wip?

Co-authored-by: Patrick Devine <patrick@infrahq.com>

CMakePresets.json

@@ -21,14 +21,16 @@
       "name": "CUDA 11",
       "inherits": [ "CUDA" ],
       "cacheVariables": {
-        "CMAKE_CUDA_ARCHITECTURES": "50;52;53;60;61;70;75;80;86"
+        "CMAKE_CUDA_ARCHITECTURES": "50;52;53;60;61;70;75;80;86",
+        "CMAKE_CUDA_FLAGS": "-Wno-deprecated-gpu-targets"
       }
     },
     {
       "name": "CUDA 12",
       "inherits": [ "CUDA" ],
       "cacheVariables": {
-        "CMAKE_CUDA_ARCHITECTURES": "50;60;61;70;75;80;86;87;89;90;90a;120"
+        "CMAKE_CUDA_ARCHITECTURES": "50;60;61;70;75;80;86;87;89;90;90a;120",
+        "CMAKE_CUDA_FLAGS": "-Wno-deprecated-gpu-targets"
       }
     },
     {

convert/convert.go

@@ -7,6 +7,7 @@ import (
 	"io"
 	"io/fs"
 	"log/slog"
+	"slices"
 	"strings"
 
 	"github.com/ollama/ollama/fs/ggml"
@@ -84,14 +85,6 @@ func (ModelParameters) specialTokenTypes() []string {
 	}
 }
 
-func (ModelParameters) writeFile(ws io.WriteSeeker, kv ggml.KV, ts []ggml.Tensor) error {
-	return ggml.WriteGGUF(ws, kv, ts)
-}
-
-func (AdapterParameters) writeFile(ws io.WriteSeeker, kv ggml.KV, ts []ggml.Tensor) error {
-	return ggml.WriteGGUF(ws, kv, ts)
-}
-
 type ModelConverter interface {
 	// KV maps parameters to LLM key-values
 	KV(*Tokenizer) ggml.KV
@@ -103,8 +96,6 @@ type ModelConverter interface {
 
 	// specialTokenTypes returns any special token types the model uses
 	specialTokenTypes() []string
-	// writeFile writes the model to the provided io.WriteSeeker
-	writeFile(io.WriteSeeker, ggml.KV, []ggml.Tensor) error
 }
 
 type moreParser interface {
@@ -119,8 +110,6 @@ type AdapterConverter interface {
 	// Replacements returns a list of string pairs to replace in tensor names.
 	// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
 	Replacements() []string
-
-	writeFile(io.WriteSeeker, ggml.KV, []ggml.Tensor) error
 }
 
 func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV ggml.KV) error {
@@ -158,7 +147,7 @@ func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV ggml.KV) error {
 		return err
 	}
 
-	return conv.writeFile(ws, conv.KV(baseKV), conv.Tensors(ts))
+	return writeFile(ws, conv.KV(baseKV), conv.Tensors(ts))
 }
 
 // Convert writes an Ollama compatible model to the provided io.WriteSeeker based on configurations
@@ -184,6 +173,8 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 	switch p.Architectures[0] {
 	case "LlamaForCausalLM":
 		conv = &llamaModel{}
+	case "Llama4ForConditionalGeneration":
+		conv = &llama4Model{}
 	case "Mistral3ForConditionalGeneration":
 		conv = &mistral3Model{}
 	case "MixtralForCausalLM":
@@ -248,5 +239,13 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 		return err
 	}
 
-	return conv.writeFile(ws, conv.KV(t), conv.Tensors(ts))
+	return writeFile(ws, conv.KV(t), conv.Tensors(ts))
+}
+
+func writeFile(ws io.WriteSeeker, kv ggml.KV, ts []ggml.Tensor) error {
+	for i := range ts {
+		ts[i].Shape = slices.Clone(ts[i].Shape)
+		slices.Reverse(ts[i].Shape)
+	}
+	return ggml.WriteGGUF(ws, kv, ts)
 }

convert/convert_llama.go

@@ -42,6 +42,8 @@ type llamaModel struct {
 	LayerNormEpsilon float32 `json:"layer_norm_epsilon"`
 	NormEpsilon      float32 `json:"norm_epsilon"`
 	HeadDim          uint32  `json:"head_dim"`
+
+	skipRepack bool
 }
 
 var _ ModelConverter = (*llamaModel)(nil)
@@ -70,6 +72,10 @@ func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
 		kv["llama.rope.dimension_count"] = p.HiddenSize / headCount
 	}
 
+	if p.HeadDim > 0 {
+		kv["llama.attention.head_dim"] = p.HeadDim
+	}
+
 	if p.RopeTheta > 0 {
 		kv["llama.rope.freq_base"] = p.RopeTheta
 	}
@@ -133,9 +139,10 @@ func (p *llamaModel) Tensors(ts []Tensor) []ggml.Tensor {
 	}
 
 	for _, t := range ts {
-		if strings.HasSuffix(t.Name(), "attn_q.weight") ||
+		if strings.HasSuffix(t.Name(), "attn_q.weight") || strings.HasSuffix(t.Name(), "attn_k.weight") {
-			strings.HasSuffix(t.Name(), "attn_k.weight") {
+			if !p.skipRepack {
-			t.SetRepacker(p.repack)
+				t.SetRepacker(p.repack)
+			}
 		}
 
 		out = append(out, ggml.Tensor{

convert/convert_llama4.go

@@ -0,0 +1,169 @@
+package convert
+
+import (
+	"slices"
+	"strings"
+
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+type llama4Model struct {
+	ModelParameters
+	TextModel struct {
+		llamaModel
+		NumExpertsPerToken     uint32 `json:"num_experts_per_tok"`
+		NumLocalExperts        uint32 `json:"num_local_experts"`
+		InterleaveMOELayerStep uint32 `json:"interleave_moe_layer_step"`
+		UseQKNorm              bool   `json:"use_qk_norm"`
+		IntermediateSizeMLP    uint32 `json:"intermediate_size_mlp"`
+		AttentionChunkSize     uint32 `json:"attention_chunk_size"`
+	} `json:"text_config"`
+	VisionModel struct {
+		NumHiddenLayers   uint32  `json:"num_hidden_layers"`
+		HiddenSize        uint32  `json:"hidden_size"`
+		IntermediateSize  uint32  `json:"intermediate_size"`
+		NumAttentionHeads uint32  `json:"num_attention_heads"`
+		ImageSize         uint32  `json:"image_size"`
+		PatchSize         uint32  `json:"patch_size"`
+		RopeTheta         float32 `json:"rope_theta"`
+		NormEpsilon       float32 `json:"norm_eps"`
+		PixelShuffleRatio float32 `json:"pixel_shuffle_ratio"`
+	} `json:"vision_config"`
+}
+
+// KV implements ModelConverter.
+func (p *llama4Model) KV(t *Tokenizer) ggml.KV {
+	kv := p.ModelParameters.KV(t)
+	kv["general.architecture"] = "llama4"
+
+	for k, v := range p.TextModel.KV(t) {
+		if strings.HasPrefix(k, "llama.") {
+			kv[strings.ReplaceAll(k, "llama.", "llama4.")] = v
+		}
+	}
+
+	kv["llama4.feed_forward_length"] = p.TextModel.IntermediateSizeMLP
+	kv["llama4.expert_feed_forward_length"] = p.TextModel.IntermediateSize
+
+	kv["llama4.expert_count"] = p.TextModel.NumLocalExperts
+	kv["llama4.expert_used_count"] = p.TextModel.NumExpertsPerToken
+	kv["llama4.interleave_moe_layer_step"] = p.TextModel.InterleaveMOELayerStep
+	kv["llama4.use_qk_norm"] = p.TextModel.UseQKNorm
+	kv["llama4.attention.chunk_size"] = p.TextModel.AttentionChunkSize
+
+	kv["llama4.vision.block_count"] = p.VisionModel.NumHiddenLayers
+	kv["llama4.vision.embedding_length"] = p.VisionModel.HiddenSize
+	kv["llama4.vision.feed_forward_length"] = p.VisionModel.IntermediateSize
+	kv["llama4.vision.attention.head_count"] = p.VisionModel.NumAttentionHeads
+	kv["llama4.vision.image_size"] = p.VisionModel.ImageSize
+	kv["llama4.vision.patch_size"] = p.VisionModel.PatchSize
+	kv["llama4.vision.rope.freq_base"] = p.VisionModel.RopeTheta
+	kv["llama4.vision.layer_norm_epsilon"] = p.VisionModel.NormEpsilon
+	kv["llama4.vision.pixel_shuffle_ratio"] = p.VisionModel.PixelShuffleRatio
+	return kv
+}
+
+// Replacements implements ModelConverter.
+func (p *llama4Model) Replacements() []string {
+	return append(
+		p.TextModel.Replacements(),
+		"language_model.", "",
+		"vision_model", "v",
+		"multi_modal_projector", "mm",
+		"feed_forward.down_proj", "ffn_down",
+		"feed_forward.up_proj", "ffn_up",
+		"feed_forward.gate_proj", "ffn_gate",
+		"feed_forward.", "ffn_",
+		"shared_expert.down_proj", "down_shexp",
+		"shared_expert.gate_proj", "gate_shexp",
+		"shared_expert.up_proj", "up_shexp",
+		"experts.down_proj", "down_exps.weight",
+		"experts.gate_up_proj", "gate_up_exps.weight",
+		"router", "gate_inp",
+		"patch_embedding.linear", "patch_embedding",
+	)
+}
+
+// Tensors implements ModelConverter.
+func (p *llama4Model) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
+
+	var textTensors []Tensor
+	for _, t := range ts {
+		if strings.HasPrefix(t.Name(), "v.") || strings.HasPrefix(t.Name(), "mm.") {
+			out = append(out, ggml.Tensor{
+				Name:     t.Name(),
+				Kind:     t.Kind(),
+				Shape:    t.Shape(),
+				WriterTo: t,
+			})
+		} else if strings.Contains(t.Name(), "ffn_gate_up_exps") {
+			// gate and up projectors are fused
+			// dims[1], dims[2] must be swapped
+			// [experts, hidden_size, intermediate_size * 2] --> [experts, intermediate_size, hidden_size]
+			halfDim := int(t.Shape()[2]) / 2
+
+			newShape := slices.Clone(t.Shape())
+			newShape[1], newShape[2] = newShape[2]/2, newShape[1]
+			for i, name := range []string{"ffn_gate_exps", "ffn_up_exps"} {
+				// clone tensor since we need separate repackers
+				tt := t.Clone()
+				tt.SetRepacker(p.repack(nil, nil, tensor.S(i*halfDim, (i+1)*halfDim)))
+				out = append(out, ggml.Tensor{
+					Name:     strings.ReplaceAll(tt.Name(), "ffn_gate_up_exps", name),
+					Kind:     tt.Kind(),
+					Shape:    newShape,
+					WriterTo: tt,
+				})
+			}
+		} else if strings.Contains(t.Name(), "ffn_down_exps") {
+			// dims[1], dims[2] must be swapped
+			// [experts, intermediate_size, hidden_size] --> [experts, hidden_size, intermediate_size]
+			t.SetRepacker(p.repack())
+			newShape := slices.Clone(t.Shape())
+			newShape[1], newShape[2] = newShape[2], newShape[1]
+			out = append(out, ggml.Tensor{
+				Name:     t.Name(),
+				Kind:     t.Kind(),
+				Shape:    newShape,
+				WriterTo: t,
+			})
+		} else {
+			textTensors = append(textTensors, t)
+		}
+	}
+
+	p.TextModel.skipRepack = true
+	out = append(out, p.TextModel.Tensors(textTensors)...)
+	return out
+}
+
+func (p *llama4Model) repack(slice ...tensor.Slice) Repacker {
+	return func(name string, data []float32, shape []uint64) ([]float32, error) {
+		dims := make([]int, len(shape))
+		for i, dim := range shape {
+			dims[i] = int(dim)
+		}
+
+		var t tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
+		t, err := t.Slice(slice...)
+		if err != nil {
+			return nil, err
+		}
+
+		if err := t.T(0, 2, 1); err != nil {
+			return nil, err
+		}
+
+		t = tensor.Materialize(t)
+		// flatten tensor so it can be return as a vector
+		if err := t.Reshape(t.Shape().TotalSize()); err != nil {
+			return nil, err
+		}
+
+		return native.VectorF32(t.(*tensor.Dense))
+	}
+}

convert/convert_test.go

@@ -11,7 +11,6 @@ import (
 	"io"
 	"io/fs"
 	"log/slog"
-	"math"
 	"os"
 	"path/filepath"
 	"slices"
@@ -48,7 +47,7 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
 	}
 	t.Cleanup(func() { r.Close() })
 
-	m, _, err := ggml.Decode(r, math.MaxInt)
+	m, _, err := ggml.Decode(r, -1)
 	if err != nil {
 		t.Fatal(err)
 	}
@@ -332,7 +331,7 @@ func TestConvertAdapter(t *testing.T) {
 			}
 			defer r.Close()
 
-			m, _, err := ggml.Decode(r, math.MaxInt)
+			m, _, err := ggml.Decode(r, -1)
 			if err != nil {
 				t.Fatal(err)
 			}

convert/reader.go

@@ -11,14 +11,15 @@ type Tensor interface {
 	Name() string
 	Shape() []uint64
 	Kind() uint32
-	SetRepacker(repacker)
+	SetRepacker(Repacker)
 	WriteTo(io.Writer) (int64, error)
+	Clone() Tensor
 }
 
 type tensorBase struct {
-	name  string
+	name     string
-	shape []uint64
+	shape    []uint64
-	repacker
+	repacker Repacker
 }
 
 func (t tensorBase) Name() string {
@@ -36,7 +37,8 @@ const (
 
 func (t tensorBase) Kind() uint32 {
 	if strings.HasSuffix(t.name, ".ffn_gate_inp.weight") ||
-		t.name == "token_types.weight" {
+		t.name == "token_types.weight" ||
+		t.name == "v.positional_embedding_vlm" {
 		// these tensors are always F32
 		return 0
 	}
@@ -51,11 +53,11 @@ func (t tensorBase) Kind() uint32 {
 	}
 }
 
