models: qwen3vl

* tests: reduce stress on CPU to 2 models

This should avoid flakes due to systems getting overloaded with 3 (or more) models running concurrently

* tests: allow slow systems to pass on timeout

If a slow system is still streaming a response, and the response
will pass validation, don't fail just because the system is slow.

* test: unload embedding models more quickly

README.md

@@ -413,6 +413,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Mayan EDMS](https://gitlab.com/mayan-edms/mayan-edms) (Open source document management system to organize, tag, search, and automate your files with powerful Ollama driven workflows.)
 - [Serene Pub](https://github.com/doolijb/serene-pub) (Beginner friendly, open source AI Roleplaying App for Windows, Mac OS and Linux. Search, download and use models with Ollama all inside the app.)
 - [Andes](https://github.com/aqerd/andes) (A Visual Studio Code extension that provides a local UI interface for Ollama models)
+- [Clueless](https://github.com/KashyapTan/clueless) (Open Source & Local Cluely: A desktop application LLM assistant to help you talk to anything on your screen using locally served Ollama models. Also undetectable to screenshare)
 
 ### Cloud
 

docs/troubleshooting.md

@@ -92,6 +92,9 @@ If none of those resolve the problem, gather additional information and file an
 - Set `CUDA_ERROR_LEVEL=50` and try again to get more diagnostic logs
 - Check dmesg for any errors `sudo dmesg | grep -i nvrm` and `sudo dmesg | grep -i nvidia`
 
+You may get more details for initialization failures by enabling debug prints in the uvm driver.  You should only use this temporarily while troubleshooting
+- `sudo rmmod nvidia_uvm` then `sudo modprobe nvidia_uvm uvm_debug_prints=1`
+
 
 ## AMD GPU Discovery
 

fs/ggml/ggml.go

@@ -57,10 +57,28 @@ func (kv KV) EmbeddingLength() uint64 {
 	return uint64(kv.Uint("embedding_length"))
 }
 
+func (kv KV) HeadCount() []uint64 {
+	headCountDefault := uint32(1)
+	headCount := kv.UintOrArrayValueAsArray("attention.head_count", headCountDefault)
+	if len(headCount) == 1 {
+		headCountDefault = headCount[0]
+	}
+	nLayers := int(kv.BlockCount())
+	if len(headCount) > nLayers {
+		slog.Warn("got more elements of attention.head_count than layers", "len(headCount)", len(headCount), "layers", nLayers)
+	}
+	out := make([]uint64, nLayers)
+	for i := range nLayers {
+		if i >= len(headCount) {
+			out[i] = uint64(headCountDefault)
+		} else {
+			out[i] = uint64(headCount[i])
+		}
+	}
+	return out
+}
+
 func (kv KV) HeadCountMax() uint64 {
-	// TODO(drifkin): using the max value can cause an overestimation. In the
-	// future if array values become more popular, we can adapt the more invasive
-	// <https://github.com/ollama/ollama/pull/10225>
 	return uint64(kv.UintOrMaxArrayValue("attention.head_count", 1))
 }
 
@@ -68,6 +86,27 @@ func (kv KV) HeadCountMin() uint64 {
 	return uint64(kv.UintOrMinArrayValue("attention.head_count", 1))
 }
 
+func (kv KV) HeadCountKV() []uint64 {
+	headCountKVDefault := uint32(1)
+	headCountKV := kv.UintOrArrayValueAsArray("attention.head_count_kv", headCountKVDefault)
+	if len(headCountKV) == 1 {
+		headCountKVDefault = headCountKV[0]
+	}
+	nLayers := int(kv.BlockCount())
+	if len(headCountKV) > nLayers {
+		slog.Warn("got more elements of attention.head_count than layers", "len(headCountKV)", len(headCountKV), "layers", nLayers)
+	}
+	out := make([]uint64, nLayers)
+	for i := range nLayers {
+		if i >= len(headCountKV) {
+			out[i] = uint64(headCountKVDefault)
+		} else {
+			out[i] = uint64(headCountKV[i])
+		}
+	}
+	return out
+}
+
 func (kv KV) HeadCountKVMax() uint64 {
 	return uint64(kv.UintOrMaxArrayValue("attention.head_count_kv", 1))
 }
@@ -100,6 +139,26 @@ func (kv KV) ChatTemplate() string {
 	return kv.String("tokenizer.chat_template")
 }
 
+// ssm architecture parameters
+
+func (kv KV) SSMConvKernel() uint64 {
+	return uint64(kv.Uint("ssm.conv_kernel"))
+}
+
+func (kv KV) SSMInnerSize() uint64 {
+	return uint64(kv.Uint("ssm.inner_size"))
+}
+
+func (kv KV) SSMStateSize() uint64 {
+	return uint64(kv.Uint("ssm.state_size"))
+}
+
+func (kv KV) SSMGroupCount() uint64 {
+	return uint64(kv.Uint("ssm.group_count"))
+}
+
+// general types
+
 func (kv KV) String(key string, defaultValue ...string) string {
 	val, _ := keyValue(kv, key, append(defaultValue, "")...)
 	return val
@@ -131,22 +190,27 @@ func (kv KV) UintOrMinArrayValue(key string, defaultValue uint32) uint32 {
 }
 
 func (kv KV) UintOrArrayValue(key string, defaultValue uint32) (uint32, uint32) {
+	arrVal := kv.UintOrArrayValueAsArray(key, defaultValue)
+	return slices.Min(arrVal), slices.Max(arrVal)
+}
+
+func (kv KV) UintOrArrayValueAsArray(key string, defaultValue uint32) []uint32 {
 	if u32, ok := keyValue(kv, key, uint32(0)); ok {
-		return u32, u32
+		return []uint32{u32}
 	} else if u32s, ok := keyValue(kv, key, &array[uint32]{}); ok {
-		min := slices.Min(u32s.values)
-		max := slices.Max(u32s.values)
-		return min, max
+		return u32s.values
 	} else if i32s, ok := keyValue(kv, key, &array[int32]{}); ok {
-		min := slices.Min(i32s.values)
-		max := slices.Max(i32s.values)
-		if min < 0 || max < 0 {
-			slog.Warn("array values are unexpectedly negative", "key", key, "min", min, "max", max)
+		dst := make([]uint32, len(i32s.values))
+		for i, v := range i32s.values {
+			if v < 0 {
+				slog.Warn("array values are unexpectedly negative", "key", key, "i", i, "v", v)
+			}
+			dst[i] = uint32(v)
 		}
-		return uint32(min), uint32(max)
+		return dst
 	}
 
-	return defaultValue, defaultValue
+	return []uint32{defaultValue}
 }
 
 func (kv KV) Strings(key string, defaultValue ...[]string) []string {
@@ -486,7 +550,9 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 
 	embedding := f.KV().EmbeddingLength()
 	heads := f.KV().HeadCountMax()
+	headsArr := f.KV().HeadCount()
 	headsKV := f.KV().HeadCountKVMax()
+	headsKVArr := f.KV().HeadCountKV()
 	vocab := uint64(f.KV()["tokenizer.ggml.tokens"].(*array[string]).size)
 
 	embeddingHeads := f.KV().EmbeddingHeadCountMax()
@@ -496,12 +562,51 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 	layers := f.Tensors().GroupLayers()
 
