parser: tidy up parameter/message parsing

This change addresses how parameters and messages are handled while parsing a Modelfile.
Currently a `MESSAGE` command creates a string that looks like "<role>: <message>" which
then has to be re-parsed, and `PARAMETER` ends up being the default data type which makes
it difficult to add other multi-part commands.

This change introduces a Message and a Parameter type which properly handle properties
such as the role and the name of the parameter.

.github/workflows/release.yaml

@@ -65,14 +65,36 @@ jobs:
             arch: amd64
             preset: 'CUDA 12'
             install: https://developer.download.nvidia.com/compute/cuda/12.8.0/local_installers/cuda_12.8.0_571.96_windows.exe
+            cuda-components:
+              - '"cudart"'
+              - '"nvcc"'
+              - '"cublas"'
+              - '"cublas_dev"'
             cuda-version: '12.8'
             flags: ''
+            runner_dir: 'cuda_v12'
+          - os: windows
+            arch: amd64
+            preset: 'CUDA 13'
+            install: https://developer.download.nvidia.com/compute/cuda/13.0.0/local_installers/cuda_13.0.0_windows.exe
+            cuda-components:
+              - '"cudart"'
+              - '"nvcc"'
+              - '"cublas"'
+              - '"cublas_dev"'
+              - '"crt"'
+              - '"nvvm"'
+              - '"nvptxcompiler"'
+            cuda-version: '13.0'
+            flags: ''
+            runner_dir: 'cuda_v13'
           - os: windows
             arch: amd64
             preset: 'ROCm 6'
             install: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q4-WinSvr2022-For-HIP.exe
             rocm-version: '6.2'
             flags: '-DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_C_FLAGS="-parallel-jobs=4 -Wno-ignored-attributes -Wno-deprecated-pragma" -DCMAKE_CXX_FLAGS="-parallel-jobs=4 -Wno-ignored-attributes -Wno-deprecated-pragma"'
+            runner_dir: ''
     runs-on: ${{ matrix.arch == 'arm64' && format('{0}-{1}', matrix.os, matrix.arch) || matrix.os }}
     environment: release
     env:
@@ -96,7 +118,7 @@ jobs:
           $ErrorActionPreference = "Stop"
           if ("${{ steps.cache-install.outputs.cache-hit }}" -ne 'true') {
             Invoke-WebRequest -Uri "${{ matrix.install }}" -OutFile "install.exe"
-            $subpackages = @("cudart", "nvcc", "cublas", "cublas_dev") | Foreach-Object {"${_}_${{ matrix.cuda-version }}"}
+            $subpackages = @(${{ join(matrix.cuda-components, ', ') }}) | Foreach-Object {"${_}_${{ matrix.cuda-version }}"}
             Start-Process -FilePath .\install.exe -ArgumentList (@("-s") + $subpackages) -NoNewWindow -Wait
           }
 
@@ -138,7 +160,7 @@ jobs:
         run: |
           Import-Module 'C:\Program Files\Microsoft Visual Studio\2022\Enterprise\Common7\Tools\Microsoft.VisualStudio.DevShell.dll'
           Enter-VsDevShell -VsInstallPath 'C:\Program Files\Microsoft Visual Studio\2022\Enterprise' -SkipAutomaticLocation  -DevCmdArguments '-arch=x64 -no_logo'
-          cmake --preset "${{ matrix.preset }}" ${{ matrix.flags }}
+          cmake --preset "${{ matrix.preset }}" ${{ matrix.flags }} -DOLLAMA_RUNNER_DIR="${{ matrix.runner_dir }}"
           cmake --build --parallel --preset "${{ matrix.preset }}"
           cmake --install build --component "${{ startsWith(matrix.preset, 'CUDA ') && 'CUDA' || startsWith(matrix.preset, 'ROCm ') && 'HIP' || 'CPU' }}" --strip --parallel 8
         env:
@@ -232,7 +254,7 @@ jobs:
             case "$COMPONENT" in
               bin/ollama)                echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
               lib/ollama/*.so*)          echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
-              lib/ollama/cuda_sbsa)      echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
+              lib/ollama/cuda_v*)        echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
               lib/ollama/cuda_jetpack5)  echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}-jetpack5.tar.in ;;
               lib/ollama/cuda_jetpack6)  echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}-jetpack6.tar.in ;;
               lib/ollama/rocm)           echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}-rocm.tar.in ;;

.github/workflows/test.yaml

@@ -46,7 +46,7 @@ jobs:
         include:
           - preset: CPU
           - preset: CUDA
-            container: nvidia/cuda:12.8.1-devel-ubuntu22.04
+            container: nvidia/cuda:13.0.0-devel-ubuntu22.04
             flags: '-DCMAKE_CUDA_ARCHITECTURES=87'
           - preset: ROCm
             container: rocm/dev-ubuntu-22.04:6.1.2
@@ -78,8 +78,17 @@ jobs:
         include:
           - preset: CPU
           - preset: CUDA
-            install: https://developer.download.nvidia.com/compute/cuda/12.8.0/local_installers/cuda_12.8.0_571.96_windows.exe
+            install: https://developer.download.nvidia.com/compute/cuda/13.0.0/local_installers/cuda_13.0.0_windows.exe
             flags: '-DCMAKE_CUDA_ARCHITECTURES=80'
+            cuda-components:
+              - '"cudart"'
+              - '"nvcc"'
+              - '"cublas"'
+              - '"cublas_dev"'
+              - '"crt"'
+              - '"nvvm"'
+              - '"nvptxcompiler"'
+            cuda-version: '13.0'
           - preset: ROCm
             install: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q4-WinSvr2022-For-HIP.exe
             flags: '-DAMDGPU_TARGETS=gfx1010 -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_C_FLAGS="-parallel-jobs=4 -Wno-ignored-attributes -Wno-deprecated-pragma" -DCMAKE_CXX_FLAGS="-parallel-jobs=4 -Wno-ignored-attributes -Wno-deprecated-pragma"'
@@ -102,7 +111,8 @@ jobs:
           $ErrorActionPreference = "Stop"
           if ("${{ steps.cache-install.outputs.cache-hit }}" -ne 'true') {
             Invoke-WebRequest -Uri "${{ matrix.install }}" -OutFile "install.exe"
-            Start-Process -FilePath .\install.exe -ArgumentList (@("-s", "cudart_12.8", "nvcc_12.8", "cublas_12.8", "cublas_dev_12.8")) -NoNewWindow -Wait
+            $subpackages = @(${{ join(matrix.cuda-components, ', ') }}) | Foreach-Object {"${_}_${{ matrix.cuda-version }}"}
+            Start-Process -FilePath .\install.exe -ArgumentList (@("-s") + $subpackages) -NoNewWindow -Wait
           }
 
           $cudaPath = (Resolve-Path "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\*").path

CMakeLists.txt

@@ -38,7 +38,7 @@ if (CMAKE_OSX_ARCHITECTURES MATCHES "x86_64")
 endif()
 
 set(OLLAMA_BUILD_DIR ${CMAKE_BINARY_DIR}/lib/ollama)
-set(OLLAMA_INSTALL_DIR ${CMAKE_INSTALL_PREFIX}/lib/ollama)
+set(OLLAMA_INSTALL_DIR ${CMAKE_INSTALL_PREFIX}/lib/ollama/${OLLAMA_RUNNER_DIR})
 
 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY         ${OLLAMA_BUILD_DIR})
 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY_DEBUG   ${OLLAMA_BUILD_DIR})
@@ -81,7 +81,7 @@ if(CMAKE_CUDA_COMPILER)
     add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-cuda)
     install(TARGETS ggml-cuda
         RUNTIME_DEPENDENCIES
-            DIRECTORIES ${CUDAToolkit_BIN_DIR} ${CUDAToolkit_LIBRARY_DIR}
+            DIRECTORIES ${CUDAToolkit_BIN_DIR} ${CUDAToolkit_BIN_DIR}/x64 ${CUDAToolkit_LIBRARY_DIR}
             PRE_INCLUDE_REGEXES cublas cublasLt cudart
             PRE_EXCLUDE_REGEXES ".*"
         RUNTIME DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT CUDA

CMakePresets.json

@@ -18,6 +18,14 @@
       "name": "CUDA",
       "inherits": [ "Default" ]
     },
+    {
+      "name": "CUDA 11",
+      "inherits": [ "CUDA" ],
+      "cacheVariables": {
+        "CMAKE_CUDA_ARCHITECTURES": "50-virtual;60-virtual;61-virtual;70-virtual;75-virtual;80-virtual;86-virtual;87-virtual;89-virtual;90-virtual",
+        "CMAKE_CUDA_FLAGS": "-Wno-deprecated-gpu-targets -t 2"
+      }
+    },
     {
       "name": "CUDA 12",
       "inherits": [ "CUDA" ],
@@ -26,6 +34,14 @@
         "CMAKE_CUDA_FLAGS": "-Wno-deprecated-gpu-targets -t 2"
       }
     },
+    {
+      "name": "CUDA 13",
+      "inherits": [ "CUDA" ],
+      "cacheVariables": {
+        "CMAKE_CUDA_ARCHITECTURES": "75-virtual;80-virtual;86-virtual;87-virtual;89-virtual;90-virtual;90a-virtual;100-virtual;110-virtual;120-virtual;121-virtual",
+        "CMAKE_CUDA_FLAGS": "-t 2"
+      }
+    },
     {
       "name": "JetPack 5",
       "inherits": [ "CUDA" ],
@@ -72,11 +88,21 @@
       "configurePreset": "CUDA",
       "targets": [ "ggml-cuda" ]
     },
+    {
+      "name": "CUDA 11",
+      "inherits": [ "CUDA" ],
+      "configurePreset": "CUDA 11"
+    },
     {
       "name": "CUDA 12",
       "inherits": [ "CUDA" ],
       "configurePreset": "CUDA 12"
     },
+    {
+      "name": "CUDA 13",
+      "inherits": [ "CUDA" ],
+      "configurePreset": "CUDA 13"
+    },
     {
       "name": "JetPack 5",
       "inherits": [ "CUDA" ],

Dockerfile

@@ -39,15 +39,35 @@ RUN --mount=type=cache,target=/root/.ccache \
         && cmake --build --parallel --preset 'CPU' \
         && cmake --install build --component CPU --strip --parallel 8
 
+FROM base AS cuda-11
+ARG CUDA11VERSION=11.8
+RUN dnf install -y cuda-toolkit-${CUDA11VERSION//./-}
+ENV PATH=/usr/local/cuda-11/bin:$PATH
+RUN --mount=type=cache,target=/root/.ccache \
+    cmake --preset 'CUDA 11' -DOLLAMA_RUNNER_DIR="cuda_v11" \
+        && cmake --build --parallel --preset 'CUDA 11' \
+        && cmake --install build --component CUDA --strip --parallel 8
+
 FROM base AS cuda-12
 ARG CUDA12VERSION=12.8
 RUN dnf install -y cuda-toolkit-${CUDA12VERSION//./-}
 ENV PATH=/usr/local/cuda-12/bin:$PATH
 RUN --mount=type=cache,target=/root/.ccache \
-    cmake --preset 'CUDA 12' \
+    cmake --preset 'CUDA 12' -DOLLAMA_RUNNER_DIR="cuda_v12"\
         && cmake --build --parallel --preset 'CUDA 12' \
         && cmake --install build --component CUDA --strip --parallel 8
 
+
+FROM base AS cuda-13
+ARG CUDA13VERSION=13.0
+RUN dnf install -y cuda-toolkit-${CUDA13VERSION//./-}
+ENV PATH=/usr/local/cuda-13/bin:$PATH
+RUN --mount=type=cache,target=/root/.ccache \
+    cmake --preset 'CUDA 13' -DOLLAMA_RUNNER_DIR="cuda_v13" \
+        && cmake --build --parallel --preset 'CUDA 13' \
+        && cmake --install build --component CUDA --strip --parallel 8
+
+
 FROM base AS rocm-6
 ENV PATH=/opt/rocm/hcc/bin:/opt/rocm/hip/bin:/opt/rocm/bin:/opt/rocm/hcc/bin:$PATH
 RUN --mount=type=cache,target=/root/.ccache \
@@ -92,10 +112,14 @@ RUN --mount=type=cache,target=/root/.cache/go-build \
     go build -trimpath -buildmode=pie -o /bin/ollama .
 
 FROM --platform=linux/amd64 scratch AS amd64
-COPY --from=cuda-12 dist/lib/ollama /lib/ollama
+# COPY --from=cuda-11 dist/lib/ollama/ /lib/ollama/
+COPY --from=cuda-12 dist/lib/ollama /lib/ollama/
+COPY --from=cuda-13 dist/lib/ollama/ /lib/ollama/
 
 FROM --platform=linux/arm64 scratch AS arm64
-COPY --from=cuda-12 dist/lib/ollama /lib/ollama/cuda_sbsa
+# COPY --from=cuda-11 dist/lib/ollama/ /lib/ollama/
+COPY --from=cuda-12 dist/lib/ollama /lib/ollama/
+COPY --from=cuda-13 dist/lib/ollama/ /lib/ollama/
 COPY --from=jetpack-5 dist/lib/ollama /lib/ollama/cuda_jetpack5
 COPY --from=jetpack-6 dist/lib/ollama /lib/ollama/cuda_jetpack6
 

README.md

@@ -414,6 +414,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [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)
+- [ollama-co2](https://github.com/carbonatedWaterOrg/ollama-co2) (FastAPI web interface for monitoring and managing local and remote Ollama servers with real-time model monitoring and concurrent downloads)
 
 ### Cloud
 

api/types.go

@@ -388,8 +388,12 @@ type EmbedRequest struct {
 	// this request.
 	KeepAlive *Duration `json:"keep_alive,omitempty"`
 
+	// Truncate truncates the input to fit the model's max sequence length.
 	Truncate *bool `json:"truncate,omitempty"`
 
+	// Dimensions truncates the output embedding to the specified dimension.
+	Dimensions int `json:"dimensions,omitempty"`
+
 	// Options lists model-specific options.
 	Options map[string]any `json:"options"`
 }

cmd/cmd.go

@@ -56,10 +56,8 @@ func ensureThinkingSupport(ctx context.Context, client *api.Client, name string)
 	if err != nil {
 		return
 	}
-	for _, cap := range resp.Capabilities {
-		if cap == model.CapabilityThinking {
-			return
-		}
+	if slices.Contains(resp.Capabilities, model.CapabilityThinking) {
+		return
 	}
 	fmt.Fprintf(os.Stderr, "warning: model %q does not support thinking output\n", name)
 }

convert/convert_bert.go

@@ -28,6 +28,7 @@ type bertModel struct {
 	LayerNormEPS          float32 `json:"layer_norm_eps"`
 	LayerNormEpsilon      float32 `json:"layer_norm_epsilon"`
 	NormEpsilon           float32 `json:"norm_epsilon"`
+	normalizeEmbeddings   bool
 
 	PoolingType uint32
 }
@@ -54,9 +55,11 @@ func (p *bertModel) parseMore(fsys fs.FS) error {
 
 	var pooling string
 	for _, m := range modules {
-		if m.Type == "sentence_transformers.models.Pooling" {
+		switch m.Type {
+		case "sentence_transformers.models.Pooling":
 			pooling = m.Path
-			break
+		case "sentence_transformers.models.Normalize":
+			p.normalizeEmbeddings = true
 		}
 	}
 