-func (t *tensorBase) SetRepacker(fn repacker) {
+func (t *tensorBase) SetRepacker(fn Repacker) {
 	t.repacker = fn
 }
 
-type repacker func(string, []float32, []uint64) ([]float32, error)
+type Repacker func(string, []float32, []uint64) ([]float32, error)
 
 func parseTensors(fsys fs.FS, replacer *strings.Replacer) ([]Tensor, error) {
 	patterns := []struct {

convert/reader_safetensors.go

@@ -94,6 +94,21 @@ type safetensor struct {
 	*tensorBase
 }
 
+func (st safetensor) Clone() Tensor {
+	return &safetensor{
+		fs:     st.fs,
+		path:   st.path,
+		dtype:  st.dtype,
+		offset: st.offset,
+		size:   st.size,
+		tensorBase: &tensorBase{
+			name:     st.name,
+			repacker: st.repacker,
+			shape:    slices.Clone(st.shape),
+		},
+	}
+}
+
 func (st safetensor) WriteTo(w io.Writer) (int64, error) {
 	f, err := st.fs.Open(st.path)
 	if err != nil {

convert/reader_torch.go

@@ -43,6 +43,17 @@ type torch struct {
 	*tensorBase
 }
 
+func (t torch) Clone() Tensor {
+	return torch{
+		storage: t.storage,
+		tensorBase: &tensorBase{
+			name:     t.name,
+			shape:    t.shape,
+			repacker: t.repacker,
+		},
+	}
+}
+
 func (pt torch) WriteTo(w io.Writer) (int64, error) {
 	return 0, nil
 }

fs/config.go

@@ -8,6 +8,6 @@ type Config interface {
 	Bool(string, ...bool) bool
 
 	Strings(string, ...[]string) []string
-	Uints(string, ...[]uint32) []uint32
+	Ints(string, ...[]int32) []int32
 	Floats(string, ...[]float32) []float32
 }

fs/ggml/ggml.go

@@ -33,7 +33,7 @@ func (kv KV) Kind() string {
 }
 
 func (kv KV) ParameterCount() uint64 {
-	return keyValue[uint64](kv, "general.parameter_count")
+	return keyValue(kv, "general.parameter_count", uint64(0))
 }
 
 func (kv KV) FileType() fileType {
@@ -105,42 +105,42 @@ func (kv KV) Bool(key string, defaultValue ...bool) bool {
 }
 
 func (kv KV) Strings(key string, defaultValue ...[]string) []string {
-	r := keyValue(kv, key, &array{})
+	return keyValue(kv, key, &array[string]{values: append(defaultValue, []string(nil))[0]}).values
-	s := make([]string, r.size)
+}
-	for i := range r.size {
-		s[i] = r.values[i].(string)
-	}
 
-	return s
+func (kv KV) Ints(key string, defaultValue ...[]int32) []int32 {
+	return keyValue(kv, key, &array[int32]{values: append(defaultValue, []int32(nil))[0]}).values
 }
 
 func (kv KV) Uints(key string, defaultValue ...[]uint32) []uint32 {
-	r := keyValue(kv, key, &array{})
+	return keyValue(kv, key, &array[uint32]{values: append(defaultValue, []uint32(nil))[0]}).values
-	s := make([]uint32, r.size)
-	for i := range r.size {
-		s[i] = uint32(r.values[i].(int32))
-	}
-
-	return s
 }
 
 func (kv KV) Floats(key string, defaultValue ...[]float32) []float32 {
-	r := keyValue(kv, key, &array{})
+	return keyValue(kv, key, &array[float32]{values: append(defaultValue, []float32(nil))[0]}).values
-	s := make([]float32, r.size)
-	for i := range r.size {
-		s[i] = float32(r.values[i].(float32))
-	}
-	return s
 }
 
 func (kv KV) OllamaEngineRequired() bool {
 	return slices.Contains([]string{
 		"gemma3",
 		"mistral3",
+		"llama4",
 	}, kv.Architecture())
 }
 
-func keyValue[T string | uint32 | uint64 | float32 | *array | bool](kv KV, key string, defaultValue ...T) T {
+type valueTypes interface {
+	uint8 | int8 | uint16 | int16 |
+		uint32 | int32 | uint64 | int64 |
+		string | float32 | float64 | bool
+}
+
+type arrayValueTypes interface {
+	*array[uint8] | *array[int8] | *array[uint16] | *array[int16] |
+		*array[uint32] | *array[int32] | *array[uint64] | *array[int64] |
+		*array[string] | *array[float32] | *array[float64] | *array[bool]
+}
+
+func keyValue[T valueTypes | arrayValueTypes](kv KV, key string, defaultValue ...T) T {
 	if !strings.HasPrefix(key, "tokenizer.") && !strings.HasPrefix(key, "general.") {
 		key = kv.Architecture() + "." + key
 	}
@@ -375,13 +375,8 @@ func DetectContentType(b []byte) string {
 // Decode decodes a GGML model from the given reader.
 //
 // It collects array values for arrays with a size less than or equal to
-// maxArraySize. If maxArraySize is 0, the default value of 1024 is used. If
+// maxArraySize. If the maxArraySize is negative, all arrays are collected.
-// the maxArraySize is negative, all arrays are collected.
 func Decode(rs io.ReadSeeker, maxArraySize int) (*GGML, int64, error) {
-	if maxArraySize == 0 {
-		maxArraySize = 1024
-	}
-
 	rs = bufioutil.NewBufferedSeeker(rs, 32<<10)
 
 	var magic uint32
@@ -420,7 +415,7 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 	embedding := f.KV().EmbeddingLength()
 	heads := f.KV().HeadCount()
 	headsKV := f.KV().HeadCountKV()
-	vocab := uint64(f.KV()["tokenizer.ggml.tokens"].(*array).size)
+	vocab := uint64(f.KV()["tokenizer.ggml.tokens"].(*array[string]).size)
 
 	embeddingHeads := f.KV().EmbeddingHeadCount()
 	embeddingHeadsK := f.KV().EmbeddingHeadCountK()
@@ -435,7 +430,7 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 	}
 
 	switch f.KV().Architecture() {
-	case "llama":
+	case "llama", "llama4":
 		fullOffload = max(
 			4*batch*(1+4*embedding+context*(1+heads)),
 			4*batch*(embedding+vocab),
@@ -449,7 +444,7 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 
 		if ffnGateExpsWeight, ok := layers["blk.0"]["ffn_gate_exps.weight"]; ok {
 			// mixtral 8x22b
-			ff := uint64(f.KV()["llama.feed_forward_length"].(uint32))
+			ff := uint64(f.KV().Uint("feed_forward_length"))
 			partialOffload = max(
 				3*ffnGateExpsWeight.Size()+4*batch*(2*ff+headsKV+embedding+context+embeddingHeads*headsKV),
 				4*(context*batch*heads+context*embeddingHeads*headsKV+batch*1024+embeddingHeads*headsKV*batch),
@@ -466,9 +461,9 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 	case "mllama":
 		var visionTokens, tiles uint64 = 1601, 4
 
-		crossAttentionLayers := f.KV().Uints("attention.cross_attention_layers")
+		crossAttentionLayers := f.KV().Ints("attention.cross_attention_layers")
 		for i := range kv {
-			if slices.Contains(crossAttentionLayers, uint32(i)) {
+			if slices.Contains(crossAttentionLayers, int32(i)) {
 				kv[i] = headsKV * (embeddingHeadsK + embeddingHeadsV) *
 					4 * // sizeof(float32)
 					visionTokens *
@@ -645,6 +640,9 @@ func (llm GGML) VisionGraphSize() (weights, graphSize uint64) {
 		graphSize = 4 * (imageSize*imageSize*numChannels +
 			embeddingLength*patchSize +
 			numPatches*numPatches*headCount)
+	case "llama4":
+		// vision graph is computed independently in the same schedule
+		// and is negligible compared to the worst case text graph
 	}
 
 	return weights, graphSize

fs/ggml/ggml_test.go

@@ -2,6 +2,7 @@ package ggml
 
 import (
 	"maps"
+	"math"
 	"slices"
 	"strconv"
 	"strings"
@@ -210,3 +211,61 @@ func TestTensorTypes(t *testing.T) {
 		})
 	}
 }
+
+func TestKeyValue(t *testing.T) {
+	kv := KV{
+		"general.architecture": "test",
+		"test.strings":         &array[string]{size: 3, values: []string{"a", "b", "c"}},
+		"test.float32s":        &array[float32]{size: 3, values: []float32{1.0, 2.0, 3.0}},
+		"test.int32s":          &array[int32]{size: 3, values: []int32{1, 2, 3}},
+		"test.uint32s":         &array[uint32]{size: 3, values: []uint32{1, 2, 3}},
+	}
+
+	if diff := cmp.Diff(kv.Strings("strings"), []string{"a", "b", "c"}); diff != "" {
+		t.Errorf("unexpected strings (-got +want):\n%s", diff)
+	}
+
+	if diff := cmp.Diff(kv.Strings("nonexistent.strings"), []string(nil)); diff != "" {
+		t.Errorf("unexpected strings (-got +want):\n%s", diff)
+	}
+
+	if diff := cmp.Diff(kv.Strings("default.strings", []string{"ollama"}), []string{"ollama"}); diff != "" {
+		t.Errorf("unexpected strings (-got +want):\n%s", diff)
+	}
+
+	if diff := cmp.Diff(kv.Floats("float32s"), []float32{1.0, 2.0, 3.0}); diff != "" {
+		t.Errorf("unexpected float32s (-got +want):\n%s", diff)
+	}
+
+	if diff := cmp.Diff(kv.Floats("nonexistent.float32s"), []float32(nil)); diff != "" {
+		t.Errorf("unexpected float32s (-got +want):\n%s", diff)
+	}
+
+	if diff := cmp.Diff(kv.Floats("default.float32s", []float32{math.MaxFloat32}), []float32{math.MaxFloat32}); diff != "" {
+		t.Errorf("unexpected float32s (-got +want):\n%s", diff)
+	}
+
+	if diff := cmp.Diff(kv.Ints("int32s"), []int32{1, 2, 3}); diff != "" {
+		t.Errorf("unexpected int8s (-got +want):\n%s", diff)
+	}
+
+	if diff := cmp.Diff(kv.Ints("nonexistent.int32s"), []int32(nil)); diff != "" {
+		t.Errorf("unexpected int8s (-got +want):\n%s", diff)
+	}
+
+	if diff := cmp.Diff(kv.Ints("default.int32s", []int32{math.MaxInt32}), []int32{math.MaxInt32}); diff != "" {
+		t.Errorf("unexpected int8s (-got +want):\n%s", diff)
+	}
+
+	if diff := cmp.Diff(kv.Uints("uint32s"), []uint32{1, 2, 3}); diff != "" {
+		t.Errorf("unexpected uint8s (-got +want):\n%s", diff)
+	}
+
+	if diff := cmp.Diff(kv.Uints("nonexistent.uint32s"), []uint32(nil)); diff != "" {
+		t.Errorf("unexpected uint8s (-got +want):\n%s", diff)
+	}
+
+	if diff := cmp.Diff(kv.Uints("default.uint32s", []uint32{math.MaxUint32}), []uint32{math.MaxUint32}); diff != "" {
+		t.Errorf("unexpected uint8s (-got +want):\n%s", diff)
+	}
+}

fs/ggml/gguf.go

@@ -36,10 +36,6 @@ type containerGGUF struct {
 	maxArraySize int
 }
 
-func (c *containerGGUF) canCollectArray(size int) bool {
-	return c.maxArraySize < 0 || size <= c.maxArraySize
-}
-
 func (c *containerGGUF) Name() string {
 	return "gguf"
 }
@@ -295,6 +291,23 @@ func readGGUFV1String(llm *gguf, r io.Reader) (string, error) {
 	return b.String(), nil
 }
 
+func readGGUFV1StringsData(llm *gguf, r io.Reader, a *array[string]) (any, error) {
+	for i := range a.size {
+		if a.values != nil {
+			e, err := readGGUFV1String(llm, r)
+			if err != nil {
+				return nil, err
+			}
+
+			a.values[i] = e
+		} else {
+			discardGGUFString(llm, r)
+		}
+	}
+
+	return a, nil
+}
+
 func discardGGUFString(llm *gguf, r io.Reader) error {
 	buf := llm.scratch[:8]
 	_, err := io.ReadFull(r, buf)
@@ -352,78 +365,44 @@ func writeGGUFString(w io.Writer, s string) error {
 	return err
 }
 
-type array struct {
+func readGGUFStringsData(llm *gguf, r io.Reader, a *array[string]) (any, error) {
-	size   int
+	for i := range a.size {
-	values []any
-}
-
-func (a *array) MarshalJSON() ([]byte, error) {
-	return json.Marshal(a.values)
-}
-
-func readGGUFV1Array(llm *gguf, r io.Reader) (*array, error) {
-	t, err := readGGUF[uint32](llm, r)
-	if err != nil {
-		return nil, err
-	}
-
-	n, err := readGGUF[uint32](llm, r)
-	if err != nil {
-		return nil, err
-	}
-
-	a := &array{size: int(n)}
-	if llm.canCollectArray(int(n)) {
-		a.values = make([]any, 0, int(n))
-	}
-
-	for i := range n {
-		var e any
-		switch t {
-		case ggufTypeUint8:
-			e, err = readGGUF[uint8](llm, r)
-		case ggufTypeInt8:
-			e, err = readGGUF[int8](llm, r)
-		case ggufTypeUint16:
-			e, err = readGGUF[uint16](llm, r)
-		case ggufTypeInt16:
-			e, err = readGGUF[int16](llm, r)
-		case ggufTypeUint32:
-			e, err = readGGUF[uint32](llm, r)
-		case ggufTypeInt32:
-			e, err = readGGUF[int32](llm, r)
-		case ggufTypeUint64:
-			e, err = readGGUF[uint64](llm, r)
-		case ggufTypeInt64:
-			e, err = readGGUF[int64](llm, r)
-		case ggufTypeFloat32:
-			e, err = readGGUF[float32](llm, r)
-		case ggufTypeFloat64:
-			e, err = readGGUF[float64](llm, r)
-		case ggufTypeBool:
-			e, err = readGGUF[bool](llm, r)
-		case ggufTypeString:
-			e, err = readGGUFV1String(llm, r)
-		default:
-			return nil, fmt.Errorf("invalid array type: %d", t)
-		}
-		if err != nil {
-			return nil, err
-		}
-
 		if a.values != nil {
+			e, err := readGGUFString(llm, r)
+			if err != nil {
+				return nil, err
+			}
+
 			a.values[i] = e
+		} else {
+			discardGGUFString(llm, r)
 		}
 	}
 
 	return a, nil
 }
 
-func readGGUFArray(llm *gguf, r io.Reader) (*array, error) {
+type array[T any] struct {
-	if llm.Version == 1 {
+	// size is the actual size of the array
-		return readGGUFV1Array(llm, r)
+	size int
-	}
 