 	bytesPerElement := kvCacheBytesPerElement(kvCacheType)
+
+	// Default for models unless special-cased below. These defaults mirror the
+	// cache usage in llama.cpp under the assumption that models without special
+	// cases below will use the llamarunner and caching will be handled by the
+	// llama.cpp layer.
+	//
+	// This also assumes that a layer without heads or headsKV set is recurrent
+	// which is usually the case. Some models (eg nemotronh) use "blocks" in
+	// place of layers where some are MLP blocks that don't have any cache.
+	// Models like this will need a special case below to be accurately
+	// estimated.
 	var kvTotal uint64
 	kv = make([]uint64, f.KV().BlockCount())
+	kvSizeAttn := uint64(0)
+	kvSizeRecurrent := uint64(0)
 	for i := range kv {
-		kv[i] = uint64(float64(context*(embeddingHeadsK+embeddingHeadsV)*headsKV) * bytesPerElement)
+		headsL := headsArr[i]
+		headsKVL := headsKVArr[i]
+		if headsL > 0 && headsKVL > 0 {
+			// full attention layer
+			// NOTE: Assumes uniform values for all attn layers
+			kv[i] = uint64(float64(context*(embeddingHeadsK+embeddingHeadsV)*headsKVL) * bytesPerElement)
+			kvSizeAttn += kv[i]
+		} else {
+			// recurrent layer
+			ssmDConv := f.KV().SSMConvKernel()
+			ssmDState := f.KV().SSMStateSize()
+			ssmDInner := f.KV().SSMInnerSize()
+			ssmNGroups := f.KV().SSMGroupCount()
+			nEmbdR := uint64(0)
+			if ssmDConv > 0 {
+				nEmbdR = (ssmDConv - 1) * (ssmDInner + 2*ssmNGroups*ssmDState)
+			}
+			nEmbdS := ssmDState * ssmDInner
+
+			// recurrent always uses F32 in llama.cpp backend
+			// https://github.com/ggml-org/llama.cpp/blob/master/src/llama-model.cpp#L18644
+			bytesPerElementRecurrent := kvCacheBytesPerElement("f32")
+
+			kv[i] = (nEmbdR + nEmbdS) * uint64(bytesPerElementRecurrent)
+			kvSizeRecurrent += kv[i]
+		}
 		kvTotal += kv[i]
 	}
+	slog.Debug("default cache size estimate", "attention MiB", float32(kvSizeAttn)/(1024.*1024.), "attention bytes", kvSizeAttn, "recurrent MiB", float32(kvSizeRecurrent)/(1024.*1024.), "recurrent bytes", kvSizeRecurrent)
 
 	switch f.KV().Architecture() {
 	case "llama", "llama4":
@@ -794,6 +899,8 @@ func kvCacheBytesPerElement(cacheType string) float64 {
 		return 1 // 1/2 of fp16
 	case "q4_0":
 		return 0.5 // 1/4 of fp16
+	case "f32":
+		return 4 // f32 (default for recurrent)
 	default:
 		return 2 // f16 (default)
 	}

harmony/harmonyparser.go

@@ -1,14 +1,13 @@
 package harmony
 
 import (
-	"encoding/json"
 	"fmt"
 	"log/slog"
-	"maps"
 	"slices"
 	"strings"
 	"unicode"
 
+	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/logutil"
 	"github.com/ollama/ollama/template"
 )
@@ -48,13 +47,12 @@ func (s harmonyParserState) String() string {
 }
 
 type HarmonyParser struct {
-	state            harmonyParserState
-	MessageStartTag  string
-	MessageEndTag    string
-	HeaderEndTag     string
-	ConstrainAllowed bool
-	acc              strings.Builder
-	lifetimeAcc      strings.Builder
+	state           harmonyParserState
+	MessageStartTag string
+	MessageEndTag   string
+	HeaderEndTag    string
+	acc             strings.Builder
+	lifetimeAcc     strings.Builder
 }
 
 type HarmonyEvent interface {
@@ -91,19 +89,28 @@ func (s *HarmonyParser) AddImplicitStart() {
 	s.acc.WriteString("<|start|>assistant")
 }
 
-// AddImplicitStartOrPrefill adds content or thinking to the accumulator else adds start tag
-func (s *HarmonyParser) AddImplicitStartOrPrefill(prefillContentOrThinking *bool) {
-	if prefillContentOrThinking != nil {
-		if *prefillContentOrThinking {
-			s.acc.WriteString("<|start|>assistant<|channel|>final<|message|>")
-			return
-		} else {
-			s.acc.WriteString("<|start|>assistant<|channel|>analysis<|message|>")
-			return
-		}
+func Prefill(lastMessage api.Message) string {
+	if lastMessage.Role != "assistant" {
+		return ""
 	}
 
-	s.AddImplicitStart()
+	switch {
+	case strings.TrimSpace(lastMessage.Content) != "":
+		return "<|start|>assistant<|channel|>final<|message|>"
+	case strings.TrimSpace(lastMessage.Thinking) != "":
+		return "<|start|>assistant<|channel|>analysis<|message|>"
+	default:
+		return ""
+	}
+}
+
+// AddImplicitStartOrPrefill adds an implicit start tag or prefill string if provided
+func (s *HarmonyParser) AddImplicitStartOrPrefill(prefillString string) {
+	if strings.TrimSpace(prefillString) != "" {
+		s.acc.WriteString(prefillString)
+	} else {
+		s.AddImplicitStart()
+	}
 }
 
 func (s *HarmonyParser) AddContent(content string) []HarmonyEvent {
@@ -329,7 +336,6 @@ func (h *HarmonyMessageHandler) AddContent(content string, toolParser *HarmonyTo
 				}
 			case "final":
 				h.state = harmonyMessageState_Normal
-				h.HarmonyParser.ConstrainAllowed = true
 			}
 		case HarmonyEventContentEmitted:
 			logutil.Trace("harmony event content", "content", event.Content, "state", h.state)
@@ -395,38 +401,6 @@ type FunctionNameMap struct {
 	harmonyToUser map[string]string
 }
 
-func (m FunctionNameMap) MarshalJSON() ([]byte, error) {
-	// necessary to avoid exposing map internals
-	type alias struct {
-		UserToHarmony map[string]string `json:"userToHarmony"`
-		HarmonyToUser map[string]string `json:"harmonyToUser"`
-	}
-	return json.Marshal(alias{
-		UserToHarmony: m.userToHarmony,
-		HarmonyToUser: m.harmonyToUser,
-	})
-}
-
-func (m *FunctionNameMap) UnmarshalJSON(b []byte) error {
-	type alias struct {
-		UserToHarmony map[string]string `json:"userToHarmony"`
-		HarmonyToUser map[string]string `json:"harmonyToUser"`
-	}
-	var a alias
-	if err := json.Unmarshal(b, &a); err != nil {
-		return err
-	}
-	if m.userToHarmony == nil {
-		m.userToHarmony = make(map[string]string)
-	}
-	if m.harmonyToUser == nil {
-		m.harmonyToUser = make(map[string]string)
-	}
-	maps.Copy(m.userToHarmony, a.UserToHarmony)
-	maps.Copy(m.harmonyToUser, a.HarmonyToUser)
-	return nil
-}
-
 func NewFunctionNameMap() *FunctionNameMap {
 	return &FunctionNameMap{
 		userToHarmony: make(map[string]string),

harmony/harmonyparser_test.go

@@ -1,7 +1,6 @@
 package harmony
 
 import (
-	"encoding/json"
 	"fmt"
 	"reflect"
 	"strings"
@@ -736,25 +735,3 @@ func TestHarmonyMessageHandlerStreamingScenarios(t *testing.T) {
 		}
 	})
 }
-
-func TestFunctionNameMapJSONRoundTrip(t *testing.T) {
-	m := NewFunctionNameMap()
-	gotConverted := m.ConvertAndAdd("get weather")
-	if gotConverted == "" {
-		t.Fatal("conversion returned empty")
-	}
-	b, err := json.Marshal(m)
-	if err != nil {
-		t.Fatalf("marshal: %v", err)
-	}
-	var m2 FunctionNameMap
-	if err := json.Unmarshal(b, &m2); err != nil {
-		t.Fatalf("unmarshal: %v", err)
-	}
-	if m2.userToHarmony["get weather"] != gotConverted {
-		t.Fatalf("userToHarmony lost: got %q want %q", m2.userToHarmony["get weather"], gotConverted)
-	}
-	if m2.harmonyToUser[gotConverted] != "get weather" {
-		t.Fatalf("harmonyToUser lost: got %q want %q", m2.harmonyToUser[gotConverted], "get weather")
-	}
-}

