fix

There are two bugs when using `/load <model>` for a model that doesn't exist, namely:
  1. it will not restore the current model settings if the current model is a thinking model; and
  2. it will crash is the current model is a non-thinking model

This bug fix saves the current runOptions and then restores them if the model load
doesn't happen. It also fixes the crash happening for non-thinking models.

.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

.gitignore

@@ -8,6 +8,7 @@
 dist
 build
 .cache
+.gocache
 *.exe
 .idea
 test_data

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
@@ -99,10 +99,12 @@ check_language(HIP)
 if(CMAKE_HIP_COMPILER)
     set(HIP_PLATFORM "amd")
 
-    find_package(hip REQUIRED)
     if(NOT AMDGPU_TARGETS)
-        list(FILTER AMDGPU_TARGETS INCLUDE REGEX "^gfx(803|902|906(:xnack-)|90c(:xnack-)|1010(:xnack-)|1011(:xnack-)|1012(:xnack-)|103[0-6]|110[0-3]|115[01]|120[01])$")
-    elseif(WIN32 AND WINDOWS_AMDGPU_TARGETS_EXCLUDE_REGEX)
+        find_package(hip REQUIRED)
+        list(FILTER AMDGPU_TARGETS INCLUDE REGEX "^gfx(803|90[012]|906(:xnack-)|90c(:xnack-)|1010(:xnack-)|1011(:xnack-)|1012(:xnack-)|103[0-6]|110[0-3]|115[0123]|120[01])$")
+    endif()
+
+    if(WIN32 AND WINDOWS_AMDGPU_TARGETS_EXCLUDE_REGEX)
         list(FILTER AMDGPU_TARGETS EXCLUDE REGEX ${WINDOWS_AMDGPU_TARGETS_EXCLUDE_REGEX})
     endif()
 

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

@@ -1,6 +1,7 @@
 # vim: filetype=dockerfile
 
 ARG FLAVOR=${TARGETARCH}
+ARG PARALLEL=8
 
 ARG ROCMVERSION=6.3.3
 ARG JETPACK5VERSION=r35.4.1
@@ -34,26 +35,51 @@ ENV LDFLAGS=-s
 FROM base AS cpu
 RUN dnf install -y gcc-toolset-11-gcc gcc-toolset-11-gcc-c++
 ENV PATH=/opt/rh/gcc-toolset-11/root/usr/bin:$PATH
+ARG PARALLEL
 RUN --mount=type=cache,target=/root/.ccache \
     cmake --preset 'CPU' \
-        && cmake --build --parallel --preset 'CPU' \
-        && cmake --install build --component CPU --strip --parallel 8
+        && cmake --build --parallel ${PARALLEL} --preset 'CPU' \
+        && cmake --install build --component CPU --strip --parallel ${PARALLEL}
+
+FROM base AS cuda-11
+ARG CUDA11VERSION=11.8
+RUN dnf install -y cuda-toolkit-${CUDA11VERSION//./-}
+ENV PATH=/usr/local/cuda-11/bin:$PATH
+ARG PARALLEL
+RUN --mount=type=cache,target=/root/.ccache \
+    cmake --preset 'CUDA 11' -DOLLAMA_RUNNER_DIR="cuda_v11" \
+        && cmake --build --parallel ${PARALLEL} --preset 'CUDA 11' \
+        && cmake --install build --component CUDA --strip --parallel ${PARALLEL}
 
 FROM base AS cuda-12
 ARG CUDA12VERSION=12.8
 RUN dnf install -y cuda-toolkit-${CUDA12VERSION//./-}
 ENV PATH=/usr/local/cuda-12/bin:$PATH
+ARG PARALLEL
 RUN --mount=type=cache,target=/root/.ccache \
-    cmake --preset 'CUDA 12' \
-        && cmake --build --parallel --preset 'CUDA 12' \
-        && cmake --install build --component CUDA --strip --parallel 8
+    cmake --preset 'CUDA 12' -DOLLAMA_RUNNER_DIR="cuda_v12"\
+        && cmake --build --parallel ${PARALLEL} --preset 'CUDA 12' \
+        && cmake --install build --component CUDA --strip --parallel ${PARALLEL}
+
+
+FROM base AS cuda-13
+ARG CUDA13VERSION=13.0
+RUN dnf install -y cuda-toolkit-${CUDA13VERSION//./-}
+ENV PATH=/usr/local/cuda-13/bin:$PATH
+ARG PARALLEL
+RUN --mount=type=cache,target=/root/.ccache \
+    cmake --preset 'CUDA 13' -DOLLAMA_RUNNER_DIR="cuda_v13" \
+        && cmake --build --parallel ${PARALLEL} --preset 'CUDA 13' \
+        && cmake --install build --component CUDA --strip --parallel ${PARALLEL}
+
 
 FROM base AS rocm-6
 ENV PATH=/opt/rocm/hcc/bin:/opt/rocm/hip/bin:/opt/rocm/bin:/opt/rocm/hcc/bin:$PATH
+ARG PARALLEL
 RUN --mount=type=cache,target=/root/.ccache \
     cmake --preset 'ROCm 6' \
-        && cmake --build --parallel --preset 'ROCm 6' \
-        && cmake --install build --component HIP --strip --parallel 8
+        && cmake --build --parallel ${PARALLEL} --preset 'ROCm 6' \
+        && cmake --install build --component HIP --strip --parallel ${PARALLEL}
 
 FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK5VERSION} AS jetpack-5
 ARG CMAKEVERSION
@@ -61,10 +87,11 @@ RUN apt-get update && apt-get install -y curl ccache \
     && curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKEVERSION}/cmake-${CMAKEVERSION}-linux-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
 COPY CMakeLists.txt CMakePresets.json .
 COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
+ARG PARALLEL
 RUN --mount=type=cache,target=/root/.ccache \
     cmake --preset 'JetPack 5' \
-        && cmake --build --parallel --preset 'JetPack 5' \
-        && cmake --install build --component CUDA --strip --parallel 8
+        && cmake --build --parallel ${PARALLEL} --preset 'JetPack 5' \
+        && cmake --install build --component CUDA --strip --parallel ${PARALLEL}
 
 FROM --platform=linux/arm64 nvcr.io/nvidia/l4t-jetpack:${JETPACK6VERSION} AS jetpack-6
 ARG CMAKEVERSION
@@ -72,10 +99,11 @@ RUN apt-get update && apt-get install -y curl ccache \
     && curl -fsSL https://github.com/Kitware/CMake/releases/download/v${CMAKEVERSION}/cmake-${CMAKEVERSION}-linux-$(uname -m).tar.gz | tar xz -C /usr/local --strip-components 1
 COPY CMakeLists.txt CMakePresets.json .
 COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
+ARG PARALLEL
 RUN --mount=type=cache,target=/root/.ccache \
     cmake --preset 'JetPack 6' \
-        && cmake --build --parallel --preset 'JetPack 6' \
-        && cmake --install build --component CUDA --strip --parallel 8
+        && cmake --build --parallel ${PARALLEL} --preset 'JetPack 6' \
+        && cmake --install build --component CUDA --strip --parallel ${PARALLEL}
 
 FROM base AS build
 WORKDIR /go/src/github.com/ollama/ollama
@@ -92,10 +120,14 @@ 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

@@ -435,6 +435,8 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Mayan EDMS](https://gitlab.com/mayan-edms/mayan-edms) (Open source document management system to organize, tag, search, and automate your files with powerful Ollama driven workflows.)
 - [Serene Pub](https://github.com/doolijb/serene-pub) (Beginner friendly, open source AI Roleplaying App for Windows, Mac OS and Linux. Search, download and use models with Ollama all inside the app.)
 - [Andes](https://github.com/aqerd/andes) (A Visual Studio Code extension that provides a local UI interface for Ollama models)
+- [Clueless](https://github.com/KashyapTan/clueless) (Open Source & Local Cluely: A desktop application LLM assistant to help you talk to anything on your screen using locally served Ollama models. Also undetectable to screenshare)
+- [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
 
@@ -624,6 +626,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [UnityCodeLama](https://github.com/HardCodeDev777/UnityCodeLama) (Unity Edtior tool to analyze scripts via Ollama)
 - [NativeMind](https://github.com/NativeMindBrowser/NativeMindExtension) (Private, on-device AI Assistant, no cloud dependencies)
 - [GMAI - Gradle Managed AI](https://gmai.premex.se/) (Gradle plugin for automated Ollama lifecycle management during build phases)
+- [NOMYO Router](https://github.com/nomyo-ai/nomyo-router) (A transparent Ollama proxy with model deployment aware routing which auto-manages multiple Ollama instances in a given network)
 
 ### Supported backends
 

api/client.go

@@ -45,6 +45,12 @@ func checkError(resp *http.Response, body []byte) error {
 		return nil
 	}
 
+	if resp.StatusCode == http.StatusUnauthorized {
+		authError := AuthorizationError{StatusCode: resp.StatusCode}
+		json.Unmarshal(body, &authError)
+		return authError
+	}
+
 	apiError := StatusError{StatusCode: resp.StatusCode}
 
 	err := json.Unmarshal(body, &apiError)
@@ -214,7 +220,8 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
 	scanner.Buffer(scanBuf, maxBufferSize)
 	for scanner.Scan() {
 		var errorResponse struct {
-			Error string `json:"error,omitempty"`
+			Error     string `json:"error,omitempty"`
+			SigninURL string `json:"signin_url,omitempty"`
 		}
 
 		bts := scanner.Bytes()
@@ -222,7 +229,13 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f
 			return fmt.Errorf("unmarshal: %w", err)
 		}
 
-		if response.StatusCode >= http.StatusBadRequest {
+		if response.StatusCode == http.StatusUnauthorized {
+			return AuthorizationError{
+				StatusCode: response.StatusCode,
+				Status:     response.Status,
+				SigninURL:  errorResponse.SigninURL,
+			}
+		} else if response.StatusCode >= http.StatusBadRequest {
 			return StatusError{
 				StatusCode:   response.StatusCode,
 				Status:       response.Status,
@@ -428,3 +441,21 @@ func (c *Client) Version(ctx context.Context) (string, error) {
 
 	return version.Version, nil
 }
+
+// Signout will signout a client for a local ollama server.
+func (c *Client) Signout(ctx context.Context) error {
+	return c.do(ctx, http.MethodPost, "/api/signout", nil, nil)
+}
+
+// Disconnect will disconnect an ollama instance from ollama.com.
+func (c *Client) Disconnect(ctx context.Context, encodedKey string) error {
+	return c.do(ctx, http.MethodDelete, fmt.Sprintf("/api/user/keys/%s", encodedKey), nil, nil)
+}
+
+func (c *Client) Whoami(ctx context.Context) (*UserResponse, error) {
+	var resp UserResponse
+	if err := c.do(ctx, http.MethodPost, "/api/me", nil, &resp); err != nil {
+		return nil, err
+	}
+	return &resp, nil
+}

api/types.go

@@ -11,6 +11,8 @@ import (
 	"strings"
 	"time"
 
+	"github.com/google/uuid"
+
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/types/model"
 )
@@ -36,6 +38,19 @@ func (e StatusError) Error() string {
 	}
 }
 
+type AuthorizationError struct {
+	StatusCode int
+	Status     string
+	SigninURL  string `json:"signin_url"`
+}
+
+func (e AuthorizationError) Error() string {
+	if e.Status != "" {
+		return e.Status
+	}
+	return "something went wrong, please see the ollama server logs for details"
+}
+
 // ImageData represents the raw binary data of an image file.
 type ImageData []byte
 
@@ -313,13 +328,29 @@ func (t *ToolFunction) String() string {
 // ChatResponse is the response returned by [Client.Chat]. Its fields are
 // similar to [GenerateResponse].
 type ChatResponse struct {
-	Model      string    `json:"model"`
-	CreatedAt  time.Time `json:"created_at"`
-	Message    Message   `json:"message"`
-	DoneReason string    `json:"done_reason,omitempty"`
+	// Model is the model name that generated the response.
+	Model string `json:"model"`
 
+	// RemoteModel is the name of the upstream model that generated the response.
+	RemoteModel string `json:"remote_model,omitempty"`
+
+	// RemoteHost is the URL of the upstream Ollama host that generated the response.
+	RemoteHost string `json:"remote_host,omitempty"`
+
+	// CreatedAt is the timestamp of the response.
+	CreatedAt time.Time `json:"created_at"`
+
+	// Message contains the message or part of a message from the model.
+	Message Message `json:"message"`
+
+	// Done specifies if the response is complete.
 	Done bool `json:"done"`
 
+	// DoneReason is the reason the model stopped generating text.
+	DoneReason string `json:"done_reason,omitempty"`
+
+	DebugInfo *DebugInfo `json:"_debug_info,omitempty"`
+
 	Metrics
 }
 
@@ -329,13 +360,6 @@ type DebugInfo struct {
 	ImageCount       int    `json:"image_count,omitempty"`
 }
 
-// DebugTemplateResponse is returned when _debug_render_only is set to true
-type DebugTemplateResponse struct {
-	Model     string    `json:"model"`
-	CreatedAt time.Time `json:"created_at"`
-	DebugInfo DebugInfo `json:"_debug_info"`
-}
-
 type Metrics struct {
 	TotalDuration      time.Duration `json:"total_duration,omitempty"`
 	LoadDuration       time.Duration `json:"load_duration,omitempty"`
@@ -388,8 +412,12 @@ 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"`
 }
@@ -427,18 +455,47 @@ type EmbeddingResponse struct {
 
 // CreateRequest is the request passed to [Client.Create].
 type CreateRequest struct {
-	Model    string `json:"model"`
-	Stream   *bool  `json:"stream,omitempty"`
+	// Model is the model name to create.
+	Model string `json:"model"`
+
+	// Stream specifies whether the response is streaming; it is true by default.
+	Stream *bool `json:"stream,omitempty"`
+
+	// Quantize is the quantization format for the model; leave blank to not change the quantization level.
 	Quantize string `json:"quantize,omitempty"`
 
-	From       string            `json:"from,omitempty"`
-	Files      map[string]string `json:"files,omitempty"`
-	Adapters   map[string]string `json:"adapters,omitempty"`
-	Template   string            `json:"template,omitempty"`
-	License    any               `json:"license,omitempty"`
-	System     string            `json:"system,omitempty"`
-	Parameters map[string]any    `json:"parameters,omitempty"`
-	Messages   []Message         `json:"messages,omitempty"`
+	// From is the name of the model or file to use as the source.
+	From string `json:"from,omitempty"`
+
+	// RemoteHost is the URL of the upstream ollama API for the model (if any).
+	RemoteHost string `json:"remote_host,omitempty"`
+
+	// Files is a map of files include when creating the model.
+	Files map[string]string `json:"files,omitempty"`
+
+	// Adapters is a map of LoRA adapters to include when creating the model.
+	Adapters map[string]string `json:"adapters,omitempty"`
+
+	// Template is the template used when constructing a request to the model.
+	Template string `json:"template,omitempty"`
+
+	// License is a string or list of strings for licenses.
+	License any `json:"license,omitempty"`
+
+	// System is the system prompt for the model.
+	System string `json:"system,omitempty"`
+
+	// Parameters is a map of hyper-parameters which are applied to the model.
+	Parameters map[string]any `json:"parameters,omitempty"`
+
+	// Messages is a list of messages added to the model before chat and generation requests.
+	Messages []Message `json:"messages,omitempty"`
+
+	Renderer string `json:"renderer,omitempty"`
+	Parser   string `json:"parser,omitempty"`
+
+	// Info is a map of additional information for the model
+	Info map[string]any `json:"info,omitempty"`
 
 	// Deprecated: set the model name with Model instead
 	Name string `json:"name"`
@@ -476,8 +533,12 @@ type ShowResponse struct {
 	Parameters    string             `json:"parameters,omitempty"`
 	Template      string             `json:"template,omitempty"`
 	System        string             `json:"system,omitempty"`
+	Renderer      string             `json:"renderer,omitempty"`
+	Parser        string             `json:"parser,omitempty"`
 	Details       ModelDetails       `json:"details,omitempty"`
 	Messages      []Message          `json:"messages,omitempty"`
+	RemoteModel   string             `json:"remote_model,omitempty"`
+	RemoteHost    string             `json:"remote_host,omitempty"`
 	ModelInfo     map[string]any     `json:"model_info,omitempty"`
 	ProjectorInfo map[string]any     `json:"projector_info,omitempty"`
 	Tensors       []Tensor           `json:"tensors,omitempty"`
@@ -536,12 +597,14 @@ type ProcessResponse struct {
 
 // ListModelResponse is a single model description in [ListResponse].
 type ListModelResponse struct {
-	Name       string       `json:"name"`
-	Model      string       `json:"model"`
-	ModifiedAt time.Time    `json:"modified_at"`
-	Size       int64        `json:"size"`
-	Digest     string       `json:"digest"`
-	Details    ModelDetails `json:"details,omitempty"`
+	Name        string       `json:"name"`
+	Model       string       `json:"model"`
+	RemoteModel string       `json:"remote_model,omitempty"`
+	RemoteHost  string       `json:"remote_host,omitempty"`
+	ModifiedAt  time.Time    `json:"modified_at"`
+	Size        int64        `json:"size"`
+	Digest      string       `json:"digest"`
+	Details     ModelDetails `json:"details,omitempty"`
 }
 
 // ProcessModelResponse is a single model description in [ProcessResponse].
@@ -565,6 +628,12 @@ type GenerateResponse struct {
 	// Model is the model name that generated the response.
 	Model string `json:"model"`
 
+	// RemoteModel is the name of the upstream model that generated the response.
+	RemoteModel string `json:"remote_model,omitempty"`
+
+	// RemoteHost is the URL of the upstream Ollama host that generated the response.
+	RemoteHost string `json:"remote_host,omitempty"`
+
 	// CreatedAt is the timestamp of the response.
 	CreatedAt time.Time `json:"created_at"`
 
@@ -588,6 +657,8 @@ type GenerateResponse struct {
 	Metrics
 
 	ToolCalls []ToolCall `json:"tool_calls,omitempty"`
+
+	DebugInfo *DebugInfo `json:"_debug_info,omitempty"`
 }
 
 // ModelDetails provides details about a model.
@@ -600,6 +671,18 @@ type ModelDetails struct {
 	QuantizationLevel string   `json:"quantization_level"`
 }
 
+// UserResponse provides information about a user.
+type UserResponse struct {
+	ID        uuid.UUID `json:"id"`
+	Email     string    `json:"email"`
+	Name      string    `json:"name"`
+	Bio       string    `json:"bio,omitempty"`
+	AvatarURL string    `json:"avatarurl,omitempty"`
+	FirstName string    `json:"firstname,omitempty"`
+	LastName  string    `json:"lastname,omitempty"`
+	Plan      string    `json:"plan,omitempty"`
+}
+
 // Tensor describes the metadata for a given tensor.
 type Tensor struct {
 	Name  string   `json:"name"`

auth/auth.go

@@ -18,21 +18,13 @@ import (
 
 const defaultPrivateKey = "id_ed25519"
 
-func keyPath() (string, error) {
+func GetPublicKey() (string, error) {
 	home, err := os.UserHomeDir()
 	if err != nil {
 		return "", err
 	}
 
-	return filepath.Join(home, ".ollama", defaultPrivateKey), nil
-}
-
-func GetPublicKey() (string, error) {
-	keyPath, err := keyPath()
-	if err != nil {
-		return "", err
-	}
-
+	keyPath := filepath.Join(home, ".ollama", defaultPrivateKey)
 	privateKeyFile, err := os.ReadFile(keyPath)
 	if err != nil {
 		slog.Info(fmt.Sprintf("Failed to load private key: %v", err))
@@ -59,11 +51,12 @@ func NewNonce(r io.Reader, length int) (string, error) {
 }
 
 func Sign(ctx context.Context, bts []byte) (string, error) {
-	keyPath, err := keyPath()
+	home, err := os.UserHomeDir()
 	if err != nil {
 		return "", err
 	}
 
+	keyPath := filepath.Join(home, ".ollama", defaultPrivateKey)
 	privateKeyFile, err := os.ReadFile(keyPath)
 	if err != nil {
 		slog.Info(fmt.Sprintf("Failed to load private key: %v", err))

cmd/cmd.go

@@ -47,6 +47,8 @@ import (
 	"github.com/ollama/ollama/version"
 )
 
+const ConnectInstructions = "To sign in, navigate to:\n    %s\n\n"
+
 // ensureThinkingSupport emits a warning if the model does not advertise thinking support
 func ensureThinkingSupport(ctx context.Context, client *api.Client, name string) {
 	if name == "" {
@@ -56,10 +58,8 @@ 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)
 }
@@ -288,7 +288,17 @@ func loadOrUnloadModel(cmd *cobra.Command, opts *runOptions) error {
 		Think: opts.Think,
 	}
 
-	return client.Generate(cmd.Context(), req, func(api.GenerateResponse) error { return nil })
+	return client.Generate(cmd.Context(), req, func(r api.GenerateResponse) error {
+		if r.RemoteModel != "" && opts.ShowConnect {
+			p.StopAndClear()
+			if strings.HasPrefix(r.RemoteHost, "https://ollama.com") {
+				fmt.Fprintf(os.Stderr, "Connecting to '%s' on 'ollama.com' ⚡\n", r.RemoteModel)
+			} else {
+				fmt.Fprintf(os.Stderr, "Connecting to '%s' on '%s'\n", r.RemoteModel, r.RemoteHost)
+			}
+		}
+		return nil
+	})
 }
 
 func StopHandler(cmd *cobra.Command, args []string) error {
@@ -309,9 +319,10 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 	interactive := true
 
 	opts := runOptions{
-		Model:    args[0],
-		WordWrap: os.Getenv("TERM") == "xterm-256color",
-		Options:  map[string]any{},
+		Model:       args[0],
+		WordWrap:    os.Getenv("TERM") == "xterm-256color",
+		Options:     map[string]any{},
+		ShowConnect: true,
 	}
 
 	format, err := cmd.Flags().GetString("format")
@@ -369,6 +380,7 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 		}
 
 		prompts = append([]string{string(in)}, prompts...)
+		opts.ShowConnect = false
 		opts.WordWrap = false
 		interactive = false
 	}
@@ -435,6 +447,15 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 
 	if interactive {
 		if err := loadOrUnloadModel(cmd, &opts); err != nil {
+			var sErr api.AuthorizationError
+			if errors.As(err, &sErr) && sErr.StatusCode == http.StatusUnauthorized {
+				fmt.Printf("You need to be signed in to Ollama to run Cloud models.\n\n")
+
+				if sErr.SigninURL != "" {
+					fmt.Printf(ConnectInstructions, sErr.SigninURL)
+				}
+				return nil
+			}
 			return err
 		}
 
@@ -455,6 +476,59 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 	return generate(cmd, opts)
 }
 
+func SigninHandler(cmd *cobra.Command, args []string) error {
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return err
+	}
+
+	user, err := client.Whoami(cmd.Context())
+	if err != nil {
+		var aErr api.AuthorizationError
+		if errors.As(err, &aErr) && aErr.StatusCode == http.StatusUnauthorized {
+			fmt.Println("You need to be signed in to Ollama to run Cloud models.")
+			fmt.Println()
+
+			if aErr.SigninURL != "" {
+				fmt.Printf(ConnectInstructions, aErr.SigninURL)
+			}
+			return nil
+		}
+		return err
+	}
+
+	if user != nil && user.Name != "" {
+		fmt.Printf("You are already signed in as user '%s'\n", user.Name)
+		fmt.Println()
+		return nil
+	}
+
+	return nil
+}
+
+func SignoutHandler(cmd *cobra.Command, args []string) error {
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return err
+	}
+
+	err = client.Signout(cmd.Context())
+	if err != nil {
+		var aErr api.AuthorizationError
+		if errors.As(err, &aErr) && aErr.StatusCode == http.StatusUnauthorized {
+			fmt.Println("You are not signed in to ollama.com")
+			fmt.Println()
+			return nil
+		} else {
+			return err
+		}
+	}
+
+	fmt.Println("You have signed out of ollama.com")
+	fmt.Println()
+	return nil
+}
+
 func PushHandler(cmd *cobra.Command, args []string) error {
 	client, err := api.ClientFromEnvironment()
 	if err != nil {
@@ -466,6 +540,25 @@ func PushHandler(cmd *cobra.Command, args []string) error {
 		return err
 	}
 
+	n := model.ParseName(args[0])
+	if strings.HasSuffix(n.Host, ".ollama.ai") || strings.HasSuffix(n.Host, ".ollama.com") {
+		_, err := client.Whoami(cmd.Context())
+		if err != nil {
+			var aErr api.AuthorizationError
+			if errors.As(err, &aErr) && aErr.StatusCode == http.StatusUnauthorized {
+				fmt.Println("You need to be signed in to push models to ollama.com.")
+				fmt.Println()
+
+				if aErr.SigninURL != "" {
+					fmt.Printf(ConnectInstructions, aErr.SigninURL)
+				}
+				return nil
+			}
+
+			return err
+		}
+	}
+
 	p := progress.NewProgress(os.Stderr)
 	defer p.Stop()
 
@@ -502,12 +595,12 @@ func PushHandler(cmd *cobra.Command, args []string) error {
 
 	request := api.PushRequest{Name: args[0], Insecure: insecure}
 
-	n := model.ParseName(args[0])
 	if err := client.Push(cmd.Context(), &request, fn); err != nil {
 		if spinner != nil {
 			spinner.Stop()
 		}
-		if strings.Contains(err.Error(), "access denied") {
+		errStr := strings.ToLower(err.Error())
+		if strings.Contains(errStr, "access denied") || strings.Contains(errStr, "unauthorized") {
 			return errors.New("you are not authorized to push to this namespace, create the model under a namespace you own")
 		}
 		return err
@@ -541,7 +634,14 @@ func ListHandler(cmd *cobra.Command, args []string) error {
 
 	for _, m := range models.Models {
 		if len(args) == 0 || strings.HasPrefix(strings.ToLower(m.Name), strings.ToLower(args[0])) {
-			data = append(data, []string{m.Name, m.Digest[:12], format.HumanBytes(m.Size), format.HumanTime(m.ModifiedAt, "Never")})
+			var size string
+			if m.RemoteModel != "" {
+				size = "-"
+			} else {
+				size = format.HumanBytes(m.Size)
+			}
+
+			data = append(data, []string{m.Name, m.Digest[:12], size, format.HumanTime(m.ModifiedAt, "Never")})
 		}
 	}
 
@@ -626,8 +726,8 @@ func DeleteHandler(cmd *cobra.Command, args []string) error {
 		KeepAlive: &api.Duration{Duration: 0},
 	}
 	if err := loadOrUnloadModel(cmd, opts); err != nil {
-		if !strings.Contains(err.Error(), "not found") {
-			return fmt.Errorf("unable to stop existing running model \"%s\": %s", args[0], err)
+		if !strings.Contains(strings.ToLower(err.Error()), "not found") {
+			fmt.Fprintf(os.Stderr, "Warning: unable to stop model '%s'\n", args[0])
 		}
 	}
 
@@ -738,12 +838,36 @@ func showInfo(resp *api.ShowResponse, verbose bool, w io.Writer) error {
 	}
 
 	tableRender("Model", func() (rows [][]string) {
+		if resp.RemoteHost != "" {
+			rows = append(rows, []string{"", "Remote model", resp.RemoteModel})
+			rows = append(rows, []string{"", "Remote URL", resp.RemoteHost})
+		}
+
 		if resp.ModelInfo != nil {
 			arch := resp.ModelInfo["general.architecture"].(string)
 			rows = append(rows, []string{"", "architecture", arch})
-			rows = append(rows, []string{"", "parameters", format.HumanNumber(uint64(resp.ModelInfo["general.parameter_count"].(float64)))})
-			rows = append(rows, []string{"", "context length", strconv.FormatFloat(resp.ModelInfo[fmt.Sprintf("%s.context_length", arch)].(float64), 'f', -1, 64)})
-			rows = append(rows, []string{"", "embedding length", strconv.FormatFloat(resp.ModelInfo[fmt.Sprintf("%s.embedding_length", arch)].(float64), 'f', -1, 64)})
+
+			var paramStr string
+			if resp.Details.ParameterSize != "" {
+				paramStr = resp.Details.ParameterSize
+			} else if v, ok := resp.ModelInfo["general.parameter_count"]; ok {
+				if f, ok := v.(float64); ok {
+					paramStr = format.HumanNumber(uint64(f))
+				}
+			}
+			rows = append(rows, []string{"", "parameters", paramStr})
+
+			if v, ok := resp.ModelInfo[fmt.Sprintf("%s.context_length", arch)]; ok {
+				if f, ok := v.(float64); ok {
+					rows = append(rows, []string{"", "context length", strconv.FormatFloat(f, 'f', -1, 64)})
+				}
+			}
+
+			if v, ok := resp.ModelInfo[fmt.Sprintf("%s.embedding_length", arch)]; ok {
+				if f, ok := v.(float64); ok {
+					rows = append(rows, []string{"", "embedding length", strconv.FormatFloat(f, 'f', -1, 64)})
+				}
+			}
 		} else {
 			rows = append(rows, []string{"", "architecture", resp.Details.Family})
 			rows = append(rows, []string{"", "parameters", resp.Details.ParameterSize})
@@ -991,6 +1115,52 @@ type runOptions struct {
 	KeepAlive    *api.Duration
 	Think        *api.ThinkValue
 	HideThinking bool
+	ShowConnect  bool
+}
+
+func (r runOptions) Copy() runOptions {
+	var messages []api.Message
+	if r.Messages != nil {
+		messages = make([]api.Message, len(r.Messages))
+		copy(messages, r.Messages)
+	}
+
+	var images []api.ImageData
+	if r.Images != nil {
+		images = make([]api.ImageData, len(r.Images))
+		copy(images, r.Images)
+	}
+
+	var opts map[string]any
+	if r.Options != nil {
+		opts = make(map[string]any, len(r.Options))
+		for k, v := range r.Options {
+			opts[k] = v
+		}
+	}
+
+	var think *api.ThinkValue
+	if r.Think != nil {
+		cThink := *r.Think
+		think = &cThink
+	}
+
+	return runOptions{
+		Model:        r.Model,
+		ParentModel:  r.ParentModel,
+		Prompt:       r.Prompt,
+		Messages:     messages,
+		WordWrap:     r.WordWrap,
+		Format:       r.Format,
+		System:       r.System,
+		Images:       images,
+		Options:      opts,
+		MultiModal:   r.MultiModal,
+		KeepAlive:    r.KeepAlive,
+		Think:        think,
+		HideThinking: r.HideThinking,
+		ShowConnect:  r.ShowConnect,
+	}
 }
 
 type displayResponseState struct {
@@ -1546,6 +1716,22 @@ func NewCLI() *cobra.Command {
 
 	pushCmd.Flags().Bool("insecure", false, "Use an insecure registry")
 