@@ -90,6 +93,7 @@ func (p *bertModel) KV(t *Tokenizer) ggml.KV {
 	kv["general.architecture"] = "bert"
 	kv["bert.attention.causal"] = false
 	kv["bert.pooling_type"] = p.PoolingType
+	kv["bert.normalize_embeddings"] = p.normalizeEmbeddings
 
 	kv["bert.block_count"] = cmp.Or(p.NLayers, p.NumHiddenLayers, p.NLayer)
 

discover/cuda_common.go

@@ -43,14 +43,15 @@ func cudaVariant(gpuInfo CudaGPUInfo) string {
 				}
 			}
 		}
-		return "sbsa"
 	}
 
-	// driver 12.0 has problems with the cuda v12 library, so run v11 on those older drivers
-	if gpuInfo.DriverMajor < 12 || (gpuInfo.DriverMajor == 12 && gpuInfo.DriverMinor == 0) {
-		// The detected driver is older than Feb 2023
-		slog.Warn("old CUDA driver detected - please upgrade to a newer driver", "version", fmt.Sprintf("%d.%d", gpuInfo.DriverMajor, gpuInfo.DriverMinor))
-		return "v11"
+	if gpuInfo.DriverMajor < 13 {
+		// The detected driver is older than 580 (Aug 2025)
+		// Warn if their CC is compatible with v13 and they should upgrade their driver to get better performance
+		if gpuInfo.computeMajor > 7 || (gpuInfo.computeMajor == 7 && gpuInfo.computeMinor >= 5) {
+			slog.Warn("old CUDA driver detected - please upgrade to a newer driver for best performance", "version", fmt.Sprintf("%d.%d", gpuInfo.DriverMajor, gpuInfo.DriverMinor))
+		}
+		return "v12"
 	}
-	return "v12"
+	return "v13"
 }

docs/api.md

@@ -1708,6 +1708,7 @@ Advanced parameters:
 - `truncate`: truncates the end of each input to fit within context length. Returns error if `false` and context length is exceeded. Defaults to `true`
 - `options`: additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`
 - `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
+- `dimensions`: number of dimensions for the embedding
 
 ### Examples
 

docs/development.md

@@ -11,6 +11,10 @@ Then build and run Ollama from the root directory of the repository:
 go run . serve
 ```
 
+> [!NOTE]
+> Ollama includes native code compiled with CGO.  From time to time these data structures can change and CGO can get out of sync resulting in unexpected crashes.  You can force a full build of the native code by running `go clean -cache` first. 
+
+
 ## macOS (Apple Silicon)
 
 macOS Apple Silicon supports Metal which is built-in to the Ollama binary. No additional steps are required.

docs/linux.md

@@ -11,12 +11,13 @@ curl -fsSL https://ollama.com/install.sh | sh
 ## Manual install
 
 > [!NOTE]
-> If you are upgrading from a prior version, you should remove the old libraries with `sudo rm -rf /usr/lib/ollama` first.
+> If you are upgrading from a prior version, you **MUST** remove the old libraries with `sudo rm -rf /usr/lib/ollama` first.
 
 Download and extract the package:
 