integration/api_test.go

@@ -410,3 +410,99 @@ func TestAPIEmbeddings(t *testing.T) {
 		t.Errorf("zero length embedding response")
 	}
 }
+
+func TestAPIToolCalling(t *testing.T) {
+	initialTimeout := 60 * time.Second
+	streamTimeout := 30 * time.Second
+	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
+	defer cancel()
+
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
+
+	modelName := "qwen3:0.6b"
+	if err := PullIfMissing(ctx, client, modelName); err != nil {
+		t.Fatalf("pull failed %s", err)
+	}
+
+	tools := []api.Tool{
+		{
+			Type: "function",
+			Function: api.ToolFunction{
+				Name:        "get_weather",
+				Description: "Get the current weather in a given location",
+				Parameters: api.ToolFunctionParameters{
+					Type:     "object",
+					Required: []string{"location"},
+					Properties: map[string]api.ToolProperty{
+						"location": {
+							Type:        api.PropertyType{"string"},
+							Description: "The city and state, e.g. San Francisco, CA",
+						},
+					},
+				},
+			},
+		},
+	}
+
+	req := api.ChatRequest{
+		Model: modelName,
+		Messages: []api.Message{
+			{
+				Role:    "user",
+				Content: "Call get_weather with location set to San Francisco.",
+			},
+		},
+		Tools: tools,
+		Options: map[string]any{
+			"temperature": 0,
+		},
+	}
+
+	stallTimer := time.NewTimer(initialTimeout)
+	var gotToolCall bool
+	var lastToolCall api.ToolCall
+
+	fn := func(response api.ChatResponse) error {
+		if len(response.Message.ToolCalls) > 0 {
+			gotToolCall = true
+			lastToolCall = response.Message.ToolCalls[len(response.Message.ToolCalls)-1]
+		}
+		if !stallTimer.Reset(streamTimeout) {
+			return fmt.Errorf("stall was detected while streaming response, aborting")
+		}
+		return nil
+	}
+
+	stream := true
+	req.Stream = &stream
+	done := make(chan int)
+	var genErr error
+	go func() {
+		genErr = client.Chat(ctx, &req, fn)
+		done <- 0
+	}()
+
+	select {
+	case <-stallTimer.C:
+		t.Errorf("tool-calling chat never started. Timed out after: %s", initialTimeout.String())
+	case <-done:
+		if genErr != nil {
+			t.Fatalf("chat failed: %v", genErr)
+		}
+
+		if !gotToolCall {
+			t.Fatalf("expected at least one tool call, got none")
+		}
+
+		if lastToolCall.Function.Name != "get_weather" {
+			t.Errorf("unexpected tool called: got %q want %q", lastToolCall.Function.Name, "get_weather")
+		}
+
+		if _, ok := lastToolCall.Function.Arguments["location"]; !ok {
+			t.Errorf("expected tool arguments to include 'location', got: %s", lastToolCall.Function.Arguments.String())
+		}
+	case <-ctx.Done():
+		t.Error("outer test context done while waiting for tool-calling chat")
+	}
+}

integration/concurrency_test.go

@@ -121,6 +121,7 @@ func TestMultiModelStress(t *testing.T) {
 	// The intent is to go 1 over what can fit so we force the scheduler to thrash
 	targetLoadCount := 0
 	slog.Info("Loading models to find how many can fit in VRAM before overflowing")
+chooseModels:
 	for i, model := range chosenModels {
 		req := &api.GenerateRequest{Model: model}
 		slog.Info("loading", "model", model)
@@ -142,6 +143,13 @@ func TestMultiModelStress(t *testing.T) {
 				slog.Info("found model load capacity", "target", targetLoadCount, "current", loaded, "chosen", chosenModels[:targetLoadCount])
 				break
 			}
+			// Effectively limit model count to 2 on CPU only systems to avoid thrashing and timeouts
+			for _, m := range models.Models {
+				if m.SizeVRAM == 0 {
+					slog.Info("model running on CPU", "name", m.Name, "target", targetLoadCount, "chosen", chosenModels[:targetLoadCount])
+					break chooseModels
+				}
+			}
 		}
 	}
 	if targetLoadCount == len(chosenModels) {

integration/context_test.go

@@ -36,7 +36,7 @@ func TestLongInputContext(t *testing.T) {
 	if err := PullIfMissing(ctx, client, req.Model); err != nil {
 		t.Fatalf("PullIfMissing failed: %v", err)
 	}
-	DoGenerate(ctx, t, client, req, []string{"russia", "germany", "france", "england", "austria", "prussia", "individuals", "coalition", "conflict"}, 120*time.Second, 10*time.Second)
+	DoGenerate(ctx, t, client, req, []string{"russia", "germany", "france", "england", "austria", "prussia", "europe", "individuals", "coalition", "conflict"}, 120*time.Second, 10*time.Second)
 }
 
 func TestContextExhaustion(t *testing.T) {

integration/embed_test.go

@@ -38,8 +38,9 @@ func TestAllMiniLMEmbeddings(t *testing.T) {
 	defer cleanup()
 
 	req := api.EmbeddingRequest{
-		Model:  "all-minilm",
-		Prompt: "why is the sky blue?",
+		Model:     "all-minilm",
+		Prompt:    "why is the sky blue?",
+		KeepAlive: &api.Duration{Duration: 10 * time.Second},
 	}
 
 	res, err := embeddingTestHelper(ctx, client, t, req)

integration/utils_test.go

@@ -502,6 +502,22 @@ func DoGenerate(ctx context.Context, t *testing.T, client *api.Client, genReq ap
 		done <- 0
 	}()
 
+	var response string
+	verify := func() {
+		// Verify the response contains the expected data
+		response = buf.String()
+		atLeastOne := false
+		for _, resp := range anyResp {
+			if strings.Contains(strings.ToLower(response), resp) {
+				atLeastOne = true
+				break
+			}
+		}
+		if !atLeastOne {
+			t.Fatalf("%s: none of %v found in %s", genReq.Model, anyResp, response)
+		}
+	}
+
 	select {
 	case <-stallTimer.C:
 		if buf.Len() == 0 {
@@ -517,21 +533,14 @@ func DoGenerate(ctx context.Context, t *testing.T, client *api.Client, genReq ap
 		if genErr != nil {
 			t.Fatalf("%s failed with %s request prompt %s", genErr, genReq.Model, genReq.Prompt)
 		}
-		// Verify the response contains the expected data
-		response := buf.String()
-		atLeastOne := false
-		for _, resp := range anyResp {
-			if strings.Contains(strings.ToLower(response), resp) {
-				atLeastOne = true
-				break
-			}
-		}
-		if !atLeastOne {
-			t.Fatalf("%s: none of %v found in %s", genReq.Model, anyResp, response)
-		}
+		verify()
 		slog.Info("test pass", "model", genReq.Model, "prompt", genReq.Prompt, "contains", anyResp, "response", response)
 	case <-ctx.Done():
-		t.Error("outer test context done while waiting for generate")
+		// On slow systems, we might timeout before some models finish rambling, so check what we have so far to see
+		// if it's considered a pass - the stallTimer will detect hangs, but we want to consider slow systems a pass
+		// if they are still generating valid responses
+		slog.Warn("outer test context done while waiting for generate")
+		verify()
 	}
 	return context
 }
@@ -599,6 +608,22 @@ func DoChat(ctx context.Context, t *testing.T, client *api.Client, req api.ChatR
 		done <- 0
 	}()
 
+	var response string
+	verify := func() {
+		// Verify the response contains the expected data
+		response = buf.String()
+		atLeastOne := false
+		for _, resp := range anyResp {
+			if strings.Contains(strings.ToLower(response), resp) {
+				atLeastOne = true
+				break
+			}
+		}
+		if !atLeastOne {
+			t.Fatalf("%s: none of %v found in \"%s\" -- request was:%v", req.Model, anyResp, response, req.Messages)
+		}
+	}
+
 	select {
 	case <-stallTimer.C:
 		if buf.Len() == 0 {
@@ -614,23 +639,14 @@ func DoChat(ctx context.Context, t *testing.T, client *api.Client, req api.ChatR
 		if genErr != nil {
 			t.Fatalf("%s failed with %s request prompt %v", genErr, req.Model, req.Messages)
 		}
-
-		// Verify the response contains the expected data
-		response := buf.String()
-		atLeastOne := false
-		for _, resp := range anyResp {
-			if strings.Contains(strings.ToLower(response), resp) {
-				atLeastOne = true
-				break
-			}
-		}
-		if !atLeastOne {
-			t.Fatalf("%s: none of %v found in \"%s\" -- request was:%v", req.Model, anyResp, response, req.Messages)
-		}
-
+		verify()
 		slog.Info("test pass", "model", req.Model, "messages", req.Messages, "contains", anyResp, "response", response)
 	case <-ctx.Done():
-		t.Error("outer test context done while waiting for generate")
+		// On slow systems, we might timeout before some models finish rambling, so check what we have so far to see
+		// if it's considered a pass - the stallTimer will detect hangs, but we want to consider slow systems a pass
+		// if they are still generating valid responses
+		slog.Warn("outer test context done while waiting for chat")
+		verify()
 	}
 	return &api.Message{Role: role, Content: buf.String()}
 }

llm/server.go

@@ -173,6 +173,8 @@ func NewLlamaServer(gpus discover.GpuInfoList, modelPath string, f *ggml.GGML, a
 		opts.NumCtx = int(trainCtx)
 	}
 