+	signinCmd := &cobra.Command{
+		Use:     "signin",
+		Short:   "Sign in to ollama.com",
+		Args:    cobra.ExactArgs(0),
+		PreRunE: checkServerHeartbeat,
+		RunE:    SigninHandler,
+	}
+
+	signoutCmd := &cobra.Command{
+		Use:     "signout",
+		Short:   "Sign out from ollama.com",
+		Args:    cobra.ExactArgs(0),
+		PreRunE: checkServerHeartbeat,
+		RunE:    SignoutHandler,
+	}
+
 	listCmd := &cobra.Command{
 		Use:     "list",
 		Aliases: []string{"ls"},
@@ -1640,6 +1826,8 @@ func NewCLI() *cobra.Command {
 		stopCmd,
 		pullCmd,
 		pushCmd,
+		signinCmd,
+		signoutCmd,
 		listCmd,
 		psCmd,
 		copyCmd,

cmd/cmd_test.go

@@ -3,10 +3,12 @@ package cmd
 import (
 	"bytes"
 	"encoding/json"
+	"fmt"
 	"io"
 	"net/http"
 	"net/http/httptest"
 	"os"
+	"reflect"
 	"strings"
 	"testing"
 	"time"
@@ -304,6 +306,8 @@ func TestDeleteHandler(t *testing.T) {
 				w.WriteHeader(http.StatusOK)
 			} else {
 				w.WriteHeader(http.StatusNotFound)
+				errPayload := `{"error":"model '%s' not found"}`
+				w.Write([]byte(fmt.Sprintf(errPayload, req.Name)))
 			}
 			return
 		}
@@ -346,7 +350,7 @@ func TestDeleteHandler(t *testing.T) {
 	}
 
 	err := DeleteHandler(cmd, []string{"test-model-not-found"})
-	if err == nil || !strings.Contains(err.Error(), "unable to stop existing running model \"test-model-not-found\"") {
+	if err == nil || !strings.Contains(err.Error(), "model 'test-model-not-found' not found") {
 		t.Fatalf("DeleteHandler failed: expected error about stopping non-existent model, got %v", err)
 	}
 }
@@ -488,9 +492,35 @@ func TestPushHandler(t *testing.T) {
 						w.(http.Flusher).Flush()
 					}
 				},
+				"/api/me": func(w http.ResponseWriter, r *http.Request) {
+					if r.Method != http.MethodPost {
+						t.Errorf("expected POST request, got %s", r.Method)
+					}
+				},
 			},
 			expectedOutput: "\nYou can find your model at:\n\n\thttps://ollama.com/test-model\n",
 		},
+		{
+			name:      "not signed in push",
+			modelName: "notsignedin-model",
+			serverResponse: map[string]func(w http.ResponseWriter, r *http.Request){
+				"/api/me": func(w http.ResponseWriter, r *http.Request) {
+					if r.Method != http.MethodPost {
+						t.Errorf("expected POST request, got %s", r.Method)
+					}
+					w.Header().Set("Content-Type", "application/json")
+					w.WriteHeader(http.StatusUnauthorized)
+					err := json.NewEncoder(w).Encode(map[string]string{
+						"error":      "unauthorized",
+						"signin_url": "https://somethingsomething",
+					})
+					if err != nil {
+						t.Fatal(err)
+					}
+				},
+			},
+			expectedOutput: "You need to be signed in to push",
+		},
 		{
 			name:      "unauthorized push",
 			modelName: "unauthorized-model",
@@ -499,12 +529,17 @@ func TestPushHandler(t *testing.T) {
 					w.Header().Set("Content-Type", "application/json")
 					w.WriteHeader(http.StatusUnauthorized)
 					err := json.NewEncoder(w).Encode(map[string]string{
-						"error": "access denied",
+						"error": "403: {\"errors\":[{\"code\":\"ACCESS DENIED\", \"message\":\"access denied\"}]}",
 					})
 					if err != nil {
 						t.Fatal(err)
 					}
 				},
+				"/api/me": func(w http.ResponseWriter, r *http.Request) {
+					if r.Method != http.MethodPost {
+						t.Errorf("expected POST request, got %s", r.Method)
+					}
+				},
 			},
 			expectedError: "you are not authorized to push to this namespace, create the model under a namespace you own",
 		},
@@ -522,6 +557,10 @@ func TestPushHandler(t *testing.T) {
 			defer mockServer.Close()
 
 			t.Setenv("OLLAMA_HOST", mockServer.URL)
+			tmpDir := t.TempDir()
+			t.Setenv("HOME", tmpDir)
+			t.Setenv("USERPROFILE", tmpDir)
+			initializeKeypair()
 
 			cmd := &cobra.Command{}
 			cmd.Flags().Bool("insecure", false, "")
@@ -557,7 +596,7 @@ func TestPushHandler(t *testing.T) {
 					t.Errorf("expected no error, got %v", err)
 				}
 				if tt.expectedOutput != "" {
-					if got := string(stdout); got != tt.expectedOutput {
+					if got := string(stdout); !strings.Contains(got, tt.expectedOutput) {
 						t.Errorf("expected output %q, got %q", tt.expectedOutput, got)
 					}
 				}
@@ -915,3 +954,286 @@ func TestNewCreateRequest(t *testing.T) {
 		})
 	}
 }
+
+func TestRunOptions_Copy(t *testing.T) {
+	// Setup test data
+	originalKeepAlive := &api.Duration{Duration: 5 * time.Minute}
+	originalThink := &api.ThinkValue{Value: "test reasoning"}
+
+	original := runOptions{
+		Model:       "test-model",
+		ParentModel: "parent-model",
+		Prompt:      "test prompt",
+		Messages: []api.Message{
+			{Role: "user", Content: "hello"},
+			{Role: "assistant", Content: "hi there"},
+		},
+		WordWrap: true,
+		Format:   "json",
+		System:   "system prompt",
+		Images: []api.ImageData{
+			[]byte("image1"),
+			[]byte("image2"),
+		},
+		Options: map[string]any{
+			"temperature": 0.7,
+			"max_tokens":  1000,
+			"top_p":       0.9,
+		},
+		MultiModal:   true,
+		KeepAlive:    originalKeepAlive,
+		Think:        originalThink,
+		HideThinking: false,
+		ShowConnect:  true,
+	}
+
+	// Test the copy
+	copied := original.Copy()
+
+	// Test 1: Verify the copy is not the same instance
+	if &copied == &original {
+		t.Error("Copy should return a different instance")
+	}
+
+	// Test 2: Verify all fields are copied correctly
+	tests := []struct {
+		name string
+		got  interface{}
+		want interface{}
+	}{
+		{"Model", copied.Model, original.Model},
+		{"ParentModel", copied.ParentModel, original.ParentModel},
+		{"Prompt", copied.Prompt, original.Prompt},
+		{"WordWrap", copied.WordWrap, original.WordWrap},
+		{"Format", copied.Format, original.Format},
+		{"System", copied.System, original.System},
+		{"MultiModal", copied.MultiModal, original.MultiModal},
+		{"HideThinking", copied.HideThinking, original.HideThinking},
+		{"ShowConnect", copied.ShowConnect, original.ShowConnect},
+	}
+
+	for _, tt := range tests {
+		if !reflect.DeepEqual(tt.got, tt.want) {
+			t.Errorf("%s mismatch: got %v, want %v", tt.name, tt.got, tt.want)
+		}
+	}
+
+	// Test 3: Verify Messages slice is deeply copied
+	if len(copied.Messages) != len(original.Messages) {
+		t.Errorf("Messages length mismatch: got %d, want %d", len(copied.Messages), len(original.Messages))
+	}
+
+	if len(copied.Messages) > 0 && &copied.Messages[0] == &original.Messages[0] {
+		t.Error("Messages should be different instances")
+	}
+
+	// Modify original to verify independence
+	if len(original.Messages) > 0 {
+		originalContent := original.Messages[0].Content
+		original.Messages[0].Content = "modified"
+		if len(copied.Messages) > 0 && copied.Messages[0].Content == "modified" {
+			t.Error("Messages should be independent after copy")
+		}
+		// Restore for other tests
+		original.Messages[0].Content = originalContent
+	}
+
+	// Test 4: Verify Images slice is deeply copied
+	if len(copied.Images) != len(original.Images) {
+		t.Errorf("Images length mismatch: got %d, want %d", len(copied.Images), len(original.Images))
+	}
+
+	if len(copied.Images) > 0 && &copied.Images[0] == &original.Images[0] {
+		t.Error("Images should be different instances")
+	}
+
+	// Modify original to verify independence
+	if len(original.Images) > 0 {
+		originalImage := original.Images[0]
+		original.Images[0] = []byte("modified")
+		if len(copied.Images) > 0 && string(copied.Images[0]) == "modified" {
+			t.Error("Images should be independent after copy")
+		}
+		// Restore for other tests
+		original.Images[0] = originalImage
+	}
+
+	// Test 5: Verify Options map is deeply copied
+	if len(copied.Options) != len(original.Options) {
+		t.Errorf("Options length mismatch: got %d, want %d", len(copied.Options), len(original.Options))
+	}
+
+	if len(copied.Options) > 0 && &copied.Options == &original.Options {
+		t.Error("Options map should be different instances")
+	}
+
+	// Modify original to verify independence
+	if len(original.Options) > 0 {
+		originalTemp := original.Options["temperature"]
+		original.Options["temperature"] = 0.9
+		if copied.Options["temperature"] == 0.9 {
+			t.Error("Options should be independent after copy")
+		}
+		// Restore for other tests
+		original.Options["temperature"] = originalTemp
+	}
+
+	// Test 6: Verify KeepAlive pointer is copied (shallow copy)
+	if copied.KeepAlive != original.KeepAlive {
+		t.Error("KeepAlive pointer should be the same (shallow copy)")
+	}
+
+	// Test 7: Verify Think pointer creates a new instance
+	if original.Think != nil && copied.Think == original.Think {
+		t.Error("Think should be a different instance")
+	}
+
+	if original.Think != nil && copied.Think != nil {
+		if !reflect.DeepEqual(copied.Think.Value, original.Think.Value) {
+			t.Errorf("Think.Value mismatch: got %v, want %v", copied.Think.Value, original.Think.Value)
+		}
+	}
+
+	// Test 8: Test with zero values
+	zeroOriginal := runOptions{}
+	zeroCopy := zeroOriginal.Copy()
+
+	if !reflect.DeepEqual(zeroCopy, zeroOriginal) {
+		fmt.Printf("orig: %#v\ncopy: %#v\n", zeroOriginal, zeroCopy)
+		t.Error("Copy of zero value should equal original zero value")
+	}
+}
+
+func TestRunOptions_Copy_EmptySlicesAndMaps(t *testing.T) {
+	// Test with empty slices and maps
+	original := runOptions{
+		Messages: []api.Message{},
+		Images:   []api.ImageData{},
+		Options:  map[string]any{},
+	}
+
+	copied := original.Copy()
+
+	if copied.Messages == nil {
+		t.Error("Empty Messages slice should remain empty, not nil")
+	}
+
+	if copied.Images == nil {
+		t.Error("Empty Images slice should remain empty, not nil")
+	}
+
+	if copied.Options == nil {
+		t.Error("Empty Options map should remain empty, not nil")
+	}
+
+	if len(copied.Messages) != 0 {
+		t.Error("Empty Messages slice should remain empty")
+	}
+
+	if len(copied.Images) != 0 {
+		t.Error("Empty Images slice should remain empty")
+	}
+
+	if len(copied.Options) != 0 {
+		t.Error("Empty Options map should remain empty")
+	}
+}
+
+func TestRunOptions_Copy_NilPointers(t *testing.T) {
+	// Test with nil pointers
+	original := runOptions{
+		KeepAlive: nil,
+		Think:     nil,
+	}
+
+	copied := original.Copy()
+
+	if copied.KeepAlive != nil {
+		t.Error("Nil KeepAlive should remain nil")
+	}
+
+	if copied.Think != nil {
+		t.Error("Nil Think should remain nil")
+	}
+}
+
+func TestRunOptions_Copy_ThinkValueVariants(t *testing.T) {
+	tests := []struct {
+		name  string
+		think *api.ThinkValue
+	}{
+		{"nil Think", nil},
+		{"bool true", &api.ThinkValue{Value: true}},
+		{"bool false", &api.ThinkValue{Value: false}},
+		{"string value", &api.ThinkValue{Value: "reasoning text"}},
+		{"int value", &api.ThinkValue{Value: 42}},
+		{"nil value", &api.ThinkValue{Value: nil}},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			original := runOptions{Think: tt.think}
+			copied := original.Copy()
+
+			if tt.think == nil {
+				if copied.Think != nil {
+					t.Error("Nil Think should remain nil")
+				}
+				return
+			}
+
+			if copied.Think == nil {
+				t.Error("Non-nil Think should not become nil")
+				return
+			}
+
+			if copied.Think == original.Think {
+				t.Error("Think should be a different instance")
+			}
+
+			if !reflect.DeepEqual(copied.Think.Value, original.Think.Value) {
+				t.Errorf("Think.Value mismatch: got %v, want %v", copied.Think.Value, original.Think.Value)
+			}
+		})
+	}
+}
+
+func TestRunOptions_Copy_Independence(t *testing.T) {
+	// Test that modifications to original don't affect copy
+	originalThink := &api.ThinkValue{Value: "original"}
+	original := runOptions{
+		Model:    "original-model",
+		Messages: []api.Message{{Role: "user", Content: "original"}},
+		Options:  map[string]any{"key": "value"},
+		Think:    originalThink,
+	}
+
+	copied := original.Copy()
+
+	// Modify original
+	original.Model = "modified-model"
+	if len(original.Messages) > 0 {
+		original.Messages[0].Content = "modified"
+	}
+	original.Options["key"] = "modified"
+	if original.Think != nil {
+		original.Think.Value = "modified"
+	}
+
+	// Verify copy is unchanged
+	if copied.Model == "modified-model" {
+		t.Error("Copy Model should not be affected by original modification")
+	}
+
+	if len(copied.Messages) > 0 && copied.Messages[0].Content == "modified" {
+		t.Error("Copy Messages should not be affected by original modification")
+	}
+
+	if copied.Options["key"] == "modified" {
+		t.Error("Copy Options should not be affected by original modification")
+	}
+
+	if copied.Think != nil && copied.Think.Value == "modified" {
+		t.Error("Copy Think should not be affected by original modification")
+	}
+}

cmd/interactive.go

@@ -195,16 +195,24 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 				fmt.Println("Usage:\n  /load <modelname>")
 				continue
 			}
+			origOpts := opts.Copy()
+
 			opts.Model = args[1]
 			opts.Messages = []api.Message{}
 			fmt.Printf("Loading model '%s'\n", opts.Model)
 			opts.Think, err = inferThinkingOption(nil, &opts, thinkExplicitlySet)
 			if err != nil {
+				if strings.Contains(err.Error(), "not found") {
+					fmt.Printf("Couldn't find model '%s'\n", opts.Model)
+					opts = origOpts.Copy()
+					continue
+				}
 				return err
 			}
 			if err := loadOrUnloadModel(cmd, &opts); err != nil {
 				if strings.Contains(err.Error(), "not found") {
-					fmt.Printf("error: %v\n", err)
+					fmt.Printf("Couldn't find model '%s'\n", opts.Model)
+					opts = origOpts.Copy()
 					continue
 				}
 				if strings.Contains(err.Error(), "does not support thinking") {

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)
 

convert/reader_safetensors.go

@@ -96,7 +96,7 @@ type safetensor struct {
 
 func (st safetensor) Kind() uint32 {
 	kind := st.tensorBase.Kind()
-	if st.dtype == "BF16" && kind != tensorKindFP32 {
+	if !strings.HasPrefix(st.name, "v.") && st.dtype == "BF16" && kind != tensorKindFP32 {
 		kind = tensorKindBF16
 	}
 

convert/reader_test.go

@@ -230,3 +230,65 @@ func TestSafetensors(t *testing.T) {
 		})
 	}
 }
+
+func TestSafetensorKind(t *testing.T) {
+	tests := []struct {
+		name     string
+		st       safetensor
+		expected uint32
+	}{
+		{
+			name: "BF16 dtype with non-v. prefix and non-FP32 base kind should return BF16",
+			st: safetensor{
+				tensorBase: &tensorBase{
+					name:  "weight.matrix",
+					shape: []uint64{10, 10}, // will default to FP16
+				},
+				dtype: "BF16",
+			},
+			expected: tensorKindBF16,
+		},
+		{
+			name: "BF16 dtype with v. prefix should return base kind",
+			st: safetensor{
+				tensorBase: &tensorBase{
+					name:  "v.weight.matrix",
+					shape: []uint64{10, 10}, // will default to FP16
+				},
+				dtype: "BF16",
+			},
+			expected: tensorKindFP16,
+		},
+		{
+			name: "BF16 dtype with FP32 base kind should return FP32",
+			st: safetensor{
+				tensorBase: &tensorBase{
+					name:  "weight.matrix",
+					shape: []uint64{10}, // will default to FP32
+				},
+				dtype: "BF16",
+			},
+			expected: tensorKindFP32,
+		},
+		{
+			name: "Non-BF16 dtype should return base kind",
+			st: safetensor{
+				tensorBase: &tensorBase{
+					name:  "weight.matrix",
+					shape: []uint64{10, 10}, // will default to FP16
+				},
+				dtype: "FP16",
+			},
+			expected: tensorKindFP16,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			result := tt.st.Kind()
+			if result != tt.expected {
+				t.Errorf("Kind() = %d, expected %d", result, tt.expected)
+			}
+		})
+	}
+}

discover/cuda_common.go

@@ -16,7 +16,7 @@ import (
 // Included to drive logic for reducing Ollama-allocated overhead on L4T/Jetson devices.
 var CudaTegra string = os.Getenv("JETSON_JETPACK")
 
-func cudaVariant(gpuInfo CudaGPUInfo) string {
+func cudaVariant(gpuInfos []CudaGPUInfo) string {
 	if runtime.GOARCH == "arm64" && runtime.GOOS == "linux" {
 		if CudaTegra != "" {
 			ver := strings.Split(CudaTegra, ".")
@@ -43,14 +43,22 @@ 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"
+	// Check GPU compute capability FIRST, lowest common denominator if multi-gpu
+	for _, gpuInfo := range gpuInfos {
+		if gpuInfo.computeMajor < 7 || (gpuInfo.computeMajor == 7 && gpuInfo.computeMinor < 5) {
+			// GPU is Pascal or older (CC <= 7.4) - use CUDA v12 (supports CC 6.1)
+			return "v12"
+		}
 	}
-	return "v12"
+
+	// GPU is Turing or newer (CC >= 7.5) - can use newer CUDA
+	if len(gpuInfos) > 0 && gpuInfos[0].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
+		slog.Warn("old CUDA driver detected - please upgrade to a newer driver for best performance", "version", fmt.Sprintf("%d.%d", gpuInfos[0].DriverMajor, gpuInfos[0].DriverMinor))
+		return "v12"
+	}
+	return "v13"
 }

discover/gpu.go

@@ -284,18 +284,8 @@ func GetGPUInfo() GpuInfoList {
 				gpuInfo.MinimumMemory = cudaMinimumMemory
 				gpuInfo.DriverMajor = driverMajor
 				gpuInfo.DriverMinor = driverMinor
-				variant := cudaVariant(gpuInfo)
 
-				// Start with our bundled libraries
-				if variant != "" {
-					variantPath := filepath.Join(LibOllamaPath, "cuda_"+variant)
-					if _, err := os.Stat(variantPath); err == nil {
-						// Put the variant directory first in the search path to avoid runtime linking to the wrong library
-						gpuInfo.DependencyPath = append([]string{variantPath}, gpuInfo.DependencyPath...)
-					}
-				}
 				gpuInfo.Name = C.GoString(&memInfo.gpu_name[0])
-				gpuInfo.Variant = variant
 
 				if int(memInfo.major) < cudaComputeMajorMin || (int(memInfo.major) == cudaComputeMajorMin && int(memInfo.minor) < cudaComputeMinorMin) {
 					unsupportedGPUs = append(unsupportedGPUs,
@@ -333,6 +323,24 @@ func GetGPUInfo() GpuInfoList {
 				// TODO potentially sort on our own algorithm instead of what the underlying GPU library does...
 				cudaGPUs = append(cudaGPUs, gpuInfo)
 			}
+			// Second pass on NVIDIA GPUs to set lowest common denominator variant and DependencyPaths
+			variant := cudaVariant(cudaGPUs)
+			var variantPath string
+			// Start with our bundled libraries
+			if variant != "" {
+				variantPath = filepath.Join(LibOllamaPath, "cuda_"+variant)
+				if _, err := os.Stat(variantPath); err != nil {
+					variantPath = ""
+				}
+			}
+
+			for i := range cudaGPUs {
+				cudaGPUs[i].Variant = variant
+				if variantPath != "" {
+					// Put the variant directory first in the search path to avoid runtime linking to the wrong library
+					cudaGPUs[i].DependencyPath = append([]string{variantPath}, cudaGPUs[i].DependencyPath...)
+				}
+			}
 		}
 
 		// Intel

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/cloud.md

@@ -0,0 +1,40 @@
+# Cloud
+
+| Ollama's cloud is currently in preview. For full documentation, see [Ollama's documentation](https://docs.ollama.com/cloud).
+
+## Cloud Models
+
+[Cloud models](https://ollama.com/cloud) are a new kind of model in Ollama that can run without a powerful GPU. Instead, cloud models are automatically offloaded to Ollama's cloud while offering the same capabilities as local models, making it possible to keep using your local tools while running larger models that wouldn’t fit on a personal computer.
+
+Ollama currently supports the following cloud models, with more coming soon:
+
+- `gpt-oss:20b-cloud`
+- `gpt-oss:120b-cloud`
+- `deepseek-v3.1:671b-cloud`
+- `qwen3-coder:480b-cloud`
+
+### Get started
+
+To run a cloud model, open the terminal and run:
+
+```
+ollama run gpt-oss:120b-cloud
+```
+
+To run cloud models with integrations that work with Ollama, first download the cloud model:
+
+```
+ollama pull qwen3-coder:480b-cloud
+```
+
+Then sign in to Ollama:
+
+```
+ollama signin
+```
+
+Finally, access the model using the model name `qwen3-coder:480b-cloud` via Ollama's local API or tooling.
+
+## Cloud API access
+
+Cloud models can also be accessed directly on ollama.com's API. For more information, see the [docs](https://docs.ollama.com/cloud).

