,
-		TimeShiftType:      "exponential",
-	}
-}
-
-// FlowMatchScheduler implements the Flow-Match Euler discrete scheduler
-type FlowMatchScheduler struct {
-	Config    *SchedulerConfig
-	Timesteps []float32 // Discretized timesteps (t from 1 to 0)
-	Sigmas    []float32 // Noise levels at each timestep
-	NumSteps  int       // Number of inference steps
-}
-
-// NewFlowMatchScheduler creates a new scheduler
-func NewFlowMatchScheduler(cfg *SchedulerConfig) *FlowMatchScheduler {
-	return &FlowMatchScheduler{
-		Config: cfg,
-	}
-}
-
-// SetTimesteps sets up the scheduler for the given number of inference steps
-func (s *FlowMatchScheduler) SetTimesteps(numSteps int) {
-	s.SetTimestepsWithMu(numSteps, 0)
-}
-
-// SetTimestepsWithMu sets up scheduler matching diffusers set_timesteps(sigmas=..., mu=...)
-func (s *FlowMatchScheduler) SetTimestepsWithMu(numSteps int, mu float32) {
-	s.NumSteps = numSteps
-
-	// diffusers: sigmas = linspace(1, 1/num_steps, num_steps)
-	// Then applies time shift, appends 0.0 at end
-	s.Sigmas = make([]float32, numSteps+1)
-
-	for i := 0; i < numSteps; i++ {
-		// linspace(1, 1/num_steps, num_steps)
-		var sigma float32
-		if numSteps == 1 {
-			sigma = 1.0
-		} else {
-			sigma = 1.0 - float32(i)/float32(numSteps-1)*(1.0-1.0/float32(numSteps))
-		}
-
-		// Apply time shift if using dynamic shifting
-		if s.Config.UseDynamicShifting && mu != 0 {
-			sigma = s.timeShift(mu, sigma)
-		} else {
-			// If not dynamic shifting, apply fixed shift scaling like diffusers
-			shift := s.Config.Shift
-			sigma = shift * sigma / (1 + (shift-1)*sigma)
-		}
-		s.Sigmas[i] = sigma
-	}
-	// Append terminal zero
-	s.Sigmas[numSteps] = 0.0
-
-	// Timesteps scaled to training range (matches diffusers: timesteps = sigmas * num_train_timesteps)
-	s.Timesteps = make([]float32, numSteps+1)
-	for i, v := range s.Sigmas {
-		s.Timesteps[i] = v * float32(s.Config.NumTrainTimesteps)
-	}
-}
-
-// timeShift applies the dynamic time shift
-func (s *FlowMatchScheduler) timeShift(mu float32, t float32) float32 {
-	if t <= 0 {
-		return 0
-	}
-	if s.Config.TimeShiftType == "linear" {
-		return mu / (mu + (1.0/t-1.0))
-	}
-	// Default: exponential
-	expMu := float32(math.Exp(float64(mu)))
-	return expMu / (expMu + (1.0/t - 1.0))
-}
-
-// Step performs one denoising step
-func (s *FlowMatchScheduler) Step(modelOutput, sample *mlx.Array, timestepIdx int) *mlx.Array {
-	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 for precision (matches diffusers)
-	sampleF32 := mlx.AsType(sample, mlx.DtypeFloat32)
-	outputF32 := mlx.AsType(modelOutput, mlx.DtypeFloat32)
-
-	scaledOutput := mlx.MulScalar(outputF32, dt)
-	result := mlx.Add(sampleF32, scaledOutput)
-
-	// Cast back to bfloat16
-	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
-func (s *FlowMatchScheduler) InitNoise(shape []int32, seed int64) *mlx.Array {
-	return mlx.RandomNormalWithDtype(shape, uint64(seed), mlx.DtypeBFloat16)
-}
-
-// CalculateShift computes the mu shift value for dynamic scheduling
-// Matches diffusers compute_empirical_mu function
-func CalculateShift(imgSeqLen int32, numSteps int) float32 {
-	a1, b1 := float32(8.73809524e-05), float32(1.89833333)
-	a2, b2 := float32(0.00016927), float32(0.45666666)
-
-	seqLen := float32(imgSeqLen)
-
-	if imgSeqLen > 4300 {
-		return a2*seqLen + b2
-	}
-
-	m200 := a2*seqLen + b2
-	m10 := a1*seqLen + b1
-
-	a := (m200 - m10) / 190.0
-	b := m200 - 200.0*a
-	return a*float32(numSteps) + b
-}

