wip

Mistral is a popular research lab making open source models. This updates
the forward pass of llama architecture models to support both llama models
and mistral models by accounting for additional metadata present in mistral
models, and finding the correct dimensions for the output projection.

CMakePresets.json

@@ -56,7 +56,7 @@
       "name": "ROCm 6",
       "inherits": [ "ROCm" ],
       "cacheVariables": {
-        "AMDGPU_TARGETS": "gfx900;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-"
+        "AMDGPU_TARGETS": "gfx900;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102;gfx1151;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-"
       }
     }
   ],

README.md

@@ -392,6 +392,8 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [1Panel](https://github.com/1Panel-dev/1Panel/) (Web-based Linux Server Management Tool)
 - [AstrBot](https://github.com/Soulter/AstrBot/) (User-friendly LLM-based multi-platform chatbot with a WebUI, supporting RAG, LLM agents, and plugins integration)
 - [Reins](https://github.com/ibrahimcetin/reins) (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)
+- [Ellama](https://github.com/zeozeozeo/ellama) (Friendly native app to chat with an Ollama instance)
+- [screenpipe](https://github.com/mediar-ai/screenpipe) Build agents powered by your screen history
 
 ### Cloud
 

cmd/cmd_test.go

@@ -757,3 +757,132 @@ func TestCreateHandler(t *testing.T) {
 		})
 	}
 }
+
+func TestNewCreateRequest(t *testing.T) {
+	tests := []struct {
+		name     string
+		from     string
+		opts     runOptions
+		expected *api.CreateRequest
+	}{
+		{
+			"basic test",
+			"newmodel",
+			runOptions{
+				Model:       "mymodel",
+				ParentModel: "",
+				Prompt:      "You are a fun AI agent",
+				Messages:    []api.Message{},
+				WordWrap:    true,
+			},
+			&api.CreateRequest{
+				From:  "mymodel",
+				Model: "newmodel",
+			},
+		},
+		{
+			"parent model test",
+			"newmodel",
+			runOptions{
+				Model:       "mymodel",
+				ParentModel: "parentmodel",
+				Messages:    []api.Message{},
+				WordWrap:    true,
+			},
+			&api.CreateRequest{
+				From:  "parentmodel",
+				Model: "newmodel",
+			},
+		},
+		{
+			"parent model as filepath test",
+			"newmodel",
+			runOptions{
+				Model:       "mymodel",
+				ParentModel: "/some/file/like/etc/passwd",
+				Messages:    []api.Message{},
+				WordWrap:    true,
+			},
+			&api.CreateRequest{
+				From:  "mymodel",
+				Model: "newmodel",
+			},
+		},
+		{
+			"parent model as windows filepath test",
+			"newmodel",
+			runOptions{
+				Model:       "mymodel",
+				ParentModel: "D:\\some\\file\\like\\etc\\passwd",
+				Messages:    []api.Message{},
+				WordWrap:    true,
+			},
+			&api.CreateRequest{
+				From:  "mymodel",
+				Model: "newmodel",
+			},
+		},
+		{
+			"options test",
+			"newmodel",
+			runOptions{
+				Model:       "mymodel",
+				ParentModel: "parentmodel",
+				Options: map[string]any{
+					"temperature": 1.0,
+				},
+			},
+			&api.CreateRequest{
+				From:  "parentmodel",
+				Model: "newmodel",
+				Parameters: map[string]any{
+					"temperature": 1.0,
+				},
+			},
+		},
+		{
+			"messages test",
+			"newmodel",
+			runOptions{
+				Model:       "mymodel",
+				ParentModel: "parentmodel",
+				System:      "You are a fun AI agent",
+				Messages: []api.Message{
+					{
+						Role:    "user",
+						Content: "hello there!",
+					},
+					{
+						Role:    "assistant",
+						Content: "hello to you!",
+					},
+				},
+				WordWrap: true,
+			},
+			&api.CreateRequest{
+				From:   "parentmodel",
+				Model:  "newmodel",
+				System: "You are a fun AI agent",
+				Messages: []api.Message{
+					{
+						Role:    "user",
+						Content: "hello there!",
+					},
+					{
+						Role:    "assistant",
+						Content: "hello to you!",
+					},
+				},
+			},
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			actual := NewCreateRequest(tt.from, tt.opts)
+			if !cmp.Equal(actual, tt.expected) {
+				t.Errorf("expected output %#v, got %#v", tt.expected, actual)
+			}
+		})
+	}
+}

cmd/interactive.go

@@ -18,6 +18,7 @@ import (
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/readline"
 	"github.com/ollama/ollama/types/errtypes"
+	"github.com/ollama/ollama/types/model"
 )
 
 type MultilineState int
@@ -459,9 +460,16 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 }
 
 func NewCreateRequest(name string, opts runOptions) *api.CreateRequest {
+	parentModel := opts.ParentModel
+
+	modelName := model.ParseName(parentModel)
+	if !modelName.IsValid() {
+		parentModel = ""
+	}
+
 	req := &api.CreateRequest{
-		Name: name,
-		From: cmp.Or(opts.ParentModel, opts.Model),
+		Model: name,
+		From:  cmp.Or(parentModel, opts.Model),
 	}
 
 	if opts.System != "" {

convert/convert.go

@@ -182,8 +182,10 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 
 	var conv ModelConverter
 	switch p.Architectures[0] {
-	case "LlamaForCausalLM", "MistralForCausalLM":
+	case "LlamaForCausalLM":
 		conv = &llamaModel{}
+	case "MistralForCausalLM":
+		conv = &mistralModel{}
 	case "MixtralForCausalLM":
 		conv = &mixtralModel{}
 	case "GemmaForCausalLM":

convert/convert_mistral.go

@@ -0,0 +1,216 @@
+package convert
+
+import (
+	"cmp"
+	"fmt"
+	"math"
+	"strings"
+
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+type mistralModel struct {
+	ModelParameters
+	NLayers               uint32  `json:"n_layers"`
+	NumHiddenLayers       uint32  `json:"num_hidden_layers"`
+	NLayer                uint32  `json:"n_layer"`
+	MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
+	NCtx                  uint32  `json:"n_ctx"`
+	HiddenSize            uint32  `json:"hidden_size"`
+	NEmbd                 uint32  `json:"n_embd"`
+	IntermediateSize      uint32  `json:"intermediate_size"`
+	NInner                uint32  `json:"n_inner"`
+	NumAttentionHeads     uint32  `json:"num_attention_heads"`
+	NHead                 uint32  `json:"n_head"`
+	NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
+	RopeTheta             float32 `json:"rope_theta"`
+	RopeScaling           struct {
+		Type                            string  `json:"type"`
+		RopeType                        string  `json:"rope_type"`
+		Factor                          float32 `json:"factor"`
+		LowFrequencyFactor              float32 `json:"low_freq_factor"`
+		HighFrequencyFactor             float32 `json:"high_freq_factor"`
+		OriginalMaxPositionalEmbeddings uint32  `json:"original_max_positional_embeddings"`
+
+		factors ropeFactor
+	} `json:"rope_scaling"`
+	RMSNormEPS       float32 `json:"rms_norm_eps"`
+	LayerNormEPS     float32 `json:"layer_norm_eps"`
+	LayerNormEpsilon float32 `json:"layer_norm_epsilon"`
+	NormEpsilon      float32 `json:"norm_epsilon"`
+	HeadDim          uint32  `json:"head_dim"`
+}
+
+func (p *mistralModel) KV(t *Tokenizer) ggml.KV {
+	kv := p.ModelParameters.KV(t)
+	kv["general.architecture"] = "mistral"
+	kv["mistral.vocab_size"] = p.VocabSize
+
+	kv["mistral.block_count"] = cmp.Or(p.NLayers, p.NumHiddenLayers, p.NLayer)
+
+	if contextLength := cmp.Or(p.MaxPositionEmbeddings, p.NCtx); contextLength > 0 {
+		kv["mistral.context_length"] = contextLength
+	}
+
+	if embeddingLength := cmp.Or(p.HiddenSize, p.NEmbd); embeddingLength > 0 {
+		kv["mistral.embedding_length"] = cmp.Or(p.HiddenSize, p.NEmbd)
+	}
+
+	if feedForwardLength := cmp.Or(p.IntermediateSize, p.NInner); feedForwardLength > 0 {
+		kv["mistral.feed_forward_length"] = cmp.Or(p.IntermediateSize, p.NInner)
+	}
+
+	kv["mistral.attention.head_count"] = cmp.Or(p.NumAttentionHeads, p.NHead)
+	kv["mistral.rope.dimension_count"] = p.HiddenSize / cmp.Or(p.NLayers, p.NumHiddenLayers, p.NLayer)
+
+	if p.RopeTheta > 0 {
+		kv["mistral.rope.freq_base"] = p.RopeTheta
+	}
+
+	if p.RopeScaling.Type == "linear" {
+		kv["mistral.rope.scaling.type"] = p.RopeScaling.Type
+		kv["mistral.rope.scaling.factor"] = p.RopeScaling.Factor
+	} else if p.RopeScaling.RopeType == "llama3" {
+		dim := p.HiddenSize / p.NumAttentionHeads
+		for i := uint32(0); i < dim; i += 2 {
+			factor := cmp.Or(p.RopeScaling.Factor, 8.0)
+			factorLow := cmp.Or(p.RopeScaling.LowFrequencyFactor, 1.0)
+			factorHigh := cmp.Or(p.RopeScaling.HighFrequencyFactor, 4.0)
+
+			original := cmp.Or(p.RopeScaling.OriginalMaxPositionalEmbeddings, 8192)
+			lambdaLow := float32(original) / factorLow
+			lambdaHigh := float32(original) / factorHigh
+
+			lambda := 2 * math.Pi * math.Pow(float64(p.RopeTheta), float64(i)/float64(dim))
+			if lambda < float64(lambdaHigh) {
+				p.RopeScaling.factors = append(p.RopeScaling.factors, 1.0)
+			} else if lambda > float64(lambdaLow) {
+				p.RopeScaling.factors = append(p.RopeScaling.factors, factor)
+			} else {
+				smooth := (float32(original)/float32(lambda) - factorLow) / (factorHigh - factorLow)
+				p.RopeScaling.factors = append(p.RopeScaling.factors, 1.0/((1-smooth)/factor+smooth))
+			}
+		}
+	}
+
+	if p.NumKeyValueHeads > 0 {
+		kv["mistral.attention.head_count_kv"] = p.NumKeyValueHeads
+	}
+
+	if p.RMSNormEPS > 0 {
+		kv["mistral.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
+	}
+
+	if layerNormEpsilon := cmp.Or(p.LayerNormEPS, p.LayerNormEpsilon, p.NormEpsilon); layerNormEpsilon > 0 {
+		kv["mistral.attention.layer_norm_epsilon"] = layerNormEpsilon
+	}
+
+	if p.HeadDim > 0 {
+		kv["mistral.attention.key_length"] = p.HeadDim
+		kv["mistral.attention.value_length"] = p.HeadDim
+	}
+
+	return kv
+}
+
+func (p *mistralModel) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
+
+	if p.RopeScaling.factors != nil {
+		out = append(out, ggml.Tensor{
+			Name:     "rope_freqs.weight",
+			Kind:     0,
+			Shape:    []uint64{uint64(len(p.RopeScaling.factors))},
+			WriterTo: p.RopeScaling.factors,
+		})
+	}
+
+	for _, t := range ts {
+		if strings.HasSuffix(t.Name(), "attn_q.weight") ||
+			strings.HasSuffix(t.Name(), "attn_k.weight") {
+			t.SetRepacker(p.repack)
+		}
+
+		if strings.HasPrefix(t.Name(), "patch_merger.") ||
+			strings.HasPrefix(t.Name(), "pre_mm_projector_output_norm.") ||
+			strings.HasPrefix(t.Name(), "vision_encoder.") ||
+			strings.HasPrefix(t.Name(), "vision_language_adapter.") {
+			continue
+		}
+
+		out = append(out, ggml.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *mistralModel) Replacements() []string {
+	return []string{
+		"tok_embeddings", "token_embd",
+		"norm", "output_norm",
+		"layers", "blk",
+		"attention_norm", "attn_norm",
+		"attention.wq", "attn_q",
+		"attention.wk", "attn_k",
+		"attention.wv", "attn_v",
+		"attention.wo", "attn_output",
+		"feed_forward.w1", "ffn_gate",
+		"feed_forward.w2", "ffn_down",
+		"feed_forward.w3", "ffn_up",
+		"ffn_norm", "ffn_norm",
+		"output", "output",
+	}
+}
+
+func (p *mistralModel) repack(name string, data []float32, shape []uint64) ([]float32, error) {
+	var dims []int
+	for _, dim := range shape {
+		dims = append(dims, int(dim))
+	}
+
+	var heads uint32
+	if strings.HasSuffix(name, "attn_q.weight") {
+		heads = p.NumAttentionHeads
+	} else if strings.HasSuffix(name, "attn_k.weight") {
+		heads = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
+	} else {
+		return nil, fmt.Errorf("unknown tensor for repack: %s", name)
+	}
+
+	n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
+	if err := n.Reshape(append([]int{int(heads), 2, dims[0] / int(heads) / 2}, dims[1:]...)...); err != nil {
+		return nil, err
+	}
+
+	if err := n.T(0, 2, 1, 3); err != nil {
+		return nil, err
+	}
+
+	if err := n.Reshape(dims...); err != nil {
+		return nil, err
+	}
+
+	if err := n.Transpose(); err != nil {
+		return nil, err
+	}
+
+	ts, err := native.SelectF32(n, 1)
+	if err != nil {
+		return nil, err
+	}
+
+	var f32s []float32
+	for _, t := range ts {
+		f32s = append(f32s, t...)
+	}
+
+	return f32s, nil
+}

convert/reader.go

@@ -62,10 +62,7 @@ func parseTensors(fsys fs.FS, replacer *strings.Replacer) ([]Tensor, error) {
 		Pattern string
 		Func    func(fs.FS, *strings.Replacer, ...string) ([]Tensor, error)
 	}{
-		{"model-*-of-*.safetensors", parseSafetensors},
-		{"model.safetensors", parseSafetensors},
-		{"adapters.safetensors", parseSafetensors},
-		{"adapter_model.safetensors", parseSafetensors},
+		{"*.safetensors", parseSafetensors},
 		{"pytorch_model-*-of-*.bin", parseTorch},
 		{"pytorch_model.bin", parseTorch},
 		{"consolidated.*.pth", parseTorch},

docs/faq.md

@@ -187,6 +187,13 @@ cloudflared tunnel --url http://localhost:11434 --http-host-header="localhost:11
 
 Ollama allows cross-origin requests from `127.0.0.1` and `0.0.0.0` by default. Additional origins can be configured with `OLLAMA_ORIGINS`.
 
+For browser extensions, you'll need to explicitly allow the extension's origin pattern. Set `OLLAMA_ORIGINS` to include `chrome-extension://*`, `moz-extension://*`, and `safari-web-extension://*` if you wish to allow all browser extensions access, or specific extensions as needed:
+
+```
+# Allow all Chrome, Firefox, and Safari extensions
+OLLAMA_ORIGINS=chrome-extension://*,moz-extension://*,safari-web-extension://* ollama serve
+```
+
 Refer to the section [above](#how-do-i-configure-ollama-server) for how to set environment variables on your platform.
 
 ## Where are models stored?

fs/ggml/ggml.go

@@ -583,39 +583,52 @@ func (f GGML) GraphSize(context, batch uint64, kvCacheType string) (kv, partialO
 }
 
 func (llm GGML) VisionGraphSize() (weights, graphSize uint64) {
+	if llm.KV().Uint("vision.block_count") == 0 {
+		return
+	}
+
+	for name, layer := range llm.Tensors().GroupLayers() {
+		if name == "v" || strings.HasPrefix(name, "v.") {
+			for _, tensor := range layer {
+				weights += tensor.Size()
+			}
+		}
+	}
+
+	imageSize := uint64(llm.KV().Uint("vision.image_size"))
+	patchSize := uint64(llm.KV().Uint("vision.patch_size"))
+	if patchSize == 0 {
+		slog.Warn("unknown patch size for vision model")
+		return
+	}
+
+	numChannels := uint64(llm.KV().Uint("vision.num_channels"))
+
+	numPatches := (imageSize / patchSize) * (imageSize / patchSize)
+	if _, ok := llm.Tensors().GroupLayers()["v"]["class_embd"]; ok {
+		numPatches++
+	}
+
+	headCount := uint64(llm.KV().Uint("vision.attention.head_count"))
+	embeddingLength := uint64(llm.KV().Uint("vision.embedding_length"))
+
 	switch llm.KV().Architecture() {
 	case "mllama":
-		for _, layer := range llm.Tensors().GroupLayers()["v"] {
-			weights += layer.Size()
-		}
-
-		kv := func(n string) uint64 {
-			if v, ok := llm.KV()["mllama.vision."+n].(uint32); ok {
-				return uint64(v)
-			}
-
-			return 0
-		}
-
-		imageSize := kv("image_size")
-
-		maxNumTiles := kv("max_num_tiles")
-		embeddingLength := kv("embedding_length")
-		headCount := kv("attention.head_count")
-
-		numPatches := (imageSize / kv("patch_size")) * (imageSize / kv("patch_size"))
-		if _, ok := llm.Tensors().GroupLayers()["v"]["class_embd"]; ok {
-			numPatches++
-		}
-
 		numPaddedPatches := numPatches + 8 - (numPatches%8)%8
 
+		maxNumTiles := uint64(llm.KV().Uint("vision.max_num_tiles"))
+
 		graphSize = 4 * (8 +
-			imageSize*imageSize*kv("num_channels")*maxNumTiles +
+			imageSize*imageSize*numChannels*maxNumTiles +
 			embeddingLength*numPatches*maxNumTiles +
 			9*embeddingLength*numPaddedPatches*maxNumTiles +
 			numPaddedPatches*maxNumTiles*numPaddedPatches*maxNumTiles*headCount)
+	case "gemma3":
+		graphSize = 4 * (imageSize*imageSize*numChannels +
+			embeddingLength*patchSize +
+			numPatches*numPatches*headCount)
 	}
+
 	return weights, graphSize
 }
 

integration/llm_image_test.go

@@ -66,6 +66,35 @@ func TestIntegrationMllama(t *testing.T) {
 	DoGenerate(ctx, t, client, req, []string{resp}, 240*time.Second, 30*time.Second)
 }
 