 ```shell
 curl -LO https://ollama.com/download/ollama-linux-amd64.tgz
+sudo rm -rf /usr/lib/ollama
 sudo tar -C /usr -xzf ollama-linux-amd64.tgz
 ```
 

envconfig/config.go

@@ -185,8 +185,6 @@ var (
 	ContextLength = Uint("OLLAMA_CONTEXT_LENGTH", 4096)
 	// Auth enables authentication between the Ollama client and server
 	UseAuth = Bool("OLLAMA_AUTH")
-	// Enable the new memory estimation logic
-	NewMemoryEstimates = Bool("OLLAMA_NEW_ESTIMATES")
 )
 
 func String(s string) func() string {
@@ -272,7 +270,6 @@ func AsMap() map[string]EnvVar {
 		"OLLAMA_MULTIUSER_CACHE":   {"OLLAMA_MULTIUSER_CACHE", MultiUserCache(), "Optimize prompt caching for multi-user scenarios"},
 		"OLLAMA_CONTEXT_LENGTH":    {"OLLAMA_CONTEXT_LENGTH", ContextLength(), "Context length to use unless otherwise specified (default: 4096)"},
 		"OLLAMA_NEW_ENGINE":        {"OLLAMA_NEW_ENGINE", NewEngine(), "Enable the new Ollama engine"},
-		"OLLAMA_NEW_ESTIMATES":     {"OLLAMA_NEW_ESTIMATES", NewMemoryEstimates(), "Enable the new memory estimation logic"},
 
 		// Informational
 		"HTTP_PROXY":  {"HTTP_PROXY", String("HTTP_PROXY")(), "HTTP proxy"},

fs/ggml/ggml.go

@@ -864,12 +864,16 @@ func (llm GGML) VisionGraphSize() (weights, graphSize uint64) {
 
 // SupportsKVCacheType checks if the requested cache type is supported
 func (f GGML) SupportsKVCacheType(cacheType string) bool {
+	if cacheType == "" || cacheType == "f16" {
+		return true
+	}
+
 	if arch := f.KV().Architecture(); slices.Contains([]string{"gptoss", "gpt-oss"}, arch) {
 		// gpt-oss uses attention with sinks which does not support quantized cache types
-		slog.Warn("model only supports non-quantized cache types ", "mode", arch)
-		return cacheType == "f16"
+		slog.Warn("model only supports non-quantized cache types", "model", arch)
+		return false
 	}
-	return slices.Contains([]string{"f16", "q8_0", "q4_0"}, cacheType)
+	return slices.Contains([]string{"q8_0", "q4_0"}, cacheType)
 }
 
 // SupportsFlashAttention checks if the model supports flash attention
@@ -879,6 +883,10 @@ func (f GGML) SupportsFlashAttention() bool {
 		return false
 	}
 
+	if arch := f.KV().Architecture(); slices.Contains([]string{"gemma2"}, arch) {
+		return false
+	}
+
 	// Check head counts match and are non-zero
 	headCountK := f.KV().EmbeddingHeadCountK()
 	headCountV := f.KV().EmbeddingHeadCountV()

harmony/harmonyparser.go

@@ -3,29 +3,15 @@ package harmony
 import (
 	"fmt"
 	"log/slog"
-	"slices"
 	"strings"
 	"unicode"
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/logutil"
-	"github.com/ollama/ollama/template"
 )
 
 type harmonyParserState int
 
-func ShouldUseHarmony(modelFamily string, template *template.Template) bool {
-	if slices.Contains([]string{"gptoss", "gpt-oss"}, modelFamily) {
-		// heuristic to check whether the template expects to be parsed via harmony:
-		// search for harmony tags that are nearly always used
-		if template.Contains("<|start|>") && template.Contains("<|end|>") {
-			return true
-		}
-	}
-
-	return false
-}
-
 const (
 	harmonyParserState_LookingForMessageStart harmonyParserState = iota
 	harmonyParserState_ParsingHeader
@@ -47,13 +33,12 @@ func (s harmonyParserState) String() string {
 }
 
 type HarmonyParser struct {
-	state              harmonyParserState
-	MessageStartTag    string
-	MessageEndTag      string
-	HeaderEndTag       string
-	constraintsAllowed bool
-	acc                strings.Builder
-	lifetimeAcc        strings.Builder
+	state           harmonyParserState
+	MessageStartTag string
+	MessageEndTag   string
+	HeaderEndTag    string
+	acc             strings.Builder
+	lifetimeAcc     strings.Builder
 }
 
 type HarmonyEvent interface {
@@ -90,32 +75,18 @@ func (s *HarmonyParser) AddImplicitStart() {
 	s.acc.WriteString("<|start|>assistant")
 }
 
-func (s *HarmonyParser) ConstraintsAllowed() bool {
-	return s.constraintsAllowed
-}
-
-func Prefill(lastMessage api.Message) string {
-	if lastMessage.Role != "assistant" {
-		return ""
-	}
-
-	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) AddImplicitStartOrPrefill(lastMessage *api.Message) {
+	if lastMessage != nil && lastMessage.Role == "assistant" {
+		// handle prefilling conditions
+		if lastMessage.Content != "" {
+			s.acc.WriteString("<|start|>assistant<|channel|>final<|message|>")
+			return
+		} else if lastMessage.Thinking != "" {
+			s.acc.WriteString("<|start|>assistant<|channel|>analysis<|message|>")
+			return
+		}
 	}
+	s.AddImplicitStart()
 }
 
 func (s *HarmonyParser) AddContent(content string) []HarmonyEvent {
@@ -294,7 +265,6 @@ type HarmonyMessageHandler struct {
 	state           harmonyMessageState
 	HarmonyParser   *HarmonyParser
 	FunctionNameMap *FunctionNameMap
-	ToolParser      *HarmonyToolCallAccumulator
 }
 
 // NewHarmonyMessageHandler creates a new message handler
@@ -307,16 +277,12 @@ func NewHarmonyMessageHandler() *HarmonyMessageHandler {
 			HeaderEndTag:    "<|message|>",
 		},
 		FunctionNameMap: NewFunctionNameMap(),
-		ToolParser: &HarmonyToolCallAccumulator{
-			state:           harmonyToolCallState_Normal,
-			currentToolName: nil,
-		},
 	}
 }
 
 // AddContent processes the content and returns the content, thinking, and tool content.
 // content and thinking are already fully parsed, but tool content still needs to be passed to the tool parser
-func (h *HarmonyMessageHandler) AddContent(content string) (string, string, string) {
+func (h *HarmonyMessageHandler) AddContent(content string, toolParser *HarmonyToolCallAccumulator) (string, string, string) {
 	contentSb := strings.Builder{}
 	thinkingSb := strings.Builder{}
 	toolContentSb := strings.Builder{}
@@ -333,20 +299,19 @@ func (h *HarmonyMessageHandler) AddContent(content string) (string, string, stri
 					// event.Header.Recipient is the tool name, something like
 					// "browser.search" for a built-in, or "functions.calc" for a
 					// custom one
-					h.ToolParser.SetToolName(event.Header.Recipient)
+					toolParser.SetToolName(event.Header.Recipient)
 				} else {
 					h.state = harmonyMessageState_Thinking
 				}
 			case "commentary":
 				if event.Header.Recipient != "" {
 					h.state = harmonyMessageState_ToolCalling
-					h.ToolParser.SetToolName(event.Header.Recipient)
+					toolParser.SetToolName(event.Header.Recipient)
 				} else {
 					h.state = harmonyMessageState_Normal
 				}
 			case "final":
 				h.state = harmonyMessageState_Normal
-				h.HarmonyParser.constraintsAllowed = true
 			}
 		case HarmonyEventContentEmitted:
 			logutil.Trace("harmony event content", "content", event.Content, "state", h.state)
@@ -364,6 +329,13 @@ func (h *HarmonyMessageHandler) AddContent(content string) (string, string, stri
 	return contentSb.String(), thinkingSb.String(), toolContentSb.String()
 }
 
+func (h *HarmonyMessageHandler) CreateToolParser() *HarmonyToolCallAccumulator {
+	return &HarmonyToolCallAccumulator{
+		state:           harmonyToolCallState_Normal,
+		currentToolName: nil,
+	}
+}
+
 type harmonyToolCallState int
 
 const (

harmony/harmonyparser_test.go

@@ -3,7 +3,6 @@ package harmony
 import (
 	"fmt"
 	"reflect"
-	"strings"
 	"testing"
 )
 
@@ -536,202 +535,3 @@ func TestFunctionConvertAndAdd(t *testing.T) {
 		})
 	}
 }
-
-func TestHarmonyMessageHandlerStreamingScenarios(t *testing.T) {
-	t.Run("thinking_then_content_streams", func(t *testing.T) {
-		handler := NewHarmonyMessageHandler()
-		handler.HarmonyParser.AddImplicitStart()
-		tp := handler.ToolParser
-		type step struct {
-			in           string
-			wantContent  string
-			wantThinking string
-		}
-		steps := []step{
-			{in: "<|channel|>analysis<|message|>Thinking...", wantThinking: "Thinking..."},
-			{in: "<|end|>", wantThinking: ""},
-			{in: "<|start|>assistant<|message|>Answer", wantContent: "Answer"},
-			{in: "<|end|>", wantContent: ""},
-		}
-		for i, s := range steps {
-			content, thinking, tool := handler.AddContent(s.in)
-			if tool != "" {
-				tp.Add(tool)
-			}
-			if content != s.wantContent || thinking != s.wantThinking {
-				t.Fatalf("step %d: got (content=%q thinking=%q), want (content=%q thinking=%q)", i, content, thinking, s.wantContent, s.wantThinking)
-			}
-		}
-	})
-
-	t.Run("content_streams_as_it_arrives", func(t *testing.T) {
-		handler := NewHarmonyMessageHandler()
-		handler.HarmonyParser.AddImplicitStart()
-		tp := handler.ToolParser
-		inputs := []string{
-			"<|start|>assistant<|message|>Hello",
-			", world",
-			"!<|end|>",
-		}
-		var got []string
-		for _, in := range inputs {
-			content, thinking, tool := handler.AddContent(in)
-			if tool != "" {
-				tp.Add(tool)
-			}
-			if thinking != "" {
-				t.Fatalf("unexpected thinking %q", thinking)
-			}
-			if content != "" {
-				got = append(got, content)
-			}
-		}
-		want := []string{"Hello", ", world", "!"}
-		if !reflect.DeepEqual(got, want) {
-			t.Fatalf("content pieces mismatch: got %v want %v", got, want)
-		}
-	})
-
-	t.Run("thinking_streams_separately_from_content", func(t *testing.T) {
-		handler := NewHarmonyMessageHandler()
-		handler.HarmonyParser.AddImplicitStart()
-		tp := handler.ToolParser
-		inputs := []string{
-			"<|channel|>analysis<|message|>Thinking...",
-			"<|end|>",
-			"<|start|>assistant<|message|>Answer",
-			"<|end|>",
-		}
-		var got []string
-		for _, in := range inputs {
-			content, thinking, tool := handler.AddContent(in)
-			if tool != "" {
-				tp.Add(tool)
-			}
-			if thinking != "" {
-				got = append(got, thinking)
-			}
-			if content != "" {
-				got = append(got, content)
-			}
-		}
-		want := []string{"Thinking...", "Answer"}
-		if !reflect.DeepEqual(got, want) {
-			t.Fatalf("content pieces mismatch: got %v want %v", got, want)
-		}
-	})
-
-	t.Run("partial_tags_buffer_until_complete", func(t *testing.T) {
-		handler := NewHarmonyMessageHandler()
-		handler.HarmonyParser.AddImplicitStart()
-		tp := handler.ToolParser
-		inputs := []string{
-			"<|chan",
-			"nel|>analysis<|mess",
-			"age|>Deep ",
-			"thought",
-			"<|end|>",
-			"<|start|>assistant<|message|>Done",
-			"<|end|>",
-		}
-		var thinkingPieces []string
-		var contentPieces []string
-		for _, in := range inputs {
-			content, thinking, tool := handler.AddContent(in)
-			if tool != "" {
-				tp.Add(tool)
-			}
-			if thinking != "" {
-				thinkingPieces = append(thinkingPieces, thinking)
-			}
-			if content != "" {
-				contentPieces = append(contentPieces, content)
-			}
-		}
-		if want := []string{"Deep ", "thought"}; !reflect.DeepEqual(thinkingPieces, want) {
-			t.Fatalf("thinking pieces mismatch: got %v want %v", thinkingPieces, want)
-		}
-		if want := []string{"Done"}; !reflect.DeepEqual(contentPieces, want) {
-			t.Fatalf("content pieces mismatch: got %v want %v", contentPieces, want)
-		}
-	})
-
-	t.Run("simple_assistant_after_analysis", func(t *testing.T) {
-		handler := NewHarmonyMessageHandler()
-		handler.HarmonyParser.AddImplicitStart()
-		tp := handler.ToolParser
-		inputs := []string{
-			"<|channel|>analysis<|message|>Think",
-			"<|end|>",
-			"<|start|>assistant<|message|>Answer",
-			"<|end|>",
-		}
-		var contentSb, thinkingSb strings.Builder
-		for _, in := range inputs {
-			content, thinking, tool := handler.AddContent(in)
-			if tool != "" {
-				tp.Add(tool)
-			}
-			contentSb.WriteString(content)
-			thinkingSb.WriteString(thinking)
-		}
-		if contentSb.String() != "Answer" {
-			t.Fatalf("content mismatch: got %q want %q", contentSb.String(), "Answer")
-		}
-		if thinkingSb.String() != "Think" {
-			t.Fatalf("thinking mismatch: got %q want %q", thinkingSb.String(), "Think")
-		}
-	})
-
-	t.Run("tool_call_parsed_and_returned_correctly", func(t *testing.T) {
-		handler := NewHarmonyMessageHandler()
-		handler.HarmonyParser.AddImplicitStart()
-		tp := handler.ToolParser
-		inputs := []string{
-			"<|channel|>commentary to=functions.calculate<|message|>{\"expression\":\"2+2\"}<|end|>",
-		}
-		for _, in := range inputs {
-			content, thinking, tool := handler.AddContent(in)
-			if content != "" || thinking != "" {
-				continue
-			}
-			if tool != "" {
-				tp.Add(tool)
-			}
-		}
-		name, args := tp.Drain()
-		if name == nil || *name != "functions.calculate" {
-			t.Fatalf("unexpected tool name: %v", name)
-		}
-		if got, want := args, "{\"expression\":\"2+2\"}"; got != want {
-			t.Fatalf("unexpected tool args: got %s want %s", got, want)
-		}
-	})
-
-	t.Run("tool_call_across_chunks", func(t *testing.T) {
-		handler := NewHarmonyMessageHandler()
-		handler.HarmonyParser.AddImplicitStart()
-		tp := handler.ToolParser
-		inputs := []string{
-			"<|channel|>commentary to=functions.calculate<|message|>{\"expression\":\"2+",
-			"2\"}",
-			"<|end|>",
-		}
-		for _, in := range inputs {
-			content, thinking, tool := handler.AddContent(in)
-			if content != "" || thinking != "" {
-				continue
-			}
-			if tool != "" {
-				tp.Add(tool)
-			}
-		}
-		name, args := tp.Drain()
-		if name == nil || *name != "functions.calculate" {
-			t.Fatalf("unexpected tool name: %v", name)
-		}
-		if got, want := args, "{\"expression\":\"2+2\"}"; got != want {
-			t.Fatalf("unexpected tool args: got %s want %s", got, want)
-		}
-	})
-}

integration/context_test.go

@@ -50,7 +50,7 @@ func TestContextExhaustion(t *testing.T) {
 	// Set up the test data
 	req := api.GenerateRequest{
 		Model:  smol,
-		Prompt: "Write me a story with a ton of emojis?",
+		Prompt: "Write me a story in english with a lot of emojis",
 		Stream: &stream,
 		Options: map[string]any{
 			"temperature": 0,

integration/utils_test.go

@@ -561,7 +561,7 @@ func GenerateRequests() ([]api.GenerateRequest, [][]string) {
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
 			}, {
 				Model:     smol,
-				Prompt:    "what is the origin of the US thanksgiving holiday? Be brief but factual in your reply",
+				Prompt:    "how do rainbows form? Be brief but factual in your reply",
 				Stream:    &stream,
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
 			}, {
@@ -579,9 +579,9 @@ func GenerateRequests() ([]api.GenerateRequest, [][]string) {
 		[][]string{
 			{"sunlight", "scattering", "interact", "color", "surface", "depth", "red", "orange", "yellow", "absorbs", "wavelength"},
 			{"soil", "organic", "earth", "black", "tan", "chemical", "processes", "pigments", "particles", "iron oxide", "rust", "air", "water", "mixture", "mixing"},
-			{"england", "english", "massachusetts", "pilgrims", "colonists", "independence", "british", "feast", "family", "gatherings", "traditions", "turkey", "colonial", "period", "harvest", "agricultural", "european settlers", "american revolution", "civil war", "16th century", "17th century", "native american", "united states", "cultural", "hardship", "autumn", "festival"},
+			{"water", "droplet", "refracted", "reflect", "color", "spectrum"},
 			{"fourth", "july", "declaration", "independence"},
-			{"nitrogen", "oxygen", "carbon", "dioxide"},
+			{"nitrogen", "oxygen", "carbon", "dioxide", "water", "vapor"},
 		}
 }
 

llama/llama.go

@@ -515,33 +515,34 @@ func (c *MtmdContext) NewEmbed(llamaContext *Context, data []byte) ([][]float32,
 	}
 	nChunks := C.mtmd_input_chunks_size(ic)
 	numEmbed := llamaContext.Model().NEmbd()
-	lastChunkSize := 0
+	embed := make([][]float32, 0)
 	for i := range int(nChunks) {
 		chunk := C.mtmd_input_chunks_get(ic, C.size_t(i))
 		numTokens := int(C.mtmd_input_chunk_get_n_tokens(chunk))
-		lastChunkSize = numTokens