x/imagegen/models/flux2/transformer.go

@@ -1,562 +0,0 @@
-//go:build mlx
-
-package flux2
-
-import (
-	"fmt"
-	"math"
-
-	"github.com/ollama/ollama/x/imagegen"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/nn"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-)
-
-// TransformerConfig holds Flux2 transformer configuration
-type TransformerConfig struct {
-	AttentionHeadDim         int32   `json:"attention_head_dim"`          // 128
-	AxesDimsRoPE             []int32 `json:"axes_dims_rope"`              // [32, 32, 32, 32]
-	Eps                      float32 `json:"eps"`                         // 1e-6
-	GuidanceEmbeds           bool    `json:"guidance_embeds"`             // false for Klein
-	InChannels               int32   `json:"in_channels"`                 // 128
-	JointAttentionDim        int32   `json:"joint_attention_dim"`         // 7680
-	MLPRatio                 float32 `json:"mlp_ratio"`                   // 3.0
-	NumAttentionHeads        int32   `json:"num_attention_heads"`         // 24
-	NumLayers                int32   `json:"num_layers"`                  // 5
-	NumSingleLayers          int32   `json:"num_single_layers"`           // 20
-	PatchSize                int32   `json:"patch_size"`                  // 1
-	RopeTheta                int32   `json:"rope_theta"`                  // 2000
-	TimestepGuidanceChannels int32   `json:"timestep_guidance_channels"`  // 256
-}
-
-// Computed dimensions
-func (c *TransformerConfig) InnerDim() int32 {
-	return c.NumAttentionHeads * c.AttentionHeadDim // 24 * 128 = 3072
-}
-
-func (c *TransformerConfig) MLPHiddenDim() int32 {
-	return int32(float32(c.InnerDim()) * c.MLPRatio) // 3072 * 3.0 = 9216
-}
-
-// TimestepEmbedder creates timestep embeddings
-// Weight names: time_guidance_embed.timestep_embedder.linear_1.weight, linear_2.weight
-type TimestepEmbedder struct {
-	Linear1  nn.LinearLayer `weight:"linear_1"`
-	Linear2  nn.LinearLayer `weight:"linear_2"`
-	EmbedDim int32          // 256
-}
-
-// Forward creates sinusoidal embeddings and projects them
-func (t *TimestepEmbedder) Forward(timesteps *mlx.Array) *mlx.Array {
-	half := t.EmbedDim / 2
-	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})
-
-	// timesteps: [B] -> [B, 1]
-	tExpanded := mlx.ExpandDims(timesteps, 1)
-	// args: [B, half]
-	args := mlx.Mul(tExpanded, freqsArr)
-
-	// [cos(args), sin(args)] -> [B, embed_dim]
-	sinEmbed := mlx.Concatenate([]*mlx.Array{mlx.Cos(args), mlx.Sin(args)}, 1)
-
-	// MLP: linear_1 -> silu -> linear_2
-	h := t.Linear1.Forward(sinEmbed)
-	h = mlx.SiLU(h)
-	return t.Linear2.Forward(h)
-}
-
-// TimeGuidanceEmbed wraps the timestep embedder
-// Weight names: time_guidance_embed.timestep_embedder.*
-type TimeGuidanceEmbed struct {
-	TimestepEmbedder *TimestepEmbedder `weight:"timestep_embedder"`
-}
-
-// Forward computes timestep embeddings
-func (t *TimeGuidanceEmbed) Forward(timesteps *mlx.Array) *mlx.Array {
-	return t.TimestepEmbedder.Forward(timesteps)
-}
-
-// Modulation computes adaptive modulation parameters
-// Weight names: double_stream_modulation_img.linear.weight, etc.
-type Modulation struct {
-	Linear nn.LinearLayer `weight:"linear"`
-}
-
-// Forward computes modulation parameters
-func (m *Modulation) Forward(temb *mlx.Array) *mlx.Array {
-	h := mlx.SiLU(temb)
-	return m.Linear.Forward(h)
-}
-
-// TransformerBlockAttn implements dual-stream attention
-// Weight names: transformer_blocks.N.attn.*
-type TransformerBlockAttn struct {
-	// Image stream (separate Q, K, V projections)
-	ToQ nn.LinearLayer `weight:"to_q"`
-	ToK nn.LinearLayer `weight:"to_k"`
-	ToV nn.LinearLayer `weight:"to_v"`
-	// Note: to_out has .0 suffix in weights, handled specially
-	ToOut0 nn.LinearLayer `weight:"to_out.0"`
-
-	// Text stream (add_ projections)
-	AddQProj nn.LinearLayer `weight:"add_q_proj"`
-	AddKProj nn.LinearLayer `weight:"add_k_proj"`
-	AddVProj nn.LinearLayer `weight:"add_v_proj"`
-	ToAddOut nn.LinearLayer `weight:"to_add_out"`
-
-	// QK norms for image stream
-	NormQ *mlx.Array `weight:"norm_q.weight"`
-	NormK *mlx.Array `weight:"norm_k.weight"`
-
-	// QK norms for text stream (added)
-	NormAddedQ *mlx.Array `weight:"norm_added_q.weight"`
-	NormAddedK *mlx.Array `weight:"norm_added_k.weight"`
-}
-
-// FeedForward implements SwiGLU MLP
-// Weight names: transformer_blocks.N.ff.linear_in.weight, linear_out.weight
-type FeedForward struct {
-	LinearIn  nn.LinearLayer `weight:"linear_in"`
-	LinearOut nn.LinearLayer `weight:"linear_out"`
-}
-
-// Forward applies SwiGLU MLP
-func (ff *FeedForward) Forward(x *mlx.Array) *mlx.Array {
-	// LinearIn outputs 2x hidden dim for SwiGLU
-	h := ff.LinearIn.Forward(x)
-	shape := h.Shape()
-	half := shape[len(shape)-1] / 2
-
-	// Split into gate and up
-	gate := mlx.Slice(h, []int32{0, 0, 0}, []int32{shape[0], shape[1], half})
-	up := mlx.Slice(h, []int32{0, 0, half}, []int32{shape[0], shape[1], shape[2]})
-
-	// SwiGLU: silu(gate) * up
-	h = mlx.Mul(mlx.SiLU(gate), up)
-	return ff.LinearOut.Forward(h)
-}
-
-// TransformerBlock implements a dual-stream transformer block
-// Weight names: transformer_blocks.N.*
-type TransformerBlock struct {
-	Attn      *TransformerBlockAttn `weight:"attn"`
-	FF        *FeedForward          `weight:"ff"`
-	FFContext *FeedForward          `weight:"ff_context"`
-
-	// Config (set after loading)
-	NHeads  int32
-	HeadDim int32
-	Scale   float32
-}
-
-// Forward applies the dual-stream block
-// imgHidden: [B, imgLen, dim]
-// txtHidden: [B, txtLen, dim]
-// imgMod, txtMod: modulation params [B, 6*dim] each
-// cos, sin: RoPE values
-func (block *TransformerBlock) Forward(imgHidden, txtHidden *mlx.Array, imgMod, txtMod *mlx.Array, cos, sin *mlx.Array) (*mlx.Array, *mlx.Array) {
-	imgShape := imgHidden.Shape()
-	B := imgShape[0]
-	imgLen := imgShape[1]
-	dim := imgShape[2]
-	txtLen := txtHidden.Shape()[1]
-
-	// Parse modulation: 6 params each (shift1, scale1, gate1, shift2, scale2, gate2)
-	imgShift1, imgScale1, imgGate1 := parseModulation3(imgMod, dim, 0)
-	imgShift2, imgScale2, imgGate2 := parseModulation3(imgMod, dim, 3)
-	txtShift1, txtScale1, txtGate1 := parseModulation3(txtMod, dim, 0)
-	txtShift2, txtScale2, txtGate2 := parseModulation3(txtMod, dim, 3)
-
-	// === Attention branch ===
-	// Modulate inputs
-	imgNorm := modulateLayerNorm(imgHidden, imgShift1, imgScale1)
-	txtNorm := modulateLayerNorm(txtHidden, txtShift1, txtScale1)
-
-	// Compute Q, K, V for image stream (separate projections)
-	imgQ := block.Attn.ToQ.Forward(imgNorm)
-	imgK := block.Attn.ToK.Forward(imgNorm)
-	imgV := block.Attn.ToV.Forward(imgNorm)
-
-	// Compute Q, K, V for text stream (add_ projections)
-	txtQ := block.Attn.AddQProj.Forward(txtNorm)
-	txtK := block.Attn.AddKProj.Forward(txtNorm)
-	txtV := block.Attn.AddVProj.Forward(txtNorm)
-
-	// Reshape for attention: [B, L, dim] -> [B, L, nheads, headDim]
-	imgQ = mlx.Reshape(imgQ, B, imgLen, block.NHeads, block.HeadDim)
-	imgK = mlx.Reshape(imgK, B, imgLen, block.NHeads, block.HeadDim)
-	imgV = mlx.Reshape(imgV, B, imgLen, block.NHeads, block.HeadDim)
-	txtQ = mlx.Reshape(txtQ, B, txtLen, block.NHeads, block.HeadDim)
-	txtK = mlx.Reshape(txtK, B, txtLen, block.NHeads, block.HeadDim)
-	txtV = mlx.Reshape(txtV, B, txtLen, block.NHeads, block.HeadDim)
-
-	// Apply QK norm (RMSNorm with learned scale)
-	imgQ = applyQKNorm(imgQ, block.Attn.NormQ)
-	imgK = applyQKNorm(imgK, block.Attn.NormK)
-	txtQ = applyQKNorm(txtQ, block.Attn.NormAddedQ)
-	txtK = applyQKNorm(txtK, block.Attn.NormAddedK)
-
-	// Concatenate for joint attention: text first, then image
-	q := mlx.Concatenate([]*mlx.Array{txtQ, imgQ}, 1)
-	k := mlx.Concatenate([]*mlx.Array{txtK, imgK}, 1)
-	v := mlx.Concatenate([]*mlx.Array{txtV, imgV}, 1)
-
-	// Apply RoPE
-	q = ApplyRoPE4D(q, cos, sin)
-	k = ApplyRoPE4D(k, cos, sin)
-
-	// Transpose for SDPA: [B, nheads, L, headDim]
-	q = mlx.Transpose(q, 0, 2, 1, 3)
-	k = mlx.Transpose(k, 0, 2, 1, 3)
-	v = mlx.Transpose(v, 0, 2, 1, 3)
-
-	// Scaled dot-product attention
-	out := mlx.ScaledDotProductAttention(q, k, v, block.Scale, false)
-
-	// Transpose back: [B, L, nheads, headDim]
-	out = mlx.Transpose(out, 0, 2, 1, 3)
-
-	// Split back into txt and img
-	totalLen := txtLen + imgLen
-	txtOut := mlx.Slice(out, []int32{0, 0, 0, 0}, []int32{B, txtLen, block.NHeads, block.HeadDim})
-	imgOut := mlx.Slice(out, []int32{0, txtLen, 0, 0}, []int32{B, totalLen, block.NHeads, block.HeadDim})
-
-	// Reshape and project
-	txtOut = mlx.Reshape(txtOut, B, txtLen, dim)
-	imgOut = mlx.Reshape(imgOut, B, imgLen, dim)
-	txtOut = block.Attn.ToAddOut.Forward(txtOut)
-	imgOut = block.Attn.ToOut0.Forward(imgOut)
-
-	// Apply gates and residual
-	imgHidden = mlx.Add(imgHidden, mlx.Mul(imgGate1, imgOut))
-	txtHidden = mlx.Add(txtHidden, mlx.Mul(txtGate1, txtOut))
-
-	// === MLP branch ===
-	imgNorm = modulateLayerNorm(imgHidden, imgShift2, imgScale2)
-	txtNorm = modulateLayerNorm(txtHidden, txtShift2, txtScale2)
-
-	imgFFOut := block.FF.Forward(imgNorm)
-	txtFFOut := block.FFContext.Forward(txtNorm)
-
-	imgHidden = mlx.Add(imgHidden, mlx.Mul(imgGate2, imgFFOut))
-	txtHidden = mlx.Add(txtHidden, mlx.Mul(txtGate2, txtFFOut))
-
-	return imgHidden, txtHidden
-}
-
-// SingleTransformerBlockAttn implements attention for single-stream blocks
-// Weight names: single_transformer_blocks.N.attn.*
-type SingleTransformerBlockAttn struct {
-	ToQKVMlpProj nn.LinearLayer `weight:"to_qkv_mlp_proj"` // Fused QKV + MLP input
-	ToOut        nn.LinearLayer `weight:"to_out"`          // Fused attn_out + MLP out
-	NormQ        *mlx.Array     `weight:"norm_q.weight"`
-	NormK        *mlx.Array     `weight:"norm_k.weight"`
-}
-
-// SingleTransformerBlock implements a single-stream transformer block
-// Weight names: single_transformer_blocks.N.*
-type SingleTransformerBlock struct {
-	Attn *SingleTransformerBlockAttn `weight:"attn"`
-
-	// Config
-	NHeads    int32
-	HeadDim   int32
-	InnerDim  int32
-	MLPHidDim int32
-	Scale     float32
-}
-
-// Forward applies the single-stream block
-// x: [B, L, dim] concatenated text+image
-// mod: modulation [B, 3*dim]
-func (block *SingleTransformerBlock) Forward(x *mlx.Array, mod *mlx.Array, cos, sin *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	L := shape[1]
-	dim := shape[2]
-
-	// Parse modulation: (shift, scale, gate)
-	shift, scale, gate := parseModulation3(mod, dim, 0)
-
-	// Modulate input
-	h := modulateLayerNorm(x, shift, scale)
-
-	// Fused projection: QKV + MLP gate/up
-	// linear1 outputs: [q, k, v, mlp_gate, mlp_up] = [dim, dim, dim, mlpHid, mlpHid]
-	qkvMlp := block.Attn.ToQKVMlpProj.Forward(h)
-
-	// Split: first 3*dim is QKV, rest is MLP
-	qkvDim := 3 * block.InnerDim
-	qkv := mlx.Slice(qkvMlp, []int32{0, 0, 0}, []int32{B, L, qkvDim})
-	mlpIn := mlx.Slice(qkvMlp, []int32{0, 0, qkvDim}, []int32{B, L, qkvMlp.Shape()[2]})
-
-	// Split QKV
-	q, k, v := splitQKV(qkv, B, L, block.InnerDim)
-
-	// Reshape for attention
-	q = mlx.Reshape(q, B, L, block.NHeads, block.HeadDim)
-	k = mlx.Reshape(k, B, L, block.NHeads, block.HeadDim)
-	v = mlx.Reshape(v, B, L, block.NHeads, block.HeadDim)
-
-	// QK norm
-	q = applyQKNorm(q, block.Attn.NormQ)
-	k = applyQKNorm(k, block.Attn.NormK)
-
-	// Apply RoPE
-	q = ApplyRoPE4D(q, cos, sin)
-	k = ApplyRoPE4D(k, cos, sin)
-
-	// Transpose for SDPA
-	q = mlx.Transpose(q, 0, 2, 1, 3)
-	k = mlx.Transpose(k, 0, 2, 1, 3)
-	v = mlx.Transpose(v, 0, 2, 1, 3)
-
-	// SDPA
-	attnOut := mlx.ScaledDotProductAttention(q, k, v, block.Scale, false)
-
-	// Transpose back and reshape
-	attnOut = mlx.Transpose(attnOut, 0, 2, 1, 3)
-	attnOut = mlx.Reshape(attnOut, B, L, block.InnerDim)
-
-	// MLP: SwiGLU
-	mlpShape := mlpIn.Shape()
-	half := mlpShape[2] / 2
-	mlpGate := mlx.Slice(mlpIn, []int32{0, 0, 0}, []int32{B, L, half})
-	mlpUp := mlx.Slice(mlpIn, []int32{0, 0, half}, []int32{B, L, mlpShape[2]})
-	mlpOut := mlx.Mul(mlx.SiLU(mlpGate), mlpUp)
-
-	// Concatenate attention and MLP for fused output
-	combined := mlx.Concatenate([]*mlx.Array{attnOut, mlpOut}, 2)
-
-	// Output projection
-	out := block.Attn.ToOut.Forward(combined)
-
-	// Apply gate and residual
-	return mlx.Add(x, mlx.Mul(gate, out))
-}
-
-// NormOut implements the output normalization with modulation
-// Weight names: norm_out.linear.weight
-type NormOut struct {
-	Linear nn.LinearLayer `weight:"linear"`
-}
-
-// Forward computes final modulated output
-func (n *NormOut) Forward(x *mlx.Array, temb *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	dim := shape[2]
-
-	// Modulation: temb -> silu -> linear -> [shift, scale]
-	mod := mlx.SiLU(temb)
-	mod = n.Linear.Forward(mod)
-
-	// Split into scale and shift (diffusers order: scale first, shift second)
-	scale := mlx.Slice(mod, []int32{0, 0}, []int32{B, dim})
-	shift := mlx.Slice(mod, []int32{0, dim}, []int32{B, 2 * dim})
-	shift = mlx.ExpandDims(shift, 1)
-	scale = mlx.ExpandDims(scale, 1)
-
-	// Modulate with RMSNorm
-	return modulateLayerNorm(x, shift, scale)
-}
-
-// Flux2Transformer2DModel is the main Flux2 transformer
-// Weight names at top level: time_guidance_embed.*, double_stream_modulation_*.*, etc.
-type Flux2Transformer2DModel struct {
-	// Timestep embedding
-	TimeGuidanceEmbed *TimeGuidanceEmbed `weight:"time_guidance_embed"`
-
-	// Shared modulation
-	DoubleStreamModulationImg *Modulation `weight:"double_stream_modulation_img"`
-	DoubleStreamModulationTxt *Modulation `weight:"double_stream_modulation_txt"`
-	SingleStreamModulation    *Modulation `weight:"single_stream_modulation"`
-
-	// Embedders
-	XEmbedder       nn.LinearLayer `weight:"x_embedder"`
-	ContextEmbedder nn.LinearLayer `weight:"context_embedder"`
-
-	// Transformer blocks
-	TransformerBlocks       []*TransformerBlock       `weight:"transformer_blocks"`
-	SingleTransformerBlocks []*SingleTransformerBlock `weight:"single_transformer_blocks"`
-
-	// Output
-	NormOut *NormOut       `weight:"norm_out"`
-	ProjOut nn.LinearLayer `weight:"proj_out"`
-
-	*TransformerConfig
-}
-
-// Load loads the Flux2 transformer from ollama blob storage.
-func (m *Flux2Transformer2DModel) Load(manifest *imagegen.ModelManifest) error {
-	fmt.Print("  Loading transformer... ")
-
-	// Load config from blob
-	var cfg TransformerConfig
-	if err := manifest.ReadConfigJSON("transformer/config.json", &cfg); err != nil {
-		return fmt.Errorf("config: %w", err)
-	}
-	m.TransformerConfig = &cfg
-
-	// Initialize slices
-	m.TransformerBlocks = make([]*TransformerBlock, cfg.NumLayers)
-	m.SingleTransformerBlocks = make([]*SingleTransformerBlock, cfg.NumSingleLayers)
-
-	// Initialize TimeGuidanceEmbed with embed dim
-	m.TimeGuidanceEmbed = &TimeGuidanceEmbed{
-		TimestepEmbedder: &TimestepEmbedder{EmbedDim: cfg.TimestepGuidanceChannels},
-	}
-
-	// Load weights from tensor blobs
-	weights, err := imagegen.LoadWeightsFromManifest(manifest, "transformer")
-	if err != nil {
-		return fmt.Errorf("weights: %w", err)
-	}
-	if err := weights.Load(0); err != nil {
-		return fmt.Errorf("load weights: %w", err)
-	}
-	defer weights.ReleaseAll()
-
-	return m.loadWeights(weights)
-}
-
-// loadWeights loads weights from any WeightSource into the model
-func (m *Flux2Transformer2DModel) loadWeights(weights safetensors.WeightSource) error {
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return fmt.Errorf("load module: %w", err)
-	}
-	m.initComputedFields()
-	fmt.Println("✓")
-	return nil
-}
-
-// initComputedFields initializes computed fields after loading weights
-func (m *Flux2Transformer2DModel) initComputedFields() {
-	cfg := m.TransformerConfig
-	innerDim := cfg.InnerDim()
-	scale := float32(1.0 / math.Sqrt(float64(cfg.AttentionHeadDim)))
-
-	// Initialize transformer blocks
-	for _, block := range m.TransformerBlocks {
-		block.NHeads = cfg.NumAttentionHeads
-		block.HeadDim = cfg.AttentionHeadDim
-		block.Scale = scale
-	}
-
-	// Initialize single transformer blocks
-	for _, block := range m.SingleTransformerBlocks {
-		block.NHeads = cfg.NumAttentionHeads
-		block.HeadDim = cfg.AttentionHeadDim
-		block.InnerDim = innerDim
-		block.MLPHidDim = cfg.MLPHiddenDim()
-		block.Scale = scale
-	}
-}
-
-// Forward runs the Flux2 transformer
-func (m *Flux2Transformer2DModel) Forward(patches, txtEmbeds *mlx.Array, timesteps *mlx.Array, rope *RoPECache) *mlx.Array {
-	patchShape := patches.Shape()
-	B := patchShape[0]
-	imgLen := patchShape[1]
-	txtLen := txtEmbeds.Shape()[1]
-
-	// Scale timestep to 0-1000 range (diffusers multiplies by 1000)
-	scaledTimesteps := mlx.MulScalar(timesteps, 1000.0)
-
-	// Compute timestep embedding
-	temb := m.TimeGuidanceEmbed.Forward(scaledTimesteps)
-
-	// Embed patches and text
-	imgHidden := m.XEmbedder.Forward(patches)
-	txtHidden := m.ContextEmbedder.Forward(txtEmbeds)
-
-	// Compute shared modulation
-	imgMod := m.DoubleStreamModulationImg.Forward(temb)
-	txtMod := m.DoubleStreamModulationTxt.Forward(temb)
-	singleMod := m.SingleStreamModulation.Forward(temb)
-
-	// Double (dual-stream) blocks
-	for _, block := range m.TransformerBlocks {
-		imgHidden, txtHidden = block.Forward(imgHidden, txtHidden, imgMod, txtMod, rope.Cos, rope.Sin)
-	}
-
-	// Concatenate for single-stream: text first, then image
-	hidden := mlx.Concatenate([]*mlx.Array{txtHidden, imgHidden}, 1)
-
-	// Single-stream blocks
-	for _, block := range m.SingleTransformerBlocks {
-		hidden = block.Forward(hidden, singleMod, rope.Cos, rope.Sin)
-	}
-
-	// Extract image portion
-	totalLen := txtLen + imgLen
-	imgOut := mlx.Slice(hidden, []int32{0, txtLen, 0}, []int32{B, totalLen, hidden.Shape()[2]})
-
-	// Final norm and projection
-	imgOut = m.NormOut.Forward(imgOut, temb)
-	return m.ProjOut.Forward(imgOut)
-}
-
-// Note: QK normalization uses mlx.RMSNorm (the fast version) directly
-// See applyQKNorm function below
-
-// compiledSwiGLU fuses: silu(gate) * up
-// Called 30x per step (10 in dual-stream + 20 in single-stream blocks)
-var compiledSwiGLU *mlx.CompiledFunc
-
-func getCompiledSwiGLU() *mlx.CompiledFunc {
-	if compiledSwiGLU == nil {
-		compiledSwiGLU = mlx.CompileShapeless(func(inputs []*mlx.Array) []*mlx.Array {
-			gate, up := inputs[0], inputs[1]
-			return []*mlx.Array{mlx.Mul(mlx.SiLU(gate), up)}
-		}, true)
-	}
-	return compiledSwiGLU
-}
-
-// Helper functions
-
-// parseModulation3 extracts 3 modulation params (shift, scale, gate) starting at offset
-func parseModulation3(mod *mlx.Array, dim int32, offset int32) (*mlx.Array, *mlx.Array, *mlx.Array) {
-	B := mod.Shape()[0]
-	start := offset * dim
-	shift := mlx.Slice(mod, []int32{0, start}, []int32{B, start + dim})
-	scale := mlx.Slice(mod, []int32{0, start + dim}, []int32{B, start + 2*dim})
-	gate := mlx.Slice(mod, []int32{0, start + 2*dim}, []int32{B, start + 3*dim})
-
-	// Expand for broadcasting [B, dim] -> [B, 1, dim]
-	shift = mlx.ExpandDims(shift, 1)
-	scale = mlx.ExpandDims(scale, 1)
-	gate = mlx.ExpandDims(gate, 1)
-
-	return shift, scale, gate
-}
-
-// modulateLayerNorm applies LayerNorm then shift/scale modulation
-// Diffusers uses LayerNorm(elementwise_affine=False) which centers the data
-func modulateLayerNorm(x *mlx.Array, shift, scale *mlx.Array) *mlx.Array {
-	// Fast LayerNorm without learnable params
-	x = mlx.LayerNorm(x, 1e-6)
-
-	// Modulate: x * (1 + scale) + shift
-	x = mlx.Mul(x, mlx.AddScalar(scale, 1.0))
-	return mlx.Add(x, shift)
-}
-
-// splitQKV splits a fused QKV tensor into Q, K, V
-func splitQKV(qkv *mlx.Array, B, L, dim int32) (*mlx.Array, *mlx.Array, *mlx.Array) {
-	q := mlx.Slice(qkv, []int32{0, 0, 0}, []int32{B, L, dim})
-	k := mlx.Slice(qkv, []int32{0, 0, dim}, []int32{B, L, 2 * dim})
-	v := mlx.Slice(qkv, []int32{0, 0, 2 * dim}, []int32{B, L, 3 * dim})
-	return q, k, v
-}
-
-// applyQKNorm applies RMSNorm with learned scale (no bias)
-// Uses the optimized mlx_fast_rms_norm
-func applyQKNorm(x *mlx.Array, scale *mlx.Array) *mlx.Array {
-	return mlx.RMSNorm(x, scale, 1e-6)
-}