+	// values is the array of values. this is nil if the array is larger than configured maxSize
+	values []T
+}
+
+func (a *array[T]) MarshalJSON() ([]byte, error) {
+	return json.Marshal(a.values)
+}
+
+func newArray[T any](size, maxSize int) *array[T] {
+	a := array[T]{size: size}
+	if maxSize < 0 || size <= maxSize {
+		a.values = make([]T, size)
+	}
+	return &a
+}
+
+func readGGUFArray(llm *gguf, r io.Reader) (any, error) {
 	t, err := readGGUF[uint32](llm, r)
 	if err != nil {
 		return nil, err
@@ -434,45 +413,55 @@ func readGGUFArray(llm *gguf, r io.Reader) (*array, error) {
 		return nil, err
 	}
 
-	a := &array{size: int(n)}
+	switch t {
-	if llm.canCollectArray(int(n)) {
+	case ggufTypeUint8:
-		a.values = make([]any, int(n))
+		a := newArray[uint8](int(n), llm.maxArraySize)
-	}
+		return readGGUFArrayData(llm, r, a)
-
+	case ggufTypeInt8:
-	for i := range n {
+		a := newArray[int8](int(n), llm.maxArraySize)
-		var e any
+		return readGGUFArrayData(llm, r, a)
-		switch t {
+	case ggufTypeUint16:
-		case ggufTypeUint8:
+		a := newArray[uint16](int(n), llm.maxArraySize)
-			e, err = readGGUF[uint8](llm, r)
+		return readGGUFArrayData(llm, r, a)
-		case ggufTypeInt8:
+	case ggufTypeInt16:
-			e, err = readGGUF[int8](llm, r)
+		a := newArray[int16](int(n), llm.maxArraySize)
-		case ggufTypeUint16:
+		return readGGUFArrayData(llm, r, a)
-			e, err = readGGUF[uint16](llm, r)
+	case ggufTypeUint32:
-		case ggufTypeInt16:
+		a := newArray[uint32](int(n), llm.maxArraySize)
-			e, err = readGGUF[int16](llm, r)
+		return readGGUFArrayData(llm, r, a)
-		case ggufTypeUint32:
+	case ggufTypeInt32:
-			e, err = readGGUF[uint32](llm, r)
+		a := newArray[int32](int(n), llm.maxArraySize)
-		case ggufTypeInt32:
+		return readGGUFArrayData(llm, r, a)
-			e, err = readGGUF[int32](llm, r)
+	case ggufTypeUint64:
-		case ggufTypeUint64:
+		a := newArray[uint64](int(n), llm.maxArraySize)
-			e, err = readGGUF[uint64](llm, r)
+		return readGGUFArrayData(llm, r, a)
-		case ggufTypeInt64:
+	case ggufTypeInt64:
-			e, err = readGGUF[int64](llm, r)
+		a := newArray[int64](int(n), llm.maxArraySize)
-		case ggufTypeFloat32:
+		return readGGUFArrayData(llm, r, a)
-			e, err = readGGUF[float32](llm, r)
+	case ggufTypeFloat32:
-		case ggufTypeFloat64:
+		a := newArray[float32](int(n), llm.maxArraySize)
-			e, err = readGGUF[float64](llm, r)
+		return readGGUFArrayData(llm, r, a)
-		case ggufTypeBool:
+	case ggufTypeFloat64:
-			e, err = readGGUF[bool](llm, r)
+		a := newArray[float64](int(n), llm.maxArraySize)
-		case ggufTypeString:
+		return readGGUFArrayData(llm, r, a)
-			if a.values != nil {
+	case ggufTypeBool:
-				e, err = readGGUFString(llm, r)
+		a := newArray[bool](int(n), llm.maxArraySize)
-			} else {
+		return readGGUFArrayData(llm, r, a)
-				err = discardGGUFString(llm, r)
+	case ggufTypeString:
-			}
+		a := newArray[string](int(n), llm.maxArraySize)
-		default:
+		if llm.Version == 1 {
-			return nil, fmt.Errorf("invalid array type: %d", t)
+			return readGGUFV1StringsData(llm, r, a)
 		}
+
+		return readGGUFStringsData(llm, r, a)
+	default:
+		return nil, fmt.Errorf("invalid array type: %d", t)
+	}
+}
+
+func readGGUFArrayData[T any](llm *gguf, r io.Reader, a *array[T]) (any, error) {
+	for i := range a.size {
+		e, err := readGGUF[T](llm, r)
 		if err != nil {
 			return nil, err
 		}
@@ -627,8 +616,8 @@ func ggufWriteTensorInfo(ws io.WriteSeeker, t Tensor) error {
 		return err
 	}
 
-	for i := range len(t.Shape) {
+	for _, n := range t.Shape {
-		if err := binary.Write(ws, binary.LittleEndian, t.Shape[len(t.Shape)-i-1]); err != nil {
+		if err := binary.Write(ws, binary.LittleEndian, n); err != nil {
 			return err
 		}
 	}

kvcache/causal.go

@@ -21,6 +21,7 @@ type shiftFn func(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, e
 type Causal struct {
 	DType      ml.DType
 	windowSize int32
+	chunkSize  int32
 
 	opts CausalOptions
 
@@ -97,6 +98,17 @@ func NewSWACache(windowSize int32, shift shiftFn) *Causal {
 	}
 }
 
+func NewChunkedAttentionCache(chunkSize int32, shift shiftFn) *Causal {
+	return &Causal{
+		windowSize: math.MaxInt32,
+		chunkSize:  chunkSize,
+		shiftFn:    shift,
+		ctxs:       make(map[int]ml.Context),
+		keys:       make(map[int]ml.Tensor),
+		values:     make(map[int]ml.Tensor),
+	}
+}
+
 func (c *Causal) Init(backend ml.Backend, dtype ml.DType, maxSequences, capacity, maxBatch int) {
 	if c.config == nil {
 		var config ml.CacheConfig
@@ -300,6 +312,7 @@ func (c *Causal) buildMask(ctx ml.Context) (ml.Tensor, error) {
 		for j := c.curCellRange.min; j <= c.curCellRange.max; j++ {
 			if !slices.Contains(c.cells[j].sequences, c.curSequences[i]) ||
 				(enabled && c.cells[j].pos > c.curPositions[i]) ||
+				c.chunkSize > 0 && c.cells[j].pos < c.curPositions[i]-c.curPositions[i]%c.chunkSize ||
 				c.cells[j].pos < c.curPositions[i]-c.windowSize {
 				mask[i*length+(j-c.curCellRange.min)] = float32(math.Inf(-1))
 			}

kvcache/causal_test.go

@@ -86,6 +86,64 @@ func TestSWA(t *testing.T) {
 	testCache(t, backend, cache, tests)
 }
 
+func TestChunkedAttention(t *testing.T) {
+	cache := NewChunkedAttentionCache(2, nil)
+	defer cache.Close()
+
+	var b testBackend
+	cache.Init(&b, ml.DTypeF16, 1, 16, 16)
+
+	x := float32(math.Inf(-1))
+
+	testCache(
+		t, &b, cache,
+		[]testCase{
+			{
+				name:          "FirstBatch",
+				in:            []float32{1, 2, 3, 4},
+				inShape:       []int{1, 1, 4},
+				seqs:          []int{0, 0, 0, 0},
+				pos:           []int32{0, 1, 2, 3},
+				expected:      []float32{1, 2, 3, 4},
+				expectedShape: []int{1, 1, 4},
+				expectedMask: []float32{
+					0, x, x, x,
+					0, 0, x, x,
+					x, x, 0, x,
+					x, x, 0, 0,
+				},
+			},
+			{
+				name:          "SecondBatch",
+				in:            []float32{5, 6, 7},
+				inShape:       []int{1, 1, 3},
+				seqs:          []int{0, 0, 0},
+				pos:           []int32{4, 5, 6},
+				expected:      []float32{1, 2, 3, 4, 5, 6, 7},
+				expectedShape: []int{1, 1, 7},
+				expectedMask: []float32{
+					x, x, x, x, 0, x, x,
+					x, x, x, x, 0, 0, x,
+					x, x, x, x, x, x, 0,
+				},
+			},
+			{
+				name:          "ThirdBatch",
+				in:            []float32{8, 9},
+				inShape:       []int{1, 1, 2},
+				seqs:          []int{0, 0},
+				pos:           []int32{7, 8},
+				expected:      []float32{1, 2, 3, 4, 5, 6, 7, 8, 9},
+				expectedShape: []int{1, 1, 9},
+				expectedMask: []float32{
+					x, x, x, x, x, x, 0, 0, x,
+					x, x, x, x, x, x, x, x, 0,
+				},
+			},
+		},
+	)
+}
+
 func TestSequences(t *testing.T) {
 	backend := &testBackend{}
 	cache := NewCausalCache(nil)
@@ -293,8 +351,16 @@ func testCache(t *testing.T, backend ml.Backend, cache Cache, tests []testCase)
 
 			context.Forward(out, mask).Compute(out, mask)
 
-			if !slices.Equal(out.Floats(), test.expected) || !slices.Equal(out.Shape(), test.expectedShape) || !slices.Equal(mask.Floats(), test.expectedMask) {
+			if !slices.Equal(out.Floats(), test.expected) {
-				t.Errorf("TestCache: have %v (shape %v); want %v (shape %v); mask: have %v (shape %v) want %v", out.Floats(), out.Shape(), test.expected, test.expectedShape, mask.Floats(), mask.Shape(), test.expectedMask)
+				t.Errorf("TestCache: have %v; want %v", out.Floats(), test.expected)
+			}
+
+			if !slices.Equal(out.Shape(), test.expectedShape) {
+				t.Errorf("TestCache: has shape %v; want %v", out.Shape(), test.expectedShape)
+			}
+
+			if !slices.Equal(mask.Floats(), test.expectedMask) {
+				t.Errorf("TestCache: have mask: have %v want %v", mask.Floats(), test.expectedMask)
 			}
 		})
 	}

llm/memory.go

@@ -414,7 +414,7 @@ func projectorMemoryRequirements(filename string) (weights, graphSize uint64) {
 	}
 	defer file.Close()
 
-	ggml, _, err := ggml.Decode(file, 0)
+	ggml, _, err := ggml.Decode(file, 1024)
 	if err != nil {
 		return 0, 0
 	}

ml/backend.go

@@ -133,6 +133,7 @@ type Tensor interface {
 	Mul(ctx Context, t2 Tensor) Tensor
 	Mulmat(ctx Context, t2 Tensor) Tensor
 	MulmatFullPrec(ctx Context, t2 Tensor) Tensor
+	MulmatID(ctx Context, t2, ids Tensor) Tensor
 
 	Softmax(ctx Context) Tensor
 	LayerNorm(ctx Context, weight, bias Tensor, eps float32) Tensor
@@ -150,6 +151,7 @@ type Tensor interface {
 	Tanh(ctx Context) Tensor
 	GELU(ctx Context) Tensor
 	SILU(ctx Context) Tensor
+	Sigmoid(ctx Context) Tensor
 
 	Reshape(ctx Context, shape ...int) Tensor
 	View(ctx Context, offset int, shape ...int) Tensor
@@ -168,6 +170,8 @@ type Tensor interface {
 	Rows(ctx Context, t2 Tensor) Tensor
 	Copy(ctx Context, t2 Tensor) Tensor
 	Duplicate(ctx Context) Tensor
+
+	TopK(ctx Context, k int) Tensor
 }
 
 // ScaledDotProductAttention implements a fused attention

ml/backend/ggml/ggml.go

@@ -884,17 +884,32 @@ func (t *Tensor) MulmatFullPrec(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
 	}
 }
 
+func (t *Tensor) MulmatID(ctx ml.Context, t2, ids ml.Tensor) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_mul_mat_id(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, ids.(*Tensor).t),
+	}
+}
+
 func (t *Tensor) LayerNorm(ctx ml.Context, w, b ml.Tensor, eps float32) ml.Tensor {
-	tt := (&Tensor{b: t.b, t: C.ggml_norm(ctx.(*Context).ctx, t.t, C.float(eps))}).Mul(ctx, w)
+	tt := C.ggml_norm(ctx.(*Context).ctx, t.t, C.float(eps))
-	if b != nil {
+	if w != nil {
-		tt = tt.Add(ctx, b)
+		tt = C.ggml_mul(ctx.(*Context).ctx, tt, w.(*Tensor).t)
+		if b != nil {
+			tt = C.ggml_add(ctx.(*Context).ctx, tt, b.(*Tensor).t)
+		}
 	}
 
-	return tt
+	return &Tensor{b: t.b, t: tt}
 }
 
 func (t *Tensor) RMSNorm(ctx ml.Context, w ml.Tensor, eps float32) ml.Tensor {
-	return (&Tensor{b: t.b, t: C.ggml_rms_norm(ctx.(*Context).ctx, t.t, C.float(eps))}).Mul(ctx, w)
+	tt := C.ggml_rms_norm(ctx.(*Context).ctx, t.t, C.float(eps))
+	if w != nil {
+		tt = C.ggml_mul(ctx.(*Context).ctx, tt, w.(*Tensor).t)
+	}
+
+	return &Tensor{b: t.b, t: tt}
 }
 
 func (t *Tensor) Pad(ctx ml.Context, shape ...int) ml.Tensor {
@@ -995,6 +1010,13 @@ func (t *Tensor) Tanh(ctx ml.Context) ml.Tensor {
 	}
 }
 