+		slog.Debug("chunk tokens", "index", i, "numTokens", numTokens)
 
 		// Encode the chunk
 		if C.int32_t(0) != C.mtmd_encode_chunk(c.c, chunk) {
 			return nil, errors.New("unable to encode mtmd image chunk")
 		}
-	}
 
-	// Get the embeddings
-	embed := make([][]float32, lastChunkSize)
-	embd := C.mtmd_get_output_embd(c.c)
-	if nil == embd {
-		return nil, errors.New("failed to get image embedding")
-	}
+		// Get the embeddings for this chunk
+		chunkEmbed := make([][]float32, numTokens)
+		chunkEmbd := C.mtmd_get_output_embd(c.c)
+		if nil == chunkEmbd {
+			continue
+		}
 
-	// Extend the embedding array for each token
-	s := unsafe.Slice((*float32)(embd), numEmbed*lastChunkSize)
-	rows := make([]float32, len(s))
-	copy(rows, s)
-	for i := range lastChunkSize {
-		embed[i] = rows[i*numEmbed : (i+1)*numEmbed]
+		// Extend the embedding array for each token
+		s := unsafe.Slice((*float32)(chunkEmbd), numTokens*numEmbed)
+		rows := make([]float32, len(s))
+		copy(rows, s)
+		for i := range numTokens {
+			chunkEmbed[i] = rows[i*numEmbed : (i+1)*numEmbed]
+		}
+		embed = append(embed, chunkEmbed...)
 	}
-
+	slog.Debug("image embeddings", "totalEmbeddings", len(embed))
 	return embed, nil
 }
 

llm/memory.go

@@ -202,7 +202,7 @@ func estimateGPULayers(gpus []discover.GpuInfo, f *ggml.GGML, projectors []strin
 	var kvct string
 	if useFlashAttention {
 		requested := strings.ToLower(envconfig.KvCacheType())
-		if requested != "" && f.SupportsKVCacheType(requested) {
+		if f.SupportsKVCacheType(requested) {
 			kvct = requested
 		}
 	}

llm/server.go

@@ -35,7 +35,6 @@ import (
 	"github.com/ollama/ollama/logutil"
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/model"
-	"github.com/ollama/ollama/parser"
 )
 
 type filteredEnv []string
@@ -149,7 +148,11 @@ func NewLlamaServer(gpus discover.GpuInfoList, modelPath string, f *ggml.GGML, a
 	var textProcessor model.TextProcessor
 	var err error
 	if envconfig.NewEngine() || f.KV().OllamaEngineRequired() {
-		textProcessor, err = model.NewTextProcessor(modelPath)
+		if len(projectors) == 0 {
+			textProcessor, err = model.NewTextProcessor(modelPath)
+		} else {
+			err = errors.New("split vision models aren't supported")
+		}
 		if err != nil {
 			// To prepare for opt-out mode, instead of treating this as an error, we fallback to the old runner
 			slog.Debug("model not yet supported by Ollama engine, switching to compatibility mode", "model", modelPath, "error", err)
@@ -162,11 +165,6 @@ func NewLlamaServer(gpus discover.GpuInfoList, modelPath string, f *ggml.GGML, a
 		}
 	}
 
-	newEstimates := textProcessor != nil && envconfig.NewMemoryEstimates()
-	if newEstimates {
-		slog.Info("enabling new memory estimates")
-	}
-
 	// Verify the requested context size is <= the model training size
 	trainCtx := f.KV().ContextLength()
 	if opts.NumCtx > int(trainCtx) && trainCtx > 0 {
@@ -221,7 +219,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, modelPath string, f *ggml.GGML, a
 
 		// Flash Attention also supports kv cache quantization
 		// Enable if the requested and kv cache type is supported by the model
-		if kvct != "" && f.SupportsKVCacheType(kvct) {
+		if f.SupportsKVCacheType(kvct) {
 			loadRequest.KvCacheType = kvct
 		} else {
 			slog.Warn("kv cache type not supported by model", "type", kvct)
@@ -434,7 +432,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, modelPath string, f *ggml.GGML, a
 			}
 		}()
 
-		if newEstimates {
+		if textProcessor != nil {
 			return &ollamaServer{llmServer: s}, nil
 		} else {
 			return &llamaServer{llmServer: s, ggml: f}, nil
@@ -1350,9 +1348,7 @@ type CompletionRequest struct {
 	Images  []ImageData
 	Options *api.Options
 
-	Grammar       string // set before sending the request to the subprocess
-	ParserType    parser.TokenParserType
-	PrefillString string
+	Grammar string // set before sending the request to the subprocess
 }
 
 // DoneReason represents the reason why a completion response is done
@@ -1379,15 +1375,13 @@ func (d DoneReason) String() string {
 }
 
 type CompletionResponse struct {
-	Content            string         `json:"content"`
-	Thinking           string         `json:"thinking"`
-	ToolCalls          []api.ToolCall `json:"tool_calls"`
-	DoneReason         DoneReason     `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"`
+	Content            string        `json:"content"`
+	DoneReason         DoneReason    `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 {
@@ -1505,8 +1499,7 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 				return fmt.Errorf("error unmarshalling llm prediction response: %v", err)
 			}
 			switch {
-			// 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 != "":
+			case strings.TrimSpace(c.Content) == lastToken:
 				tokenRepeat++
 			default:
 				lastToken = strings.TrimSpace(c.Content)
@@ -1519,14 +1512,16 @@ func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn fu
 				return ctx.Err()
 			}
 
+			if c.Content != "" {
+				fn(CompletionResponse{
+					Content: c.Content,
+				})
+			}
+
 			if c.Done {
 				fn(c)
 				return nil
 			}
-
-			if c.Content != "" || c.Thinking != "" || len(c.ToolCalls) > 0 {
-				fn(c)
-			}
 		}
 	}
 

ml/backend.go

@@ -416,6 +416,7 @@ type Tensor interface {
 	AddID(ctx Context, t2, ids Tensor) Tensor
 
 	Softmax(ctx Context) Tensor
+	L2Norm(ctx Context, eps float32) Tensor
 	LayerNorm(ctx Context, weight, bias Tensor, eps float32) Tensor
 	RMSNorm(ctx Context, weight Tensor, eps float32) Tensor
 	Scale(ctx Context, s float64) Tensor

ml/backend/ggml/ggml.go

@@ -1205,6 +1205,13 @@ func (t *Tensor) AddID(ctx ml.Context, t2, ids ml.Tensor) ml.Tensor {
 	}
 }
 
+func (t *Tensor) L2Norm(ctx ml.Context, eps float32) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_l2_norm(ctx.(*Context).ctx, t.t, C.float(eps)),
+	}
+}
+
 func (t *Tensor) LayerNorm(ctx ml.Context, w, b ml.Tensor, eps float32) ml.Tensor {
 	tt := C.ggml_norm(ctx.(*Context).ctx, t.t, C.float(eps))
 	if w != nil {

ml/nn/pooling/pooling.go

@@ -0,0 +1,36 @@
+package pooling
+
+import (
+	"github.com/ollama/ollama/ml"
+)
+
+type Type uint32
+
+const (
+	TypeNone Type = iota
+	TypeMean
+	TypeCLS
+	TypeLast
+	TypeRank
+
+	TypeUnknown     = 0xFFFFFFFE
+	TypeUnspecified = 0xFFFFFFFF
+)
+
+func Pooling(ctx ml.Context, hiddenStates ml.Tensor, poolingType Type) ml.Tensor {
+	switch poolingType {
+	case TypeNone:
+		return hiddenStates
+	case TypeMean:
+		hiddenStates = hiddenStates.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx).Mean(ctx)
+		return hiddenStates.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
+	case TypeCLS:
+		return hiddenStates.View(ctx, 0, hiddenStates.Dim(0))
+	case TypeLast:
+		panic("not implemented")
+	case TypeRank:
+		panic("not implemented")
+	default:
+		panic("not implemented")
+	}
+}

model/input/input.go

@@ -54,10 +54,9 @@ type Batch struct {
 	// Inputs is the input tokens, including placeholders for multimodal inputs.
 	Inputs ml.Tensor
 
-	// Multimodal is a set of multimodal embeddings previously created by
-	// EncodeMultimodal, along with an index into Inputs. Unused for text-only
-	// models or for batches without multimodal elements.
-	Multimodal []MultimodalIndex
+	// Outputs are the set of indicies into Inputs for which output data should
+	// be returned.
+	Outputs ml.Tensor
 
 	// Positions is the position for each Input, relative to its sequence. Equal
 	// in length to Inputs.
@@ -66,7 +65,8 @@ type Batch struct {
 	// Sequences is the sequence for each Input. Equal in length to Inputs.
 	Sequences []int
 
-	// Outputs are the set of indicies into Inputs for which output data should
-	// be returned.
-	Outputs []int32
+	// Multimodal is a set of multimodal embeddings previously created by
+	// EncodeMultimodal, along with an index into Inputs. Unused for text-only
+	// models or for batches without multimodal elements.
+	Multimodal []MultimodalIndex
 }

model/model.go

@@ -24,7 +24,11 @@ import (
 	"github.com/ollama/ollama/model/input"
 )
 
-var ErrNoVisionModel = errors.New("this model is missing data required for image input")
+var (
+	ErrNoVisionModel        = errors.New("this model is missing data required for image input")
+	ErrUnsupportedModel     = errors.New("model not supported")
+	ErrUnsupportedTokenizer = errors.New("tokenizer not supported")
+)
 
 // Model implements a specific model architecture, defining the forward pass and any model-specific configuration
 type Model interface {
@@ -242,7 +246,7 @@ func setPointer(base Base, v reflect.Value, tags []Tag) {
 		vv = vv.Elem()
 	}
 
-	vv = vv.Elem()
+	vv = reflect.Indirect(vv)
 	if v.IsNil() {
 		vv = reflect.New(v.Type().Elem()).Elem()
 	}

model/models/bert/model.go

@@ -0,0 +1,181 @@
+package bert
+
+import (
+	"cmp"
+	"math"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/ml/nn/pooling"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type Model struct {
+	model.Base
+	model.TextProcessor
+
+	TokenEmbedding     *nn.Embedding `gguf:"token_embd"`
+	TypeEmbedding      *nn.Embedding `gguf:"token_types"`
+	PositionEmbedding  *nn.Embedding `gguf:"position_embd"`
+	TokenEmbeddingNorm *nn.LayerNorm `gguf:"token_embd_norm"`
+
+	Layers []EncoderLayer `gguf:"blk"`
+
+	Options
+}
+
+// Forward implements model.Model.
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
+	hiddenStates = hiddenStates.Add(ctx, m.TypeEmbedding.Weight.View(ctx, 0, m.hiddenSize))
+	hiddenStates = hiddenStates.Add(ctx, m.PositionEmbedding.Forward(ctx, ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))))
+	hiddenStates = m.TokenEmbeddingNorm.Forward(ctx, hiddenStates, m.eps)
+
+	for _, layer := range m.Layers {
+		hiddenStates = layer.Forward(ctx, hiddenStates, &m.Options)
+	}
+
+	hiddenStates = pooling.Pooling(ctx, hiddenStates, m.poolingType)
+	if m.normalize {
+		hiddenStates = hiddenStates.L2Norm(ctx, 1e-12)
+	}
+
+	return hiddenStates, nil
+}
+
+type EncoderLayer struct {
+	*Attention
+	AttentionNorm *nn.LayerNorm `gguf:"attn_output_norm"`
+
+	*MLP
+	MLPNorm *nn.LayerNorm `gguf:"layer_output_norm"`
+}
+
+func (e *EncoderLayer) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
+	// Attention
+	residual := hiddenStates
+	hiddenStates = e.Attention.Forward(ctx, hiddenStates, opts)
+	hiddenStates = hiddenStates.Add(ctx, residual)
+	hiddenStates = e.AttentionNorm.Forward(ctx, hiddenStates, opts.eps)
+
+	// MLP
+	residual = hiddenStates
+	hiddenStates = e.MLP.Forward(ctx, hiddenStates, opts)
+	hiddenStates = hiddenStates.Add(ctx, residual)
+	hiddenStates = e.MLPNorm.Forward(ctx, hiddenStates, opts.eps)
+
+	return hiddenStates
+}
+
+type Attention struct {
+	Query     *nn.Linear    `gguf:"attn_q"`
+	QueryNorm *nn.LayerNorm `gguf:"attn_q_norm"`
+
+	Key     *nn.Linear    `gguf:"attn_k"`
+	KeyNorm *nn.LayerNorm `gguf:"attn_k_norm"`
+
+	Value *nn.Linear `gguf:"attn_v"`
+
+	Output *nn.Linear `gguf:"attn_output"`
+}
+
+func (a *Attention) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
+	batchSize := hiddenStates.Dim(1)
+
+	query := a.Query.Forward(ctx, hiddenStates)
+	if a.QueryNorm != nil {
+		query = a.QueryNorm.Forward(ctx, query, opts.eps)
+	}
+	query = query.Reshape(ctx, opts.headDim(), opts.numHeads, batchSize)
+
+	key := a.Key.Forward(ctx, hiddenStates)
+	if a.KeyNorm != nil {
+		key = a.KeyNorm.Forward(ctx, key, opts.eps)
+	}
+	key = key.Reshape(ctx, opts.headDim(), cmp.Or(opts.numKVHeads, opts.numHeads), batchSize)
+
+	value := a.Value.Forward(ctx, hiddenStates)
+	value = value.Reshape(ctx, opts.headDim(), cmp.Or(opts.numKVHeads, opts.numHeads), batchSize)
+
+	attention := nn.Attention(ctx, query, key, value, 1/math.Sqrt(float64(opts.headDim())), nil)
+	attention = attention.Reshape(ctx, opts.hiddenSize, batchSize)
+	return a.Output.Forward(ctx, attention)
+}
+
+type MLP struct {
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+}
+
+func (m *MLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
+	return m.Down.Forward(ctx, m.Up.Forward(ctx, hiddenStates).GELU(ctx))
+}
+
+type Options struct {
+	hiddenSize,
+	numHeads,
+	numKVHeads,
+	keyLength,
+	valueLength int
+	poolingType pooling.Type
+	eps         float32
+	normalize   bool
+}
+
+func (o Options) headDim() int {
+	return cmp.Or(o.keyLength, o.valueLength, o.hiddenSize/o.numHeads)
+}
+
+func New(c fs.Config) (model.Model, error) {
+	var processor model.TextProcessor
+	switch c.String("tokenizer.ggml.model", "bert") {
+	case "bert":
+		processor = model.NewWordPiece(
+			&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(cmp.Or(
+						c.Uint("tokenizer.ggml.cls_token_id"),
+						c.Uint("tokenizer.ggml.bos_token_id"),
+					)),
+				},
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", true),
+				EOS: []int32{
+					int32(cmp.Or(
+						c.Uint("tokenizer.ggml.separator_token_id"),
+						//nolint:misspell
+						// NOTE: "seperator_token_id" is a typo in model metadata but we need to
+						// support it for compatibility.
+						c.Uint("tokenizer.ggml.seperator_token_id"),
+						c.Uint("tokenizer.ggml.eos_token_id"),
+					)),
+				},
+			},
+		)
+	default:
+		return nil, model.ErrUnsupportedTokenizer
+	}
+
+	return &Model{
+		TextProcessor: processor,
+		Layers:        make([]EncoderLayer, 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")),
+			eps:         c.Float("attention.layer_norm_epsilon"),
+			poolingType: pooling.Type(c.Uint("pooling_type")),
+			normalize:   c.Bool("normalize_embeddings", true),
+		},
+	}, nil
+}
+
+func init() {
+	model.Register("bert", New)
+	model.Register("bert_embed", New)
+}

model/models/gemma2/model.go

@@ -24,7 +24,7 @@ type Options struct {
 
 type Model struct {
 	model.Base
-	model.SentencePieceModel
+	model.SentencePiece
 
 	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
 	Layers         []Layer       `gguf:"blk"`
@@ -40,7 +40,7 @@ const (
 
 func New(c fs.Config) (model.Model, error) {
 	m := Model{
-		SentencePieceModel: model.NewSentencePieceModel(
+		SentencePiece: model.NewSentencePiece(
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Scores: c.Floats("tokenizer.ggml.scores"),
@@ -176,7 +176,6 @@ func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs, outputs ml.Ten
 
 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))
 
 	hiddenState := m.TokenEmbedding.Forward(ctx, batch.Inputs)
 	hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.Options.hiddenSize)))
@@ -193,7 +192,7 @@ func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 
 		var lastLayerOutputs ml.Tensor
 		if i == len(m.Layers)-1 {
-			lastLayerOutputs = outputs
+			lastLayerOutputs = batch.Outputs
 		}
 
 		hiddenState = layer.Forward(ctx, hiddenState, positions, lastLayerOutputs, m.Cache, m.Options)

model/models/gemma3/embed.go

@@ -1,49 +1,38 @@
 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/ml/nn/pooling"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
 )
 
 type embedModel struct {
 	model.Base
-	model.SentencePieceModel
+	model.SentencePiece
 
 	*TextModel
-	PoolingType uint32
+	poolingType pooling.Type
 
 	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")
-	}
-
+	hiddenStates = pooling.Pooling(ctx, hiddenStates, m.poolingType)
 	for _, dense := range m.Dense {
 		hiddenStates = dense.Forward(ctx, hiddenStates)
 	}
-
+	hiddenStates = hiddenStates.L2Norm(ctx, 1e-12)
 	return hiddenStates, nil
 }
 
 func newEmbedModel(c fs.Config) (model.Model, error) {