x/imagegen/models/flux2/vae.go

@@ -1,804 +0,0 @@
-//go:build mlx
-
-package flux2
-
-import (
-	"fmt"
-	"math"
-
-	"github.com/ollama/ollama/x/imagegen"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/nn"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-	"github.com/ollama/ollama/x/imagegen/vae"
-)
-
-// VAEConfig holds AutoencoderKLFlux2 configuration
-type VAEConfig struct {
-	ActFn             string  `json:"act_fn"`              // "silu"
-	BatchNormEps      float32 `json:"batch_norm_eps"`      // 0.0001
-	BatchNormMomentum float32 `json:"batch_norm_momentum"` // 0.1
-	BlockOutChannels  []int32 `json:"block_out_channels"`  // [128, 256, 512, 512]
-	ForceUpcast       bool    `json:"force_upcast"`        // true
-	InChannels        int32   `json:"in_channels"`         // 3
-	LatentChannels    int32   `json:"latent_channels"`     // 32
-	LayersPerBlock    int32   `json:"layers_per_block"`    // 2
-	MidBlockAddAttn   bool    `json:"mid_block_add_attention"` // true
-	NormNumGroups     int32   `json:"norm_num_groups"`     // 32
-	OutChannels       int32   `json:"out_channels"`        // 3
-	PatchSize         []int32 `json:"patch_size"`          // [2, 2]
-	SampleSize        int32   `json:"sample_size"`         // 1024
-	UsePostQuantConv  bool    `json:"use_post_quant_conv"` // true
-	UseQuantConv      bool    `json:"use_quant_conv"`      // true
-}
-
-// BatchNorm2D implements 2D batch normalization with running statistics
-type BatchNorm2D struct {
-	RunningMean *mlx.Array // [C]
-	RunningVar  *mlx.Array // [C]
-	Weight      *mlx.Array // [C] gamma
-	Bias        *mlx.Array // [C] beta
-	Eps         float32
-	Momentum    float32
-}
-
-// Forward applies batch normalization (inference mode - uses running stats)
-// Input and output are in NHWC format [B, H, W, C]
-func (bn *BatchNorm2D) Forward(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	C := shape[3]
-
-	// Reshape stats for broadcasting [1, 1, 1, C]
-	mean := mlx.Reshape(bn.RunningMean, 1, 1, 1, C)
-	variance := mlx.Reshape(bn.RunningVar, 1, 1, 1, C)
-
-	// Normalize: (x - mean) / sqrt(var + eps)
-	xNorm := mlx.Sub(x, mean)
-	xNorm = mlx.Div(xNorm, mlx.Sqrt(mlx.AddScalar(variance, bn.Eps)))
-
-	// Scale and shift (only if affine=True)
-	if bn.Weight != nil {
-		weight := mlx.Reshape(bn.Weight, 1, 1, 1, C)
-		xNorm = mlx.Mul(xNorm, weight)
-	}
-	if bn.Bias != nil {
-		bias := mlx.Reshape(bn.Bias, 1, 1, 1, C)
-		xNorm = mlx.Add(xNorm, bias)
-	}
-
-	return xNorm
-}
-
-// Denormalize inverts the batch normalization
-// Used when decoding latents
-func (bn *BatchNorm2D) Denormalize(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	C := shape[3]
-
-	// Reshape stats for broadcasting [1, 1, 1, C]
-	mean := mlx.Reshape(bn.RunningMean, 1, 1, 1, C)
-	variance := mlx.Reshape(bn.RunningVar, 1, 1, 1, C)
-
-	// Inverse: first undo affine, then undo normalization
-	// For affine=False: x_denorm = x * sqrt(var + eps) + mean
-	if bn.Bias != nil {
-		bias := mlx.Reshape(bn.Bias, 1, 1, 1, C)
-		x = mlx.Sub(x, bias)
-	}
-	if bn.Weight != nil {
-		weight := mlx.Reshape(bn.Weight, 1, 1, 1, C)
-		x = mlx.Div(x, weight)
-	}
-	x = mlx.Mul(x, mlx.Sqrt(mlx.AddScalar(variance, bn.Eps)))
-	x = mlx.Add(x, mean)
-
-	return x
-}
-
-// GroupNormLayer implements group normalization
-// Reused from zimage package pattern
-type GroupNormLayer struct {
-	Weight    *mlx.Array `weight:"weight"`
-	Bias      *mlx.Array `weight:"bias"`
-	NumGroups int32
-	Eps       float32
-}
-
-// Forward applies group normalization
-// Input and output are in NHWC format [B, H, W, C]
-func (gn *GroupNormLayer) Forward(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	H := shape[1]
-	W := shape[2]
-	C := shape[3]
-
-	// Reshape to [B, H, W, groups, C/groups]
-	groupSize := C / gn.NumGroups
-	x = mlx.Reshape(x, B, H, W, gn.NumGroups, groupSize)
-
-	// Compute mean and variance per group
-	mean := mlx.Mean(x, 1, true)
-	mean = mlx.Mean(mean, 2, true)
-	mean = mlx.Mean(mean, 4, true)
-
-	xCentered := mlx.Sub(x, mean)
-
-	sq := mlx.Square(xCentered)
-	variance := mlx.Mean(sq, 1, true)
-	variance = mlx.Mean(variance, 2, true)
-	variance = mlx.Mean(variance, 4, true)
-
-	// Normalize
-	xNorm := mlx.Div(xCentered, mlx.Sqrt(mlx.AddScalar(variance, gn.Eps)))
-
-	// Reshape back to [B, H, W, C]
-	xNorm = mlx.Reshape(xNorm, B, H, W, C)
-
-	// Scale and shift
-	if gn.Weight != nil {
-		weight := mlx.Reshape(gn.Weight, 1, 1, 1, C)
-		xNorm = mlx.Mul(xNorm, weight)
-	}
-	if gn.Bias != nil {
-		bias := mlx.Reshape(gn.Bias, 1, 1, 1, C)
-		xNorm = mlx.Add(xNorm, bias)
-	}
-
-	return xNorm
-}
-
-// Conv2D represents a 2D convolution layer (reused pattern)
-type Conv2D struct {
-	Weight  *mlx.Array `weight:"weight"`
-	Bias    *mlx.Array `weight:"bias,optional"`
-	Stride  int32
-	Padding int32
-}
-
-// Transform implements safetensors.Transformer to transpose weights from PyTorch's OIHW to MLX's OHWI.
-func (conv *Conv2D) Transform(field string, arr *mlx.Array) *mlx.Array {
-	if field == "Weight" {
-		return mlx.Transpose(arr, 0, 2, 3, 1)
-	}
-	return arr
-}
-
-// Forward applies convolution (NHWC format)
-func (conv *Conv2D) Forward(x *mlx.Array) *mlx.Array {
-	out := mlx.Conv2d(x, conv.Weight, conv.Stride, conv.Padding)
-
-	if conv.Bias != nil {
-		bias := mlx.Reshape(conv.Bias, 1, 1, 1, conv.Bias.Dim(0))
-		out = mlx.Add(out, bias)
-	}
-
-	return out
-}
-
-// ResnetBlock2D implements a ResNet block for VAE
-type ResnetBlock2D struct {
-	Norm1        *GroupNormLayer `weight:"norm1"`
-	Conv1        *Conv2D         `weight:"conv1"`
-	Norm2        *GroupNormLayer `weight:"norm2"`
-	Conv2        *Conv2D         `weight:"conv2"`
-	ConvShortcut *Conv2D         `weight:"conv_shortcut,optional"`
-}
-
-// Forward applies the ResNet block
-func (rb *ResnetBlock2D) Forward(x *mlx.Array) *mlx.Array {
-	h := rb.Norm1.Forward(x)
-	h = mlx.SiLU(h)
-	h = rb.Conv1.Forward(h)
-
-	h = rb.Norm2.Forward(h)
-	h = mlx.SiLU(h)
-	h = rb.Conv2.Forward(h)
-
-	if rb.ConvShortcut != nil {
-		x = rb.ConvShortcut.Forward(x)
-	}
-
-	return mlx.Add(h, x)
-}
-
-// VAEAttentionBlock implements self-attention for VAE
-type VAEAttentionBlock struct {
-	GroupNorm *GroupNormLayer `weight:"group_norm"`
-	ToQ       nn.LinearLayer  `weight:"to_q"`
-	ToK       nn.LinearLayer  `weight:"to_k"`
-	ToV       nn.LinearLayer  `weight:"to_v"`
-	ToOut     nn.LinearLayer  `weight:"to_out.0"`
-}
-
-// Forward applies attention (NHWC format)
-func (ab *VAEAttentionBlock) Forward(x *mlx.Array) *mlx.Array {
-	residual := x
-	shape := x.Shape()
-	B := shape[0]
-	H := shape[1]
-	W := shape[2]
-	C := shape[3]
-
-	h := ab.GroupNorm.Forward(x)
-	h = mlx.Reshape(h, B, H*W, C)
-
-	q := ab.ToQ.Forward(h)
-	k := ab.ToK.Forward(h)
-	v := ab.ToV.Forward(h)
-
-	q = mlx.ExpandDims(q, 1)
-	k = mlx.ExpandDims(k, 1)
-	v = mlx.ExpandDims(v, 1)
-
-	scale := float32(1.0 / math.Sqrt(float64(C)))
-	out := mlx.ScaledDotProductAttention(q, k, v, scale, false)
-	out = mlx.Squeeze(out, 1)
-
-	out = ab.ToOut.Forward(out)
-	out = mlx.Reshape(out, B, H, W, C)
-	out = mlx.Add(out, residual)
-
-	return out
-}
-
-// UpDecoderBlock2D implements an upsampling decoder block
-type UpDecoderBlock2D struct {
-	ResnetBlocks []*ResnetBlock2D
-	Upsample     *Conv2D
-}
-
-// Forward applies the up decoder block
-func (ub *UpDecoderBlock2D) Forward(x *mlx.Array) *mlx.Array {
-	for _, resnet := range ub.ResnetBlocks {
-		x = resnet.Forward(x)
-	}
-
-	if ub.Upsample != nil {
-		x = upsample2x(x)
-		x = ub.Upsample.Forward(x)
-	}
-
-	return x
-}
-
-// upsample2x performs 2x nearest neighbor upsampling
-func upsample2x(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	H := shape[1]
-	W := shape[2]
-
-	hIdx := mlx.ArangeInt(0, H, 1, mlx.DtypeInt32)
-	hIdx = mlx.Reshape(hIdx, H, 1)
-	hIdx = mlx.BroadcastTo(hIdx, []int32{H, 2})
-	hIdx = mlx.Reshape(hIdx, H*2)
-
-	wIdx := mlx.ArangeInt(0, W, 1, mlx.DtypeInt32)
-	wIdx = mlx.Reshape(wIdx, W, 1)
-	wIdx = mlx.BroadcastTo(wIdx, []int32{W, 2})
-	wIdx = mlx.Reshape(wIdx, W*2)
-
-	x = mlx.Take(x, hIdx, 1)
-	x = mlx.Take(x, wIdx, 2)
-
-	return x
-}
-
-// VAEMidBlock is the middle block with attention
-type VAEMidBlock struct {
-	Resnet1   *ResnetBlock2D
-	Attention *VAEAttentionBlock
-	Resnet2   *ResnetBlock2D
-}
-
-// Forward applies the mid block
-func (mb *VAEMidBlock) Forward(x *mlx.Array) *mlx.Array {
-	x = mb.Resnet1.Forward(x)
-	x = mb.Attention.Forward(x)
-	x = mb.Resnet2.Forward(x)
-	return x
-}
-
-// DefaultTilingConfig returns reasonable defaults for tiled decoding
-// Matches diffusers: tile_latent_min_size=64, tile_overlap_factor=0.25
-func DefaultTilingConfig() *vae.TilingConfig {
-	return vae.DefaultTilingConfig()
-}
-
-// AutoencoderKLFlux2 is the Flux2 VAE with BatchNorm
-type AutoencoderKLFlux2 struct {
-	Config *VAEConfig
-
-	// Encoder components (for image editing)
-	EncoderConvIn  *Conv2D
-	EncoderMid     *VAEMidBlock
-	EncoderDown    []*DownEncoderBlock2D
-	EncoderNormOut *GroupNormLayer
-	EncoderConvOut *Conv2D
-
-	// Decoder components
-	DecoderConvIn  *Conv2D
-	DecoderMid     *VAEMidBlock
-	DecoderUp      []*UpDecoderBlock2D
-	DecoderNormOut *GroupNormLayer
-	DecoderConvOut *Conv2D
-
-	// Quant conv layers
-	QuantConv     *Conv2D
-	PostQuantConv *Conv2D
-
-	// BatchNorm for latent normalization
-	LatentBN *BatchNorm2D
-
-	// Tiling configuration (nil = no tiling)
-	Tiling *vae.TilingConfig
-}
-
-// DownEncoderBlock2D implements a downsampling encoder block
-type DownEncoderBlock2D struct {
-	ResnetBlocks []*ResnetBlock2D
-	Downsample   *Conv2D
-}
-
-// Forward applies the down encoder block
-func (db *DownEncoderBlock2D) Forward(x *mlx.Array) *mlx.Array {
-	for _, resnet := range db.ResnetBlocks {
-		x = resnet.Forward(x)
-	}
-
-	if db.Downsample != nil {
-		// Pad then conv with stride 2
-		x = mlx.Pad(x, []int32{0, 0, 0, 1, 0, 1, 0, 0})
-		x = db.Downsample.Forward(x)
-	}
-
-	return x
-}
-
-// Load loads the Flux2 VAE from ollama blob storage.
-func (m *AutoencoderKLFlux2) Load(manifest *imagegen.ModelManifest) error {
-	fmt.Print("  Loading VAE... ")
-
-	// Load config from blob
-	var cfg VAEConfig
-	if err := manifest.ReadConfigJSON("vae/config.json", &cfg); err != nil {
-		return fmt.Errorf("config: %w", err)
-	}
-	m.Config = &cfg
-
-	// Load weights from tensor blobs
-	weights, err := imagegen.LoadWeightsFromManifest(manifest, "vae")
-	if err != nil {
-		return fmt.Errorf("weights: %w", err)
-	}
-	if err := weights.Load(0); err != nil {
-		return fmt.Errorf("load weights: %w", err)
-	}
-	defer weights.ReleaseAll()
-
-	return m.loadWeights(weights, &cfg)
-}
-
-// loadWeights loads VAE weights from any WeightSource
-func (m *AutoencoderKLFlux2) loadWeights(weights safetensors.WeightSource, cfg *VAEConfig) error {
-	var err error
-
-	// Load encoder components (for image conditioning)
-	if err := m.loadEncoderWeights(weights, cfg); err != nil {
-		return fmt.Errorf("encoder: %w", err)
-	}
-
-	// Load decoder conv_in
-	m.DecoderConvIn = &Conv2D{Stride: 1, Padding: 1}
-	if err := safetensors.LoadModule(m.DecoderConvIn, weights, "decoder.conv_in"); err != nil {
-		return fmt.Errorf("decoder.conv_in: %w", err)
-	}
-
-	// Load mid block
-	m.DecoderMid, err = loadVAEMidBlock(weights, "decoder.mid_block", cfg.NormNumGroups)
-	if err != nil {
-		return fmt.Errorf("decoder.mid_block: %w", err)
-	}
-
-	// Load up blocks
-	numBlocks := len(cfg.BlockOutChannels)
-	m.DecoderUp = make([]*UpDecoderBlock2D, numBlocks)
-	for i := 0; i < numBlocks; i++ {
-		prefix := fmt.Sprintf("decoder.up_blocks.%d", i)
-		hasUpsample := i < numBlocks-1
-		m.DecoderUp[i], err = loadUpDecoderBlock2D(weights, prefix, cfg.LayersPerBlock+1, cfg.NormNumGroups, hasUpsample)
-		if err != nil {
-			return fmt.Errorf("%s: %w", prefix, err)
-		}
-	}
-
-	// Load decoder conv_norm_out and conv_out
-	m.DecoderNormOut = &GroupNormLayer{NumGroups: cfg.NormNumGroups, Eps: 1e-5}
-	if err := safetensors.LoadModule(m.DecoderNormOut, weights, "decoder.conv_norm_out"); err != nil {
-		return fmt.Errorf("decoder.conv_norm_out: %w", err)
-	}
-
-	m.DecoderConvOut = &Conv2D{Stride: 1, Padding: 1}
-	if err := safetensors.LoadModule(m.DecoderConvOut, weights, "decoder.conv_out"); err != nil {
-		return fmt.Errorf("decoder.conv_out: %w", err)
-	}
-
-	// Load post_quant_conv