+func (t *Tensor) Sigmoid(ctx ml.Context) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_sigmoid_inplace(ctx.(*Context).ctx, t.t),
+	}
+}
+
 func (t *Tensor) Unpad(ctx ml.Context, shape ...int) ml.Tensor {
 	if len(shape) != 4 {
 		panic("expected 4 dimensions")
@@ -1158,3 +1180,10 @@ func (t *Tensor) Duplicate(ctx ml.Context) ml.Tensor {
 		t: C.ggml_dup(ctx.(*Context).ctx, t.t),
 	}
 }
+
+func (t *Tensor) TopK(ctx ml.Context, k int) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_top_k(ctx.(*Context).ctx, t.t, C.int(k)),
+	}
+}

model/models/gemma2/model.go

@@ -42,7 +42,7 @@ func New(c fs.Config) (model.Model, error) {
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Scores: c.Floats("tokenizer.ggml.scores"),
-				Types:  c.Uints("tokenizer.ggml.token_type"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
 				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id")),
 				EOS:    int32(c.Uint("tokenizer.ggml.eos_token_id")),
 			},

model/models/gemma3/model.go

@@ -59,7 +59,7 @@ func New(c fs.Config) (model.Model, error) {
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Scores: c.Floats("tokenizer.ggml.scores"),
-				Types:  c.Uints("tokenizer.ggml.token_type"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
 				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id")),
 				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
 				EOS:    int32(1),

model/models/gemma3/model_text.go

@@ -49,7 +49,7 @@ func newTextModel(c fs.Config) *TextModel {
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Scores: c.Floats("tokenizer.ggml.scores"),
-				Types:  c.Uints("tokenizer.ggml.token_type"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
 				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id")),
 				EOS:    int32(c.Uint("tokenizer.ggml.eos_token_id")),
 			},

model/models/llama/model.go

@@ -41,7 +41,7 @@ func New(c fs.Config) (model.Model, error) {
 			c.String("tokenizer.ggml.pretokenizer", `(?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+`),
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
-				Types:  c.Uints("tokenizer.ggml.token_type"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),
 				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id")),
 				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),

model/models/llama4/model.go

@@ -0,0 +1,189 @@
+package llama4
+
+import (
+	"bytes"
+	"image"
+	"slices"
+	"sync"
+
+	"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 Model struct {
+	model.Base
+	model.BytePairEncoding
+	ImageProcessor
+
+	*VisionModel `gguf:"v,vision"`
+	*Projector   `gguf:"mm"`
+	*TextModel
+}
+
+type Projector struct {
+	Linear1 *nn.Linear `gguf:"linear_1"`
+}
+
+func (p *Projector) Forward(ctx ml.Context, visionOutputs ml.Tensor) ml.Tensor {
+	return p.Linear1.Forward(ctx, visionOutputs)
+}
+
+func New(c fs.Config) (model.Model, error) {
+	m := Model{
+		BytePairEncoding: model.NewBytePairEncoding(
+			c.String("tokenizer.ggml.pretokenizer",
+				`[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n/]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
+				Merges: c.Strings("tokenizer.ggml.merges"),
+				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id")),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+				EOS:    int32(c.Uint("tokenizer.ggml.eos_token_id")),
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+			},
+		),
+		ImageProcessor: newImageProcessor(c),
+		VisionModel:    newVisionModel(c),
+		TextModel:      newTextModel(c),
+	}
+
+	m.Cache = kvcache.NewWrapperCache(
+		kvcache.NewChunkedAttentionCache(int32(c.Uint("attention.chunk_size", 8192)), m.Shift),
+		kvcache.NewCausalCache(m.Shift),
+	)
+
+	return &m, nil
+}
+
+func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) (any, error) {
+	if len(m.VisionModel.Layers) < 1 {
+		return nil, model.ErrNoVisionModel
+	}
+
+	img, _, err := image.Decode(bytes.NewReader(multimodalData))
+	if err != nil {
+		return nil, err
+	}
+
+	pixelsLocal, pixelsGlobal, size, err := m.ProcessImage(img)
+	if err != nil {
+		return nil, err
+	}
+
+	tilesLocal, err := ctx.Input().FromFloatSlice(pixelsLocal, size.X, size.Y, m.numChannels)
+	if err != nil {
+		return nil, err
+	}
+
+	ratioW, ratioH := size.X/m.imageSize, size.Y/m.imageSize
+
+	tilesLocal = tilesLocal.Reshape(ctx, size.X/ratioW, ratioW, size.Y, m.numChannels).Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	tilesLocal = tilesLocal.Reshape(ctx, size.X/ratioW*size.Y/ratioH, ratioH, ratioW, m.numChannels).Permute(ctx, 0, 3, 2, 1).Contiguous(ctx)
+	tilesLocal = tilesLocal.Reshape(ctx, size.X/ratioW, size.Y/ratioH, m.numChannels, ratioH*ratioW)
+
+	pixelValues := tilesLocal
+
+	if len(pixelsGlobal) > 0 {
+		tilesGlobal, err := ctx.Input().FromFloatSlice(pixelsGlobal, m.imageSize, m.imageSize, m.numChannels)
+		if err != nil {
+			return nil, err
+		}
+
+		pixelValues = pixelValues.Concat(ctx, tilesGlobal, 3)
+	}
+
+	visionOutputs := m.VisionModel.Forward(ctx, pixelValues)
+	visionOutputs = visionOutputs.Reshape(ctx, visionOutputs.Dim(0), visionOutputs.Dim(1)*visionOutputs.Dim(2)*visionOutputs.Dim(3))
+	projectedOutputs := m.Projector.Forward(ctx, visionOutputs)
+	return &chunks{Model: m, Tensor: projectedOutputs, aspectRatio: image.Point{ratioW, ratioH}}, nil
+}
+
+type chunks struct {
+	*Model
+	ml.Tensor
+	aspectRatio image.Point
+
+	dataOnce sync.Once
+	data     []float32
+}
+
+type chunk struct {
+	*chunks
+	s, n int
+}
+
+func (r *chunk) floats() []float32 {
+	r.dataOnce.Do(func() {
+		temp := r.Backend().NewContext()
+		defer temp.Close()
+		temp.Forward(r.Tensor).Compute(r.Tensor)
+		r.data = r.Floats()
+	})
+
+	return r.data[r.s*r.Dim(0) : (r.s+r.n)*r.Dim(0)]
+}
+
+func (m *Model) PostTokenize(inputs []input.Input) ([]input.Input, error) {
+	var result []input.Input
+	for _, inp := range inputs {
+		if inp.Multimodal == nil {
+			result = append(result, inp)
+			continue
+		}
+
+		t := inp.Multimodal.(*chunks)
+		var imageInputs []input.Input
+		imageInputs = append(imageInputs, input.Input{Token: 200080}) // <|image_start|>
+
+		var offset int
+		patchesPerChunk := t.Dim(1)
+		if t.aspectRatio.Y*t.aspectRatio.X > 1 {
+			patchesPerChunk = t.Dim(1) / (t.aspectRatio.X*t.aspectRatio.Y + 1)
+
+			for range t.aspectRatio.Y {
+				for x := range t.aspectRatio.X {
+					imageInputs = append(imageInputs, input.Input{Token: 200092, Multimodal: &chunk{t, offset, patchesPerChunk}, MultimodalHash: inp.MultimodalHash, SameBatch: patchesPerChunk}) // <|patch|>
+					imageInputs = append(imageInputs, slices.Repeat([]input.Input{{Token: 200092}}, patchesPerChunk-1)...)
+					if x < t.aspectRatio.X-1 {
+						imageInputs = append(imageInputs, input.Input{Token: 200084}) // <|tile_x_separator|>
+					}
+					offset += patchesPerChunk
+				}
+
+				imageInputs = append(imageInputs, input.Input{Token: 200085}) // <|tile_y_separator|>
+			}
+		}
+
+		imageInputs = append(imageInputs, input.Input{Token: 200090})                                                                                                                 // <|image|>
+		imageInputs = append(imageInputs, input.Input{Token: 200092, Multimodal: &chunk{t, offset, patchesPerChunk}, MultimodalHash: inp.MultimodalHash, SameBatch: patchesPerChunk}) // <|patch|>
+		imageInputs = append(imageInputs, slices.Repeat([]input.Input{{Token: 200092}}, patchesPerChunk-1)...)
+		imageInputs = append(imageInputs, input.Input{Token: 200080}) // <|image_end|>
+
+		result = append(result, imageInputs...)
+	}
+
+	return result, nil
+}
+
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	positions, err := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	if err != nil {
+		return nil, err
+	}
+
+	outputs, err := ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs))
+	if err != nil {
+		return nil, err
+	}
+
+	return m.TextModel.Forward(ctx, batch.Inputs, positions, outputs, batch, m.Cache), nil
+}
+
+func init() {
+	model.Register("llama4", New)
+}