+	opts.NumBatch = min(opts.NumBatch, opts.NumCtx)
+
 	loadRequest := LoadRequest{LoraPath: adapters, KvSize: opts.NumCtx * numParallel, BatchSize: opts.NumBatch, Parallel: numParallel, MultiUserCache: envconfig.MultiUserCache()}
 
 	defaultThreads := discover.GetSystemInfo().GetOptimalThreadCount()
@@ -1347,9 +1349,9 @@ type CompletionRequest struct {
 	Images  []ImageData
 	Options *api.Options
 
-	Grammar        string // set before sending the request to the subprocess
-	UseHarmony     bool
-	PrefillContent *bool
+	Grammar       string // set before sending the request to the subprocess
+	UseHarmony    bool
+	PrefillString string
 }
 
 // DoneReason represents the reason why a completion response is done
@@ -1362,6 +1364,8 @@ const (
 	DoneReasonLength
 	// DoneReasonConnectionClosed indicates the completion stopped due to the connection being closed
 	DoneReasonConnectionClosed
+	// DoneReasonTokenRepeatLimit indicates the completion stopped due to a token repeat limit
+	DoneReasonTokenRepeatLimit
 )
 
 func (d DoneReason) String() string {
@@ -1370,6 +1374,8 @@ func (d DoneReason) String() string {
 		return "length"
 	case DoneReasonStop:
 		return "stop"
+	case DoneReasonTokenRepeatLimit:
+		return "token_repeat_limit"
 	default:
 		return "" // closed
 	}
@@ -1502,7 +1508,8 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 				return fmt.Errorf("error unmarshalling llm prediction response: %v", err)
 			}
 			switch {
-			case lastToken != "" && (strings.TrimSpace(c.Content) == lastToken || strings.TrimSpace(c.Thinking) == lastToken):
+			// TODO(parthsareen): token repeat limit is now handled in the runner, this currently support legacy model and can be removed in the future
+			case strings.TrimSpace(c.Content) == lastToken && c.Content != "":
 				tokenRepeat++
 			default:
 				lastToken = strings.TrimSpace(c.Content)

model/bytepairencoding.go

@@ -201,12 +201,11 @@ func (bpe BytePairEncoding) Encode(s string, addSpecial bool) ([]int32, error) {
 		}
 	}
 
-	logutil.Trace("encoded", "string", s, "ids", ids)
-
 	if addSpecial && len(ids) > 0 {
 		ids = bpe.vocab.addSpecials(ids)
 	}
 
+	logutil.Trace("encoded", "string", s, "ids", ids)
 	return ids, nil
 }
 

model/model.go

@@ -5,6 +5,7 @@ import (
 	"fmt"
 	_ "image/jpeg"
 	_ "image/png"
+	"math"
 	"os"
 	"reflect"
 	"strconv"
@@ -103,6 +104,10 @@ func New(modelPath string, params ml.BackendParams) (Model, error) {
 	}
 
 	arch := b.Config().Architecture()
+	if b.Config().Uint("pooling_type", math.MaxUint32) != math.MaxUint32 {
+		arch = arch + "_embed"
+	}
+
 	f, ok := models[arch]
 	if !ok {
 		return nil, fmt.Errorf("unsupported model architecture %q", arch)

model/models/gemma3/embed.go

@@ -0,0 +1,73 @@
+package gemma3
+
+import (
+	"errors"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type embedModel struct {
+	model.Base
+	model.SentencePieceModel
+
+	*TextModel
+	PoolingType uint32
+
+	Dense [2]*nn.Linear `gguf:"dense"`
+}
+
+func (m *embedModel) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	batch.Outputs = batch.Positions // return all positions
+	hiddenStates := m.TextModel.Forward(ctx, batch, m.Cache)
+
+	switch m.PoolingType {
+	case 0: // None
+	case 1: // Mean
+		hiddenStates = hiddenStates.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx).Mean(ctx)
+		hiddenStates = hiddenStates.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
+	default:
+		return nil, errors.New("unsupported pooling type")
+	}
+
+	for _, dense := range m.Dense {
+		hiddenStates = dense.Forward(ctx, hiddenStates)
+	}
+
+	return hiddenStates, nil
+}
+
+func newEmbedModel(c fs.Config) (model.Model, error) {
+	m := &embedModel{
+		SentencePieceModel: model.NewSentencePieceModel(
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Scores: c.Floats("tokenizer.ggml.scores"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+				BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+				EOS: append(
+					[]int32{
+						int32(c.Uint("tokenizer.ggml.eos_token_id")),
+						int32(c.Uint("tokenizer.ggml.eot_token_id", 106)),
+					},
+					c.Ints("tokenizer.ggml.eos_token_ids")...,
+				),
+			},
+		),
+		TextModel:   newTextModel(c),
+		PoolingType: c.Uint("pooling_type", 0),
+	}
+
+	m.Cache = kvcache.NewWrapperCache(
+		kvcache.NewSWACache(int32(c.Uint("attention.sliding_window")), m.Shift),
+		kvcache.NewCausalCache(m.Shift),
+	)
+
+	return m, nil
+}

model/models/gemma3/model.go

@@ -141,12 +141,11 @@ func (m *Model) PostTokenize(inputs []*input.Input) ([]*input.Input, error) {
 }
 
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
-	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
-	outputs := ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs))
-
-	return m.TextModel.Forward(ctx, batch.Inputs, positions, outputs, batch, m.Cache), nil
+	hiddenStates := m.TextModel.Forward(ctx, batch, m.Cache)
+	return m.Output.Forward(ctx, hiddenStates), nil
 }
 
 func init() {
 	model.Register("gemma3", New)
+	model.Register("gemma3_embed", newEmbedModel)
 }

model/models/gemma3/model_text.go

@@ -159,8 +159,11 @@ func (l *TextLayer) Forward(ctx ml.Context, layer int, hiddenState, positionIDs,
 	return hiddenState.Add(ctx, residual)
 }
 
-func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor, batch input.Batch, cache kvcache.Cache) ml.Tensor {
-	hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
+func (m *TextModel) Forward(ctx ml.Context, batch input.Batch, cache kvcache.Cache) ml.Tensor {
+	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	outputs := ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs))
+
+	hiddenState := m.TokenEmbedding.Forward(ctx, batch.Inputs)
 	hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.TextConfig.hiddenSize)))
 
 	// set image embeddings
@@ -198,5 +201,5 @@ func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor
 	}
 
 	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
-	return m.Output.Forward(ctx, hiddenState)
+	return hiddenState
 }

model/models/models.go

@@ -12,4 +12,5 @@ import (
 	_ "github.com/ollama/ollama/model/models/qwen2"
 	_ "github.com/ollama/ollama/model/models/qwen25vl"
 	_ "github.com/ollama/ollama/model/models/qwen3"
+	_ "github.com/ollama/ollama/model/models/qwen3vl"
 )

model/models/qwen25vl/model.go

@@ -44,8 +44,8 @@ func New(c fs.Config) (model.Model, error) {
 			},
 		),
 		TextModel:      NewTextModel(c),
-		VisionModel:    newVisionModel(c),
-		ImageProcessor: newImageProcessor(c),
+		VisionModel:    NewVisionModel(c),
+		ImageProcessor: NewImageProcessor(c),
 	}
 
 	m.Cache = kvcache.NewCausalCache(m.TextModel.Shift)
@@ -65,8 +65,8 @@ func (m *Model) PixelValues(ctx ml.Context, multimodalData []byte) (ml.Tensor, *
 	}
 