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
 ```
 

docs/troubleshooting.md

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

docs/turbo.md

@@ -1,107 +0,0 @@
-# Turbo
-
-> ⚠️ Turbo is preview
-
-Ollama’s [Turbo](https://ollama.com/turbo) is a new way to run open-source models with acceleration from datacenter-grade hardware.
-
-Currently, the following models are available in Turbo:
-
-- `gpt-oss:20b`
-- `gpt-oss:120b`
-
-## Get started
-
-### Ollama for macOS & Windows
-
-Download Ollama
-
-- Select a model such as `gpt-oss:20b` or `gpt-oss:120b`
-- Click on **Turbo**. You’ll be prompted to create an account or sign in
-
-### Ollama’s CLI
-
-- [Sign up](https://ollama.com/signup) for an Ollama account
-- Add your Ollama key [to ollama.com](https://ollama.com/settings/keys).
-
-  On macOS and Linux:
-
-  ```shell
-  cat ~/.ollama/id_ed25519.pub
-  ```
-
-  On Windows:
-
-  ```
-  type "%USERPROFILE%\.ollama\id_ed25519.pub"
-  ```
-
-- Then run a model setting `OLLAMA_HOST` to `ollama.com`:
-  ```shell
-  OLLAMA_HOST=ollama.com ollama run gpt-oss:120b
-  ```
-
-### Ollama’s Python library
-
-- Download Ollama's [Python library](https://github.com/ollama/ollama-python)
-- [Sign up](https://ollama.com/signup) for an Ollama account
-- Create an API key by visiting https://ollama.com/settings/keys
-
-```python
-from ollama import Client
-
-client = Client(
-    host="https://ollama.com",
-    headers={'Authorization': '<api key>'}
-)
-
-messages = [
-  {
-    'role': 'user',
-    'content': 'Why is the sky blue?',
-  },
-]
-
-for part in client.chat('gpt-oss:120b', messages=messages, stream=True):
-  print(part['message']['content'], end='', flush=True)
-```
-
-### Ollama’s JavaScript library
-
-- Download Ollama's [JavaScript library](https://github.com/ollama/ollama-js)
-- [Sign up](https://ollama.com/signup) for an Ollama account
-- Create an API key by visiting https://ollama.com/settings/keys
-
-```typescript
-import { Ollama } from 'ollama';
-
-const ollama = new Ollama({
-  host: 'https://ollama.com',
-  headers: {
-	  Authorization: "Bearer <api key>"
-  }
-});
-
-const response = await ollama.chat({
-  model: 'gpt-oss:120b',
-  messages: [{ role: 'user', content: 'Explain quantum computing' }],
-  stream: true
-});
-
-for await (const part of response) {
-    process.stdout.write(part.message.content)
-}
-```
-
-### Community integrations
-
-Turbo mode is also compatible with several community integrations.
-
-#### Open WebUI
-
-- Go to **settings** → **Admin settings** → **Connections**
-- Under **Ollama API,** click **+**
-- For the **URL** put `https://ollama.com`
-- For the **API key,** create an API key on https://ollama.com/settings/keys and add it.
-- Click **Save**
-
-Now, if you navigate to the model selector, Turbo models should be available under **External**.

envconfig/config.go

@@ -134,6 +134,17 @@ func LoadTimeout() (loadTimeout time.Duration) {
 	return loadTimeout
 }
 
+func Remotes() []string {
+	var r []string
+	raw := strings.TrimSpace(Var("OLLAMA_REMOTES"))
+	if raw == "" {
+		r = []string{"ollama.com"}
+	} else {
+		r = strings.Split(raw, ",")
+	}
+	return r
+}
+
 func Bool(k string) func() bool {
 	return func() bool {
 		if s := Var(k); s != "" {
@@ -185,8 +196,6 @@ 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 +281,7 @@ 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"},
+		"OLLAMA_REMOTES":           {"OLLAMA_REMOTES", Remotes(), "Allowed hosts for remote models (default \"ollama.com\")"},
 
 		// Informational
 		"HTTP_PROXY":  {"HTTP_PROXY", String("HTTP_PROXY")(), "HTTP proxy"},

fs/ggml/ggml.go

@@ -57,10 +57,28 @@ func (kv KV) EmbeddingLength() uint64 {
 	return uint64(kv.Uint("embedding_length"))
 }
 
+func (kv KV) HeadCount() []uint64 {
+	headCountDefault := uint32(1)
+	headCount := kv.UintOrArrayValueAsArray("attention.head_count", headCountDefault)
+	if len(headCount) == 1 {
+		headCountDefault = headCount[0]
+	}
+	nLayers := int(kv.BlockCount())
+	if len(headCount) > nLayers {
+		slog.Warn("got more elements of attention.head_count than layers", "len(headCount)", len(headCount), "layers", nLayers)
+	}
+	out := make([]uint64, nLayers)
+	for i := range nLayers {
+		if i >= len(headCount) {
+			out[i] = uint64(headCountDefault)
+		} else {
+			out[i] = uint64(headCount[i])
+		}
+	}
+	return out
+}
+
 func (kv KV) HeadCountMax() uint64 {
-	// TODO(drifkin): using the max value can cause an overestimation. In the
-	// future if array values become more popular, we can adapt the more invasive
-	// <https://github.com/ollama/ollama/pull/10225>
 	return uint64(kv.UintOrMaxArrayValue("attention.head_count", 1))
 }
 
@@ -68,6 +86,27 @@ func (kv KV) HeadCountMin() uint64 {
 	return uint64(kv.UintOrMinArrayValue("attention.head_count", 1))
 }
 
+func (kv KV) HeadCountKV() []uint64 {
+	headCountKVDefault := uint32(1)
+	headCountKV := kv.UintOrArrayValueAsArray("attention.head_count_kv", headCountKVDefault)
+	if len(headCountKV) == 1 {
+		headCountKVDefault = headCountKV[0]
+	}
+	nLayers := int(kv.BlockCount())
+	if len(headCountKV) > nLayers {
+		slog.Warn("got more elements of attention.head_count than layers", "len(headCountKV)", len(headCountKV), "layers", nLayers)
+	}
+	out := make([]uint64, nLayers)
+	for i := range nLayers {
+		if i >= len(headCountKV) {
+			out[i] = uint64(headCountKVDefault)
+		} else {
+			out[i] = uint64(headCountKV[i])
+		}
+	}
+	return out
+}
+
 func (kv KV) HeadCountKVMax() uint64 {
 	return uint64(kv.UintOrMaxArrayValue("attention.head_count_kv", 1))
 }
@@ -100,6 +139,26 @@ func (kv KV) ChatTemplate() string {
 	return kv.String("tokenizer.chat_template")
 }
 
+// ssm architecture parameters
+
+func (kv KV) SSMConvKernel() uint64 {
+	return uint64(kv.Uint("ssm.conv_kernel"))
+}
+
+func (kv KV) SSMInnerSize() uint64 {
+	return uint64(kv.Uint("ssm.inner_size"))
+}
+
+func (kv KV) SSMStateSize() uint64 {
+	return uint64(kv.Uint("ssm.state_size"))
+}
+
+func (kv KV) SSMGroupCount() uint64 {
+	return uint64(kv.Uint("ssm.group_count"))
+}
+
+// general types
+
 func (kv KV) String(key string, defaultValue ...string) string {
 	val, _ := keyValue(kv, key, append(defaultValue, "")...)
 	return val
@@ -131,22 +190,27 @@ func (kv KV) UintOrMinArrayValue(key string, defaultValue uint32) uint32 {
 }
 
 func (kv KV) UintOrArrayValue(key string, defaultValue uint32) (uint32, uint32) {
+	arrVal := kv.UintOrArrayValueAsArray(key, defaultValue)
+	return slices.Min(arrVal), slices.Max(arrVal)
+}
+
+func (kv KV) UintOrArrayValueAsArray(key string, defaultValue uint32) []uint32 {
 	if u32, ok := keyValue(kv, key, uint32(0)); ok {
-		return u32, u32
+		return []uint32{u32}
 	} else if u32s, ok := keyValue(kv, key, &array[uint32]{}); ok {
-		min := slices.Min(u32s.values)
-		max := slices.Max(u32s.values)
-		return min, max
+		return u32s.values
 	} else if i32s, ok := keyValue(kv, key, &array[int32]{}); ok {
-		min := slices.Min(i32s.values)
-		max := slices.Max(i32s.values)
-		if min < 0 || max < 0 {
-			slog.Warn("array values are unexpectedly negative", "key", key, "min", min, "max", max)
+		dst := make([]uint32, len(i32s.values))
+		for i, v := range i32s.values {
+			if v < 0 {
+				slog.Warn("array values are unexpectedly negative", "key", key, "i", i, "v", v)
+			}
+			dst[i] = uint32(v)
 		}
-		return uint32(min), uint32(max)
+		return dst
 	}
 
-	return defaultValue, defaultValue
+	return []uint32{defaultValue}
 }
 
 func (kv KV) Strings(key string, defaultValue ...[]string) []string {
@@ -179,6 +243,8 @@ func (kv KV) OllamaEngineRequired() bool {
 		"gemma3",
 		"gemma3n",
 		"mistral3",
+		"qwen3",
+		"qwen3moe",
 		"llama4",
 		"mllama",
 		"qwen25vl",
@@ -486,7 +552,9 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 
 	embedding := f.KV().EmbeddingLength()
 	heads := f.KV().HeadCountMax()
+	headsArr := f.KV().HeadCount()
 	headsKV := f.KV().HeadCountKVMax()
+	headsKVArr := f.KV().HeadCountKV()
 	vocab := uint64(f.KV()["tokenizer.ggml.tokens"].(*array[string]).size)
 
 	embeddingHeads := f.KV().EmbeddingHeadCountMax()
@@ -496,12 +564,51 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
 	layers := f.Tensors().GroupLayers()
 
 	bytesPerElement := kvCacheBytesPerElement(kvCacheType)
+
+	// Default for models unless special-cased below. These defaults mirror the
+	// cache usage in llama.cpp under the assumption that models without special
+	// cases below will use the llamarunner and caching will be handled by the
+	// llama.cpp layer.
+	//
+	// This also assumes that a layer without heads or headsKV set is recurrent
+	// which is usually the case. Some models (eg nemotronh) use "blocks" in
+	// place of layers where some are MLP blocks that don't have any cache.
+	// Models like this will need a special case below to be accurately
+	// estimated.
 	var kvTotal uint64
 	kv = make([]uint64, f.KV().BlockCount())
+	kvSizeAttn := uint64(0)
+	kvSizeRecurrent := uint64(0)
 	for i := range kv {
-		kv[i] = uint64(float64(context*(embeddingHeadsK+embeddingHeadsV)*headsKV) * bytesPerElement)
+		headsL := headsArr[i]
+		headsKVL := headsKVArr[i]
+		if headsL > 0 && headsKVL > 0 {
+			// full attention layer
+			// NOTE: Assumes uniform values for all attn layers
+			kv[i] = uint64(float64(context*(embeddingHeadsK+embeddingHeadsV)*headsKVL) * bytesPerElement)
+			kvSizeAttn += kv[i]
+		} else {
+			// recurrent layer
+			ssmDConv := f.KV().SSMConvKernel()
+			ssmDState := f.KV().SSMStateSize()
+			ssmDInner := f.KV().SSMInnerSize()
+			ssmNGroups := f.KV().SSMGroupCount()
+			nEmbdR := uint64(0)
+			if ssmDConv > 0 {
+				nEmbdR = (ssmDConv - 1) * (ssmDInner + 2*ssmNGroups*ssmDState)
+			}
+			nEmbdS := ssmDState * ssmDInner
+
+			// recurrent always uses F32 in llama.cpp backend
+			// https://github.com/ggml-org/llama.cpp/blob/master/src/llama-model.cpp#L18644
+			bytesPerElementRecurrent := kvCacheBytesPerElement("f32")
+
+			kv[i] = (nEmbdR + nEmbdS) * uint64(bytesPerElementRecurrent)
+			kvSizeRecurrent += kv[i]
+		}
 		kvTotal += kv[i]
 	}
+	slog.Debug("default cache size estimate", "attention MiB", float32(kvSizeAttn)/(1024.*1024.), "attention bytes", kvSizeAttn, "recurrent MiB", float32(kvSizeRecurrent)/(1024.*1024.), "recurrent bytes", kvSizeRecurrent)
 
 	switch f.KV().Architecture() {
 	case "llama", "llama4":
@@ -759,12 +866,16 @@ 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
@@ -774,6 +885,10 @@ 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()
@@ -794,6 +909,8 @@ func kvCacheBytesPerElement(cacheType string) float64 {
 		return 1 // 1/2 of fp16
 	case "q4_0":
 		return 0.5 // 1/4 of fp16
+	case "f32":
+		return 4 // f32 (default for recurrent)
 	default:
 		return 2 // f16 (default)
 	}

harmony/harmonyparser.go

@@ -1,7 +1,7 @@
 package harmony
 
 import (
-	"context"
+	"encoding/json"
 	"fmt"
 	"log/slog"
 	"strings"
@@ -266,6 +266,8 @@ type HarmonyMessageHandler struct {
 	state           harmonyMessageState
 	HarmonyParser   *HarmonyParser
 	FunctionNameMap *FunctionNameMap
+	toolAccumulator *HarmonyToolCallAccumulator
+	convertedTools  map[string]struct{}
 }
 
 // NewHarmonyMessageHandler creates a new message handler
@@ -278,6 +280,7 @@ func NewHarmonyMessageHandler() *HarmonyMessageHandler {
 			HeaderEndTag:    "<|message|>",
 		},
 		FunctionNameMap: NewFunctionNameMap(),
+		convertedTools:  make(map[string]struct{}),
 	}
 }
 
@@ -292,7 +295,7 @@ func (h *HarmonyMessageHandler) AddContent(content string, toolParser *HarmonyTo
 	for _, event := range events {
 		switch event := event.(type) {
 		case HarmonyEventHeaderComplete:
-			slog.Log(context.TODO(), logutil.LevelTrace, "harmony event header complete", "header", event.Header)
+			logutil.Trace("harmony event header complete", "header", event.Header)
 			switch event.Header.Channel {
 			case "analysis":
 				if event.Header.Recipient != "" {
@@ -315,7 +318,7 @@ func (h *HarmonyMessageHandler) AddContent(content string, toolParser *HarmonyTo
 				h.state = harmonyMessageState_Normal
 			}
 		case HarmonyEventContentEmitted:
-			slog.Log(context.TODO(), logutil.LevelTrace, "harmony event content", "content", event.Content, "state", h.state)
+			logutil.Trace("harmony event content", "content", event.Content, "state", h.state)
 			if h.state == harmonyMessageState_Normal {
 				contentSb.WriteString(event.Content)
 			} else if h.state == harmonyMessageState_Thinking {
@@ -385,8 +388,85 @@ func NewFunctionNameMap() *FunctionNameMap {
 	}
 }
 
+// Init initializes the handler with tools and optional last message
+// Implements the Parser interface
+func (h *HarmonyMessageHandler) Init(tools []api.Tool, lastMessage *api.Message) []api.Tool {
+	// Initialize the harmony parser
+	if h.HarmonyParser == nil {
+		h.HarmonyParser = &HarmonyParser{
+			MessageStartTag: "<|start|>",
+			MessageEndTag:   "<|end|>",
+			HeaderEndTag:    "<|message|>",
+		}
+	}
+
+	// Handle prefill for chat mode
+	if lastMessage != nil {
+		h.HarmonyParser.AddImplicitStartOrPrefill(lastMessage)
+	} else {
+		h.HarmonyParser.AddImplicitStart()
+	}
+
+	// Initialize tool accumulator
+	h.toolAccumulator = h.CreateToolParser()
+
+	// Process tools and return renamed versions
+	if len(tools) == 0 {
+		return tools
+	}
+
+	processedTools := make([]api.Tool, len(tools))
+	copy(processedTools, tools)
+	for i, tool := range processedTools {
+		if tool.Function.Name != "" {
+			processedTools[i].Function.Name = h.FunctionNameMap.ConvertAndAdd(tool.Function.Name)
+			h.convertedTools[tool.Function.Name] = struct{}{}
+		}
+	}
+	return processedTools
+}
+
+// Add implements the Parser interface - processes streamed content and extracts content, thinking, and tool calls
+func (h *HarmonyMessageHandler) Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error) {
+	content, thinking, toolContent := h.AddContent(s, h.toolAccumulator)
+	if toolContent != "" {
+		h.toolAccumulator.Add(toolContent)
+	}
+
+	// tool calls always happen one at a time, and always at the end of a message,
+	// so for simplicity we defer parsing them until we know we're done
+	if done {
+		toolName, raw := h.toolAccumulator.Drain()
+		if toolName != nil {
+			name := strings.TrimPrefix(*toolName, "functions.")
+			name = h.FunctionNameMap.OriginalFromConverted(name)
+			var args api.ToolCallFunctionArguments
+			if err := json.Unmarshal([]byte(raw), &args); err != nil {
+				return "", "", nil, fmt.Errorf("error parsing tool call: raw='%s', err=%w", raw, err)
+			}
+			calls = append(calls, api.ToolCall{Function: api.ToolCallFunction{Name: name, Arguments: args}})
+		}
+	}
+
+	return content, thinking, calls, nil
+}
+
+// HasToolSupport implements the Parser interface
+func (h *HarmonyMessageHandler) HasToolSupport() bool {
+	return true
+}
+
+// HasThinkingSupport implements the Parser interface
+func (h *HarmonyMessageHandler) HasThinkingSupport() bool {
+	return true
+}
+
 func (m *FunctionNameMap) ConvertAndAdd(userFunctionName string) string {
 	harmonyFunctionName := m.deriveName(userFunctionName)
+	// built-in functions should not be renamed
+	if userFunctionName == "browser.open" || userFunctionName == "browser.search" || userFunctionName == "browser.find" || userFunctionName == "python" {
+		harmonyFunctionName = userFunctionName
+	}
 	m.userToHarmony[userFunctionName] = harmonyFunctionName
 	m.harmonyToUser[harmonyFunctionName] = userFunctionName
 	return harmonyFunctionName

harmony/harmonyparser_test.go

@@ -513,6 +513,7 @@ func TestFunctionConvertAndAdd(t *testing.T) {
 		{name: "dupes from different user-specified names", in: []string{"get weather", "get_weather", "get-weather"}, want: []string{"get_weather", "get_weather_2", "get_weather_3"}},
 		{name: "non dupes after dupes", in: []string{"get weather", "get_weather", "get-weather", "something-different"}, want: []string{"get_weather", "get_weather_2", "get_weather_3", "something_different"}},
 		{name: "multiple sets of dupes", in: []string{"a", "a", "b", "a", "a", "b", "a"}, want: []string{"a", "a_2", "b", "a_3", "a_4", "b_2", "a_5"}},
+		{name: "built-in functions should not be renamed", in: []string{"browser.open", "python", "not.a.built-in.function", "browser.not_a_real_built_in"}, want: []string{"browser.open", "python", "not_a_built_in_function", "browser_not_a_real_built_in"}},
 	}
 
 	for i, tt := range tests {

integration/README.md

@@ -12,3 +12,6 @@ The integration tests have 2 modes of operating.
 
 > [!IMPORTANT]
 > Before running the tests locally without the "test existing" setting, compile ollama from the top of the source tree  `go build .` in addition to GPU support with cmake if applicable on your platform.  The integration tests expect to find an ollama binary at the top of the tree.
+
+
+Many tests use a default small model suitable to run on many systems.  You can override this default model by setting `OLLAMA_TEST_DEFAULT_MODEL`

integration/api_test.go

@@ -22,13 +22,12 @@ func TestAPIGenerate(t *testing.T) {
 	// Set up the test data
 	req := api.GenerateRequest{
 		Model:  smol,
-		Prompt: "why is the sky blue? be brief",
+		Prompt: blueSkyPrompt,
 		Options: map[string]interface{}{
 			"temperature": 0,
 			"seed":        123,
 		},
 	}
-	anyResp := []string{"rayleigh", "scattering"}
 
 	client, _, cleanup := InitServerConnection(ctx, t)
 	defer cleanup()
@@ -120,14 +119,14 @@ func TestAPIGenerate(t *testing.T) {
 				// Verify the response contains the expected data
 				response := buf.String()
 				atLeastOne := false
-				for _, resp := range anyResp {
+				for _, resp := range blueSkyExpected {
 					if strings.Contains(strings.ToLower(response), resp) {
 						atLeastOne = true
 						break
 					}
 				}
 				if !atLeastOne {
-					t.Errorf("none of %v found in %s", anyResp, response)
+					t.Errorf("none of %v found in %s", blueSkyExpected, response)
 				}
 			case <-ctx.Done():
 				t.Error("outer test context done while waiting for generate")
@@ -181,7 +180,7 @@ func TestAPIChat(t *testing.T) {
 		Messages: []api.Message{
 			{
 				Role:    "user",
-				Content: "why is the sky blue?  be brief",
+				Content: blueSkyPrompt,
 			},
 		},
 		Options: map[string]interface{}{
@@ -189,7 +188,6 @@ func TestAPIChat(t *testing.T) {
 			"seed":        123,
 		},
 	}
-	anyResp := []string{"rayleigh", "scattering"}
 
 	client, _, cleanup := InitServerConnection(ctx, t)
 	defer cleanup()
@@ -279,14 +277,14 @@ func TestAPIChat(t *testing.T) {
 				// Verify the response contains the expected data
 				response := buf.String()
 				atLeastOne := false
-				for _, resp := range anyResp {
+				for _, resp := range blueSkyExpected {
 					if strings.Contains(strings.ToLower(response), resp) {
 						atLeastOne = true
 						break
 					}
 				}
 				if !atLeastOne {
-					t.Errorf("none of %v found in %s", anyResp, response)
+					t.Errorf("none of %v found in %s", blueSkyExpected, response)
 				}
 			case <-ctx.Done():
 				t.Error("outer test context done while waiting for chat")
@@ -410,3 +408,99 @@ func TestAPIEmbeddings(t *testing.T) {
 		t.Errorf("zero length embedding response")
 	}
 }
+
+func TestAPIToolCalling(t *testing.T) {
+	initialTimeout := 60 * time.Second
+	streamTimeout := 30 * time.Second
+	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
+	defer cancel()
+
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
+
+	modelName := "qwen3:0.6b"
+	if err := PullIfMissing(ctx, client, modelName); err != nil {
+		t.Fatalf("pull failed %s", err)
+	}
+
+	tools := []api.Tool{
+		{
+			Type: "function",
+			Function: api.ToolFunction{
+				Name:        "get_weather",
+				Description: "Get the current weather in a given location",
+				Parameters: api.ToolFunctionParameters{
+					Type:     "object",
+					Required: []string{"location"},
+					Properties: map[string]api.ToolProperty{
+						"location": {
+							Type:        api.PropertyType{"string"},
+							Description: "The city and state, e.g. San Francisco, CA",
+						},
+					},
+				},
+			},
+		},
+	}
+
+	req := api.ChatRequest{
+		Model: modelName,
+		Messages: []api.Message{
+			{
+				Role:    "user",
+				Content: "Call get_weather with location set to San Francisco.",
+			},
+		},
+		Tools: tools,
+		Options: map[string]any{
+			"temperature": 0,
+		},
+	}
+
+	stallTimer := time.NewTimer(initialTimeout)
+	var gotToolCall bool
+	var lastToolCall api.ToolCall
+
+	fn := func(response api.ChatResponse) error {
+		if len(response.Message.ToolCalls) > 0 {
+			gotToolCall = true
+			lastToolCall = response.Message.ToolCalls[len(response.Message.ToolCalls)-1]
+		}
+		if !stallTimer.Reset(streamTimeout) {
+			return fmt.Errorf("stall was detected while streaming response, aborting")
+		}
+		return nil
+	}
+
+	stream := true
+	req.Stream = &stream
+	done := make(chan int)
+	var genErr error
+	go func() {
+		genErr = client.Chat(ctx, &req, fn)
+		done <- 0
+	}()
+
+	select {
+	case <-stallTimer.C:
+		t.Errorf("tool-calling chat never started. Timed out after: %s", initialTimeout.String())
+	case <-done:
+		if genErr != nil {
+			t.Fatalf("chat failed: %v", genErr)
+		}
+
+		if !gotToolCall {
+			t.Fatalf("expected at least one tool call, got none")
+		}
+
+		if lastToolCall.Function.Name != "get_weather" {
+			t.Errorf("unexpected tool called: got %q want %q", lastToolCall.Function.Name, "get_weather")
+		}
+
+		if _, ok := lastToolCall.Function.Arguments["location"]; !ok {
+			t.Errorf("expected tool arguments to include 'location', got: %s", lastToolCall.Function.Arguments.String())
+		}
+	case <-ctx.Done():
+		t.Error("outer test context done while waiting for tool-calling chat")
+	}
+}

integration/basic_test.go

@@ -19,14 +19,14 @@ func TestBlueSky(t *testing.T) {
 	// Set up the test data
 	req := api.GenerateRequest{
 		Model:  smol,
-		Prompt: "why is the sky blue?",
+		Prompt: blueSkyPrompt,
 		Stream: &stream,
 		Options: map[string]any{
 			"temperature": 0,
 			"seed":        123,
 		},
 	}
-	GenerateTestHelper(ctx, t, req, []string{"rayleigh", "scattering"})
+	GenerateTestHelper(ctx, t, req, blueSkyExpected)
 }
 
 func TestUnicode(t *testing.T) {
@@ -110,12 +110,12 @@ func TestUnicodeModelDir(t *testing.T) {
 
 	req := api.GenerateRequest{
 		Model:  smol,
-		Prompt: "why is the sky blue?",
+		Prompt: blueSkyPrompt,
 		Stream: &stream,
 		Options: map[string]any{
 			"temperature": 0,
 			"seed":        123,
 		},
 	}
-	GenerateTestHelper(ctx, t, req, []string{"rayleigh", "scattering"})
+	GenerateTestHelper(ctx, t, req, blueSkyExpected)
 }

integration/concurrency_test.go

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

integration/context_test.go

@@ -36,7 +36,7 @@ func TestLongInputContext(t *testing.T) {
 	if err := PullIfMissing(ctx, client, req.Model); err != nil {
 		t.Fatalf("PullIfMissing failed: %v", err)
 	}
-	DoGenerate(ctx, t, client, req, []string{"russia", "germany", "france", "england", "austria", "prussia", "individuals", "coalition", "conflict"}, 120*time.Second, 10*time.Second)
+	DoGenerate(ctx, t, client, req, []string{"russia", "germany", "france", "england", "austria", "prussia", "europe", "individuals", "coalition", "conflict"}, 120*time.Second, 10*time.Second)
 }
 
 func TestContextExhaustion(t *testing.T) {
@@ -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,
@@ -63,11 +63,11 @@ func TestContextExhaustion(t *testing.T) {
 	if err := PullIfMissing(ctx, client, req.Model); err != nil {
 		t.Fatalf("PullIfMissing failed: %v", err)
 	}
-	DoGenerate(ctx, t, client, req, []string{"once", "upon", "lived", "sunny", "cloudy", "clear", "water"}, 120*time.Second, 10*time.Second)
+	DoGenerate(ctx, t, client, req, []string{"once", "upon", "lived", "sunny", "cloudy", "clear", "water", "time", "travel", "world"}, 120*time.Second, 10*time.Second)
 }
 
 // Send multiple generate requests with prior context and ensure the response is coherant and expected
-func TestGenerateWithHistory(t *testing.T) {
+func TestParallelGenerateWithHistory(t *testing.T) {
 	modelOverride := ollamaEngineChatModels[0] // Most recent ollama engine model
 	req, resp := GenerateRequests()
 	numParallel := 2
@@ -113,8 +113,48 @@ func TestGenerateWithHistory(t *testing.T) {
 	wg.Wait()
 }
 
+// Send generate requests with prior context and ensure the response is coherant and expected
+func TestGenerateWithHistory(t *testing.T) {
+	req := api.GenerateRequest{
+		Model:     smol,
+		Prompt:    rainbowPrompt,
+		Stream:    &stream,
+		KeepAlive: &api.Duration{Duration: 10 * time.Second},
+		Options: map[string]any{
+			"num_ctx": 16384,
+		},
+	}
+
+	softTimeout, hardTimeout := getTimeouts(t)
+	ctx, cancel := context.WithTimeout(context.Background(), hardTimeout)
+	defer cancel()
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
+
+	// Get the server running (if applicable) warm the model up with a single initial request
+	slog.Info("loading", "model", req.Model)
+	err := client.Generate(ctx,
+		&api.GenerateRequest{Model: req.Model, KeepAlive: &api.Duration{Duration: 10 * time.Second}, Options: req.Options},
+		func(response api.GenerateResponse) error { return nil },
+	)
+	if err != nil {
+		t.Fatalf("failed to load model %s: %s", req.Model, err)
+	}
+
+	req.Context = DoGenerate(ctx, t, client, req, rainbowExpected, 30*time.Second, 20*time.Second)
+
+	for i := 0; i < len(rainbowFollowups); i++ {
+		req.Prompt = rainbowFollowups[i]
+		if time.Now().Sub(started) > softTimeout {
+			slog.Info("exceeded soft timeout, winding down test")
+			return
+		}
+		req.Context = DoGenerate(ctx, t, client, req, rainbowExpected, 30*time.Second, 20*time.Second)
+	}
+}
+
 // Send multiple chat requests with prior context and ensure the response is coherant and expected
-func TestChatWithHistory(t *testing.T) {
+func TestParallelChatWithHistory(t *testing.T) {
 	modelOverride := ollamaEngineChatModels[0] // Most recent ollama engine model
 	req, resp := ChatRequests()
 	numParallel := 2