+func TestIntegrationSplitBatch(t *testing.T) {
+	image, err := base64.StdEncoding.DecodeString(imageEncoding)
+	require.NoError(t, err)
+	req := api.GenerateRequest{
+		Model: "gemma3:4b",
+		// Fill up a chunk of the batch so the image will partially spill over into the next one
+		System: "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed aliquet, justo in malesuada lobortis, odio ligula volutpat quam, quis faucibus ipsum magna quis sapien. Aliquam in venenatis diam, eu viverra magna. Phasellus imperdiet hendrerit volutpat. Vivamus sem ex, facilisis placerat felis non, dictum elementum est. Phasellus aliquam imperdiet lacus, eget placerat ligula sodales vel. Pellentesque nec auctor mi. Curabitur arcu nisi, faucibus eget nunc id, viverra interdum mi. Curabitur ornare ipsum ex, ac euismod ex aliquam in. Vestibulum id magna at purus accumsan fermentum. Proin scelerisque posuere nunc quis interdum. Maecenas sed mollis nisl. Etiam vitae ipsum interdum, placerat est quis, tincidunt velit. Nullam tempor nibh non lorem volutpat efficitur. Cras laoreet diam imperdiet ipsum auctor bibendum. Suspendisse ultrices urna sed metus sagittis suscipit. Quisque ullamcorper aliquam nibh ut mollis. Aenean dapibus mauris pharetra, venenatis elit ac, hendrerit odio. Cras vestibulum erat tempor, lobortis justo eu, lobortis ipsum. Nam laoreet dapibus sem. Proin vel diam ultrices, elementum ante et, ornare lectus. Proin eu accumsan nisl. Praesent ac ex vitae ipsum vulputate tristique facilisis sit amet lacus. Nullam faucibus magna a pellentesque pretium. Nunc lacinia ullamcorper sollicitudin. Donec vitae accumsan turpis, sed porttitor est. Donec porttitor mi vitae augue faucibus, vel mollis diam tincidunt.",
+		Prompt: "what does the text in this image say?",
+		Stream: &stream,
+		Options: map[string]interface{}{
+			"seed":        42,
+			"temperature": 0.0,
+		},
+		Images: []api.ImageData{
+			image,
+		},
+	}
+
+	// Note: sometimes it returns "the ollamas" sometimes "the ollams"
+	resp := "the ollam"
+	ctx, cancel := context.WithTimeout(context.Background(), 3*time.Minute)
+	defer cancel()
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
+	require.NoError(t, PullIfMissing(ctx, client, req.Model))
+	// llava models on CPU can be quite slow to start,
+	DoGenerate(ctx, t, client, req, []string{resp}, 120*time.Second, 30*time.Second)
+}
+
 const imageEncoding = `iVBORw0KGgoAAAANSUhEUgAAANIAAAB4CAYAAACHHqzKAAAAAXNSR0IArs4c6QAAAIRlWElmTU0AKgAAAAgABQESAAMAAAABAAEAAAEaAAUAAAABAAAASgEb
 AAUAAAABAAAAUgEoAAMAAAABAAIAAIdpAAQAAAABAAAAWgAAAAAAAABIAAAAAQAAAEgAAAABAAOgAQADAAAAAQABAACgAgAEAAAAAQAAANKgAwAEAAAAAQAA
 AHgAAAAAXdsepgAAAAlwSFlzAAALEwAACxMBAJqcGAAAAVlpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6

llama/llama.cpp/src/llama-arch.cpp

@@ -37,6 +37,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_MINICPM3,         "minicpm3"         },
     { LLM_ARCH_GEMMA,            "gemma"            },
     { LLM_ARCH_GEMMA2,           "gemma2"           },
+    { LLM_ARCH_GEMMA3,           "gemma3"           },
     { LLM_ARCH_STARCODER2,       "starcoder2"       },
     { LLM_ARCH_MAMBA,            "mamba"            },
     { LLM_ARCH_XVERSE,           "xverse"           },
@@ -804,6 +805,24 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
         },
     },
+    {
+        LLM_ARCH_GEMMA3,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
+        },
+    },
     {
         LLM_ARCH_STARCODER2,
         {

llama/llama.cpp/src/llama-arch.h

@@ -41,6 +41,7 @@ enum llm_arch {
     LLM_ARCH_MINICPM3,
     LLM_ARCH_GEMMA,
     LLM_ARCH_GEMMA2,
+    LLM_ARCH_GEMMA3,
     LLM_ARCH_STARCODER2,
     LLM_ARCH_MAMBA,
     LLM_ARCH_XVERSE,

llama/llama.cpp/src/llama-model.cpp

@@ -878,6 +878,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                }
             } break;
+        case LLM_ARCH_GEMMA3:
+            {
+            } break;
         case LLM_ARCH_STARCODER2:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -2537,6 +2540,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
                     }
                 } break;
+            case LLM_ARCH_GEMMA3:
+                {
+                } break;
             case LLM_ARCH_STARCODER2:
                 {
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -4029,6 +4035,7 @@ enum llama_rope_type llama_model_rope_type(const struct llama_model * model) {
         case LLM_ARCH_PHIMOE:
         case LLM_ARCH_GEMMA:
         case LLM_ARCH_GEMMA2:
+        case LLM_ARCH_GEMMA3:
         case LLM_ARCH_STARCODER2:
         case LLM_ARCH_OPENELM:
         case LLM_ARCH_GPTNEOX:

llama/llama.cpp/src/llama-quant.cpp

@@ -737,6 +737,15 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
         // This used to be a regex, but <regex> has an extreme cost to compile times.
         bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
 
+        // don't quantize vision stuff
+        quantize &= name.find("v.blk.") == std::string::npos;
+
+        quantize &= name.find("mm.mm_input_projection.weight") == std::string::npos;
+        quantize &= name.find("mm.mm_soft_emb_norm.weight") == std::string::npos;
+        quantize &= name.find("v.patch_embedding.weight") == std::string::npos;
+        quantize &= name.find("v.position_embedding.weight") == std::string::npos;
+        quantize &= name.find("v.post_layernorm.weight") == std::string::npos;
+
         // quantize only 2D and 3D tensors (experts)
         quantize &= (ggml_n_dims(tensor) >= 2);
 

llama/patches/0021-gemma3-quantization.patch

@@ -0,0 +1,113 @@
+From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
+From: Patrick Devine <patrick@infrahq.com>
+Date: Fri, 14 Mar 2025 16:33:23 -0700
+Subject: [PATCH] gemma3 quantization
+
+---
+ src/llama-arch.cpp  | 19 +++++++++++++++++++
+ src/llama-arch.h    |  1 +
+ src/llama-model.cpp |  7 +++++++
+ src/llama-quant.cpp |  9 +++++++++
+ 4 files changed, 36 insertions(+)
+
+diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
+index b6f20286..b443fcd3 100644
+--- a/src/llama-arch.cpp
++++ b/src/llama-arch.cpp
+@@ -37,6 +37,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
+     { LLM_ARCH_MINICPM3,         "minicpm3"         },
+     { LLM_ARCH_GEMMA,            "gemma"            },
+     { LLM_ARCH_GEMMA2,           "gemma2"           },
++    { LLM_ARCH_GEMMA3,           "gemma3"           },
+     { LLM_ARCH_STARCODER2,       "starcoder2"       },
+     { LLM_ARCH_MAMBA,            "mamba"            },
+     { LLM_ARCH_XVERSE,           "xverse"           },
+@@ -804,6 +805,24 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
+             { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
+         },
+     },
++    {
++        LLM_ARCH_GEMMA3,
++        {
++            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
++            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
++            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
++            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
++            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
++            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
++            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
++            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
++            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
++            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
++            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
++            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
++            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
++        },
++    },
+     {
+         LLM_ARCH_STARCODER2,
+         {
+diff --git a/src/llama-arch.h b/src/llama-arch.h
+index ec742224..aad92a5d 100644
+--- a/src/llama-arch.h
++++ b/src/llama-arch.h
+@@ -41,6 +41,7 @@ enum llm_arch {
+     LLM_ARCH_MINICPM3,
+     LLM_ARCH_GEMMA,
+     LLM_ARCH_GEMMA2,
++    LLM_ARCH_GEMMA3,
+     LLM_ARCH_STARCODER2,
+     LLM_ARCH_MAMBA,
+     LLM_ARCH_XVERSE,
+diff --git a/src/llama-model.cpp b/src/llama-model.cpp
+index ab1a07d1..70183041 100644
+--- a/src/llama-model.cpp
++++ b/src/llama-model.cpp
+@@ -878,6 +878,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
+                     default: type = LLM_TYPE_UNKNOWN;
+                }
+             } break;
++        case LLM_ARCH_GEMMA3:
++            {
++            } break;
+         case LLM_ARCH_STARCODER2:
+             {
+                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+@@ -2537,6 +2540,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
+                         layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+                     }
+                 } break;
++            case LLM_ARCH_GEMMA3:
++                {
++                } break;
+             case LLM_ARCH_STARCODER2:
+                 {
+                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+@@ -4029,6 +4035,7 @@ enum llama_rope_type llama_model_rope_type(const struct llama_model * model) {
+         case LLM_ARCH_PHIMOE:
+         case LLM_ARCH_GEMMA:
+         case LLM_ARCH_GEMMA2:
++        case LLM_ARCH_GEMMA3:
+         case LLM_ARCH_STARCODER2:
+         case LLM_ARCH_OPENELM:
+         case LLM_ARCH_GPTNEOX:
+diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
+index 6eb1da08..d2f3a510 100644
+--- a/src/llama-quant.cpp
++++ b/src/llama-quant.cpp
+@@ -737,6 +737,15 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
+         // This used to be a regex, but <regex> has an extreme cost to compile times.
+         bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
+ 
++        // don't quantize vision stuff
++        quantize &= name.find("v.blk.") == std::string::npos;
++
++        quantize &= name.find("mm.mm_input_projection.weight") == std::string::npos;
++        quantize &= name.find("mm.mm_soft_emb_norm.weight") == std::string::npos;
++        quantize &= name.find("v.patch_embedding.weight") == std::string::npos;
++        quantize &= name.find("v.position_embedding.weight") == std::string::npos;
++        quantize &= name.find("v.post_layernorm.weight") == std::string::npos;
++
+         // quantize only 2D and 3D tensors (experts)
+         quantize &= (ggml_n_dims(tensor) >= 2);
+ 

llm/memory.go

@@ -218,8 +218,8 @@ func EstimateGPULayers(gpus []discover.GpuInfo, f *ggml.GGML, projectors []strin
 		if blk, ok := layers[fmt.Sprintf("blk.%d", i)]; ok {
 			layerSize = blk.Size()
 			layerSize += kv / f.KV().BlockCount()
+			memoryWeights += blk.Size()
 		}
-		memoryWeights += layerSize
 
 		if opts.NumGPU >= 0 && layerCount >= opts.NumGPU {
 			// Stop allocating on GPU(s) once we hit the users target NumGPU
@@ -376,7 +376,7 @@ func (m MemoryEstimate) LogValue() slog.Value {
 				// memory of the weights
 				"total", format.HumanBytes2(m.memoryWeights),
 				// memory of repeating layers
-				"repeating", format.HumanBytes2(m.memoryWeights-m.memoryLayerOutput),
+				"repeating", format.HumanBytes2(m.memoryWeights),
 				// memory of non-repeating layers
 				"nonrepeating", format.HumanBytes2(m.memoryLayerOutput),
 			),

llm/server.go

@@ -402,7 +402,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, modelPath string, f *ggml.GGML, a
 			s.cmd.Env = append(s.cmd.Env, visibleDevicesEnv+"="+visibleDevicesEnvVal)
 		}
 
-		slog.Info("starting llama server", "cmd", s.cmd.String())
+		slog.Info("starting llama server", "cmd", s.cmd)
 		if envconfig.Debug() {
 			filteredEnv := []string{}
 			for _, ev := range s.cmd.Env {
@@ -470,7 +470,7 @@ const ( // iota is reset to 0
 	ServerStatusError
 )
 
-func (s ServerStatus) ToString() string {
+func (s ServerStatus) String() string {
 	switch s {
 	case ServerStatusReady:
 		return "llm server ready"
@@ -485,12 +485,9 @@ func (s ServerStatus) ToString() string {
 	}
 }
 
-type ServerStatusResp struct {
-	Status          string  `json:"status"`
-	SlotsIdle       int     `json:"slots_idle"`
-	SlotsProcessing int     `json:"slots_processing"`
-	Error           string  `json:"error"`
-	Progress        float32 `json:"progress"`
+type ServerStatusResponse struct {
+	Status   ServerStatus `json:"status"`
+	Progress float32      `json:"progress"`
 }
 
 func (s *llmServer) getServerStatus(ctx context.Context) (ServerStatus, error) {
@@ -502,7 +499,7 @@ func (s *llmServer) getServerStatus(ctx context.Context) (ServerStatus, error) {
 		}
 		if s.cmd.ProcessState.ExitCode() == -1 {
 			// Most likely a signal killed it, log some more details to try to help troubleshoot
-			slog.Warn("llama runner process no longer running", "sys", s.cmd.ProcessState.Sys(), "string", s.cmd.ProcessState.String())
+			slog.Warn("llama runner process no longer running", "sys", s.cmd.ProcessState.Sys(), "string", s.cmd.ProcessState)
 		}
 		return ServerStatusError, fmt.Errorf("llama runner process no longer running: %d %s", s.cmd.ProcessState.ExitCode(), msg)
 	}
@@ -527,21 +524,19 @@ func (s *llmServer) getServerStatus(ctx context.Context) (ServerStatus, error) {
 		return ServerStatusError, fmt.Errorf("read health request: %w", err)
 	}
 
-	var status ServerStatusResp
-	if err := json.Unmarshal(body, &status); err != nil {
+	var ssr ServerStatusResponse
+	if err := json.Unmarshal(body, &ssr); err != nil {
 		return ServerStatusError, fmt.Errorf("health unmarshal encode response: %w", err)
 	}
 
-	switch status.Status {
-	case "ok":
-		return ServerStatusReady, nil
-	case "no slot available":
-		return ServerStatusNoSlotsAvailable, nil
-	case "loading model":
-		s.loadProgress = status.Progress
-		return ServerStatusLoadingModel, nil
+	switch ssr.Status {
+	case ServerStatusLoadingModel:
+		s.loadProgress = ssr.Progress
+		return ssr.Status, nil
+	case ServerStatusReady, ServerStatusNoSlotsAvailable:
+		return ssr.Status, nil
 	default:
-		return ServerStatusError, fmt.Errorf("server error: %+v", status)
+		return ssr.Status, fmt.Errorf("server error: %+v", ssr)
 	}
 }
 
@@ -616,7 +611,7 @@ func (s *llmServer) WaitUntilRunning(ctx context.Context) error {
 		status, _ := s.getServerStatus(ctx)
 		if lastStatus != status && status != ServerStatusReady {
 			// Only log on status changes
-			slog.Info("waiting for server to become available", "status", status.ToString())
+			slog.Info("waiting for server to become available", "status", status)
 		}
 		switch status {
 		case ServerStatusReady:
@@ -630,7 +625,7 @@ func (s *llmServer) WaitUntilRunning(ctx context.Context) error {
 				slog.Debug(fmt.Sprintf("model load progress %0.2f", s.loadProgress))
 				stallTimer = time.Now().Add(stallDuration)
 			} else if !fullyLoaded && int(s.loadProgress*100.0) >= 100 {
-				slog.Debug("model load completed, waiting for server to become available", "status", status.ToString())
+				slog.Debug("model load completed, waiting for server to become available", "status", status)
 				stallTimer = time.Now().Add(stallDuration)
 				fullyLoaded = true
 			}
@@ -671,63 +666,26 @@ type ImageData struct {
 	AspectRatioID int    `json:"aspect_ratio_id"`
 }
 
-type completion struct {
-	Content      string `json:"content"`
-	Model        string `json:"model"`
-	Prompt       string `json:"prompt"`
-	Stop         bool   `json:"stop"`
-	StoppedLimit bool   `json:"stopped_limit"`
-
-	Timings struct {
-		PredictedN  int     `json:"predicted_n"`
-		PredictedMS float64 `json:"predicted_ms"`
-		PromptN     int     `json:"prompt_n"`
-		PromptMS    float64 `json:"prompt_ms"`
-	}
-}
-
 type CompletionRequest struct {
 	Prompt  string
 	Format  json.RawMessage
 	Images  []ImageData
 	Options *api.Options
+
+	Grammar string // set before sending the request to the subprocess
 }
 
 type CompletionResponse struct {
-	Content            string
-	DoneReason         string
-	Done               bool
-	PromptEvalCount    int
-	PromptEvalDuration time.Duration
-	EvalCount          int
-	EvalDuration       time.Duration
+	Content            string        `json:"content"`
+	DoneReason         string        `json:"done_reason"`
+	Done               bool          `json:"done"`
+	PromptEvalCount    int           `json:"prompt_eval_count"`
+	PromptEvalDuration time.Duration `json:"prompt_eval_duration"`
+	EvalCount          int           `json:"eval_count"`
+	EvalDuration       time.Duration `json:"eval_duration"`
 }
 
 func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn func(CompletionResponse)) error {
-	request := map[string]any{
-		"prompt":            req.Prompt,
-		"stream":            true,
-		"n_predict":         req.Options.NumPredict,
-		"n_keep":            req.Options.NumKeep,
-		"main_gpu":          req.Options.MainGPU,
-		"temperature":       req.Options.Temperature,
-		"top_k":             req.Options.TopK,
-		"top_p":             req.Options.TopP,
-		"min_p":             req.Options.MinP,
-		"typical_p":         req.Options.TypicalP,
-		"repeat_last_n":     req.Options.RepeatLastN,
-		"repeat_penalty":    req.Options.RepeatPenalty,
-		"presence_penalty":  req.Options.PresencePenalty,
-		"frequency_penalty": req.Options.FrequencyPenalty,
-		"mirostat":          req.Options.Mirostat,
-		"mirostat_tau":      req.Options.MirostatTau,
-		"mirostat_eta":      req.Options.MirostatEta,
-		"seed":              req.Options.Seed,
-		"stop":              req.Options.Stop,
-		"image_data":        req.Images,
-		"cache_prompt":      true,
-	}
-
 	if len(req.Format) > 0 {
 		switch string(req.Format) {
 		case `null`, `""`:
@@ -735,7 +693,7 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 			// these as "not set".
 			break
 		case `"json"`:
-			request["grammar"] = grammarJSON
+			req.Grammar = grammarJSON
 		default:
 			if req.Format[0] != '{' {
 				return fmt.Errorf("invalid format: %q; expected \"json\" or a valid JSON Schema object", req.Format)
@@ -746,10 +704,15 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 			if g == nil {
 				return fmt.Errorf("invalid JSON schema in format")
 			}
-			request["grammar"] = string(g)
+			req.Grammar = string(g)
 		}
 	}
 