 	// Calculate tensor dimensions
-	patchDim := m.ImageProcessor.numChannels * m.ImageProcessor.temporalPatchSize *
-		m.ImageProcessor.patchSize * m.ImageProcessor.patchSize
+	patchDim := m.ImageProcessor.NumChannels * m.ImageProcessor.TemporalPatchSize *
+		m.ImageProcessor.PatchSize * m.ImageProcessor.PatchSize
 	numPatches := grid.Temporal * grid.Height * grid.Width
 
 	pixelValues := ctx.Input().FromFloatSlice(f32s, patchDim, numPatches)

model/models/qwen25vl/model_vision.go

@@ -345,8 +345,8 @@ func (m *VisionModel) PositionalEmbedding(ctx ml.Context, grid *Grid) ml.Tensor
 	return positionalEmbedding
 }
 
-// newVisionModel creates a new instance of the Qwen vision model
-func newVisionModel(c fs.Config) *VisionModel {
+// NewVisionModel creates a new instance of the Qwen vision model
+func NewVisionModel(c fs.Config) *VisionModel {
 	patchSize := int(c.Uint("vision.patch_size", 14))
 	hiddenSize := int(c.Uint("vision.embedding_length", 1280))
 	numHeads := int(c.Uint("vision.attention.head_count", 16))

model/models/qwen25vl/process_image.go

@@ -11,40 +11,40 @@ import (
 
 // ImageProcessor contains configuration for the Qwen 2.5 VL image processing
 type ImageProcessor struct {
-	numChannels       int
-	patchSize         int
-	temporalPatchSize int
-	mergeSize         int
-	minPixels         int
-	maxPixels         int
-	factor            int
-	rescaleFactor     float32
-	imageMean         []float32
-	imageStd          []float32
+	NumChannels       int
+	PatchSize         int
+	TemporalPatchSize int
+	MergeSize         int
+	MinPixels         int
+	MaxPixels         int
+	Factor            int
+	RescaleFactor     float32
+	ImageMean         []float32
+	ImageStd          []float32
 }
 
 // newImageProcessor creates a new image processor with default values
-func newImageProcessor(c fs.Config) ImageProcessor {
+func NewImageProcessor(c fs.Config) ImageProcessor {
 	patchSize := int(c.Uint("vision.patch_size", 14))
 	mergeSize := int(c.Uint("vision.spatial_merge_size", 2))
 
 	return ImageProcessor{
-		numChannels:       int(c.Uint("vision.num_channels", 3)), // not set
-		patchSize:         patchSize,
-		temporalPatchSize: 2,
-		mergeSize:         mergeSize,
-		minPixels:         56 * 56,
-		maxPixels:         int(c.Uint("vision.max_pixels", 28*28*1280)), // 1MP limit
-		factor:            patchSize * mergeSize,
-		rescaleFactor:     1.0 / 255.0,
-		imageMean:         imageproc.ClipDefaultMean[:],
-		imageStd:          imageproc.ClipDefaultSTD[:],
+		NumChannels:       int(c.Uint("vision.num_channels", 3)), // not set
+		PatchSize:         patchSize,
+		TemporalPatchSize: 2,
+		MergeSize:         mergeSize,
+		MinPixels:         56 * 56,
+		MaxPixels:         int(c.Uint("vision.max_pixels", 28*28*1280)), // 1MP limit
+		Factor:            patchSize * mergeSize,
+		RescaleFactor:     1.0 / 255.0,
+		ImageMean:         imageproc.ClipDefaultMean[:],
+		ImageStd:          imageproc.ClipDefaultSTD[:],
 	}
 }
 
 // SmartResize implements the smart resize algorithm
 func (p *ImageProcessor) SmartResize(height, width int) (int, int) {
-	factor := p.factor
+	factor := p.Factor
 
 	if height < factor || width < factor {
 		panic(fmt.Sprintf("height:%d or width:%d must be larger than factor:%d", height, width, factor))
@@ -57,13 +57,13 @@ func (p *ImageProcessor) SmartResize(height, width int) (int, int) {
 	hBar := round(float64(height)/float64(factor)) * factor
 	wBar := round(float64(width)/float64(factor)) * factor
 
-	if hBar*wBar > p.maxPixels {
-		beta := math.Sqrt(float64(height*width) / float64(p.maxPixels))
+	if hBar*wBar > p.MaxPixels {
+		beta := math.Sqrt(float64(height*width) / float64(p.MaxPixels))
 
 		hBar = int(math.Floor(float64(height)/beta/float64(factor))) * factor
 		wBar = int(math.Floor(float64(width)/beta/float64(factor))) * factor
-	} else if hBar*wBar < p.minPixels {
-		beta := math.Sqrt(float64(p.minPixels) / float64(height*width))
+	} else if hBar*wBar < p.MinPixels {
+		beta := math.Sqrt(float64(p.MinPixels) / float64(height*width))
 
 		hBar = int(math.Ceil(float64(height)*beta/float64(factor))) * factor
 		wBar = int(math.Ceil(float64(width)*beta/float64(factor))) * factor
@@ -90,16 +90,16 @@ func (p *ImageProcessor) ProcessImage(img image.Image) ([]float32, *Grid, error)
 
 	normalizedPixels := imageproc.Normalize(
 		resizedImg,
-		[3]float32{p.imageMean[0], p.imageMean[1], p.imageMean[2]},
-		[3]float32{p.imageStd[0], p.imageStd[1], p.imageStd[2]},
+		[3]float32{p.ImageMean[0], p.ImageMean[1], p.ImageMean[2]},
+		[3]float32{p.ImageStd[0], p.ImageStd[1], p.ImageStd[2]},
 		true, // rescale
 		true, // channelFirst
 	)
 
 	// Calculate grid dimensions
 	grid := &Grid{
-		Height:   resizedHeight / p.patchSize,
-		Width:    resizedWidth / p.patchSize,
+		Height:   resizedHeight / p.PatchSize,
+		Width:    resizedWidth / p.PatchSize,
 		Temporal: 1, // For single images, temporal dimension is 1
 	}
 
@@ -113,10 +113,10 @@ func (p *ImageProcessor) ProcessImage(img image.Image) ([]float32, *Grid, error)
 }
 
 func (p *ImageProcessor) createPatches(pixels []float32, height, width int, grid *Grid) ([]float32, error) {
-	channels := p.numChannels
-	patchSize := p.patchSize
-	mergeSize := p.mergeSize
-	temporalPatchSize := p.temporalPatchSize
+	channels := p.NumChannels
+	patchSize := p.PatchSize
+	mergeSize := p.MergeSize
+	temporalPatchSize := p.TemporalPatchSize
 
 	// Calculate output dimensions
 	numPatches := grid.Temporal * grid.Height * grid.Width