model/models/llama4/model_text.go

@@ -0,0 +1,259 @@
+package llama4
+
+import (
+	"cmp"
+	"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/input"
+)
+
+type TextAttention struct {
+	Query       *nn.Linear `gguf:"attn_q"`
+	Key         *nn.Linear `gguf:"attn_k"`
+	Value       *nn.Linear `gguf:"attn_v"`
+	Output      *nn.Linear `gguf:"attn_output"`
+	RopeFactors ml.Tensor  `gguf:"rope_factors"`
+}
+
+func (sa *TextAttention) Forward(ctx ml.Context, hiddenStates, positions, attentionScales ml.Tensor, cache kvcache.Cache, useRope bool, opts *TextOptions) ml.Tensor {
+	batchSize, headDim := hiddenStates.Dim(1), cmp.Or(opts.headDim, opts.hiddenSize/opts.numHeads)
+
+	query := sa.Query.Forward(ctx, hiddenStates)
+	key := sa.Key.Forward(ctx, hiddenStates)
+	value := sa.Value.Forward(ctx, hiddenStates)
+
+	query = query.Reshape(ctx, headDim, opts.numHeads, batchSize)
+	key = key.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
+	value = value.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
+
+	if useRope {
+		query = query.RoPE(ctx, positions, sa.RopeFactors, uint32(opts.ropeDim), uint32(0), opts.ropeBase, opts.ropeScale)
+		key = key.RoPE(ctx, positions, sa.RopeFactors, uint32(opts.ropeDim), uint32(0), opts.ropeBase, opts.ropeScale)
+	}
+
+	if opts.useQKNorm {
+		query = query.RMSNorm(ctx, nil, opts.eps)
+		key = key.RMSNorm(ctx, nil, opts.eps)
+	}
+
+	if attentionScales != nil && !useRope {
+		query = query.Mul(ctx, attentionScales)
+	}
+
+	attention := nn.Attention(ctx, query, key, value, 1./math.Sqrt(float64(headDim)), cache)
+	attention = attention.Reshape(ctx, opts.hiddenSize, batchSize)
+	return sa.Output.Forward(ctx, attention)
+}
+
+type TextMLP struct {
+	Gate *nn.Linear `gguf:"ffn_gate"`
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+}
+
+func (mlp *TextMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *TextOptions) ml.Tensor {
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenStates))
+	return mlp.Down.Forward(ctx, hiddenStates)
+}
+
+type TextExperts struct {
+	Gate ml.Tensor `gguf:"ffn_gate_exps.weight"`
+	Up   ml.Tensor `gguf:"ffn_up_exps.weight"`
+	Down ml.Tensor `gguf:"ffn_down_exps.weight"`
+}
+
+func (e *TextExperts) Forward(ctx ml.Context, hiddenStates, routerLogits ml.Tensor, opts *TextOptions) ml.Tensor {
+	experts := routerLogits.TopK(ctx, opts.numExpertsUsed)
+	scores := routerLogits.Sigmoid(ctx).Reshape(ctx, 1, opts.numExperts, hiddenStates.Dim(1)).Rows(ctx, experts)
+
+	hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0), 1, hiddenStates.Dim(1))
+	hiddenStates = hiddenStates.Repeat(ctx, 1, opts.numExpertsUsed)
+	hiddenStates = hiddenStates.Mul(ctx, scores)
+
+	upStates := e.Up.MulmatID(ctx, hiddenStates, experts)
+	gateStates := e.Gate.MulmatID(ctx, hiddenStates, experts)
+	downStates := e.Down.MulmatID(ctx, upStates.Mul(ctx, gateStates.SILU(ctx)), experts)
+
+	nextStates := downStates.View(ctx, 0, hiddenStates.Dim(0), downStates.Stride(2), hiddenStates.Dim(2))
+	for i := 1; i < opts.numExpertsUsed; i++ {
+		nextStates.Add(ctx, downStates.View(ctx, i*downStates.Stride(1), hiddenStates.Dim(0), downStates.Stride(2), hiddenStates.Dim(2)))
+	}
+
+	return nextStates
+}
+
+// TextSharedExpert is TextMLP with different tensor names
+type TextSharedExpert struct {
+	Gate *nn.Linear `gguf:"ffn_gate_shexp"`
+	Up   *nn.Linear `gguf:"ffn_up_shexp"`
+	Down *nn.Linear `gguf:"ffn_down_shexp"`
+}
+
+func (mlp *TextSharedExpert) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *TextOptions) ml.Tensor {
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenStates))
+	return mlp.Down.Forward(ctx, hiddenStates)
+}
+
+type TextMOE struct {
+	Router       *nn.Linear `gguf:"ffn_gate_inp"`
+	Experts      *TextExperts
+	SharedExpert *TextSharedExpert
+}
+
+func (moe *TextMOE) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *TextOptions) ml.Tensor {
+	hiddenDim, sequenceLength, batchSize := hiddenStates.Dim(0), hiddenStates.Dim(1), hiddenStates.Dim(2)
+	hiddenStates = hiddenStates.Reshape(ctx, hiddenDim, sequenceLength*batchSize)
+	routerLogits := moe.Router.Forward(ctx, hiddenStates)
+
+	sharedStates := moe.SharedExpert.Forward(ctx, hiddenStates, opts)
+	routedStates := moe.Experts.Forward(ctx, hiddenStates, routerLogits, opts)
+	return sharedStates.Add(ctx, routedStates)
+}
+
+type TextFeedForward interface {
+	Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *TextOptions) ml.Tensor
+}
+
+type TextLayer struct {
+	AttentionNorm *nn.LayerNorm `gguf:"attn_norm"`
+	Attention     *TextAttention
+
+	FFNNorm     *nn.LayerNorm `gguf:"ffn_norm"`
+	FeedForward TextFeedForward
+}
+
+func (d *TextLayer) Forward(ctx ml.Context, hiddenStates, positions, attentionScales, outputs ml.Tensor, cache kvcache.Cache, useRope bool, opts *TextOptions) ml.Tensor {
+	residual := hiddenStates
+
+	// self attention
+	hiddenStates = d.AttentionNorm.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = d.Attention.Forward(ctx, hiddenStates, positions, attentionScales, cache, useRope, opts)
+
+	if outputs != nil {
+		hiddenStates = hiddenStates.Rows(ctx, outputs)
+		residual = residual.Rows(ctx, outputs)
+	}
+
+	hiddenStates = hiddenStates.Add(ctx, residual)
+	residual = hiddenStates
+
+	hiddenStates = d.FFNNorm.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = d.FeedForward.Forward(ctx, hiddenStates, opts)
+
+	return residual.Add(ctx, hiddenStates)
+}
+
+type TextOptions struct {
+	hiddenSize                    int
+	numHeads, numKVHeads, headDim int
+	numExperts, numExpertsUsed    int
+	ropeDim                       int
+	ropeBase, ropeScale           float32
+	eps                           float32
+	interleaveLayerStep           int
+	noRopeInterval                int
+	useQKNorm                     bool
+	attentionTemperatureTuning    bool
+	attentionScale                float64
+	attentionFloorScale           float64
+}
+
+type TextModel struct {
+	Layers []TextLayer `gguf:"blk"`
+
+	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
+	OutputNorm     *nn.LayerNorm `gguf:"output_norm"`
+	Output         *nn.Linear    `gguf:"output,alt:token_embd"`
+
+	*TextOptions
+}
+
+func newTextModel(c fs.Config) *TextModel {
+	layers := make([]TextLayer, c.Uint("block_count"))
+	interleaveLayerStep := c.Uint("interleave_moe_layer_step", 1)
+	for i := range layers {
+		if (i+1)%int(interleaveLayerStep) == 0 {
+			layers[i] = TextLayer{FeedForward: &TextMOE{}}
+		} else {
+			layers[i] = TextLayer{FeedForward: &TextMLP{}}
+		}
+	}
+
+	return &TextModel{
+		Layers: layers,
+		TextOptions: &TextOptions{
+			hiddenSize:                 int(c.Uint("embedding_length")),
+			numHeads:                   int(c.Uint("attention.head_count")),
+			numKVHeads:                 int(c.Uint("attention.head_count_kv")),
+			headDim:                    int(c.Uint("attention.head_dim", 128)),
+			numExperts:                 int(c.Uint("expert_count")),
+			numExpertsUsed:             int(c.Uint("expert_used_count")),
+			ropeDim:                    int(c.Uint("rope.dimension_count")),
+			ropeBase:                   c.Float("rope.freq_base"),
+			ropeScale:                  c.Float("rope.freq_scale", 1),
+			eps:                        c.Float("attention.layer_norm_rms_epsilon"),
+			interleaveLayerStep:        int(c.Uint("interleave_moe_layer_step", 1)),
+			noRopeInterval:             int(c.Uint("no_rope_interval", 4)),
+			useQKNorm:                  c.Bool("use_qk_norm", true),
+			attentionTemperatureTuning: c.Bool("attention.temperature_tuning", true),
+			attentionScale:             float64(c.Float("attention.scale", 0.1)),
+			attentionFloorScale:        float64(c.Float("attention.floor_scale", 8192)),
+		},
+	}
+}
+
+func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor, batch input.Batch, cache kvcache.Cache) ml.Tensor {
+	hiddenStates := m.TokenEmbedding.Forward(ctx, inputs).Duplicate(ctx)
+
+	for _, mi := range batch.Multimodal {
+		f32s := mi.Multimodal.(*chunk).floats()
+		img, err := ctx.Input().FromFloatSlice(f32s, len(f32s)/m.hiddenSize, m.hiddenSize)
+		if err != nil {
+			panic(err)
+		}
+
+		ctx.Forward(img.Copy(ctx, hiddenStates.View(ctx, mi.Index*hiddenStates.Stride(1), img.Dim(0)*img.Dim(1))))
+	}
+
+	var attentionScales ml.Tensor
+	if m.attentionTemperatureTuning {
+		scales := make([]float32, len(batch.Positions))
+		for i, p := range batch.Positions {
+			scales[i] = float32(math.Log(math.Floor(((float64(p)+1.0)/float64(m.attentionFloorScale))+1.0))*m.attentionScale + 1.0)
+		}
+
+		var err error
+		attentionScales, err = ctx.Input().FromFloatSlice(scales, 1, 1, len(scales))
+		if err != nil {
+			panic(err)
+		}
+	}
+
+	for i, layer := range m.Layers {
+		cache.SetLayer(i)
+		wc := cache.(*kvcache.WrapperCache)
+		wc.SetLayerType(1)
+		useChunkedAttention := (i+1)%m.noRopeInterval != 0
+		if useChunkedAttention {
+			wc.SetLayerType(0)
+		}
+
+		var lastLayerOutputs ml.Tensor
+		if i == len(m.Layers)-1 {
+			lastLayerOutputs = outputs
+		}
+
+		hiddenStates = layer.Forward(ctx, hiddenStates, positions, attentionScales, lastLayerOutputs, cache, useChunkedAttention, m.TextOptions)
+	}
+
+	hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.eps)
+	return m.Output.Forward(ctx, hiddenStates)
+}
+
+func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	return key.RoPE(ctx, shift, m.Layers[layer].Attention.RopeFactors, uint32(0), uint32(m.ropeDim), m.ropeBase, m.ropeScale), nil
+}

model/models/llama4/model_vision.go

@@ -0,0 +1,256 @@
+package llama4
+
+import (
+	"math"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+)
+
+type VisionAttention struct {
+	Query  *nn.Linear `gguf:"attn_q"`
+	Key    *nn.Linear `gguf:"attn_k"`
+	Value  *nn.Linear `gguf:"attn_v"`
+	Output *nn.Linear `gguf:"attn_output"`
+}
+
+// applyVisionRotaryEmbedding applies 2D rotary embedding to the input tensor.
+// This is equivalent to the Pytorch implmentation using half rotations:
+//
+//	cos, sin = torch.cos(freqs), torch.sin(freqs)
+//	cos = cos.unsqueeze(-1)
+//	sin = sin.unsqueeze(-1)
+//	t = t.reshape(*t.shape[:-1], -1, 2)
+//	t_out = (t * cos) + (_rotate_half(t) * sin)
+//	t_out = t_out.flatten(3)
+//
+// Which is equivalent to the Pytorch implementation using complex numbers:
+//
+//	t_ = torch.view_as_complex(t.float().reshape(*t.shape[:-1], -1, 2))
+//	freqs_ci = reshape_for_broadcast(freqs_ci=freq_cis, t=t_)  # freqs_ci[:,:,None,:]
+//	freqs_ci = freqs_ci.to(t_.device)
+//	t_out = torch.view_as_real(t_ * freqs_ci).flatten(3)
+//
+// Due to the 1) the dimensional and 2) the datatype limitations of current backends,
+// we need to use a different approach to achieve the same result.
+func applyVisionRotaryEmbedding(ctx ml.Context, t, cos, sin ml.Tensor) ml.Tensor {
+	width, height, channels, tiles := t.Dim(0), t.Dim(1), t.Dim(2), t.Dim(3)
+
+	t = t.Reshape(ctx, 2, t.Dim(0)/2, t.Dim(1)*t.Dim(2)*t.Dim(3))
+
+	// t1 = t[..., 0::2]
+	t1 := t.View(ctx, 0, 1, t.Stride(1), t.Dim(1), t.Stride(2), t.Dim(2)).Contiguous(ctx)
+	t1 = t1.Reshape(ctx, width/2, height, channels, tiles)
+
+	// t2 = t[..., 1::2]
+	t2 := t.View(ctx, t.Stride(0), 1, t.Stride(1), t.Dim(1), t.Stride(2), t.Dim(2)).Contiguous(ctx)
+	t2 = t2.Reshape(ctx, width/2, height, channels, tiles)
+
+	// cos_out = torch.stack((t1 * cos, t2 * cos), dim=-1)
+	cosOut := t1.Mul(ctx, cos).Concat(ctx, t2.Mul(ctx, cos), 0)
+	cosOut = cosOut.Reshape(ctx, cosOut.Dim(0)/2, 2, cosOut.Dim(1)*cosOut.Dim(2)*cosOut.Dim(3))
+	cosOut = cosOut.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
+	cosOut = cosOut.Reshape(ctx, width, height, channels, tiles)
+
+	// sin_out = torch.stack((-t2 * sin, t1 * sin), dim=-1)
+	sinOut := t2.Neg(ctx).Mul(ctx, sin).Concat(ctx, t1.Mul(ctx, sin), 0)
+	sinOut = sinOut.Reshape(ctx, sinOut.Dim(0)/2, 2, sinOut.Dim(1)*sinOut.Dim(2)*sinOut.Dim(3))
+	sinOut = sinOut.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
+	sinOut = sinOut.Reshape(ctx, width, height, channels, tiles)
+
+	return cosOut.Add(ctx, sinOut)
+}
+
+func (sa *VisionAttention) Forward(ctx ml.Context, hiddenState, cos, sin ml.Tensor, opts *VisionOptions) ml.Tensor {
+	headDim := opts.hiddenSize / opts.numHeads
+
+	query := sa.Query.Forward(ctx, hiddenState)
+	key := sa.Key.Forward(ctx, hiddenState)
+	value := sa.Value.Forward(ctx, hiddenState)
+
+	query = query.Reshape(ctx, headDim, opts.numHeads, query.Dim(1), query.Dim(2))
+	key = key.Reshape(ctx, headDim, opts.numHeads, key.Dim(1), key.Dim(2))
+	value = value.Reshape(ctx, headDim, opts.numHeads, value.Dim(1), value.Dim(2))
+
+	query = applyVisionRotaryEmbedding(ctx, query, cos, sin)
+	key = applyVisionRotaryEmbedding(ctx, key, cos, sin)
+
+	attention := nn.Attention(ctx, query, key, value, 1./math.Sqrt(float64(headDim)), nil)
+	attention = attention.Reshape(ctx, opts.hiddenSize, attention.Dim(2), attention.Dim(3))
+	return sa.Output.Forward(ctx, attention)
+}
+
+type VisionMLP struct {
+	FC1 *nn.Linear `gguf:"fc1"`
+	FC2 *nn.Linear `gguf:"fc2"`
+}
+
+func (mlp *VisionMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *VisionOptions) ml.Tensor {
+	hiddenStates = mlp.FC1.Forward(ctx, hiddenStates).GELU(ctx)
+	hiddenStates = mlp.FC2.Forward(ctx, hiddenStates)
+	return hiddenStates
+}
+
+type VisionLayer struct {
+	InputLayerNorm *nn.LayerNorm `gguf:"attn_norm"`
+	*VisionAttention
+
+	PostAttentionNorm *nn.LayerNorm `gguf:"ffn_norm"`
+	*VisionMLP        `gguf:"mlp"`
+}
+
+func (e *VisionLayer) Forward(ctx ml.Context, hiddenStates, cos, sin ml.Tensor, opts *VisionOptions) ml.Tensor {
+	residual := hiddenStates
+
+	// self attention
+	hiddenStates = e.InputLayerNorm.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = e.VisionAttention.Forward(ctx, hiddenStates, cos, sin, opts)
+	hiddenStates = hiddenStates.Add(ctx, residual)
+
+	// MLP
+	residual = hiddenStates
+	hiddenStates = e.PostAttentionNorm.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = e.VisionMLP.Forward(ctx, hiddenStates, opts)
+	hiddenStates = hiddenStates.Add(ctx, residual)
+
+	return hiddenStates
+}
+
+type VisionAdapter struct {
+	FC1 *nn.Linear `gguf:"mlp.fc1"`
+	FC2 *nn.Linear `gguf:"mlp.fc2"`
+}
+
+func (a *VisionAdapter) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *VisionOptions) ml.Tensor {
+	patches := hiddenStates.Dim(1)
+	patchSize := int(math.Sqrt(float64(patches)))
+
+	hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0), patchSize, patchSize, hiddenStates.Dim(2))
+
+	channels, width, height, tiles := hiddenStates.Dim(0), hiddenStates.Dim(1), hiddenStates.Dim(2), hiddenStates.Dim(3)
+
+	channels, width = int(float32(channels)/opts.pixelShuffleRatio), int(float32(width)*opts.pixelShuffleRatio)
+	hiddenStates = hiddenStates.Reshape(ctx, channels, width, height, tiles)
+	hiddenStates = hiddenStates.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+
+	channels, height = int(float32(channels)/opts.pixelShuffleRatio), int(float32(height)*opts.pixelShuffleRatio)
+	hiddenStates = hiddenStates.Reshape(ctx, channels, width, height, tiles)
+	hiddenStates = hiddenStates.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+
+	hiddenStates = hiddenStates.Reshape(ctx, channels, width*height, tiles)
+
+	hiddenStates = a.FC1.Forward(ctx, hiddenStates).GELU(ctx)
+	hiddenStates = a.FC2.Forward(ctx, hiddenStates).GELU(ctx)
+	return hiddenStates
+}
+
+type VisionOptions struct {
+	hiddenSize, numHeads int
+	imageSize, patchSize int
+
+	ropeTheta         float32
+	eps               float32
+	pixelShuffleRatio float32
+}
+
+type PatchEmbedding struct {
+	*nn.Linear
+}
+
+func (p *PatchEmbedding) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *VisionOptions) ml.Tensor {
+	kernel := ctx.Input().Empty(ml.DTypeF32, opts.patchSize, opts.patchSize, hiddenStates.Dim(2))
+	hiddenStates = kernel.IM2Col(ctx, hiddenStates, opts.patchSize, opts.patchSize, 0, 0, 1, 1)
+	hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0), hiddenStates.Dim(1)*hiddenStates.Dim(2), hiddenStates.Dim(3))
+	return p.Linear.Forward(ctx, hiddenStates)
+}
+
+type VisionModel struct {
+	Layers []VisionLayer `gguf:"blk"`
+
+	*PatchEmbedding     `gguf:"patch_embedding"`
+	ClassEmbedding      ml.Tensor `gguf:"class_embedding"`
+	PositionalEmbedding ml.Tensor `gguf:"positional_embedding_vlm"`
+
+	LayerNormPre  *nn.LayerNorm `gguf:"layernorm_pre"`
+	LayerNormPost *nn.LayerNorm `gguf:"layernorm_post"`
+
+	*VisionAdapter `gguf:"vision_adapter"`
+
+	*VisionOptions
+}
+
+func newVisionModel(c fs.Config) *VisionModel {
+	return &VisionModel{
+		Layers: make([]VisionLayer, c.Uint("vision.block_count")),
+		VisionOptions: &VisionOptions{
+			hiddenSize:        int(c.Uint("vision.embedding_length")),
+			numHeads:          int(c.Uint("vision.attention.head_count")),
+			imageSize:         int(c.Uint("vision.image_size")),
+			patchSize:         int(c.Uint("vision.patch_size")),
+			ropeTheta:         float32(c.Float("vision.rope.freq_base")),
+			eps:               c.Float("vision.layer_norm_epsilon"),
+			pixelShuffleRatio: float32(c.Float("vision.pixel_shuffle_ratio")),
+		},
+	}
+}
+
+func (m *VisionModel) Forward(ctx ml.Context, pixelValues ml.Tensor) ml.Tensor {
+	hiddenStates := m.PatchEmbedding.Forward(ctx, pixelValues, m.VisionOptions)
+	hiddenStates = hiddenStates.Concat(ctx, m.ClassEmbedding.Repeat(ctx, 2, hiddenStates.Dim(2)), 1)
+
+	hiddenStates = hiddenStates.Add(ctx, m.PositionalEmbedding)
+	hiddenStates = m.LayerNormPre.Forward(ctx, hiddenStates, m.eps)
+
+	cos, sin := m.rotaryEmbedding(ctx)
+	for _, layer := range m.Layers {
+		hiddenStates = layer.Forward(ctx, hiddenStates, cos, sin, m.VisionOptions)
+	}
+
+	hiddenStates = m.LayerNormPost.Forward(ctx, hiddenStates, m.eps)
+	hiddenStates = hiddenStates.Unpad(ctx, 0, 1, 0, 0)
+	hiddenStates = m.VisionAdapter.Forward(ctx, hiddenStates, m.VisionOptions)
+	return hiddenStates
+}
+
+// floorDiv is a helper function to perform floor division. This mimics PyTorch's div(round_mode='floor') function
+// which in turn mimics Python's // operator.
+func floorDiv[T int | int16 | int32 | int64 | uint | uint16 | uint32 | uint64](a, b T) T {
+	if b == 0 {
+		panic("division by zero")
+	}
+
+	if (a >= 0 && b > 0) || (a <= 0 && b < 0) || a%b == 0 {
+		return a / b
+	}
+
+	return a/b - 1
+}
+
+func (m *VisionModel) rotaryEmbedding(ctx ml.Context) (ml.Tensor, ml.Tensor) {
+	patchesPerSide := m.imageSize / m.patchSize
+	numPatches := patchesPerSide*patchesPerSide + 1
+
+	headDim := m.hiddenSize / m.numHeads
+	freqDim := headDim / 2
+
+	freqs := make([]float32, numPatches*freqDim)
+	for i := range numPatches - 1 {
+		for j := 0; j < freqDim; j += 2 {
+			positionX := i*freqDim/2 + j/2
+			positionY := (i+numPatches)*freqDim/2 + j/2
+			ropeFreq := math.Pow(float64(m.ropeTheta), float64(j)*2/float64(headDim))
+			freqs[positionX] = float32(float64(1+i-floorDiv(i, patchesPerSide)*patchesPerSide) / ropeFreq)
+			freqs[positionY] = float32(float64(1+floorDiv(i, patchesPerSide)) / ropeFreq)
+		}
+	}
+
+	ropeFreqs, err := ctx.Input().FromFloatSlice(freqs, freqDim/2, numPatches, 2)
+	if err != nil {
+		panic(err)
+	}
+
+	ropeFreqs = ropeFreqs.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	ropeFreqs = ropeFreqs.Reshape(ctx, freqDim, 1, numPatches)
+	return ropeFreqs.Cos(ctx), ropeFreqs.Sin(ctx)
+}