+	if req.Options == nil {
+		opts := api.DefaultOptions()
+		req.Options = &opts
+	}
+
 	if err := s.sem.Acquire(ctx, 1); err != nil {
 		if errors.Is(err, context.Canceled) {
 			slog.Info("aborting completion request due to client closing the connection")
@@ -770,7 +733,7 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 	if err != nil {
 		return err
 	} else if status != ServerStatusReady {
-		return fmt.Errorf("unexpected server status: %s", status.ToString())
+		return fmt.Errorf("unexpected server status: %s", status)
 	}
 
 	// Handling JSON marshaling with special characters unescaped.
@@ -778,7 +741,7 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 	enc := json.NewEncoder(buffer)
 	enc.SetEscapeHTML(false)
 
-	if err := enc.Encode(request); err != nil {
+	if err := enc.Encode(req); err != nil {
 		return fmt.Errorf("failed to marshal data: %v", err)
 	}
 
@@ -829,7 +792,7 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 				evt = line
 			}
 
-			var c completion
+			var c CompletionResponse
 			if err := json.Unmarshal(evt, &c); err != nil {
 				return fmt.Errorf("error unmarshalling llm prediction response: %v", err)
 			}
@@ -853,20 +816,8 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 				})
 			}
 
-			if c.Stop {
-				doneReason := "stop"
-				if c.StoppedLimit {
-					doneReason = "length"
-				}
-
-				fn(CompletionResponse{
-					Done:               true,
-					DoneReason:         doneReason,
-					PromptEvalCount:    c.Timings.PromptN,
-					PromptEvalDuration: parseDurationMs(c.Timings.PromptMS),
-					EvalCount:          c.Timings.PredictedN,
-					EvalDuration:       parseDurationMs(c.Timings.PredictedMS),
-				})
+			if c.Done {
+				fn(c)
 				return nil
 			}
 		}
@@ -914,7 +865,7 @@ func (s *llmServer) Embedding(ctx context.Context, input string) ([]float32, err
 	if err != nil {
 		return nil, err
 	} else if status != ServerStatusReady {
-		return nil, fmt.Errorf("unexpected server status: %s", status.ToString())
+		return nil, fmt.Errorf("unexpected server status: %s", status)
 	}
 
 	data, err := json.Marshal(EmbeddingRequest{Content: input})
@@ -1059,12 +1010,3 @@ func (s *llmServer) EstimatedVRAMByGPU(gpuID string) uint64 {
 	}
 	return 0
 }
-
-func parseDurationMs(ms float64) time.Duration {
-	dur, err := time.ParseDuration(fmt.Sprintf("%fms", ms))
-	if err != nil {
-		panic(err)
-	}
-
-	return dur
-}

model/input/input.go

@@ -15,6 +15,12 @@ type Input struct {
 	// stored in Multimodal, used for caching and comparing
 	// equality.
 	MultimodalHash uint64
+
+	// SameBatch forces the following number of tokens to be processed
+	// in a single batch, breaking and extending batches as needed.
+	// Useful for things like images that must be processed in one
+	// shot.
+	SameBatch int
 }
 
 // MultimodalIndex is a multimodal element (such as an image)

model/model.go

@@ -60,7 +60,7 @@ type MultimodalProcessor interface {
 	// This function is also responsible for updating MultimodalHash for any Multimodal
 	// that is modified to ensure that there is a unique hash value that accurately
 	// represents the contents.
-	PostTokenize(ml.Context, []input.Input) ([]input.Input, error)
+	PostTokenize([]input.Input) ([]input.Input, error)
 }
 
 // Base implements the common fields and methods for all models

model/models/gemma3/model.go

@@ -2,10 +2,9 @@ package gemma3
 
 import (
 	"bytes"
-	"encoding/binary"
-	"hash/fnv"
 	"image"
 	"math"
+	"slices"
 
 	"github.com/ollama/ollama/kvcache"
 	"github.com/ollama/ollama/ml"
@@ -112,36 +111,23 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) (any, er
 	return visionOutputs, nil
 }
 
-type imageToken struct {
-	embedding ml.Tensor
-	index     int
-}
-
-func (m *Model) PostTokenize(ctx ml.Context, inputs []input.Input) ([]input.Input, error) {
+func (m *Model) PostTokenize(inputs []input.Input) ([]input.Input, error) {
 	var result []input.Input
-	fnvHash := fnv.New64a()
 
 	for _, inp := range inputs {
 		if inp.Multimodal == nil {
 			result = append(result, inp)
 		} else {
-			imageInputs := []input.Input{
-				{Token: 108},    // "\n\n"
-				{Token: 255999}, // "<start_of_image>""
-			}
-			result = append(result, imageInputs...)
-
-			// add image embeddings
 			inputMultimodal := inp.Multimodal.(ml.Tensor)
 
-			for i := range inputMultimodal.Dim(1) {
-				fnvHash.Reset()
-				binary.Write(fnvHash, binary.NativeEndian, inp.MultimodalHash)
-				fnvHash.Write([]byte{byte(i)})
+			result = append(result,
+				input.Input{Token: 108, SameBatch: inputMultimodal.Dim(1) + 3},               // "\n\n"
+				input.Input{Token: 255999},                                                   // "<start_of_image>""
+				input.Input{Multimodal: inputMultimodal, MultimodalHash: inp.MultimodalHash}, // image data is on the first placeholder
+			)
 
-				imageToken := imageToken{embedding: inputMultimodal, index: i}
-				result = append(result, input.Input{Multimodal: imageToken, MultimodalHash: fnvHash.Sum64()})
-			}
+			// add image token placeholders
+			result = append(result, slices.Repeat([]input.Input{{Token: 0}}, inputMultimodal.Dim(1)-1)...)
 
 			result = append(result,
 				input.Input{Token: 256000}, // <end_of_image>

model/models/gemma3/model_text.go

@@ -171,53 +171,20 @@ func (l *TextLayer) Forward(ctx ml.Context, layer int, hiddenState, positionIDs,
 	return hiddenState.Add(ctx, residual)
 }
 
-func setImageEmbeddings(ctx ml.Context, hiddenState ml.Tensor, multimodal []input.MultimodalIndex) []int {
-	var embedding ml.Tensor
-	var src, dst, length int
-	var except []int
-
-	for _, image := range multimodal {
-		imageToken := image.Multimodal.(imageToken)
-		imageSrc := imageToken.index
-		imageDst := image.Index
-
-		if embedding == nil {
-			embedding = imageToken.embedding
-			src = imageSrc
-			dst = imageDst
-			length = 1
-		} else if embedding == imageToken.embedding && imageSrc+1 == src && imageDst+1 == dst {
-			src = imageSrc
-			dst = imageDst
-			length++
-		} else if embedding == imageToken.embedding && src+length == imageSrc && dst+length == imageDst {
-			length++
-		} else {
-			visionOutputs := embedding.View(ctx, src*embedding.Stride(1), length*embedding.Dim(0))
-			ctx.Forward(visionOutputs.Copy(ctx, hiddenState.View(ctx, dst*hiddenState.Stride(1), length*hiddenState.Dim(0))))
-
-			embedding = imageToken.embedding
-			src = imageSrc
-			dst = imageDst
-			length = 1
-		}
-
-		except = append(except, imageDst)
-	}
-
-	if embedding != nil {
-		visionOutputs := embedding.View(ctx, src*embedding.Stride(1), length*embedding.Dim(0))
-		ctx.Forward(visionOutputs.Copy(ctx, hiddenState.View(ctx, dst*hiddenState.Stride(1), length*hiddenState.Dim(0))))
-	}
-
-	return except
-}
-
 func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor, opts input.Options, cache kvcache.Cache) ml.Tensor {
 	hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
 	hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.TextOptions.hiddenSize)))
 
-	except := setImageEmbeddings(ctx, hiddenState, opts.Multimodal)
+	// set image embeddings
+	var except []int
+	for _, image := range opts.Multimodal {
+		visionOutputs := image.Multimodal.(ml.Tensor)
+		ctx.Forward(visionOutputs.Copy(ctx, hiddenState.View(ctx, image.Index*hiddenState.Stride(1), visionOutputs.Dim(0)*visionOutputs.Dim(1))))
+
+		for i := range visionOutputs.Dim(1) {
+			except = append(except, image.Index+i)
+		}
+	}
 
 	for i, layer := range m.Layers {
 		// gemma alternates between the sliding window (local) and causal (global)

model/models/llama/model.go

@@ -13,9 +13,9 @@ import (
 )
 
 type Options struct {
-	hiddenSize, numHeads, numKVHeads int
-	eps, ropeBase, ropeScale         float32
-	ropeDim                          uint32
+	hiddenSize, numHeads, numKVHeads, headDim int
+	eps, ropeBase, ropeScale                  float32
+	ropeDim                                   uint32
 }
 
 type Model struct {
@@ -37,6 +37,8 @@ func New(c ml.Config) (model.Model, error) {
 
 	m := Model{
 		BytePairEncoding: model.NewBytePairEncoding(
+			// TODO: need to set this in the conversion for mistral:
+			// 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+
 			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"),
@@ -53,6 +55,7 @@ func New(c ml.Config) (model.Model, error) {
 			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.key_length")),
 			eps:        c.Float("attention.layer_norm_rms_epsilon"),
 			ropeBase:   c.Float("rope.freq_base"),
 			ropeScale:  c.Float("rope.freq_scale", 1),
@@ -75,24 +78,36 @@ type SelfAttention struct {
 
 func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
 	batchSize := hiddenState.Dim(1)
-	headDim := opts.hiddenSize / opts.numHeads
 	ropeType := uint32(0)
+	// Get head dimension - use explicit value if available, otherwise calculate
+	headDim := opts.headDim
+	if headDim == 0 {
+		headDim = opts.hiddenSize / opts.numHeads
+	}
 
+	// Query projection and reshape
 	q := sa.Query.Forward(ctx, hiddenState)
 	q = q.Reshape(ctx, headDim, opts.numHeads, batchSize)
 	q = q.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
 
+	// Key projection and reshape
 	k := sa.Key.Forward(ctx, hiddenState)
 	k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
 	k = k.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
 
+	// Value projection and reshape
 	v := sa.Value.Forward(ctx, hiddenState)
 	v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
 
+	// Attention computation
 	scaleFactor := 1.0 / math.Sqrt(float64(headDim))
 	kqv := nn.Attention(ctx, q, k, v, scaleFactor, cache)
-	kqv = kqv.Reshape(ctx, opts.hiddenSize, batchSize)
 
+	// Reshape attention output for final projection
+	outputDim := headDim * opts.numHeads
+	kqv = kqv.Reshape(ctx, outputDim, batchSize)
+
+	// Apply output projection
 	return sa.Output.Forward(ctx, kqv)
 }
 

model/models/mistral/model.go

@@ -0,0 +1,193 @@
+package llama
+
+import (
+	"fmt"
+	"math"
+	"strings"
+
+	"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 {
+	hiddenSize, numHeads, numKVHeads, headDim int
+	eps, ropeBase, ropeScale                  float32
+	ropeDim                                   uint32
+}
+
+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 ml.Config) (model.Model, error) {
+	if !strings.EqualFold(c.String("tokenizer.ggml.model"), "gpt2") {
+		return nil, fmt.Errorf("tokenizer %s not yet supported", c.String("tokenizer.ggml.model"))
+	}
+
+	m := Model{
+		BytePairEncoding: model.NewBytePairEncoding(
+			// TODO: need to set this in the conversion for mistral:
+			// 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+
+			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+`),
+			// 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"),
+				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),
+			},
+		),
+		Layers: make([]Layer, c.Uint("block_count")),
+		Options: &Options{
+			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.key_length")),
+			eps:        c.Float("attention.layer_norm_rms_epsilon"),
+			ropeBase:   c.Float("rope.freq_base"),
+			ropeScale:  c.Float("rope.freq_scale", 1),
+			ropeDim:    c.Uint("rope.dimension_count"),
+		},
+	}
+
+	m.Cache = kvcache.NewCausalCache(m.Shift)
+
+	return &m, nil
+}
+
+type SelfAttention 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_freqs.weight"`
+}
+
+func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
+	batchSize := hiddenState.Dim(1)
+	ropeType := uint32(0)
+	// Get head dimension - use explicit value if available, otherwise calculate
+	headDim := opts.headDim
+	if headDim == 0 {
+		headDim = opts.hiddenSize / opts.numHeads
+	}
+
+	// Query projection and reshape
+	q := sa.Query.Forward(ctx, hiddenState)
+	q = q.Reshape(ctx, headDim, opts.numHeads, batchSize)
+	q = q.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
+
+	// Key projection and reshape
+	k := sa.Key.Forward(ctx, hiddenState)
+	k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
+	k = k.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
+
+	// Value projection and reshape
+	v := sa.Value.Forward(ctx, hiddenState)
+	v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
+
+	// Attention computation
+	scaleFactor := 1.0 / math.Sqrt(float64(headDim))
+	kqv := nn.Attention(ctx, q, k, v, scaleFactor, cache)
+
+	// Reshape attention output for final projection
+	outputDim := headDim * opts.numHeads
+	kqv = kqv.Reshape(ctx, outputDim, batchSize)
+
+	// Apply output projection
+	return sa.Output.Forward(ctx, kqv)
+}
+
+func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	return key.RoPE(ctx, shift, m.Layers[layer].SelfAttention.RopeFactors, uint32(0), m.ropeDim, m.ropeBase, m.ropeScale), nil
+}
+
+type MLP struct {
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+	Gate *nn.Linear `gguf:"ffn_gate"`
+}
+
+func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *Options) ml.Tensor {
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	return mlp.Down.Forward(ctx, hiddenState)
+}
+
+type Layer struct {
+	AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
+	SelfAttention *SelfAttention
+	MLPNorm       *nn.RMSNorm `gguf:"ffn_norm"`
+	MLP           *MLP
+}
+
+func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs, outputs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
+	residual := hiddenState
+
+	hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.SelfAttention.Forward(ctx, hiddenState, positionIDs, cache, opts)
+
+	// In the final layer (outputs != nil), optimize by pruning to just the token positions
+	// we need logits for.
+	if outputs != nil {
+		hiddenState = hiddenState.Rows(ctx, outputs)
+		residual = residual.Rows(ctx, outputs)
+	}
+
+	hiddenState = hiddenState.Add(ctx, residual)
+	residual = hiddenState
+
+	hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.MLP.Forward(ctx, hiddenState, opts)
+	return hiddenState.Add(ctx, residual)
+}
+
+func (m *Model) Forward(ctx ml.Context, opts input.Options) (ml.Tensor, error) {
+	inputs, err := ctx.Input().FromIntSlice(opts.Inputs, len(opts.Inputs))
+	if err != nil {
+		return nil, err
+	}
+
+	positions, err := ctx.Input().FromIntSlice(opts.Positions, len(opts.Positions))
+	if err != nil {
+		return nil, err
+	}
+
+	outputs, err := ctx.Output().FromIntSlice(opts.Outputs, len(opts.Outputs))
+	if err != nil {
+		return nil, err
+	}
+
+	hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
+
+	for i, layer := range m.Layers {
+		m.Cache.SetLayer(i)
+
+		var lastLayerOutputs ml.Tensor
+		if i == len(m.Layers)-1 {
+			lastLayerOutputs = outputs
+		}
+
+		hiddenState = layer.Forward(ctx, hiddenState, positions, lastLayerOutputs, m.Cache, m.Options)
+	}
+
+	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
+	return m.Output.Forward(ctx, hiddenState), nil
+}
+
+func init() {
+	model.Register("mistral", New)
+}

model/models/mllama/model.go

@@ -106,17 +106,17 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) (any, er
 	return m.Projector.Forward(ctx, crossAttentionStates), nil
 }
 
-func (m *Model) PostTokenize(ctx ml.Context, inputs []input.Input) ([]input.Input, error) {
+func (m *Model) PostTokenize(inputs []input.Input) ([]input.Input, error) {
 	var images []input.Input
 	fnvHash := fnv.New64a()
 
 	for i := range inputs {
 		if inputs[i].Multimodal == nil {
 			if len(images) > 0 {
-				inputs[i].Multimodal = images[0].Multimodal
+				inputs[i].Multimodal = []ml.Tensor{images[0].Multimodal.(ml.Tensor)}
 				inputs[i].MultimodalHash = images[0].MultimodalHash
 				for j := 1; j < len(images); j++ {
-					inputs[i].Multimodal = inputs[i].Multimodal.(ml.Tensor).Concat(ctx, images[j].Multimodal.(ml.Tensor), 3)
+					inputs[i].Multimodal = append(inputs[i].Multimodal.([]ml.Tensor), images[0].Multimodal.(ml.Tensor))
 					fnvHash.Reset()
 					binary.Write(fnvHash, binary.NativeEndian, inputs[i].MultimodalHash)
 					binary.Write(fnvHash, binary.NativeEndian, inputs[j].MultimodalHash)
@@ -138,7 +138,10 @@ func (m *Model) PostTokenize(ctx ml.Context, inputs []input.Input) ([]input.Inpu
 func (m *Model) Forward(ctx ml.Context, opts input.Options) (ml.Tensor, error) {
 	var crossAttentionStates ml.Tensor
 	if len(opts.Multimodal) > 0 {
-		crossAttentionStates = opts.Multimodal[len(opts.Multimodal)-1].Multimodal.(ml.Tensor)
+		images := opts.Multimodal[len(opts.Multimodal)-1].Multimodal.([]ml.Tensor)
+		if len(images) > 0 {
+			crossAttentionStates = images[len(images)-1]
+		}
 	}
 
 	inputs, err := ctx.Input().FromIntSlice(opts.Inputs, len(opts.Inputs))

model/models/models.go

@@ -4,5 +4,6 @@ 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/mistral"
 	_ "github.com/ollama/ollama/model/models/mllama"
 )

model/process_text.go

@@ -263,6 +263,10 @@ func (bpe BytePairEncoding) Encode(s string, addSpecial bool) ([]int32, error) {
 					continue
 				}
 