model/models/qwen3vl/model.go

@@ -0,0 +1,153 @@
+package qwen3vl
+
+import (
+	"bytes"
+	"fmt"
+	"image"
+	"slices"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+	"github.com/ollama/ollama/model/models/qwen25vl"
+	"github.com/ollama/ollama/model/models/qwen3"
+)
+
+type Model struct {
+	model.Base
+	model.BytePairEncoding
+
+	TextModel *qwen3.Model
+	*qwen25vl.VisionModel
+
+	qwen25vl.ImageProcessor
+}
+
+var _ model.MultimodalProcessor = (*Model)(nil)
+
+func New(c fs.Config) (model.Model, error) {
+	textModel, err := qwen3.New(c)
+	if err != nil {
+		return nil, err
+	}
+
+	m := &Model{
+		BytePairEncoding: model.NewBytePairEncoding(
+			c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
+				Merges: c.Strings("tokenizer.ggml.merges"),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+				BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+				EOS: append(
+					[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
+					c.Ints("tokenizer.ggml.eos_token_ids")...,
+				),
+			},
+		),
+		TextModel:      textModel.(*qwen3.Model),
+		VisionModel:    qwen25vl.NewVisionModel(c),
+		ImageProcessor: qwen25vl.NewImageProcessor(c),
+	}
+
+	m.Cache = kvcache.NewCausalCache(m.TextModel.Shift)
+
+	return m, nil
+}
+
+func (m *Model) PixelValues(ctx ml.Context, multimodalData []byte) (ml.Tensor, *qwen25vl.Grid, error) {
+	image, _, err := image.Decode(bytes.NewReader(multimodalData))
+	if err != nil {
+		return nil, nil, err
+	}
+
+	f32s, grid, err := m.ImageProcessor.ProcessImage(image)
+	if err != nil {
+		return nil, nil, err
+	}
+
+	// Calculate tensor dimensions
+	patchDim := m.ImageProcessor.NumChannels * m.ImageProcessor.TemporalPatchSize *
+		m.ImageProcessor.PatchSize * m.ImageProcessor.PatchSize
+	numPatches := grid.Temporal * grid.Height * grid.Width
+
+	pixelValues := ctx.Input().FromFloatSlice(f32s, patchDim, numPatches)
+
+	return pixelValues, grid, nil
+}
+
+func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) ([]input.Multimodal, error) {
+	if len(m.VisionModel.Layers) == 0 {
+		return nil, model.ErrNoVisionModel
+	}
+
+	pixels, grid, err := m.PixelValues(ctx, multimodalData)
+	if err != nil {
+		return nil, err
+	}
+
+	visionOutputs := m.VisionModel.Forward(ctx, pixels, grid)
+	return []input.Multimodal{{Tensor: visionOutputs}}, nil
+}
+
+// PostTokenize arranges Qwen-3-VL's inputs for the forward pass
+func (m *Model) PostTokenize(inputs []*input.Input) ([]*input.Input, error) {
+	var result []*input.Input
+
+	var (
+		imageToken       int32 = 151655
+		visionStartToken int32 = 151652
+		visionEndToken   int32 = 151653
+	)
+
+	nImg := 0
+	for _, inp := range inputs {
+		if inp.Multimodal == nil {
+			// If not a multimodal input, add it to the result unchanged
+			result = append(result, inp)
+		} else {
+			// Adding the 'Picture' prefix is a hack, at the time of writing there is no way to prefix
+			// the image tokens with a prompt, so we add a prefix here
+			nImg++
+			pre, err := m.Encode(fmt.Sprintf(" Picture %d: ", nImg), true)
+			if err != nil {
+				return nil, fmt.Errorf("failed to encode image prompt: %w", err)
+			}
+			for i := range pre {
+				result = append(result, &input.Input{Token: pre[i]})
+			}
+
+			patchesPerChunk := inp.Multimodal[0].Tensor.Dim(1)
+
+			// First add the vision start token
+			result = append(result, &input.Input{Token: visionStartToken})
+
+			// Add the image token with the multimodal tensor data at the first position
+			result = append(result, &input.Input{
+				Token:          imageToken,
+				Multimodal:     inp.Multimodal,
+				MultimodalHash: inp.MultimodalHash,
+				SameBatch:      patchesPerChunk,
+			})
+
+			// Add the placeholder tokens for the remaining positions (tokensPerGrid-1)
+			result = append(result, slices.Repeat([]*input.Input{{Token: imageToken}}, patchesPerChunk-1)...)
+
+			result = append(result, &input.Input{Token: visionEndToken})
+		}
+	}
+
+	return result, nil
+}
+
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	return m.TextModel.Forward(ctx, batch)
+}
+
+func init() {
+	model.Register("qwen3vl", New)
+}

model/sentencepiece.go

@@ -181,12 +181,11 @@ func (spm SentencePieceModel) Encode(s string, addSpecial bool) ([]int32, error)
 		}
 	}
 
-	logutil.Trace("encoded", "string", s, "ids", ids)
-
 	if addSpecial && len(ids) > 0 {
 		ids = spm.vocab.addSpecials(ids)
 	}
 
+	logutil.Trace("encoded", "string", s, "ids", ids)
 	return ids, nil
 }
 

model/vocabulary.go

@@ -49,7 +49,7 @@ func (v *Vocabulary) addSpecials(ids []int32) []int32 {
 			slog.Warn("adding bos token to prompt which already has it", "id", v.BOS)
 		}
 
-		slog.Debug("adding bos token to prompt", "id", v.BOS)
+		slog.Debug("adding bos token to prompt", "id", v.BOS[0])
 		ids = append([]int32{v.BOS[0]}, ids...)
 	}
 
@@ -58,7 +58,7 @@ func (v *Vocabulary) addSpecials(ids []int32) []int32 {
 			slog.Warn("adding eos token to prompt which already has it", "id", v.EOS)
 		}
 
-		slog.Debug("adding eos token to prompt", "id", v.EOS)
+		slog.Debug("adding eos token to prompt", "id", v.EOS[0])
 		ids = append(ids, v.EOS[0])
 	}
 

parser/parser.go

@@ -246,7 +246,7 @@ func filesForModel(path string) ([]string, error) {
 		for _, match := range matches {
 			if ct, err := detectContentType(match); err != nil {
 				return nil, err
-			} else if ct != contentType {
+			} else if len(contentType) > 0 && ct != contentType {
 				return nil, fmt.Errorf("invalid content type: expected %s for %s", ct, match)
 			}
 		}
@@ -255,7 +255,8 @@ func filesForModel(path string) ([]string, error) {
 	}
 
 	var files []string
-	if st, _ := glob(filepath.Join(path, "*.safetensors"), "application/octet-stream"); len(st) > 0 {
+	// some safetensors files do not properly match "application/octet-stream", so skip checking their contentType
+	if st, _ := glob(filepath.Join(path, "*.safetensors"), ""); 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...)

runner/ollamarunner/cache.go

@@ -34,8 +34,8 @@ type InputCache struct {
 func NewInputCache(model model.Model, kvCacheType string, kvSize int32, numSlots int, batchSize int, multiUserCache bool) (*InputCache, error) {
 	numCtx := kvSize / int32(numSlots)
 
-	if numCtx < 1 {
-		return nil, fmt.Errorf("must have at least one kv cache entry per parallel sequence (kv: %v parallel: %v)", kvSize, numSlots)
+	if int(numCtx) < batchSize {
+		return nil, fmt.Errorf("kv size must be at least as large as batch size * parallel (kv: %v batch: %v parallel: %v)", kvSize, batchSize, numSlots)
 	}
 
 	slots := make([]InputCacheSlot, numSlots)
@@ -70,11 +70,9 @@ func kvCacheTypeFromStr(s string) ml.DType {
 }
 
 func (c *InputCache) Close() {
-	if c == nil {
-		return
+	if c != nil && c.cache != nil {
+		c.cache.Close()
 	}
-
-	c.cache.Close()
 }
 
 // Locking: Operations on InputCacheSlot (including finding one
@@ -95,7 +93,7 @@ type InputCacheSlot struct {
 	lastUsed time.Time
 }
 
-func (c *InputCache) LoadCacheSlot(prompt []*input.Input) (*InputCacheSlot, []*input.Input, error) {
+func (c *InputCache) LoadCacheSlot(prompt []*input.Input, cachePrompt bool) (*InputCacheSlot, []*input.Input, error) {
 	var slot *InputCacheSlot
 	var numPast int32
 	var err error
@@ -113,6 +111,10 @@ func (c *InputCache) LoadCacheSlot(prompt []*input.Input) (*InputCacheSlot, []*i
 		return nil, nil, err
 	}
 
+	if !cachePrompt {
+		numPast = 0
+	}
+
 	slot.InUse = true
 	slot.lastUsed = time.Now()
 

runner/ollamarunner/cache_test.go

@@ -393,7 +393,7 @@ func TestLoadCacheSlot(t *testing.T) {
 
 	for _, tt := range tests {
 		t.Run(tt.name, func(t *testing.T) {
-			slot, remainingPrompt, err := tt.cache.LoadCacheSlot(tt.prompt)
+			slot, remainingPrompt, err := tt.cache.LoadCacheSlot(tt.prompt, true)
 
 			// Check error state
 			if (err != nil) != tt.wantErr {

runner/ollamarunner/runner.go

@@ -11,6 +11,7 @@ import (
 	"image"
 	"log"
 	"log/slog"
+	"math"
 	"net"
 	"net/http"
 	"os"
@@ -406,6 +407,8 @@ func (s *Server) removeSequence(seqIndex int, reason llm.DoneReason) {
 func (s *Server) run(ctx context.Context) {
 	s.ready.Wait()
 