model/models/llama4/process_image.go

@@ -0,0 +1,167 @@
+package llama4
+
+import (
+	"cmp"
+	"image"
+	"math"
+	"slices"
+	"sort"
+
+	"golang.org/x/image/draw"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/model/imageproc"
+)
+
+type ImageProcessor struct {
+	imageSize, patchSize, numChannels, maxUpscalingSize int
+}
+
+func newImageProcessor(c fs.Config) ImageProcessor {
+	return ImageProcessor{
+		imageSize:        int(c.Uint("vision.image_size")),
+		patchSize:        int(c.Uint("vision.patch_size")),
+		numChannels:      int(c.Uint("vision.num_channels", 3)),
+		maxUpscalingSize: int(c.Uint("vision.max_upscaling_size", 448)),
+	}
+}
+
+func factors(n int) []int {
+	var result []int
+	seen := make(map[int]bool)
+
+	for i := 1; i <= n/2; i++ {
+		if n%i == 0 && !seen[i] {
+			result = append(result, i)
+			seen[i] = true
+		}
+	}
+
+	result = append(result, n)
+	sort.Ints(result)
+
+	return result
+}
+
+func (p ImageProcessor) supportedResolutions() []image.Point {
+	var resolutions []image.Point
+
+	aspectMap := make(map[float64][]image.Point)
+	for i := p.patchSize; i >= 1; i-- {
+		for _, f := range factors(i) {
+			x := f
+			y := i / f
+			k := float64(y) / float64(x)
+			aspectMap[k] = append(aspectMap[k], image.Point{x, y})
+		}
+	}
+
+	for _, v := range aspectMap {
+		for _, i := range v {
+			resolutions = append(resolutions, image.Point{i.X * p.imageSize, i.Y * p.imageSize})
+		}
+	}
+
+	return resolutions
+}
+
+func (p ImageProcessor) bestResolution(img image.Point, possibleResolutions []image.Point, resizeToMaxCanvas bool) image.Point {
+	w, h := img.X, img.Y
+
+	scales := make([]float64, len(possibleResolutions))
+
+	for i, res := range possibleResolutions {
+		scaleW := float64(res.X) / float64(w)
+		scaleH := float64(res.Y) / float64(h)
+		scale := math.Min(scaleW, scaleH)
+
+		scales[i] = scale
+	}
+
+	minAboveOne := func(scales []float64) (float64, bool) {
+		min := math.MaxFloat64
+		found := false
+
+		for _, s := range scales {
+			if s >= 1.0 && s < min {
+				min = s
+				found = true
+			}
+		}
+
+		return min, found
+	}
+
+	bestScale, ok := minAboveOne(scales)
+	if resizeToMaxCanvas || !ok {
+		bestScale = slices.Max(scales)
+	}
+
+	var bestOptions []image.Point
+	for i, scale := range scales {
+		if math.Abs(scale-bestScale) < 1e-6 {
+			bestOptions = append(bestOptions, possibleResolutions[i])
+		}
+	}
+
+	var chosenResolution image.Point
+	if len(bestOptions) > 1 {
+		chosenResolution = slices.MinFunc(bestOptions, func(a, b image.Point) int {
+			return cmp.Compare(a.X*a.Y, b.X*b.Y)
+		})
+	} else {
+		chosenResolution = bestOptions[0]
+	}
+
+	return chosenResolution
+}
+
+func (p ImageProcessor) maxResolution(imageRes, targetRes image.Point) image.Point {
+	scaleW := float64(targetRes.X) / float64(imageRes.X)
+	scaleH := float64(targetRes.Y) / float64(imageRes.Y)
+
+	var newRes image.Point
+	if scaleW < scaleH {
+		newRes = image.Point{
+			targetRes.X,
+			int(math.Min(math.Floor(float64(imageRes.Y)*scaleW), float64(targetRes.Y))),
+		}
+	} else {
+		newRes = image.Point{
+			int(math.Min(math.Floor(float64(imageRes.X)*scaleH), float64(targetRes.X))),
+			targetRes.Y,
+		}
+	}
+
+	return newRes
+}
+
+func (p ImageProcessor) pad(src image.Image, outputSize image.Point) image.Image {
+	dst := image.NewRGBA(image.Rect(0, 0, outputSize.X, outputSize.Y))
+	draw.Draw(dst, src.Bounds(), src, image.Point{}, draw.Over)
+	return dst
+}
+
+func (p ImageProcessor) ProcessImage(img image.Image) (pixelsLocal, pixelsGlobal []float32, targetSize image.Point, _ error) {
+	img = imageproc.Composite(img)
+
+	targetSize = p.bestResolution(img.Bounds().Max, p.supportedResolutions(), false)
+	targetSizeWithoutDistortion := targetSize
+	if p.maxUpscalingSize > 0 {
+		targetSizeWithoutDistortion = p.maxResolution(img.Bounds().Max, targetSize)
+		targetSizeWithoutDistortion.X = min(max(img.Bounds().Max.X, p.maxUpscalingSize), targetSize.X)
+		targetSizeWithoutDistortion.Y = min(max(img.Bounds().Max.Y, p.maxUpscalingSize), targetSize.Y)
+	}
+
+	newSizeWithoutDistortion := p.maxResolution(img.Bounds().Max, targetSizeWithoutDistortion)
+
+	padded := p.pad(imageproc.Resize(img, newSizeWithoutDistortion, imageproc.ResizeBilinear), targetSize)
+	pixelsLocal = imageproc.Normalize(padded, imageproc.ImageNetStandardMean, imageproc.ImageNetStandardSTD, true, true)
+
+	if targetSize.X/p.imageSize*targetSize.Y/p.imageSize > 1 {
+		padded := imageproc.Resize(img, image.Point{p.imageSize, p.imageSize}, imageproc.ResizeBilinear)
+		pixelsGlobal = imageproc.Normalize(padded, imageproc.ImageNetStandardMean, imageproc.ImageNetStandardSTD, true, true)
+	}
+
+	return pixelsLocal, pixelsGlobal, targetSize, nil
+}