@@ -164,3 +204,55 @@ func TestChatWithHistory(t *testing.T) {
 	}
 	wg.Wait()
 }
+
+// Send generate requests with prior context and ensure the response is coherant and expected
+func TestChatWithHistory(t *testing.T) {
+	req := api.ChatRequest{
+		Model:     smol,
+		Stream:    &stream,
+		KeepAlive: &api.Duration{Duration: 10 * time.Second},
+		Options: map[string]any{
+			"num_ctx": 16384,
+		},
+		Messages: []api.Message{
+			{
+				Role:    "user",
+				Content: rainbowPrompt,
+			},
+		},
+	}
+
+	softTimeout, hardTimeout := getTimeouts(t)
+	ctx, cancel := context.WithTimeout(context.Background(), hardTimeout)
+	defer cancel()
+	client, _, cleanup := InitServerConnection(ctx, t)
+	defer cleanup()
+
+	// Get the server running (if applicable) warm the model up with a single initial request
+	slog.Info("loading", "model", req.Model)
+	err := client.Generate(ctx,
+		&api.GenerateRequest{Model: req.Model, KeepAlive: &api.Duration{Duration: 10 * time.Second}, Options: req.Options},
+		func(response api.GenerateResponse) error { return nil },
+	)
+	if err != nil {
+		t.Fatalf("failed to load model %s: %s", req.Model, err)
+	}
+
+	assistant := DoChat(ctx, t, client, req, rainbowExpected, 30*time.Second, 20*time.Second)
+
+	for i := 0; i < len(rainbowFollowups); i++ {
+		if time.Now().Sub(started) > softTimeout {
+			slog.Info("exceeded soft timeout, winding down test")
+			return
+		}
+		req.Messages = append(req.Messages,
+			*assistant,
+			api.Message{Role: "user", Content: rainbowFollowups[i]},
+		)
+
+		assistant = DoChat(ctx, t, client, req, rainbowExpected, 30*time.Second, 20*time.Second)
+		if assistant == nil {
+			t.Fatalf("didn't get an assistant response for context")
+		}
+	}
+}

integration/embed_test.go

@@ -8,6 +8,7 @@ import (
 	"testing"
 	"time"
 
+	"github.com/google/go-cmp/cmp"
 	"github.com/ollama/ollama/api"
 )
 
@@ -38,14 +39,14 @@ func TestAllMiniLMEmbeddings(t *testing.T) {
 	defer cleanup()
 
 	req := api.EmbeddingRequest{
-		Model:  "all-minilm",
-		Prompt: "why is the sky blue?",
+		Model:     "all-minilm",
+		Prompt:    "why is the sky blue?",
+		KeepAlive: &api.Duration{Duration: 10 * time.Second},
 	}
 
 	res, err := embeddingTestHelper(ctx, client, t, req)
-
 	if err != nil {
-		t.Fatalf("error: %v", err)
+		t.Fatal(err)
 	}
 
 	if len(res.Embedding) != 384 {
@@ -73,9 +74,8 @@ func TestAllMiniLMEmbed(t *testing.T) {
 	}
 
 	res, err := embedTestHelper(ctx, client, t, req)
-
 	if err != nil {
-		t.Fatalf("error: %v", err)
+		t.Fatal(err)
 	}
 
 	if len(res.Embeddings) != 1 {
@@ -111,9 +111,8 @@ func TestAllMiniLMBatchEmbed(t *testing.T) {
 	}
 
 	res, err := embedTestHelper(ctx, client, t, req)
-
 	if err != nil {
-		t.Fatalf("error: %v", err)
+		t.Fatal(err)
 	}
 
 	if len(res.Embeddings) != 2 {
@@ -155,93 +154,135 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
 
 	truncTrue, truncFalse := true, false
 
-	type testReq struct {
-		Name    string
-		Request api.EmbedRequest
+	want, err := embedTestHelper(ctx, client, t, api.EmbedRequest{
+		Model: "all-minilm",
+		Input: "why",
+	})
+	if err != nil {
+		t.Fatal(err)
 	}
 
-	reqs := []testReq{
+	cases := []struct {
+		name    string
+		request api.EmbedRequest
+		check   func(*api.EmbedResponse, error)
+	}{
 		{
-			Name: "Target Truncation",
-			Request: api.EmbedRequest{
+			name: "target truncation",
+			request: api.EmbedRequest{
 				Model: "all-minilm",
 				Input: "why",
 			},
-		},
-		{
-			Name: "Default Truncate",
-			Request: api.EmbedRequest{
-				Model:   "all-minilm",
-				Input:   "why is the sky blue?",
-				Options: map[string]any{"num_ctx": 1},
+			check: func(got *api.EmbedResponse, err error) {
+				if err != nil {
+					t.Fatal(err)
+				}
+
+				if diff := cmp.Diff(want.Embeddings[0], got.Embeddings[0]); diff != "" {
+					t.Errorf("embedding mismatch (-want +got):\n%s", diff)
+				}
 			},
 		},
 		{
-			Name: "Explicit Truncate",
-			Request: api.EmbedRequest{
+			name: "default truncate",
+			request: api.EmbedRequest{
+				Model:   "all-minilm",
+				Input:   "why is the sky blue?",
+				Options: map[string]any{"num_ctx": 3},
+			},
+			check: func(got *api.EmbedResponse, err error) {
+				if err != nil {
+					t.Fatal(err)
+				}
+				if diff := cmp.Diff(want.Embeddings[0], got.Embeddings[0]); diff != "" {
+					t.Errorf("embedding mismatch (-want +got):\n%s", diff)
+				}
+			},
+		},
+		{
+			name: "explicit truncate",
+			request: api.EmbedRequest{
+				Model:    "all-minilm",
+				Input:    "why is the sky blue?",
+				Truncate: &truncTrue,
+				Options:  map[string]any{"num_ctx": 3},
+			},
+			check: func(got *api.EmbedResponse, err error) {
+				if err != nil {
+					t.Fatal(err)
+				}
+				if diff := cmp.Diff(want.Embeddings[0], got.Embeddings[0]); diff != "" {
+					t.Errorf("embedding mismatch (-want +got):\n%s", diff)
+				}
+			},
+		},
+		{
+			name: "truncate error",
+			request: api.EmbedRequest{
+				Model:    "all-minilm",
+				Input:    "why is the sky blue?",
+				Truncate: &truncFalse,
+				Options:  map[string]any{"num_ctx": 3},
+			},
+			check: func(res *api.EmbedResponse, err error) {
+				if err.Error() != "input exceeds maximum context length" {
+					t.Fatalf("expected truncation error, got: %v", err)
+				}
+			},
+		},
+		{
+			name: "input after truncate error",
+			request: api.EmbedRequest{
 				Model:    "all-minilm",
 				Input:    "why is the sky blue?",
 				Truncate: &truncTrue,
 				Options:  map[string]any{"num_ctx": 1},
 			},
+			check: func(res *api.EmbedResponse, err error) {
+				if err.Error() != "input after truncation exceeds maximum context length" {
+					t.Fatalf("expected truncation error, got: %v", err)
+				}
+			},
+		},
+		{
+			name: "input after truncate error",
+			request: api.EmbedRequest{
+				Model:    "all-minilm",
+				Input:    "why is the sky blue?",
+				Truncate: &truncTrue,
+				Options:  map[string]any{"num_ctx": 0},
+			},
+			check: func(res *api.EmbedResponse, err error) {
+				if err.Error() != "input after truncation exceeds maximum context length" {
+					t.Fatalf("expected truncation error, got: %v", err)
+				}
+			},
 		},
 	}
 
-	res := make(map[string]*api.EmbedResponse)
-
-	for _, req := range reqs {
-		response, err := embedTestHelper(ctx, client, t, req.Request)
-		if err != nil {
-			t.Fatalf("error: %v", err)
-		}
-		res[req.Name] = response
-	}
-
-	if res["Target Truncation"].Embeddings[0][0] != res["Default Truncate"].Embeddings[0][0] {
-		t.Fatal("expected default request to truncate correctly")
-	}
-
-	if res["Default Truncate"].Embeddings[0][0] != res["Explicit Truncate"].Embeddings[0][0] {
-		t.Fatal("expected default request and truncate true request to be the same")
-	}
-
-	// check that truncate set to false returns an error if context length is exceeded
-	_, err := embedTestHelper(ctx, client, t, api.EmbedRequest{
-		Model:    "all-minilm",
-		Input:    "why is the sky blue?",
-		Truncate: &truncFalse,
-		Options:  map[string]any{"num_ctx": 1},
-	})
-
-	if err == nil {
-		t.Fatal("expected error, got nil")
+	for _, req := range cases {
+		t.Run(req.name, func(t *testing.T) {
+			req.check(embedTestHelper(ctx, client, t, req.request))
+		})
 	}
 }
 
 func embeddingTestHelper(ctx context.Context, client *api.Client, t *testing.T, req api.EmbeddingRequest) (*api.EmbeddingResponse, error) {
+	t.Helper()
+
 	if err := PullIfMissing(ctx, client, req.Model); err != nil {
-		t.Fatalf("failed to pull model %s: %v", req.Model, err)
+		t.Fatal(err)
 	}
 
-	response, err := client.Embeddings(ctx, &req)
-
-	if err != nil {
-		return nil, err
-	}
-
-	return response, nil
+	return client.Embeddings(ctx, &req)
 }
 
 func embedTestHelper(ctx context.Context, client *api.Client, t *testing.T, req api.EmbedRequest) (*api.EmbedResponse, error) {
+	t.Helper()
+
 	if err := PullIfMissing(ctx, client, req.Model); err != nil {
-		t.Fatalf("failed to pull model %s: %v", req.Model, err)
+		t.Fatal(err)
 	}
 
-	response, err := client.Embed(ctx, &req)
-
-	if err != nil {
-		return nil, err
-	}
-
-	return response, nil
+	return client.Embed(ctx, &req)
 }

integration/library_models_test.go

@@ -4,7 +4,9 @@ package integration
 
 import (
 	"context"
+	"fmt"
 	"log/slog"
+	"os"
 	"testing"
 	"time"
 
@@ -20,6 +22,7 @@ func TestLibraryModelsGenerate(t *testing.T) {
 	defer cancel()
 	client, _, cleanup := InitServerConnection(ctx, t)
 	defer cleanup()
+	targetArch := os.Getenv("OLLAMA_TEST_ARCHITECTURE")
 
 	chatModels := libraryChatModels
 	for _, model := range chatModels {
@@ -30,16 +33,26 @@ func TestLibraryModelsGenerate(t *testing.T) {
 			if err := PullIfMissing(ctx, client, model); err != nil {
 				t.Fatalf("pull failed %s", err)
 			}
+			if targetArch != "" {
+				resp, err := client.Show(ctx, &api.ShowRequest{Name: model})
+				if err != nil {
+					t.Fatalf("unable to show model: %s", err)
+				}
+				arch := resp.ModelInfo["general.architecture"].(string)
+				if arch != targetArch {
+					t.Skip(fmt.Sprintf("Skipping %s architecture %s != %s", model, arch, targetArch))
+				}
+			}
 			req := api.GenerateRequest{
 				Model:     model,
-				Prompt:    "why is the sky blue?",
+				Prompt:    blueSkyPrompt,
 				KeepAlive: &api.Duration{Duration: 10 * time.Second},
 				Options: map[string]interface{}{
 					"temperature": 0.1,
 					"seed":        123,
 				},
 			}
-			anyResp := []string{"rayleigh", "scatter", "atmosphere", "nitrogen", "oxygen", "wavelength"}
+			anyResp := blueSkyExpected
 			// Special cases
 			if model == "duckdb-nsql" {
 				anyResp = []string{"select", "from"}

integration/model_arch_test.go

@@ -68,14 +68,13 @@ func TestModelsGenerate(t *testing.T) {
 			// TODO - fiddle with context size
 			req := api.GenerateRequest{
 				Model:  model,
-				Prompt: "why is the sky blue?",
+				Prompt: blueSkyPrompt,
 				Options: map[string]interface{}{
 					"temperature": 0,
 					"seed":        123,
 				},
 			}
-			anyResp := []string{"rayleigh", "scattering", "atmosphere", "nitrogen", "oxygen"}
-			DoGenerate(ctx, t, client, req, anyResp, 120*time.Second, 30*time.Second)
+			DoGenerate(ctx, t, client, req, blueSkyExpected, 120*time.Second, 30*time.Second)
 		})
 	}
 }

integration/model_perf_test.go

@@ -40,6 +40,18 @@ var (
 // cat int.log | grep MODEL_PERF_HEADER | head -1| cut -f2- -d: > perf.csv
 // cat int.log | grep MODEL_PERF_DATA | cut -f2- -d: >> perf.csv
 func TestModelsPerf(t *testing.T) {
+	if s := os.Getenv("OLLAMA_NEW_ENGINE"); s != "" {
+		doModelPerfTest(t, ollamaEngineChatModels)
+	} else {
+		doModelPerfTest(t, append(ollamaEngineChatModels, llamaRunnerChatModels...))
+	}
+}
+
+func TestLibraryModelsPerf(t *testing.T) {
+	doModelPerfTest(t, libraryChatModels)
+}
+
+func doModelPerfTest(t *testing.T, chatModels []string) {
 	softTimeout, hardTimeout := getTimeouts(t)
 	slog.Info("Setting timeouts", "soft", softTimeout, "hard", hardTimeout)
 	ctx, cancel := context.WithTimeout(context.Background(), hardTimeout)
@@ -65,14 +77,12 @@ func TestModelsPerf(t *testing.T) {
 	}
 	longPrompt := "summarize the following: " + string(data)
 
-	var chatModels []string
-	if s := os.Getenv("OLLAMA_NEW_ENGINE"); s != "" {
-		chatModels = ollamaEngineChatModels
-	} else {
-		chatModels = append(ollamaEngineChatModels, llamaRunnerChatModels...)
-	}
+	targetArch := os.Getenv("OLLAMA_TEST_ARCHITECTURE")
 
 	for _, model := range chatModels {
+		if !strings.Contains(model, ":") {
+			model = model + ":latest"
+		}
 		t.Run(model, func(t *testing.T) {
 			if time.Now().Sub(started) > softTimeout {
 				t.Skip("skipping remaining tests to avoid excessive runtime")
@@ -88,6 +98,9 @@ func TestModelsPerf(t *testing.T) {
 			}
 			arch := resp.ModelInfo["general.architecture"].(string)
 			maxContext = int(resp.ModelInfo[fmt.Sprintf("%s.context_length", arch)].(float64))
+			if targetArch != "" && arch != targetArch {
+				t.Skip(fmt.Sprintf("Skipping %s architecture %s != %s", model, arch, targetArch))
+			}
 
 			if maxVram > 0 {
 				resp, err := client.List(ctx)
@@ -151,8 +164,8 @@ func TestModelsPerf(t *testing.T) {
 					prompt  string
 					anyResp []string
 				}{
-					{"why is the sky blue?", []string{"rayleigh", "scattering", "atmosphere", "nitrogen", "oxygen"}},
-					{maxPrompt, []string{"shakespeare", "oppression", "sorrows", "gutenberg", "child", "license", "sonnet", "melancholy"}},
+					{blueSkyPrompt, blueSkyExpected},
+					{maxPrompt, []string{"shakespeare", "oppression", "sorrows", "gutenberg", "child", "license", "sonnet", "melancholy", "love", "sorrow", "beauty"}},
 				}
 				var gpuPercent int
 				for _, tc := range testCases {
@@ -241,11 +254,12 @@ func TestModelsPerf(t *testing.T) {
 							}
 						}
 					}
+					// Round the logged prompt count for comparisons across versions/configurations which can vary slightly
 					fmt.Fprintf(os.Stderr, "MODEL_PERF_HEADER:%s,%s,%s,%s,%s,%s,%s\n",
 						"MODEL",
 						"CONTEXT",
 						"GPU PERCENT",
-						"PROMPT COUNT",
+						"APPROX PROMPT COUNT",
 						"LOAD TIME",
 						"PROMPT EVAL TPS",
 						"EVAL TPS",
@@ -254,7 +268,7 @@ func TestModelsPerf(t *testing.T) {
 						model,
 						numCtx,
 						gpuPercent,
-						resp.PromptEvalCount,
+						(resp.PromptEvalCount/10)*10,
 						float64(resp.LoadDuration)/1000000000.0,
 						float64(resp.PromptEvalCount)/(float64(resp.PromptEvalDuration)/1000000000.0),
 						float64(resp.EvalCount)/(float64(resp.EvalDuration)/1000000000.0),

integration/quantization_test.go

@@ -76,7 +76,7 @@ func TestQuantization(t *testing.T) {
 				stream := true
 				genReq := api.GenerateRequest{
 					Model:     newName,
-					Prompt:    "why is the sky blue?",
+					Prompt:    blueSkyPrompt,
 					KeepAlive: &api.Duration{Duration: 3 * time.Second},
 					Options: map[string]any{
 						"seed":        42,
@@ -88,14 +88,13 @@ func TestQuantization(t *testing.T) {
 
 				// Some smaller quantizations can cause models to have poor quality
 				// or get stuck in repetition loops, so we stop as soon as we have any matches
-				anyResp := []string{"rayleigh", "scattering", "day", "sun", "moon", "color", "nitrogen", "oxygen"}
 				reqCtx, reqCancel := context.WithCancel(ctx)
 				atLeastOne := false
 				var buf bytes.Buffer
 				genfn := func(response api.GenerateResponse) error {
 					buf.Write([]byte(response.Response))
 					fullResp := strings.ToLower(buf.String())
-					for _, resp := range anyResp {
+					for _, resp := range blueSkyExpected {
 						if strings.Contains(fullResp, resp) {
 							atLeastOne = true
 							t.Log(fullResp)

integration/utils_test.go

@@ -256,13 +256,29 @@ var (
 		"snowflake-arctic-embed",
 		"snowflake-arctic-embed2",
 	}
+
+	blueSkyPrompt   = "why is the sky blue? Be brief but factual in your reply"
+	blueSkyExpected = []string{"rayleigh", "scatter", "atmosphere", "nitrogen", "oxygen", "wavelength", "interact"}
+
+	rainbowPrompt    = "how do rainbows form? Be brief but factual in your reply"
+	rainbowFollowups = []string{
+		"Explain the physics involved in them.  Be breif in your reply",
+		"Explain the chemistry involved in them.  Be breif in your reply",
+		"Explain the quantum mechanics involved in them. Be breif in your reply",
+		"What are common myths related to them? Be brief in your reply",
+		"What are common fairytales related to them? Be brief in your reply",
+		"Can they form if there is no rain?  Be breif in your reply",
+		"Can they form if there are no clouds?  Be breif in your reply",
+		"Do they happen on other planets? Be brief in your reply",
+	}
+	rainbowExpected = []string{"water", "droplet", "mist", "glow", "refracted", "reflect", "color", "spectrum", "frequency", "end", "gold", "fortune", "blessing", "prosperity"}
 )
 
 func init() {
 	lifecycle.InitLogging()
-	custom := os.Getenv("OLLAMA_TEST_SMOL_MODEL")
+	custom := os.Getenv("OLLAMA_TEST_DEFAULT_MODEL")
 	if custom != "" {
-		slog.Info("setting smol test model to " + custom)
+		slog.Info("setting default test model to " + custom)
 		smol = custom
 	}
 }
@@ -502,6 +518,22 @@ func DoGenerate(ctx context.Context, t *testing.T, client *api.Client, genReq ap
 		done <- 0
 	}()
 
+	var response string
+	verify := func() {
+		// Verify the response contains the expected data
+		response = buf.String()
+		atLeastOne := false
+		for _, resp := range anyResp {
+			if strings.Contains(strings.ToLower(response), resp) {
+				atLeastOne = true
+				break
+			}
+		}
+		if !atLeastOne {
+			t.Fatalf("%s: none of %v found in %s", genReq.Model, anyResp, response)
+		}
+	}
+
 	select {
 	case <-stallTimer.C:
 		if buf.Len() == 0 {
@@ -517,21 +549,14 @@ func DoGenerate(ctx context.Context, t *testing.T, client *api.Client, genReq ap
 		if genErr != nil {
 			t.Fatalf("%s failed with %s request prompt %s", genErr, genReq.Model, genReq.Prompt)
 		}
-		// Verify the response contains the expected data
-		response := buf.String()
-		atLeastOne := false
-		for _, resp := range anyResp {
-			if strings.Contains(strings.ToLower(response), resp) {
-				atLeastOne = true
-				break
-			}
-		}
-		if !atLeastOne {
-			t.Fatalf("%s: none of %v found in %s", genReq.Model, anyResp, response)
-		}
+		verify()
 		slog.Info("test pass", "model", genReq.Model, "prompt", genReq.Prompt, "contains", anyResp, "response", response)
 	case <-ctx.Done():
-		t.Error("outer test context done while waiting for generate")
+		// On slow systems, we might timeout before some models finish rambling, so check what we have so far to see
+		// if it's considered a pass - the stallTimer will detect hangs, but we want to consider slow systems a pass
+		// if they are still generating valid responses
+		slog.Warn("outer test context done while waiting for generate")
+		verify()
 	}
 	return context
 }
@@ -552,7 +577,7 @@ 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},
 			}, {
@@ -568,11 +593,11 @@ 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"},
+			{"sunlight", "scatter", "interact", "color", "surface", "depth", "red", "orange", "yellow", "absorb", "wavelength", "water", "molecule"},
+			{"soil", "organic", "earth", "black", "tan", "chemical", "processes", "pigment", "particle", "iron oxide", "rust", "air", "water", "wet", "mixture", "mixing", "mineral", "element", "decomposed", "matter", "wavelength"},
+			{"water", "droplet", "refract", "reflect", "color", "spectrum", "raindrop"},
 			{"fourth", "july", "declaration", "independence"},
-			{"nitrogen", "oxygen", "carbon", "dioxide"},
+			{"nitrogen", "oxygen", "carbon", "dioxide", "water", "vapor", "fluid", "particles", "gas"},
 		}
 }
 
@@ -599,6 +624,22 @@ func DoChat(ctx context.Context, t *testing.T, client *api.Client, req api.ChatR
 		done <- 0
 	}()
 
+	var response string
+	verify := func() {
+		// Verify the response contains the expected data
+		response = buf.String()
+		atLeastOne := false
+		for _, resp := range anyResp {
+			if strings.Contains(strings.ToLower(response), resp) {
+				atLeastOne = true
+				break
+			}
+		}
+		if !atLeastOne {
+			t.Fatalf("%s: none of %v found in \"%s\" -- request was:%v", req.Model, anyResp, response, req.Messages)
+		}
+	}
+
 	select {
 	case <-stallTimer.C:
 		if buf.Len() == 0 {
@@ -614,23 +655,14 @@ func DoChat(ctx context.Context, t *testing.T, client *api.Client, req api.ChatR
 		if genErr != nil {
 			t.Fatalf("%s failed with %s request prompt %v", genErr, req.Model, req.Messages)
 		}
-
-		// Verify the response contains the expected data
-		response := buf.String()
-		atLeastOne := false
-		for _, resp := range anyResp {
-			if strings.Contains(strings.ToLower(response), resp) {
-				atLeastOne = true
-				break
-			}
-		}
-		if !atLeastOne {
-			t.Fatalf("%s: none of %v found in \"%s\" -- request was:%v", req.Model, anyResp, response, req.Messages)
-		}
-
+		verify()
 		slog.Info("test pass", "model", req.Model, "messages", req.Messages, "contains", anyResp, "response", response)
 	case <-ctx.Done():
-		t.Error("outer test context done while waiting for generate")
+		// On slow systems, we might timeout before some models finish rambling, so check what we have so far to see
+		// if it's considered a pass - the stallTimer will detect hangs, but we want to consider slow systems a pass
+		// if they are still generating valid responses
+		slog.Warn("outer test context done while waiting for chat")
+		verify()
 	}
 	return &api.Message{Role: role, Content: buf.String()}
 }

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
 }
 

llama/patches/0025-harden-uncaught-exception-registration.patch

@@ -0,0 +1,28 @@
+From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
+From: Daniel Hiltgen <daniel@ollama.com>
+Date: Fri, 29 Aug 2025 16:53:08 -0700
+Subject: [PATCH] harden uncaught exception registration
+
+---
+ ggml/src/ggml.cpp | 8 ++++++--
+ 1 file changed, 6 insertions(+), 2 deletions(-)
+
+diff --git a/ggml/src/ggml.cpp b/ggml/src/ggml.cpp
+index 0d388d45..f5bcb446 100644
+--- a/ggml/src/ggml.cpp
++++ b/ggml/src/ggml.cpp
+@@ -19,8 +19,12 @@ static bool ggml_uncaught_exception_init = []{
+         return false;
+     }
+     const auto prev{std::get_terminate()};
+-    GGML_ASSERT(prev != ggml_uncaught_exception);
+-    previous_terminate_handler = prev;
++    // GGML_ASSERT(prev != ggml_uncaught_exception);
++    if (prev != ggml_uncaught_exception) {
++        previous_terminate_handler = prev;
++    } else {
++        GGML_LOG_WARN("%s double registration of ggml_uncaught_exception\n", __func__);
++    }
+     std::set_terminate(ggml_uncaught_exception);
+     return true;
+ }();

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

@@ -148,7 +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)
@@ -161,11 +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 {
@@ -173,6 +172,8 @@ func NewLlamaServer(gpus discover.GpuInfoList, modelPath string, f *ggml.GGML, a
 		opts.NumCtx = int(trainCtx)
 	}
 
+	opts.NumBatch = min(opts.NumBatch, opts.NumCtx)
+
 	loadRequest := LoadRequest{LoraPath: adapters, KvSize: opts.NumCtx * numParallel, BatchSize: opts.NumBatch, Parallel: numParallel, MultiUserCache: envconfig.MultiUserCache()}
 
 	defaultThreads := discover.GetSystemInfo().GetOptimalThreadCount()
@@ -218,7 +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)
@@ -431,7 +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
@@ -678,8 +679,12 @@ func (s *ollamaServer) Load(ctx context.Context, gpus discover.GpuInfoList, requ
 
 	if !(len(gpus) == 1 && gpus[0].Library == "cpu") {
 		for _, gpu := range gpus {
+			available := gpu.FreeMemory - envconfig.GpuOverhead() - gpu.MinimumMemory
+			if gpu.FreeMemory < envconfig.GpuOverhead()+gpu.MinimumMemory {
+				available = 0
+			}
 			slog.Info("gpu memory", "id", gpu.ID,
-				"available", format.HumanBytes2(gpu.FreeMemory-envconfig.GpuOverhead()-gpu.MinimumMemory),
+				"available", format.HumanBytes2(available),
 				"free", format.HumanBytes2(gpu.FreeMemory),
 				"minimum", format.HumanBytes2(gpu.MinimumMemory),
 				"overhead", format.HumanBytes2(envconfig.GpuOverhead()))
@@ -861,7 +866,7 @@ func (s *ollamaServer) createLayout(systemInfo discover.SystemInfo, systemGPUs d
 		}
 		layers[i] += memory.CPU.Weights[i].Size
 		layers[i] += memory.CPU.Cache[i].Size
-		slog.Log(context.TODO(), logutil.LevelTrace, "layer to assign", "layer", i, "size", format.HumanBytes2(layers[i]))
+		logutil.Trace("layer to assign", "layer", i, "size", format.HumanBytes2(layers[i]))
 	}
 
 	gpuLayers := ml.GPULayersList{}

logutil/logutil.go

@@ -1,9 +1,12 @@
 package logutil
 
 import (
+	"context"
 	"io"
 	"log/slog"
 	"path/filepath"
+	"runtime"
+	"time"
 )
 
 const LevelTrace slog.Level = -8
@@ -27,3 +30,19 @@ func NewLogger(w io.Writer, level slog.Level) *slog.Logger {
 		},
 	}))
 }
+
+type key string
+
+func Trace(msg string, args ...any) {
+	TraceContext(context.WithValue(context.TODO(), key("skip"), 1), msg, args...)
+}
+
+func TraceContext(ctx context.Context, msg string, args ...any) {
+	if logger := slog.Default(); logger.Enabled(ctx, LevelTrace) {
+		skip, _ := ctx.Value(key("skip")).(int)
+		pc, _, _, _ := runtime.Caller(1 + skip)
+		record := slog.NewRecord(time.Now(), LevelTrace, msg, pc)
+		record.Add(args...)
+		logger.Handler().Handle(ctx, record)
+	}
+}

ml/backend.go

@@ -266,7 +266,7 @@ func (m DeviceMemory) LogValue() slog.Value {
 // allocation is guaranteed to be provided so that if it failed, the caller can
 // accommodate that to make forward progress.
 type BackendMemory struct {
-	// InputsWeights are always located on the CPU and cannot be moved
+	// InputWeights are always located on the CPU and cannot be moved
 	InputWeights Memory
 
 	// CPU model components are located in system memory. This does not
@@ -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
@@ -429,12 +430,13 @@ type Tensor interface {
 	Sin(ctx Context) Tensor
 	Cos(ctx Context) Tensor
 	Tanh(ctx Context) Tensor
-	GELU(ctx Context) Tensor
-	QuickGELU(ctx Context) Tensor
-	SILU(ctx Context) Tensor
-	RELU(ctx Context) Tensor
+	GELU(ctx Context, up ...Tensor) Tensor
+	SILU(ctx Context, up ...Tensor) Tensor
+	RELU(ctx Context, up ...Tensor) Tensor
 	Sigmoid(ctx Context) Tensor
-	SwiGLU(ctx Context, up Tensor, alpha, limit float32) Tensor
+
+	// AlphaLimitSILU is a variant of SILU that clamps the input to the range [-limit, limit]
+	SILUAlphaLimit(ctx Context, up Tensor, alpha, limit float32) Tensor
 
 	Reshape(ctx Context, shape ...int) Tensor
 	View(ctx Context, offset int, shape ...int) Tensor

ml/backend/ggml/ggml.go

@@ -271,7 +271,7 @@ func New(modelPath string, params ml.BackendParams) (ml.Backend, error) {
 			tt := C.ggml_new_tensor(ctxs[bt], kind, C.int(len(t.source.Shape)), (*C.int64_t)(unsafe.Pointer(&t.source.Shape[0])))
 			C.ggml_set_name(tt, cname)
 
-			slog.Log(context.TODO(), logutil.LevelTrace, "created tensor", "name", name, "shape", t.source.Shape, "dtype", t.source.Kind, "buffer_type", C.GoString(C.ggml_backend_buft_name(bt)))