+				if id := bpe.vocab.Encode(pair.value); id < 0 {
+					continue
+				}
+
 				merges[pair.a].runes = append(left.runes, right.runes...)
 				merges[pair.b].runes = nil
 

model/process_text_test.go

@@ -209,6 +209,326 @@ func TestLlama(t *testing.T) {
 	})
 }
 
+// tekken loads the Tekken tokenizer for testing
+func tekken(t testing.TB) TextProcessor {
+	t.Helper()
+
+	// Load tokenizer config from mistral-small
+	tokenizerConfigPath := filepath.Join("testdata", "mistral-small", "tokenizer_config.json")
+	configFile, err := os.Open(tokenizerConfigPath)
+	if err != nil {
+		t.Fatal(err)
+	}
+	defer configFile.Close()
+
+	var config struct {
+		AddBosToken bool `json:"add_bos_token"`
+		AddEosToken bool `json:"add_eos_token"`
+		BosToken    struct {
+			Content string `json:"content"`
+		} `json:"bos_token"`
+		EosToken struct {
+			Content string `json:"content"`
+		} `json:"eos_token"`
+	}
+	if err := json.NewDecoder(configFile).Decode(&config); err != nil {
+		t.Fatal(err)
+	}
+
+	// Load tokenizer.json which contains the vocabulary and other settings
+	tokenizerJsonPath := filepath.Join("testdata", "mistral-small", "tokenizer.json")
+	tokenizerFile, err := os.Open(tokenizerJsonPath)
+	if err != nil {
+		t.Fatal(err)
+	}
+	defer tokenizerFile.Close()
+
+	var tokenizerData struct {
+		Model struct {
+			Type   string           `json:"type"`
+			Vocab  map[string]int32 `json:"vocab"`
+			Merges []string         `json:"merges"`
+		} `json:"model"`
+		AddedTokens []struct {
+			Id      int32  `json:"id"`
+			Content string `json:"content"`
+			Special bool   `json:"special"`
+		} `json:"added_tokens"`
+		PreTokenizer struct {
+			Type          string `json:"type"`
+			Pretokenizers []struct {
+				Type    string `json:"type"`
+				Pattern struct {
+					String string `json:"String"`
+				} `json:"pattern"`
+				Behavior string `json:"behavior"`
+			} `json:"pretokenizers"`
+		} `json:"pre_tokenizer"`
+	}
+	if err := json.NewDecoder(tokenizerFile).Decode(&tokenizerData); err != nil {
+		t.Fatal(err)
+	}
+
+	// Extract the pattern from pre_tokenizer if available
+	var pattern string
+	if tokenizerData.PreTokenizer.Type == "Sequence" && len(tokenizerData.PreTokenizer.Pretokenizers) > 0 {
+		pattern = tokenizerData.PreTokenizer.Pretokenizers[0].Pattern.String
+	}
+
+	// Combine regular vocab and added tokens
+	vocab := tokenizerData.Model.Vocab
+
+	// Add special tokens from added_tokens
+	for _, token := range tokenizerData.AddedTokens {
+		vocab[token.Content] = token.Id
+	}
+
+	// Create vocabulary arrays
+	maxId := int32(-1)
+	for _, id := range vocab {
+		if id > maxId {
+			maxId = id
+		}
+	}
+
+	vocabSize := int(maxId + 1)
+	types := make([]uint32, vocabSize)
+	tokens := make([]string, vocabSize)
+	scores := make([]float32, vocabSize)
+
+	for token, id := range vocab {
+		tokens[id] = token
+		types[id] = TOKEN_TYPE_NORMAL
+
+		// Assign appropriate token types for special tokens
+		if token == "<s>" {
+			types[id] = TOKEN_TYPE_CONTROL
+		} else if token == "</s>" {
+			types[id] = TOKEN_TYPE_CONTROL
+		} else if token == "[INST]" || token == "[/INST]" {
+			types[id] = TOKEN_TYPE_CONTROL
+		}
+	}
+
+	// In Tekken, we don't need to load merges separately as they're part of the model
+	var merges []string
+
+	// Create vocabulary object
+	vocabObj := &Vocabulary{
+		Values: tokens,
+		Types:  types,
+		Scores: scores,
+		Merges: merges,
+		BOS:    vocab[config.BosToken.Content],
+		EOS:    vocab[config.EosToken.Content],
+		AddBOS: config.AddBosToken,
+		AddEOS: config.AddEosToken,
+	}
+
+	// Use pattern from tokenizer.json if available
+	if pattern != "" {
+		// Ensure pattern has proper escaping for Go regexp
+		pattern = strings.ReplaceAll(pattern, "p{", "\\p{")
+		return NewBytePairEncoding(pattern, vocabObj)
+	}
+
+	// Fallback pattern if not found
+	return NewBytePairEncoding(
+		`\p{L}+|\p{N}+|[^\s\p{L}\p{N}]+|\s+`,
+		vocabObj,
+	)
+}
+
+func TestTekken(t *testing.T) {
+	// Skip if the test data isn't available
+	if _, err := os.Stat(filepath.Join("testdata", "mistral-small")); os.IsNotExist(err) {
+		t.Skip("Mistral-small test data not available")
+	}
+
+	tokenizer := tekken(t)
+
+	t.Run("whitespace_handling", func(t *testing.T) {
+		t.Parallel()
+
+		// The key difference from SentencePiece is that Tekken doesn't prepend whitespace
+		cases := []struct {
+			input    string
+			expected string
+		}{
+			{" hello", " hello"},
+			{"hello ", "hello "},
+			{"hello world", "hello world"},
+			{" hello world ", " hello world "},
+		}
+
+		for _, tc := range cases {
+			ids, err := tokenizer.Encode(tc.input, false)
+			if err != nil {
+				t.Errorf("Failed to encode %q: %v", tc.input, err)
+				continue
+			}
+
+			decoded, err := tokenizer.Decode(ids)
+			if err != nil {
+				t.Errorf("Failed to decode tokens for %q: %v", tc.input, err)
+				continue
+			}
+
+			if decoded != tc.expected {
+				t.Errorf("Whitespace handling: got %q, want %q", decoded, tc.expected)
+			}
+		}
+	})
+
+	t.Run("chat_templates", func(t *testing.T) {
+		t.Parallel()
+
+		// Test the Tekken chat template format which doesn't have spaces after special tokens
+		templates := []struct {
+			input       string
+			expectSpace bool // whether we expect a space after special tokens
+		}{
+			{"<s>[INST]user message[/INST]", false},
+			{"<s>[INST] user message[/INST]", true},
+			{"<s>[INST]user message [/INST]", true},
+		}
+
+		for _, tc := range templates {
+			ids, err := tokenizer.Encode(tc.input, false)
+			if err != nil {
+				t.Errorf("Failed to encode %q: %v", tc.input, err)
+				continue
+			}
+
+			decoded, err := tokenizer.Decode(ids)
+			if err != nil {
+				t.Errorf("Failed to decode tokens for %q: %v", tc.input, err)
+				continue
+			}
+
+			// Check if there's a space after special tokens
+			hasSpaceAfterINST := strings.Contains(decoded, "[INST] ")
+
+			if hasSpaceAfterINST != tc.expectSpace {
+				t.Errorf("Chat template space handling: got space=%v, want space=%v for %q",
+					hasSpaceAfterINST, tc.expectSpace, tc.input)
+			}
+		}
+	})
+
+	t.Run("special_tokens", func(t *testing.T) {
+		t.Parallel()
+
+		// Test how Tekken handles special tokens
+		cases := []struct {
+			input    string
+			expected []string // We'll check if these tokens are in the decoded output
+		}{
+			{"<s>[INST]hello[/INST]", []string{"<s>", "[INST]", "hello", "[/INST]"}},
+			{"[INST]hello[/INST]</s>", []string{"[INST]", "hello", "[/INST]", "</s>"}},
+			{"<s>[INST]hello[/INST]</s>[INST]again[/INST]", []string{"<s>", "[INST]", "hello", "[/INST]", "</s>", "[INST]", "again", "[/INST]"}},
+		}
+
+		for _, tc := range cases {
+			ids, err := tokenizer.Encode(tc.input, false)
+			if err != nil {
+				t.Errorf("Failed to encode %q: %v", tc.input, err)
+				continue
+			}
+
+			decoded, err := tokenizer.Decode(ids)
+			if err != nil {
+				t.Errorf("Failed to decode tokens for %q: %v", tc.input, err)
+				continue
+			}
+
+			for _, expected := range tc.expected {
+				if !strings.Contains(decoded, expected) {
+					t.Errorf("Special token handling: %q missing in decoded output %q", expected, decoded)
+				}
+			}
+		}
+	})
+
+	t.Run("vocabulary_coverage", func(t *testing.T) {
+		t.Parallel()
+
+		// Tekken has a larger vocabulary, so test coverage of various token types
+		samples := []string{
+			"Hello world!",
+			"This is a test of the Tekken tokenizer.",
+			"It has a considerably larger vocabulary size.",
+			"Special characters: !@#$%^&*()",
+			"Numbers: 1234567890",
+			"Multiple languages: こんにちは 你好 안녕하세요",
+			"Code snippets: def function(): return True",
+		}
+
+		for _, sample := range samples {
+			ids, err := tokenizer.Encode(sample, false)
+			if err != nil {
+				t.Errorf("Failed to encode %q: %v", sample, err)
+				continue
+			}
+
+			decoded, err := tokenizer.Decode(ids)
+			if err != nil {
+				t.Errorf("Failed to decode tokens for %q: %v", sample, err)
+				continue
+			}
+
+			if decoded != sample {
+				t.Errorf("Vocabulary coverage: got %q, want %q", decoded, sample)
+			}
+		}
+	})
+
+	t.Run("splitting_behavior", func(t *testing.T) {
+		t.Parallel()
+
+		// Test the splitting behavior which might differ from SentencePiece
+		cases := map[string][]string{
+			"Hello World!": {"Hello", " World", "!"},
+			"user message": {"user", " message"},
+			"[INST]hello":  {"[INST]", "hello"},
+			"hello[/INST]": {"hello", "[/INST]"},
+		}
+
+		for s, want := range cases {
+			got := slices.Collect(tokenizer.(*BytePairEncoding).split(s))
+			if diff := cmp.Diff(want, got); diff != "" {
+				t.Errorf("Splitting behavior no match (-want +got):\n%s", diff)
+			}
+		}
+	})
+
+	t.Run("full_chat_sequence", func(t *testing.T) {
+		t.Parallel()
+
+		// Test a complete chat sequence with Tekken's format
+		chatSequence := "<s>[INST]user message[/INST]assistant message</s>[INST]new user message[/INST]"
+
+		ids, err := tokenizer.Encode(chatSequence, false)
+		if err != nil {
+			t.Fatalf("Failed to encode chat sequence: %v", err)
+		}
+
+		decoded, err := tokenizer.Decode(ids)
+		if err != nil {
+			t.Fatalf("Failed to decode chat sequence tokens: %v", err)
+		}
+
+		// In Tekken, the whitespace shouldn't be added after special tokens
+		if strings.Contains(decoded, "[INST] ") {
+			t.Errorf("Tekken chat sequence has unexpected space after [INST]: %q", decoded)
+		}
+
+		if strings.Contains(decoded, "[/INST] ") {
+			t.Errorf("Tekken chat sequence has unexpected space after [/INST]: %q", decoded)
+		}
+	})
+}
+
 func BenchmarkBytePairEncoding(b *testing.B) {
 	tokenizer := llama(b)
 	bts, err := os.ReadFile(filepath.Join("testdata", "war-and-peace.txt"))

parser/parser.go

@@ -211,16 +211,10 @@ func filesForModel(path string) ([]string, error) {
 	}
 
 	var files []string
-	if st, _ := glob(filepath.Join(path, "model*.safetensors"), "application/octet-stream"); len(st) > 0 {
+	if st, _ := glob(filepath.Join(path, "*.safetensors"), "application/octet-stream"); len(st) > 0 {
 		// safetensors files might be unresolved git lfs references; skip if they are
 		// covers model-x-of-y.safetensors, model.fp32-x-of-y.safetensors, model.safetensors
 		files = append(files, st...)
-	} else if st, _ := glob(filepath.Join(path, "adapters.safetensors"), "application/octet-stream"); len(st) > 0 {
-		// covers adapters.safetensors
-		files = append(files, st...)
-	} else if st, _ := glob(filepath.Join(path, "adapter_model.safetensors"), "application/octet-stream"); len(st) > 0 {
-		// covers adapter_model.safetensors
-		files = append(files, st...)
 	} else if pt, _ := glob(filepath.Join(path, "pytorch_model*.bin"), "application/zip"); len(pt) > 0 {
 		// pytorch files might also be unresolved git lfs references; skip if they are
 		// covers pytorch_model-x-of-y.bin, pytorch_model.fp32-x-of-y.bin, pytorch_model.bin

runner/llamarunner/runner.go

@@ -24,6 +24,7 @@ import (
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/llama"
+	"github.com/ollama/ollama/llm"
 	"github.com/ollama/ollama/runner/common"
 )
 
@@ -99,7 +100,7 @@ type NewSequenceParams struct {
 	embedding      bool
 }
 
-func (s *Server) NewSequence(prompt string, images []ImageData, params NewSequenceParams) (*Sequence, error) {
+func (s *Server) NewSequence(prompt string, images []llm.ImageData, params NewSequenceParams) (*Sequence, error) {
 	s.ready.Wait()
 
 	startTime := time.Now()
@@ -163,7 +164,7 @@ func (s *Server) NewSequence(prompt string, images []ImageData, params NewSequen
 // inputs processes the prompt and images into a list of inputs
 // by splitting the prompt on [img-<n>] tags, tokenizing text and
 // generating image embeddings for each image
-func (s *Server) inputs(prompt string, images []ImageData) ([]input, error) {
+func (s *Server) inputs(prompt string, images []llm.ImageData) ([]input, error) {
 	var inputs []input
 	var parts []string
 	var matches [][]string
@@ -229,7 +230,7 @@ type Server struct {
 	image *ImageContext
 
 	// status for external health reporting - loading, ready to serve, etc.
-	status ServerStatus
+	status llm.ServerStatus
 
 	// current progress on loading the model
 	progress float32
@@ -541,75 +542,18 @@ func (s *Server) processBatch(tokenBatch *llama.Batch, embedBatch *llama.Batch)
 	return nil
 }
 
-// TODO (jmorganca): use structs from the api package to avoid duplication
-// this way the api acts as a proxy instead of using a different api for the
-// runner
-type Options struct {
-	api.Runner
-
-	NumKeep          int      `json:"n_keep"`
-	Seed             int      `json:"seed"`
-	NumPredict       int      `json:"n_predict"`
-	TopK             int      `json:"top_k"`
-	TopP             float32  `json:"top_p"`
-	MinP             float32  `json:"min_p"`
-	TypicalP         float32  `json:"typical_p"`
-	RepeatLastN      int      `json:"repeat_last_n"`
-	Temperature      float32  `json:"temperature"`
-	RepeatPenalty    float32  `json:"repeat_penalty"`
-	PresencePenalty  float32  `json:"presence_penalty"`
-	FrequencyPenalty float32  `json:"frequency_penalty"`
-	Mirostat         int      `json:"mirostat"`
-	MirostatTau      float32  `json:"mirostat_tau"`
-	MirostatEta      float32  `json:"mirostat_eta"`
-	Stop             []string `json:"stop"`
-}
-
-type ImageData struct {
-	Data          []byte `json:"data"`
-	ID            int    `json:"id"`
-	AspectRatioID int    `json:"aspect_ratio_id"`
-}
-
-type CompletionRequest struct {
-	Prompt      string      `json:"prompt"`
-	Images      []ImageData `json:"image_data"`
-	Grammar     string      `json:"grammar"`
-	CachePrompt bool        `json:"cache_prompt"`
-
-	Options
-}
-
-type Timings struct {
-	PredictedN  int     `json:"predicted_n"`
-	PredictedMS float64 `json:"predicted_ms"`
-	PromptN     int     `json:"prompt_n"`
-	PromptMS    float64 `json:"prompt_ms"`
-}
-
-type CompletionResponse struct {
-	Content string `json:"content"`
-	Stop    bool   `json:"stop"`
-
-	Model        string  `json:"model,omitempty"`
-	Prompt       string  `json:"prompt,omitempty"`
-	StoppedLimit bool    `json:"stopped_limit,omitempty"`
-	PredictedN   int     `json:"predicted_n,omitempty"`
-	PredictedMS  float64 `json:"predicted_ms,omitempty"`
-	PromptN      int     `json:"prompt_n,omitempty"`
-	PromptMS     float64 `json:"prompt_ms,omitempty"`
-
-	Timings Timings `json:"timings"`
-}
-
 func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
-	var req CompletionRequest
-	req.Options = Options(api.DefaultOptions())
+	var req llm.CompletionRequest
 	if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
 		http.Error(w, "Bad request", http.StatusBadRequest)
 		return
 	}
 
+	if req.Options == nil {
+		opts := api.DefaultOptions()
+		req.Options = &opts
+	}
+
 	// Set the headers to indicate streaming
 	w.Header().Set("Content-Type", "application/json")
 	w.Header().Set("Transfer-Encoding", "chunked")
@@ -620,26 +564,28 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 		return
 	}
 