 	m := &embedModel{
-		SentencePieceModel: model.NewSentencePieceModel(
+		SentencePiece: model.NewSentencePiece(
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Scores: c.Floats("tokenizer.ggml.scores"),
@@ -61,7 +50,7 @@ func newEmbedModel(c fs.Config) (model.Model, error) {
 			},
 		),
 		TextModel:   newTextModel(c),
-		PoolingType: c.Uint("pooling_type", 0),
+		poolingType: pooling.Type(c.Uint("pooling_type", 0)),
 	}
 
 	m.Cache = kvcache.NewWrapperCache(

model/models/gemma3/model.go

@@ -16,7 +16,7 @@ import (
 
 type Model struct {
 	model.Base
-	model.SentencePieceModel
+	model.SentencePiece
 
 	*VisionModel `gguf:"v"`
 	*TextModel
@@ -55,7 +55,7 @@ func (p *MultiModalProjector) Forward(ctx ml.Context, visionOutputs ml.Tensor, i
 
 func New(c fs.Config) (model.Model, error) {
 	m := Model{
-		SentencePieceModel: model.NewSentencePieceModel(
+		SentencePiece: model.NewSentencePiece(
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Scores: c.Floats("tokenizer.ggml.scores"),

model/models/gemma3/model_text.go

@@ -161,7 +161,6 @@ func (l *TextLayer) Forward(ctx ml.Context, layer int, hiddenState, positionIDs,
 
 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)))
@@ -194,7 +193,7 @@ func (m *TextModel) Forward(ctx ml.Context, batch input.Batch, cache kvcache.Cac
 
 		var lastLayerOutputs ml.Tensor
 		if i == len(m.Layers)-1 {
-			lastLayerOutputs = outputs
+			lastLayerOutputs = batch.Outputs
 		}
 
 		hiddenState = layer.Forward(ctx, i, hiddenState, positions, lastLayerOutputs, cache, m.TextConfig)

model/models/gemma3n/model.go

@@ -10,7 +10,7 @@ import (
 
 type Model struct {
 	model.Base
-	model.SentencePieceModel
+	model.SentencePiece
 
 	*TextModel
 }
@@ -23,7 +23,7 @@ func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 func New(c fs.Config) (model.Model, error) {
 	m := Model{
 		TextModel: newTextModel(c),
-		SentencePieceModel: model.NewSentencePieceModel(
+		SentencePiece: model.NewSentencePiece(
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Scores: c.Floats("tokenizer.ggml.scores"),

model/models/gemma3n/model_text.go

@@ -83,7 +83,7 @@ func (m *TextModel) Forward(ctx ml.Context, batch input.Batch, cache kvcache.Cac
 
 	hiddenStates = hiddenStates.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx).Mean(ctx)
 	hiddenStates = hiddenStates.Permute(ctx, 2, 0, 1, 3).Contiguous(ctx)
-	hiddenStates = hiddenStates.Rows(ctx, ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs)))
+	hiddenStates = hiddenStates.Rows(ctx, batch.Outputs)
 
 	hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.eps)
 	return m.Output.Forward(ctx, hiddenStates), nil

model/models/gptoss/model.go

@@ -41,8 +41,8 @@ func (m *Transformer) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, err
 		}
 
 		var outputs ml.Tensor
-		if len(batch.Outputs) > 0 && i == len(m.TransformerBlocks)-1 {
-			outputs = ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs))
+		if i == len(m.TransformerBlocks)-1 {
+			outputs = batch.Outputs
 		}
 
 		hiddenStates = block.Forward(ctx, hiddenStates, positions, outputs, one, m.Cache, &m.Options)

model/models/llama/model.go

@@ -160,7 +160,7 @@ func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 
 		var outputs ml.Tensor
 		if i == len(m.Layers)-1 {
-			outputs = ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs))
+			outputs = batch.Outputs
 		}
 
 		hiddenState = layer.Forward(ctx, hiddenState, positions, outputs, m.Cache, m.Options)

model/models/llama4/model.go

@@ -176,9 +176,7 @@ 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
+	return m.TextModel.Forward(ctx, batch.Inputs, positions, batch.Outputs, batch, m.Cache), nil
 }
 
 func init() {

model/models/mistral3/model.go

@@ -159,9 +159,8 @@ 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
+	return m.TextModel.Forward(ctx, batch.Inputs, positions, batch.Outputs, batch, m.Cache), nil
 }
 
 func init() {

model/models/mllama/model.go

@@ -107,10 +107,9 @@ 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))
 
 	// TODO: attention mask, cross attention mask
-	return m.TextModel.Forward(ctx, batch.Inputs, positions, outputs, crossAttentionStates, nil, m.Cache.(*kvcache.WrapperCache)), nil
+	return m.TextModel.Forward(ctx, batch.Inputs, positions, batch.Outputs, crossAttentionStates, nil, m.Cache.(*kvcache.WrapperCache)), nil
 }
 
 func init() {

model/models/models.go

@@ -1,6 +1,7 @@
 package models
 
 import (
+	_ "github.com/ollama/ollama/model/models/bert"
 	_ "github.com/ollama/ollama/model/models/gemma2"
 	_ "github.com/ollama/ollama/model/models/gemma3"
 	_ "github.com/ollama/ollama/model/models/gemma3n"

model/models/qwen2/model.go

@@ -111,7 +111,7 @@ func (m Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 
 		var outputs ml.Tensor
 		if i == len(m.Layers)-1 {
-			outputs = ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs))
+			outputs = batch.Outputs
 		}
 
 		hiddenStates = layer.Forward(ctx, hiddenStates, positions, outputs, m.Cache, &m.Options)

model/models/qwen25vl/model.go

@@ -140,9 +140,8 @@ 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)
+	return m.TextModel.Forward(ctx, batch.Inputs, positions, batch.Outputs, batch, m.Cache)
 }
 
 func init() {

model/models/qwen3/model.go

@@ -165,7 +165,7 @@ func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 
 		var outputs ml.Tensor
 		if i == len(m.Layers)-1 {
-			outputs = ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs))
+			outputs = batch.Outputs
 		}
 
 		hiddenStates = layer.Forward(ctx, hiddenStates, positions, outputs, m.Cache, m.Options)

model/sentencepiece.go

@@ -12,18 +12,18 @@ import (
 
 const spmWhitespaceSep = "▁"
 
-type SentencePieceModel struct {
+type SentencePiece struct {
 	maxTokenLen int
 	vocab       *Vocabulary
 }
 
-var _ TextProcessor = (*SentencePieceModel)(nil)
+var _ TextProcessor = (*SentencePiece)(nil)
 
-func (spm SentencePieceModel) Vocabulary() *Vocabulary {
+func (spm SentencePiece) Vocabulary() *Vocabulary {
 	return spm.vocab
 }
 
-func NewSentencePieceModel(vocab *Vocabulary) SentencePieceModel {
+func NewSentencePiece(vocab *Vocabulary) SentencePiece {
 	logutil.Trace("Tokens", "num tokens", len(vocab.Values), "vals", vocab.Values[:5], "scores", vocab.Scores[:5], "types", vocab.Types[:5])
 
 	counter := map[int]int{}
@@ -42,17 +42,17 @@ func NewSentencePieceModel(vocab *Vocabulary) SentencePieceModel {
 		"user defined", counter[TOKEN_TYPE_USER_DEFINED], "unused", counter[TOKEN_TYPE_UNUSED], "byte", counter[TOKEN_TYPE_BYTE],
 		"max token len", maxTokenLen)
 
-	return SentencePieceModel{
+	return SentencePiece{
 		maxTokenLen: maxTokenLen,
 		vocab:       vocab,
 	}
 }
 
-func (spm SentencePieceModel) Is(id int32, special Special) bool {
+func (spm SentencePiece) Is(id int32, special Special) bool {
 	return spm.vocab.Is(id, special)
 }
 
-func (spm SentencePieceModel) Encode(s string, addSpecial bool) ([]int32, error) {
+func (spm SentencePiece) Encode(s string, addSpecial bool) ([]int32, error) {
 	fragments := []fragment{{value: s}}
 	for _, special := range spm.vocab.SpecialVocabulary() {
 		id := spm.vocab.Encode(special)
@@ -218,7 +218,7 @@ func (q *queue) Pop() interface{} {
 	return item
 }
 
-func (spm SentencePieceModel) Decode(ids []int32) (string, error) {
+func (spm SentencePiece) Decode(ids []int32) (string, error) {
 	var sb strings.Builder
 	for _, id := range ids {
 		data := spm.vocab.Decode(id)

model/sentencepiece_test.go

@@ -12,7 +12,7 @@ import (
 	"github.com/ollama/ollama/convert/sentencepiece"
 )
 
-func loadSentencePieceVocab(t *testing.T) SentencePieceModel {
+func loadSentencePieceVocab(t *testing.T) SentencePiece {
 	t.Helper()
 
 	bts, err := os.ReadFile(filepath.Join("testdata", "gemma2", "tokenizer.model"))
@@ -45,7 +45,7 @@ func loadSentencePieceVocab(t *testing.T) SentencePieceModel {
 		}
 	}
 
-	return NewSentencePieceModel(&v)
+	return NewSentencePiece(&v)
 }
 
 func TestSentencePieceEncode(t *testing.T) {
@@ -115,7 +115,7 @@ func TestSentencePieceEncode(t *testing.T) {
 	})
 }
 
-func TestSentencePieceModelDecodeByteTokens(t *testing.T) {
+func TestSentencePieceDecodeByteTokens(t *testing.T) {
 	vocab := &Vocabulary{
 		Values: []string{
 			"normal",
@@ -134,7 +134,7 @@ func TestSentencePieceModelDecodeByteTokens(t *testing.T) {
 		Scores: []float32{0, 0, 0, 0, 0},
 	}
 
-	spm := NewSentencePieceModel(vocab)
+	spm := NewSentencePiece(vocab)
 
 	tests := []struct {
 		name     string

model/wordpiece.go

@@ -0,0 +1,167 @@
+package model
+
+import (
+	"fmt"
+	"iter"
+	"strings"
+	"unicode"
+
+	"github.com/ollama/ollama/logutil"
+)
+
+type WordPiece struct {
+	vocab *Vocabulary
+}
+
+// ggmlPrefix is the prefix used by GGML vocabularies to indicate word boundaries.
+// this differs from original word piece which uses "##" to indicate subwords.
+const ggmlPrefix = "▁"
+
+var wordPieceReplacer = strings.NewReplacer(
+	" .", ".",
+	" ?", "?",
+	" !", "!",
+	" ,", ",",
+	" ' ", "'",
+	" n't", "n't",
+	" 'm", "'m",
+	" do not", " don't",
+	" 's", "'s",
+	" 've", "'ve",
+	" 're", "'re",
+)
+
+// Decode implements TextProcessor.
+func (wpm WordPiece) Decode(ids []int32) (string, error) {
+	var sb strings.Builder
+	for i, id := range ids {
+		if id < 0 || int(id) >= len(wpm.vocab.Values) {
+			return "", fmt.Errorf("invalid token id: %d", id)
+		}
+
+		var separator string
+		piece := wpm.vocab.Values[id]
+		if i > 0 &&
+			(strings.HasPrefix(piece, ggmlPrefix) ||
+				(strings.HasPrefix(piece, "[") && strings.HasSuffix(piece, "]"))) {
+			separator = " "
+		}
+
+		sb.WriteString(wordPieceReplacer.Replace(separator + strings.TrimPrefix(piece, ggmlPrefix)))
+	}
+
+	return sb.String(), nil
+}
+
+// words splits a string into words, treating CJK characters as separate words.
+// TODO: this is specifically for BERT and may need to be adjusted or refactored for other models.
+func (wpm WordPiece) words(s string) iter.Seq[string] {
+	return func(yield func(string) bool) {
+		runes := make([]rune, 0, len(s)*3)
+		for _, r := range s {
+			switch {
+			case r >= 0x4E00 && r <= 0x9FFF,
+				r >= 0x3400 && r <= 0x4DBF,
+				r >= 0x20000 && r <= 0x2A6DF,
+				r >= 0x2A700 && r <= 0x2B73F,
+				r >= 0x2B740 && r <= 0x2B81F,
+				r >= 0x2B820 && r <= 0x2CEAF,
+				r >= 0xF900 && r <= 0xFAFF,
+				r >= 0x2F800 && r <= 0x2FA1F:
+				runes = append(runes, ' ', r, ' ')
+			default:
+				runes = append(runes, r)
+			}
+		}
+
+		for w := range strings.FieldsFuncSeq(string(runes), unicode.IsSpace) {
+			// split on but keep punctuation
+			var start int
+			for start < len(w) {
+				end := strings.IndexFunc(w[start:], unicode.IsPunct)
+				if end < 0 {
+					end = len(w) - start
+				} else if end == 0 {
+					end = 1
+				}
+
+				if !yield(w[start : start+end]) {
+					return
+				}
+
+				start += end
+			}
+		}
+	}
+}
+
+// Encode implements TextProcessor.
+func (wpm WordPiece) Encode(s string, addSpecial bool) ([]int32, error) {
+	var ids []int32
+
+	// TODO: use [UNK] from config
+	unk := wpm.vocab.Encode("[UNK]")
+	for word := range wpm.words(s) {
+		var start int
+		var pieces []int32
+		for start < len(word) {
+			end := len(word)
+
+			var piece int32
+			for start < end {
+				subword := word[start:end]
+				if start == 0 {
+					subword = ggmlPrefix + subword
+				}
+
+				// TODO: some models might not want [ToLower]
+				piece = wpm.vocab.Encode(strings.ToLower(subword))
+				if piece >= 0 {
+					break
+				}
+
+				end--
+			}
+
+			if piece < 0 {
+				// Unknown token
+				pieces = pieces[:0]
+				break
+			}
+
+			pieces = append(pieces, piece)
+			start = end
+		}
+
+		if len(pieces) > 0 {
+			ids = append(ids, pieces...)
+		} else {
+			ids = append(ids, unk)
+		}
+	}
+
+	if addSpecial && len(ids) > 0 {
+		ids = wpm.vocab.addSpecials(ids)
+	}
+
+	logutil.Trace("encoded", "string", s, "ids", ids)
+	return ids, nil
+}
+
+// Is implements TextProcessor.
+func (wpm WordPiece) Is(id int32, special Special) bool {
+	return wpm.vocab.Is(id, special)
+}
+
+// Vocabulary implements TextProcessor.
+func (wpm WordPiece) Vocabulary() *Vocabulary {
+	return wpm.vocab
+}
+
+var _ TextProcessor = (*WordPiece)(nil)
+
+func NewWordPiece(vocab *Vocabulary) WordPiece {
+	return WordPiece{
+		vocab: vocab,
+	}
+}

model/wordpiece_test.go

@@ -0,0 +1,51 @@
+package model
+
+import (
+	"slices"
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+)
+
+func TestWordPiece(t *testing.T) {
+	wpm := NewWordPiece(
+		&Vocabulary{
+			Values: []string{"[UNK]", "[CLS]", "[SEP]", "▁hello", "▁world", "s", "▁!", "▁@", "▁#"},
+			AddBOS: true,
+			AddEOS: true,
+			BOS:    []int32{1},
+			EOS:    []int32{2},
+		})
+
+	ids, err := wpm.Encode("Hello world!", true)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	if diff := cmp.Diff([]int32{1, 3, 4, 6, 2}, ids); diff != "" {
+		t.Errorf("unexpected ids (-want +got):\n%s", diff)
+	}
+
+	words, err := wpm.Decode(ids)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	if diff := cmp.Diff("[CLS] hello world! [SEP]", words); diff != "" {
+		t.Errorf("unexpected words (-want +got):\n%s", diff)
+	}
+}
+
+func TestWordPieceWords(t *testing.T) {
+	var wpm WordPiece
+
+	basic := slices.Collect(wpm.words("Hey friend!     How are you?!?"))
+	if diff := cmp.Diff([]string{"Hey", "friend", "!", "How", "are", "you", "?", "!", "?"}, basic); diff != "" {
+		t.Errorf("unexpected words (-want +got):\n%s", diff)
+	}
+
+	chinese := slices.Collect(wpm.words("野口里佳 Noguchi Rika"))
+	if diff := cmp.Diff([]string{"野", "口", "里", "佳", "Noguchi", "Rika"}, chinese); diff != "" {
+		t.Errorf("unexpected words (-want +got):\n%s", diff)
+	}
+}

openai/openai.go

@@ -76,8 +76,9 @@ type JsonSchema struct {
 }
 
 type EmbedRequest struct {
-	Input any    `json:"input"`
-	Model string `json:"model"`
+	Input      any    `json:"input"`
+	Model      string `json:"model"`
+	Dimensions int    `json:"dimensions,omitempty"`
 }
 
 type StreamOptions struct {
@@ -1005,7 +1006,7 @@ func EmbeddingsMiddleware() gin.HandlerFunc {
 		}
 
 		var b bytes.Buffer
-		if err := json.NewEncoder(&b).Encode(api.EmbedRequest{Model: req.Model, Input: req.Input}); err != nil {
+		if err := json.NewEncoder(&b).Encode(api.EmbedRequest{Model: req.Model, Input: req.Input, Dimensions: req.Dimensions}); err != nil {
 			c.AbortWithStatusJSON(http.StatusInternalServerError, NewError(http.StatusInternalServerError, err.Error()))
 			return
 		}

parser/parser.go

@@ -62,14 +62,15 @@ func (f Modelfile) CreateRequest(relativeDir string) (*api.CreateRequest, error)
 	for _, c := range f.Commands {
 		switch c.Name {
 		case "model":
-			path, err := expandPath(c.Args, relativeDir)
+			name := c.Args.(string)
+			path, err := expandPath(name, relativeDir)
 			if err != nil {
 				return nil, err
 			}
 
 			digestMap, err := fileDigestMap(path)
 			if errors.Is(err, os.ErrNotExist) {
-				req.From = c.Args
+				req.From = name
 				continue
 			} else if err != nil {
 				return nil, err
@@ -83,7 +84,8 @@ func (f Modelfile) CreateRequest(relativeDir string) (*api.CreateRequest, error)
 				}
 			}
 		case "adapter":
-			path, err := expandPath(c.Args, relativeDir)
+			adapter := c.Args.(string)
+			path, err := expandPath(adapter, relativeDir)
 			if err != nil {
 				return nil, err
 			}
@@ -95,21 +97,25 @@ func (f Modelfile) CreateRequest(relativeDir string) (*api.CreateRequest, error)
 