+	supportsAsync := s.model.Backend().Config().Uint("pooling_type", math.MaxUint32) == math.MaxUint32
+
 	var activeBatch batchState
 	for {
 		select {
@@ -419,7 +422,12 @@ func (s *Server) run(ctx context.Context) {
 			if err != nil {
 				panic(err)
 			}
-			go s.computeBatch(activeBatch)
+
+			if supportsAsync {
+				go s.computeBatch(activeBatch)
+			} else {
+				s.computeBatch(activeBatch)
+			}
 		}
 	}
 }
@@ -430,12 +438,12 @@ func (s *Server) forwardBatch(pendingBatch batchState) (nextBatch batchState, er
 	// before setting up the next batch so the seqs inputs are ready to receive their
 	// token values and we get the correct input pointers for the batchInputs
 	if pendingBatch.ctx != nil {
-		slog.Log(context.TODO(), logutil.LevelTrace, "forwardBatch waiting for compute to start", "pendingBatch.id", pendingBatch.id)
+		logutil.Trace("forwardBatch waiting for compute to start", "pendingBatch.id", pendingBatch.id)
 		<-pendingBatch.computeStartedCh
-		slog.Log(context.TODO(), logutil.LevelTrace, "forwardBatch compute started, setting up next batch", "pendingBatch.id", pendingBatch.id, "id", s.batchID)
+		logutil.Trace("forwardBatch compute started, setting up next batch", "pendingBatch.id", pendingBatch.id, "id", s.batchID)
 		nextBatch.inputsReadyCh = pendingBatch.outputsReadyCh // Chain the ouputs from the pending batch to the next inputs batch
 	} else {
-		slog.Log(context.TODO(), logutil.LevelTrace, "forwardBatch no pending batch detected", "batchID", s.batchID)
+		logutil.Trace("forwardBatch no pending batch detected", "batchID", s.batchID)
 		// No pendingBatch, so the inputs will be ready in the seqs immediately
 		nextBatch.inputsReadyCh = make(chan struct{}, 1)
 		nextBatch.inputsReadyCh <- struct{}{}
@@ -547,7 +555,7 @@ func (s *Server) forwardBatch(pendingBatch batchState) (nextBatch batchState, er
 			if i+1 == len(seq.inputs) {
 				batch.Outputs = append(batch.Outputs, int32(len(batchInputs)-1))
 			}
-			slog.Log(context.TODO(), logutil.LevelTrace, "forwardBatch iBatch", "batchID", s.batchID, "seqIdx", seqIdx, "seq.iBatch", seq.iBatch, "i+1", i+1, "len(seq.inputs)", len(seq.inputs))
+			logutil.Trace("forwardBatch iBatch", "batchID", s.batchID, "seqIdx", seqIdx, "seq.iBatch", seq.iBatch, "i+1", i+1, "len(seq.inputs)", len(seq.inputs))
 			seq.pendingInputs = append(seq.pendingInputs, inp)
 		}
 
@@ -561,7 +569,7 @@ func (s *Server) forwardBatch(pendingBatch batchState) (nextBatch batchState, er
 	}
 
 	if len(batchInputs) == 0 {
-		slog.Log(context.TODO(), logutil.LevelTrace, "forwardBatch no batchInputs, going idle", "batchID", s.batchID)
+		logutil.Trace("forwardBatch no batchInputs, going idle", "batchID", s.batchID)
 		nextBatch.ctx.Close()
 		nextBatch.ctx = nil
 		return
@@ -590,14 +598,14 @@ func (s *Server) computeBatch(activeBatch batchState) {
 	defer activeBatch.ctx.Close()
 
 	// Wait until inputs are ready
-	slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: waiting for inputs to be ready", "batchID", activeBatch.id)
+	logutil.Trace("computeBatch: waiting for inputs to be ready", "batchID", activeBatch.id)
 	<-activeBatch.inputsReadyCh
-	slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: inputs are ready", "batchID", activeBatch.id)
+	logutil.Trace("computeBatch: inputs are ready", "batchID", activeBatch.id)
 
 	// Once we complete, signal the next batch of inputs are ready
 	// This will unblock the next computeBatch, or forwardBatch if new seqs come in
 	defer func() {
-		slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: outputs are ready", "batchID", activeBatch.id)
+		logutil.Trace("computeBatch: outputs are ready", "batchID", activeBatch.id)
 		activeBatch.outputsReadyCh <- struct{}{}
 	}()
 
@@ -627,7 +635,7 @@ func (s *Server) computeBatch(activeBatch batchState) {
 		// Detect if the sequence we're processing has already been completed and replaced
 		// with a new sequence
 		if seq != activeBatch.seqs[i] {
-			slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: sequence replaced, discarding its results", "batchID", activeBatch.id, "seqIdx", i)
+			logutil.Trace("computeBatch: sequence replaced, discarding its results", "batchID", activeBatch.id, "seqIdx", i)
 			continue
 		}
 
@@ -667,18 +675,19 @@ func (s *Server) computeBatch(activeBatch batchState) {
 	activeBatch.batch.Inputs.SetValueFromIntSlice(batchInputs)
 	activeBatch.ctx.ComputeWithNotify(
 		func() {
-			slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: signaling computeStartedCh", "batchID", activeBatch.id)
+			logutil.Trace("computeBatch: signaling computeStartedCh", "batchID", activeBatch.id)
 			activeBatch.computeStartedCh <- struct{}{}
 		},
 		activeBatch.modelOutput)
-	logits := activeBatch.modelOutput.Floats()
 
-	slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: logits ready", "batchID", activeBatch.id)
+	outputs := activeBatch.modelOutput.Floats()
+
+	logutil.Trace("computeBatch: logits ready", "batchID", activeBatch.id)
 
 	s.mu.Lock()
 	defer s.mu.Unlock()
 
-	slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: decoding", "batchID", activeBatch.id)
+	logutil.Trace("computeBatch: decoding", "batchID", activeBatch.id)
 	for i, seq := range s.seqs {
 		if seq == nil || nextBatchTokens[i] == nil {
 			continue
@@ -690,16 +699,15 @@ func (s *Server) computeBatch(activeBatch batchState) {
 
 		// if done processing the prompt, generate an embedding and return
 		if seq.embeddingOnly {
-			// TODO(jessegross): Embedding support
-			slog.Warn("generation of embedding outputs not yet supported", "id", activeBatch.id, "seqIdx", i)
+			seq.embedding <- outputs
 			s.removeSequence(i, llm.DoneReasonStop)
 			continue
 		}
 
 		// sample a token
-		vocabSize := len(logits) / len(activeBatch.batch.Outputs)
-		slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: vocab details", "batchID", activeBatch.id, "seqIdx", i, "len(logits)", len(logits), "len(activeBatch.batch.Outputs)", len(activeBatch.batch.Outputs), "vocabSize", vocabSize, "iBatches", iBatches)
-		token, err := seq.sampler.Sample(logits[iBatches[i]*vocabSize : (iBatches[i]+1)*vocabSize])
+		vocabSize := len(outputs) / len(activeBatch.batch.Outputs)
+		logutil.Trace("computeBatch: vocab details", "batchID", activeBatch.id, "seqIdx", i, "len(logits)", len(outputs), "len(activeBatch.batch.Outputs)", len(activeBatch.batch.Outputs), "vocabSize", vocabSize, "iBatches", iBatches)
+		token, err := seq.sampler.Sample(outputs[iBatches[i]*vocabSize : (iBatches[i]+1)*vocabSize])
 		if err != nil {
 			s.hardErrCh <- fmt.Errorf("failed to sample token: %w", err)
 			return
@@ -712,7 +720,7 @@ func (s *Server) computeBatch(activeBatch batchState) {
 			// TODO (jmorganca): we should send this back
 			// as it's important for the /api/generate context
 			// seq.responses <- piece
-			slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: EOS", "batchID", activeBatch.id, "seqIdx", i)
+			logutil.Trace("computeBatch: EOS", "batchID", activeBatch.id, "seqIdx", i)
 			s.removeSequence(i, llm.DoneReasonStop)
 			continue
 		}
@@ -778,7 +786,7 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 	var harmonyToolParser *harmony.HarmonyToolCallAccumulator
 	if req.UseHarmony {
 		harmonyMessageHandler = harmony.NewHarmonyMessageHandler()
-		harmonyMessageHandler.HarmonyParser.AddImplicitStartOrPrefill(req.PrefillContent)
+		harmonyMessageHandler.HarmonyParser.AddImplicitStartOrPrefill(req.PrefillString)
 		harmonyToolParser = harmonyMessageHandler.CreateToolParser()
 	}
 