-	var samplingParams llama.SamplingParams
-	samplingParams.TopK = req.TopK
-	samplingParams.TopP = req.TopP
-	samplingParams.MinP = req.MinP
-	samplingParams.TypicalP = req.TypicalP
-	samplingParams.Temp = req.Temperature
-	samplingParams.RepeatLastN = req.RepeatLastN
-	samplingParams.PenaltyRepeat = req.RepeatPenalty
-	samplingParams.PenaltyFreq = req.FrequencyPenalty
-	samplingParams.PenaltyPresent = req.PresencePenalty
-	samplingParams.Mirostat = req.Mirostat
-	samplingParams.MirostatTau = req.MirostatTau
-	samplingParams.MirostatEta = req.MirostatEta
-	samplingParams.Seed = uint32(req.Seed)
-	samplingParams.Grammar = req.Grammar
+	// Extract options from the CompletionRequest
+	samplingParams := llama.SamplingParams{
+		TopK:           req.Options.TopK,
+		TopP:           req.Options.TopP,
+		MinP:           req.Options.MinP,
+		TypicalP:       req.Options.TypicalP,
+		Temp:           req.Options.Temperature,
+		RepeatLastN:    req.Options.RepeatLastN,
+		PenaltyRepeat:  req.Options.RepeatPenalty,
+		PenaltyFreq:    req.Options.FrequencyPenalty,
+		PenaltyPresent: req.Options.PresencePenalty,
+		Mirostat:       req.Options.Mirostat,
+		MirostatTau:    req.Options.MirostatTau,
+		MirostatEta:    req.Options.MirostatEta,
+		Seed:           uint32(req.Options.Seed),
+		Grammar:        req.Grammar,
+	}
 
 	seq, err := s.NewSequence(req.Prompt, req.Images, NewSequenceParams{
-		numPredict:     req.NumPredict,
-		stop:           req.Stop,
-		numKeep:        req.NumKeep,
+		numPredict:     req.Options.NumPredict,
+		stop:           req.Options.Stop,
+		numKeep:        req.Options.NumKeep,
 		samplingParams: &samplingParams,
 		embedding:      false,
 	})
@@ -662,7 +608,7 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 	found := false
 	for i, sq := range s.seqs {
 		if sq == nil {
-			seq.cache, seq.inputs, err = s.cache.LoadCacheSlot(seq.inputs, req.CachePrompt)
+			seq.cache, seq.inputs, err = s.cache.LoadCacheSlot(seq.inputs, true)
 			if err != nil {
 				s.mu.Unlock()
 				http.Error(w, fmt.Sprintf("Failed to load cache: %v", err), http.StatusInternalServerError)
@@ -691,7 +637,7 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 			return
 		case content, ok := <-seq.responses:
 			if ok {
-				if err := json.NewEncoder(w).Encode(&CompletionResponse{
+				if err := json.NewEncoder(w).Encode(&llm.CompletionResponse{
 					Content: content,
 				}); err != nil {
 					http.Error(w, fmt.Sprintf("failed to encode response: %v", err), http.StatusInternalServerError)
@@ -702,15 +648,17 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 				flusher.Flush()
 			} else {
 				// Send the final response
-				if err := json.NewEncoder(w).Encode(&CompletionResponse{
-					Stop:         true,
-					StoppedLimit: seq.doneReason == "limit",
-					Timings: Timings{
-						PromptN:     seq.numPromptInputs,
-						PromptMS:    float64(seq.startGenerationTime.Sub(seq.startProcessingTime).Milliseconds()),
-						PredictedN:  seq.numDecoded,
-						PredictedMS: float64(time.Since(seq.startGenerationTime).Milliseconds()),
-					},
+				doneReason := "stop"
+				if seq.doneReason == "limit" {
+					doneReason = "length"
+				}
+				if err := json.NewEncoder(w).Encode(&llm.CompletionResponse{
+					Done:               true,
+					DoneReason:         doneReason,
+					PromptEvalCount:    seq.numPromptInputs,
+					PromptEvalDuration: seq.startGenerationTime.Sub(seq.startProcessingTime),
+					EvalCount:          seq.numDecoded,
+					EvalDuration:       time.Since(seq.startGenerationTime),
 				}); err != nil {
 					http.Error(w, fmt.Sprintf("failed to encode final response: %v", err), http.StatusInternalServerError)
 				}
@@ -721,17 +669,8 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 	}
 }
 
-type EmbeddingRequest struct {
-	Content     string `json:"content"`
-	CachePrompt bool   `json:"cache_prompt"`
-}
-
-type EmbeddingResponse struct {
-	Embedding []float32 `json:"embedding"`
-}
-
 func (s *Server) embeddings(w http.ResponseWriter, r *http.Request) {
-	var req EmbeddingRequest
+	var req llm.EmbeddingRequest
 	if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
 		http.Error(w, fmt.Sprintf("bad request: %s", err), http.StatusBadRequest)
 		return
@@ -761,7 +700,7 @@ func (s *Server) embeddings(w http.ResponseWriter, r *http.Request) {
 	found := false
 	for i, sq := range s.seqs {
 		if sq == nil {
-			seq.cache, seq.inputs, err = s.cache.LoadCacheSlot(seq.inputs, req.CachePrompt)
+			seq.cache, seq.inputs, err = s.cache.LoadCacheSlot(seq.inputs, false)
 			if err != nil {
 				s.mu.Unlock()
 				http.Error(w, fmt.Sprintf("Failed to load cache: %v", err), http.StatusInternalServerError)
@@ -782,41 +721,17 @@ func (s *Server) embeddings(w http.ResponseWriter, r *http.Request) {
 
 	embedding := <-seq.embedding
 
-	if err := json.NewEncoder(w).Encode(&EmbeddingResponse{
+	if err := json.NewEncoder(w).Encode(&llm.EmbeddingResponse{
 		Embedding: embedding,
 	}); err != nil {
 		http.Error(w, fmt.Sprintf("failed to encode response: %v", err), http.StatusInternalServerError)
 	}
 }
 
-type HealthResponse struct {
-	Status   string  `json:"status"`
-	Progress float32 `json:"progress"`
-}
-
-type ServerStatus int
-
-const (
-	ServerStatusReady ServerStatus = iota
-	ServerStatusLoadingModel
-	ServerStatusError
-)
-
-func (s ServerStatus) ToString() string {
-	switch s {
-	case ServerStatusReady:
-		return "ok"
-	case ServerStatusLoadingModel:
-		return "loading model"
-	default:
-		return "server error"
-	}
-}
-
 func (s *Server) health(w http.ResponseWriter, r *http.Request) {
 	w.Header().Set("Content-Type", "application/json")
-	if err := json.NewEncoder(w).Encode(&HealthResponse{
-		Status:   s.status.ToString(),
+	if err := json.NewEncoder(w).Encode(&llm.ServerStatusResponse{
+		Status:   s.status,
 		Progress: s.progress,
 	}); err != nil {
 		http.Error(w, fmt.Sprintf("failed to encode response: %v", err), http.StatusInternalServerError)
@@ -879,7 +794,7 @@ func (s *Server) loadModel(
 		panic(err)
 	}
 
-	s.status = ServerStatusReady
+	s.status = llm.ServerStatusReady
 	s.ready.Done()
 }
 
@@ -937,7 +852,7 @@ func Execute(args []string) error {
 		parallel:  *parallel,
 		seqs:      make([]*Sequence, *parallel),
 		seqsSem:   semaphore.NewWeighted(int64(*parallel)),
-		status:    ServerStatusLoadingModel,
+		status:    llm.ServerStatusLoadingModel,
 	}
 
 	var tensorSplitFloats []float32

runner/ollamarunner/cache.go

@@ -107,6 +107,7 @@ func (c *InputCache) LoadCacheSlot(prompt []input.Input, cachePrompt bool) (*Inp
 		return nil, nil, err
 	}
 
+	// TODO (brucemacd): cachePrompt is always true for completion, but false for embedding, can this be improved?
 	if !cachePrompt {
 		numPast = 0
 	}

runner/ollamarunner/runner.go

@@ -24,6 +24,7 @@ import (
 	"golang.org/x/sync/semaphore"
 
 	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/llm"
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
@@ -33,10 +34,14 @@ import (
 	_ "github.com/ollama/ollama/model/models"
 )
 
+type contextList struct {
+	list []ml.Context
+}
+
 type Sequence struct {
-	// ctx for allocating tensors that last the lifetime of the sequence, such as
+	// ctxs are used for allocating tensors that last the lifetime of the sequence, such as
 	// multimodal embeddings
-	ctx ml.Context
+	ctxs *contextList
 
 	// batch index
 	iBatch int
@@ -94,13 +99,12 @@ type NewSequenceParams struct {
 	embedding  bool
 }
 
-func (s *Server) NewSequence(prompt string, images []ImageData, params NewSequenceParams) (*Sequence, error) {
+func (s *Server) NewSequence(prompt string, images []llm.ImageData, params NewSequenceParams) (*Sequence, error) {
 	s.ready.Wait()
 
 	startTime := time.Now()
-	ctx := s.model.Backend().NewContext()
 
-	inputs, err := s.inputs(ctx, prompt, images)
+	inputs, ctxs, err := s.inputs(prompt, images)
 	if err != nil {
 		return nil, fmt.Errorf("failed to process inputs: %w", err)
 	} else if len(inputs) == 0 {
@@ -126,7 +130,7 @@ func (s *Server) NewSequence(prompt string, images []ImageData, params NewSequen
 	// TODO(jessegross): Ingest cached history for grammar
 
 	return &Sequence{
-		ctx:                 ctx,
+		ctxs:                ctxs,
 		inputs:              inputs,
 		numPromptInputs:     len(inputs),
 		startProcessingTime: startTime,
@@ -145,7 +149,7 @@ func (s *Server) NewSequence(prompt string, images []ImageData, params NewSequen
 // inputs processes the prompt and images into a list of inputs
 // by splitting the prompt on [img-<n>] tags, tokenizing text and
 // decoding images
-func (s *Server) inputs(ctx ml.Context, prompt string, images []ImageData) ([]input.Input, error) {
+func (s *Server) inputs(prompt string, images []llm.ImageData) ([]input.Input, *contextList, error) {
 	var inputs []input.Input
 	var parts []string
 	var matches [][]string
@@ -160,14 +164,25 @@ func (s *Server) inputs(ctx ml.Context, prompt string, images []ImageData) ([]in
 		parts = []string{prompt}
 	}
 
+	var contexts contextList
+	runtime.AddCleanup(&contexts, func(ctxs []ml.Context) {
+		for _, ctx := range ctxs {
+			ctx.Close()
+		}
+	}, contexts.list)
+
 	postTokenize := false
 	for i, part := range parts {
 		// text - tokenize
 		tokens, err := s.model.(model.TextProcessor).Encode(part, i == 0)
 		if err != nil {
-			return nil, err
+			return nil, nil, err
 		}
 
+		for _, t := range tokens {
+			decoded, _ := s.model.(model.TextProcessor).Decode([]int32{t})
+			fmt.Println("token", t, "decoded", decoded)
+		}
 		for _, t := range tokens {
 			inputs = append(inputs, input.Input{Token: t})
 		}
@@ -185,12 +200,14 @@ func (s *Server) inputs(ctx ml.Context, prompt string, images []ImageData) ([]in
 			}
 
 			if imageIndex < 0 {
-				return nil, fmt.Errorf("invalid image index: %d", n)
+				return nil, nil, fmt.Errorf("invalid image index: %d", n)
 			}
 
+			ctx := s.model.Backend().NewContext()
+			contexts.list = append(contexts.list, ctx)
 			imageEmbeddings, err := multimodalProcessor.EncodeMultimodal(ctx, images[imageIndex].Data)
 			if err != nil {
-				return nil, err
+				return nil, nil, err
 			}
 
 			s.multimodalHash.Reset()
@@ -204,13 +221,13 @@ func (s *Server) inputs(ctx ml.Context, prompt string, images []ImageData) ([]in
 
 	if visionModel && postTokenize {
 		var err error
-		inputs, err = multimodalProcessor.PostTokenize(ctx, inputs)
+		inputs, err = multimodalProcessor.PostTokenize(inputs)
 		if err != nil {
-			return nil, err
+			return nil, nil, err
 		}
 	}
 
-	return inputs, nil
+	return inputs, &contexts, nil
 }
 
 type Server struct {
@@ -222,7 +239,7 @@ type Server struct {
 	model model.Model
 
 	// status for external health reporting - loading, ready to serve, etc.
-	status ServerStatus
+	status llm.ServerStatus
 
 	// current progress on loading the model
 	progress float32
@@ -305,7 +322,6 @@ func (s *Server) removeSequence(seqIndex int, reason string) {
 	close(seq.responses)
 	close(seq.embedding)
 	seq.cache.InUse = false
-	seq.ctx.Close()
 	s.seqs[seqIndex] = nil
 	s.seqsSem.Release(1)
 }
@@ -351,6 +367,8 @@ func (s *Server) processBatch() error {
 			seq.cache.Inputs = []input.Input{}
 		}
 
+		batchSize := s.batchSize
+
 		for j, inp := range seq.inputs {
 			if int32(len(seq.cache.Inputs)+len(seq.pendingInputs)+1) > s.cache.numCtx {
 				if len(seq.pendingInputs) == 0 {
@@ -363,7 +381,15 @@ func (s *Server) processBatch() error {
 				}
 			}
 
-			if j >= s.batchSize {
+			// If we are required to put following inputs into a single batch then extend the
+			// batch size. Since we are only extending the size the minimum amount possible, this
+			// will cause a break if we have pending inputs.
+			minBatch := 1 + inp.SameBatch
+			if minBatch > batchSize {
+				batchSize = minBatch
+			}
+
+			if len(seq.pendingInputs)+minBatch > batchSize {
 				break
 			}
 
@@ -501,75 +527,18 @@ func (s *Server) processBatch() error {
 	return nil
 }
 
-// TODO (jmorganca): use structs from the api package to avoid duplication
-// this way the api acts as a proxy instead of using a different api for the
-// runner
-type Options struct {
-	api.Runner
-
-	NumKeep          int      `json:"n_keep"`
-	Seed             int      `json:"seed"`
-	NumPredict       int      `json:"n_predict"`
-	TopK             int      `json:"top_k"`
-	TopP             float32  `json:"top_p"`
-	MinP             float32  `json:"min_p"`
-	TypicalP         float32  `json:"typical_p"`
-	RepeatLastN      int      `json:"repeat_last_n"`
-	Temperature      float32  `json:"temperature"`
-	RepeatPenalty    float32  `json:"repeat_penalty"`
-	PresencePenalty  float32  `json:"presence_penalty"`
-	FrequencyPenalty float32  `json:"frequency_penalty"`
-	Mirostat         int      `json:"mirostat"`
-	MirostatTau      float32  `json:"mirostat_tau"`
-	MirostatEta      float32  `json:"mirostat_eta"`
-	Stop             []string `json:"stop"`
-}
-
-type ImageData struct {
-	Data          []byte `json:"data"`
-	ID            int    `json:"id"`
-	AspectRatioID int    `json:"aspect_ratio_id"`
-}
-
-type CompletionRequest struct {
-	Prompt      string      `json:"prompt"`
-	Images      []ImageData `json:"image_data"`
-	Grammar     string      `json:"grammar"`
-	CachePrompt bool        `json:"cache_prompt"`
-
-	Options
-}
-
-type Timings struct {
-	PredictedN  int     `json:"predicted_n"`
-	PredictedMS float64 `json:"predicted_ms"`
-	PromptN     int     `json:"prompt_n"`
-	PromptMS    float64 `json:"prompt_ms"`
-}
-
-type CompletionResponse struct {
-	Content string `json:"content"`
-	Stop    bool   `json:"stop"`
-
-	Model        string  `json:"model,omitempty"`
-	Prompt       string  `json:"prompt,omitempty"`
-	StoppedLimit bool    `json:"stopped_limit,omitempty"`
-	PredictedN   int     `json:"predicted_n,omitempty"`
-	PredictedMS  float64 `json:"predicted_ms,omitempty"`
-	PromptN      int     `json:"prompt_n,omitempty"`
-	PromptMS     float64 `json:"prompt_ms,omitempty"`
-
-	Timings Timings `json:"timings"`
-}
-
 func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
-	var req CompletionRequest
-	req.Options = Options(api.DefaultOptions())
+	var req llm.CompletionRequest
 	if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
 		http.Error(w, "Bad request", http.StatusBadRequest)
 		return
 	}
 
+	if req.Options == nil {
+		opts := api.DefaultOptions()
+		req.Options = &opts
+	}
+
 	// Set the headers to indicate streaming
 	w.Header().Set("Content-Type", "application/json")
 	w.Header().Set("Transfer-Encoding", "chunked")
@@ -591,18 +560,18 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 	}
 
 	sampler := sample.NewSampler(
-		req.Temperature,
-		req.TopK,
-		req.TopP,
-		req.MinP,
-		req.Seed,
+		req.Options.Temperature,
+		req.Options.TopK,
+		req.Options.TopP,
+		req.Options.MinP,
+		req.Options.Seed,
 		grammar,
 	)
 
 	seq, err := s.NewSequence(req.Prompt, req.Images, NewSequenceParams{
-		numPredict: req.NumPredict,
-		stop:       req.Stop,
-		numKeep:    int32(req.NumKeep),
+		numPredict: req.Options.NumPredict,
+		stop:       req.Options.Stop,
+		numKeep:    int32(req.Options.NumKeep),
 		sampler:    sampler,
 		embedding:  false,
 	})
@@ -625,7 +594,7 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 	found := false
 	for i, sq := range s.seqs {
 		if sq == nil {
-			seq.cache, seq.inputs, err = s.cache.LoadCacheSlot(seq.inputs, req.CachePrompt)
+			seq.cache, seq.inputs, err = s.cache.LoadCacheSlot(seq.inputs, true)
 			if err != nil {
 				s.mu.Unlock()
 				http.Error(w, fmt.Sprintf("Failed to load cache: %v", err), http.StatusInternalServerError)
@@ -652,7 +621,7 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 			return
 		case content, ok := <-seq.responses:
 			if ok {
-				if err := json.NewEncoder(w).Encode(&CompletionResponse{
+				if err := json.NewEncoder(w).Encode(&llm.CompletionResponse{
 					Content: content,
 				}); err != nil {
 					http.Error(w, fmt.Sprintf("failed to encode response: %v", err), http.StatusInternalServerError)
@@ -663,15 +632,17 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 				flusher.Flush()
 			} else {
 				// Send the final response
-				if err := json.NewEncoder(w).Encode(&CompletionResponse{
-					Stop:         true,
-					StoppedLimit: seq.doneReason == "limit",
-					Timings: Timings{
-						PromptN:     seq.numPromptInputs,
-						PromptMS:    float64(seq.startGenerationTime.Sub(seq.startProcessingTime).Milliseconds()),
-						PredictedN:  seq.numPredicted,
-						PredictedMS: float64(time.Since(seq.startGenerationTime).Milliseconds()),
-					},
+				doneReason := "stop"
+				if seq.doneReason == "limit" {
+					doneReason = "length"
+				}
+				if err := json.NewEncoder(w).Encode(&llm.CompletionResponse{
+					Done:               true,
+					DoneReason:         doneReason,
+					PromptEvalCount:    seq.numPromptInputs,
+					PromptEvalDuration: seq.startGenerationTime.Sub(seq.startProcessingTime),
+					EvalCount:          seq.numPredicted,
+					EvalDuration:       time.Since(seq.startGenerationTime),
 				}); err != nil {
 					http.Error(w, fmt.Sprintf("failed to encode final response: %v", err), http.StatusInternalServerError)
 				}
@@ -682,43 +653,10 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 	}
 }
 