 			req.Adapters = digestMap
 		case "template":
-			req.Template = c.Args
+			template := c.Args.(string)
+			req.Template = template
 		case "system":
-			req.System = c.Args
+			system := c.Args.(string)
+			req.System = system
 		case "license":
-			licenses = append(licenses, c.Args)
+			license := c.Args.(string)
+			licenses = append(licenses, license)
 		case "message":
-			role, msg, _ := strings.Cut(c.Args, ": ")
-			messages = append(messages, api.Message{Role: role, Content: msg})
-		default:
+			msg := c.Args.(*Message)
+			messages = append(messages, api.Message{Role: msg.Role, Content: msg.Content})
+		case "parameter":
 			if slices.Contains(deprecatedParameters, c.Name) {
-				fmt.Printf("warning: parameter %s is deprecated\n", c.Name)
+				fmt.Printf("warning: parameter '%s' is deprecated\n", c.Name)
 				break
 			}
 
-			ps, err := api.FormatParams(map[string][]string{c.Name: {c.Args}})
+			param := c.Args.(*Parameter)
+			ps, err := api.FormatParams(map[string][]string{param.Name: {param.Value}})
 			if err != nil {
 				return nil, err
 			}
@@ -123,6 +129,8 @@ func (f Modelfile) CreateRequest(relativeDir string) (*api.CreateRequest, error)
 					params[k] = v
 				}
 			}
+		default:
+			return nil, fmt.Errorf("warning: unknown command '%s'", c.Name)
 		}
 	}
 
@@ -312,7 +320,17 @@ func filesForModel(path string) ([]string, error) {
 
 type Command struct {
 	Name string
-	Args string
+	Args any
+}
+
+type Parameter struct {
+	Name  string
+	Value string
+}
+
+type Message struct {
+	Role    string
+	Content string
 }
 
 func (c Command) String() string {
@@ -321,12 +339,16 @@ func (c Command) String() string {
 	case "model":
 		fmt.Fprintf(&sb, "FROM %s", c.Args)
 	case "license", "template", "system", "adapter":
-		fmt.Fprintf(&sb, "%s %s", strings.ToUpper(c.Name), quote(c.Args))
+		data := c.Args.(string)
+		fmt.Fprintf(&sb, "%s %s", strings.ToUpper(c.Name), quote(data))
 	case "message":
-		role, message, _ := strings.Cut(c.Args, ": ")
-		fmt.Fprintf(&sb, "MESSAGE %s %s", role, quote(message))
+		data := c.Args.(*Message)
+		fmt.Fprintf(&sb, "MESSAGE %s %s", data.Role, quote(data.Content))
+	case "parameter":
+		data := c.Args.(*Parameter)
+		fmt.Fprintf(&sb, "PARAMETER %s %s", data.Name, quote(data.Value))
 	default:
-		fmt.Fprintf(&sb, "PARAMETER %s %s", c.Name, quote(c.Args))
+		fmt.Printf("unknown command '%s'\n", c.Name)
 	}
 
 	return sb.String()
@@ -366,7 +388,6 @@ func ParseFile(r io.Reader) (*Modelfile, error) {
 	var curr state
 	var currLine int = 1
 	var b bytes.Buffer
-	var role string
 
 	var f Modelfile
 
@@ -413,6 +434,7 @@ func ParseFile(r io.Reader) (*Modelfile, error) {
 				case "parameter":
 					// transition to stateParameter which sets command name
 					next = stateParameter
+					cmd.Name = s
 				case "message":
 					// transition to stateMessage which validates the message role
 					next = stateMessage
@@ -421,16 +443,37 @@ func ParseFile(r io.Reader) (*Modelfile, error) {
 					cmd.Name = s
 				}
 			case stateParameter:
-				cmd.Name = b.String()
+				s, ok := unquote(strings.TrimSpace(b.String()))
+				if !ok || isSpace(r) {
+					if _, err := b.WriteRune(r); err != nil {
+						return nil, err
+					}
+
+					continue
+				}
+				cmd.Args = &Parameter{
+					Name: s,
+				}
 			case stateMessage:
-				if !isValidMessageRole(b.String()) {
+				s, ok := unquote(strings.TrimSpace(b.String()))
+				if !ok || isSpace(r) {
+					if _, err := b.WriteRune(r); err != nil {
+						return nil, err
+					}
+
+					continue
+				}
+
+				if !isValidMessageRole(s) {
 					return nil, &ParserError{
 						LineNumber: currLine,
 						Msg:        errInvalidMessageRole.Error(),
 					}
 				}
 
-				role = b.String()
+				cmd.Args = &Message{
+					Role: s,
+				}
 			case stateComment, stateNil:
 				// pass
 			case stateValue:
@@ -443,12 +486,16 @@ func ParseFile(r io.Reader) (*Modelfile, error) {
 					continue
 				}
 
-				if role != "" {
-					s = role + ": " + s
-					role = ""
+				switch cmd.Name {
+				case "parameter":
+					p := cmd.Args.(*Parameter)
+					p.Value = s
+				case "message":
+					m := cmd.Args.(*Message)
+					m.Content = s
+				default:
+					cmd.Args = s
 				}
-
-				cmd.Args = s
 				f.Commands = append(f.Commands, cmd)
 			}
 
@@ -473,11 +520,16 @@ func ParseFile(r io.Reader) (*Modelfile, error) {
 			return nil, io.ErrUnexpectedEOF
 		}
 
-		if role != "" {
-			s = role + ": " + s
+		switch cmd.Name {
+		case "parameter":
+			c := cmd.Args.(*Parameter)
+			c.Value = s
+		case "message":
+			c := cmd.Args.(*Message)
+			c.Content = s
+		default:
+			cmd.Args = s
 		}
-
-		cmd.Args = s
 		f.Commands = append(f.Commands, cmd)
 	default:
 		return nil, io.ErrUnexpectedEOF

parser/parser_test.go

@@ -47,8 +47,8 @@ TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
 		{Name: "model", Args: "model1"},
 		{Name: "adapter", Args: "adapter1"},
 		{Name: "license", Args: "MIT"},
-		{Name: "param1", Args: "value1"},
-		{Name: "param2", Args: "value2"},
+		{Name: "parameter", Args: &Parameter{"param1", "value1"}},
+		{Name: "parameter", Args: &Parameter{"param2", "value2"}},
 		{Name: "template", Args: "{{ if .System }}<|start_header_id|>system<|end_header_id|>\n\n{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>\n\n{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>\n\n{{ .Response }}<|eot_id|>"},
 	}
 
@@ -80,8 +80,8 @@ TEMPLATE """   {{ if .System }}<|start_header_id|>system<|end_header_id|>
 		{Name: "model", Args: "     model 1"},
 		{Name: "adapter", Args: "adapter3"},
 		{Name: "license", Args: "MIT       "},
-		{Name: "param1", Args: "value1"},
-		{Name: "param2", Args: "value2"},
+		{Name: "parameter", Args: &Parameter{"param1", "value1"}},
+		{Name: "parameter", Args: &Parameter{"param2", "value2"}},
 		{Name: "template", Args: "   {{ if .System }}<|start_header_id|>system<|end_header_id|>\n\n{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>\n\n{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>\n\n{{ .Response }}<|eot_id|>   "},
 	}
 
@@ -101,7 +101,7 @@ func TestParseFileFrom(t *testing.T) {
 		},
 		{
 			"FROM \"FOO BAR\"\nPARAMETER param1 value1",
-			[]Command{{Name: "model", Args: "FOO BAR"}, {Name: "param1", Args: "value1"}},
+			[]Command{{Name: "model", Args: "FOO BAR"}, {Name: "parameter", Args: &Parameter{"param1", "value1"}}},
 			nil,
 		},
 		{
@@ -149,12 +149,12 @@ func TestParseFileFrom(t *testing.T) {
 		},
 		{
 			"PARAMETER param1 value1\nFROM foo",
-			[]Command{{Name: "param1", Args: "value1"}, {Name: "model", Args: "foo"}},
+			[]Command{{Name: "parameter", Args: &Parameter{"param1", "value1"}}, {Name: "model", Args: "foo"}},
 			nil,
 		},
 		{
 			"PARAMETER what the \nFROM lemons make lemonade ",
-			[]Command{{Name: "what", Args: "the"}, {Name: "model", Args: "lemons make lemonade"}},
+			[]Command{{Name: "parameter", Args: &Parameter{"what", "the"}}, {Name: "model", Args: "lemons make lemonade"}},
 			nil,
 		},
 	}
@@ -211,7 +211,7 @@ MESSAGE system You are a file parser. Always parse things.
 `,
 			[]Command{
 				{Name: "model", Args: "foo"},
-				{Name: "message", Args: "system: You are a file parser. Always parse things."},
+				{Name: "message", Args: &Message{"system", "You are a file parser. Always parse things."}},
 			},
 			nil,
 		},
@@ -221,7 +221,7 @@ FROM foo
 MESSAGE system You are a file parser. Always parse things.`,
 			[]Command{
 				{Name: "model", Args: "foo"},
-				{Name: "message", Args: "system: You are a file parser. Always parse things."},
+				{Name: "message", Args: &Message{"system", "You are a file parser. Always parse things."}},
 			},
 			nil,
 		},
@@ -234,9 +234,9 @@ MESSAGE assistant Hello, I want to parse all the things!
 `,
 			[]Command{
 				{Name: "model", Args: "foo"},
-				{Name: "message", Args: "system: You are a file parser. Always parse things."},
-				{Name: "message", Args: "user: Hey there!"},
-				{Name: "message", Args: "assistant: Hello, I want to parse all the things!"},
+				{Name: "message", Args: &Message{"system", "You are a file parser. Always parse things."}},
+				{Name: "message", Args: &Message{"user", "Hey there!"}},
+				{Name: "message", Args: &Message{"assistant", "Hello, I want to parse all the things!"}},
 			},
 			nil,
 		},
@@ -244,12 +244,12 @@ MESSAGE assistant Hello, I want to parse all the things!
 			`
 FROM foo
 MESSAGE system """
-You are a multiline file parser. Always parse things.
+You are a multiline file "parser". Always parse things.
 """
 			`,
 			[]Command{
 				{Name: "model", Args: "foo"},
-				{Name: "message", Args: "system: \nYou are a multiline file parser. Always parse things.\n"},
+				{Name: "message", Args: &Message{"system", "\nYou are a multiline file \"parser\". Always parse things.\n"}},
 			},
 			nil,
 		},
@@ -514,7 +514,7 @@ func TestParseFileParameters(t *testing.T) {
 
 			assert.Equal(t, []Command{
 				{Name: "model", Args: "foo"},
-				{Name: v.name, Args: v.value},
+				{Name: "parameter", Args: &Parameter{v.name, v.value}},
 			}, modelfile.Commands)
 		})
 	}
@@ -617,8 +617,8 @@ SYSTEM You are a utf16 file.
 
 	expected := []Command{
 		{Name: "model", Args: "bob"},
-		{Name: "param1", Args: "1"},
-		{Name: "param2", Args: "4096"},
+		{Name: "parameter", Args: &Parameter{"param1", "1"}},
+		{Name: "parameter", Args: &Parameter{"param2", "4096"}},
 		{Name: "system", Args: "You are a utf16 file."},
 	}
 

parser/token_parser.go

@@ -1,135 +0,0 @@
-package parser
-
-import (
-	"encoding/json"
-	"errors"
-	"strings"
-
-	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/harmony"
-)
-
-type TokenParserType int
-
-const (
-	TokenParserTypeDefault TokenParserType = iota
-	TokenParserTypeHarmony
-)
-
-type TokenParser struct {
-	messageHandler MessageHandler
-	parserEngine   ParserInternals
-	toolParser     ToolParser
-	lastToken      string
-	tokenRepeat    int
-	repeatLimit    int
-}
-
-const defaultTokenRepeatLimit = 30
-
-type MessageHandler interface {
-	AddContent(token string) (content, thinking string, toolContent string)
-}
-
-type ParserInternals interface {
-	AddImplicitStartOrPrefill(prefillString string)
-	ConstraintsAllowed() bool
-}
-
-type ToolParser interface {
-	Add(token string)
-	Drain() (toolName *string, toolContent string)
-}
-
-// Default implementation for the TokenParser interface as a no-op passthrough
-type defaultMessageHandler struct{}
-
-func (defaultMessageHandler) AddContent(token string) (string, string, string) {
-	return token, "", ""
-}
-
-type defaultEngine struct{}
-
-func (defaultEngine) AddImplicitStartOrPrefill(prefillString string) {}
-
-func (defaultEngine) ConstraintsAllowed() bool {
-	return true
-}
-
-type defaultToolParser struct{}
-
-func (defaultToolParser) Add(token string) {}
-
-func (defaultToolParser) Drain() (*string, string) { return nil, "" }
-
-func NewTokenParser(parserType TokenParserType, prefillString string) TokenParser {
-	switch parserType {
-	case TokenParserTypeHarmony:
-		harmonyMessageHandler := harmony.NewHarmonyMessageHandler()
-		harmonyMessageHandler.HarmonyParser.AddImplicitStartOrPrefill(prefillString)
-		return TokenParser{
-			messageHandler: harmonyMessageHandler,
-			parserEngine:   harmonyMessageHandler.HarmonyParser,
-			toolParser:     harmonyMessageHandler.ToolParser,
-			repeatLimit:    defaultTokenRepeatLimit,
-		}
-
-	default:
-		return TokenParser{
-			messageHandler: defaultMessageHandler{},
-			parserEngine:   defaultEngine{},
-			toolParser:     defaultToolParser{},
-			repeatLimit:    30,
-		}
-	}
-}
-
-func (p *TokenParser) AddContent(token string) (string, string, error) {
-	if p.repeatLimitReached(token) {
-		return "", "", errors.New("token repeat limit reached")
-	}
-	content, thinking, toolContent := p.messageHandler.AddContent(token)
-	p.toolParser.Add(toolContent)
-	return content, thinking, nil
-}
-
-// repeatLimitReached updates repeat counters and returns true if the repeat limit is reached.
-func (p *TokenParser) repeatLimitReached(token string) bool {
-	if p == nil {
-		return false
-	}
-	trimmed := strings.TrimSpace(token)
-	if trimmed == p.lastToken {
-		p.tokenRepeat++
-	} else {
-		p.tokenRepeat = 0
-	}
-	p.lastToken = trimmed
-
-	return p.tokenRepeat >= p.repeatLimit
-}
-
-func (p *TokenParser) ConstraintsAllowed() bool {
-	return p.parserEngine.ConstraintsAllowed()
-}
-
-// TODO: update to work with multiple toolcalls - unmarshalling should also happen on parser level
-func (p *TokenParser) Drain() []api.ToolCall {
-	toolName, toolContent := p.toolParser.Drain()
-	if toolName != nil {
-		*toolName = strings.TrimPrefix(*toolName, "functions.")
-		var args api.ToolCallFunctionArguments
-		if err := json.Unmarshal([]byte(toolContent), &args); err != nil {
-			return nil
-		}
-		return []api.ToolCall{
-			{
-				Function: api.ToolCallFunction{
-					Name:      *toolName,
-					Arguments: args,
-				},
-			},
-		}
-	}
-	return nil
-}

runner/ollamarunner/runner.go

@@ -18,7 +18,6 @@ import (
 	"reflect"
 	"regexp"
 	"runtime"
-	"runtime/debug"
 	"strconv"
 	"strings"
 	"sync"
@@ -35,7 +34,6 @@ import (
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
-	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/runner/common"
 	"github.com/ollama/ollama/sample"
 
@@ -62,11 +60,6 @@ type Sequence struct {
 	// tokens that have been generated but not returned yet (e.g. for stop sequences)
 	pendingResponses []string
 
-	// startGate
-	startGate *sync.Mutex
-
-	grammarReady bool
-
 	// input cache being used by this sequence
 	cache *InputCacheSlot
 
@@ -169,7 +162,6 @@ func (s *Server) NewSequence(prompt string, images []llm.ImageData, params NewSe
 
 	// TODO(jessegross): Ingest cached history for grammar
 
-	startGate := &sync.Mutex{}
 	return &Sequence{
 		ctxs:                ctxs,
 		mmStore:             mmStore,
@@ -185,8 +177,6 @@ func (s *Server) NewSequence(prompt string, images []llm.ImageData, params NewSe
 		embeddingOnly:       params.embedding,
 		stop:                params.stop,
 		numKeep:             params.numKeep,
-		startGate:           startGate,
-		grammarReady:        false,
 	}, nil
 }
 
@@ -477,6 +467,7 @@ func (s *Server) forwardBatch(pendingBatch batchState) (nextBatch batchState, er
 
 	// Prepare the seqs and batch, but defer the input token values as we may not be ready yet
 	var batchInputs []*input.Input
+	var batchOutputs []int32
 	var batch input.Batch
 
 	resumeSeq := -1
@@ -559,9 +550,9 @@ func (s *Server) forwardBatch(pendingBatch batchState) (nextBatch batchState, er
 			batch.Positions = append(batch.Positions, int32(len(seq.cache.Inputs)+len(seq.pendingInputs)))
 			batch.Sequences = append(batch.Sequences, seq.cache.Id)
 
-			seq.iBatch = len(batch.Outputs)
-			if i+1 == len(seq.inputs) {
-				batch.Outputs = append(batch.Outputs, int32(len(batchInputs)-1))
+			seq.iBatch = len(batchOutputs)
+			if i+1 == len(seq.inputs) || seq.embeddingOnly {
+				batchOutputs = append(batchOutputs, int32(len(batchInputs)-1))
 			}
 			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)
@@ -586,6 +577,7 @@ func (s *Server) forwardBatch(pendingBatch batchState) (nextBatch batchState, er
 
 	// Actual batchInputs values will be injected into the batch.Inputs tensor before calling Compute
 	batch.Inputs = nextBatch.ctx.Input().Empty(ml.DTypeI32, len(batchInputs))