-	if cfg.UsePostQuantConv {
-		m.PostQuantConv = &Conv2D{Stride: 1, Padding: 0}
-		if err := safetensors.LoadModule(m.PostQuantConv, weights, "post_quant_conv"); err != nil {
-			return fmt.Errorf("post_quant_conv: %w", err)
-		}
-	}
-
-	// Load latent BatchNorm (affine=False, so no weight/bias)
-	bnMean, err := weights.GetTensor("bn.running_mean")
-	if err != nil {
-		return fmt.Errorf("bn.running_mean: %w", err)
-	}
-	bnVar, err := weights.GetTensor("bn.running_var")
-	if err != nil {
-		return fmt.Errorf("bn.running_var: %w", err)
-	}
-	m.LatentBN = &BatchNorm2D{
-		RunningMean: bnMean,
-		RunningVar:  bnVar,
-		Weight:      nil, // affine=False
-		Bias:        nil, // affine=False
-		Eps:         cfg.BatchNormEps,
-		Momentum:    cfg.BatchNormMomentum,
-	}
-
-	fmt.Println("✓")
-	return nil
-}
-
-// loadVAEMidBlock loads the mid block.
-func loadVAEMidBlock(weights safetensors.WeightSource, prefix string, numGroups int32) (*VAEMidBlock, error) {
-	resnet1, err := loadResnetBlock2D(weights, prefix+".resnets.0", numGroups)
-	if err != nil {
-		return nil, err
-	}
-
-	attention, err := loadVAEAttentionBlock(weights, prefix+".attentions.0", numGroups)
-	if err != nil {
-		return nil, err
-	}
-
-	resnet2, err := loadResnetBlock2D(weights, prefix+".resnets.1", numGroups)
-	if err != nil {
-		return nil, err
-	}
-
-	return &VAEMidBlock{
-		Resnet1:   resnet1,
-		Attention: attention,
-		Resnet2:   resnet2,
-	}, nil
-}
-
-// loadResnetBlock2D loads a ResNet block.
-func loadResnetBlock2D(weights safetensors.WeightSource, prefix string, numGroups int32) (*ResnetBlock2D, error) {
-	block := &ResnetBlock2D{
-		Norm1:        &GroupNormLayer{NumGroups: numGroups, Eps: 1e-5},
-		Conv1:        &Conv2D{Stride: 1, Padding: 1},
-		Norm2:        &GroupNormLayer{NumGroups: numGroups, Eps: 1e-5},
-		Conv2:        &Conv2D{Stride: 1, Padding: 1},
-		ConvShortcut: &Conv2D{Stride: 1, Padding: 0}, // Pre-allocate for optional loading
-	}
-	if err := safetensors.LoadModule(block, weights, prefix); err != nil {
-		return nil, err
-	}
-	// If ConvShortcut wasn't loaded (no weights found), nil it out
-	if block.ConvShortcut.Weight == nil {
-		block.ConvShortcut = nil
-	}
-	return block, nil
-}
-
-// loadVAEAttentionBlock loads an attention block using LoadModule.
-func loadVAEAttentionBlock(weights safetensors.WeightSource, prefix string, numGroups int32) (*VAEAttentionBlock, error) {
-	ab := &VAEAttentionBlock{
-		GroupNorm: &GroupNormLayer{NumGroups: numGroups, Eps: 1e-5},
-	}
-	if err := safetensors.LoadModule(ab, weights, prefix); err != nil {
-		return nil, err
-	}
-	return ab, nil
-}
-
-// loadUpDecoderBlock2D loads an up decoder block.
-func loadUpDecoderBlock2D(weights safetensors.WeightSource, prefix string, numLayers, numGroups int32, hasUpsample bool) (*UpDecoderBlock2D, error) {
-	resnets := make([]*ResnetBlock2D, numLayers)
-	for i := int32(0); i < numLayers; i++ {
-		resPrefix := fmt.Sprintf("%s.resnets.%d", prefix, i)
-		resnet, err := loadResnetBlock2D(weights, resPrefix, numGroups)
-		if err != nil {
-			return nil, err
-		}
-		resnets[i] = resnet
-	}
-
-	var upsample *Conv2D
-	if hasUpsample {
-		upsample = &Conv2D{Stride: 1, Padding: 1}
-		if err := safetensors.LoadModule(upsample, weights, prefix+".upsamplers.0.conv"); err != nil {
-			return nil, err
-		}
-	}
-
-	return &UpDecoderBlock2D{
-		ResnetBlocks: resnets,
-		Upsample:     upsample,
-	}, nil
-}
-
-// Patchify converts latents [B, C, H, W] to patches [B, H*W/4, C*4] using 2x2 patches
-// This is the inverse of the VAE's patchify for feeding to transformer
-func (vae *AutoencoderKLFlux2) Patchify(latents *mlx.Array) *mlx.Array {
-	shape := latents.Shape()
-	B := shape[0]
-	C := shape[1]
-	H := shape[2]
-	W := shape[3]
-
-	patchH := vae.Config.PatchSize[0]
-	patchW := vae.Config.PatchSize[1]
-
-	pH := H / patchH
-	pW := W / patchW
-
-	// [B, C, H, W] -> [B, C, pH, patchH, pW, patchW]
-	x := mlx.Reshape(latents, B, C, pH, patchH, pW, patchW)
-	// [B, C, pH, patchH, pW, patchW] -> [B, pH, pW, C, patchH, patchW]
-	x = mlx.Transpose(x, 0, 2, 4, 1, 3, 5)
-	// [B, pH, pW, C, patchH, patchW] -> [B, pH*pW, C*patchH*patchW]
-	return mlx.Reshape(x, B, pH*pW, C*patchH*patchW)
-}
-
-// Unpatchify converts patches [B, L, C*4] back to [B, C, H, W]
-func (vae *AutoencoderKLFlux2) Unpatchify(patches *mlx.Array, pH, pW, C int32) *mlx.Array {
-	shape := patches.Shape()
-	B := shape[0]
-
-	patchH := vae.Config.PatchSize[0]
-	patchW := vae.Config.PatchSize[1]
-
-	// [B, pH*pW, C*patchH*patchW] -> [B, pH, pW, C, patchH, patchW]
-	x := mlx.Reshape(patches, B, pH, pW, C, patchH, patchW)
-	// [B, pH, pW, C, patchH, patchW] -> [B, C, pH, patchH, pW, patchW]
-	x = mlx.Transpose(x, 0, 3, 1, 4, 2, 5)
-	// [B, C, pH, patchH, pW, patchW] -> [B, C, H, W]
-	H := pH * patchH
-	W := pW * patchW
-	return mlx.Reshape(x, B, C, H, W)
-}
-
-// denormalizePatchified applies inverse batch normalization to patchified latents.
-// Input: [B, L, 128] where 128 = 32 latent channels * 4 (2x2 patch)
-// Output: [B, L, 128] denormalized
-func (vae *AutoencoderKLFlux2) denormalizePatchified(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	C := shape[2] // 128
-
-	// Reshape stats for broadcasting [1, 1, C]
-	mean := mlx.Reshape(vae.LatentBN.RunningMean, 1, 1, C)
-	variance := mlx.Reshape(vae.LatentBN.RunningVar, 1, 1, C)
-
-	// Inverse BN (affine=False): x_denorm = x * sqrt(var + eps) + mean
-	if vae.LatentBN.Bias != nil {
-		bias := mlx.Reshape(vae.LatentBN.Bias, 1, 1, C)
-		x = mlx.Sub(x, bias)
-	}
-	if vae.LatentBN.Weight != nil {
-		weight := mlx.Reshape(vae.LatentBN.Weight, 1, 1, C)
-		x = mlx.Div(x, weight)
-	}
-	x = mlx.Mul(x, mlx.Sqrt(mlx.AddScalar(variance, vae.LatentBN.Eps)))
-	x = mlx.Add(x, mean)
-
-	return x
-}
-
-// Decode decodes latent patches to images.
-// If Tiling is set, uses tiled decoding to reduce memory for large images.
-// latents: [B, L, C*4] patchified latents from transformer
-// pH, pW: patch grid dimensions
-// Returns: [B, 3, H, W] image tensor
-func (v *AutoencoderKLFlux2) Decode(latents *mlx.Array, pH, pW int32) *mlx.Array {
-	// Denormalize patchified latents
-	z := v.denormalizePatchified(latents)
-
-	// Unpatchify: [B, L, C*4] -> [B, C, H, W]
-	z = v.Unpatchify(z, pH, pW, v.Config.LatentChannels)
-
-	// Convert NCHW -> NHWC for processing
-	z = mlx.Transpose(z, 0, 2, 3, 1)
-
-	// Use tiled decoding if enabled
-	if v.Tiling != nil {
-		mlx.Eval(z)
-		return vae.DecodeTiled(z, v.Tiling, v.decodeTile)
-	}
-
-	// Direct decode (no tiling)
-	h := v.decodeTile(z)
-	h = mlx.ClipScalar(h, 0.0, 1.0, true, true)
-	h = mlx.Transpose(h, 0, 3, 1, 2)
-	return h
-}
-
-// decodeTile decodes a single latent tile to pixels (internal helper)
-// z: [B, H, W, C] latent tile in NHWC format
-// Returns: [B, H*8, W*8, 3] pixel tile in NHWC format (before clipping)
-func (vae *AutoencoderKLFlux2) decodeTile(z *mlx.Array) *mlx.Array {
-	// Post-quant conv
-	if vae.PostQuantConv != nil {
-		z = vae.PostQuantConv.Forward(z)
-	}
-
-	// Decoder
-	h := vae.DecoderConvIn.Forward(z)
-	h = vae.DecoderMid.Forward(h)
-
-	for _, upBlock := range vae.DecoderUp {
-		h = upBlock.Forward(h)
-	}
-
-	h = vae.DecoderNormOut.Forward(h)
-	h = mlx.SiLU(h)
-	h = vae.DecoderConvOut.Forward(h)
-
-	// VAE outputs [-1, 1], convert to [0, 1]
-	h = mlx.MulScalar(h, 0.5)
-	h = mlx.AddScalar(h, 0.5)
-
-	return h
-}
-
-// loadEncoderWeights loads the encoder components for image conditioning
-func (m *AutoencoderKLFlux2) loadEncoderWeights(weights safetensors.WeightSource, cfg *VAEConfig) error {
-	var err error
-
-	// Load encoder conv_in
-	m.EncoderConvIn = &Conv2D{Stride: 1, Padding: 1}
-	if err := safetensors.LoadModule(m.EncoderConvIn, weights, "encoder.conv_in"); err != nil {
-		return fmt.Errorf("encoder.conv_in: %w", err)
-	}
-
-	// Load encoder down blocks
-	numBlocks := len(cfg.BlockOutChannels)
-	m.EncoderDown = make([]*DownEncoderBlock2D, numBlocks)
-	for i := 0; i < numBlocks; i++ {
-		prefix := fmt.Sprintf("encoder.down_blocks.%d", i)
-		hasDownsample := i < numBlocks-1
-		m.EncoderDown[i], err = loadDownEncoderBlock2D(weights, prefix, cfg.LayersPerBlock, cfg.NormNumGroups, hasDownsample)
-		if err != nil {
-			return fmt.Errorf("%s: %w", prefix, err)
-		}
-	}
-
-	// Load encoder mid block
-	m.EncoderMid, err = loadVAEMidBlock(weights, "encoder.mid_block", cfg.NormNumGroups)
-	if err != nil {
-		return fmt.Errorf("encoder.mid_block: %w", err)
-	}
-
-	// Load encoder conv_norm_out and conv_out
-	m.EncoderNormOut = &GroupNormLayer{NumGroups: cfg.NormNumGroups, Eps: 1e-5}
-	if err := safetensors.LoadModule(m.EncoderNormOut, weights, "encoder.conv_norm_out"); err != nil {
-		return fmt.Errorf("encoder.conv_norm_out: %w", err)
-	}
-
-	m.EncoderConvOut = &Conv2D{Stride: 1, Padding: 1}
-	if err := safetensors.LoadModule(m.EncoderConvOut, weights, "encoder.conv_out"); err != nil {
-		return fmt.Errorf("encoder.conv_out: %w", err)
-	}
-
-	// Load quant_conv (for encoding)
-	if cfg.UseQuantConv {
-		m.QuantConv = &Conv2D{Stride: 1, Padding: 0}
-		if err := safetensors.LoadModule(m.QuantConv, weights, "quant_conv"); err != nil {
-			return fmt.Errorf("quant_conv: %w", err)
-		}
-	}
-
-	return nil
-}
-
-// loadDownEncoderBlock2D loads a down encoder block.
-func loadDownEncoderBlock2D(weights safetensors.WeightSource, prefix string, numLayers, numGroups int32, hasDownsample bool) (*DownEncoderBlock2D, error) {
-	resnets := make([]*ResnetBlock2D, numLayers)
-	for i := int32(0); i < numLayers; i++ {
-		resPrefix := fmt.Sprintf("%s.resnets.%d", prefix, i)
-		resnet, err := loadResnetBlock2D(weights, resPrefix, numGroups)
-		if err != nil {
-			return nil, err
-		}
-		resnets[i] = resnet
-	}
-
-	var downsample *Conv2D
-	if hasDownsample {
-		downsample = &Conv2D{Stride: 2, Padding: 0}
-		if err := safetensors.LoadModule(downsample, weights, prefix+".downsamplers.0.conv"); err != nil {
-			return nil, err
-		}
-	}
-
-	return &DownEncoderBlock2D{
-		ResnetBlocks: resnets,
-		Downsample:   downsample,
-	}, nil
-}
-
-// EncodeImage encodes an image to normalized latents.
-// image: [B, 3, H, W] image tensor in [-1, 1]
-// Returns: [B, L, C*4] patchified normalized latents
-func (vae *AutoencoderKLFlux2) EncodeImage(image *mlx.Array) *mlx.Array {
-	// Convert NCHW -> NHWC
-	x := mlx.Transpose(image, 0, 2, 3, 1)
-
-	// Encoder
-	h := vae.EncoderConvIn.Forward(x)
-
-	for _, downBlock := range vae.EncoderDown {
-		h = downBlock.Forward(h)
-	}
-
-	h = vae.EncoderMid.Forward(h)
-	h = vae.EncoderNormOut.Forward(h)
-	h = mlx.SiLU(h)
-	h = vae.EncoderConvOut.Forward(h)
-
-	// Quant conv outputs [B, H, W, 2*latent_channels] (mean + logvar)
-	if vae.QuantConv != nil {
-		h = vae.QuantConv.Forward(h)
-	}
-
-	// Take only the mean (first latent_channels) - deterministic encoding
-	// h is [B, H, W, 64] -> take first 32 channels for mean
-	shape := h.Shape()
-	latentChannels := vae.Config.LatentChannels // 32
-	h = mlx.Slice(h, []int32{0, 0, 0, 0}, []int32{shape[0], shape[1], shape[2], latentChannels})
-
-	// Convert NHWC -> NCHW for patchifying
-	h = mlx.Transpose(h, 0, 3, 1, 2)
-
-	// Patchify: [B, C, H, W] -> [B, L, C*4]
-	h = vae.Patchify(h)
-
-	// Apply BatchNorm on patchified latents [B, L, 128]
-	// The BatchNorm has 128 channels matching the patchified dimension
-	h = vae.normalizePatchified(h)
-
-	return h
-}
-
-// normalizePatchified applies batch normalization to patchified latents.
-// Input: [B, L, 128] where 128 = 32 latent channels * 4 (2x2 patch)
-// Output: [B, L, 128] normalized
-func (vae *AutoencoderKLFlux2) normalizePatchified(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	C := shape[2] // 128
-
-	// Reshape stats for broadcasting [1, 1, C]
-	mean := mlx.Reshape(vae.LatentBN.RunningMean, 1, 1, C)
-	variance := mlx.Reshape(vae.LatentBN.RunningVar, 1, 1, C)
-
-	// Normalize: (x - mean) / sqrt(var + eps)
-	xNorm := mlx.Sub(x, mean)
-	xNorm = mlx.Div(xNorm, mlx.Sqrt(mlx.AddScalar(variance, vae.LatentBN.Eps)))
-
-	// Scale and shift (only if affine=True)
-	if vae.LatentBN.Weight != nil {
-		weight := mlx.Reshape(vae.LatentBN.Weight, 1, 1, C)
-		xNorm = mlx.Mul(xNorm, weight)
-	}
-	if vae.LatentBN.Bias != nil {
-		bias := mlx.Reshape(vae.LatentBN.Bias, 1, 1, C)
-		xNorm = mlx.Add(xNorm, bias)
-	}
-
-	return xNorm
-}