+			logutil.Trace("created tensor", "name", name, "shape", t.source.Shape, "dtype", t.source.Kind, "buffer_type", C.GoString(C.ggml_backend_buft_name(bt)))
 
 			size := pad(C.ggml_backend_buft_get_alloc_size(bt, tt), C.ggml_backend_buft_get_alignment(bt))
 			if layer == -1 {
@@ -378,7 +378,7 @@ func New(modelPath string, params ml.BackendParams) (ml.Backend, error) {
 	}
 
 	for bs := range maps.Values(bbs) {
-		slog.Log(context.TODO(), logutil.LevelTrace, "model weights", "buffer", C.GoString(C.ggml_backend_buffer_name(bs)),
+		logutil.Trace("model weights", "buffer", C.GoString(C.ggml_backend_buffer_name(bs)),
 			"size", format.HumanBytes2(uint64(C.ggml_backend_buffer_get_size(bs))))
 	}
 
@@ -811,7 +811,7 @@ func (c *Context) Reserve() {
 			}
 		}
 
-		slog.Log(context.TODO(), logutil.LevelTrace, "compute graph", "backend", C.GoString(C.ggml_backend_name(c.b.schedBackends[i])),
+		logutil.Trace("compute graph", "backend", C.GoString(C.ggml_backend_name(c.b.schedBackends[i])),
 			"buffer_type", C.GoString(C.ggml_backend_buft_name(c.b.schedBufts[i])), "size", format.HumanBytes2(uint64(bufferStatus.size)))
 	}
 
@@ -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 {
@@ -1424,35 +1431,46 @@ func (t *Tensor) IM2Col(ctx ml.Context, t2 ml.Tensor, s0, s1, p0, p1, d0, d1 int
 	}
 }
 
-func (t *Tensor) GELU(ctx ml.Context) ml.Tensor {
+func (t *Tensor) GELU(ctx ml.Context, t2 ...ml.Tensor) ml.Tensor {
+	if len(t2) > 0 {
+		return &Tensor{
+			b: t.b,
+			t: C.ggml_geglu_split(ctx.(*Context).ctx, t.t, t2[0].(*Tensor).t),
+		}
+	}
 	return &Tensor{
 		b: t.b,
 		t: C.ggml_gelu_inplace(ctx.(*Context).ctx, t.t),
 	}
 }
 
-func (t *Tensor) QuickGELU(ctx ml.Context) ml.Tensor {
-	return &Tensor{
-		b: t.b,
-		t: C.ggml_gelu_quick_inplace(ctx.(*Context).ctx, t.t),
+func (t *Tensor) SILU(ctx ml.Context, t2 ...ml.Tensor) ml.Tensor {
+	if len(t2) > 0 {
+		return &Tensor{
+			b: t.b,
+			t: C.ggml_swiglu_split(ctx.(*Context).ctx, t.t, t2[0].(*Tensor).t),
+		}
 	}
-}
-
-func (t *Tensor) SILU(ctx ml.Context) ml.Tensor {
 	return &Tensor{
 		b: t.b,
 		t: C.ggml_silu_inplace(ctx.(*Context).ctx, t.t),
 	}
 }
 
-func (t *Tensor) RELU(ctx ml.Context) ml.Tensor {
+func (t *Tensor) RELU(ctx ml.Context, t2 ...ml.Tensor) ml.Tensor {
+	if len(t2) > 0 {
+		return &Tensor{
+			b: t.b,
+			t: C.ggml_reglu_split(ctx.(*Context).ctx, t.t, t2[0].(*Tensor).t),
+		}
+	}
 	return &Tensor{
 		b: t.b,
 		t: C.ggml_relu_inplace(ctx.(*Context).ctx, t.t),
 	}
 }
 
-func (t *Tensor) SwiGLU(ctx ml.Context, up ml.Tensor, alpha, limit float32) ml.Tensor {
+func (t *Tensor) SILUAlphaLimit(ctx ml.Context, up ml.Tensor, alpha, limit float32) ml.Tensor {
 	return &Tensor{
 		b: t.b,
 		t: C.ggml_swiglu_oai(ctx.(*Context).ctx, t.t, up.(*Tensor).t, C.float(alpha), C.float(limit)),

ml/backend/ggml/ggml/src/ggml.cpp

@@ -19,8 +19,12 @@ static bool ggml_uncaught_exception_init = []{
         return false;
     }
     const auto prev{std::get_terminate()};
-    GGML_ASSERT(prev != ggml_uncaught_exception);
-    previous_terminate_handler = prev;
+    // GGML_ASSERT(prev != ggml_uncaught_exception);
+    if (prev != ggml_uncaught_exception) {
+        previous_terminate_handler = prev;
+    } else {
+        GGML_LOG_WARN("%s double registration of ggml_uncaught_exception\n", __func__);
+    }
     std::set_terminate(ggml_uncaught_exception);
     return true;
 }();

ml/nn/attention.go

@@ -26,6 +26,7 @@ func Attention(ctx ml.Context, query, key, value ml.Tensor, scale float64, cache
 }
 
 func AttentionWithSinks(ctx ml.Context, query, key, value, sinks ml.Tensor, scale float64, cache kvcache.Cache) ml.Tensor {
+	ctx.Forward(query)
 	if key != nil && value != nil {
 		if query.Dim(0) != key.Dim(0) {
 			panic(fmt.Errorf("d_k in attention operation does not match between query(%v) and key(%v)", query.Dim(0), key.Dim(0)))
@@ -39,6 +40,7 @@ func AttentionWithSinks(ctx ml.Context, query, key, value, sinks ml.Tensor, scal
 			panic(fmt.Errorf("seq_len_k in attention operation does not match between key(%v) and value(%v)", key.Dim(2), value.Dim(2)))
 		}
 
+		ctx.Forward(key, value)
 		if cache != nil {
 			cache.Put(ctx, key, value)
 		}

ml/nn/pooling/pooling.go

@@ -0,0 +1,42 @@
+package pooling
+
+import (
+	"github.com/ollama/ollama/ml"
+)
+
+type Type uint32
+
+const (
+	TypeNone Type = iota
+	TypeMean
+	TypeCLS
+	TypeLast
+)
+
+func (t Type) String() string {
+	switch t {
+	case TypeMean:
+		return "Mean"
+	case TypeCLS:
+		return "CLS"
+	case TypeLast:
+		return "Last"
+	default:
+		return "Unknown"
+	}
+}
+
+func (t Type) Forward(ctx ml.Context, hiddenStates ml.Tensor) ml.Tensor {
+	switch t {
+	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:
+		hiddenStates = hiddenStates.View(ctx, (hiddenStates.Dim(1)-1)*hiddenStates.Stride(1), hiddenStates.Dim(0))
+		return hiddenStates
+	default:
+		panic("unknown pooling type")
+	}
+}

ml/nn/pooling/pooling_test.go

@@ -0,0 +1,79 @@
+package pooling_test
+
+import (
+	"bytes"
+	"os"
+	"slices"
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+	"github.com/ollama/ollama/discover"
+	fsggml "github.com/ollama/ollama/fs/ggml"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/backend/ggml"
+	"github.com/ollama/ollama/ml/nn/pooling"
+)
+
+func setup(tb testing.TB, n int) ml.Backend {
+	tb.Helper()
+
+	f, err := os.CreateTemp(tb.TempDir(), "*.bin")
+	if err != nil {
+		tb.Fatal(err)
+	}
+	defer f.Close()
+
+	if err := fsggml.WriteGGUF(f, fsggml.KV{
+		"general.architecture": "test",
+		"test.block_count":     uint32(1),
+	}, []*fsggml.Tensor{
+		{Name: "blk.0.weight", Shape: []uint64{1}, WriterTo: bytes.NewBuffer(make([]byte, 4))},
+	}); err != nil {
+		tb.Fatal(err)
+	}
+
+	var gpuLayers ml.GPULayersList
+	if gpus := discover.GetGPUInfo(); len(gpus) > 0 {
+		gpuLayers = append(gpuLayers, ml.GPULayers{
+			ID: gpus[0].ID,
+			Layers: slices.Collect(func(yield func(int) bool) {
+				for i := range n {
+					if !yield(i) {
+						return
+					}
+				}
+			}),
+		})
+	}
+	b, err := ggml.New(f.Name(), ml.BackendParams{AllocMemory: true, GPULayers: gpuLayers})
+	if err != nil {
+		tb.Fatal(err)
+	}
+
+	return b
+}
+
+func TestForward(t *testing.T) {
+	cases := map[pooling.Type][]float32{
+		pooling.TypeMean: {4, 5, 6, 7, 8, 9, 10, 11},
+		pooling.TypeCLS:  {0, 1, 2, 3, 4, 5, 6, 7},
+		pooling.TypeLast: {8, 9, 10, 11, 12, 13, 14, 15},
+	}
+	for typ, want := range cases {
+		t.Run(typ.String(), func(t *testing.T) {
+			b := setup(t, 99)
+			defer b.Close()
+
+			ctx := b.NewContext()
+			defer ctx.Close()
+
+			tt := ctx.Input().Arange(0, 16, 1, ml.DTypeF32).Reshape(ctx, 8, 2)
+			tt = typ.Forward(ctx, tt)
+
+			ctx.Forward(tt).Compute(tt)
+			if diff := cmp.Diff(want, tt.Floats()); diff != "" {
+				t.Error(diff)
+			}
+		})
+	}
+}

model/bytepairencoding.go

@@ -2,10 +2,10 @@ package model
 
 import (
 	"cmp"
-	"context"
 	"fmt"
 	"iter"
 	"log/slog"
+	"slices"
 	"strings"
 
 	"github.com/dlclark/regexp2"
@@ -14,16 +14,28 @@ import (
 )
 
 type BytePairEncoding struct {
-	pre   *regexp2.Regexp
-	vocab *Vocabulary
+	vocab   *Vocabulary
+	regexps []*regexp2.Regexp
 }
 
 var _ TextProcessor = (*BytePairEncoding)(nil)
 
-func NewBytePairEncoding(pre string, vocab *Vocabulary) BytePairEncoding {
+func NewBytePairEncoding(vocab *Vocabulary, pretokenizers ...string) BytePairEncoding {
+	if len(pretokenizers) == 0 {
+		// set default byte-level pretokenizer if none provided, e.g.
+		// https://github.com/huggingface/tokenizers/blob/main/tokenizers/src/pre_tokenizers/byte_level.rs#L44
+		pretokenizers = []string{`'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+`}
+	}
+
 	return BytePairEncoding{
-		pre:   regexp2.MustCompile(pre, regexp2.None),
 		vocab: vocab,
+		regexps: slices.Collect(func(yield func(*regexp2.Regexp) bool) {
+			for _, p := range pretokenizers {
+				if !yield(regexp2.MustCompile(p, regexp2.RE2)) {
+					return
+				}
+			}
+		}),
 	}
 }
 
@@ -36,13 +48,36 @@ func (bpe BytePairEncoding) Is(id int32, special Special) bool {
 }
 
 func (bpe *BytePairEncoding) split(s string) iter.Seq[string] {
-	return func(yield func(string) bool) {
-		for m, _ := bpe.pre.FindStringMatch(s); m != nil; m, _ = bpe.pre.FindNextMatch(m) {
-			if !yield(m.String()) {
-				break
+	parts := []string{s}
+	for _, re := range bpe.regexps {
+		parts = slices.Collect(func(yield func(string) bool) {
+			for _, part := range parts {
+				r := []rune(part)
+				var offset int
+				for m, _ := re.FindRunesMatch(r); m != nil; m, _ = re.FindNextMatch(m) {
+					if offset-m.Index != 0 {
+						if !yield(string(r[:m.Index])) {
+							return
+						}
+					}
+
+					if !yield(m.String()) {
+						return
+					}
+
+					offset = m.Index + m.Length
+				}
+
+				if offset < len(r) {
+					if !yield(string(r[offset:])) {
+						return
+					}
+				}
 			}
-		}
+		})
 	}
+
+	return slices.Values(parts)
 }
 
 // fragment is a string fragment and their corresponding token IDs
@@ -202,12 +237,11 @@ func (bpe BytePairEncoding) Encode(s string, addSpecial bool) ([]int32, error) {
 		}
 	}
 
-	slog.Log(context.TODO(), logutil.LevelTrace, "encoded", "string", s, "ids", ids)
-
 	if addSpecial && len(ids) > 0 {
 		ids = bpe.vocab.addSpecials(ids)
 	}
 
+	logutil.Trace("encoded", "string", s, "ids", ids)
 	return ids, nil
 }
 
@@ -243,6 +277,6 @@ func (bpe BytePairEncoding) Decode(ids []int32) (string, error) {
 		}
 	}
 
-	slog.Log(context.TODO(), logutil.LevelTrace, "decoded", "string", sb.String(), "from", lazyIdsString{ids: ids})
+	logutil.Trace("decoded", "string", sb.String(), "from", lazyIdsString{ids: ids})
 	return sb.String(), nil
 }

model/bytepairencoding_test.go

@@ -59,12 +59,12 @@ func llama(t testing.TB) BytePairEncoding {
 	}
 
 	return NewBytePairEncoding(
-		`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
 		&Vocabulary{
 			Values: tokens,
 			Types:  types,
 			Merges: merges,
 		},
+		"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
 	)
 }
 
@@ -282,3 +282,41 @@ func BenchmarkBytePairEncoding(b *testing.B) {
 		})
 	}
 }
+
+func TestSplit(t *testing.T) {
+	cases := []struct {
+		name string
+		patterns,
+		want []string
+	}{
+		{
+			name: "default",
+			want: []string{"Hello", ",", " WORLD", "!!", " How", "'s", " it", " going", "?", " 123", " 一二三"},
+		},
+		{
+			name: "unicode",
+			patterns: []string{
+				"\\p{N}{1,3}",
+				`[一-龥぀-ゟ゠-ヿ]+`,
+				"[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\r\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\r\n]*|\\s*[\r\n]+|\\s+(?!\\S)|\\s+",
+			},
+			want: []string{"Hello", ",", " WORLD", "!!", " How", "'s", " it", " going", "?", " ", "123", " ", "一二三"},
+		},
+		{
+			name: "individual digits",
+			patterns: []string{
+				"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+			},
+			want: []string{"Hello", ",", " WORLD", "!!", " How", "'s", " it", " going", "?", " ", "1", "2", "3", " 一二三"},
+		},
+	}
+
+	for _, tt := range cases {
+		t.Run(tt.name, func(t *testing.T) {
+			tokenizer := NewBytePairEncoding(nil, tt.patterns...)
+			if diff := cmp.Diff(tt.want, slices.Collect(tokenizer.split("Hello, WORLD!! How's it going? 123 一二三"))); diff != "" {
+				t.Errorf("no match (-theirs +ours):\n%s", diff)
+			}
+		})
+	}
+}

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

@@ -1,7 +1,6 @@
 package model
 
 import (
-	"context"
 	"errors"
 	"fmt"
 	_ "image/jpeg"
@@ -22,10 +21,15 @@ import (
 	"github.com/ollama/ollama/logutil"
 	"github.com/ollama/ollama/ml"
 	_ "github.com/ollama/ollama/ml/backend"
+	"github.com/ollama/ollama/ml/nn/pooling"
 	"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 {
@@ -104,19 +108,12 @@ func New(modelPath string, params ml.BackendParams) (Model, error) {
 		return nil, err
 	}
 
-	arch := b.Config().Architecture()
-	f, ok := models[arch]
-	if !ok {
-		return nil, fmt.Errorf("unsupported model architecture %q", arch)
-	}
-
-	m, err := f(b.Config())
+	m, err := modelForArch(b.Config())
 	if err != nil {
 		return nil, err
 	}
 
 	base := Base{b: b, config: m.Config()}
-
 	v := reflect.ValueOf(m)
 	v.Elem().Set(populateFields(base, v.Elem()))
 	return m, nil
@@ -128,30 +125,38 @@ func NewTextProcessor(s string) (TextProcessor, error) {
 		return nil, err
 	}
 	defer r.Close()
+
 	meta, err := fsggml.Decode(r, -1)
 	if err != nil {
 		return nil, err
 	}
-	return getTextProcessor(meta.KV())
-}
 
-func getTextProcessor(kv fsggml.KV) (TextProcessor, error) {
-	arch := kv.Architecture()
-	f, ok := models[arch]
-	if !ok {
-		return nil, fmt.Errorf("unsupported model architecture %q", arch)
-	}
-	m, err := f(kv)
+	m, err := modelForArch(meta.KV())
 	if err != nil {
 		return nil, err
 	}
+
 	tp, ok := m.(TextProcessor)
 	if !ok {
-		return nil, fmt.Errorf("%v is not a TextProcessor", m)
+		return nil, ErrUnsupportedTokenizer
 	}
 	return tp, nil
 }
 
+func modelForArch(c fs.Config) (Model, error) {
+	arch := c.Architecture()
+	if pooling.Type(c.Uint("pooling_type")) != pooling.TypeNone {
+		arch = arch + "_embed"
+	}
+
+	f, ok := models[arch]
+	if !ok {
+		return nil, ErrUnsupportedModel
+	}
+
+	return f(c)
+}
+
 func populateFields(base Base, v reflect.Value, tags ...Tag) reflect.Value {
 	t := v.Type()
 
@@ -167,38 +172,47 @@ func populateFields(base Base, v reflect.Value, tags ...Tag) reflect.Value {
 			// make a copy
 			tagsCopy := tags
 			if tag := t.Field(i).Tag.Get("gguf"); tag != "" {
-				tagsCopy = append(tagsCopy, ParseTags(tag))
+				tagsCopy = append(tagsCopy, parseTag(tag))
 			}
 
 			if tt == reflect.TypeOf((*Base)(nil)).Elem() {
 				vv.Set(reflect.ValueOf(base))
 			} else if tt == reflect.TypeOf((*ml.Tensor)(nil)).Elem() {
-				var fn func([]Tag) [][]string
-				fn = func(tags []Tag) (names [][]string) {
+				var fn func([]Tag, string, string) [][]string
+				fn = func(tags []Tag, prefix, suffix string) (fullNames [][]string) {
 					if len(tags) > 0 {
-						localNames := []string{tags[0].Name}
-						localNames = append(localNames, tags[0].Alternate...)
-
-						for _, localName := range localNames {
-							fullName := []string{localName}
-							nested := fn(tags[1:])
-							if len(nested) > 0 {
-								for _, rest := range nested {
-									names = append(names, append(fullName, rest...))
+						var names []string
+						if tags[0].name != "" {
+							for _, n := range append([]string{tags[0].name}, tags[0].alternatives...) {
+								names = append(names, prefix+n+suffix)
+							}
+						}
+						childNames := fn(tags[1:], tags[0].prefix, tags[0].suffix)
+						if len(names) == 0 {
+							// current tag has no name, use child names only
+							fullNames = append(fullNames, childNames...)
+						} else if len(childNames) == 0 {
+							// current tag has names but no children, create branches for each name
+							for _, name := range names {
+								fullNames = append(fullNames, []string{name})
+							}
+						} else {
+							// merge each name with each child
+							for _, name := range names {
+								for _, childName := range childNames {
+									fullNames = append(fullNames, append([]string{name}, childName...))
 								}
-							} else {
-								names = append(names, fullName)
 							}
 						}
 					}
 
-					return names
+					return fullNames
 				}
 
-				names := fn(tagsCopy)
+				names := fn(tagsCopy, "", "")
 				for _, name := range names {
 					if tensor := base.Backend().Get(strings.Join(name, ".")); tensor != nil {
-						slog.Log(context.TODO(), logutil.LevelTrace, "found tensor", "", tensor)
+						logutil.Trace("found tensor", "", tensor)
 						vv.Set(reflect.ValueOf(tensor))
 						break
 					}
@@ -209,9 +223,9 @@ func populateFields(base Base, v reflect.Value, tags ...Tag) reflect.Value {
 				for i := range vv.Len() {
 					vvv := vv.Index(i)
 					if vvv.Kind() == reflect.Pointer || vvv.Kind() == reflect.Interface {
-						setPointer(base, vvv, append(tagsCopy, Tag{Name: strconv.Itoa(i)}))
+						setPointer(base, vvv, append(tagsCopy, Tag{name: strconv.Itoa(i)}))
 					} else {
-						vvv.Set(populateFields(base, vvv, append(tagsCopy, Tag{Name: strconv.Itoa(i)})...))
+						vvv.Set(populateFields(base, vvv, append(tagsCopy, Tag{name: strconv.Itoa(i)})...))
 					}
 				}
 			}
@@ -239,7 +253,7 @@ 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()
 	}
@@ -250,18 +264,31 @@ func setPointer(base Base, v reflect.Value, tags []Tag) {
 }
 
 type Tag struct {
-	Name      string
-	Alternate []string
+	name,
+	// prefix and suffix are applied to child tags
+	prefix,
+	suffix string
+	alternatives []string
 }
 
-func ParseTags(s string) (tag Tag) {
+func parseTag(s string) (tag Tag) {
 	parts := strings.Split(s, ",")
 	if len(parts) > 0 {
-		tag.Name = parts[0]
+		tag.name = parts[0]
 
 		for _, part := range parts[1:] {
-			if value, ok := strings.CutPrefix(part, "alt:"); ok {
-				tag.Alternate = append(tag.Alternate, value)
+			if value, ok := strings.CutPrefix(part, "alt:"); ok && tag.name == "" {
+				// elevate alternative to primary if no primary given
+				tag.name = value
+				slog.Warn("gguf tag has alt: but no primary name", "tag", s)
+			} else if ok {
+				tag.alternatives = append(tag.alternatives, value)
+			}
+			if value, ok := strings.CutPrefix(part, "pre:"); ok {
+				tag.prefix = value
+			}
+			if value, ok := strings.CutPrefix(part, "suf:"); ok {
+				tag.suffix = value
 			}
 		}
 	}

model/model_test.go

@@ -1,9 +1,9 @@
 package model
 
 import (
+	"errors"
 	"reflect"
 	"slices"
-	"strings"
 	"testing"
 
 	"github.com/google/go-cmp/cmp"
@@ -12,7 +12,6 @@ import (
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/ml/backend/ggml"
 	"github.com/ollama/ollama/ml/nn"
-	"github.com/ollama/ollama/model/input"
 )
 
 func TestParseTags(t *testing.T) {
@@ -23,14 +22,14 @@ func TestParseTags(t *testing.T) {
 		{
 			value: "output",
 			want: Tag{
-				Name: "output",
+				name: "output",
 			},
 		},
 		{
 			value: "output,alt:token_embd",
 			want: Tag{
-				Name: "output",
-				Alternate: []string{
+				name: "output",
+				alternatives: []string{
 					"token_embd",
 				},
 			},
@@ -39,8 +38,8 @@ func TestParseTags(t *testing.T) {
 
 	for _, tt := range cases {
 		t.Run(tt.value, func(t *testing.T) {
-			got := ParseTags(tt.value)
-			if diff := cmp.Diff(tt.want, got); diff != "" {
+			got := parseTag(tt.value)
+			if diff := cmp.Diff(tt.want, got, cmp.AllowUnexported((Tag{}))); diff != "" {
 				t.Errorf("ParseTags() returned unexpected values (-want +got):\n%s", diff)
 			}
 		})
@@ -126,6 +125,7 @@ func TestPopulateFieldsAlternateName(t *testing.T) {
 		Input  *nn.Embedding `gguf:"input"`
 		Output *nn.Linear    `gguf:"output,alt:input"`
 		Nested *nested       `gguf:"nested"`
+		Tensor ml.Tensor     `gguf:"leaf,alt:tensor"`
 	}
 
 	var m fakeModel
@@ -134,6 +134,7 @@ func TestPopulateFieldsAlternateName(t *testing.T) {
 		names: []string{
 			"input.weight",
 			"nested.b.weight",
+			"leaf",
 		},
 	}}, v.Elem()))
 
@@ -143,44 +144,115 @@ func TestPopulateFieldsAlternateName(t *testing.T) {
 		Nested: &nested{
 			Weight: &nn.Linear{Weight: &fakeTensor{Name: "nested.b.weight"}},
 		},
+		Tensor: &fakeTensor{Name: "leaf"},
 	}, m); diff != "" {
 		t.Errorf("populateFields() set incorrect values (-want +got):\n%s", diff)
 	}
 }
 
-func TestGetTextProcessor(t *testing.T) {
-	tp, err := getTextProcessor(fsggml.KV{})
-	if err == nil {
-		t.Error("expected error")
-	} else if !strings.Contains(err.Error(), "unsupported model architecture") {
-		t.Errorf("unexpected error: %v", err)
-	} else if tp != nil {
-		t.Error("expected nil tp")
+func TestPopulateFieldsPrefixSuffixName(t *testing.T) {
+	type fakeBlock struct {
+		A  *nn.Linear `gguf:"a"`
+		B  *nn.Linear `gguf:",pre:b_"`
+		C  *nn.Linear `gguf:",suf:_c"`
+		XY *nn.Linear `gguf:",pre:x_,suf:_y"`
 	}
 
-	models["dummy"] = func(fs.Config) (Model, error) {
-		return notTextProcessorModel{}, nil
+	type fakeModel struct {
+		Blocks []fakeBlock `gguf:"blk"`
 	}
-	tp, err = getTextProcessor(fsggml.KV{"general.architecture": "dummy"})
-	if err == nil {
-		t.Error("expected error")
-	} else if !strings.Contains(err.Error(), "not a TextProcessor") {
-		t.Errorf("unexpected error: %v", err)
-	} else if tp != nil {
-		t.Error("expected nil tp")
+
+	m := fakeModel{
+		Blocks: make([]fakeBlock, 2),
+	}
+	v := reflect.ValueOf(&m)
+	v.Elem().Set(populateFields(Base{b: &fakeBackend{
+		names: []string{
+			"blk.0.a.weight",
+			"blk.0.b_weight",
+			"blk.0.b_bias",
+			"blk.0.weight_c",
+			"blk.0.x_weight_y",
+			"blk.1.a.weight",
+			"blk.1.b_weight",
+			"blk.1.b_bias",
+			"blk.1.weight_c",
+			"blk.1.x_weight_y",
+		},
+	}}, v.Elem()))
+
+	if diff := cmp.Diff(fakeModel{
+		Blocks: []fakeBlock{
+			{
+				A:  &nn.Linear{Weight: &fakeTensor{Name: "blk.0.a.weight"}},
+				B:  &nn.Linear{Weight: &fakeTensor{Name: "blk.0.b_weight"}, Bias: &fakeTensor{Name: "blk.0.b_bias"}},
+				C:  &nn.Linear{Weight: &fakeTensor{Name: "blk.0.weight_c"}},
+				XY: &nn.Linear{Weight: &fakeTensor{Name: "blk.0.x_weight_y"}},
+			},
+			{
+				A:  &nn.Linear{Weight: &fakeTensor{Name: "blk.1.a.weight"}},
+				B:  &nn.Linear{Weight: &fakeTensor{Name: "blk.1.b_weight"}, Bias: &fakeTensor{Name: "blk.1.b_bias"}},
+				C:  &nn.Linear{Weight: &fakeTensor{Name: "blk.1.weight_c"}},
+				XY: &nn.Linear{Weight: &fakeTensor{Name: "blk.1.x_weight_y"}},
+			},
+		},
+	}, m); diff != "" {
+		t.Errorf("populateFields() set incorrect values (-want +got):\n%s", diff)
 	}
 }
 
-type notTextProcessorModel struct{}
+func TestModelForArch(t *testing.T) {
+	type fakeModel struct {
+		Model
+	}
 
-func (notTextProcessorModel) Forward(ml.Context, input.Batch) (ml.Tensor, error) {
-	panic("unimplemented")
-}
+	type fakeEmbeddingModel struct {
+		Model
+	}
 
-func (notTextProcessorModel) Backend() ml.Backend {
-	panic("unimplemented")
-}
+	models["model"] = func(c fs.Config) (Model, error) { return fakeModel{}, nil }
+	models["model_embed"] = func(c fs.Config) (Model, error) { return fakeEmbeddingModel{}, nil }
 
-func (notTextProcessorModel) Config() config {
-	panic("unimplemented")
+	cases := []struct {
+		name   string
+		config fs.Config
+		want   any
+		err    error
+	}{
+		{
+			name: "model",
+			config: fsggml.KV{
+				"general.architecture": "model",
+			},
+			want: fakeModel{},
+		},
+		{
+			name: "embedding",
+			config: fsggml.KV{
+				"general.architecture": "model",
+				"model.pooling_type":   uint32(1),
+			},
+			want: fakeEmbeddingModel{},
+		},
+		{
+			name: "unsupported",
+			config: fsggml.KV{
+				"general.architecture": "unsupported",
+			},
+			err: ErrUnsupportedModel,
+		},
+	}
+
+	for _, tt := range cases {
+		t.Run(tt.name, func(t *testing.T) {
+			got, err := modelForArch(tt.config)
+			if !errors.Is(err, tt.err) {
+				t.Fatal(err)
+			}
+
+			if diff := cmp.Diff(tt.want, got); diff != "" {
+				t.Errorf("modelForArch() returned unexpected values (-want +got):\n%s", diff)
+			}
+		})
+	}
 }

model/models/bert/embed.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 = m.poolingType.Forward(ctx, hiddenStates)
+	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/deepseek2/model.go

@@ -0,0 +1,324 @@
+package deepseek2
+
+// uses deepseek 2 architecture but written based on deepseek 3 model
+
+import (
+	"math"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/ml/nn/fast"
+	"github.com/ollama/ollama/ml/nn/rope"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type Options struct {
+	numExpertsUsed      int
+	numExperts          int
+	normTopKProb        bool
+	routedScalingFactor float32
+
+	kvLoraRank,
+	qkNopeHeadDim,
+	qkRopeHeadDim,
+	kqNopeHeadDim,
+	qkHeadDim int
+	qLoraRank int
+	vHeadDim  int
+
+	hiddenSize,
+	numHeads,
+	numKVHeads,
+	keyLength,
+	valueLength,
+	originalContextLength int
+
+	eps,
+	ropeBase,
+	ropeScale float32
+	kqScale float64
+}
+
+func (o Options) RoPEOptions() []func(*rope.Options) {
+	attnFactor := float32(1.0 / (1.0 + 0.1*math.Log(float64(o.ropeScale))))
+	return []func(*rope.Options){
+		rope.WithOriginalContextLength(o.originalContextLength),
+		rope.WithExtrapolationFactor(1.),
+		rope.WithAttentionFactor(attnFactor),
+	}
+}
+
+type Attention struct {
+	Q *nn.Linear `gguf:"attn_q"`
+
+	QA     *nn.Linear  `gguf:"attn_q_a"`
+	QANorm *nn.RMSNorm `gguf:"attn_q_a_norm"`
+	QB     *nn.Linear  `gguf:"attn_q_b"`
+
+	KVA     *nn.Linear  `gguf:"attn_kv_a_mqa"`
+	KVANorm *nn.RMSNorm `gguf:"attn_kv_a_norm"`
+	KVB     *nn.Linear  `gguf:"attn_kv_b"`
+
+	Output *nn.Linear `gguf:"attn_out,alt:attn_output"`
+}
+
+func (attn *Attention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
+	seqLength := hiddenStates.Dim(1)
+
+	var query ml.Tensor
+	if opts.qLoraRank == 0 { // nil {
+		query = attn.Q.Forward(ctx, hiddenStates)
+	} else {
+		query = attn.QA.Forward(ctx, hiddenStates)
+		query = attn.QANorm.Forward(ctx, query, opts.eps)
+		query = attn.QB.Forward(ctx, query)
+	}
+
+	query = query.Reshape(ctx, query.Dim(0)/opts.numHeads, opts.numHeads, seqLength)
+
+	qPass := query.View(ctx, 0,
+		opts.qkNopeHeadDim, query.Stride(1),
+		query.Dim(1), query.Stride(2),
+		query.Dim(2))
+
+	qRot := query.View(ctx, opts.qkNopeHeadDim*query.Stride(0),
+		opts.qkRopeHeadDim, query.Stride(1),
+		query.Dim(1), query.Stride(2),
+		query.Dim(2))
+
+	compressedKV := attn.KVA.Forward(ctx, hiddenStates)
+
+	kPass := compressedKV.View(ctx, 0, opts.kvLoraRank, compressedKV.Stride(1), compressedKV.Dim(1))
+	kRot := compressedKV.View(ctx, opts.kvLoraRank*compressedKV.Stride(0),
+		opts.qkRopeHeadDim, compressedKV.Stride(1),
+		1, compressedKV.Stride(1),
+		compressedKV.Dim(1))
+
+	kPass = attn.KVANorm.Forward(ctx, kPass, opts.eps)
+	kPass = attn.KVB.Forward(ctx, kPass)
+
+	kv := kPass.Reshape(ctx, kPass.Dim(0)/opts.numKVHeads, opts.numKVHeads, seqLength)
+	kPass = kv.View(ctx, 0, opts.kqNopeHeadDim, kv.Stride(1), kv.Dim(1), kv.Stride(2), kv.Dim(2))
+	value := kv.View(ctx, opts.kqNopeHeadDim*kv.Stride(0),
+		opts.vHeadDim, kv.Stride(1),
+		kv.Dim(1), kv.Stride(2),