+	batch.Outputs = nextBatch.ctx.Input().FromIntSlice(batchOutputs, len(batchOutputs))
 	nextBatch.modelOutput, err = model.Forward(nextBatch.ctx, s.model, batch)
 	if err != nil {
 		err = fmt.Errorf("failed to build graph: %w", err)
@@ -713,20 +705,13 @@ func (s *Server) computeBatch(activeBatch batchState) {
 		}
 
 		// sample a token
-		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)
-
-		if !seq.grammarReady {
-			seq.startGate.Lock()
-		}
+		vocabSize := len(outputs) / activeBatch.batch.Outputs.Dim(0)
+		logutil.Trace("computeBatch: vocab details", "batchID", activeBatch.id, "seqIdx", i, "len(logits)", len(outputs), "len(activeBatch.batch.Outputs)", activeBatch.batch.Outputs.Dim(0), "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
 		}
-		if !seq.grammarReady {
-			seq.startGate.Unlock()
-		}
 
 		nextBatchTokens[i].Token = token
 
@@ -829,7 +814,7 @@ 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{
@@ -844,12 +829,6 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 		return
 	}
 
-	tokenParser := parser.NewTokenParser(req.ParserType, req.PrefillString)
-	// this accounts for the default case and also the case where there is a prefill which moves the state of the parser to allow for constraints
-	if tokenParser.ConstraintsAllowed() {
-		seq.grammarReady = true
-	}
-
 	// Ensure there is a place to put the sequence, released when removed from s.seqs
 	if err := s.seqsSem.Acquire(r.Context(), 1); err != nil {
 		if errors.Is(err, context.Canceled) {
@@ -893,28 +872,8 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 			return
 		case content, ok := <-seq.responses:
 			if ok {
-				if !seq.grammarReady {
-					seq.startGate.Lock()
-				}
-				var thinking string
-				var err error
-				content, thinking, err = tokenParser.AddContent(content)
-				if err != nil {
-					http.Error(w, err.Error(), http.StatusInternalServerError)
-					close(seq.quit)
-					return
-				}
-
-				// only apply the grammar once
-				if tokenParser.ConstraintsAllowed() && !seq.grammarReady {
-					seq.sampler.SetGrammar(grammar, &s.mu)
-					seq.grammarReady = true
-					seq.startGate.Unlock()
-				}
-
 				if err := json.NewEncoder(w).Encode(&llm.CompletionResponse{
-					Content:  content,
-					Thinking: thinking,
+					Content: content,
 				}); err != nil {
 					http.Error(w, fmt.Sprintf("failed to encode response: %v", err), http.StatusInternalServerError)
 					close(seq.quit)
@@ -923,9 +882,7 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 
 				flusher.Flush()
 			} else {
-				toolCalls := tokenParser.Drain()
 				if err := json.NewEncoder(w).Encode(&llm.CompletionResponse{
-					ToolCalls:          toolCalls,
 					Done:               true,
 					DoneReason:         seq.doneReason,
 					PromptEvalCount:    seq.numPromptInputs,
@@ -938,9 +895,6 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 
 				return
 			}
-			if !seq.grammarReady {
-				seq.startGate.Unlock()
-			}
 		}
 	}
 }
@@ -1094,12 +1048,8 @@ func (s *Server) reserveWorstCaseGraph() error {
 		batch.Positions[i] = int32(i)
 	}
 
-	batch.Outputs = make([]int32, s.parallel)
-	for i := range batch.Outputs {
-		batch.Outputs[i] = int32(i)
-	}
-
 	batch.Inputs = ctx.Input().FromIntSlice(batchInputs, len(batchInputs))
+	batch.Outputs = ctx.Input().Empty(ml.DTypeI32, s.parallel)
 
 	cache := s.model.Config().Cache
 	if cache != nil {
@@ -1133,9 +1083,13 @@ func (s *Server) allocModel(
 	// Convert memory allocation panics to errors
 	defer func() {
 		if r := recover(); r != nil {
-			debug.PrintStack()
 			if err, ok := r.(error); ok {
-				panicErr = err
+				var noMem ml.ErrNoMem
+				if errors.As(err, &noMem) {
+					panicErr = noMem
+				} else {
+					panic(r)
+				}
 			} else {
 				panic(r)
 			}

sample/samplers.go

@@ -5,7 +5,6 @@ import (
 	"math"
 	"math/rand/v2"
 	"slices"
-	"sync"
 
 	"github.com/ollama/ollama/llama"
 	"github.com/ollama/ollama/model"
@@ -26,12 +25,6 @@ type Sampler struct {
 	grammar     *GrammarSampler
 }
 
-func (s *Sampler) SetGrammar(grammar *GrammarSampler, mutex *sync.Mutex) {
-	mutex.Lock()
-	defer mutex.Unlock()
-	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")

scripts/build_windows.ps1

@@ -78,7 +78,7 @@ function checkEnv() {
 }
 
 
-function buildOllama() {
+function buildCPU() {
     mkdir -Force -path "${script:DIST_DIR}\"
     if ($script:ARCH -ne "arm64") {
         Remove-Item -ea 0 -recurse -force -path "${script:SRC_DIR}\dist\windows-${script:ARCH}"
@@ -90,20 +90,72 @@ function buildOllama() {
         if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
         & cmake --install build --component CPU --strip
         if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
+    }
+}
 
+function buildCUDA11() {
+    # CUDA v11 claims to be compatible with MSVC 2022, but the latest updates are no longer compatible
+    # 19.40 is the last compiler version that works, but recent udpates are 19.43
+    # So this pins to MSVC 2019 for best compatibility
+    mkdir -Force -path "${script:DIST_DIR}\"
+    if ($script:ARCH -ne "arm64") {
         $hashEnv = @{}
         Get-ChildItem env: | foreach { $hashEnv[$_.Name] = $_.Value }
-        if ("$script:CUDA_DIRS".Contains("v12")) {
-            $hashEnv.Keys | foreach { if ($_.Contains("CUDA_PATH_V12")) { $v12="$_" }}
-            $env:CUDAToolkit_ROOT=$hashEnv[$v12]
-            write-host "Building CUDA v12 backend libraries"
-            & cmake --fresh --preset "CUDA 12" --install-prefix $script:DIST_DIR
+        if ("$script:CUDA_DIRS".Contains("v11")) {
+            $hashEnv.Keys | foreach { if ($_.Contains("CUDA_PATH_V11")) { $x=$hashEnv[$_]; if (test-path -literalpath "$x\bin\nvcc.exe" ) { $cuda=$x}  }}
+            write-host "Building CUDA v11 backend libraries $cuda"
+            $env:CUDAToolkit_ROOT=$cuda
+            & cmake --fresh --preset "CUDA 11" -T cuda="$cuda" -DCMAKE_CUDA_COMPILER="$cuda\bin\nvcc.exe" -G "Visual Studio 16 2019" --install-prefix $script:DIST_DIR -DOLLAMA_RUNNER_DIR="cuda_v11"
+            if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
+            & cmake --build --preset "CUDA 11"  --config Release --parallel $script:JOBS
+            if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
+            & cmake --install build --component "CUDA" --strip
+            if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
+        }
+    }
+}
+
+function buildCUDA12() {
+    mkdir -Force -path "${script:DIST_DIR}\"
+    if ($script:ARCH -ne "arm64") {
+        $hashEnv = @{}
+        Get-ChildItem env: | foreach { $hashEnv[$_.Name] = $_.Value }
+        if ("$script:CUDA_DIRS".Contains("v12.8")) {
+            $hashEnv.Keys | foreach { if ($_.Contains("CUDA_PATH_V12_8")) { $x=$hashEnv[$_]; if (test-path -literalpath "$x\bin\nvcc.exe" ) { $cuda=$x}  }}
+            write-host "Building CUDA v12 backend libraries $cuda"
+            $env:CUDAToolkit_ROOT=$cuda
+            & cmake --fresh --preset "CUDA 12" -T cuda="$cuda" --install-prefix $script:DIST_DIR -DOLLAMA_RUNNER_DIR="cuda_v12"
             if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
             & cmake --build --preset "CUDA 12"  --config Release --parallel $script:JOBS
             if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
             & cmake --install build --component "CUDA" --strip
             if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
         }
+    }
+}
+
+function buildCUDA13() {
+    mkdir -Force -path "${script:DIST_DIR}\"
+    if ($script:ARCH -ne "arm64") {
+        $hashEnv = @{}
+        Get-ChildItem env: | foreach { $hashEnv[$_.Name] = $_.Value }
+        if ("$script:CUDA_DIRS".Contains("v13")) {
+            $hashEnv.Keys | foreach { if ($_.Contains("CUDA_PATH_V13")) { $x=$hashEnv[$_]; if (test-path -literalpath "$x\bin\nvcc.exe" ) { $cuda=$x}  }}
+            $env:CUDAToolkit_ROOT=$cuda
+            write-host "Building CUDA v13 backend libraries $cuda"
+            & cmake --fresh --preset "CUDA 13" -T cuda="$cuda" --install-prefix $script:DIST_DIR -DOLLAMA_RUNNER_DIR="cuda_v13"
+            if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
+            & cmake --build --preset "CUDA 13"  --config Release --parallel $script:JOBS
+            if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
+            & cmake --install build --component "CUDA" --strip
+            if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
+        }
+    }
+}
+
+function buildROCm() {
+    mkdir -Force -path "${script:DIST_DIR}\"
+    if ($script:ARCH -ne "arm64") {
         if ($env:HIP_PATH) {
             write-host "Building ROCm backend libraries"
             if (-Not (get-command -ErrorAction silent ninja)) {
@@ -129,6 +181,10 @@ function buildOllama() {
             if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
         }
     }
+}
+
+function buildOllama() {
+    mkdir -Force -path "${script:DIST_DIR}\"
     write-host "Building ollama CLI"
     & go build -trimpath -ldflags "-s -w -X=github.com/ollama/ollama/version.Version=$script:VERSION -X=github.com/ollama/ollama/server.mode=release" .
     if ($LASTEXITCODE -ne 0) { exit($LASTEXITCODE)}
@@ -236,6 +292,10 @@ function distZip() {
 checkEnv
 try {
     if ($($args.count) -eq 0) {
+        buildCPU
+        buildCUDA12
+        buildCUDA13
+        buildROCm
         buildOllama
         buildApp
         gatherDependencies

server/routes.go

@@ -36,7 +36,6 @@ import (
 	"github.com/ollama/ollama/llm"
 	"github.com/ollama/ollama/logutil"
 	"github.com/ollama/ollama/openai"
-	"github.com/ollama/ollama/parser"
 	"github.com/ollama/ollama/server/internal/client/ollama"
 	"github.com/ollama/ollama/server/internal/registry"
 	"github.com/ollama/ollama/template"
@@ -47,6 +46,18 @@ import (
 	"github.com/ollama/ollama/version"
 )
 
+func shouldUseHarmony(model *Model) bool {
+	if slices.Contains([]string{"gptoss", "gpt-oss"}, model.Config.ModelFamily) {
+		// heuristic to check whether the template expects to be parsed via harmony:
+		// search for harmony tags that are nearly always used
+		if model.Template.Contains("<|start|>") && model.Template.Contains("<|end|>") {
+			return true
+		}
+	}
+
+	return false
+}
+
 func experimentEnabled(name string) bool {
 	return slices.Contains(strings.Split(os.Getenv("OLLAMA_EXPERIMENT"), ","), name)
 }
@@ -196,17 +207,13 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 		return
 	}
 
-	useHarmony := harmony.ShouldUseHarmony(m.Config.ModelFamily, m.Template) && !req.Raw
-	var parserType parser.TokenParserType
+	useHarmony := shouldUseHarmony(m) && !req.Raw
+	var harmonyMessageHandler *harmony.HarmonyMessageHandler
+	var harmonyToolParser *harmony.HarmonyToolCallAccumulator
 	if useHarmony {
-		parserType = parser.TokenParserTypeHarmony
-	} else {
-		parserType = parser.TokenParserTypeDefault
-	}
-	var functionNameMap *harmony.FunctionNameMap
-
-	if useHarmony {
-		functionNameMap = harmony.NewFunctionNameMap()
+		harmonyMessageHandler = harmony.NewHarmonyMessageHandler()
+		harmonyMessageHandler.HarmonyParser.AddImplicitStart()
+		harmonyToolParser = harmonyMessageHandler.CreateToolParser()
 	}
 
 	// Validate Think value: string values currently only allowed for gptoss models
@@ -350,19 +357,16 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 		var sb strings.Builder
 		defer close(ch)
 		if err := r.Completion(c.Request.Context(), llm.CompletionRequest{
-			Prompt:     prompt,
-			Images:     images,
-			Format:     req.Format,
-			Options:    opts,
-			ParserType: parserType,
+			Prompt:  prompt,
+			Images:  images,
+			Format:  req.Format,
+			Options: opts,
 		}, func(cr llm.CompletionResponse) {
 			res := api.GenerateResponse{
 				Model:     req.Model,
 				CreatedAt: time.Now().UTC(),
 				Response:  cr.Content,
 				Done:      cr.Done,
-				Thinking:  cr.Thinking,
-				ToolCalls: cr.ToolCalls,
 				Metrics: api.Metrics{
 					PromptEvalCount:    cr.PromptEvalCount,
 					PromptEvalDuration: cr.PromptEvalDuration,
@@ -371,22 +375,12 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 				},
 			}
 
-			if res.Done {
-				res.DoneReason = cr.DoneReason.String()
-				res.TotalDuration = time.Since(checkpointStart)
-				res.LoadDuration = checkpointLoaded.Sub(checkpointStart)
-			}
-
 			if useHarmony {
-				for i, tool := range res.ToolCalls {
-					res.ToolCalls[i].Function.Name = functionNameMap.OriginalFromConverted(tool.Function.Name)
-				}
-				if res.Response != "" || res.Thinking != "" || len(res.ToolCalls) > 0 || res.Done {
-					ch <- res
-				}
-				return
-			}
-			if thinkingState != nil {
+				content, thinking, toolContent := harmonyMessageHandler.AddContent(cr.Content, harmonyToolParser)
+				res.Response = content
+				res.Thinking = thinking
+				harmonyToolParser.Add(toolContent)
+			} else if thinkingState != nil {
 				thinking, content := thinkingState.AddContent(cr.Content)
 				res.Thinking = thinking
 				res.Response = content
@@ -397,6 +391,30 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 			}
 
 			if cr.Done {
+				if useHarmony {
+					toolName, toolContent := harmonyToolParser.Drain()
+					if toolName != nil {
+						*toolName = strings.TrimPrefix(*toolName, "functions.")
+						var args api.ToolCallFunctionArguments
+						if err := json.Unmarshal([]byte(toolContent), &args); err != nil {
+							errStr := fmt.Sprintf("error parsing tool call: raw='%s', err=%s", toolContent, err.Error())
+							ch <- gin.H{"error": errStr}
+							return
+						}
+
+						res.ToolCalls = append(res.ToolCalls, api.ToolCall{
+							Function: api.ToolCallFunction{
+								Name:      *toolName,
+								Arguments: args,
+							},
+						})
+					}
+				}
+
+				res.DoneReason = cr.DoneReason.String()
+				res.TotalDuration = time.Since(checkpointStart)
+				res.LoadDuration = checkpointLoaded.Sub(checkpointStart)
+
 				if !req.Raw {
 					tokens, err := r.Tokenize(c.Request.Context(), prompt+sb.String())
 					if err != nil {
@@ -470,7 +488,6 @@ func (s *Server) EmbedHandler(c *gin.Context) {
 	}
 
 	truncate := true
-
 	if req.Truncate != nil && !*req.Truncate {
 		truncate = false
 	}
@@ -537,7 +554,16 @@ func (s *Server) EmbedHandler(c *gin.Context) {
 				return
 			}
 
+			if bos := kvData.Uint("tokenizer.ggml.bos_token_id"); tokens[0] != int(bos) && kvData.Bool("add_bos_token", true) {
+				ctxLen--
+			}
+
+			if eos := kvData.Uint("tokenizer.ggml.eos_token_id"); tokens[len(tokens)-1] != int(eos) && kvData.Bool("add_eos_token", true) {
+				ctxLen--
+			}
+
 			tokens = tokens[:ctxLen]
+
 			s, err = r.Detokenize(c.Request.Context(), tokens)
 			if err != nil {
 				c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
@@ -558,7 +584,12 @@ func (s *Server) EmbedHandler(c *gin.Context) {
 			if err != nil {
 				return err
 			}
-			embeddings[i] = normalize(embedding)
+			// TODO: this first normalization should be done by the model
+			embedding = normalize(embedding)
+			if req.Dimensions > 0 && req.Dimensions < len(embedding) {
+				embedding = normalize(embedding[:req.Dimensions])
+			}
+			embeddings[i] = embedding
 			return nil
 		})
 	}
@@ -584,11 +615,7 @@ func normalize(vec []float32) []float32 {
 		sum += v * v
 	}
 
-	norm := float32(0.0)
-	if sum > 0 {
-		norm = float32(1.0 / math.Sqrt(float64(sum)))
-	}
-
+	norm := float32(1.0 / max(math.Sqrt(float64(sum)), 1e-12))