@@ -814,7 +822,7 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 		req.Options.TopP,
 		req.Options.MinP,
 		req.Options.Seed,
-		nil,
+		grammar,
 	)
 
 	seq, err := s.NewSequence(req.Prompt, req.Images, NewSequenceParams{
@@ -843,7 +851,7 @@ 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)
+			seq.cache, seq.inputs, err = s.cache.LoadCacheSlot(seq.inputs, true)
 			if err != nil {
 				s.mu.Unlock()
 				s.seqsSem.Release(1)
@@ -864,13 +872,10 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 		http.Error(w, "could not find an available sequence", http.StatusInternalServerError)
 		return
 	}
+	var lastToken string
+	tokenRepeat := 0
+	const tokenRepeatLimit = 30
 
-	// TODO(parthsareen): generalize grammar enablement on the fly for all thinking models
-	if harmonyMessageHandler == nil {
-		seq.sampler.SetGrammar(grammar)
-	}
-
-	grammarSet := false
 	for {
 		select {
 		case <-r.Context().Done():
@@ -878,15 +883,22 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 			return
 		case content, ok := <-seq.responses:
 			if ok {
+				if strings.TrimSpace(content) == lastToken {
+					tokenRepeat++
+				}
+				if tokenRepeat == tokenRepeatLimit {
+					http.Error(w, "token repeat limit reached", http.StatusInternalServerError)
+					seq.doneReason = llm.DoneReasonTokenRepeatLimit
+					close(seq.quit)
+					return
+				}
+				lastToken = strings.TrimSpace(content)
+
 				var thinking string
 				if harmonyMessageHandler != nil {
 					var toolContent string
 					content, thinking, toolContent = harmonyMessageHandler.AddContent(content, harmonyToolParser)
 					harmonyToolParser.Add(toolContent)
-					if harmonyMessageHandler.HarmonyParser.ConstrainAllowed && !grammarSet {
-						seq.sampler.SetGrammar(grammar)
-						grammarSet = true
-					}
 				}
 
 				if err := json.NewEncoder(w).Encode(&llm.CompletionResponse{
@@ -939,6 +951,67 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 	}
 }
 
+func (s *Server) embeddings(w http.ResponseWriter, r *http.Request) {
+	if s.model.Backend().Config().Uint("pooling_type", math.MaxUint32) == math.MaxUint32 {
+		http.Error(w, "this model does not support embeddings", http.StatusNotImplemented)
+		return
+	}
+
+	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
+	}
+
+	w.Header().Set("Content-Type", "application/json")
+	seq, err := s.NewSequence(req.Content, nil, NewSequenceParams{embedding: true})
+	if err != nil {
+		http.Error(w, fmt.Sprintf("failed to create new sequence: %v", err), http.StatusInternalServerError)
+		return
+	}
+
+	if err := s.seqsSem.Acquire(r.Context(), 1); err != nil {
+		if errors.Is(err, context.Canceled) {
+			slog.Info("aborting embedding request due to client closing the connection")
+		} else {
+			http.Error(w, fmt.Sprintf("failed to acquire semaphore: %v", err), http.StatusInternalServerError)
+		}
+		return
+	}
+
+	s.mu.Lock()
+	found := false
+	for i, sq := range s.seqs {
+		if sq == nil {
+			seq.cache, seq.inputs, err = s.cache.LoadCacheSlot(seq.inputs, false)
+			if err != nil {
+				s.mu.Unlock()
+				s.seqsSem.Release(1)
+				http.Error(w, fmt.Sprintf("failed to load cache: %v", err), http.StatusInternalServerError)
+				return
+			}
+
+			s.seqs[i] = seq
+			s.cond.Signal()
+			found = true
+			break
+		}
+	}
+	s.mu.Unlock()
+
+	if !found {
+		s.seqsSem.Release(1)
+		http.Error(w, "could not find an available sequence", http.StatusInternalServerError)
+		return
+	}
+
+	if err := json.NewEncoder(w).Encode(&llm.EmbeddingResponse{
+		Embedding: <-seq.embedding,
+	}); err != nil {
+		http.Error(w, fmt.Sprintf("failed to encode response: %v", err), http.StatusInternalServerError)
+	}
+}
+
 func (s *Server) health(w http.ResponseWriter, r *http.Request) {
 	w.Header().Set("Content-Type", "application/json")
 	if err := json.NewEncoder(w).Encode(&llm.ServerStatusResponse{
@@ -1255,10 +1328,7 @@ func Execute(args []string) error {
 	mux := http.NewServeMux()
 	// TODO: support embeddings
 	mux.HandleFunc("POST /load", server.load)
-	mux.HandleFunc("POST /embedding", func(w http.ResponseWriter, r *http.Request) {
-		http.Error(w, "this model does not support embeddings", http.StatusNotImplemented)
-	})
-
+	mux.HandleFunc("POST /embedding", server.embeddings)
 	mux.HandleFunc("POST /completion", server.completion)
 	mux.HandleFunc("GET /health", server.health)
 

sample/samplers.go

@@ -25,10 +25,6 @@ type Sampler struct {
 	grammar     *GrammarSampler
 }
 
-func (s *Sampler) SetGrammar(grammar *GrammarSampler) {
-	s.grammar = grammar
-}
-
 func (s *Sampler) Sample(logits []float32) (int32, error) {
 	if len(logits) == 0 {
 		return -1, errors.New("sample: no logits provided to sample")

server/routes.go

@@ -1595,26 +1595,11 @@ func (s *Server) ChatHandler(c *gin.Context) {
 
 	processedTools := req.Tools
 	var functionNameMap *harmony.FunctionNameMap
-	var prefillContentOrThinking *bool
+	var prefillString string
+	// TODO(parthsareen): this can be abstracted to not be model specific and potentially moved to the runner
 	if useHarmony {
+		prefillString = harmony.Prefill(msgs[len(msgs)-1])
 		functionNameMap = harmony.NewFunctionNameMap()
-		var lastMessage *api.Message
-		if len(msgs) > 0 {
-			lastMessage = &msgs[len(msgs)-1]
-		}
-
-		// prefill content or thinking flag if the last message is an assistant message
-		if lastMessage != nil && lastMessage.Role == "assistant" {
-			if lastMessage.Content != "" {
-				trueVal := true
-				// true sets content to be prefilled
-				prefillContentOrThinking = &trueVal
-			} else if lastMessage.Thinking != "" {
-				// false sets thinking to be prefilled
-				falseVal := false
-				prefillContentOrThinking = &falseVal
-			}
-		}
 		// make a copy of tools to pass to the chat prompt. Function names may be
 		// renamed to be valid Harmony function names.
 		processedTools = make([]api.Tool, len(req.Tools))
@@ -1673,12 +1658,12 @@ func (s *Server) ChatHandler(c *gin.Context) {
 		defer close(ch)
 
 		if err := r.Completion(c.Request.Context(), llm.CompletionRequest{
-			Prompt:         prompt,
-			Images:         images,
-			Format:         req.Format,
-			Options:        opts,
-			UseHarmony:     useHarmony,
-			PrefillContent: prefillContentOrThinking,
+			Prompt:        prompt,
+			Images:        images,
+			Format:        req.Format,
+			Options:       opts,
+			UseHarmony:    useHarmony,
+			PrefillString: prefillString,
 		}, func(r llm.CompletionResponse) {
 			res := api.ChatResponse{
 				Model:     req.Model,
@@ -1699,10 +1684,10 @@ func (s *Server) ChatHandler(c *gin.Context) {
 			}
 
 			if useHarmony {
-				// only send messages with meaningful content (empty messages confuse clients)
 				for i, tool := range res.Message.ToolCalls {
 					res.Message.ToolCalls[i].Function.Name = functionNameMap.OriginalFromConverted(tool.Function.Name)
 				}
+				// only send messages with meaningful content (empty messages confuse clients)
 				if res.Message.Content != "" || res.Message.Thinking != "" || len(res.Message.ToolCalls) > 0 || res.Done {
 					ch <- res
 				}