+		kv.Dim(2)).Contiguous(ctx)
+
+	qRot = fast.RoPE(ctx, qRot, positions, opts.qkRopeHeadDim, opts.ropeBase, 1./opts.ropeScale, opts.RoPEOptions()...)
+	kRot = fast.RoPE(ctx, kRot, positions, opts.qkRopeHeadDim, opts.ropeBase, 1./opts.ropeScale, opts.RoPEOptions()...)
+
+	kRot = kRot.Repeat(ctx, 1, qPass.Dim(1))
+
+	query = qRot.Concat(ctx, qPass, 0)
+	key := kRot.Concat(ctx, kPass, 0)
+
+	attention := nn.Attention(ctx, query, key, value, opts.kqScale, cache)
+	attention = attention.Reshape(ctx, attention.Dim(0)*attention.Dim(1), seqLength)
+	return attn.Output.Forward(ctx, attention)
+}
+
+type MLP interface {
+	Forward(ml.Context, ml.Tensor, *Options) ml.Tensor
+}
+
+type sparse struct {
+	Router       *nn.Linear `gguf:"ffn_gate_inp"`
+	Gate         *nn.Linear `gguf:"ffn_gate_exps"`
+	Up           *nn.Linear `gguf:"ffn_up_exps"`
+	Down         *nn.Linear `gguf:"ffn_down_exps"`
+	SharedExpert *dense     `gguf:",suf:_shexp"`
+	ExpProbsBias ml.Tensor  `gguf:"exp_probs_b.bias,alt:exp_probs_b"`
+}
+
+func (moe *sparse) Moe(ctx ml.Context, hiddenStates, topKIndices, topKWeights ml.Tensor, opts *Options) ml.Tensor {
+	hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0), 1, hiddenStates.Dim(1))
+
+	upStates := moe.Up.Weight.MulmatID(ctx, hiddenStates, topKIndices)
+	hiddenStates = moe.Gate.Weight.MulmatID(ctx, hiddenStates, topKIndices)
+	hiddenStates = hiddenStates.SILU(ctx, upStates)
+
+	experts := moe.Down.Weight.MulmatID(ctx, hiddenStates, topKIndices)
+	experts = experts.Mul(ctx, topKWeights)
+	nextStates := experts.View(ctx, 0, experts.Dim(0), experts.Stride(2), experts.Dim(2))
+	for i := 1; i < opts.numExpertsUsed; i++ {
+		nextStates = nextStates.Add(ctx, experts.View(ctx, i*experts.Stride(1), experts.Dim(0), experts.Stride(2), experts.Dim(2)))
+	}
+	return nextStates
+}
+
+func (moe *sparse) topKIndices(ctx ml.Context, scores ml.Tensor, opts *Options) ml.Tensor {
+	scores = scores.Add(ctx, moe.ExpProbsBias)
+	topKIndices := scores.TopK(ctx, opts.numExpertsUsed)
+	return topKIndices
+}
+
+func (moe *sparse) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
+	residuals := hiddenStates
+
+	routerLogits := moe.Router.Forward(ctx, hiddenStates)
+	scores := routerLogits.Sigmoid(ctx)
+	topKIndices := moe.topKIndices(ctx, scores, opts)
+	topKWeights := scores.Reshape(ctx, 1, opts.numExperts, hiddenStates.Dim(1)).Rows(ctx, topKIndices)
+
+	if opts.normTopKProb {
+		topKWeights = topKWeights.Reshape(ctx, opts.numExpertsUsed, hiddenStates.Dim(1))
+		topKWeights = topKWeights.Div(ctx, topKWeights.SumRows(ctx))
+		topKWeights = topKWeights.Reshape(ctx, 1, opts.numExpertsUsed, hiddenStates.Dim(1))
+	}
+
+	topKWeights = topKWeights.Scale(ctx, float64(opts.routedScalingFactor))
+	hiddenStates = moe.Moe(ctx, hiddenStates, topKIndices, topKWeights, opts)
+	sharedExpertResult := moe.SharedExpert.Forward(ctx, residuals, opts)
+
+	hiddenStates = hiddenStates.Add(ctx, sharedExpertResult)
+	return hiddenStates
+}
+
+type dense struct {
+	Gate *nn.Linear `gguf:"ffn_gate"`
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+}
+
+func (mlp *dense) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
+	return mlp.Down.Forward(ctx, hiddenStates)
+}
+
+type Layer struct {
+	AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
+	Attention     *Attention
+
+	MLPNorm *nn.RMSNorm `gguf:"ffn_norm"`
+	MLP     MLP
+}
+
+func (t *Layer) Forward(ctx ml.Context, hiddenStates, positions, outputs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
+	residual := hiddenStates
+	hiddenStates = t.AttentionNorm.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = t.Attention.Forward(ctx, hiddenStates, positions, cache, opts)
+
+	if outputs != nil {
+		hiddenStates = hiddenStates.Rows(ctx, outputs)
+		residual = residual.Rows(ctx, outputs)
+	}
+
+	hiddenStates = hiddenStates.Add(ctx, residual)
+	residual = hiddenStates
+
+	hiddenStates = t.MLPNorm.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = t.MLP.Forward(ctx, hiddenStates, opts)
+	hiddenStates = hiddenStates.Add(ctx, residual)
+	return hiddenStates
+}
+
+type Model struct {
+	model.Base
+	model.BytePairEncoding
+
+	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
+	Layers         []Layer       `gguf:"blk"`
+
+	OutputNorm *nn.RMSNorm `gguf:"output_norm"`
+	Output     *nn.Linear  `gguf:"output,alt:token_embd"`
+
+	*Options
+}
+
+func New(c fs.Config) (model.Model, error) {
+	layers := make([]Layer, c.Uint("block_count"))
+
+	firstDenseLayerIndex := int(c.Uint("leading_dense_block_count"))
+	for i := range layers {
+		if i < firstDenseLayerIndex {
+			layers[i].MLP = &dense{}
+		} else {
+			layers[i].MLP = &sparse{}
+		}
+	}
+
+	mScale := float32(1.0 + float64(c.Float("rope.scaling.yarn_log_multiplier"))*math.Log(float64(c.Float("rope.scaling.factor"))))
+	kqScale := float64(mScale) * float64(mScale) / math.Sqrt(float64(c.Uint("attention.key_length")))
+
+	m := Model{
+		BytePairEncoding: model.NewBytePairEncoding(
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
+				Merges: c.Strings("tokenizer.ggml.merges"),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+				BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+				EOS: append(
+					[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
+					c.Ints("tokenizer.ggml.eos_token_ids")...,
+				),
+			},
+			// Split regex into multiple parts (according to DeepSeek3's regex)
+			"\\p{N}{1,3}",
+			`[一-龥぀-ゟ゠-ヿ]+`,
+			"[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\r\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\r\n]*|\\s*[\r\n]+|\\s+(?!\\S)|\\s+",
+		),
+		Layers: layers,
+		Options: &Options{
+			hiddenSize:     int(c.Uint("embedding_length")),
+			numHeads:       int(c.Uint("attention.head_count")),
+			numKVHeads:     int(c.Uint("attention.head_count_kv")),
+			keyLength:      int(c.Uint("attention.key_length")),
+			valueLength:    int(c.Uint("attention.value_length")),
+			eps:            c.Float("attention.layer_norm_rms_epsilon"),
+			ropeBase:       c.Float("rope.freq_base"),
+			ropeScale:      c.Float("rope.scaling.factor", 1),
+			numExperts:     int(c.Uint("expert_count")),
+			numExpertsUsed: int(c.Uint("expert_used_count")),
+			normTopKProb:   c.Bool("expert_weights_norm", true),
+
+			qLoraRank:     int(c.Uint("attention.q_lora_rank")), //&qLoraRankVal,
+			kvLoraRank:    int(c.Uint("attention.kv_lora_rank")),
+			qkHeadDim:     int(c.Uint("attention.key_length")),
+			vHeadDim:      int(c.Uint("attention.value_length")),
+			qkRopeHeadDim: int(c.Uint("rope.dimension_count")),
+			qkNopeHeadDim: int(c.Uint("attention.key_length")) - int(c.Uint("rope.dimension_count")),
+			kqNopeHeadDim: int(c.Uint("attention.key_length")) - int(c.Uint("rope.dimension_count")),
+
+			routedScalingFactor:   c.Float("expert_weights_scale"),
+			originalContextLength: int(c.Uint("rope.scaling.original_context_length")),
+
+			kqScale: kqScale,
+		},
+	}
+
+	m.Cache = kvcache.NewCausalCache(m.Shift)
+	return &m, nil
+}
+
+func (m Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	return fast.RoPE(ctx, key, shift, m.qkRopeHeadDim, m.ropeBase, 1./m.ropeScale, m.RoPEOptions()...), nil
+}
+
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+
+	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
+
+	for i, layer := range m.Layers {
+		m.Cache.SetLayer(i)
+
+		var outputs ml.Tensor
+		if i == len(m.Layers)-1 {
+			outputs = batch.Outputs
+		}
+
+		hiddenStates = layer.Forward(ctx, hiddenStates, positions, outputs, m.Cache, m.Options)
+	}
+
+	hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.eps)
+	return m.Output.Forward(ctx, hiddenStates), nil
+}
+
+func init() {
+	model.Register("deepseek2", 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"),
@@ -63,7 +63,7 @@ func New(c fs.Config) (model.Model, error) {
 			attnValLen:        int(c.Uint("attention.value_length")),
 			eps:               c.Float("attention.layer_norm_rms_epsilon"),
 			ropeBase:          c.Float("rope.freq_base", 10000.0),
-			ropeScale:         c.Float("rope.freq_scale", 1.0),
+			ropeScale:         c.Float("rope.scaling.factor", 1.0),
 			attnLogitSoftcap:  c.Float("attn_logit_softcapping"),
 			finalLogitSoftcap: c.Float("final_logit_softcapping"),
 		},
@@ -88,7 +88,7 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
 
 	q := sa.Query.Forward(ctx, hiddenState)
 	q = q.Reshape(ctx, opts.attnKeyLen, opts.numHeads, batchSize)
-	q = fast.RoPE(ctx, q, positionIDs, opts.attnKeyLen, opts.ropeBase, opts.ropeScale, rope.WithTypeNeoX())
+	q = fast.RoPE(ctx, q, positionIDs, opts.attnKeyLen, opts.ropeBase, 1./opts.ropeScale, rope.WithTypeNeoX())
 
 	if opts.largeModelScaling {
 		q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.hiddenSize/opts.numHeads)))
@@ -98,7 +98,7 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
 
 	k := sa.Key.Forward(ctx, hiddenState)
 	k = k.Reshape(ctx, opts.attnKeyLen, opts.numKVHeads, batchSize)
-	k = fast.RoPE(ctx, k, positionIDs, opts.attnKeyLen, opts.ropeBase, opts.ropeScale, rope.WithTypeNeoX())
+	k = fast.RoPE(ctx, k, positionIDs, opts.attnKeyLen, opts.ropeBase, 1./opts.ropeScale, rope.WithTypeNeoX())
 
 	v := sa.Value.Forward(ctx, hiddenState)
 	v = v.Reshape(ctx, opts.attnValLen, opts.numKVHeads, batchSize)
@@ -128,7 +128,7 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
 }
 
 func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
-	return fast.RoPE(ctx, key, shift, m.Options.attnKeyLen, m.Options.ropeBase, m.Options.ropeScale, rope.WithTypeNeoX()), nil
+	return fast.RoPE(ctx, key, shift, m.Options.attnKeyLen, m.Options.ropeBase, 1/m.Options.ropeScale, rope.WithTypeNeoX()), nil
 }
 
 type MLP struct {
@@ -138,7 +138,7 @@ type MLP struct {
 }
 
 func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *Options) ml.Tensor {
-	hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx, mlp.Up.Forward(ctx, hiddenState))
 	return mlp.Down.Forward(ctx, hiddenState)
 }
 
@@ -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

@@ -0,0 +1,62 @@
+package gemma3
+
+import (
+	"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.SentencePiece
+
+	*TextModel
+	poolingType pooling.Type
+
+	Dense [2]*nn.Linear `gguf:"dense"`
+}
+
+func (m *embedModel) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	hiddenStates := m.TextModel.Forward(ctx, batch, m.Cache)
+	hiddenStates = m.poolingType.Forward(ctx, hiddenStates)
+	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{
+		SentencePiece: model.NewSentencePiece(
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Scores: c.Floats("tokenizer.ggml.scores"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+				BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+				EOS: append(
+					[]int32{
+						int32(c.Uint("tokenizer.ggml.eos_token_id")),
+						int32(c.Uint("tokenizer.ggml.eot_token_id", 106)),
+					},
+					c.Ints("tokenizer.ggml.eos_token_ids")...,
+				),
+			},
+		),
+		TextModel:   newTextModel(c),
+		poolingType: pooling.Type(c.Uint("pooling_type", 0)),
+	}
+
+	m.Cache = kvcache.NewWrapperCache(
+		kvcache.NewSWACache(int32(c.Uint("attention.sliding_window")), m.Shift),
+		kvcache.NewCausalCache(m.Shift),
+	)
+
+	return m, nil
+}

model/models/gemma3/model.go

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

model/models/gemma3/model_text.go

@@ -53,7 +53,10 @@ func newTextModel(c fs.Config) *TextModel {
 			eps:            c.Float("attention.layer_norm_rms_epsilon", 1e-06),
 			ropeLocalBase:  c.Float("rope.local.freq_base", 10000.0),
 			ropeGlobalBase: c.Float("rope.global.freq_base", 1000000.0),
-			ropeScale:      c.Float("rope.freq_scale", 1.0),
+			ropeScale:      1,
+			// NOTE: the rope.scaling.factor is set incorrectly in the official QAT weights
+			//       (8 instead of 1)
+			// ropeScale:      c.Float("rope.scaling.factor", 1.0),
 		},
 	}
 
@@ -84,7 +87,7 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, pos
 	q := sa.Query.Forward(ctx, hiddenState)
 	q = q.Reshape(ctx, opts.attnKeyLen, opts.numHeads, batchSize)
 	q = sa.QueryNorm.Forward(ctx, q, opts.eps)
-	q = fast.RoPE(ctx, q, positionIDs, opts.attnKeyLen, ropeBase, opts.ropeScale, rope.WithTypeNeoX())
+	q = fast.RoPE(ctx, q, positionIDs, opts.attnKeyLen, ropeBase, 1./opts.ropeScale, rope.WithTypeNeoX())
 
 	if opts.largeModelScaling {
 		q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.hiddenSize/opts.numHeads)))
@@ -95,7 +98,7 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, pos
 	k := sa.Key.Forward(ctx, hiddenState)
 	k = k.Reshape(ctx, opts.attnKeyLen, opts.numKVHeads, batchSize)
 	k = sa.KeyNorm.Forward(ctx, k, opts.eps)
-	k = fast.RoPE(ctx, k, positionIDs, opts.attnKeyLen, ropeBase, opts.ropeScale, rope.WithTypeNeoX())
+	k = fast.RoPE(ctx, k, positionIDs, opts.attnKeyLen, ropeBase, 1./opts.ropeScale, rope.WithTypeNeoX())
 
 	v := sa.Value.Forward(ctx, hiddenState)
 	v = v.Reshape(ctx, opts.attnValLen, opts.numKVHeads, batchSize)
@@ -113,7 +116,7 @@ func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.T
 		ropeBase = m.TextConfig.ropeGlobalBase
 	}
 
-	return fast.RoPE(ctx, key, shift, m.TextConfig.attnKeyLen, ropeBase, m.TextConfig.ropeScale, rope.WithTypeNeoX()), nil
+	return fast.RoPE(ctx, key, shift, m.TextConfig.attnKeyLen, ropeBase, 1/m.TextConfig.ropeScale, rope.WithTypeNeoX()), nil
 }
 
 type TextMLP struct {
@@ -123,7 +126,7 @@ type TextMLP struct {
 }
 
 func (mlp *TextMLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextConfig) ml.Tensor {
-	hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx, mlp.Up.Forward(ctx, hiddenState))
 	return mlp.Down.Forward(ctx, hiddenState)
 }
 
@@ -159,8 +162,10 @@ func (l *TextLayer) Forward(ctx ml.Context, layer int, hiddenState, positionIDs,
 	return hiddenState.Add(ctx, residual)
 }
 
-func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor, batch input.Batch, cache kvcache.Cache) ml.Tensor {
-	hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
+func (m *TextModel) Forward(ctx ml.Context, batch input.Batch, cache kvcache.Cache) ml.Tensor {
+	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+
+	hiddenState := m.TokenEmbedding.Forward(ctx, batch.Inputs)
 	hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.TextConfig.hiddenSize)))
 
 	// set image embeddings
@@ -191,12 +196,12 @@ func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor
 
 		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)
 	}
 
 	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
-	return m.Output.Forward(ctx, hiddenState)
+	return hiddenState
 }

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
@@ -95,7 +95,7 @@ func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.T
 		ropeBase = m.ropeBaseLocal
 	}
 
-	return fast.RoPE(ctx, key, shift, m.headDim(), ropeBase, m.ropeScale, rope.WithTypeNeoX()), nil
+	return fast.RoPE(ctx, key, shift, m.headDim(), ropeBase, 1./m.ropeScale, rope.WithTypeNeoX()), nil
 }
 
 type TextScaledWordEmbedding struct {
@@ -170,8 +170,7 @@ func (d TextLayer) Forward(ctx ml.Context, hiddenStates, perLayerInput, position
 	}
 
 	active = d.PerLayerInputGate.Forward(ctx, active)
-	active = active.GELU(ctx)
-	active = active.Mul(ctx, perLayerInput)
+	active = active.GELU(ctx, perLayerInput)
 
 	active = d.PerLayerProjection.Forward(ctx, active)
 	active = d.PostPerLayerNorm.Forward(ctx, active, opts.eps)
@@ -257,14 +256,14 @@ func (attn TextAttention) Forward(ctx ml.Context, hiddenStates, positions ml.Ten
 	query := attn.Query.Forward(ctx, hiddenStates)
 	query = query.Reshape(ctx, opts.headDim(), opts.numHeads, batchSize)
 	query = attn.QueryNorm.Forward(ctx, query, opts.eps)
-	query = fast.RoPE(ctx, query, positions, opts.headDim(), ropeBase, opts.ropeScale, rope.WithTypeNeoX())
+	query = fast.RoPE(ctx, query, positions, opts.headDim(), ropeBase, 1./opts.ropeScale, rope.WithTypeNeoX())
 
 	var key, value ml.Tensor
 	if !sharedKV {
 		key = attn.Key.Forward(ctx, hiddenStates)
 		key = key.Reshape(ctx, opts.headDim(), opts.numKVHeads, batchSize)
 		key = attn.KeyNorm.Forward(ctx, key, opts.eps)
-		key = fast.RoPE(ctx, key, positions, opts.headDim(), ropeBase, opts.ropeScale, rope.WithTypeNeoX())
+		key = fast.RoPE(ctx, key, positions, opts.headDim(), ropeBase, 1./opts.ropeScale, rope.WithTypeNeoX())
 
 		value = attn.Value.Forward(ctx, hiddenStates)
 		value = value.Reshape(ctx, opts.headDim(), opts.numKVHeads, batchSize)
@@ -292,7 +291,7 @@ func (mlp TextMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, activationSpa
 		hiddenStates = hiddenStates.Sub(ctx, cutoff).RELU(ctx)
 	}
 
-	hiddenStates = hiddenStates.GELU(ctx).Mul(ctx, upStates)
+	hiddenStates = hiddenStates.GELU(ctx, upStates)
 	hiddenStates = mlp.Down.Forward(ctx, hiddenStates)
 	return hiddenStates
 }
@@ -350,7 +349,7 @@ func newTextModel(c fs.Config) *TextModel {
 			eps:           c.Float("attention.layer_norm_rms_epsilon", 1e-06),
 			ropeBase:      c.Float("rope.freq_base", 1_000_000),
 			ropeBaseLocal: c.Float("rope.freq_base_local", 10_000),
-			ropeScale:     c.Float("rope.freq_scale", 1.0),
+			ropeScale:     c.Float("rope.scaling.factor", 1.0),
 
 			slidingWindowPattern:    c.Bools("attention.sliding_window_pattern"),
 			activationSparsityScale: c.Floats("activation_sparsity_scale"),

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)
@@ -210,7 +210,7 @@ func (mlp *MLPBlock) Forward(ctx ml.Context, hiddenStates, one ml.Tensor, opts *
 		up = mlp.Up.Forward(ctx, hiddenStates, selectedExperts)
 	}
 
-	hiddenStates = gate.SwiGLU(ctx, up, 1.702, 7)
+	hiddenStates = gate.SILUAlphaLimit(ctx, up, 1.702, 7)
 
 	experts := mlp.Down.Forward(ctx, hiddenStates, selectedExperts)
 	experts = experts.Mul(ctx, routingWeights)
@@ -227,17 +227,6 @@ func New(c fs.Config) (model.Model, error) {
 	m := Transformer{
 		TransformerBlocks: make([]TransformerBlock, c.Uint("block_count")),
 		BytePairEncoding: model.NewBytePairEncoding(
-			c.String("tokenizer.ggml.pretokenizer",
-				strings.Join([]string{
-					`[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?`,
-					`[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?`,
-					`\p{N}{1,3}`,
-					` ?[^\s\p{L}\p{N}]+[\r\n/]*`,
-					`\s*[\r\n]+`,
-					`\s+(?!\S)`,
-					`\s+`,
-				}, "|"),
-			),
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
@@ -250,6 +239,15 @@ func New(c fs.Config) (model.Model, error) {
 					c.Ints("tokenizer.ggml.eos_token_ids")...,
 				),
 			},
+			strings.Join([]string{
+				`[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?`,
+				`[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?`,
+				`\p{N}{1,3}`,
+				` ?[^\s\p{L}\p{N}]+[\r\n/]*`,
+				`\s*[\r\n]+`,
+				`\s+(?!\S)`,
+				`\s+`,
+			}, "|"),
 		),
 		Options: Options{
 			hiddenSize:            int(c.Uint("embedding_length")),

model/models/llama/model.go

@@ -2,7 +2,6 @@ package llama
 
 import (
 	"cmp"
-	"fmt"
 	"math"
 
 	"github.com/ollama/ollama/fs"
@@ -23,51 +22,80 @@ type Options struct {
 
 type Model struct {
 	model.Base
-	model.BytePairEncoding
+	model.TextProcessor
 
 	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
 	Layers         []Layer       `gguf:"blk"`
 	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
 	Output         *nn.Linear    `gguf:"output,alt:token_embd"`
 
-	*Options
+	Options
 }
 
 func New(c fs.Config) (model.Model, error) {
-	// This model currently only supports the gpt2 tokenizer
-	if c.String("tokenizer.ggml.model") == "llama" {
-		return nil, fmt.Errorf("unsupported tokenizer: llama")
+	if c.Uint("expert_count") > 0 {
+		// TODO: support mixtures of experts
+		return nil, model.ErrUnsupportedModel
 	}
-	// Best effort detection of library/deepseek-coder model(s) which are incompatible
-	if c.String("general.name") == "deepseek-ai" {
-		return nil, fmt.Errorf("unsupported model: %s", c.String("general.name"))
-	}
-	m := Model{
-		BytePairEncoding: model.NewBytePairEncoding(
-			c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
-			&model.Vocabulary{
-				Values: c.Strings("tokenizer.ggml.tokens"),
-				Types:  c.Ints("tokenizer.ggml.token_type"),
-				Merges: c.Strings("tokenizer.ggml.merges"),
-				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
-				BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
-				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
-				EOS: append(
-					[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
-					c.Ints("tokenizer.ggml.eos_token_ids")...,
-				),
-			},
+
+	var processor model.TextProcessor
+	vocabulary := model.Vocabulary{
+		Values: c.Strings("tokenizer.ggml.tokens"),
+		Scores: c.Floats("tokenizer.ggml.scores"),
+		Types:  c.Ints("tokenizer.ggml.token_type"),
+		Merges: c.Strings("tokenizer.ggml.merges"),
+		AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+		BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
+		AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+		EOS: append(
+			[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
+			c.Ints("tokenizer.ggml.eos_token_ids")...,
 		),
-		Layers: make([]Layer, c.Uint("block_count")),
-		Options: &Options{
+	}
+	switch c.String("tokenizer.ggml.model") {
+	case "gpt2":
+		var pretokenizers []string
+		switch c.String("tokenizer.ggml.pre") {
+		case "default":
+			// no-op use the default bpe pretokenizer
+		case "qwen2":
+			pretokenizers = []string{
+				"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+			}
+		case "refact":
+			pretokenizers = []string{
+				`\p{N}`,
+				`'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+`,
+			}
+		case "tekken":
+			pretokenizers = []string{
+				"[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+			}
+		default:
+			// use a llama-style pretokenizer
+			pretokenizers = []string{
+				"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+			}
+		}
+		processor = model.NewBytePairEncoding(&vocabulary, pretokenizers...)
+	case "llama":
+		processor = model.NewSentencePiece(&vocabulary)
+	default:
+		return nil, model.ErrUnsupportedTokenizer
+	}
+
+	m := Model{
+		TextProcessor: processor,
+		Layers:        make([]Layer, c.Uint("block_count")),
+		Options: Options{
 			hiddenSize: int(c.Uint("embedding_length")),
 			numHeads:   int(c.Uint("attention.head_count")),
 			numKVHeads: int(c.Uint("attention.head_count_kv")),
 			headDim:    int(c.Uint("attention.key_length")),
 			ropeDim:    int(c.Uint("rope.dimension_count")),
 			eps:        c.Float("attention.layer_norm_rms_epsilon"),
-			ropeBase:   c.Float("rope.freq_base"),
-			ropeScale:  c.Float("rope.freq_scale", 1),
+			ropeBase:   c.Float("rope.freq_base", 1e5),
+			ropeScale:  c.Float("rope.scaling.factor", 1),
 		},
 	}
 
@@ -98,8 +126,8 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positions ml.Tenso
 	value := sa.Value.Forward(ctx, hiddenState)
 	value = value.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
 
-	query = fast.RoPE(ctx, query, positions, ropeDim, opts.ropeBase, opts.ropeScale, rope.WithFactors(sa.RopeFactors))
-	key = fast.RoPE(ctx, key, positions, ropeDim, opts.ropeBase, opts.ropeScale, rope.WithFactors(sa.RopeFactors))
+	query = fast.RoPE(ctx, query, positions, ropeDim, opts.ropeBase, 1./opts.ropeScale, rope.WithFactors(sa.RopeFactors))
+	key = fast.RoPE(ctx, key, positions, ropeDim, opts.ropeBase, 1./opts.ropeScale, rope.WithFactors(sa.RopeFactors))
 
 	attention := nn.Attention(ctx, query, key, value, 1.0/math.Sqrt(float64(headDim)), cache)
 	attention = attention.Reshape(ctx, headDim*opts.numHeads, batchSize)
@@ -108,7 +136,7 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positions ml.Tenso
 
 func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
 	ropeDim := cmp.Or(m.ropeDim, m.hiddenSize/m.numHeads)
-	return fast.RoPE(ctx, key, shift, ropeDim, m.ropeBase, m.ropeScale, rope.WithFactors(m.Layers[layer].SelfAttention.RopeFactors)), nil
+	return fast.RoPE(ctx, key, shift, ropeDim, m.ropeBase, 1./m.ropeScale, rope.WithFactors(m.Layers[layer].SelfAttention.RopeFactors)), nil
 }
 
 type MLP struct {
@@ -118,7 +146,7 @@ type MLP struct {
 }
 
 func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *Options) ml.Tensor {
-	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx, mlp.Up.Forward(ctx, hiddenState))
 	return mlp.Down.Forward(ctx, hiddenState)
 }
 
@@ -160,10 +188,10 @@ 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)
+		hiddenState = layer.Forward(ctx, hiddenState, positions, outputs, m.Cache, &m.Options)
 	}
 
 	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)

model/models/llama4/model.go

@@ -34,8 +34,6 @@ func (p *Projector) Forward(ctx ml.Context, visionOutputs ml.Tensor) ml.Tensor {
 func New(c fs.Config) (model.Model, error) {
 	m := Model{
 		BytePairEncoding: model.NewBytePairEncoding(
-			c.String("tokenizer.ggml.pretokenizer",
-				`[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n/]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
@@ -48,6 +46,7 @@ func New(c fs.Config) (model.Model, error) {
 					c.Ints("tokenizer.ggml.eos_token_ids")...,
 				),
 			},
+			`[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n/]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
 		),
 		ImageProcessor: newImageProcessor(c),
 		VisionModel:    newVisionModel(c),
@@ -176,9 +175,7 @@ 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/llama4/model_text.go

@@ -33,8 +33,8 @@ func (sa *TextAttention) Forward(ctx ml.Context, hiddenStates, positions, attent
 	value = value.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
 
 	if useRope {
-		query = fast.RoPE(ctx, query, positions, opts.ropeDim, opts.ropeBase, opts.ropeScale, rope.WithFactors(sa.RopeFactors))
-		key = fast.RoPE(ctx, key, positions, opts.ropeDim, opts.ropeBase, opts.ropeScale, rope.WithFactors(sa.RopeFactors))
+		query = fast.RoPE(ctx, query, positions, opts.ropeDim, opts.ropeBase, 1./opts.ropeScale, rope.WithFactors(sa.RopeFactors))
+		key = fast.RoPE(ctx, key, positions, opts.ropeDim, opts.ropeBase, 1./opts.ropeScale, rope.WithFactors(sa.RopeFactors))