x/imagegen/models/qwen3/text_encoder.go

@@ -1,390 +0,0 @@
-//go:build mlx
-
-// Package qwen3 provides a shared Qwen3 text encoder used by multiple image generation models.
-package qwen3
-
-import (
-	"fmt"
-	"math"
-
-	"github.com/ollama/ollama/x/imagegen"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/nn"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-	"github.com/ollama/ollama/x/imagegen/tokenizer"
-)
-
-// Config holds Qwen3 text encoder configuration
-type Config struct {
-	HiddenSize        int32   `json:"hidden_size"`
-	NumHiddenLayers   int32   `json:"num_hidden_layers"`
-	IntermediateSize  int32   `json:"intermediate_size"`
-	NumAttentionHeads int32   `json:"num_attention_heads"`
-	NumKeyValueHeads  int32   `json:"num_key_value_heads"`
-	VocabSize         int32   `json:"vocab_size"`
-	RMSNormEps        float32 `json:"rms_norm_eps"`
-	RopeTheta         float32 `json:"rope_theta"`
-	HeadDim           int32   `json:"head_dim"`
-}
-
-// Attention implements Qwen3 attention with QK norms
-type Attention struct {
-	QProj nn.LinearLayer `weight:"q_proj"`
-	KProj nn.LinearLayer `weight:"k_proj"`
-	VProj nn.LinearLayer `weight:"v_proj"`
-	OProj nn.LinearLayer `weight:"o_proj"`
-	QNorm *nn.RMSNorm    `weight:"q_norm"`
-	KNorm *nn.RMSNorm    `weight:"k_norm"`
-	// Computed fields
-	NHeads    int32
-	NKVHeads  int32
-	HeadDim   int32
-	Scale     float32
-	RopeTheta float32
-}
-
-// applyRoPEQwen3 applies the custom RoPE for Qwen3 text encoder
-func applyRoPEQwen3(x *mlx.Array, seqLen int32, theta float32) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	L := shape[1]
-	H := shape[2]
-	D := shape[3]
-	half := D / 2
-
-	freqsArr := make([]float32, half)
-	logTheta := float32(math.Log(float64(theta)))
-	for i := int32(0); i < half; i++ {
-		freqsArr[i] = float32(math.Exp(float64(-logTheta * float32(i) / float32(half))))
-	}
-	freqs := mlx.NewArray(freqsArr, []int32{half})
-
-	posArr := make([]float32, seqLen)
-	for i := int32(0); i < seqLen; i++ {
-		posArr[i] = float32(i)
-	}
-	pos := mlx.NewArray(posArr, []int32{seqLen})
-
-	posExpanded := mlx.Reshape(pos, seqLen, 1)
-	freqsExpanded := mlx.Reshape(freqs, 1, half)
-	args := mlx.Mul(posExpanded, freqsExpanded)
-
-	cosVals := mlx.Cos(args)
-	sinVals := mlx.Sin(args)
-	cosVals = mlx.Reshape(cosVals, seqLen, 1, half)
-	sinVals = mlx.Reshape(sinVals, seqLen, 1, half)
-
-	x1 := mlx.Slice(x, []int32{0, 0, 0, 0}, []int32{B, L, H, half})
-	x2 := mlx.Slice(x, []int32{0, 0, 0, half}, []int32{B, L, H, D})
-
-	part1 := mlx.Sub(mlx.Mul(x1, cosVals), mlx.Mul(x2, sinVals))
-	part2 := mlx.Add(mlx.Mul(x1, sinVals), mlx.Mul(x2, cosVals))
-
-	return mlx.Concatenate([]*mlx.Array{part1, part2}, 3)
-}
-
-// Forward computes attention with causal masking and optional padding mask
-func (attn *Attention) Forward(x *mlx.Array, mask *mlx.Array, maskMode string) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	L := shape[1]
-
-	q := attn.QProj.Forward(x)
-	k := attn.KProj.Forward(x)
-	v := attn.VProj.Forward(x)
-
-	q = mlx.Reshape(q, B, L, attn.NHeads, attn.HeadDim)
-	k = mlx.Reshape(k, B, L, attn.NKVHeads, attn.HeadDim)
-	v = mlx.Reshape(v, B, L, attn.NKVHeads, attn.HeadDim)
-
-	// QK norm uses 1e-6 hardcoded (Qwen3 specific)
-	q = attn.QNorm.Forward(q, 1e-6)
-	k = attn.KNorm.Forward(k, 1e-6)
-
-	q = applyRoPEQwen3(q, L, attn.RopeTheta)
-	k = applyRoPEQwen3(k, L, attn.RopeTheta)
-
-	q = mlx.Transpose(q, 0, 2, 1, 3)
-	k = mlx.Transpose(k, 0, 2, 1, 3)
-	v = mlx.Transpose(v, 0, 2, 1, 3)
-
-	if attn.NKVHeads < attn.NHeads {
-		repeats := attn.NHeads / attn.NKVHeads
-		k = repeatKV(k, repeats)
-		v = repeatKV(v, repeats)
-	}
-
-	out := mlx.ScaledDotProductAttentionWithSinks(q, k, v, attn.Scale, maskMode, mask, nil)
-
-	out = mlx.Transpose(out, 0, 2, 1, 3)
-	out = mlx.Reshape(out, B, L, attn.NHeads*attn.HeadDim)
-
-	out = attn.OProj.Forward(out)
-
-	return out
-}
-
-// repeatKV repeats key/value heads for GQA
-func repeatKV(x *mlx.Array, repeats int32) *mlx.Array {
-	if repeats == 1 {
-		return x
-	}
-	shape := x.Shape()
-	x = mlx.ExpandDims(x, 2)
-	x = mlx.Tile(x, []int32{1, 1, repeats, 1, 1})
-	return mlx.Reshape(x, shape[0], shape[1]*repeats, shape[2], shape[3])
-}
-
-// MLP implements Qwen3 SwiGLU MLP
-type MLP struct {
-	GateProj nn.LinearLayer `weight:"gate_proj"`
-	UpProj   nn.LinearLayer `weight:"up_proj"`
-	DownProj nn.LinearLayer `weight:"down_proj"`
-}
-
-// Forward applies the MLP
-func (m *MLP) Forward(x *mlx.Array) *mlx.Array {
-	gate := m.GateProj.Forward(x)
-	gate = mlx.SiLU(gate)
-	up := m.UpProj.Forward(x)
-	h := mlx.Mul(gate, up)
-	return m.DownProj.Forward(h)
-}
-
-// Block represents a single Qwen3 transformer block
-type Block struct {
-	Attention         *Attention  `weight:"self_attn"`
-	MLP               *MLP        `weight:"mlp"`
-	InputLayerNorm    *nn.RMSNorm `weight:"input_layernorm"`
-	PostAttnLayerNorm *nn.RMSNorm `weight:"post_attention_layernorm"`
-}
-
-// Forward applies the Qwen3 block
-func (qb *Block) Forward(x *mlx.Array, eps float32, mask *mlx.Array, maskMode string) *mlx.Array {
-	h := qb.InputLayerNorm.Forward(x, eps)
-	attnOut := qb.Attention.Forward(h, mask, maskMode)
-	x = mlx.Add(x, attnOut)
-
-	h = qb.PostAttnLayerNorm.Forward(x, eps)
-	mlpOut := qb.MLP.Forward(h)
-	x = mlx.Add(x, mlpOut)
-
-	return x
-}
-
-// TextEncoder is the full Qwen3 encoder
-type TextEncoder struct {
-	EmbedTokens *nn.Embedding `weight:"model.embed_tokens"`
-	Layers      []*Block      `weight:"model.layers"`
-	FinalNorm   *nn.RMSNorm   `weight:"model.norm"`
-	*Config
-}
-
-// Load loads the Qwen3 text encoder from ollama blob storage.
-func (m *TextEncoder) Load(manifest *imagegen.ModelManifest, configPath string) error {
-	fmt.Print("  Loading text encoder... ")
-
-	// Load config from blob
-	var cfg Config
-	if err := manifest.ReadConfigJSON(configPath, &cfg); err != nil {
-		return fmt.Errorf("config: %w", err)
-	}
-	m.Config = &cfg
-	m.Layers = make([]*Block, cfg.NumHiddenLayers)
-
-	// Load weights from tensor blobs
-	weights, err := imagegen.LoadWeightsFromManifest(manifest, "text_encoder")
-	if err != nil {
-		return fmt.Errorf("weights: %w", err)
-	}
-	if err := weights.Load(0); err != nil {
-		return fmt.Errorf("load weights: %w", err)
-	}
-	defer weights.ReleaseAll()
-
-	return m.loadWeights(weights)
-}
-
-// loadWeights loads weights from any WeightSource into the model
-func (m *TextEncoder) loadWeights(weights safetensors.WeightSource) error {
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return fmt.Errorf("load module: %w", err)
-	}
-	m.initComputedFields()
-	fmt.Println("✓")
-	return nil
-}
-
-// initComputedFields initializes computed fields after loading weights
-func (m *TextEncoder) initComputedFields() {
-	cfg := m.Config
-	m.FinalNorm.Eps = cfg.RMSNormEps
-	for _, block := range m.Layers {
-		// Attention
-		block.Attention.NHeads = cfg.NumAttentionHeads
-		block.Attention.NKVHeads = cfg.NumKeyValueHeads
-		block.Attention.HeadDim = cfg.HeadDim
-		block.Attention.Scale = float32(1.0 / math.Sqrt(float64(cfg.HeadDim)))
-		block.Attention.RopeTheta = cfg.RopeTheta
-		block.Attention.QNorm.Eps = cfg.RMSNormEps
-		block.Attention.KNorm.Eps = cfg.RMSNormEps
-		// Block norms
-		block.InputLayerNorm.Eps = cfg.RMSNormEps
-		block.PostAttnLayerNorm.Eps = cfg.RMSNormEps
-	}
-}
-
-// Forward encodes text tokens with provided attention mask (LxL) and mask mode.
-func (te *TextEncoder) Forward(tokens *mlx.Array, attnMask *mlx.Array, maskMode string) *mlx.Array {
-	h := te.EmbedTokens.Forward(tokens)
-	eps := te.RMSNormEps
-
-	for _, layer := range te.Layers {
-		h = layer.Forward(h, eps, attnMask, maskMode)
-	}
-
-	// Apply final RMS norm
-	h = te.FinalNorm.Forward(h, eps)
-
-	return h
-}
-
-// ForwardWithLayerOutputs encodes text tokens and returns hidden states from specified layers.
-// This is used by Flux2 which needs embeddings from specific intermediate layers.
-func (te *TextEncoder) ForwardWithLayerOutputs(tokens *mlx.Array, layerIndices []int, attnMask *mlx.Array, maskMode string) []*mlx.Array {
-	h := te.EmbedTokens.Forward(tokens)
-	eps := te.RMSNormEps
-
-	outputs := make([]*mlx.Array, len(layerIndices))
-	layerSet := make(map[int]int)
-	for i, idx := range layerIndices {
-		layerSet[idx] = i
-	}
-
-	for i, layer := range te.Layers {
-		h = layer.Forward(h, eps, attnMask, maskMode)
-		if outIdx, ok := layerSet[i]; ok {
-			outputs[outIdx] = h
-		}
-	}
-
-	return outputs
-}
-
-// ApplyChatTemplate wraps prompt in Qwen3 chat format.
-// If think is true, adds the <think></think> block after the assistant tag
-// (matches tokenizer.apply_chat_template with enable_thinking=False in Python).
-func ApplyChatTemplate(prompt string, think bool) string {
-	base := "<|im_start|>user\n" + prompt + "<|im_end|>\n<|im_start|>assistant\n"
-	if think {
-		return base + "<think>\n\n</think>\n\n"
-	}
-	return base
-}
-
-// EncodePrompt encodes a text prompt using the tokenizer and encoder.
-// If think is true, includes the <think></think> block in the chat template.
-func (te *TextEncoder) EncodePrompt(tok *tokenizer.Tokenizer, prompt string, maxLen int, think bool) (*mlx.Array, *mlx.Array) {
-	formattedPrompt := ApplyChatTemplate(prompt, think)
-
-	tokens := tok.Encode(formattedPrompt, false)
-
-	if len(tokens) > maxLen {
-		tokens = tokens[:maxLen]
-	}
-
-	maskData := make([]float32, maxLen)
-	for i := 0; i < len(tokens); i++ {
-		maskData[i] = 1.0
-	}
-
-	// Get PAD token (different from EOS for Qwen3)
-	padToken := tok.PAD()
-	if padToken < 0 {
-		padToken = tok.EOS() // fallback
-	}
-
-	paddedTokens := make([]int32, maxLen)
-	copy(paddedTokens, tokens)
-	for i := len(tokens); i < maxLen; i++ {
-		paddedTokens[i] = padToken
-	}
-
-	tokensArr := mlx.NewArrayInt32(paddedTokens, []int32{1, int32(maxLen)})
-	maskArr := mlx.NewArray(maskData, []int32{1, int32(maxLen)})
-
-	// Build combined causal + PAD mask [L, L]
-	// mask[i,j] = 0 if (j <= i AND valid[j]) else -inf
-	L := int32(maxLen)
-	validLen := int32(len(tokens))
-	combinedMaskData := make([]float32, L*L)
-	negInf := float32(-1e9)
-	for i := int32(0); i < L; i++ {
-		for j := int32(0); j < L; j++ {
-			idx := i*L + j
-			if j <= i && j < validLen {
-				combinedMaskData[idx] = 0
-			} else {
-				combinedMaskData[idx] = negInf
-			}
-		}
-	}
-	maskMat := mlx.NewArray(combinedMaskData, []int32{L, L})
-
-	embeddings := te.Forward(tokensArr, maskMat, "")
-
-	return embeddings, maskArr
-}
-
-// EncodePromptWithLayers encodes a text prompt and returns embeddings from specified layers.
-// Used by Flux2 which concatenates embeddings from multiple intermediate layers.
-// If think is true, includes the <think></think> block in the chat template.
-// Returns embeddings and padded sequence length.
-func (te *TextEncoder) EncodePromptWithLayers(tok *tokenizer.Tokenizer, prompt string, maxLen int, layerIndices []int, think bool) (*mlx.Array, int32) {
-	formattedPrompt := ApplyChatTemplate(prompt, think)
-	tokens := tok.Encode(formattedPrompt, false)
-
-	if len(tokens) > maxLen {
-		tokens = tokens[:maxLen]
-	}
-
-	// Pad to maxLen
-	padToken := tok.PAD()
-	if padToken < 0 {
-		padToken = tok.EOS() // fallback
-	}
-	padded := make([]int32, maxLen)
-	copy(padded, tokens)
-	for i := len(tokens); i < maxLen; i++ {
-		padded[i] = padToken
-	}
-	tokensArr := mlx.NewArrayInt32(padded, []int32{1, int32(maxLen)})
-
-	// Build combined causal + PAD mask [L, L]
-	// mask[i,j] = 0 if (j <= i AND valid[j]) else -inf
-	// This combines causal masking with PAD token masking
-	L := int32(maxLen)
-	validLen := int32(len(tokens))
-	maskData := make([]float32, L*L)
-	negInf := float32(-1e9)
-	for i := int32(0); i < L; i++ {
-		for j := int32(0); j < L; j++ {
-			idx := i*L + j
-			if j <= i && j < validLen {
-				maskData[idx] = 0 // allowed: causal OK and not PAD
-			} else {
-				maskData[idx] = negInf // blocked: future or PAD
-			}
-		}
-	}
-	maskMat := mlx.NewArray(maskData, []int32{L, L})
-
-	layerOutputs := te.ForwardWithLayerOutputs(tokensArr, layerIndices, maskMat, "")
-
-	// Concatenate layer outputs along the hidden dimension
-	// Each output is [B, L, hidden_dim], result is [B, L, num_layers * hidden_dim]
-	embeddings := mlx.Concatenate(layerOutputs, 2)
-
-	// Return embeddings and padded length
-	return embeddings, int32(maxLen)
-}