-type EmbeddingRequest struct {
-	Content     string `json:"content"`
-	CachePrompt bool   `json:"cache_prompt"`
-}
-
-type EmbeddingResponse struct {
-	Embedding []float32 `json:"embedding"`
-}
-
-type HealthResponse struct {
-	Status   string  `json:"status"`
-	Progress float32 `json:"progress"`
-}
-
-type ServerStatus int
-
-const (
-	ServerStatusReady ServerStatus = iota
-	ServerStatusLoadingModel
-	ServerStatusError
-)
-
-func (s ServerStatus) ToString() string {
-	switch s {
-	case ServerStatusReady:
-		return "ok"
-	case ServerStatusLoadingModel:
-		return "loading model"
-	default:
-		return "server error"
-	}
-}
-
 func (s *Server) health(w http.ResponseWriter, r *http.Request) {
 	w.Header().Set("Content-Type", "application/json")
-	if err := json.NewEncoder(w).Encode(&HealthResponse{
-		Status:   s.status.ToString(),
+	if err := json.NewEncoder(w).Encode(&llm.ServerStatusResponse{
+		Status:   s.status,
 		Progress: s.progress,
 	}); err != nil {
 		http.Error(w, fmt.Sprintf("failed to encode response: %v", err), http.StatusInternalServerError)
@@ -772,7 +710,7 @@ func (s *Server) loadModel(
 	s.seqs = make([]*Sequence, s.parallel)
 	s.seqsSem = semaphore.NewWeighted(int64(s.parallel))
 
-	s.status = ServerStatusReady
+	s.status = llm.ServerStatusReady
 	s.ready.Done()
 }
 
@@ -824,7 +762,7 @@ func Execute(args []string) error {
 
 	server := &Server{
 		batchSize: *batchSize,
-		status:    ServerStatusLoadingModel,
+		status:    llm.ServerStatusLoadingModel,
 	}
 
 	// TODO(jessegross): Parameters that need to be implemented:

scripts/build_darwin.sh

@@ -8,7 +8,7 @@ usage() {
     exit 1
 }
 
-export VERSION=${VERSION:-$(git describe --tags --dirty)}
+export VERSION=${VERSION:-$(git describe --tags --first-parent --abbrev=7 --long --dirty --always | sed -e "s/^v//g")}
 export GOFLAGS="'-ldflags=-w -s \"-X=github.com/ollama/ollama/version.Version=${VERSION#v}\" \"-X=github.com/ollama/ollama/server.mode=release\"'"
 export CGO_CPPFLAGS='-mmacosx-version-min=11.3'
 

server/internal/cache/blob/cache.go

@@ -146,7 +146,7 @@ func debugger(err *error) func(step string) {
 // be in either of the following forms:
 //
 //	@<digest>
-//	<name>
+//	<name>@<digest>
 //	<name>
 //
 // If a digest is provided, it is returned as is and nothing else happens.
@@ -160,8 +160,6 @@ func debugger(err *error) func(step string) {
 // hashed is passed to a PutBytes call to ensure that the manifest is in the
 // blob store. This is done to ensure that future calls to [Get] succeed in
 // these cases.
-//
-// TODO(bmizerany): Move Links/Resolve/etc. out of this package.
 func (c *DiskCache) Resolve(name string) (Digest, error) {
 	name, digest := splitNameDigest(name)
 	if digest != "" {
@@ -279,18 +277,6 @@ func (c *DiskCache) Get(d Digest) (Entry, error) {
 // It returns an error if either the name or digest is invalid, or if link
 // creation encounters any issues.
 func (c *DiskCache) Link(name string, d Digest) error {
-	// TODO(bmizerany): Move link handling from cache to registry.
-	//
-	// We originally placed links in the cache due to its storage
-	// knowledge. However, the registry likely offers better context for
-	// naming concerns, and our API design shouldn't be tightly coupled to
-	// our on-disk format.
-	//
-	// Links work effectively when independent from physical location -
-	// they can reference content with matching SHA regardless of storage
-	// location. In an upcoming change, we plan to shift this
-	// responsibility to the registry where it better aligns with the
-	// system's conceptual model.
 	manifest, err := c.manifestPath(name)
 	if err != nil {
 		return err
@@ -341,7 +327,9 @@ func (c *DiskCache) GetFile(d Digest) string {
 	return absJoin(c.dir, "blobs", filename)
 }
 
-// Links returns a sequence of links in the cache in lexical order.
+// Links returns a sequence of link names. The sequence is in lexical order.
+// Names are converted from their relative path form to their name form but are
+// not guaranteed to be valid. Callers should validate the names before using.
 func (c *DiskCache) Links() iter.Seq2[string, error] {
 	return func(yield func(string, error) bool) {
 		for path, err := range c.links() {
@@ -414,12 +402,14 @@ func (c *DiskCache) links() iter.Seq2[string, error] {
 }
 
 type checkWriter struct {
-	d    Digest
 	size int64
-	n    int64
-	h    hash.Hash
+	d    Digest
 	f    *os.File
-	err  error
+	h    hash.Hash
+
+	w   io.Writer // underlying writer; set by creator
+	n   int64
+	err error
 
 	testHookBeforeFinalWrite func(*os.File)
 }
@@ -435,6 +425,10 @@ func (w *checkWriter) seterr(err error) error {
 // underlying writer is guaranteed to be the last byte of p as verified by the
 // hash.
 func (w *checkWriter) Write(p []byte) (int, error) {
+	if w.err != nil {
+		return 0, w.err
+	}
+
 	_, err := w.h.Write(p)
 	if err != nil {
 		return 0, w.seterr(err)
@@ -453,7 +447,7 @@ func (w *checkWriter) Write(p []byte) (int, error) {
 	if nextSize > w.size {
 		return 0, w.seterr(fmt.Errorf("content exceeds expected size: %d > %d", nextSize, w.size))
 	}
-	n, err := w.f.Write(p)
+	n, err := w.w.Write(p)
 	w.n += int64(n)
 	return n, w.seterr(err)
 }
@@ -493,10 +487,12 @@ func (c *DiskCache) copyNamedFile(name string, file io.Reader, out Digest, size
 
 	// Copy file to f, but also into h to double-check hash.
 	cw := &checkWriter{
-		d:                        out,
-		size:                     size,
-		h:                        sha256.New(),
-		f:                        f,
+		d:    out,
+		size: size,
+		h:    sha256.New(),
+		f:    f,
+		w:    f,
+
 		testHookBeforeFinalWrite: c.testHookBeforeFinalWrite,
 	}
 	n, err := io.Copy(cw, file)
@@ -532,11 +528,6 @@ func splitNameDigest(s string) (name, digest string) {
 var errInvalidName = errors.New("invalid name")
 
 func nameToPath(name string) (_ string, err error) {
-	if strings.Contains(name, "@") {
-		// TODO(bmizerany): HACK: Fix names.Parse to validate.
-		// TODO(bmizerany): merge with default parts (maybe names.Merge(a, b))
-		return "", errInvalidName
-	}
 	n := names.Parse(name)
 	if !n.IsFullyQualified() {
 		return "", errInvalidName
@@ -547,8 +538,7 @@ func nameToPath(name string) (_ string, err error) {
 func absJoin(pp ...string) string {
 	abs, err := filepath.Abs(filepath.Join(pp...))
 	if err != nil {
-		// Likely a bug bug or a bad OS problem. Just panic.
-		panic(err)
+		panic(err) // this should never happen
 	}
 	return abs
 }

server/internal/cache/blob/chunked.go

@@ -0,0 +1,73 @@
+package blob
+
+import (
+	"crypto/sha256"
+	"errors"
+	"io"
+	"os"
+)
+
+// Chunk represents a range of bytes in a blob.
+type Chunk struct {
+	Start int64
+	End   int64
+}
+
+// Size returns end minus start plus one.
+func (c Chunk) Size() int64 {
+	return c.End - c.Start + 1
+}
+
+// Chunker writes to a blob in chunks.
+// Its zero value is invalid. Use [DiskCache.Chunked] to create a new Chunker.
+type Chunker struct {
+	digest Digest
+	size   int64
+	f      *os.File // nil means pre-validated
+}
+
+// Chunked returns a new Chunker, ready for use storing a blob of the given
+// size in chunks.
+//
+// Use [Chunker.Put] to write data to the blob at specific offsets.
+func (c *DiskCache) Chunked(d Digest, size int64) (*Chunker, error) {
+	name := c.GetFile(d)
+	info, err := os.Stat(name)
+	if err == nil && info.Size() == size {
+		return &Chunker{}, nil
+	}
+	f, err := os.OpenFile(name, os.O_CREATE|os.O_WRONLY, 0o666)
+	if err != nil {
+		return nil, err
+	}
+	return &Chunker{digest: d, size: size, f: f}, nil
+}
+
+// Put copies chunk.Size() bytes from r to the blob at the given offset,
+// merging the data with the existing blob. It returns an error if any. As a
+// special case, if r has less than chunk.Size() bytes, Put returns
+// io.ErrUnexpectedEOF.
+func (c *Chunker) Put(chunk Chunk, d Digest, r io.Reader) error {
+	if c.f == nil {
+		return nil
+	}
+
+	cw := &checkWriter{
+		d:    d,
+		size: chunk.Size(),
+		h:    sha256.New(),
+		f:    c.f,
+		w:    io.NewOffsetWriter(c.f, chunk.Start),
+	}
+
+	_, err := io.CopyN(cw, r, chunk.Size())
+	if err != nil && errors.Is(err, io.EOF) {
+		return io.ErrUnexpectedEOF
+	}
+	return err
+}
+
+// Close closes the underlying file.
+func (c *Chunker) Close() error {
+	return c.f.Close()
+}

server/internal/cache/blob/digest.go

@@ -63,6 +63,10 @@ func (d Digest) Short() string {
 	return fmt.Sprintf("%x", d.sum[:4])
 }
 
+func (d Digest) Sum() [32]byte {
+	return d.sum
+}
+
 func (d Digest) Compare(other Digest) int {
 	return slices.Compare(d.sum[:], other.sum[:])
 }

server/internal/chunks/chunks.go

@@ -1,78 +0,0 @@
-package chunks
-
-import (
-	"fmt"
-	"iter"
-	"strconv"
-	"strings"
-)
-
-type Chunk struct {
-	Start, End int64
-}
-
-func New(start, end int64) Chunk {
-	return Chunk{start, end}
-}
-
-// ParseRange parses a string in the form "unit=range" where unit is a string
-// and range is a string in the form "start-end". It returns the unit and the
-// range as a Chunk.
-func ParseRange(s string) (unit string, _ Chunk, _ error) {
-	unit, r, _ := strings.Cut(s, "=")
-	if r == "" {
-		return unit, Chunk{}, nil
-	}
-	c, err := Parse(r)
-	if err != nil {
-		return "", Chunk{}, err
-	}
-	return unit, c, err
-}
-
-// Parse parses a string in the form "start-end" and returns the Chunk.
-func Parse(s string) (Chunk, error) {
-	startStr, endStr, _ := strings.Cut(s, "-")
-	start, err := strconv.ParseInt(startStr, 10, 64)
-	if err != nil {
-		return Chunk{}, fmt.Errorf("invalid start: %v", err)
-	}
-	end, err := strconv.ParseInt(endStr, 10, 64)
-	if err != nil {
-		return Chunk{}, fmt.Errorf("invalid end: %v", err)
-	}
-	if start > end {
-		return Chunk{}, fmt.Errorf("invalid range %d-%d: start > end", start, end)
-	}
-	return Chunk{start, end}, nil
-}
-
-// Of returns a sequence of contiguous Chunks of size chunkSize that cover
-// the range [0, size), in order.
-func Of(size, chunkSize int64) iter.Seq[Chunk] {
-	return func(yield func(Chunk) bool) {
-		for start := int64(0); start < size; start += chunkSize {
-			end := min(start+chunkSize-1, size-1)
-			if !yield(Chunk{start, end}) {
-				break
-			}
-		}
-	}
-}
-
-// Count returns the number of Chunks of size chunkSize needed to cover the
-// range [0, size).
-func Count(size, chunkSize int64) int64 {
-	return (size + chunkSize - 1) / chunkSize
-}
-
-// Size returns end minus start plus one.
-func (c Chunk) Size() int64 {
-	return c.End - c.Start + 1
-}
-
-// String returns the string representation of the Chunk in the form
-// "{start}-{end}".
-func (c Chunk) String() string {
-	return fmt.Sprintf("%d-%d", c.Start, c.End)
-}

server/internal/chunks/chunks_test.go

@@ -1,65 +0,0 @@
-package chunks
-
-import (
-	"slices"
-	"testing"
-)
-
-func TestOf(t *testing.T) {
-	cases := []struct {
-		total     int64
-		chunkSize int64
-		want      []Chunk
-	}{
-		{0, 1, nil},
-		{1, 1, []Chunk{{0, 0}}},
-		{1, 2, []Chunk{{0, 0}}},
-		{2, 1, []Chunk{{0, 0}, {1, 1}}},
-		{10, 9, []Chunk{{0, 8}, {9, 9}}},
-	}
-
-	for _, tt := range cases {
-		got := slices.Collect(Of(tt.total, tt.chunkSize))
-		if !slices.Equal(got, tt.want) {
-			t.Errorf("[%d/%d]: got %v; want %v", tt.total, tt.chunkSize, got, tt.want)
-		}
-	}
-}
-
-func TestSize(t *testing.T) {
-	cases := []struct {
-		c    Chunk
-		want int64
-	}{
-		{Chunk{0, 0}, 1},
-		{Chunk{0, 1}, 2},
-		{Chunk{3, 4}, 2},
-	}
-
-	for _, tt := range cases {
-		got := tt.c.Size()
-		if got != tt.want {
-			t.Errorf("%v: got %d; want %d", tt.c, got, tt.want)
-		}
-	}
-}
-
-func TestCount(t *testing.T) {
-	cases := []struct {
-		total     int64
-		chunkSize int64
-		want      int64
-	}{
-		{0, 1, 0},
-		{1, 1, 1},
-		{1, 2, 1},
-		{2, 1, 2},
-		{10, 9, 2},
-	}
-	for _, tt := range cases {
-		got := Count(tt.total, tt.chunkSize)
-		if got != tt.want {
-			t.Errorf("[%d/%d]: got %d; want %d", tt.total, tt.chunkSize, got, tt.want)
-		}
-	}
-}

server/internal/client/ollama/registry.go

@@ -19,11 +19,13 @@ import (
 	"fmt"
 	"io"
 	"io/fs"
+	"iter"
 	"log/slog"
 	"net/http"
 	"os"
 	"path/filepath"
 	"runtime"
+	"runtime/debug"
 	"slices"
 	"strconv"
 	"strings"
@@ -35,10 +37,8 @@ import (
 	"golang.org/x/sync/errgroup"
 
 	"github.com/ollama/ollama/server/internal/cache/blob"
-	"github.com/ollama/ollama/server/internal/chunks"
 	"github.com/ollama/ollama/server/internal/internal/backoff"
 	"github.com/ollama/ollama/server/internal/internal/names"
-	"github.com/ollama/ollama/server/internal/internal/syncs"
 
 	_ "embed"
 )
@@ -66,12 +66,7 @@ var (
 const (
 	// DefaultChunkingThreshold is the threshold at which a layer should be
 	// split up into chunks when downloading.
-	DefaultChunkingThreshold = 128 << 20
-
-	// DefaultMaxChunkSize is the default maximum size of a chunk to
-	// download. It is configured based on benchmarks and aims to strike a
-	// balance between download speed and memory usage.
-	DefaultMaxChunkSize = 8 << 20
+	DefaultChunkingThreshold = 64 << 20
 )
 
 var defaultCache = sync.OnceValues(func() (*blob.DiskCache, error) {
@@ -211,8 +206,7 @@ type Registry struct {
 	// pushing or pulling models. If zero, the number of streams is
 	// determined by [runtime.GOMAXPROCS].
 	//
-	// Clients that want "unlimited" streams should set this to a large
-	// number.
+	// A negative value means no limit.
 	MaxStreams int
 
 	// ChunkingThreshold is the maximum size of a layer to download in a single
@@ -266,6 +260,7 @@ func DefaultRegistry() (*Registry, error) {
 	}
 
 	var rc Registry
+	rc.UserAgent = UserAgent()
 	rc.Key, err = ssh.ParseRawPrivateKey(keyPEM)
 	if err != nil {
 		return nil, err
@@ -281,25 +276,24 @@ func DefaultRegistry() (*Registry, error) {
 	return &rc, nil
 }
 
-func (r *Registry) maxStreams() int {
-	n := cmp.Or(r.MaxStreams, runtime.GOMAXPROCS(0))
+func UserAgent() string {
+	buildinfo, _ := debug.ReadBuildInfo()
+	return fmt.Sprintf("ollama/%s (%s %s) Go/%s",
+		buildinfo.Main.Version,
+		runtime.GOARCH,
+		runtime.GOOS,
+		runtime.Version(),
+	)
+}
 