 	}
 
 	if opts.useQKNorm {
@@ -58,14 +58,14 @@ type TextMLP struct {
 }
 
 func (mlp *TextMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *TextOptions) ml.Tensor {
-	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenStates))
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
 	return mlp.Down.Forward(ctx, hiddenStates)
 }
 
 type TextExperts struct {
-	Gate *nn.Linear `gguf:"ffn_gate_exps"`
-	Up   *nn.Linear `gguf:"ffn_up_exps"`
-	Down *nn.Linear `gguf:"ffn_down_exps"`
+	Gate *nn.LinearBatch `gguf:"ffn_gate_exps"`
+	Up   *nn.LinearBatch `gguf:"ffn_up_exps"`
+	Down *nn.LinearBatch `gguf:"ffn_down_exps"`
 }
 
 func (e *TextExperts) Forward(ctx ml.Context, hiddenStates, routerLogits ml.Tensor, opts *TextOptions) ml.Tensor {
@@ -76,9 +76,9 @@ func (e *TextExperts) Forward(ctx ml.Context, hiddenStates, routerLogits ml.Tens
 	hiddenStates = hiddenStates.Repeat(ctx, 1, opts.numExpertsUsed)
 	hiddenStates = hiddenStates.Mul(ctx, scores)
 
-	upStates := e.Up.Weight.MulmatID(ctx, hiddenStates, experts)
-	gateStates := e.Gate.Weight.MulmatID(ctx, hiddenStates, experts)
-	downStates := e.Down.Weight.MulmatID(ctx, upStates.Mul(ctx, gateStates.SILU(ctx)), experts)
+	upStates := e.Up.Forward(ctx, hiddenStates, experts)
+	gateStates := e.Gate.Forward(ctx, hiddenStates, experts)
+	downStates := e.Down.Forward(ctx, upStates.Mul(ctx, gateStates.SILU(ctx)), experts)
 
 	nextStates := downStates.View(ctx, 0, hiddenStates.Dim(0), downStates.Stride(2), hiddenStates.Dim(2))
 	for i := 1; i < opts.numExpertsUsed; i++ {
@@ -88,22 +88,10 @@ func (e *TextExperts) Forward(ctx ml.Context, hiddenStates, routerLogits ml.Tens
 	return nextStates
 }
 
-// TextSharedExpert is TextMLP with different tensor names
-type TextSharedExpert struct {
-	Gate *nn.Linear `gguf:"ffn_gate_shexp"`
-	Up   *nn.Linear `gguf:"ffn_up_shexp"`
-	Down *nn.Linear `gguf:"ffn_down_shexp"`
-}
-
-func (mlp *TextSharedExpert) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *TextOptions) ml.Tensor {
-	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenStates))
-	return mlp.Down.Forward(ctx, hiddenStates)
-}
-
 type TextMOE struct {
 	Router       *nn.Linear `gguf:"ffn_gate_inp"`
 	Experts      *TextExperts
-	SharedExpert *TextSharedExpert
+	SharedExpert *TextMLP `gguf:",suf:_shexp"`
 }
 
 func (moe *TextMOE) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *TextOptions) ml.Tensor {
@@ -196,7 +184,7 @@ func newTextModel(c fs.Config) *TextModel {
 			numExpertsUsed:             int(c.Uint("expert_used_count")),
 			ropeDim:                    int(c.Uint("rope.dimension_count")),
 			ropeBase:                   c.Float("rope.freq_base"),
-			ropeScale:                  c.Float("rope.freq_scale", 1),
+			ropeScale:                  c.Float("rope.scaling.factor", 1),
 			eps:                        c.Float("attention.layer_norm_rms_epsilon"),
 			interleaveLayerStep:        int(c.Uint("interleave_moe_layer_step", 1)),
 			noRopeInterval:             int(c.Uint("no_rope_interval", 4)),
@@ -248,5 +236,5 @@ func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor
 }
 
 func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
-	return fast.RoPE(ctx, key, shift, m.ropeDim, m.ropeBase, m.ropeScale, rope.WithFactors(m.Layers[layer].Attention.RopeFactors)), nil
+	return fast.RoPE(ctx, key, shift, m.ropeDim, m.ropeBase, 1./m.ropeScale, rope.WithFactors(m.Layers[layer].Attention.RopeFactors)), nil
 }

model/models/mistral3/model.go

@@ -33,7 +33,6 @@ var _ model.TextProcessor = (*Model)(nil)
 func New(c fs.Config) (model.Model, error) {
 	m := &Model{
 		BytePairEncoding: model.NewBytePairEncoding(
-			c.String("tokenizer.ggml.pretokenizer", `[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n/]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
@@ -46,6 +45,7 @@ func New(c fs.Config) (model.Model, error) {
 					c.Ints("tokenizer.ggml.eos_token_ids")...,
 				),
 			},
+			`[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n/]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
 		),
 		TextModel:           newTextModel(c),
 		VisionModel:         newVisionModel(c),
@@ -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/mistral3/model_text.go

@@ -40,11 +40,11 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
 
 	q := sa.Query.Forward(ctx, hiddenState)
 	q = q.Reshape(ctx, headDim, opts.numHeads, batchSize)
-	q = fast.RoPE(ctx, q, positionIDs, opts.ropeDim, opts.ropeBase, opts.ropeScale)
+	q = fast.RoPE(ctx, q, positionIDs, opts.ropeDim, opts.ropeBase, 1./opts.ropeScale)
 
 	k := sa.Key.Forward(ctx, hiddenState)
 	k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
-	k = fast.RoPE(ctx, k, positionIDs, opts.ropeDim, opts.ropeBase, opts.ropeScale)
+	k = fast.RoPE(ctx, k, positionIDs, opts.ropeDim, opts.ropeBase, 1./opts.ropeScale)
 
 	v := sa.Value.Forward(ctx, hiddenState)
 	v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
@@ -55,7 +55,7 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
 }
 
 func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
-	return fast.RoPE(ctx, key, shift, m.ropeDim, m.ropeBase, m.ropeScale), nil
+	return fast.RoPE(ctx, key, shift, m.ropeDim, m.ropeBase, 1./m.ropeScale), nil
 }
 
 type MLP struct {
@@ -65,7 +65,7 @@ type MLP struct {
 }
 
 func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextOptions) ml.Tensor {
-	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx, mlp.Up.Forward(ctx, hiddenState))
 	return mlp.Down.Forward(ctx, hiddenState)
 }
 
@@ -132,7 +132,7 @@ func newTextModel(c fs.Config) *TextModel {
 			ropeDim:    int(c.Uint("rope.dimension_count")),
 			eps:        c.Float("attention.layer_norm_rms_epsilon"),
 			ropeBase:   c.Float("rope.freq_base"),
-			ropeScale:  c.Float("rope.freq_scale", 1),
+			ropeScale:  c.Float("rope.scaling.factor", 1),
 		},
 	}
 }

model/models/mistral3/model_vision.go

@@ -51,7 +51,7 @@ type VisionMLP struct {
 }
 
 func (mlp *VisionMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *VisionModelOptions) ml.Tensor {
-	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenStates))
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
 	return mlp.Down.Forward(ctx, hiddenStates)
 }
 

model/models/mllama/model.go

@@ -33,7 +33,6 @@ const (
 func New(c fs.Config) (model.Model, error) {
 	m := Model{
 		BytePairEncoding: model.NewBytePairEncoding(
-			c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
@@ -46,6 +45,7 @@ func New(c fs.Config) (model.Model, error) {
 					c.Ints("tokenizer.ggml.eos_token_ids")...,
 				),
 			},
+			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
 		),
 		ImageProcessor: newImageProcessor(c),
 		VisionModel:    newVisionModel(c),
@@ -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/mllama/model_text.go

@@ -26,11 +26,11 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions ml.T
 
 	query := sa.Query.Forward(ctx, hiddenState)
 	query = query.Reshape(ctx, headDim, opts.numHeads, batchSize)
-	query = fast.RoPE(ctx, query, positions, opts.ropeDim, opts.ropeBase, opts.ropeScale, rope.WithFactors(sa.RopeFactors))
+	query = fast.RoPE(ctx, query, positions, opts.ropeDim, opts.ropeBase, 1./opts.ropeScale, rope.WithFactors(sa.RopeFactors))
 
 	key := sa.Key.Forward(ctx, hiddenState)
 	key = key.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
-	key = fast.RoPE(ctx, key, positions, opts.ropeDim, opts.ropeBase, opts.ropeScale, rope.WithFactors(sa.RopeFactors))
+	key = fast.RoPE(ctx, key, positions, opts.ropeDim, opts.ropeBase, 1./opts.ropeScale, rope.WithFactors(sa.RopeFactors))
 
 	value := sa.Value.Forward(ctx, hiddenState)
 	value = value.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
@@ -45,7 +45,7 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions ml.T
 func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
 	// This will only get called for layers in the cache, which are just the self attention layers
 	if sa, ok := m.Transformer.Layers[layer].(*TextSelfAttentionDecoderLayer); ok {
-		return fast.RoPE(ctx, key, shift, m.ropeDim, m.ropeBase, m.ropeScale, rope.WithFactors(sa.SelfAttention.RopeFactors)), nil
+		return fast.RoPE(ctx, key, shift, m.ropeDim, m.ropeBase, 1./m.ropeScale, rope.WithFactors(sa.SelfAttention.RopeFactors)), nil
 	}
 
 	return key, nil
@@ -58,7 +58,7 @@ type TextMLP struct {
 }
 
 func (mlp *TextMLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextModelOptions) ml.Tensor {
-	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx, mlp.Up.Forward(ctx, hiddenState))
 	return mlp.Down.Forward(ctx, hiddenState)
 }
 
@@ -244,7 +244,7 @@ func newTextModel(c fs.Config) *TextModel {
 			ropeDim:              int(c.Uint("rope.dimension_count")),
 			eps:                  c.Float("attention.layer_norm_rms_epsilon"),
 			ropeBase:             c.Float("rope.freq_base"),
-			ropeScale:            c.Float("rope.freq_scale", 1),
+			ropeScale:            c.Float("rope.scaling.factor", 1),
 			crossAttentionLayers: c.Ints("attention.cross_attention_layers"),
 		},
 	}

model/models/models.go

@@ -1,6 +1,8 @@
 package models
 
 import (
+	_ "github.com/ollama/ollama/model/models/bert"
+	_ "github.com/ollama/ollama/model/models/deepseek2"
 	_ "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

@@ -43,8 +43,8 @@ func (attn Attention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor,
 	value := attn.Value.Forward(ctx, hiddenStates)
 	value = value.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
 
-	query = fast.RoPE(ctx, query, positions, ropeDim, opts.ropeBase, opts.ropeScale, rope.WithTypeNeoX())
-	key = fast.RoPE(ctx, key, positions, ropeDim, opts.ropeBase, opts.ropeScale, rope.WithTypeNeoX())
+	query = fast.RoPE(ctx, query, positions, ropeDim, opts.ropeBase, 1./opts.ropeScale, rope.WithTypeNeoX())
+	key = fast.RoPE(ctx, key, positions, ropeDim, opts.ropeBase, 1./opts.ropeScale, rope.WithTypeNeoX())
 
 	attention := nn.Attention(ctx, query, key, value, 1.0/math.Sqrt(float64(headDim)), cache)
 	attention = attention.Reshape(ctx, headDim*opts.numHeads, batchSize)
@@ -59,7 +59,7 @@ type MLP struct {
 }
 
 func (mlp MLP) Forward(ctx ml.Context, hiddenStates ml.Tensor) ml.Tensor {
-	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenStates))
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
 	return mlp.Down.Forward(ctx, hiddenStates)
 }
 
@@ -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)
@@ -124,7 +124,7 @@ func (m Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 
 func (m Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
 	ropeDim := cmp.Or(m.ropeDim, m.hiddenSize/m.numHeads)
-	return fast.RoPE(ctx, key, shift, ropeDim, m.ropeBase, m.ropeScale, rope.WithTypeNeoX()), nil
+	return fast.RoPE(ctx, key, shift, ropeDim, m.ropeBase, 1./m.ropeScale, rope.WithTypeNeoX()), nil
 }
 
 func New(c fs.Config) (model.Model, error) {
@@ -139,7 +139,6 @@ func New(c fs.Config) (model.Model, error) {
 	m := Model{
 		Layers: make([]DecoderLayer, c.Uint("block_count")),
 		BytePairEncoding: model.NewBytePairEncoding(
-			c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
@@ -152,6 +151,7 @@ func New(c fs.Config) (model.Model, error) {
 					c.Ints("tokenizer.ggml.eos_token_ids")...,
 				),
 			},
+			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
 		),
 		Options: Options{
 			hiddenSize: int(c.Uint("embedding_length")),
@@ -160,7 +160,7 @@ func New(c fs.Config) (model.Model, error) {
 			headDim:    int(c.Uint("attention.key_length")),
 			ropeDim:    int(c.Uint("rope.dimension_count")),
 			ropeBase:   c.Float("rope.freq_base"),
-			ropeScale:  c.Float("rope.freq_scale", 1),
+			ropeScale:  c.Float("rope.scaling.factor", 1),
 			eps:        c.Float("attention.layer_norm_rms_epsilon"),
 		},
 	}

model/models/qwen25vl/model.go

@@ -29,7 +29,6 @@ var _ model.MultimodalProcessor = (*Model)(nil)
 func New(c fs.Config) (model.Model, error) {
 	m := &Model{
 		BytePairEncoding: model.NewBytePairEncoding(
-			c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
@@ -42,6 +41,7 @@ func New(c fs.Config) (model.Model, error) {
 					c.Ints("tokenizer.ggml.eos_token_ids")...,
 				),
 			},
+			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
 		),
 		TextModel:      NewTextModel(c),
 		VisionModel:    newVisionModel(c),
@@ -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/qwen25vl/model_text.go

@@ -38,7 +38,7 @@ func NewTextModel(c fs.Config) *TextModel {
 			originalContextLength: int(c.Uint("context_length", 128000)),
 			eps:                   c.Float("attention.layer_norm_rms_epsilon"),
 			ropeBase:              c.Float("rope.freq_base"),
-			ropeScale:             c.Float("rope.freq_scale", 1),
+			ropeScale:             c.Float("rope.scaling.factor", 1),
 		},
 	}
 
@@ -60,11 +60,11 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
 
 	q := sa.Query.Forward(ctx, hiddenState)
 	q = q.Reshape(ctx, headDim, opts.numHeads, batchSize)
-	q = fast.RoPE(ctx, q, positionIDs, opts.ropeDim, opts.ropeBase, opts.ropeScale, rope.WithOriginalContextLength(opts.originalContextLength), rope.WithTypeNeoX())
+	q = fast.RoPE(ctx, q, positionIDs, opts.ropeDim, opts.ropeBase, 1./opts.ropeScale, rope.WithOriginalContextLength(opts.originalContextLength), rope.WithTypeNeoX())
 
 	k := sa.Key.Forward(ctx, hiddenState)
 	k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
-	k = fast.RoPE(ctx, k, positionIDs, opts.ropeDim, opts.ropeBase, opts.ropeScale, rope.WithOriginalContextLength(opts.originalContextLength), rope.WithTypeNeoX())
+	k = fast.RoPE(ctx, k, positionIDs, opts.ropeDim, opts.ropeBase, 1./opts.ropeScale, rope.WithOriginalContextLength(opts.originalContextLength), rope.WithTypeNeoX())
 
 	v := sa.Value.Forward(ctx, hiddenState)
 	v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
@@ -78,7 +78,7 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
 
 // Shift applies rotary position embeddings to the key tensor for causal attention caching
 func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
-	return fast.RoPE(ctx, key, shift, m.ropeDim, m.ropeBase, m.ropeScale, rope.WithOriginalContextLength(m.originalContextLength), rope.WithTypeNeoX()), nil
+	return fast.RoPE(ctx, key, shift, m.ropeDim, m.ropeBase, 1./m.ropeScale, rope.WithOriginalContextLength(m.originalContextLength), rope.WithTypeNeoX()), nil
 }
 
 // MLP implements the feed-forward network component with SwiGLU activation
@@ -90,7 +90,7 @@ type MLP struct {
 
 func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextOptions) ml.Tensor {
 	// Apply SwiGLU activation gating
-	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx, mlp.Up.Forward(ctx, hiddenState))
 	// Project back to hidden dimension
 	return mlp.Down.Forward(ctx, hiddenState)
 }

model/models/qwen25vl/model_vision.go

@@ -100,8 +100,7 @@ type VisionMLP struct {
 func (mlp *VisionMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *VisionModelOptions) ml.Tensor {
 	// Using activation as specified in config (likely GELU or SiLU/Swish)
 	gateOutput := mlp.Gate.Forward(ctx, hiddenStates)
-	upOutput := mlp.Up.Forward(ctx, hiddenStates)
-	hiddenStates = gateOutput.SILU(ctx).Mul(ctx, upOutput)
+	hiddenStates = gateOutput.SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
 
 	return mlp.Down.Forward(ctx, hiddenStates)
 }

model/models/qwen3/embed.go

@@ -0,0 +1,73 @@
+package qwen3
+
+import (
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn/pooling"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type embedModel struct {
+	model.Base
+	model.BytePairEncoding
+
+	*Model
+	poolingType pooling.Type
+}
+
+func (m *embedModel) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	hiddenStates, err := m.forward(ctx, batch)
+	if err != nil {
+		return nil, err
+	}
+
+	hiddenStates = m.poolingType.Forward(ctx, hiddenStates)
+	hiddenStates = hiddenStates.L2Norm(ctx, 1e-12)
+	return hiddenStates, nil
+}
+
+func newEmbed(c fs.Config) (model.Model, error) {
+	layers := make([]Layer, c.Uint("block_count"))
+	for i := range layers {
+		layers[i].MLP = &dense{}
+	}
+	m := embedModel{
+		BytePairEncoding: model.NewBytePairEncoding(
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Types:  c.Ints("tokenizer.ggml.token_type"),
+				Merges: c.Strings("tokenizer.ggml.merges"),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+				BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+				EOS: append(
+					[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
+					c.Ints("tokenizer.ggml.eos_token_ids")...,
+				),
+			},
+			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
+		),
+		Model: &Model{
+			Layers: layers,
+			Options: &Options{
+				hiddenSize:     int(c.Uint("embedding_length")),
+				numHeads:       int(c.Uint("attention.head_count")),
+				numKVHeads:     int(c.Uint("attention.head_count_kv")),
+				keyLength:      int(c.Uint("attention.key_length")),
+				valueLength:    int(c.Uint("attention.value_length")),
+				eps:            c.Float("attention.layer_norm_rms_epsilon"),
+				ropeBase:       c.Float("rope.freq_base"),
+				ropeScale:      c.Float("rope.freq_scale", 1),
+				numExperts:     int(c.Uint("expert_count")),
+				numExpertsUsed: int(c.Uint("expert_used_count")),
+				normTopKProb:   c.Bool("norm_top_k_prob", true),
+			},
+		},
+		poolingType: pooling.Type(c.Uint("pooling_type")),
+	}
+
+	m.Cache = kvcache.NewCausalCache(m.Shift)
+	return &m, nil
+}

model/models/qwen3/model.go

@@ -30,10 +30,10 @@ func (o Options) headDim() int {
 }
 
 type Attention struct {
-	QueryNorm *nn.RMSNorm `gguf:"attn_q_norm"`
 	Query     *nn.Linear  `gguf:"attn_q"`
-	KeyNorm   *nn.RMSNorm `gguf:"attn_k_norm"`
+	QueryNorm *nn.RMSNorm `gguf:"attn_q_norm"`
 	Key       *nn.Linear  `gguf:"attn_k"`
+	KeyNorm   *nn.RMSNorm `gguf:"attn_k_norm"`
 	Value     *nn.Linear  `gguf:"attn_v"`
 	Output    *nn.Linear  `gguf:"attn_output"`
 }
@@ -52,8 +52,8 @@ func (sa *Attention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor,
 	query = sa.QueryNorm.Forward(ctx, query, opts.eps)
 	key = sa.KeyNorm.Forward(ctx, key, opts.eps)
 
-	query = fast.RoPE(ctx, query, positions, opts.headDim(), opts.ropeBase, opts.ropeScale, rope.WithTypeNeoX())
-	key = fast.RoPE(ctx, key, positions, opts.headDim(), opts.ropeBase, opts.ropeScale, rope.WithTypeNeoX())
+	query = fast.RoPE(ctx, query, positions, opts.headDim(), opts.ropeBase, 1./opts.ropeScale, rope.WithTypeNeoX())
+	key = fast.RoPE(ctx, key, positions, opts.headDim(), opts.ropeBase, 1./opts.ropeScale, rope.WithTypeNeoX())
 
 	attention := nn.Attention(ctx, query, key, value, 1./math.Sqrt(float64(opts.headDim())), cache)
 	attention = attention.Reshape(ctx, attention.Dim(0)*attention.Dim(1), batchSize)
@@ -65,10 +65,10 @@ type MLP interface {
 }
 
 type sparse struct {
-	Router *nn.Linear `gguf:"ffn_gate_inp"`
-	Gate   *nn.Linear `gguf:"ffn_gate_exps"`
-	Up     *nn.Linear `gguf:"ffn_up_exps"`
-	Down   *nn.Linear `gguf:"ffn_down_exps"`
+	Router *nn.Linear      `gguf:"ffn_gate_inp"`
+	Gate   *nn.LinearBatch `gguf:"ffn_gate_exps"`
+	Up     *nn.LinearBatch `gguf:"ffn_up_exps"`
+	Down   *nn.LinearBatch `gguf:"ffn_down_exps"`
 }
 
 func (mlp *sparse) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options) ml.Tensor {
@@ -87,13 +87,9 @@ func (mlp *sparse) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *Options
 
 	hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0), 1, hiddenStates.Dim(1))
 
-	upStates := mlp.Up.Weight.MulmatID(ctx, hiddenStates, selectedExperts)
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates, selectedExperts).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates, selectedExperts))
 
-	hiddenStates = mlp.Gate.Weight.MulmatID(ctx, hiddenStates, selectedExperts)
-	hiddenStates = hiddenStates.SILU(ctx)
-	hiddenStates = hiddenStates.Mul(ctx, upStates)
-
-	experts := mlp.Down.Weight.MulmatID(ctx, hiddenStates, selectedExperts)
+	experts := mlp.Down.Forward(ctx, hiddenStates, selectedExperts)
 	experts = experts.Mul(ctx, routingWeights)
 
 	nextStates := experts.View(ctx, 0, experts.Dim(0), experts.Stride(2), experts.Dim(2))
@@ -111,7 +107,8 @@ type dense struct {
 }
 
 func (mlp *dense) Forward(ctx ml.Context, hiddenStates ml.Tensor, _ *Options) ml.Tensor {
-	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenStates))
+	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).
+		SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
 	return mlp.Down.Forward(ctx, hiddenStates)
 }
 
@@ -154,29 +151,39 @@ type Model struct {
 	*Options
 }
 
-// Forward implements model.Model.
 func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	hiddenStates, err := m.forward(ctx, batch)
+	if err != nil {
+		return nil, err
+	}
+
+	return m.Output.Forward(ctx, hiddenStates), nil
+}
+
+// Forward implements model.Model.
+func (m *Model) forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
 	positions := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
 
 	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
 
 	for i, layer := range m.Layers {
-		m.Cache.SetLayer(i)
+		if m.Cache != nil {
+			m.Cache.SetLayer(i)
+		}
 
 		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)
 	}
 
-	hiddenStates = m.OutputNorm.Forward(ctx, hiddenStates, m.eps)
-	return m.Output.Forward(ctx, hiddenStates), nil
+	return m.OutputNorm.Forward(ctx, hiddenStates, m.eps), nil
 }
 
 func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
-	return fast.RoPE(ctx, key, shift, m.headDim(), m.ropeBase, m.ropeScale, rope.WithTypeNeoX()), nil
+	return fast.RoPE(ctx, key, shift, m.headDim(), m.ropeBase, 1./m.ropeScale, rope.WithTypeNeoX()), nil
 }
 
 var _ model.Model = (*Model)(nil)
@@ -193,7 +200,6 @@ func New(c fs.Config) (model.Model, error) {
 
 	m := Model{
 		BytePairEncoding: model.NewBytePairEncoding(
-			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
 			&model.Vocabulary{
 				Values: c.Strings("tokenizer.ggml.tokens"),
 				Types:  c.Ints("tokenizer.ggml.token_type"),
@@ -206,6 +212,7 @@ func New(c fs.Config) (model.Model, error) {
 					c.Ints("tokenizer.ggml.eos_token_ids")...,
 				),
 			},
+			`(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`,
 		),
 		Layers: layers,
 		Options: &Options{
@@ -216,7 +223,7 @@ func New(c fs.Config) (model.Model, error) {
 			valueLength:    int(c.Uint("attention.value_length")),
 			eps:            c.Float("attention.layer_norm_rms_epsilon"),
 			ropeBase:       c.Float("rope.freq_base"),
-			ropeScale:      c.Float("rope.freq_scale", 1),
+			ropeScale:      c.Float("rope.scaling.factor", 1),
 			numExperts:     int(c.Uint("expert_count")),
 			numExpertsUsed: int(c.Uint("expert_used_count")),
 			normTopKProb:   c.Bool("norm_top_k_prob", true),
@@ -230,4 +237,5 @@ func New(c fs.Config) (model.Model, error) {
 func init() {
 	model.Register("qwen3", New)
 	model.Register("qwen3moe", New)
+	model.Register("qwen3_embed", newEmbed)
 }

model/parsers/parsers.go

@@ -0,0 +1,49 @@
+package parsers
+
+import (
+	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/harmony"
+)
+
+type Parser interface {
+	// Init initializes the parser with tools and optional last message for chat prefill
+	// Returns processed tools if the parser needs to modify them (e.g., harmony renames them)
+	Init(tools []api.Tool, lastMessage *api.Message) []api.Tool
+	// Add processes streamed content and returns parsed content, thinking, and tool calls
+	// The done flag indicates if this is the last chunk (used for draining accumulators)
+	Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error)
+	HasToolSupport() bool
+	HasThinkingSupport() bool
+}
+
+func ParserForName(name string) Parser {
+	switch name {
+	case "qwen3-coder":
+		parser := &Qwen3CoderParser{}
+		return parser
+	case "passthrough":
+		return &PassthroughParser{}
+	case "harmony":
+		return harmony.NewHarmonyMessageHandler()
+	default:
+		return nil
+	}
+}
+
+type PassthroughParser struct{}
+
+func (p *PassthroughParser) Init(tools []api.Tool, lastMessage *api.Message) []api.Tool {
+	return tools // passthrough doesn't modify tools
+}
+
+func (p *PassthroughParser) Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error) {
+	return s, "", nil, nil
+}
+
+func (p *PassthroughParser) HasToolSupport() bool {
+	return false
+}
+
+func (p *PassthroughParser) HasThinkingSupport() bool {
+	return false
+}

model/parsers/qwen3coder.go

@@ -0,0 +1,463 @@
+package parsers
+
+import (
+	"context"
+	"encoding/json"
+	"encoding/xml"
+	"fmt"
+	"log/slog"
+	"math"
+	"regexp"
+	"strconv"
+	"strings"
+	"unicode"
+	"unicode/utf8"
+
+	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/logutil"
+)
+
+type qwenParserState int
+
+const (
+	toolOpenTag  = "<tool_call>"
+	toolCloseTag = "</tool_call>"
+)
+
+const (
+	qwenParserState_LookingForToolStart qwenParserState = iota
+	qwenParserState_CollectingToolContent
+)
+
+type Qwen3CoderParser struct {
+	state qwenParserState
+	acc   strings.Builder
+	tools []api.Tool
+}
+
+func (p *Qwen3CoderParser) HasToolSupport() bool {
+	return true
+}
+
+func (p *Qwen3CoderParser) HasThinkingSupport() bool {
+	return false
+}
+
+func (p *Qwen3CoderParser) Init(tools []api.Tool, lastMessage *api.Message) []api.Tool {
+	p.tools = tools
+	return tools // Qwen doesn't modify tools
+}
+
+func (p *Qwen3CoderParser) Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error) {
+	p.acc.WriteString(s)
+
+	events := p.parseEvents()
+
+	var toolCalls []api.ToolCall
+	var sb strings.Builder
+	for _, event := range events {
+		switch event := event.(type) {
+		case qwenEventRawToolCall:
+			toolCall, err := parseToolCall(event, p.tools)
+			if err != nil {
+				slog.Warn("qwen tool call parsing failed", "error", err)
+				return "", "", nil, err
+			}
+			toolCalls = append(toolCalls, toolCall)
+		case qwenEventContent:
+			// TODO(drifkin): if the same turn contains multiple interleaved content
+			// events, we naively append them together here. See the note below about
+			// `qwenEvent`s for more details