x/imagegen/models/qwen_image/qwen_image.go

@@ -17,13 +17,13 @@ import (
 // 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
+	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       ProgressFunc // Optional progress callback
 
 	// Layer caching (DeepCache/Learning-to-Cache speedup)
 	LayerCache    bool // Enable layer caching (default: false)
@@ -31,6 +31,9 @@ type GenerateConfig struct {
 	CacheLayers   int  // Number of shallow layers to cache (default: 25)
 }
 
+// ProgressFunc is called during generation with step progress.
+type ProgressFunc func(step, totalSteps int)
+
 // Model represents a Qwen-Image diffusion model.
 type Model struct {
 	ModelPath   string
@@ -114,7 +117,7 @@ func (m *Model) Generate(prompt string, width, height int32, steps int, seed int
 }
 
 // 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) {
+func (m *Model) GenerateWithProgress(prompt string, width, height int32, steps int, seed int64, progress ProgressFunc) (*mlx.Array, error) {
 	return m.GenerateFromConfig(&GenerateConfig{
 		Prompt:   prompt,
 		Width:    width,
@@ -126,7 +129,7 @@ func (m *Model) GenerateWithProgress(prompt string, width, height int32, steps i
 }
 
 // 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) {
+func (m *Model) GenerateWithCFG(prompt, negativePrompt string, width, height int32, steps int, seed int64, cfgScale float32, progress ProgressFunc) (*mlx.Array, error) {
 	return m.GenerateFromConfig(&GenerateConfig{
 		Prompt:         prompt,
 		NegativePrompt: negativePrompt,

x/imagegen/models/qwen_image_edit/qwen_image_edit.go

@@ -18,15 +18,18 @@ import (
 // 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
+	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       ProgressFunc // Optional progress callback
 }
 
+// ProgressFunc is called during generation with step progress.
+type ProgressFunc func(step, totalSteps int)
+
 // Model represents a Qwen-Image-Edit diffusion model.
 type Model struct {
 	ModelPath     string

x/imagegen/models/zimage/text_encoder.go

@@ -3,17 +3,287 @@
 package zimage
 
 import (
-	"github.com/ollama/ollama/x/imagegen/models/qwen3"
+	"fmt"
+	"math"
+
+	"github.com/ollama/ollama/x/imagegen"
+	"github.com/ollama/ollama/x/imagegen/mlx"
+	"github.com/ollama/ollama/x/imagegen/nn"
+	"github.com/ollama/ollama/x/imagegen/safetensors"
+	"github.com/ollama/ollama/x/imagegen/tokenizer"
 )
 
-// Re-export types from shared qwen3 package for backwards compatibility
-type (
-	Qwen3Config      = qwen3.Config
-	Qwen3Attention   = qwen3.Attention
-	Qwen3MLP         = qwen3.MLP
-	Qwen3Block       = qwen3.Block
-	Qwen3TextEncoder = qwen3.TextEncoder
-)
+// Qwen3Config holds Qwen3 text encoder configuration
+type Qwen3Config 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"`
+}
+
+// Qwen3Attention implements Qwen3 attention with QK norms
+type Qwen3Attention struct {
+	QProj nn.LinearLayer `weight:"q_proj"`
+	KProj nn.LinearLayer `weight:"k_proj"`
+	VProj nn.LinearLayer `weight:"v_proj"`
+	OProj nn.LinearLayer `weight:"o_proj"`
+	QNorm *nn.RMSNorm    `weight:"q_norm"`
+	KNorm *nn.RMSNorm    `weight:"k_norm"`
+	// Computed fields
+	NHeads    int32
+	NKVHeads  int32
+	HeadDim   int32
+	Scale     float32
+	RopeTheta float32
+}
+
+// applyRoPEQwen3 applies the custom RoPE for Qwen3 text encoder
+func applyRoPEQwen3(x *mlx.Array, seqLen int32, theta float32) *mlx.Array {
+	shape := x.Shape()
+	B := shape[0]
+	L := shape[1]
+	H := shape[2]
+	D := shape[3]
+	half := D / 2
+
+	freqsArr := make([]float32, half)
+	logTheta := float32(math.Log(float64(theta)))
+	for i := int32(0); i < half; i++ {
+		freqsArr[i] = float32(math.Exp(float64(-logTheta * float32(i) / float32(half))))
+	}
+	freqs := mlx.NewArray(freqsArr, []int32{half})
+
+	posArr := make([]float32, seqLen)
+	for i := int32(0); i < seqLen; i++ {
+		posArr[i] = float32(i)
+	}
+	pos := mlx.NewArray(posArr, []int32{seqLen})
+
+	posExpanded := mlx.Reshape(pos, seqLen, 1)
+	freqsExpanded := mlx.Reshape(freqs, 1, half)
+	args := mlx.Mul(posExpanded, freqsExpanded)
+
+	cosVals := mlx.Cos(args)
+	sinVals := mlx.Sin(args)
+	cosVals = mlx.Reshape(cosVals, seqLen, 1, half)
+	sinVals = mlx.Reshape(sinVals, seqLen, 1, half)
+
+	x1 := mlx.Slice(x, []int32{0, 0, 0, 0}, []int32{B, L, H, half})
+	x2 := mlx.Slice(x, []int32{0, 0, 0, half}, []int32{B, L, H, D})
+
+	part1 := mlx.Sub(mlx.Mul(x1, cosVals), mlx.Mul(x2, sinVals))
+	part2 := mlx.Add(mlx.Mul(x1, sinVals), mlx.Mul(x2, cosVals))
+
+	return mlx.Concatenate([]*mlx.Array{part1, part2}, 3)
+}
+
+// Forward computes attention with causal masking
+func (attn *Qwen3Attention) Forward(x *mlx.Array) *mlx.Array {
+	shape := x.Shape()
+	B := shape[0]
+	L := shape[1]
+
+	q := attn.QProj.Forward(x)
+	k := attn.KProj.Forward(x)
+	v := attn.VProj.Forward(x)
+
+	q = mlx.Reshape(q, B, L, attn.NHeads, attn.HeadDim)
+	k = mlx.Reshape(k, B, L, attn.NKVHeads, attn.HeadDim)
+	v = mlx.Reshape(v, B, L, attn.NKVHeads, attn.HeadDim)
+
+	// QK norm uses 1e-6 hardcoded (Qwen3 specific)
+	q = attn.QNorm.Forward(q, 1e-6)
+	k = attn.KNorm.Forward(k, 1e-6)
+
+	q = applyRoPEQwen3(q, L, attn.RopeTheta)
+	k = applyRoPEQwen3(k, L, attn.RopeTheta)
+
+	q = mlx.Transpose(q, 0, 2, 1, 3)
+	k = mlx.Transpose(k, 0, 2, 1, 3)
+	v = mlx.Transpose(v, 0, 2, 1, 3)
+
+	if attn.NKVHeads < attn.NHeads {
+		repeats := attn.NHeads / attn.NKVHeads
+		k = repeatKV(k, repeats)
+		v = repeatKV(v, repeats)
+	}
+
+	out := mlx.ScaledDotProductAttention(q, k, v, attn.Scale, true)
+
+	out = mlx.Transpose(out, 0, 2, 1, 3)
+	out = mlx.Reshape(out, B, L, attn.NHeads*attn.HeadDim)
+
+	out = attn.OProj.Forward(out)
+
+	return out
+}
+
+// repeatKV repeats key/value heads for GQA
+func repeatKV(x *mlx.Array, repeats int32) *mlx.Array {
+	if repeats == 1 {
+		return x
+	}
+	shape := x.Shape()
+	x = mlx.ExpandDims(x, 2)
+	x = mlx.Tile(x, []int32{1, 1, repeats, 1, 1})
+	return mlx.Reshape(x, shape[0], shape[1]*repeats, shape[2], shape[3])
+}
+
+// Qwen3MLP implements Qwen3 SwiGLU MLP
+type Qwen3MLP struct {
+	GateProj nn.LinearLayer `weight:"gate_proj"`
+	UpProj   nn.LinearLayer `weight:"up_proj"`
+	DownProj nn.LinearLayer `weight:"down_proj"`
+}
+
+// Forward applies the MLP
+func (m *Qwen3MLP) Forward(x *mlx.Array) *mlx.Array {
+	gate := m.GateProj.Forward(x)
+	gate = mlx.SiLU(gate)
+	up := m.UpProj.Forward(x)
+	h := mlx.Mul(gate, up)
+	return m.DownProj.Forward(h)
+}
+
+// Qwen3Block represents a single Qwen3 transformer block
+type Qwen3Block struct {
+	Attention         *Qwen3Attention `weight:"self_attn"`
+	MLP               *Qwen3MLP       `weight:"mlp"`
+	InputLayerNorm    *nn.RMSNorm     `weight:"input_layernorm"`
+	PostAttnLayerNorm *nn.RMSNorm     `weight:"post_attention_layernorm"`
+}
+
+// Forward applies the Qwen3 block
+func (qb *Qwen3Block) Forward(x *mlx.Array, eps float32) *mlx.Array {
+	h := qb.InputLayerNorm.Forward(x, eps)
+	attnOut := qb.Attention.Forward(h)
+	x = mlx.Add(x, attnOut)
+
+	h = qb.PostAttnLayerNorm.Forward(x, eps)
+	mlpOut := qb.MLP.Forward(h)
+	x = mlx.Add(x, mlpOut)
+
+	return x
+}
+
+// Qwen3TextEncoder is the full Qwen3 encoder for Z-Image
+type Qwen3TextEncoder struct {
+	EmbedTokens *nn.Embedding   `weight:"model.embed_tokens"`
+	Layers      []*Qwen3Block   `weight:"model.layers"`
+	FinalNorm   *nn.RMSNorm     `weight:"model.norm"`
+	*Qwen3Config
+}
+
+// Load loads the Qwen3 text encoder from ollama blob storage.
+func (m *Qwen3TextEncoder) Load(manifest *imagegen.ModelManifest) error {
+	fmt.Print("  Loading text encoder... ")
+
+	// Load config from blob
+	var cfg Qwen3Config
+	if err := manifest.ReadConfigJSON("text_encoder/config.json", &cfg); err != nil {
+		return fmt.Errorf("config: %w", err)
+	}
+	m.Qwen3Config = &cfg
+	m.Layers = make([]*Qwen3Block, cfg.NumHiddenLayers)
+
+	// Load weights from tensor blobs
+	weights, err := imagegen.LoadWeightsFromManifest(manifest, "text_encoder")
+	if err != nil {
+		return fmt.Errorf("weights: %w", err)
+	}
+	if err := weights.Load(0); err != nil {
+		return fmt.Errorf("load weights: %w", err)
+	}
+	defer weights.ReleaseAll()
+
+	return m.loadWeights(weights)
+}
+
+// loadWeights loads weights from any WeightSource into the model
+func (m *Qwen3TextEncoder) loadWeights(weights safetensors.WeightSource) error {
+	if err := safetensors.LoadModule(m, weights, ""); err != nil {
+		return fmt.Errorf("load module: %w", err)
+	}
+	m.initComputedFields()
+	fmt.Println("✓")
+	return nil
+}
+
+// initComputedFields initializes computed fields after loading weights
+func (m *Qwen3TextEncoder) initComputedFields() {
+	cfg := m.Qwen3Config
+	m.FinalNorm.Eps = cfg.RMSNormEps
+	for _, block := range m.Layers {
+		// Attention
+		block.Attention.NHeads = cfg.NumAttentionHeads
+		block.Attention.NKVHeads = cfg.NumKeyValueHeads
+		block.Attention.HeadDim = cfg.HeadDim
+		block.Attention.Scale = float32(1.0 / math.Sqrt(float64(cfg.HeadDim)))
+		block.Attention.RopeTheta = cfg.RopeTheta
+		block.Attention.QNorm.Eps = cfg.RMSNormEps
+		block.Attention.KNorm.Eps = cfg.RMSNormEps
+		// Block norms
+		block.InputLayerNorm.Eps = cfg.RMSNormEps
+		block.PostAttnLayerNorm.Eps = cfg.RMSNormEps
+	}
+}
+
+// Forward encodes text tokens
+func (te *Qwen3TextEncoder) Forward(tokens *mlx.Array) *mlx.Array {
+	h := te.EmbedTokens.Forward(tokens)
+	eps := te.RMSNormEps
+
+	for _, layer := range te.Layers {
+		h = layer.Forward(h, eps)
+	}
+
+	// Apply final RMS norm
+	h = te.FinalNorm.Forward(h, eps)
+
+	return h
+}
 