-	// Large downloads require a writter stream, so ensure we have at least
-	// two streams to avoid a deadlock.
-	return max(n, 2)
+func (r *Registry) maxStreams() int {
+	return cmp.Or(r.MaxStreams, runtime.GOMAXPROCS(0))
 }
 
 func (r *Registry) maxChunkingThreshold() int64 {
 	return cmp.Or(r.ChunkingThreshold, DefaultChunkingThreshold)
 }
 
-// chunkSizeFor returns the chunk size for a layer of the given size. If the
-// size is less than or equal to the max chunking threshold, the size is
-// returned; otherwise, the max chunk size is returned.
-func (r *Registry) maxChunkSize() int64 {
-	return cmp.Or(r.MaxChunkSize, DefaultMaxChunkSize)
-}
-
 type PushParams struct {
 	// From is an optional destination name for the model. If empty, the
 	// destination name is the same as the source name.
@@ -426,6 +420,21 @@ func canRetry(err error) bool {
 	return re.Status >= 500
 }
 
+// trackingReader is an io.Reader that tracks the number of bytes read and
+// calls the update function with the layer, the number of bytes read.
+//
+// It always calls update with a nil error.
+type trackingReader struct {
+	r io.Reader
+	n *atomic.Int64
+}
+
+func (r *trackingReader) Read(p []byte) (n int, err error) {
+	n, err = r.r.Read(p)
+	r.n.Add(int64(n))
+	return
+}
+
 // Pull pulls the model with the given name from the remote registry into the
 // cache.
 //
@@ -434,11 +443,6 @@ func canRetry(err error) bool {
 // typically slower than splitting the model up across layers, and is mostly
 // utilized for layers of type equal to "application/vnd.ollama.image".
 func (r *Registry) Pull(ctx context.Context, name string) error {
-	scheme, n, _, err := r.parseNameExtended(name)
-	if err != nil {
-		return err
-	}
-
 	m, err := r.Resolve(ctx, name)
 	if err != nil {
 		return err
@@ -457,126 +461,95 @@ func (r *Registry) Pull(ctx context.Context, name string) error {
 		return err == nil && info.Size == l.Size
 	}
 
-	t := traceFromContext(ctx)
-
-	g, ctx := errgroup.WithContext(ctx)
-	g.SetLimit(r.maxStreams())
-
 	layers := m.Layers
 	if m.Config != nil && m.Config.Digest.IsValid() {
 		layers = append(layers, m.Config)
 	}
 
-	for _, l := range layers {
+	// Send initial layer trace events to allow clients to have an
+	// understanding of work to be done before work starts.
+	t := traceFromContext(ctx)
+	skip := make([]bool, len(layers))
+	for i, l := range layers {
+		t.update(l, 0, nil)
 		if exists(l) {
+			skip[i] = true
 			t.update(l, l.Size, ErrCached)
+		}
+	}
+
+	g, ctx := errgroup.WithContext(ctx)
+	g.SetLimit(r.maxStreams())
+	for i, l := range layers {
+		if skip[i] {
 			continue
 		}
 
-		blobURL := fmt.Sprintf("%s://%s/v2/%s/%s/blobs/%s", scheme, n.Host(), n.Namespace(), n.Model(), l.Digest)
-		req, err := r.newRequest(ctx, "GET", blobURL, nil)
+		chunked, err := c.Chunked(l.Digest, l.Size)
 		if err != nil {
 			t.update(l, 0, err)
 			continue
 		}
+		defer chunked.Close()
 
-		t.update(l, 0, nil)
-
-		if l.Size <= r.maxChunkingThreshold() {
-			g.Go(func() error {
-				// TODO(bmizerany): retry/backoff like below in
-				// the chunking case
-				res, err := sendRequest(r.client(), req)
-				if err != nil {
-					return err
-				}
-				defer res.Body.Close()
-				err = c.Put(l.Digest, res.Body, l.Size)
-				if err == nil {
-					t.update(l, l.Size, nil)
-				}
-				return err
-			})
-		} else {
-			q := syncs.NewRelayReader()
+		var progress atomic.Int64
+		for cs, err := range r.chunksums(ctx, name, l) {
+			if err != nil {
+				t.update(l, progress.Load(), err)
+				break
+			}
 
 			g.Go(func() (err error) {
-				defer func() { q.CloseWithError(err) }()
-				return c.Put(l.Digest, q, l.Size)
-			})
+				defer func() { t.update(l, progress.Load(), err) }()
 
-			var progress atomic.Int64
-
-			// We want to avoid extra round trips per chunk due to
-			// redirects from the registry to the blob store, so
-			// fire an initial request to get the final URL and
-			// then use that URL for the chunk requests.
-			req.Header.Set("Range", "bytes=0-0")
-			res, err := sendRequest(r.client(), req)
-			if err != nil {
-				return err
-			}
-			res.Body.Close()
-			req = res.Request.WithContext(req.Context())
-
-			wp := writerPool{size: r.maxChunkSize()}
-
-			for chunk := range chunks.Of(l.Size, r.maxChunkSize()) {
-				if ctx.Err() != nil {
-					break
-				}
-
-				ticket := q.Take()
-				g.Go(func() (err error) {
-					defer func() {
-						if err != nil {
-							q.CloseWithError(err)
-						}
-						ticket.Close()
-						t.update(l, progress.Load(), err)
-					}()
-
-					for _, err := range backoff.Loop(ctx, 3*time.Second) {
-						if err != nil {
-							return err
-						}
-						err := func() error {
-							req := req.Clone(req.Context())
-							req.Header.Set("Range", fmt.Sprintf("bytes=%s", chunk))
-							res, err := sendRequest(r.client(), req)
-							if err != nil {
-								return err
-							}
-							defer res.Body.Close()
-
-							tw := wp.get()
-							tw.Reset(ticket)
-							defer wp.put(tw)
-
-							_, err = io.CopyN(tw, res.Body, chunk.Size())
-							if err != nil {
-								return maybeUnexpectedEOF(err)
-							}
-							if err := tw.Flush(); err != nil {
-								return err
-							}
-
-							total := progress.Add(chunk.Size())
-							if total >= l.Size {
-								q.Close()
-							}
-							return nil
-						}()
-						if !canRetry(err) {
-							return err
-						}
+				for _, err := range backoff.Loop(ctx, 3*time.Second) {
+					if err != nil {
+						return err
 					}
-					return nil
-				})
-			}
+					err := func() error {
+						req, err := http.NewRequestWithContext(ctx, "GET", cs.URL, nil)
+						if err != nil {
+							return err
+						}
+						req.Header.Set("Range", fmt.Sprintf("bytes=%d-%d", cs.Chunk.Start, cs.Chunk.End))
+						res, err := sendRequest(r.client(), req)
+						if err != nil {
+							return err
+						}
+						defer res.Body.Close()
+
+						// Count bytes towards
+						// progress, as they arrive, so
+						// that our bytes piggyback
+						// other chunk updates on
+						// completion.
+						//
+						// This tactic is enough to
+						// show "smooth" progress given
+						// the current CLI client. In
+						// the near future, the server
+						// should report download rate
+						// since it knows better than
+						// a client that is measuring
+						// rate based on wall-clock
+						// time-since-last-update.
+						body := &trackingReader{r: res.Body, n: &progress}
+
+						err = chunked.Put(cs.Chunk, cs.Digest, body)
+						if err != nil {
+							return err
+						}
+
+						return nil
+					}()
+					if !canRetry(err) {
+						return err
+					}
+				}
+				return nil
+			})
 		}
 	}
-
 	if err := g.Wait(); err != nil {
 		return err
 	}
@@ -615,8 +588,6 @@ type Manifest struct {
 	Config *Layer `json:"config"`
 }
 
-var emptyDigest, _ = blob.ParseDigest("sha256:0000000000000000000000000000000000000000000000000000000000000000")
-
 // Layer returns the layer with the given
 // digest, or nil if not found.
 func (m *Manifest) Layer(d blob.Digest) *Layer {
@@ -643,10 +614,9 @@ func (m Manifest) MarshalJSON() ([]byte, error) {
 		// last phase of the commit which expects it, but does nothing
 		// with it. This will be fixed in a future release of
 		// ollama.com.
-		Config *Layer `json:"config"`
+		Config Layer `json:"config"`
 	}{
-		M:      M(m),
-		Config: &Layer{Digest: emptyDigest},
+		M: M(m),
 	}
 	return json.Marshal(v)
 }
@@ -736,6 +706,123 @@ func (r *Registry) Resolve(ctx context.Context, name string) (*Manifest, error)
 	return m, nil
 }
 
+type chunksum struct {
+	URL    string
+	Chunk  blob.Chunk
+	Digest blob.Digest
+}
+
+// chunksums returns a sequence of chunksums for the given layer. If the layer is under the
+// chunking threshold, a single chunksum is returned that covers the entire layer. If the layer
+// is over the chunking threshold, the chunksums are read from the chunksums endpoint.
+func (r *Registry) chunksums(ctx context.Context, name string, l *Layer) iter.Seq2[chunksum, error] {
+	return func(yield func(chunksum, error) bool) {
+		scheme, n, _, err := r.parseNameExtended(name)
+		if err != nil {
+			yield(chunksum{}, err)
+			return
+		}
+
+		if l.Size < r.maxChunkingThreshold() {
+			// any layer under the threshold should be downloaded
+			// in one go.
+			cs := chunksum{
+				URL: fmt.Sprintf("%s://%s/v2/%s/%s/blobs/%s",
+					scheme,
+					n.Host(),
+					n.Namespace(),
+					n.Model(),
+					l.Digest,
+				),
+				Chunk:  blob.Chunk{Start: 0, End: l.Size - 1},
+				Digest: l.Digest,
+			}
+			yield(cs, nil)
+			return
+		}
+
+		// A chunksums response is a sequence of chunksums in a
+		// simple, easy to parse line-oriented format.
+		//
+		// Example:
+		//
+		//     >> GET /v2/<namespace>/<model>/chunksums/<digest>
+		//
+		//     << HTTP/1.1 200 OK
+		//     << Content-Location: <blobURL>
+		//     <<
+		//     << <digest> <start>-<end>
+		//     << ...
+		//
+		// The blobURL is the URL to download the chunks from.
+
+		chunksumsURL := fmt.Sprintf("%s://%s/v2/%s/%s/chunksums/%s",
+			scheme,
+			n.Host(),
+			n.Namespace(),
+			n.Model(),
+			l.Digest,
+		)
+
+		req, err := r.newRequest(ctx, "GET", chunksumsURL, nil)
+		if err != nil {
+			yield(chunksum{}, err)
+			return
+		}
+		res, err := sendRequest(r.client(), req)
+		if err != nil {
+			yield(chunksum{}, err)
+			return
+		}
+		defer res.Body.Close()
+		if res.StatusCode != 200 {
+			err := fmt.Errorf("chunksums: unexpected status code %d", res.StatusCode)
+			yield(chunksum{}, err)
+			return
+		}
+		blobURL := res.Header.Get("Content-Location")
+
+		s := bufio.NewScanner(res.Body)
+		s.Split(bufio.ScanWords)
+		for {
+			if !s.Scan() {
+				if s.Err() != nil {
+					yield(chunksum{}, s.Err())
+				}
+				return
+			}
+			d, err := blob.ParseDigest(s.Bytes())
+			if err != nil {
+				yield(chunksum{}, fmt.Errorf("invalid digest: %q", s.Bytes()))
+				return
+			}
+
+			if !s.Scan() {
+				err := s.Err()
+				if err == nil {
+					err = fmt.Errorf("missing chunk range for digest %s", d)
+				}
+				yield(chunksum{}, err)
+				return
+			}
+			chunk, err := parseChunk(s.Bytes())
+			if err != nil {
+				yield(chunksum{}, fmt.Errorf("invalid chunk range for digest %s: %q", d, s.Bytes()))
+				return
+			}
+
+			cs := chunksum{
+				URL:    blobURL,
+				Chunk:  chunk,
+				Digest: d,
+			}
+			if !yield(cs, nil) {
+				return
+			}
+		}
+	}
+}
+
 func (r *Registry) client() *http.Client {
 	if r.HTTPClient != nil {
 		return r.HTTPClient
@@ -898,13 +985,6 @@ func checkData(url string) string {
 	return fmt.Sprintf("GET,%s,%s", url, zeroSum)
 }
 
-func maybeUnexpectedEOF(err error) error {
-	if errors.Is(err, io.EOF) {
-		return io.ErrUnexpectedEOF
-	}
-	return err
-}
-
 type publicError struct {
 	wrapped error
 	message string
@@ -991,27 +1071,22 @@ func splitExtended(s string) (scheme, name, digest string) {
 	return scheme, s, digest
 }
 
-type writerPool struct {
-	size int64 // set by the caller
-
-	mu sync.Mutex
-	ws []*bufio.Writer
-}
-
-func (p *writerPool) get() *bufio.Writer {
-	p.mu.Lock()
-	defer p.mu.Unlock()
-	if len(p.ws) == 0 {
-		return bufio.NewWriterSize(nil, int(p.size))
+// parseChunk parses a string in the form "start-end" and returns the Chunk.
+func parseChunk[S ~string | ~[]byte](s S) (blob.Chunk, error) {
+	startPart, endPart, found := strings.Cut(string(s), "-")
+	if !found {
+		return blob.Chunk{}, fmt.Errorf("chunks: invalid range %q: missing '-'", s)
 	}
-	w := p.ws[len(p.ws)-1]
-	p.ws = p.ws[:len(p.ws)-1]
-	return w
-}
-
-func (p *writerPool) put(w *bufio.Writer) {
-	p.mu.Lock()
-	defer p.mu.Unlock()
-	w.Reset(nil)
-	p.ws = append(p.ws, w)
+	start, err := strconv.ParseInt(startPart, 10, 64)
+	if err != nil {
+		return blob.Chunk{}, fmt.Errorf("chunks: invalid start to %q: %v", s, err)
+	}
+	end, err := strconv.ParseInt(endPart, 10, 64)
+	if err != nil {
+		return blob.Chunk{}, fmt.Errorf("chunks: invalid end to %q: %v", s, err)
+	}
+	if start > end {
+		return blob.Chunk{}, fmt.Errorf("chunks: invalid range %q: start > end", s)
+	}
+	return blob.Chunk{Start: start, End: end}, nil
 }

server/internal/client/ollama/registry_test.go

@@ -21,7 +21,6 @@ import (
 	"time"
 
 	"github.com/ollama/ollama/server/internal/cache/blob"
-	"github.com/ollama/ollama/server/internal/chunks"
 	"github.com/ollama/ollama/server/internal/testutil"
 )
 
@@ -428,7 +427,7 @@ func TestRegistryPullCached(t *testing.T) {
 	err := rc.Pull(ctx, "single")
 	testutil.Check(t, err)
 
-	want := []int64{6}
+	want := []int64{0, 6}
 	if !errors.Is(errors.Join(errs...), ErrCached) {
 		t.Errorf("errs = %v; want %v", errs, ErrCached)
 	}
@@ -531,54 +530,6 @@ func TestRegistryPullMixedCachedNotCached(t *testing.T) {
 	}
 }
 
-func TestRegistryPullChunking(t *testing.T) {
-	rc, _ := newClient(t, func(w http.ResponseWriter, r *http.Request) {
-		t.Log("request:", r.URL.Host, r.Method, r.URL.Path, r.Header.Get("Range"))
-		if r.URL.Host != "blob.store" {
-			// The production registry redirects to the blob store.
-			http.Redirect(w, r, "http://blob.store"+r.URL.Path, http.StatusFound)
-			return
-		}
-		if strings.Contains(r.URL.Path, "/blobs/") {
-			rng := r.Header.Get("Range")
-			if rng == "" {
-				http.Error(w, "missing range", http.StatusBadRequest)
-				return
-			}
-			_, c, err := chunks.ParseRange(r.Header.Get("Range"))
-			if err != nil {
-				panic(err)
-			}
-			io.WriteString(w, "remote"[c.Start:c.End+1])
-			return
-		}
-		fmt.Fprintf(w, `{"layers":[{"digest":%q,"size":6}]}`, blob.DigestFromBytes("remote"))
-	})
-
-	// Force chunking by setting the threshold to less than the size of the
-	// layer.
-	rc.ChunkingThreshold = 3
-	rc.MaxChunkSize = 3
-
-	var reads []int64
-	ctx := WithTrace(t.Context(), &Trace{
-		Update: func(d *Layer, n int64, err error) {
-			if err != nil {
-				t.Errorf("update %v %d %v", d, n, err)
-			}
-			reads = append(reads, n)
-		},
-	})
-
-	err := rc.Pull(ctx, "remote")
-	testutil.Check(t, err)
-
-	want := []int64{0, 3, 6}
-	if !slices.Equal(reads, want) {
-		t.Errorf("reads = %v; want %v", reads, want)
-	}
-}
-
 func TestRegistryResolveByDigest(t *testing.T) {
 	check := testutil.Checker(t)
 

server/internal/cmd/oppbench/oppbench.go

@@ -1,11 +0,0 @@
-package main
-
-import (
-	"fmt"
-	"os"
-)
-
-func main() {
-	fmt.Println("Run as 'go test -bench=.' to run the benchmarks")
-	os.Exit(1)
-}