model/models/llama4/process_image_test.go

@@ -0,0 +1,300 @@
+package llama4
+
+import (
+	"cmp"
+	"image"
+	"image/color"
+	"reflect"
+	"slices"
+	"testing"
+
+	gocmp "github.com/google/go-cmp/cmp"
+)
+
+func TestFactors(t *testing.T) {
+	tests := []struct {
+		name     string
+		input    int
+		expected []int
+	}{
+		{
+			name:     "factors of 1",
+			input:    1,
+			expected: []int{1},
+		},
+		{
+			name:     "factors of 2",
+			input:    2,
+			expected: []int{1, 2},
+		},
+		{
+			name:     "factors of 6",
+			input:    6,
+			expected: []int{1, 2, 3, 6},
+		},
+		{
+			name:     "factors of 28",
+			input:    28,
+			expected: []int{1, 2, 4, 7, 14, 28},
+		},
+		{
+			name:     "factors of 49",
+			input:    49,
+			expected: []int{1, 7, 49},
+		},
+		{
+			name:     "factors of 97 (prime)",
+			input:    97,
+			expected: []int{1, 97},
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			actual := factors(tt.input)
+			if !reflect.DeepEqual(actual, tt.expected) {
+				t.Errorf("factors(%d) = %v; want %v", tt.input, actual, tt.expected)
+			}
+		})
+	}
+}
+
+func TestSupportedResolutions(t *testing.T) {
+	expectedResolutions := []image.Point{
+		{X: 3360, Y: 336},
+		{X: 672, Y: 2688},
+		{X: 336, Y: 1344},
+		{X: 336, Y: 4032},
+		{X: 1008, Y: 1344},
+		{X: 1344, Y: 1008},
+		{X: 336, Y: 1680},
+		{X: 1680, Y: 336},
+		{X: 336, Y: 5040},
+		{X: 4032, Y: 336},
+		{X: 2352, Y: 336},
+		{X: 2688, Y: 672},
+		{X: 1344, Y: 336},
+		{X: 5376, Y: 336},
+		{X: 2352, Y: 672},
+		{X: 672, Y: 1008},
+		{X: 1008, Y: 672},
+		{X: 336, Y: 5376},
+		{X: 1680, Y: 1008},
+		{X: 5040, Y: 336},
+		{X: 336, Y: 3024},
+		{X: 3024, Y: 336},
+		{X: 336, Y: 2688},
+		{X: 672, Y: 1344},
+		{X: 336, Y: 672},
+		{X: 336, Y: 2352},
+		{X: 2016, Y: 672},
+		{X: 1008, Y: 336},
+		{X: 336, Y: 3360},
+		{X: 336, Y: 4368},
+		{X: 1008, Y: 1680},
+		{X: 336, Y: 4704},
+		{X: 4704, Y: 336},
+		{X: 1344, Y: 672},
+		{X: 672, Y: 336},
+		{X: 2688, Y: 336},
+		{X: 3696, Y: 336},
+		{X: 2016, Y: 336},
+		{X: 1344, Y: 1344},
+		{X: 1008, Y: 1008},
+		{X: 672, Y: 672},
+		{X: 336, Y: 336},
+		{X: 4368, Y: 336},
+		{X: 672, Y: 2016},
+		{X: 336, Y: 1008},
+		{X: 336, Y: 3696},
+		{X: 672, Y: 1680},
+		{X: 1680, Y: 672},
+		{X: 336, Y: 2016},
+		{X: 672, Y: 2352},
+	}
+
+	sortResolutionFunc := func(a, b image.Point) int {
+		return cmp.Or(cmp.Compare(a.X, b.X), cmp.Compare(a.Y, b.Y))
+	}
+
+	slices.SortStableFunc(expectedResolutions, sortResolutionFunc)
+
+	imgProc := ImageProcessor{
+		imageSize:        336,
+		patchSize:        16,
+		numChannels:      3,
+		maxUpscalingSize: 448,
+	}
+
+	actualResolutions := imgProc.supportedResolutions()
+	slices.SortStableFunc(actualResolutions, sortResolutionFunc)
+
+	if diff := gocmp.Diff(expectedResolutions, actualResolutions); diff != "" {
+		t.Errorf("supportedResolutions() mismatch (-want +got):\n%s", diff)
+	}
+}
+
+func TestBestResolution(t *testing.T) {
+	tests := []struct {
+		name        string
+		size        image.Point
+		resolutions []image.Point
+		max         bool
+		expected    image.Point
+	}{
+		{
+			"normal",
+			image.Point{800, 600},
+			[]image.Point{
+				{300, 200},
+				{640, 480},
+				{800, 600},
+				{1024, 768},
+				{1600, 1200},
+			},
+			false,
+			image.Point{800, 600},
+		},
+		{
+			"max",
+			image.Point{800, 600},
+			[]image.Point{
+				{300, 200},
+				{640, 480},
+				{800, 600},
+				{1024, 768},
+				{1600, 1200},
+			},
+			true,
+			image.Point{1600, 1200},
+		},
+		{
+			"mid",
+			image.Point{1000, 700},
+			[]image.Point{
+				{300, 200},
+				{640, 480},
+				{800, 600},
+				{1024, 768},
+				{1600, 1200},
+			},
+			false,
+			image.Point{1024, 768},
+		},
+		{
+			"smol",
+			image.Point{100, 100},
+			[]image.Point{
+				{300, 200},
+				{640, 480},
+				{800, 600},
+				{1024, 768},
+				{1600, 1200},
+			},
+			false,
+			image.Point{300, 200},
+		},
+		{
+			"huge",
+			image.Point{10000, 10000},
+			[]image.Point{
+				{300, 200},
+				{640, 480},
+				{800, 600},
+				{1024, 768},
+				{1600, 1200},
+			},
+			false,
+			image.Point{1600, 1200},
+		},
+	}
+
+	p := ImageProcessor{}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			actual := p.bestResolution(tt.size, tt.resolutions, tt.max)
+			if diff := gocmp.Diff(tt.expected, actual); diff != "" {
+				t.Errorf("best resolution mismatch (-want +got):\n%s", diff)
+			}
+		})
+	}
+}
+
+func TestMaxResolution(t *testing.T) {
+	tests := []struct {
+		name      string
+		origRes   image.Point
+		targetRes image.Point
+		expected  image.Point
+	}{
+		{
+			"normal",
+			image.Point{800, 600},
+			image.Point{800, 600},
+			image.Point{800, 600},
+		},
+		{
+			"skew",
+			image.Point{800, 600},
+			image.Point{1100, 700},
+			image.Point{933, 700},
+		},
+	}
+
+	p := ImageProcessor{}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			actual := p.maxResolution(tt.origRes, tt.targetRes)
+			if !reflect.DeepEqual(actual, tt.expected) {
+				t.Errorf("max resolution; got %v want %v", actual, tt.expected)
+			}
+		})
+	}
+}
+
+func TestProcessImage(t *testing.T) {
+	imgProc := ImageProcessor{
+		imageSize:        336,
+		patchSize:        16,
+		numChannels:      3,
+		maxUpscalingSize: 448,
+	}
+
+	generateImage := func(seed int) image.Image {
+		width, height := 20, 10
+		img := image.NewRGBA(image.Rect(0, 0, width, height))
+
+		for x := range width {
+			// Use the seed to vary color generation
+			r := uint8((seed + x*11) % 256)
+			g := uint8((seed + x*17) % 256)
+			b := uint8((seed + x*23) % 256)
+
+			c := color.RGBA{R: r, G: g, B: b, A: 255}
+			for y := range height {
+				img.Set(x, y, c)
+			}
+		}
+
+		return img
+	}
+
+	pixelsLocal, pixelsGlobal, targetSize, err := imgProc.ProcessImage(generateImage(12))
+	if err != nil {
+		t.Error(err)
+	}
+
+	if n := len(pixelsLocal); n != 336*336*3 {
+		t.Errorf("unexpected size of f32s: %d", n)
+	}
+
+	if n := len(pixelsGlobal); n > 0 {
+		t.Errorf("unexpected size of f32s: %d", n)
+	}
+
+	if !targetSize.Eq(image.Point{336, 336}) {
+		t.Errorf("unexpected target size: %v", targetSize)
+	}
+}

model/models/mistral3/model_text.go

@@ -152,7 +152,7 @@ func NewTextModel(c fs.Config) (*TextModel, error) {
 			c.String("tokenizer.ggml.pretokenizer", `[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n/]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
-				Types:  c.Uints("tokenizer.ggml.token_type"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),
 				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id", 1)),
 				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),

model/models/mllama/model.go

@@ -43,7 +43,7 @@ func New(c fs.Config) (model.Model, error) {
 			c.String("tokenizer.ggml.pretokenizer", `(?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+`),
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
-				Types:  c.Uints("tokenizer.ggml.token_type"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
 				Merges: c.Strings("tokenizer.ggml.merges"),
 				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id")),
 				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),

model/models/mllama/model_text.go

@@ -177,7 +177,7 @@ type TextDecoder struct {
 func (d *TextDecoder) Forward(ctx ml.Context, hiddenState, positionIDs, outputs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
 	for i, layer := range d.Layers {
 		layerType := selfAttentionLayer
-		if slices.Contains(opts.crossAttentionLayers, uint32(i)) {
+		if slices.Contains(opts.crossAttentionLayers, int32(i)) {
 			layerType = crossAttentionLayer
 		}
 
@@ -202,7 +202,7 @@ type TextModelOptions struct {
 	eps, ropeBase, ropeScale         float32
 	ropeDim                          uint32
 
-	crossAttentionLayers []uint32
+	crossAttentionLayers []int32
 }
 
 type TextModel struct {
@@ -225,7 +225,7 @@ func newTextModel(c fs.Config) *TextModel {
 	var decoderLayers []TextDecoderLayer
 	for i := range c.Uint("block_count") {
 		var textDecoderLayer TextDecoderLayer
-		if slices.Contains(c.Uints("attention.cross_attention_layers"), i) {
+		if slices.Contains(c.Ints("attention.cross_attention_layers"), int32(i)) {
 			textDecoderLayer = &TextCrossAttentionDecoderLayer{}
 		} else {
 			textDecoderLayer = &TextSelfAttentionDecoderLayer{}
@@ -244,7 +244,7 @@ func newTextModel(c fs.Config) *TextModel {
 			ropeBase:             c.Float("rope.freq_base"),
 			ropeScale:            c.Float("rope.freq_scale", 1),
 			ropeDim:              c.Uint("rope.dimension_count"),
-			crossAttentionLayers: c.Uints("attention.cross_attention_layers"),
+			crossAttentionLayers: c.Ints("attention.cross_attention_layers"),
 		},
 	}
 }

model/models/mllama/model_vision.go

@@ -96,10 +96,10 @@ type VisionEncoder struct {
 	Layers []VisionEncoderLayer
 }
 
-func (e *VisionEncoder) Forward(ctx ml.Context, hiddenState ml.Tensor, intermediateLayersIndices []uint32, opts *VisionModelOptions) (ml.Tensor, []ml.Tensor) {
+func (e *VisionEncoder) Forward(ctx ml.Context, hiddenState ml.Tensor, intermediateLayersIndices []int32, opts *VisionModelOptions) (ml.Tensor, []ml.Tensor) {
 	var intermediateHiddenStates []ml.Tensor
 	for i, layer := range e.Layers {
-		if slices.Contains(intermediateLayersIndices, uint32(i)) {
+		if slices.Contains(intermediateLayersIndices, int32(i)) {
 			intermediateHiddenStates = append(intermediateHiddenStates, hiddenState.Reshape(ctx, append([]int{1}, hiddenState.Shape()...)...))
 		}
 
@@ -154,7 +154,7 @@ type VisionModelOptions struct {
 	imageSize, patchSize           int
 	eps                            float32
 
-	intermediateLayersIndices []uint32
+	intermediateLayersIndices []int32
 }
 
 type VisionModel struct {
@@ -229,7 +229,7 @@ func newVisionModel(c fs.Config) *VisionModel {
 
 			eps: c.Float("vision.attention.layer_norm_epsilon"),
 
-			intermediateLayersIndices: c.Uints("vision.intermediate_layers_indices"),
+			intermediateLayersIndices: c.Ints("vision.intermediate_layers_indices"),
 		},
 	}
 }

model/models/models.go

@@ -4,6 +4,7 @@ import (
 	_ "github.com/ollama/ollama/model/models/gemma2"
 	_ "github.com/ollama/ollama/model/models/gemma3"
 	_ "github.com/ollama/ollama/model/models/llama"
+	_ "github.com/ollama/ollama/model/models/llama4"
 	_ "github.com/ollama/ollama/model/models/mistral3"
 	_ "github.com/ollama/ollama/model/models/mllama"
 )

model/process_text.go

@@ -37,7 +37,7 @@ type TextProcessor interface {
 
 type Vocabulary struct {
 	Values []string
-	Types  []uint32
+	Types  []int32
 	Scores []float32
 	Merges []string
 

model/process_text_spm_test.go

@@ -35,9 +35,9 @@ func loadSentencePieceVocab(t *testing.T) SentencePieceModel {
 			sentencepiece.ModelProto_SentencePiece_CONTROL,
 			sentencepiece.ModelProto_SentencePiece_UNUSED,
 			sentencepiece.ModelProto_SentencePiece_BYTE:
-			v.Types = append(v.Types, uint32(t))
+			v.Types = append(v.Types, int32(t))
 		default:
-			tt := uint32(sentencepiece.ModelProto_SentencePiece_NORMAL)
+			tt := int32(sentencepiece.ModelProto_SentencePiece_NORMAL)
 			// todo parse the special tokens file
 			//   - this will roundtrip correctly but the <start_of_turn> and
 			//     <end_of_turn> tokens aren't processed
@@ -124,7 +124,7 @@ func TestSentencePieceModelDecodeByteTokens(t *testing.T) {
 			"<0xC3>",
 			"<0xA3>",
 		},
-		Types: []uint32{
+		Types: []int32{
 			TOKEN_TYPE_NORMAL,
 			TOKEN_TYPE_BYTE,
 			TOKEN_TYPE_BYTE,

model/process_text_test.go

@@ -28,7 +28,7 @@ func llama(t testing.TB) BytePairEncoding {
 		t.Fatal(err)
 	}
 
-	types := make([]uint32, len(vocab))
+	types := make([]int32, len(vocab))
 	tokens := make([]string, len(vocab))
 	for token, id := range vocab {
 		tokens[id] = token

sample/samplers_test.go

@@ -74,7 +74,6 @@ func modelHelper(t testing.TB) model.BytePairEncoding {
 		t.Fatal(err)
 	}
 
-	types := make([]uint32, len(vocab))
 	tokens := make([]string, len(vocab))
 	for token, id := range vocab {
 		tokens[id] = token
@@ -86,7 +85,7 @@ func modelHelper(t testing.TB) model.BytePairEncoding {
 		``,
 		&model.Vocabulary{
 			Values: tokens,
-			Types:  types,
+			Types:  make([]int32, len(vocab)),
 			Merges: merges,
 		},
 	)

server/create.go

@@ -295,7 +295,7 @@ func convertFromSafetensors(files map[string]string, baseLayers []*layerGGML, is
 	}
 	defer bin.Close()
 
-	f, _, err := ggml.Decode(bin, 0)
+	f, _, err := ggml.Decode(bin, 1024)
 	if err != nil {
 		return nil, err
 	}
@@ -457,7 +457,7 @@ func quantizeLayer(layer *layerGGML, quantizeType string, fn func(resp api.Progr
 		return nil, err
 	}
 
-	f, _, err := ggml.Decode(temp, 0)
+	f, _, err := ggml.Decode(temp, 1024)
 	if err != nil {
 		slog.Error(fmt.Sprintf("error decoding ggml: %s\n", err))
 		return nil, err
@@ -499,7 +499,7 @@ func ggufLayers(digest string, fn func(resp api.ProgressResponse)) ([]*layerGGML
 
 	var offset int64
 	for offset < stat.Size() {
-		f, n, err := ggml.Decode(blob, 0)
+		f, n, err := ggml.Decode(blob, 1024)
 		if errors.Is(err, io.EOF) {
 			break
 		} else if err != nil {

server/images.go

@@ -75,7 +75,7 @@ func (m *Model) Capabilities() []model.Capability {
 	if err == nil {
 		defer r.Close()
 
-		f, _, err := ggml.Decode(r, 0)
+		f, _, err := ggml.Decode(r, 1024)
 		if err == nil {
 			if _, ok := f.KV()[fmt.Sprintf("%s.pooling_type", f.KV().Architecture())]; ok {
 				capabilities = append(capabilities, model.CapabilityEmbedding)

server/internal/registry/server.go

@@ -73,8 +73,13 @@ type statusCodeRecorder struct {
 func (r *statusCodeRecorder) WriteHeader(status int) {
 	if r._status == 0 {
 		r._status = status
+		r.ResponseWriter.WriteHeader(status)
 	}
-	r.ResponseWriter.WriteHeader(status)
+}
+
+func (r *statusCodeRecorder) Write(b []byte) (int, error) {
+	r._status = r.status()
+	return r.ResponseWriter.Write(b)
 }
 
 var (

server/model.go

@@ -64,7 +64,7 @@ func parseFromModel(ctx context.Context, name model.Name, fn func(api.ProgressRe
 			}
 			defer blob.Close()
 