 // ApplyChatTemplate wraps prompt in Qwen3 chat format
-var ApplyChatTemplate = qwen3.ApplyChatTemplate
+func ApplyChatTemplate(prompt string) string {
+	return "<|im_start|>user\n" + prompt + "<|im_end|>\n<|im_start|>assistant\n"
+}
+
+// EncodePrompt encodes a text prompt using the tokenizer and encoder
+func (te *Qwen3TextEncoder) EncodePrompt(tok *tokenizer.Tokenizer, prompt string, maxLen int) (*mlx.Array, *mlx.Array) {
+	formattedPrompt := ApplyChatTemplate(prompt)
+
+	tokens := tok.Encode(formattedPrompt, false)
+
+	if len(tokens) > maxLen {
+		tokens = tokens[:maxLen]
+	}
+
+	maskData := make([]float32, maxLen)
+	for i := 0; i < len(tokens); i++ {
+		maskData[i] = 1.0
+	}
+
+	// Get PAD token (different from EOS for Qwen3)
+	padToken := tok.PAD()
+	if padToken < 0 {
+		padToken = tok.EOS() // fallback
+	}
+
+	paddedTokens := make([]int32, maxLen)
+	copy(paddedTokens, tokens)
+	for i := len(tokens); i < maxLen; i++ {
+		paddedTokens[i] = padToken
+	}
+
+	tokensArr := mlx.NewArrayInt32(paddedTokens, []int32{1, int32(maxLen)})
+	maskArr := mlx.NewArray(maskData, []int32{1, int32(maxLen)})
+
+	embeddings := te.Forward(tokensArr)
+
+	return embeddings, maskArr
+}

x/imagegen/models/zimage/vae.go

@@ -9,7 +9,6 @@ import (
 	"github.com/ollama/ollama/x/imagegen"
 	"github.com/ollama/ollama/x/imagegen/mlx"
 	"github.com/ollama/ollama/x/imagegen/safetensors"
-	"github.com/ollama/ollama/x/imagegen/vae"
 )
 
 // VAEConfig holds VAE decoder configuration
@@ -637,9 +636,6 @@ type VAEDecoder struct {
 	UpBlocks    []*UpDecoderBlock2D
 	ConvNormOut *GroupNormLayer
 	ConvOut     *Conv2D
-
-	// Tiling configuration (nil = no tiling)
-	Tiling *vae.TilingConfig
 }
 
 // Load loads the VAE decoder from ollama blob storage.
@@ -734,60 +730,45 @@ func (m *VAEDecoder) loadWeights(weights safetensors.WeightSource, cfg *VAEConfi
 
 // Decode decodes latents to images.
 // Input latents are in NCHW format, output is in NCHW format.
-// If Tiling is set, uses tiled decoding to reduce memory for large images.
-func (v *VAEDecoder) Decode(latents *mlx.Array) *mlx.Array {
+// Internally uses NHWC format (MLX native) for all operations.
+func (vae *VAEDecoder) Decode(latents *mlx.Array) *mlx.Array {
 	// Scale latents
-	z := mlx.DivScalar(latents, v.Config.ScalingFactor)
-	z = mlx.AddScalar(z, v.Config.ShiftFactor)
+	z := mlx.DivScalar(latents, vae.Config.ScalingFactor)
+	z = mlx.AddScalar(z, vae.Config.ShiftFactor)
 	// Convert NCHW -> NHWC for internal processing
 	z = mlx.Transpose(z, 0, 2, 3, 1)
-
-	// Use tiled decoding if enabled
-	if v.Tiling != nil {
-		mlx.Eval(z)
-		return vae.DecodeTiled(z, v.Tiling, v.decodeTile)
-	}
-
-	// Direct decode
-	h := v.decodeTile(z)
-	h = mlx.ClipScalar(h, 0.0, 1.0, true, true)
-	// Convert NHWC -> NCHW for output
-	h = mlx.Transpose(h, 0, 3, 1, 2)
-	mlx.Eval(h)
-	return h
-}
-
-// decodeTile decodes a single latent tile to pixels.
-// Input: [B, H, W, C] latent tile in NHWC format (already scaled)
-// Output: [B, H*8, W*8, 3] pixel tile in NHWC format
-func (v *VAEDecoder) decodeTile(z *mlx.Array) *mlx.Array {
-	h := v.ConvIn.Forward(z)
+	h := vae.ConvIn.Forward(z)
 	mlx.Eval(h)
 
 	prev := h
-	h = v.MidBlock.Forward(h)
+	h = vae.MidBlock.Forward(h)
 	prev.Free()
 
-	for _, upBlock := range v.UpBlocks {
+	for _, upBlock := range vae.UpBlocks {
 		prev = h
 		h = upBlock.Forward(h)
 		prev.Free()
 	}
 
 	prev = h
-	h = v.ConvNormOut.Forward(h)
+	h = vae.ConvNormOut.Forward(h)
 	mlx.Eval(h) // Eval after GroupNorm to avoid grid dimension issues
 	prev.Free()
 
 	prev = h
 	h = mlx.SiLU(h)
-	h = v.ConvOut.Forward(h)
+	h = vae.ConvOut.Forward(h)
 	mlx.Eval(h)
 	prev.Free()
 
 	// VAE outputs [-1, 1], convert to [0, 1]
 	h = mlx.MulScalar(h, 0.5)
 	h = mlx.AddScalar(h, 0.5)
+	h = mlx.ClipScalar(h, 0.0, 1.0, true, true)
+
+	// Convert NHWC -> NCHW for output
+	h = mlx.Transpose(h, 0, 3, 1, 2)
+	mlx.Eval(h)
 
 	return h
 }

x/imagegen/models/zimage/zimage.go

@@ -12,20 +12,19 @@ import (
 	"github.com/ollama/ollama/x/imagegen/cache"
 	"github.com/ollama/ollama/x/imagegen/mlx"
 	"github.com/ollama/ollama/x/imagegen/tokenizer"
-	"github.com/ollama/ollama/x/imagegen/vae"
 )
 
 // 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: 9 for turbo)
-	Seed           int64                 // Random seed
-	Progress       func(step, totalSteps int) // Optional progress callback
-	CapturePath    string                // GPU capture path (debug)
+	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: 9 for turbo)
+	Seed           int64        // Random seed
+	Progress       ProgressFunc // Optional progress callback
+	CapturePath    string       // GPU capture path (debug)
 
 	// TeaCache options (timestep embedding aware caching)
 	TeaCache          bool    // TeaCache is always enabled for faster inference
@@ -35,6 +34,9 @@ type GenerateConfig struct {
 	FusedQKV bool // Enable fused QKV projection (default: false)
 }
 
+// ProgressFunc is called during generation with step progress.
+type ProgressFunc func(step, totalSteps int)
+
 // Model represents a Z-Image diffusion model.
 type Model struct {
 	ModelName   string
@@ -91,7 +93,7 @@ func (m *Model) Load(modelName string) error {
 
 	// Load text encoder
 	m.TextEncoder = &Qwen3TextEncoder{}
-	if err := m.TextEncoder.Load(manifest, "text_encoder/config.json"); err != nil {
+	if err := m.TextEncoder.Load(manifest); err != nil {
 		return fmt.Errorf("text encoder: %w", err)
 	}
 	mlx.Eval(mlx.Collect(m.TextEncoder)...)
@@ -137,7 +139,7 @@ func (m *Model) Generate(prompt string, width, height int32, steps int, seed int
 }
 
 // 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) {
+func (m *Model) GenerateWithProgress(prompt string, width, height int32, steps int, seed int64, progress ProgressFunc) (*mlx.Array, error) {
 	return m.GenerateFromConfig(context.Background(), &GenerateConfig{
 		Prompt:   prompt,
 		Width:    width,
@@ -149,7 +151,7 @@ func (m *Model) GenerateWithProgress(prompt string, width, height int32, steps i
 }
 
 // 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) {
+func (m *Model) GenerateWithCFG(prompt, negativePrompt string, width, height int32, steps int, seed int64, cfgScale float32, progress ProgressFunc) (*mlx.Array, error) {
 	return m.GenerateFromConfig(context.Background(), &GenerateConfig{
 		Prompt:         prompt,
 		NegativePrompt: negativePrompt,
@@ -177,16 +179,9 @@ func (m *Model) GenerateFromConfig(ctx context.Context, cfg *GenerateConfig) (*m
 	return result, nil
 }
 
-// GenerateImage implements runner.ImageModel interface.
-func (m *Model) GenerateImage(ctx context.Context, prompt string, width, height int32, steps int, seed int64, progress func(step, total int)) (*mlx.Array, error) {
-	return m.GenerateFromConfig(ctx, &GenerateConfig{
-		Prompt:   prompt,
-		Width:    width,
-		Height:   height,
-		Steps:    steps,
-		Seed:     seed,
-		Progress: progress,
-	})
+// 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.
@@ -227,9 +222,9 @@ func (m *Model) generate(ctx context.Context, cfg *GenerateConfig) (*mlx.Array,
 	// Text encoding with padding to multiple of 32
 	var posEmb, negEmb *mlx.Array
 	{
-		posEmb, _ = m.TextEncoder.EncodePrompt(m.Tokenizer, cfg.Prompt, 512, false)
+		posEmb, _ = m.TextEncoder.EncodePrompt(m.Tokenizer, cfg.Prompt, 512)
 		if useCFG {
-			negEmb, _ = m.TextEncoder.EncodePrompt(m.Tokenizer, cfg.NegativePrompt, 512, false)
+			negEmb, _ = m.TextEncoder.EncodePrompt(m.Tokenizer, cfg.NegativePrompt, 512)
 		}
 
 		// Pad both to same length (multiple of 32)
@@ -453,11 +448,7 @@ func (m *Model) generate(ctx context.Context, cfg *GenerateConfig) (*mlx.Array,
 		teaCache.Free()
 	}
 
-	// VAE decode - enable tiling for larger images to reduce memory
-	// VAE attention is O(n²) on latent pixels, tiling helps significantly
-	if latentH > 64 || latentW > 64 {
-		m.VAEDecoder.Tiling = vae.DefaultTilingConfig()
-	}
+	// VAE decode
 	decoded := m.VAEDecoder.Decode(latents)
 	latents.Free()
 

x/imagegen/runner/runner.go

@@ -19,7 +19,6 @@ import (
 
 	"github.com/ollama/ollama/x/imagegen"
 	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/models/flux2"
 	"github.com/ollama/ollama/x/imagegen/models/zimage"
 )
 
@@ -41,15 +40,10 @@ type Response struct {
 	Total   int    `json:"total,omitempty"`
 }
 
-// ImageModel is the interface for image generation models
-type ImageModel interface {
-	GenerateImage(ctx context.Context, prompt string, width, height int32, steps int, seed int64, progress func(step, total int)) (*mlx.Array, error)
-}
-
 // Server holds the model and handles requests
 type Server struct {
 	mu        sync.Mutex
-	model     ImageModel
+	model     *zimage.Model
 	modelName string
 }
 
@@ -86,25 +80,10 @@ func Execute(args []string) error {
 			requiredMemory/(1024*1024*1024), availableMemory/(1024*1024*1024))
 	}
 