server/internal/cmd/oppbench/oppbench_test.go

@@ -1,107 +0,0 @@
-package main
-
-import (
-	"bytes"
-	"context"
-	"fmt"
-	"io"
-	"net/http"
-	"os"
-	"path/filepath"
-	"runtime"
-	"sync/atomic"
-	"testing"
-	"time"
-
-	"github.com/ollama/ollama/server/internal/chunks"
-	"golang.org/x/sync/errgroup"
-)
-
-func BenchmarkDownload(b *testing.B) {
-	run := func(fileSize, chunkSize int64) {
-		name := fmt.Sprintf("size=%d/chunksize=%d", fileSize, chunkSize)
-		b.Run(name, func(b *testing.B) { benchmarkDownload(b, fileSize, chunkSize) })
-	}
-
-	run(100<<20, 8<<20)
-	run(100<<20, 16<<20)
-	run(100<<20, 32<<20)
-	run(100<<20, 64<<20)
-	run(100<<20, 128<<20) // 1 chunk
-}
-
-func run(ctx context.Context, c *http.Client, chunk chunks.Chunk) error {
-	const blobURL = "https://ollama.com/v2/x/x/blobs/sha256-4824460d29f2058aaf6e1118a63a7a197a09bed509f0e7d4e2efb1ee273b447d"
-	req, err := http.NewRequestWithContext(ctx, "GET", blobURL, nil)
-	if err != nil {
-		return err
-	}
-	req.Header.Set("Range", fmt.Sprintf("bytes=%s", chunk))
-	res, err := c.Do(req)
-	if err != nil {
-		return err
-	}
-	defer res.Body.Close()
-
-	_, err = io.CopyN(io.Discard, res.Body, chunk.Size()) // will io.EOF on short read
-	return err
-}
-
-var sleepTime atomic.Int64
-
-func benchmarkDownload(b *testing.B, fileSize, chunkSize int64) {
-	client := &http.Client{
-		Transport: func() http.RoundTripper {
-			tr := http.DefaultTransport.(*http.Transport).Clone()
-			tr.DisableKeepAlives = true
-			return tr
-		}(),
-	}
-	defer client.CloseIdleConnections()
-
-	// warm up the client
-	run(context.Background(), client, chunks.New(0, 1<<20))
-
-	b.SetBytes(fileSize)
-	b.ReportAllocs()
-
-	// Give our CDN a min to breathe between benchmarks.
-	time.Sleep(time.Duration(sleepTime.Swap(3)))
-
-	for b.Loop() {
-		g, ctx := errgroup.WithContext(b.Context())
-		g.SetLimit(runtime.GOMAXPROCS(0))
-		for chunk := range chunks.Of(fileSize, chunkSize) {
-			g.Go(func() error { return run(ctx, client, chunk) })
-		}
-		if err := g.Wait(); err != nil {
-			b.Fatal(err)
-		}
-	}
-}
-
-func BenchmarkWrite(b *testing.B) {
-	b.Run("chunksize=1MiB", func(b *testing.B) { benchmarkWrite(b, 1<<20) })
-}
-
-func benchmarkWrite(b *testing.B, chunkSize int) {
-	b.ReportAllocs()
-
-	dir := b.TempDir()
-	f, err := os.Create(filepath.Join(dir, "write-single"))
-	if err != nil {
-		b.Fatal(err)
-	}
-	defer f.Close()
-
-	data := make([]byte, chunkSize)
-	b.SetBytes(int64(chunkSize))
-	r := bytes.NewReader(data)
-	for b.Loop() {
-		r.Reset(data)
-		_, err := io.Copy(f, r)
-		if err != nil {
-			b.Fatal(err)
-		}
-	}
-}

server/internal/registry/server.go

@@ -1,6 +1,5 @@
-// Package registry provides an http.Handler for handling local Ollama API
-// requests for performing tasks related to the ollama.com model registry and
-// the local disk cache.
+// Package registry implements an http.Handler for handling local Ollama API
+// model management requests. See [Local] for details.
 package registry
 
 import (
@@ -10,6 +9,7 @@ import (
 	"fmt"
 	"io"
 	"log/slog"
+	"maps"
 	"net/http"
 	"sync"
 	"time"
@@ -18,16 +18,11 @@ import (
 	"github.com/ollama/ollama/server/internal/client/ollama"
 )
 
-// Local is an http.Handler for handling local Ollama API requests for
-// performing tasks related to the ollama.com model registry combined with the
-// local disk cache.
+// Local implements an http.Handler for handling local Ollama API model
+// management requests, such as pushing, pulling, and deleting models.
 //
-// It is not concern of Local, or this package, to handle model creation, which
-// proceeds any registry operations for models it produces.
-//
-// NOTE: The package built for dealing with model creation should use
-// [DefaultCache] to access the blob store and not attempt to read or write
-// directly to the blob disk cache.
+// It can be arranged for all unknown requests to be passed through to a
+// fallback handler, if one is provided.
 type Local struct {
 	Client *ollama.Registry // required
 	Logger *slog.Logger     // required
@@ -63,6 +58,7 @@ func (e serverError) Error() string {
 var (
 	errMethodNotAllowed = &serverError{405, "method_not_allowed", "method not allowed"}
 	errNotFound         = &serverError{404, "not_found", "not found"}
+	errModelNotFound    = &serverError{404, "not_found", "model not found"}
 	errInternalError    = &serverError{500, "internal_error", "internal server error"}
 )
 
@@ -175,8 +171,16 @@ func (s *Local) serveHTTP(rec *statusCodeRecorder, r *http.Request) {
 }
 
 type params struct {
-	DeprecatedName string `json:"name"`  // Use [params.model]
-	Model          string `json:"model"` // Use [params.model]
+	// DeprecatedName is the name of the model to push, pull, or delete,
+	// but is deprecated. New clients should use [Model] instead.
+	//
+	// Use [model()] to get the model name for both old and new API requests.
+	DeprecatedName string `json:"name"`
+
+	// Model is the name of the model to push, pull, or delete.
+	//
+	// Use [model()] to get the model name for both old and new API requests.
+	Model string `json:"model"`
 
 	// AllowNonTLS is a flag that indicates a client using HTTP
 	// is doing so, deliberately.
@@ -189,9 +193,18 @@ type params struct {
 	// confusing flags such as this.
 	AllowNonTLS bool `json:"insecure"`
 
-	// ProgressStream is a flag that indicates the client is expecting a stream of
-	// progress updates.
-	ProgressStream bool `json:"stream"`
+	// Stream, if true, will make the server send progress updates in a
+	// streaming of JSON objects. If false, the server will send a single
+	// JSON object with the final status as "success", or an error object
+	// if an error occurred.
+	//
+	// Unfortunately, this API was designed to be a bit awkward. Stream is
+	// defined to default to true if not present, so we need a way to check
+	// if the client decisively it to false. So, we use a pointer to a
+	// bool. Gross.
+	//
+	// Use [stream()] to get the correct value for this field.
+	Stream *bool `json:"stream"`
 }
 
 // model returns the model name for both old and new API requests.
@@ -199,6 +212,13 @@ func (p params) model() string {
 	return cmp.Or(p.Model, p.DeprecatedName)
 }
 
+func (p params) stream() bool {
+	if p.Stream == nil {
+		return true
+	}
+	return *p.Stream
+}
+
 func (s *Local) handleDelete(_ http.ResponseWriter, r *http.Request) error {
 	if r.Method != "DELETE" {
 		return errMethodNotAllowed
@@ -212,16 +232,16 @@ func (s *Local) handleDelete(_ http.ResponseWriter, r *http.Request) error {
 		return err
 	}
 	if !ok {
-		return &serverError{404, "not_found", "model not found"}
+		return errModelNotFound
 	}
-	if s.Prune == nil {
-		return nil
+	if s.Prune != nil {
+		return s.Prune()
 	}
-	return s.Prune()
+	return nil
 }
 
 type progressUpdateJSON struct {
-	Status    string      `json:"status"`
+	Status    string      `json:"status,omitempty,omitzero"`
 	Digest    blob.Digest `json:"digest,omitempty,omitzero"`
 	Total     int64       `json:"total,omitempty,omitzero"`
 	Completed int64       `json:"completed,omitempty,omitzero"`
@@ -237,6 +257,17 @@ func (s *Local) handlePull(w http.ResponseWriter, r *http.Request) error {
 		return err
 	}
 
+	enc := json.NewEncoder(w)
+	if !p.stream() {
+		if err := s.Client.Pull(r.Context(), p.model()); err != nil {
+			if errors.Is(err, ollama.ErrModelNotFound) {
+				return errModelNotFound
+			}
+			return err
+		}
+		return enc.Encode(progressUpdateJSON{Status: "success"})
+	}
+
 	maybeFlush := func() {
 		fl, _ := w.(http.Flusher)
 		if fl != nil {
@@ -246,69 +277,67 @@ func (s *Local) handlePull(w http.ResponseWriter, r *http.Request) error {
 	defer maybeFlush()
 
 	var mu sync.Mutex
-	enc := json.NewEncoder(w)
-	enc.Encode(progressUpdateJSON{Status: "pulling manifest"})
+	progress := make(map[*ollama.Layer]int64)
 
-	ctx := ollama.WithTrace(r.Context(), &ollama.Trace{
-		Update: func(l *ollama.Layer, n int64, err error) {
-			mu.Lock()
-			defer mu.Unlock()
+	progressCopy := make(map[*ollama.Layer]int64, len(progress))
+	pushUpdate := func() {
+		defer maybeFlush()
 
-			// TODO(bmizerany): coalesce these updates; writing per
-			// update is expensive
+		// TODO(bmizerany): This scales poorly with more layers due to
+		// needing to flush out them all in one big update. We _could_
+		// just flush on the changed ones, or just track the whole
+		// download. Needs more thought. This is fine for now.
+		mu.Lock()
+		maps.Copy(progressCopy, progress)
+		mu.Unlock()
+		for l, n := range progress {
 			enc.Encode(progressUpdateJSON{
 				Digest:    l.Digest,
-				Status:    "pulling",
 				Total:     l.Size,
 				Completed: n,
 			})
+		}
+	}
+
+	t := time.NewTicker(time.Hour) // "unstarted" timer
+	start := sync.OnceFunc(func() {
+		pushUpdate()
+		t.Reset(100 * time.Millisecond)
+	})
+	ctx := ollama.WithTrace(r.Context(), &ollama.Trace{
+		Update: func(l *ollama.Layer, n int64, err error) {
+			if n > 0 {
+				start() // flush initial state
+			}
+			mu.Lock()
+			progress[l] = n
+			mu.Unlock()
 		},
 	})
 
 	done := make(chan error, 1)
 	go func() {
-		// TODO(bmizerany): continue to support non-streaming responses
 		done <- s.Client.Pull(ctx, p.model())
 	}()
 
-	func() {
-		t := time.NewTicker(100 * time.Millisecond)
-		defer t.Stop()
-		for {
-			select {
-			case <-t.C:
-				mu.Lock()
-				maybeFlush()
-				mu.Unlock()
-			case err := <-done:
-				if err != nil {
-					var status string
-					if errors.Is(err, ollama.ErrModelNotFound) {
-						status = fmt.Sprintf("error: model %q not found", p.model())
-						enc.Encode(progressUpdateJSON{Status: status})
-					} else {
-						status = fmt.Sprintf("error: %v", err)
-						enc.Encode(progressUpdateJSON{Status: status})
-					}
-					return
+	for {
+		select {
+		case <-t.C:
+			pushUpdate()
+		case err := <-done:
+			pushUpdate()
+			if err != nil {
+				var status string
+				if errors.Is(err, ollama.ErrModelNotFound) {
+					status = fmt.Sprintf("error: model %q not found", p.model())
+				} else {
+					status = fmt.Sprintf("error: %v", err)
 				}
-
-				// These final updates are not strictly necessary, because they have
-				// already happened at this point. Our pull handler code used to do
-				// these steps after, not during, the pull, and they were slow, so we
-				// wanted to provide feedback to users what was happening. For now, we
-				// keep them to not jar users who are used to seeing them. We can phase
-				// them out with a new and nicer UX later. One without progress bars
-				// and digests that no one cares about.
-				enc.Encode(progressUpdateJSON{Status: "verifying layers"})
-				enc.Encode(progressUpdateJSON{Status: "writing manifest"})
-				enc.Encode(progressUpdateJSON{Status: "success"})
-				return
+				enc.Encode(progressUpdateJSON{Status: status})
 			}
+			return nil
 		}
-	}()
-
-	return nil
+	}
 }
 
 func decodeUserJSON[T any](r io.Reader) (T, error) {

server/internal/registry/server_test.go

@@ -4,7 +4,6 @@ import (
 	"bytes"
 	"context"
 	"encoding/json"
-	"fmt"
 	"io"
 	"io/fs"
 	"net"
@@ -160,7 +159,6 @@ var registryFS = sync.OnceValue(func() fs.FS {
 	// to \n when parsing the txtar on Windows.
 	data := bytes.ReplaceAll(registryTXT, []byte("\r\n"), []byte("\n"))
 	a := txtar.Parse(data)
-	fmt.Printf("%q\n", a.Comment)
 	fsys, err := txtar.FS(a)
 	if err != nil {
 		panic(err)
@@ -179,7 +177,7 @@ func TestServerPull(t *testing.T) {
 			w.WriteHeader(404)
 			io.WriteString(w, `{"errors": [{"code": "MANIFEST_UNKNOWN", "message": "manifest unknown"}]}`)
 		default:
-			t.Logf("serving file: %s", r.URL.Path)
+			t.Logf("serving blob: %s", r.URL.Path)
 			modelsHandler.ServeHTTP(w, r)
 		}
 	})
@@ -188,7 +186,7 @@ func TestServerPull(t *testing.T) {
 		t.Helper()
 
 		if got.Code != 200 {
-			t.Fatalf("Code = %d; want 200", got.Code)
+			t.Errorf("Code = %d; want 200", got.Code)
 		}
 		gotlines := got.Body.String()
 		t.Logf("got:\n%s", gotlines)
@@ -197,35 +195,29 @@ func TestServerPull(t *testing.T) {
 			want, unwanted := strings.CutPrefix(want, "!")
 			want = strings.TrimSpace(want)
 			if !unwanted && !strings.Contains(gotlines, want) {
-				t.Fatalf("! missing %q in body", want)
+				t.Errorf("! missing %q in body", want)
 			}
 			if unwanted && strings.Contains(gotlines, want) {
-				t.Fatalf("! unexpected %q in body", want)
+				t.Errorf("! unexpected %q in body", want)
 			}
 		}
 	}
 
 	got := s.send(t, "POST", "/api/pull", `{"model": "BOOM"}`)
 	checkResponse(got, `
-		{"status":"pulling manifest"}
 		{"status":"error: request error https://example.com/v2/library/BOOM/manifests/latest: registry responded with status 999: boom"}
 	`)
 
 	got = s.send(t, "POST", "/api/pull", `{"model": "smol"}`)
 	checkResponse(got, `
-		{"status":"pulling manifest"}
-		{"status":"pulling","digest":"sha256:68e0ec597aee59d35f8dc44942d7b17d471ade10d3aca07a5bb7177713950312","total":5}
-		{"status":"pulling","digest":"sha256:ca3d163bab055381827226140568f3bef7eaac187cebd76878e0b63e9e442356","total":3}
-		{"status":"pulling","digest":"sha256:68e0ec597aee59d35f8dc44942d7b17d471ade10d3aca07a5bb7177713950312","total":5,"completed":5}
-		{"status":"pulling","digest":"sha256:ca3d163bab055381827226140568f3bef7eaac187cebd76878e0b63e9e442356","total":3,"completed":3}
-		{"status":"verifying layers"}
-		{"status":"writing manifest"}
-		{"status":"success"}
+		{"digest":"sha256:68e0ec597aee59d35f8dc44942d7b17d471ade10d3aca07a5bb7177713950312","total":5}
+		{"digest":"sha256:ca3d163bab055381827226140568f3bef7eaac187cebd76878e0b63e9e442356","total":3}
+		{"digest":"sha256:68e0ec597aee59d35f8dc44942d7b17d471ade10d3aca07a5bb7177713950312","total":5,"completed":5}
+		{"digest":"sha256:ca3d163bab055381827226140568f3bef7eaac187cebd76878e0b63e9e442356","total":3,"completed":3}
 	`)
 
 	got = s.send(t, "POST", "/api/pull", `{"model": "unknown"}`)
 	checkResponse(got, `
-		{"status":"pulling manifest"}
 		{"status":"error: model \"unknown\" not found"}
 	`)
 
@@ -240,19 +232,39 @@ func TestServerPull(t *testing.T) {
 
 	got = s.send(t, "POST", "/api/pull", `{"model": "://"}`)
 	checkResponse(got, `
-		{"status":"pulling manifest"}
 		{"status":"error: invalid or missing name: \"\""}
-
-		!verifying
-		!writing
-		!success
 	`)
+
+	// Non-streaming pulls
+	got = s.send(t, "POST", "/api/pull", `{"model": "://", "stream": false}`)
+	checkErrorResponse(t, got, 400, "bad_request", "invalid or missing name")
+	got = s.send(t, "POST", "/api/pull", `{"model": "smol", "stream": false}`)
+	checkResponse(got, `
+		{"status":"success"}
+		!digest
+		!total
+		!completed
+	`)
+	got = s.send(t, "POST", "/api/pull", `{"model": "unknown", "stream": false}`)
+	checkErrorResponse(t, got, 404, "not_found", "model not found")
 }
 
 func TestServerUnknownPath(t *testing.T) {
 	s := newTestServer(t, nil)
 	got := s.send(t, "DELETE", "/api/unknown", `{}`)
 	checkErrorResponse(t, got, 404, "not_found", "not found")
+
+	var fellback bool
+	s.Fallback = http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
+		fellback = true
+	})
+	got = s.send(t, "DELETE", "/api/unknown", `{}`)
+	if !fellback {
+		t.Fatal("expected Fallback to be called")
+	}
+	if got.Code != 200 {
+		t.Fatalf("Code = %d; want 200", got.Code)
+	}
 }
 
 func checkErrorResponse(t *testing.T, got *httptest.ResponseRecorder, status int, code, msg string) {

server/prompt.go

@@ -26,7 +26,6 @@ func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.
 	var system []api.Message
 
 	isMllama := checkMllamaModelFamily(m)
-	isGemma3 := checkGemma3ModelFamily(m)
 
 	var imageNumTokens int
 	// TODO: Ideally we would compute this from the projector metadata but some pieces are implementation dependent
@@ -41,7 +40,7 @@ func chatPrompt(ctx context.Context, m *Model, tokenize tokenizeFunc, opts *api.
 	n := len(msgs) - 1
 	// in reverse, find all messages that fit into context window
 	for i := n; i >= 0; i-- {
-		if (isMllama || isGemma3) && len(msgs[i].Images) > 1 {
+		if isMllama && len(msgs[i].Images) > 1 {
 			return "", nil, errTooManyImages
 		}
 
@@ -158,12 +157,3 @@ func checkMllamaModelFamily(m *Model) bool {
 	}
 	return false
 }
-
-func checkGemma3ModelFamily(m *Model) bool {
-	for _, arch := range m.Config.ModelFamilies {
-		if arch == "gemma3" {
-			return true
-		}
-	}
-	return false
-}