-			f, _, err := ggml.Decode(blob, 0)
+			f, _, err := ggml.Decode(blob, 1024)
 			if err != nil {
 				return nil, err
 			}

server/python_tools.go

@@ -0,0 +1,226 @@
+package server
+
+import (
+	"fmt"
+	"regexp"
+	"strconv"
+	"strings"
+
+	"github.com/ollama/ollama/api"
+)
+
+var (
+	pythonFuncRegex = regexp.MustCompile(`(\w+)\((.*?)\)`)
+	braces          = map[rune]rune{
+		'[':  ']',
+		'{':  '}',
+		'(':  ')',
+		'"':  '"',
+		'\'': '\'',
+	}
+)
+
+// parsePythonValue converts a Python value string to its appropriate Go type
+func parsePythonValue(value string) (any, error) {
+	value = strings.TrimSpace(value)
+
+	// string
+	if (strings.HasPrefix(value, "\"") && strings.HasSuffix(value, "\"")) ||
+		(strings.HasPrefix(value, "'") && strings.HasSuffix(value, "'")) {
+		// Remove quotes
+		result := value[1 : len(value)-1]
+		return result, nil
+	}
+
+	// bool
+	switch strings.ToLower(value) {
+	case "true":
+		return true, nil
+	case "false":
+		return false, nil
+	case "none":
+		return nil, nil
+	}
+
+	// int
+	if i, err := strconv.Atoi(value); err == nil {
+		return i, nil
+	}
+
+	// float
+	if f, err := strconv.ParseFloat(value, 64); err == nil {
+		return f, nil
+	}
+
+	// list
+	if strings.HasPrefix(value, "[") && strings.HasSuffix(value, "]") {
+		listStr := value[1 : len(value)-1]
+		var list []any
+		stack := []rune{}
+		start := 0
+
+		for i, char := range listStr {
+			if len(stack) != 0 && char == braces[stack[len(stack)-1]] {
+				stack = stack[:len(stack)-1]
+			} else if _, ok := braces[char]; ok {
+				stack = append(stack, char)
+			}
+
+			if len(stack) == 0 && (char == ',' || i == len(listStr)-1) {
+				end := i
+				if i == len(listStr)-1 {
+					end = i + 1
+				}
+				item := strings.TrimSpace(listStr[start:end])
+				if val, err := parsePythonValue(item); err == nil {
+					list = append(list, val)
+				} else {
+					return nil, fmt.Errorf("invalid list item: %s", item)
+				}
+				start = i + 1
+			}
+		}
+		return list, nil
+	}
+
+	// dictionary
+	if strings.HasPrefix(value, "{") && strings.HasSuffix(value, "}") && strings.Contains(value, ":") {
+		dictStr := value[1 : len(value)-1]
+		dict := make(map[any]any)
+		stack := []rune{}
+		start := 0
+		for i, char := range dictStr {
+			if len(stack) != 0 && char == braces[stack[len(stack)-1]] {
+				stack = stack[:len(stack)-1]
+			} else if _, ok := braces[char]; ok {
+				stack = append(stack, char)
+			}
+			if len(stack) == 0 && (char == ',' || i == len(dictStr)-1) {
+				end := i
+				if i == len(dictStr)-1 {
+					end = i + 1
+				}
+				item := strings.TrimSpace(dictStr[start:end])
+				kv := strings.SplitN(item, ":", 2)
+				if len(kv) != 2 {
+					return nil, fmt.Errorf("invalid dictionary key-value pair: %s", item)
+				}
+
+				key, err := parsePythonValue(strings.TrimSpace(kv[0]))
+				if err != nil {
+					return nil, fmt.Errorf("invalid dictionary key: %s", kv[0])
+				}
+
+				val, err := parsePythonValue(strings.TrimSpace(kv[1]))
+				if err != nil {
+					return nil, fmt.Errorf("invalid dictionary value: %s", kv[1])
+				}
+
+				dict[key] = val
+				start = i + 1
+			}
+		}
+		return dict, nil
+	}
+
+	// sets (stored as lists)
+	if strings.HasPrefix(value, "{") && strings.HasSuffix(value, "}") {
+		setStr := value[1 : len(value)-1]
+		var list []any
+		stack := []rune{}
+		start := 0
+		for i, char := range setStr {
+			if len(stack) != 0 && char == braces[stack[len(stack)-1]] {
+				stack = stack[:len(stack)-1]
+			} else if _, ok := braces[char]; ok {
+				stack = append(stack, char)
+			}
+			if len(stack) == 0 && (char == ',' || i == len(setStr)-1) {
+				end := i
+				if i == len(setStr)-1 {
+					end = i + 1
+				}
+				item := strings.TrimSpace(setStr[start:end])
+				if val, err := parsePythonValue(item); err == nil {
+					list = append(list, val)
+				} else {
+					return nil, fmt.Errorf("invalid set item: %s", item)
+				}
+				start = i + 1
+			}
+		}
+		return list, nil
+	}
+
+	return nil, fmt.Errorf("invalid Python value: %s", value)
+}
+
+// parsePythonToolCall parses Python function calls from a string
+// it supports keyword arguments, as well as multiple functions in a single string
+func parsePythonToolCall(s string) ([]api.ToolCall, error) {
+	matches := pythonFuncRegex.FindAllStringSubmatchIndex(s, -1)
+	if len(matches) == 0 {
+		return nil, fmt.Errorf("no Python function calls found")
+	}
+
+	var toolCalls []api.ToolCall
+	for _, match := range matches {
+		name := s[match[2]:match[3]]
+		args := s[match[4]:match[5]]
+		var arguments api.ToolCallFunctionArguments
+		if len(args) == 0 {
+			toolCalls = append(toolCalls, api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name: name,
+				},
+			})
+			continue
+		}
+
+		start := 0
+		stack := []rune{}
+		for i, char := range args {
+			if len(stack) != 0 && char == braces[stack[len(stack)-1]] {
+				stack = stack[:len(stack)-1]
+			} else if _, ok := braces[char]; ok {
+				stack = append(stack, char)
+			}
+			if len(stack) == 0 && (char == ',' || i == len(args)-1) {
+				end := i
+				if i == len(args)-1 {
+					end = i + 1
+				}
+				kv := strings.SplitN(args[start:end], "=", 2)
+				if len(kv) == 2 {
+					key := strings.TrimSpace(kv[0])
+					valueStr := strings.TrimSpace(kv[1])
+
+					// Parse the value into appropriate type
+					value, err := parsePythonValue(valueStr)
+					if err != nil {
+						return nil, fmt.Errorf("failed to parse value for key %q: %v", key, err)
+					}
+
+					arguments[key] = value
+				} else {
+					return nil, fmt.Errorf("invalid argument format: %q", args[start:end])
+				}
+				start = i + 1
+			}
+		}
+
+		if len(arguments) > 0 {
+			toolCalls = append(toolCalls, api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      name,
+					Arguments: arguments,
+				},
+			})
+		}
+	}
+
+	if len(toolCalls) > 0 {
+		return toolCalls, nil
+	}
+	return nil, fmt.Errorf("failed to parse any valid tool calls")
+}

server/python_tools_test.go

@@ -0,0 +1,269 @@
+package server
+
+import (
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+	"github.com/ollama/ollama/api"
+)
+
+func TestParsePythonFunctionCall(t *testing.T) {
+	t1 := api.ToolCall{
+		Function: api.ToolCallFunction{
+			Name: "get_current_weather",
+			Arguments: api.ToolCallFunctionArguments{
+				"location": "San Francisco, CA",
+				"format":   "fahrenheit",
+			},
+		},
+	}
+
+	t2 := api.ToolCall{
+		Function: api.ToolCallFunction{
+			Name: "get_forecast",
+			Arguments: api.ToolCallFunctionArguments{
+				"days":     5,
+				"location": "Seattle",
+			},
+		},
+	}
+
+	t3 := api.ToolCall{
+		Function: api.ToolCallFunction{
+			Name: "get_current_weather",
+			Arguments: api.ToolCallFunctionArguments{
+				"list":   []any{1, 2, 3},
+				"int":    -1,
+				"float":  1.23,
+				"string": "hello",
+			},
+		},
+	}
+	t4 := api.ToolCall{
+		Function: api.ToolCallFunction{
+			Name: "get_current_weather",
+		},
+	}
+
+	cases := []struct {
+		name  string
+		input string
+		want  []api.ToolCall
+		err   bool
+	}{
+		{
+			name:  "malformed function call - missing closing paren",
+			input: "get_current_weather(location=\"San Francisco\"",
+			err:   true,
+		},
+		{
+			name:  "empty function call",
+			input: "get_current_weather()",
+			want:  []api.ToolCall{t4},
+			err:   false,
+		},
+		{
+			name:  "single valid function call",
+			input: "get_current_weather(location=\"San Francisco, CA\", format=\"fahrenheit\")",
+			want:  []api.ToolCall{t1},
+		},
+		{
+			name:  "multiple valid function calls",
+			input: "get_current_weather(location=\"San Francisco, CA\", format=\"fahrenheit\") get_forecast(days=5, location=\"Seattle\")",
+			want:  []api.ToolCall{t1, t2},
+		},
+		{
+			name:  "multiple valid function calls with list",
+			input: "get_current_weather(list=[1,2,3], int=-1, float=1.23, string=\"hello\")",
+			want:  []api.ToolCall{t3},
+		},
+		{
+			name:  "positional arguments not supported",
+			input: "get_current_weather(1, 2, 3)",
+			err:   true,
+		},
+		{
+			name:  "invalid argument format without equals",
+			input: "get_current_weather(\"San Francisco\")",
+			err:   true,
+		},
+		{
+			name:  "nested lists",
+			input: "get_current_weather(data=[[1,2],[3,4]])",
+			want: []api.ToolCall{{
+				Function: api.ToolCallFunction{
+					Name: "get_current_weather",
+					Arguments: api.ToolCallFunctionArguments{
+						"data": []any{[]any{1, 2}, []any{3, 4}},
+					},
+				},
+			}},
+		},
+		{
+			name:  "boolean and none values",
+			input: "get_current_weather(active=true, enabled=false, value=None)",
+			want: []api.ToolCall{{
+				Function: api.ToolCallFunction{
+					Name: "get_current_weather",
+					Arguments: api.ToolCallFunctionArguments{
+						"active":  true,
+						"enabled": false,
+						"value":   nil,
+					},
+				},
+			}},
+		},
+		{
+			name:  "single vs double quotes",
+			input: "get_current_weather(str1='single', str2=\"double\")",
+			want: []api.ToolCall{{
+				Function: api.ToolCallFunction{
+					Name: "get_current_weather",
+					Arguments: api.ToolCallFunctionArguments{
+						"str1": "single",
+						"str2": "double",
+					},
+				},
+			}},
+		},
+		{
+			name:  "whitespace handling",
+			input: "get_current_weather( location = \"San Francisco\" , temp = 72 )",
+			want: []api.ToolCall{{
+				Function: api.ToolCallFunction{
+					Name: "get_current_weather",
+					Arguments: api.ToolCallFunctionArguments{
+						"location": "San Francisco",
+						"temp":     72,
+					},
+				},
+			}},
+		},
+	}
+
+	for _, tt := range cases {
+		t.Run(tt.name, func(t *testing.T) {
+			got, err := parsePythonToolCall(tt.input)
+			if (err != nil) != tt.err {
+				t.Fatalf("expected error: %v, got error: %v", tt.err, err)
+			}
+			if tt.err {
+				return
+			}
+			if diff := cmp.Diff(got, tt.want); diff != "" {
+				t.Errorf("mismatch (-got +want):\n%s", diff)
+			}
+		})
+	}
+}
+
+func TestParsePythonValue(t *testing.T) {
+	cases := []struct {
+		name  string
+		input string
+		want  any
+		err   bool
+	}{
+		{
+			name:  "string with double quotes",
+			input: "\"hello\"",
+			want:  "hello",
+		},
+		{
+			name:  "string with single quotes",
+			input: "'world'",
+			want:  "world",
+		},
+		{
+			name:  "integer",
+			input: "42",
+			want:  42,
+		},
+		{
+			name:  "float",
+			input: "3.14",
+			want:  3.14,
+		},
+		{
+			name:  "boolean true",
+			input: "True",
+			want:  true,
+		},
+		{
+			name:  "boolean false",
+			input: "False",
+			want:  false,
+		},
+		{
+			name:  "none/null",
+			input: "None",
+			want:  nil,
+		},
+		{
+			name:  "simple list",
+			input: "[1, 2, 3]",
+			want:  []any{1, 2, 3},
+		},
+		{
+			name:  "nested list",
+			input: "[1, [2, 3], 4]",
+			want:  []any{1, []any{2, 3}, 4},
+		},
+		{
+			name:  "mixed type list",
+			input: "[1, \"two\", 3.0, true]",
+			want:  []any{1, "two", 3.0, true},
+		},
+		{
+			name:  "invalid list",
+			input: "[1, 2,",
+			want:  nil,
+			err:   true,
+		},
+		{
+			name:  "dictionaries",
+			input: "{'a': 1, 'b': 2}",
+			want:  map[any]any{"a": 1, "b": 2},
+			err:   false,
+		},
+		{
+			name:  "int dictionary",
+			input: "{1: 2}",
+			want:  map[any]any{1: 2},
+			err:   false,
+		},
+		{
+			name:  "mixed type dictionary",
+			input: "{'a': 1, 'b': 2.0, 'c': True}",
+			want:  map[any]any{"a": 1, "b": 2.0, "c": true},
+			err:   false,
+		},
+		{
+			name:  "invalid dictionary - missing closing brace",
+			input: "{'a': 1, 'b': 2",
+			want:  nil,
+			err:   true,
+		},
+		{
+			name:  "sets",
+			input: "{1, 2, 3}",
+			want:  []any{1, 2, 3},
+			err:   false,
+		},
+	}
+
+	for _, tt := range cases {
+		t.Run(tt.name, func(t *testing.T) {
+			got, err := parsePythonValue(tt.input)
+			if (err != nil) != tt.err {
+				t.Fatalf("expected error: %v, got error: %v", tt.err, err)
+			}
+			if tt.err {
+				return
+			}
+			if diff := cmp.Diff(got, tt.want); diff != "" {
+				t.Errorf("mismatch (-got +want):\n%s", diff)
+			}
+		})
+	}
+}