-	// Detect model type and load appropriate model
-	modelType := imagegen.DetectModelType(*modelName)
-	slog.Info("detected model type", "type", modelType)
-
-	var model ImageModel
-	switch modelType {
-	case "Flux2KleinPipeline":
-		m := &flux2.Model{}
-		if err := m.Load(*modelName); err != nil {
-			return fmt.Errorf("failed to load model: %w", err)
-		}
-		model = m
-	default:
-		// Default to Z-Image for ZImagePipeline, FluxPipeline, etc.
-		m := &zimage.Model{}
-		if err := m.Load(*modelName); err != nil {
-			return fmt.Errorf("failed to load model: %w", err)
-		}
-		model = m
+	// Load model
+	model := &zimage.Model{}
+	if err := model.Load(*modelName); err != nil {
+		return fmt.Errorf("failed to load model: %w", err)
 	}
 
 	server := &Server{
@@ -180,19 +159,26 @@ func (s *Server) completionHandler(w http.ResponseWriter, r *http.Request) {
 		return
 	}
 
-	// Generate image using the common interface
+	// Generate image
 	ctx := r.Context()
-	enc := json.NewEncoder(w)
-
-	// Progress callback streams step updates
-	progress := func(step, total int) {
-		resp := Response{Step: step, Total: total}
-		enc.Encode(resp)
-		w.Write([]byte("\n"))
-		flusher.Flush()
-	}
-
-	img, err := s.model.GenerateImage(ctx, req.Prompt, req.Width, req.Height, req.Steps, req.Seed, progress)
+	img, err := s.model.GenerateFromConfig(ctx, &zimage.GenerateConfig{
+		Prompt: req.Prompt,
+		Width:  req.Width,
+		Height: req.Height,
+		Steps:  req.Steps,
+		Seed:   req.Seed,
+		Progress: func(step, total int) {
+			resp := Response{
+				Step:  step,
+				Total: total,
+				Done:  false,
+			}
+			data, _ := json.Marshal(resp)
+			w.Write(data)
+			w.Write([]byte("\n"))
+			flusher.Flush()
+		},
+	})
 
 	if err != nil {
 		// Don't send error for cancellation

x/imagegen/safetensors/loader.go

@@ -17,24 +17,6 @@ type WeightSource interface {
 	GetTensor(name string) (*mlx.Array, error)
 	ListTensors() []string
 	HasTensor(name string) bool
-	Quantization() string // Returns "FP4", "FP8", or ""
-}
-
-// quantizationParams returns groupSize, bits, mode for a quantization type.
-// Returns defaults (32, 8, "affine") for unknown types (backward compatibility).
-func quantizationParams(quantization string) (groupSize, bits int, mode string) {
-	switch strings.ToUpper(quantization) {
-	case "FP4":
-		return 32, 4, "affine"
-	default:
-		return 32, 8, "affine" // FP8 or unknown
-	}
-}
-
-// Transformer allows structs to transform weight arrays before assignment.
-// Implement this to apply operations like transpose during loading.
-type Transformer interface {
-	Transform(field string, arr *mlx.Array) *mlx.Array
 }
 
 // LoadModule loads weights into a struct using reflection and struct tags.
@@ -154,10 +136,6 @@ func loadStruct(v reflect.Value, weights WeightSource, prefix string, errs *[]st
 					}
 					continue
 				}
-				// Transform before assigning if parent implements Transformer
-				if t, ok := v.Addr().Interface().(Transformer); ok {
-					arr = t.Transform(field.Name, arr)
-				}
 				fieldVal.Set(reflect.ValueOf(arr))
 				continue
 			}
@@ -245,21 +223,19 @@ func LoadLinearLayer(weights WeightSource, path string) (nn.LinearLayer, error)
 			qbiases, _ = weights.GetTensor(qbiasPath)
 		}
 
-		groupSize, bits, mode := quantizationParams(weights.Quantization())
-
 		if mlx.MetalIsAvailable() {
 			return &nn.QuantizedLinear{
 				Weight:    weight,
 				Scales:    scales,
 				QBiases:   qbiases,
 				Bias:      bias,
-				GroupSize: groupSize,
-				Bits:      bits,
-				Mode:      mode,
+				GroupSize: 32,
+				Bits:      8,
+				Mode:      "affine",
 			}, nil
 		}
 
-		dequantized := mlx.Dequantize(weight, scales, qbiases, groupSize, bits, mode)
+		dequantized := mlx.Dequantize(weight, scales, qbiases, 32, 8, "affine")
 		return nn.NewLinear(dequantized, bias), nil
 	}
 

x/imagegen/safetensors/safetensors.go

@@ -298,11 +298,6 @@ func (mw *ModelWeights) HasTensor(name string) bool {
 	return ok
 }
 
-// Quantization returns empty string for directory-based weights (not quantized).
-func (mw *ModelWeights) Quantization() string {
-	return ""
-}
-
 // ReleaseAll releases all cached native file handles.
 func (mw *ModelWeights) ReleaseAll() {
 	for path, native := range mw.nativeCache {

x/imagegen/tokenizer/tokenizer.go

@@ -510,11 +510,7 @@ func loadFromTokenizerJSON(data []byte, dir string) (*Tokenizer, error) {
 		t.vocab.Merges[merge] = i
 	}
 
-	// Add all added_tokens to vocabulary and special tokens map.
-	// HuggingFace treats ALL added_tokens as special for tokenization purposes -
-	// they bypass BPE and get their own token ID. The "special" flag just indicates
-	// if it's a "truly special" token like BOS/EOS/PAD, but for tokenization we need
-	// to treat all added_tokens as special to match HuggingFace behavior.
+	// Add special tokens to vocabulary
 	for _, tok := range raw.AddedTokens {
 		if int(tok.ID) >= len(t.vocab.Values) {
 			newValues := make([]string, tok.ID+1)
@@ -522,7 +518,9 @@ func loadFromTokenizerJSON(data []byte, dir string) (*Tokenizer, error) {
 			t.vocab.Values = newValues
 		}
 		t.vocab.Values[tok.ID] = tok.Content
-		t.specialTokens[tok.Content] = tok.ID // Add ALL added_tokens to special tokens
+		if tok.Special {
+			t.specialTokens[tok.Content] = tok.ID
+		}
 	}
 
 	// Load special token configuration from companion files

x/imagegen/vae/tiling.go

@@ -1,215 +0,0 @@
-//go:build mlx
-
-// Package vae provides shared utilities for VAE (Variational Autoencoder) operations.
-package vae
-
-import (
-	"github.com/ollama/ollama/x/imagegen/mlx"
-)
-
-// TilingConfig holds configuration for tiled VAE decoding.
-// This is a general technique to reduce memory usage when decoding large latents.
-type TilingConfig struct {
-	TileSize int32 // Tile size in latent space (e.g., 64 latent → 512 pixels for 8x VAE)
-	Overlap  int32 // Overlap in latent space (e.g., 16 latent = 25% of 64)
-}
-
-// DefaultTilingConfig returns reasonable defaults matching diffusers.
-// tile_latent_min_size=64, tile_overlap_factor=0.25
-func DefaultTilingConfig() *TilingConfig {
-	return &TilingConfig{
-		TileSize: 64, // 64 latent pixels
-		Overlap:  16, // 25% overlap
-	}
-}
-
-// decodedTile holds a decoded tile's pixel data and dimensions
-type decodedTile struct {
-	data   []float32
-	height int32
-	width  int32
-}
-
-// DecodeTiled decodes latents using tiled processing with overlap blending.
-// This reduces memory usage for large images by processing in overlapping tiles.
-//
-// Parameters:
-//   - latents: [1, H, W, C] latent tensor in NHWC format
-//   - cfg: tiling configuration (tile size and overlap)
-//   - decoder: function to decode a single tile [1, H, W, C] -> [1, H*scale, W*scale, 3]
-//
-// Returns: [1, 3, H*scale, W*scale] decoded image in NCHW format
-func DecodeTiled(latents *mlx.Array, cfg *TilingConfig, decoder func(*mlx.Array) *mlx.Array) *mlx.Array {
-	shape := latents.Shape()
-	H := shape[1] // latent height
-	W := shape[2] // latent width
-	C := shape[3]
-
-	tileLatentSize := cfg.TileSize
-	overlapLatent := cfg.Overlap
-
-	// If image is small enough, just decode normally
-	if H <= tileLatentSize && W <= tileLatentSize {
-		decoded := decoder(latents)
-		decoded = mlx.AsType(decoded, mlx.DtypeFloat32)
-		decoded = mlx.ClipScalar(decoded, 0.0, 1.0, true, true)
-		decoded = mlx.Transpose(decoded, 0, 3, 1, 2) // NHWC -> NCHW
-		return decoded
-	}
-
-	// Calculate tiling parameters (matching diffusers)
-	overlapSize := tileLatentSize - overlapLatent // stride in latent space
-
-	// Blend extent in pixel space (assumes 8x upscale, adjust if needed)
-	// For other scale factors, this could be made configurable
-	tileSampleSize := tileLatentSize * 8     // tile size in pixels after 8x upscale
-	blendExtent := overlapLatent * 8         // blend region in pixels
-	rowLimit := tileSampleSize - blendExtent // non-overlapping region per tile
-
-	// Phase 1: Decode all tiles and store in 2D grid
-	var rows [][]decodedTile
-
-	for i := int32(0); i < H; i += overlapSize {
-		var row []decodedTile
-		for j := int32(0); j < W; j += overlapSize {
-			// Extract tile (may be smaller at edges)
-			i2 := min(i+tileLatentSize, H)
-			j2 := min(j+tileLatentSize, W)
-
-			tile := mlx.Slice(latents, []int32{0, i, j, 0}, []int32{1, i2, j2, C})
-			decoded := decoder(tile)
-			decoded = mlx.AsType(decoded, mlx.DtypeFloat32)
-			mlx.Eval(decoded)
-
-			decodedShape := decoded.Shape()
-			tileH := decodedShape[1]
-			tileW := decodedShape[2]
-			tileData := decoded.Data()
-			decoded.Free()
-
-			row = append(row, decodedTile{data: tileData, height: tileH, width: tileW})
-		}
-		rows = append(rows, row)
-	}
-
-	// Phase 2: Blend adjacent tiles (modifies in place)
-	for i := range rows {
-		for j := range rows[i] {
-			tile := &rows[i][j]
-
-			// Blend with tile above
-			if i > 0 {
-				above := &rows[i-1][j]
-				blendV(above, tile, blendExtent)
-			}
-
-			// Blend with tile to the left
-			if j > 0 {
-				left := &rows[i][j-1]
-				blendH(left, tile, blendExtent)
-			}
-		}
-	}
-
-	// Phase 3: Calculate crop dimensions for each tile
-	colWidths := make([]int32, len(rows[0]))
-	for j := range rows[0] {
-		keepW := rowLimit
-		if int32(j+1)*overlapSize >= W {
-			keepW = rows[0][j].width
-		}
-		colWidths[j] = keepW
-	}
-
-	rowHeights := make([]int32, len(rows))
-	for i := range rows {
-		keepH := rowLimit
-		if int32(i+1)*overlapSize >= H {
-			keepH = rows[i][0].height
-		}
-		rowHeights[i] = keepH
-	}
-
-	// Calculate total dimensions
-	var totalW, totalH int32
-	for _, w := range colWidths {
-		totalW += w
-	}
-	for _, h := range rowHeights {
-		totalH += h
-	}
-
-	// Phase 4: Assemble final image by interleaving tiles row-by-row
-	finalData := make([]float32, totalH*totalW*3)
-
-	dstY := int32(0)
-	for i, row := range rows {
-		keepH := rowHeights[i]
-
-		for y := int32(0); y < keepH; y++ {
-			dstX := int32(0)
-			for j, tile := range row {
-				keepW := colWidths[j]
-
-				for x := int32(0); x < keepW; x++ {
-					for c := int32(0); c < 3; c++ {
-						srcIdx := (y*tile.width + x) * 3 + c
-						dstIdx := ((dstY + y) * totalW + (dstX + x)) * 3 + c
-						finalData[dstIdx] = tile.data[srcIdx]
-					}
-				}
-				dstX += keepW
-			}
-		}
-		dstY += keepH
-	}
-
-	// Create mlx array [1, H, W, 3] then transpose to NCHW [1, 3, H, W]
-	result := mlx.NewArray(finalData, []int32{1, totalH, totalW, 3})
-	result = mlx.Transpose(result, 0, 3, 1, 2)
-	result = mlx.ClipScalar(result, 0.0, 1.0, true, true)
-
-	return result
-}
-
-// blendV blends the bottom of 'above' tile into top of 'current' tile (vertical blend)
-// Matches diffusers blend_v formula
-func blendV(above, current *decodedTile, blendExtent int32) {
-	blend := min(blendExtent, min(above.height, current.height))
-	if blend <= 0 {
-		return
-	}
-
-	w := min(above.width, current.width)
-	for y := int32(0); y < blend; y++ {
-		alpha := float32(y) / float32(blend)
-		for x := int32(0); x < w; x++ {
-			for c := int32(0); c < 3; c++ {
-				aboveIdx := ((above.height - blend + y) * above.width + x) * 3 + c
-				currIdx := (y * current.width + x) * 3 + c
-				current.data[currIdx] = above.data[aboveIdx]*(1-alpha) + current.data[currIdx]*alpha
-			}
-		}
-	}
-}
-
-// blendH blends the right of 'left' tile into left of 'current' tile (horizontal blend)
-// Matches diffusers blend_h formula
-func blendH(left, current *decodedTile, blendExtent int32) {
-	blend := min(blendExtent, min(left.width, current.width))
-	if blend <= 0 {
-		return
-	}
-
-	h := min(left.height, current.height)
-	for y := int32(0); y < h; y++ {
-		for x := int32(0); x < blend; x++ {
-			alpha := float32(x) / float32(blend)
-			for c := int32(0); c < 3; c++ {
-				leftIdx := (y * left.width + (left.width - blend + x)) * 3 + c
-				currIdx := (y * current.width + x) * 3 + c
-				current.data[currIdx] = left.data[leftIdx]*(1-alpha) + current.data[currIdx]*alpha
-			}
-		}
-	}
-}

x/imagegen/weights.go

@@ -106,21 +106,6 @@ func (mw *ManifestWeights) HasTensor(name string) bool {
 	return ok
 }
 
-// Quantization returns the model's quantization type from model_index.json.
-// Returns empty string if not quantized or unknown.
-func (mw *ManifestWeights) Quantization() string {
-	if mw.manifest == nil {
-		return ""
-	}
-	var index struct {
-		Quantization string `json:"quantization"`
-	}
-	if err := mw.manifest.ReadConfigJSON("model_index.json", &index); err != nil {
-		return ""
-	}
-	return index.Quantization
-}
-
 // ReleaseAll frees all native handles and clears the tensor cache.
 func (mw *ManifestWeights) ReleaseAll() {
 	for _, sf := range mw.nativeCache {