 	for i := range vec {
 		vec[i] *= norm
 	}
@@ -1598,27 +1625,27 @@ func (s *Server) ChatHandler(c *gin.Context) {
 	}
 	msgs = filterThinkTags(msgs, m)
 
-	useHarmony := harmony.ShouldUseHarmony(m.Config.ModelFamily, m.Template)
-	var parserType parser.TokenParserType
-	if useHarmony {
-		parserType = parser.TokenParserTypeHarmony
-	} else {
-		parserType = parser.TokenParserTypeDefault
-	}
+	var harmonyMessageHandler *harmony.HarmonyMessageHandler
+	var harmonyToolParser *harmony.HarmonyToolCallAccumulator
+
+	useHarmony := shouldUseHarmony(m)
 
 	processedTools := req.Tools
-	var functionNameMap *harmony.FunctionNameMap
-	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()
+		harmonyMessageHandler = harmony.NewHarmonyMessageHandler()
+		var lastMessage *api.Message
+		if len(msgs) > 0 {
+			lastMessage = &msgs[len(msgs)-1]
+		}
+		harmonyMessageHandler.HarmonyParser.AddImplicitStartOrPrefill(lastMessage)
+		harmonyToolParser = harmonyMessageHandler.CreateToolParser()
+
 		// 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))
 		copy(processedTools, req.Tools)
 		for i, tool := range processedTools {
-			processedTools[i].Function.Name = functionNameMap.ConvertAndAdd(tool.Function.Name)
+			processedTools[i].Function.Name = harmonyMessageHandler.FunctionNameMap.ConvertAndAdd(tool.Function.Name)
 		}
 	}
 
@@ -1671,17 +1698,15 @@ 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,
-			ParserType:    parserType,
-			PrefillString: prefillString,
+			Prompt:  prompt,
+			Images:  images,
+			Format:  req.Format,
+			Options: opts,
 		}, func(r llm.CompletionResponse) {
 			res := api.ChatResponse{
 				Model:     req.Model,
 				CreatedAt: time.Now().UTC(),
-				Message:   api.Message{Role: "assistant", Content: r.Content, Thinking: r.Thinking, ToolCalls: r.ToolCalls},
+				Message:   api.Message{Role: "assistant", Content: r.Content},
 				Done:      r.Done,
 				Metrics: api.Metrics{
 					PromptEvalCount:    r.PromptEvalCount,
@@ -1697,13 +1722,31 @@ func (s *Server) ChatHandler(c *gin.Context) {
 			}
 
 			if useHarmony {
-				for i, tool := range res.Message.ToolCalls {
-					res.Message.ToolCalls[i].Function.Name = functionNameMap.OriginalFromConverted(tool.Function.Name)
+				content, thinking, toolContent := harmonyMessageHandler.AddContent(r.Content, harmonyToolParser)
+				res.Message.Content = content
+				res.Message.Thinking = thinking
+				harmonyToolParser.Add(toolContent)
+
+				if r.Done {
+					toolName, toolContent := harmonyToolParser.Drain()
+					if toolName != nil {
+						*toolName = strings.TrimPrefix(*toolName, "functions.")
+						*toolName = harmonyMessageHandler.FunctionNameMap.OriginalFromConverted(*toolName)
+						var args api.ToolCallFunctionArguments
+						if err := json.Unmarshal([]byte(toolContent), &args); err != nil {
+							errStr := fmt.Sprintf("error parsing tool call: raw='%s', err=%s", toolContent, err.Error())
+							ch <- gin.H{"error": errStr}
+							return
+						}
+						res.Message.ToolCalls = []api.ToolCall{{Function: api.ToolCallFunction{Name: *toolName, Arguments: args}}}
+					}
 				}
+
 				// 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
 				}
+
 				return
 			}
 

server/routes_harmony_streaming_test.go

@@ -7,6 +7,7 @@ import (
 	"bytes"
 	"context"
 	"encoding/json"
+	"net/http"
 	"strings"
 	"testing"
 	"time"
@@ -117,7 +118,7 @@ func TestChatHarmonyParserStreamingRealtime(t *testing.T) {
 			name: "content streams as it arrives",
 			steps: []step{
 				{
-					input:       llm.CompletionResponse{Content: "Hello", Done: false},
+					input:       llm.CompletionResponse{Content: "<|message|>Hello", Done: false},
 					wantContent: "Hello",
 				},
 				{
@@ -125,7 +126,7 @@ func TestChatHarmonyParserStreamingRealtime(t *testing.T) {
 					wantContent: ", world",
 				},
 				{
-					input:       llm.CompletionResponse{Content: "!", Done: true, DoneReason: llm.DoneReasonStop},
+					input:       llm.CompletionResponse{Content: "!<|end|>", Done: true, DoneReason: llm.DoneReasonStop},
 					wantContent: "!",
 				},
 			},
@@ -134,15 +135,20 @@ func TestChatHarmonyParserStreamingRealtime(t *testing.T) {
 			name: "thinking streams separately from content",
 			steps: []step{
 				{
-					input:        llm.CompletionResponse{Thinking: "Thinking...", Done: false},
+					input:        llm.CompletionResponse{Content: "<|channel|>analysis<|message|>Thinking...", Done: false},
 					wantThinking: "Thinking...",
 				},
 				{
-					input:       llm.CompletionResponse{Content: "Answer", Done: false},
-					wantContent: "Answer",
+					input: llm.CompletionResponse{Content: "<|end|>", Done: false},
+					// No output expected - just closes the analysis message and resets state to normal
 				},
 				{
-					input: llm.CompletionResponse{Done: true, DoneReason: llm.DoneReasonStop},
+					input:       llm.CompletionResponse{Content: "<|start|>assistant<|message|>Answer", Done: false},
+					wantContent: "Answer", // After message end, state is reset to normal
+				},
+				{
+					input: llm.CompletionResponse{Content: "<|end|>", Done: true, DoneReason: llm.DoneReasonStop},
+					// No output expected - just closes the assistant message
 				},
 			},
 		},
@@ -150,16 +156,24 @@ func TestChatHarmonyParserStreamingRealtime(t *testing.T) {
 			name: "partial tags buffer until complete",
 			steps: []step{
 				{
-					input:        llm.CompletionResponse{Thinking: "Deep ", Done: false},
+					input: llm.CompletionResponse{Content: "<|chan", Done: false},
+					// No output - partial tag
+				},
+				{
+					input: llm.CompletionResponse{Content: "nel|>analysis<|mess", Done: false},
+					// No output - still building tags
+				},
+				{
+					input:        llm.CompletionResponse{Content: "age|>Deep ", Done: false},
 					wantThinking: "Deep ",
 				},
 				{
-					input:        llm.CompletionResponse{Thinking: "thought", Done: false},
+					input:        llm.CompletionResponse{Content: "thought<|end|>", Done: false},
 					wantThinking: "thought",
 				},
 				{
-					input:       llm.CompletionResponse{Content: "Done", Done: true, DoneReason: llm.DoneReasonStop},
-					wantContent: "Done",
+					input:       llm.CompletionResponse{Content: "<|start|>assistant<|message|>Done<|end|>", Done: true, DoneReason: llm.DoneReasonStop},
+					wantContent: "Done", // After message end, state is reset to normal
 				},
 			},
 		},
@@ -167,7 +181,7 @@ func TestChatHarmonyParserStreamingRealtime(t *testing.T) {
 			name: "simple assistant after analysis",
 			steps: []step{
 				{
-					input:        llm.CompletionResponse{Thinking: "Think", Content: "Answer", Done: true, DoneReason: llm.DoneReasonStop},
+					input:        llm.CompletionResponse{Content: "<|channel|>analysis<|message|>Think<|end|><|start|>assistant<|message|>Answer<|end|>", Done: true, DoneReason: llm.DoneReasonStop},
 					wantContent:  "Answer",
 					wantThinking: "Think",
 				},
@@ -177,7 +191,7 @@ func TestChatHarmonyParserStreamingRealtime(t *testing.T) {
 			name: "tool call parsed and returned correctly",
 			steps: []step{
 				{
-					input:       llm.CompletionResponse{Content: "The weather is sunny", ToolCalls: []api.ToolCall{{Function: api.ToolCallFunction{Name: "get_weather", Arguments: api.ToolCallFunctionArguments{"location": "San Francisco"}}}}, Done: true, DoneReason: llm.DoneReasonStop},
+					input:       llm.CompletionResponse{Content: "<|channel|>commentary to=functions.get_weather<|message|>{\"location\":\"San Francisco\"}<|end|><|start|>assistant<|message|>The weather is sunny<|end|>", Done: true, DoneReason: llm.DoneReasonStop},
 					wantContent: "The weather is sunny",
 					wantToolCalls: []api.ToolCall{
 						{
@@ -196,10 +210,15 @@ func TestChatHarmonyParserStreamingRealtime(t *testing.T) {
 			name: "tool call with streaming JSON across chunks",
 			steps: []step{
 				{
-					input: llm.CompletionResponse{Done: false},
+					input: llm.CompletionResponse{Content: "<|channel|>commentary to=functions.calculate<|message|>{\"expr", Done: false},
+					// No output yet - incomplete JSON
 				},
 				{
-					input: llm.CompletionResponse{ToolCalls: []api.ToolCall{{Function: api.ToolCallFunction{Name: "calculate", Arguments: api.ToolCallFunctionArguments{"expression": "2+2"}}}}, Done: true},
+					input: llm.CompletionResponse{Content: "ession\":\"2+", Done: false},
+					// Still no output - incomplete JSON
+				},
+				{
+					input: llm.CompletionResponse{Content: "2\"}", Done: true},
 					wantToolCalls: []api.ToolCall{
 						{
 							Function: api.ToolCallFunction{
@@ -381,9 +400,9 @@ func TestChatHarmonyParserStreamingSimple(t *testing.T) {
 	gin.SetMode(gin.TestMode)
 
 	mockResponses := []llm.CompletionResponse{
-		{Content: "First ", Done: false},
+		{Content: "<|message|>First ", Done: false},
 		{Content: "chunk ", Done: false},
-		{Content: "here", Done: true, DoneReason: llm.DoneReasonStop},
+		{Content: "here<|end|>", Done: true, DoneReason: llm.DoneReasonStop},
 	}
 
 	mock := mockRunner{
@@ -488,3 +507,189 @@ func TestChatHarmonyParserStreamingSimple(t *testing.T) {
 		t.Errorf("expected at least 2 content chunks for streaming, got %d", contentChunks)
 	}
 }
+
+func TestChatHarmonyParserStreaming(t *testing.T) {
+	gin.SetMode(gin.TestMode)
+
+	type expectedChunk struct {
+		afterResponse int    // Which mock response this chunk should appear after
+		content       string // Expected content in this chunk
+		thinking      string // Expected thinking in this chunk
+	}
+
+	testCases := []struct {
+		name           string
+		mockResponses  []llm.CompletionResponse
+		expectedChunks []expectedChunk
+		wantContent    string
+		wantThinking   string
+	}{
+		{
+			name: "simple message without thinking",
+			mockResponses: []llm.CompletionResponse{
+				{Content: "<|start|>assistant<|message|>Hello, ", Done: false},
+				{Content: "how can I help?", Done: false},
+				{Content: "<|end|>", Done: true, DoneReason: llm.DoneReasonStop},
+			},
+			expectedChunks: []expectedChunk{
+				{afterResponse: 1, content: "Hello, "},
+				{afterResponse: 2, content: "how can I help?"},
+			},
+			wantContent: "Hello, how can I help?",
+		},
+		{
+			name: "message with analysis channel for thinking",
+			mockResponses: []llm.CompletionResponse{
+				{Content: "<|channel|>analysis<|message|>", Done: false},
+				{Content: "Let me think ", Done: false},
+				{Content: "about this problem...", Done: false},
+				{Content: "<|end|>", Done: false},
+				{Content: "<|start|>assistant<|message|>", Done: false},
+				{Content: "The answer ", Done: false},
+				{Content: "is 42", Done: false},
+				{Content: "<|end|>", Done: true, DoneReason: llm.DoneReasonStop},
+			},
+			expectedChunks: []expectedChunk{
+				{afterResponse: 2, thinking: "Let me think "},
+				{afterResponse: 3, thinking: "about this problem..."},
+				{afterResponse: 6, content: "The answer "},
+				{afterResponse: 7, content: "is 42"},
+			},
+			wantContent:  "The answer is 42",
+			wantThinking: "Let me think about this problem...",
+		},
+		{
+			name: "streaming with partial tags across boundaries",
+			mockResponses: []llm.CompletionResponse{
+				{Content: "<|chan", Done: false},
+				{Content: "nel|>analy", Done: false},
+				{Content: "sis<|mess", Done: false},
+				{Content: "age|>Think", Done: false},
+				{Content: "ing deeply...<|end|>", Done: false},
+				{Content: "<|start|>assi", Done: false},
+				{Content: "stant<|message|>Result ", Done: false},
+				{Content: "computed<|e", Done: false},
+				{Content: "nd|>", Done: true, DoneReason: llm.DoneReasonStop},
+			},
+			expectedChunks: []expectedChunk{
+				{afterResponse: 4, thinking: "Think"},
+				{afterResponse: 5, thinking: "ing deeply..."},
+				{afterResponse: 7, content: "Result "},
+				{afterResponse: 8, content: "computed"},
+			},
+			wantContent:  "Result computed",
+			wantThinking: "Thinking deeply...",
+		},
+	}
+
+	for _, tc := range testCases {
+		t.Run(tc.name, func(t *testing.T) {
+			// Channel to synchronize mock responses with chunk verification
+			responsesSent := make(chan int, len(tc.mockResponses))
+
+			mock := mockRunner{
+				CompletionFn: func(ctx context.Context, r llm.CompletionRequest, fn func(llm.CompletionResponse)) error {
+					// Send mock responses one at a time, notifying when each is sent
+					for i, resp := range tc.mockResponses {
+						fn(resp)
+						responsesSent <- i + 1
+					}
+					close(responsesSent)
+					return nil
+				},
+			}
+
+			s := Server{
+				sched: &Scheduler{
+					pendingReqCh:  make(chan *LlmRequest, 1),
+					finishedReqCh: make(chan *LlmRequest, 1),
+					expiredCh:     make(chan *runnerRef, 1),
+					unloadedCh:    make(chan any, 1),
+					loaded:        make(map[string]*runnerRef),
+					newServerFn:   newMockServer(&mock),
+					getGpuFn:      discover.GetGPUInfo,
+					getCpuFn:      discover.GetCPUInfo,
+					reschedDelay:  250 * time.Millisecond,
+					loadFn: func(req *LlmRequest, _ *ggml.GGML, _ discover.GpuInfoList, _ bool) bool {
+						req.successCh <- &runnerRef{
+							llama: &mock,
+						}
+						return false
+					},
+				},
+			}
+
+			go s.sched.Run(t.Context())
+
+			// Create a minimal model
+			_, digest := createHarmonyTestModel(t)
+
+			// Create model with passthrough template
+			stream := false
+			w := createRequest(t, s.CreateHandler, api.CreateRequest{
+				Model:    "harmony-test",
+				Files:    map[string]string{"file.gguf": digest},
+				Template: `<|start|><|end|>{{ with .Tools }}{{ end }}{{ .Prompt }}`,
+				Stream:   &stream,
+			})
+
+			if w.Code != http.StatusOK {
+				t.Fatalf("failed to create model: %d", w.Code)
+			}
+
+			// Test chat endpoint with streaming
+			streamTrue := true
+			w = createRequest(t, s.ChatHandler, api.ChatRequest{
+				Model:    "harmony-test",
+				Messages: []api.Message{{Role: "user", Content: "Hello"}},
+				Stream:   &streamTrue,
+				Tools:    getTestTools(),
+			})
+
+			if w.Code != http.StatusOK {
+				t.Fatalf("chat request failed: %d - %s", w.Code, w.Body.String())
+			}
+
+			// Parse streaming response
+			var chunks []api.ChatResponse
+			var content, thinking strings.Builder
+
+			decoder := json.NewDecoder(w.Body)
+			for decoder.More() {
+				var chunk api.ChatResponse
+				if err := decoder.Decode(&chunk); err != nil {
+					t.Fatalf("failed to decode chunk: %v", err)
+				}
+				chunks = append(chunks, chunk)
+
+				// Accumulate content and thinking from each chunk
+				content.WriteString(chunk.Message.Content)
+				thinking.WriteString(chunk.Message.Thinking)
+
+				// Debug output
+				t.Logf("Chunk %d: content=%q thinking=%q done=%v", len(chunks), chunk.Message.Content, chunk.Message.Thinking, chunk.Done)
+			}
+
+			// Verify we got streaming chunks
+			if len(chunks) == 0 {
+				t.Fatal("expected streaming chunks, got none")
+			}
+
+			gotContent := content.String()
+			gotThinking := thinking.String()
+
+			if gotContent != tc.wantContent {
+				t.Errorf("content mismatch: got %q, want %q", gotContent, tc.wantContent)
+			}
+			if gotThinking != tc.wantThinking {
+				t.Errorf("thinking mismatch: got %q, want %q", gotThinking, tc.wantThinking)
+			}
+
+			// Verify last chunk has done=true
+			lastChunk := chunks[len(chunks)-1]
+			if !lastChunk.Done {
+				t.Error("expected last chunk to have done=true")
+			}
+		})
+	}
+}