+			sb.WriteString(event.content)
+		}
+	}
+
+	return sb.String(), "", toolCalls, nil
+}
+
+func (p *Qwen3CoderParser) parseEvents() []qwenEvent {
+	var all []qwenEvent
+
+	keepLooping := true
+	for keepLooping {
+		var events []qwenEvent
+		events, keepLooping = eat(p)
+		if len(events) > 0 {
+			all = append(all, events...)
+		}
+	}
+
+	if len(all) > 0 {
+		slog.Log(context.TODO(), logutil.LevelTrace, "qwen events parsed", "events", all, "state", p.state, "acc", p.acc.String())
+	}
+
+	return all
+}
+
+// we use some internal event types in order to communicate between `Add` and
+// `eat`. We do this to support interleaving content and parallel tool calls in
+// the parser, even though qwen3-coder isn't supposed to do this. Our API
+// doesn't currently support models outputting multiple messages in a turn, so
+// we wouldn't be able to represent it yet, but there's no reason to prevent the
+// parser from supporting it, especially for future models if they end up using
+// a similar format.
+type qwenEvent interface {
+	isQwenEvent()
+}
+
+type qwenEventRawToolCall struct {
+	raw string
+}
+
+type qwenEventContent struct {
+	content string
+}
+
+func (qwenEventContent) isQwenEvent()     {}
+func (qwenEventRawToolCall) isQwenEvent() {}
+
+// eat consumes the parser's buffer, and returns a list of any unambiguous
+// events from the current parser state. If the parser transitions to another
+// state, it may have additional events to emit on the next call, which is what
+// the second return value indicates
+func eat(p *Qwen3CoderParser) ([]qwenEvent, bool) {
+	var events []qwenEvent
+
+	switch p.state {
+	case qwenParserState_LookingForToolStart:
+		if strings.Contains(p.acc.String(), toolOpenTag) {
+			// we found a full tool open tag, so we can emit the content before the
+			// tag, being sure to trim any trailing whitespace
+			split := strings.SplitN(p.acc.String(), toolOpenTag, 2)
+			before := split[0]
+			before = strings.TrimRightFunc(before, unicode.IsSpace)
+			if len(before) > 0 {
+				events = append(events, qwenEventContent{content: before})
+			}
+			after := split[1]
+			p.acc.Reset()
+			p.acc.WriteString(after)
+			p.state = qwenParserState_CollectingToolContent
+			return events, true
+		} else if overlap := overlap(p.acc.String(), toolOpenTag); overlap > 0 {
+			// we found a partial tool open tag, so we can emit the unambiguous part,
+			// which is the (trailing-whitespace trimmed) content before the partial
+			// tool open tag
+			beforePartialTag := p.acc.String()[:len(p.acc.String())-overlap]
+			trailingWhitespaceLen := trailingWhitespaceLen(beforePartialTag)
+			ambiguousStart := len(beforePartialTag) - trailingWhitespaceLen
+			unambiguous := p.acc.String()[:ambiguousStart]
+			ambiguous := p.acc.String()[ambiguousStart:]
+			p.acc.Reset()
+			p.acc.WriteString(ambiguous)
+			events = append(events, qwenEventContent{content: unambiguous})
+			return events, false
+		} else {
+			// we found content that is entirely not a tool call. We should withhold
+			// any trailing whitespace in case this is the end of the content
+			whitespaceLen := trailingWhitespaceLen(p.acc.String())
+			ambiguousStart := len(p.acc.String()) - whitespaceLen
+			unambiguous := p.acc.String()[:ambiguousStart]
+			ambiguous := p.acc.String()[ambiguousStart:]
+			p.acc.Reset()
+			p.acc.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, qwenEventContent{content: unambiguous})
+			}
+			return events, false
+		}
+	case qwenParserState_CollectingToolContent:
+		if strings.Contains(p.acc.String(), toolCloseTag) {
+			split := strings.SplitN(p.acc.String(), toolCloseTag, 2)
+			before := split[0]
+			if len(before) == 0 {
+				slog.Warn("qwen tool call closing tag found but no content before it")
+			}
+			// remove any whitespace between the tool call and any content after it
+			after := strings.TrimLeftFunc(split[1], unicode.IsSpace)
+			p.acc.Reset()
+			p.acc.WriteString(after)
+			events = append(events, qwenEventRawToolCall{raw: before})
+			p.state = qwenParserState_LookingForToolStart
+			return events, true
+		} else {
+			// note that we don't need to check the overlap here because we only plan
+			// on parsing the tool call once we see the full closing tag. We don't
+			// stream back the unparsed tool content, so there's no need to be eager
+			// here
+			return events, false
+		}
+	default:
+		panic("unreachable")
+	}
+}
+
+// TODO(drifkin): move this to a shared location
+// longest overlap between suffix of s and prefix of delim
+func overlap(s, delim string) int {
+	max := min(len(delim), len(s))
+	for i := max; i > 0; i-- {
+		if strings.HasSuffix(s, delim[:i]) {
+			return i
+		}
+	}
+	return 0
+}
+
+func trailingWhitespaceLen(s string) int {
+	remaining := s
+	total := 0
+	for len(remaining) > 0 {
+		r, size := utf8.DecodeLastRuneInString(remaining)
+		// if it's an invalid utf8 rune, assume it isn't whitespace
+		if r == utf8.RuneError && size == 1 {
+			break
+		}
+		if !unicode.IsSpace(r) {
+			break
+		}
+		total += size
+		remaining = remaining[:len(remaining)-size]
+	}
+	return total
+}
+
+type XMLFunctionCall struct {
+	XMLName    xml.Name       `xml:"function"`
+	Name       string         `xml:"name,attr"`
+	Parameters []XMLParameter `xml:"parameter"`
+}
+
+type XMLParameter struct {
+	Name  string `xml:"name,attr"`
+	Value string `xml:",chardata"`
+}
+
+// parseToolCall parses a raw tool call string into an api.ToolCall.
+// The raw string follows an xml-like format, here's an example:
+//
+// <function=get_current_temperature>
+// <parameter=location>
+// San Francisco
+// </parameter>
+// <parameter=unit>
+// celsius
+// </parameter>
+// </function>
+func parseToolCall(raw qwenEventRawToolCall, tools []api.Tool) (api.ToolCall, error) {
+	toolCall := api.ToolCall{}
+
+	xmlString := transformToXML(raw.raw)
+
+	var functionCall XMLFunctionCall
+	err := xml.Unmarshal([]byte(xmlString), &functionCall)
+	if err != nil {
+		return api.ToolCall{}, err
+	}
+
+	toolCall.Function = api.ToolCallFunction{
+		Name: functionCall.Name,
+	}
+
+	// Find the matching tool to get parameter types
+	var matchedTool *api.Tool
+	for i := range tools {
+		if tools[i].Function.Name == functionCall.Name {
+			matchedTool = &tools[i]
+			break
+		}
+	}
+
+	toolCall.Function.Arguments = make(api.ToolCallFunctionArguments)
+	for _, parameter := range functionCall.Parameters {
+		// Look up the parameter type if we found the tool
+		var paramType api.PropertyType
+		if matchedTool != nil && matchedTool.Function.Parameters.Properties != nil {
+			if prop, ok := matchedTool.Function.Parameters.Properties[parameter.Name]; ok {
+				paramType = prop.Type
+			}
+		}
+
+		toolCall.Function.Arguments[parameter.Name] = parseValue(parameter.Value, paramType)
+	}
+
+	return toolCall, nil
+}
+
+// parseValue converts a raw string value to the appropriate type based on the parameter type specification.
+//
+// For union types (multiple types in PropertyType, which we support but doesn't
+// seem as though the reference parser does type coercion with those types in
+// mind) we use a type precedence approach:
+// 1. null - checked first regardless of declared types (matches reference implementation)
+// 2. boolean - only "true"/"false" are valid booleans
+// 3. integer - must parse as a whole number
+// 4. number - must parse as numeric (returns int if no decimal part)
+// 5. array - must parse as valid JSON array
+// 6. object - must parse as valid JSON object
+// 7. string - always succeeds (least specific type)
+//
+// This precedence ensures we return the most specific type that successfully parses,
+// following the principle of least surprise. For example, with PropertyType{"string", "number"},
+// "123" becomes 123 (number), while "hello" becomes "hello" (string).
+func parseValue(raw string, paramType api.PropertyType) any {
+	// first remove a single leading newlines, and a single trailing newline (if
+	// they exist). This follows the reference implementation
+	raw = strings.TrimPrefix(raw, "\n")
+	raw = strings.TrimSuffix(raw, "\n")
+
+	// Check for null first (case-insensitive) - this takes precedence over any type
+	if strings.ToLower(raw) == "null" {
+		return nil
+	}
+
+	// If no type is specified, default to string
+	if len(paramType) == 0 {
+		return raw
+	}
+
+	// Check if any of the specified types match, using type precedence
+	// Order: boolean -> integer -> number -> array -> object -> string
+	typeSet := make(map[string]bool)
+	for _, t := range paramType {
+		typeSet[t] = true
+	}
+
+	// Try boolean first (most restrictive)
+	if typeSet["boolean"] {
+		lower := strings.ToLower(raw)
+		switch lower {
+		case "true":
+			return true
+		case "false":
+			return false
+		}
+		// If not a valid boolean but boolean is the only type, return false (matching reference)
+		if len(paramType) == 1 {
+			return false
+		}
+		// Otherwise try other types
+	}
+
+	// Try integer
+	if typeSet["integer"] {
+		if i, err := strconv.ParseInt(raw, 10, 64); err == nil {
+			// Return as int if it fits in int32, otherwise int64
+			if i >= math.MinInt32 && i <= math.MaxInt32 {
+				return int(i)
+			}
+			return i
+		}
+		// If integer is the only type and parsing failed, fall back to string
+		if len(paramType) == 1 {
+			return raw
+		}
+	}
+
+	// Try number (float)
+	if typeSet["number"] {
+		if f, err := strconv.ParseFloat(raw, 64); err == nil {
+			// If the number has no decimal part, return as int (matching reference)
+			if f == math.Trunc(f) {
+				i := int64(f)
+				if i >= math.MinInt32 && i <= math.MaxInt32 {
+					return int(i)
+				}
+				return i
+			}
+			return f
+		}
+		// If number is the only type and parsing failed, fall back to string
+		if len(paramType) == 1 {
+			return raw
+		}
+	}
+
+	// Try array
+	if typeSet["array"] {
+		var arr []any
+		if err := json.Unmarshal([]byte(raw), &arr); err == nil {
+			return arr
+		}
+		// If array is the only type and parsing failed, fall back to string
+		if len(paramType) == 1 {
+			return raw
+		}
+	}
+
+	// Try object
+	if typeSet["object"] {
+		var obj map[string]any
+		if err := json.Unmarshal([]byte(raw), &obj); err == nil {
+			return obj
+		}
+		// If object is the only type and parsing failed, fall back to string
+		if len(paramType) == 1 {
+			return raw
+		}
+	}
+
+	// String always succeeds (or if "string" is in the type set)
+	if typeSet["string"] {
+		return raw
+	}
+
+	// If we get here, none of the types matched and string wasn't an option
+	// We return string as a fallback. The reference implementation will attempt
+	// to parse the value as a python literal, but we purposefully don't support
+	// that
+	return raw
+}
+
+var (
+	qwenTagRegex    = regexp.MustCompile(`<(\w+)=([^>]+)>`)
+	qwenXMLTagRegex = regexp.MustCompile(`</?(?:function|parameter)(?:\s+name="[^"]*")?>`)
+)
+
+// transformToXML transforms a raw qwen tool call with xml-like tags into valid
+// xml so that it can be parsed by any xml parser
+func transformToXML(raw string) string {
+	// take the form `<tag=abc>` and transform it to `<tag name="abc">`, taking
+	// care to properly escape the string that becomes the attribute value
+	transformed := qwenTagRegex.ReplaceAllStringFunc(raw, func(match string) string {
+		groups := qwenTagRegex.FindStringSubmatch(match)
+		tag := groups[1]
+		var escapedValue strings.Builder
+		xml.EscapeText(&escapedValue, []byte(groups[2]))
+		return fmt.Sprintf(`<%s name="%s">`, tag, escapedValue.String())
+	})
+
+	// Walk the resulting string, escaping any character data that sits between the
+	// xml tags we just emitted
+	var out strings.Builder
+	lastIdx := 0
+	for _, loc := range qwenXMLTagRegex.FindAllStringIndex(transformed, -1) {
+		if loc[0] > lastIdx {
+			escapeTextNode(&out, transformed[lastIdx:loc[0]])
+		}
+		out.WriteString(transformed[loc[0]:loc[1]])
+		lastIdx = loc[1]
+	}
+	if lastIdx < len(transformed) {
+		escapeTextNode(&out, transformed[lastIdx:])
+	}
+
+	return out.String()
+}
+
+// escapeTextNode escapes XML character data without altering other characters
+// like newlines or tabs (which is why we don't use xml.EscapeText for this)
+func escapeTextNode(sb *strings.Builder, s string) {
+	for _, r := range s {
+		switch r {
+		case '&':
+			sb.WriteString("&amp;")
+		case '<':
+			sb.WriteString("&lt;")
+		case '>':
+			sb.WriteString("&gt;")
+		default:
+			sb.WriteRune(r)
+		}
+	}
+}

model/renderers/qwen3coder.go

@@ -0,0 +1,217 @@
+package renderers
+
+import (
+	"encoding/json"
+	"fmt"
+	"reflect"
+	"strings"
+
+	"github.com/ollama/ollama/api"
+)
+
+var (
+	imStartTag = "<|im_start|>"
+	imEndTag   = "<|im_end|>"
+)
+
+// renderAdditionalKeys renders all JSON fields except the ones in handledKeys
+// This follows the same approach from the reference implementation, which gives
+// a particular key ordering
+func renderAdditionalKeys(obj any, handledKeys map[string]bool) string {
+	data, err := json.Marshal(obj)
+	if err != nil {
+		return ""
+	}
+
+	var m map[string]any
+	if err := json.Unmarshal(data, &m); err != nil {
+		return ""
+	}
+
+	var sb strings.Builder
+	for key, value := range m {
+		if handledKeys[key] {
+			continue
+		}
+
+		// Check if value is a map or array (needs JSON serialization)
+		switch v := value.(type) {
+		case map[string]any, []any:
+			jsonBytes, _ := json.Marshal(v)
+			// TODO(drifkin): it would be nice to format the JSON here similarly to
+			// python's default json.dumps behavior (spaces after commas and colons).
+			// This would let us be byte-for-byte compatible with the reference
+			// implementation for most common inputs
+			jsonStr := string(jsonBytes)
+			sb.WriteString("\n<" + key + ">" + jsonStr + "</" + key + ">")
+		case nil:
+			continue
+		default:
+			// Simple types, convert to string
+			sb.WriteString("\n<" + key + ">" + fmt.Sprintf("%v", value) + "</" + key + ">")
+		}
+	}
+
+	return sb.String()
+}
+
+func Qwen3CoderRenderer(messages []api.Message, tools []api.Tool, _ *api.ThinkValue) (string, error) {
+	var sb strings.Builder
+
+	// filter out system messages and choose the first (if any) to win
+	var systemMessage string
+	var filteredMessages []api.Message
+	for _, message := range messages {
+		if message.Role != "system" {
+			filteredMessages = append(filteredMessages, message)
+			continue
+		}
+
+		if systemMessage == "" {
+			systemMessage = message.Content
+		}
+	}
+
+	if systemMessage != "" || len(tools) > 0 {
+		sb.WriteString(imStartTag + "system\n")
+
+		// if we have tools but no system message, match the reference implementation by providing a default system message
+		if systemMessage == "" {
+			systemMessage = "You are Qwen, a helpful AI assistant that can interact with a computer to solve tasks."
+		}
+
+		sb.WriteString(systemMessage)
+
+		if len(tools) > 0 {
+			sb.WriteString("\n\n# Tools\n\nYou have access to the following functions:\n\n")
+			sb.WriteString("<tools>")
+			for _, tool := range tools {
+				sb.WriteString("\n")
+				sb.WriteString("<function>\n")
+				sb.WriteString("<name>" + tool.Function.Name + "</name>")
+				if tool.Function.Description != "" {
+					sb.WriteString("\n<description>" + tool.Function.Description + "</description>")
+				}
+				sb.WriteString("\n<parameters>")
+
+				for name, prop := range tool.Function.Parameters.Properties {
+					sb.WriteString("\n<parameter>")
+					sb.WriteString("\n<name>" + name + "</name>")
+
+					if len(prop.Type) > 0 {
+						// TODO(!!!)(drifkin): we should match the reference implementation for
+						// more complex types here instead of using this format
+						sb.WriteString("\n<type>" + prop.ToTypeScriptType() + "</type>")
+					}
+
+					if prop.Description != "" {
+						sb.WriteString("\n<description>" + prop.Description + "</description>")
+					}
+
+					// Render any additional keys not already handled
+					handledKeys := map[string]bool{
+						"type":        true,
+						"description": true,
+					}
+					sb.WriteString(renderAdditionalKeys(prop, handledKeys))
+
+					sb.WriteString("\n</parameter>")
+				}
+
+				// Render extra keys for parameters (everything except 'type' and 'properties')
+				paramHandledKeys := map[string]bool{
+					"type":       true,
+					"properties": true,
+				}
+				sb.WriteString(renderAdditionalKeys(tool.Function.Parameters, paramHandledKeys))
+
+				sb.WriteString("\n</parameters>")
+				sb.WriteString("\n</function>")
+			}
+			sb.WriteString("\n</tools>")
+			sb.WriteString("\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>")
+		}
+
+		sb.WriteString(imEndTag + "\n")
+	}
+
+	for i, message := range filteredMessages {
+		lastMessage := i == len(filteredMessages)-1
+		prefill := lastMessage && message.Role == "assistant"
+		switch message.Role {
+		case "assistant":
+			if len(message.ToolCalls) > 0 {
+				sb.WriteString(imStartTag + "assistant\n")
+				if message.Content != "" {
+					sb.WriteString(message.Content + "\n")
+				}
+				for _, toolCall := range message.ToolCalls {
+					sb.WriteString("\n<tool_call>\n<function=" + toolCall.Function.Name + ">")
+					for name, value := range toolCall.Function.Arguments {
+						valueStr := formatToolCallArgument(value)
+						sb.WriteString("\n<parameter=" + name + ">\n" + valueStr + "\n</parameter>")
+					}
+					sb.WriteString("\n</function>\n</tool_call>")
+				}
+				sb.WriteString("<|im_end|>\n")
+			} else {
+				sb.WriteString(imStartTag + "assistant\n")
+				sb.WriteString(message.Content)
+				if !prefill {
+					sb.WriteString(imEndTag + "\n")
+				}
+			}
+		case "tool":
+			// consecutive tool responses should share a single `<im_start>user`, but
+			// have their own <tool_response> tags
+
+			// only start a new user block if this is the first tool response
+			if i == 0 || filteredMessages[i-1].Role != "tool" {
+				sb.WriteString(imStartTag + "user\n")
+			}
+
+			sb.WriteString("<tool_response>\n")
+			sb.WriteString(message.Content)
+			sb.WriteString("\n</tool_response>\n")
+
+			// close the user block only if this is the last tool response
+			if i == len(filteredMessages)-1 || filteredMessages[i+1].Role != "tool" {
+				sb.WriteString(imEndTag + "\n")
+			}
+		default:
+			sb.WriteString(imStartTag + message.Role + "\n")
+			sb.WriteString(message.Content)
+			sb.WriteString(imEndTag + "\n")
+		}
+
+		if lastMessage && !prefill {
+			sb.WriteString(imStartTag + "assistant\n")
+		}
+	}
+
+	return sb.String(), nil
+}
+
+func formatToolCallArgument(value any) string {
+	if value == nil {
+		return "null"
+	}
+
+	switch v := value.(type) {
+	case string:
+		return v
+	case []byte:
+		return string(v)
+	}
+
+	if reflect.TypeOf(value) != nil {
+		kind := reflect.TypeOf(value).Kind()
+		if kind == reflect.Map || kind == reflect.Slice || kind == reflect.Array {
+			if marshalled, err := json.Marshal(value); err == nil {
+				return string(marshalled)
+			}
+		}
+	}
+
+	return fmt.Sprintf("%v", value)
+}

model/renderers/qwen3coder_test.go

@@ -0,0 +1,338 @@
+package renderers
+
+import (
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+	"github.com/ollama/ollama/api"
+)
+
+func TestQwen3CoderRenderer(t *testing.T) {
+	tests := []struct {
+		name     string
+		msgs     []api.Message
+		tools    []api.Tool
+		expected string
+	}{
+		{
+			name: "basic",
+			msgs: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant."},
+				{Role: "user", Content: "Hello, how are you?"},
+			},
+			expected: `<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+Hello, how are you?<|im_end|>
+<|im_start|>assistant
+`,
+		},
+		{
+			name: "with tools and response",
+			msgs: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant with access to tools."},
+				{Role: "user", Content: "What is the weather like in San Francisco?"},
+				{
+					Role:    "assistant",
+					Content: "I'll check the weather in San Francisco for you.",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: map[string]any{
+									"unit": "fahrenheit",
+								},
+							},
+						},
+					},
+				},
+				{Role: "tool", Content: "{\"location\": \"San Francisco, CA\", \"temperature\": 68, \"condition\": \"partly cloudy\", \"humidity\": 65, \"wind_speed\": 12}", ToolName: "get_weather"},
+				{Role: "user", Content: "That sounds nice! What about New York?"},
+			},
+			tools: []api.Tool{
+				{Function: api.ToolFunction{
+					Name:        "get_weather",
+					Description: "Get the current weather in a given location",
+					Parameters: api.ToolFunctionParameters{
+						Required: []string{"unit"},
+						Properties: map[string]api.ToolProperty{
+							"unit": {Type: api.PropertyType{"string"}, Enum: []any{"celsius", "fahrenheit"}, Description: "The unit of temperature"},
+							// TODO(drifkin): add multiple params back once we have predictable
+							// order via some sort of ordered map type (see
+							// <https://github.com/ollama/ollama/issues/12244>)
+							/*
+								"location": {Type: api.PropertyType{"string"}, Description: "The city and state, e.g. San Francisco, CA"},
+							*/
+						},
+					},
+				}},
+			},
+			expected: `<|im_start|>system
+You are a helpful assistant with access to tools.
+
+# Tools
+
+You have access to the following functions:
+
+<tools>
+<function>
+<name>get_weather</name>
+<description>Get the current weather in a given location</description>
+<parameters>
+<parameter>
+<name>unit</name>
+<type>string</type>
+<description>The unit of temperature</description>
+<enum>["celsius","fahrenheit"]</enum>
+</parameter>
+<required>["unit"]</required>
+</parameters>
+</function>
+</tools>
+
+If you choose to call a function ONLY reply in the following format with NO suffix:
+
+<tool_call>
+<function=example_function_name>
+<parameter=example_parameter_1>
+value_1
+</parameter>
+<parameter=example_parameter_2>
+This is the value for the second parameter
+that can span
+multiple lines
+</parameter>
+</function>
+</tool_call>
+
+<IMPORTANT>
+Reminder:
+- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags
+- Required parameters MUST be specified
+- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after
+- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls
+</IMPORTANT><|im_end|>
+<|im_start|>user
+What is the weather like in San Francisco?<|im_end|>
+<|im_start|>assistant
+I'll check the weather in San Francisco for you.
+
+<tool_call>
+<function=get_weather>
+<parameter=unit>
+fahrenheit
+</parameter>
+</function>
+</tool_call><|im_end|>
+<|im_start|>user
+<tool_response>
+{"location": "San Francisco, CA", "temperature": 68, "condition": "partly cloudy", "humidity": 65, "wind_speed": 12}
+</tool_response>
+<|im_end|>
+<|im_start|>user
+That sounds nice! What about New York?<|im_end|>
+<|im_start|>assistant
+`,
+		},
+		{
+			name: "parallel tool calls",
+			msgs: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant with access to tools."},
+				{Role: "user", Content: "call double(1) and triple(2)"},
+				{Role: "assistant", Content: "I'll call double(1) and triple(2) for you.", ToolCalls: []api.ToolCall{
+					{Function: api.ToolCallFunction{Name: "double", Arguments: map[string]any{"number": "1"}}},
+					{Function: api.ToolCallFunction{Name: "triple", Arguments: map[string]any{"number": "2"}}},
+				}},
+				{Role: "tool", Content: "{\"number\": 2}", ToolName: "double"},
+				{Role: "tool", Content: "{\"number\": 6}", ToolName: "triple"},
+			},
+			tools: []api.Tool{
+				{Function: api.ToolFunction{Name: "double", Description: "Double a number", Parameters: api.ToolFunctionParameters{Properties: map[string]api.ToolProperty{
+					"number": {Type: api.PropertyType{"string"}, Description: "The number to double"},
+				}}}},
+				{Function: api.ToolFunction{Name: "triple", Description: "Triple a number", Parameters: api.ToolFunctionParameters{Properties: map[string]api.ToolProperty{
+					"number": {Type: api.PropertyType{"string"}, Description: "The number to triple"},
+				}}}},
+			},
+			expected: `<|im_start|>system
+You are a helpful assistant with access to tools.
+
+# Tools
+
+You have access to the following functions:
+
+<tools>
+<function>
+<name>double</name>
+<description>Double a number</description>
+<parameters>
+<parameter>
+<name>number</name>
+<type>string</type>
+<description>The number to double</description>
+</parameter>
+</parameters>
+</function>
+<function>
+<name>triple</name>
+<description>Triple a number</description>
+<parameters>
+<parameter>
+<name>number</name>
+<type>string</type>
+<description>The number to triple</description>
+</parameter>
+</parameters>
+</function>
+</tools>
+
+If you choose to call a function ONLY reply in the following format with NO suffix:
+
+<tool_call>
+<function=example_function_name>
+<parameter=example_parameter_1>
+value_1
+</parameter>
+<parameter=example_parameter_2>
+This is the value for the second parameter
+that can span
+multiple lines
+</parameter>
+</function>
+</tool_call>
+
+<IMPORTANT>
+Reminder:
+- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags
+- Required parameters MUST be specified
+- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after
+- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls
+</IMPORTANT><|im_end|>
+<|im_start|>user
+call double(1) and triple(2)<|im_end|>
+<|im_start|>assistant
+I'll call double(1) and triple(2) for you.
+
+<tool_call>
+<function=double>
+<parameter=number>
+1
+</parameter>
+</function>
+</tool_call>
+<tool_call>
+<function=triple>
+<parameter=number>
+2
+</parameter>
+</function>
+</tool_call><|im_end|>
+<|im_start|>user
+<tool_response>
+{"number": 2}
+</tool_response>
+<tool_response>
+{"number": 6}
+</tool_response>
+<|im_end|>
+<|im_start|>assistant
+`,
+		},
+		{
+			name: "prefill",
+			msgs: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant."},
+				{Role: "user", Content: "Tell me something interesting."},
+				{Role: "assistant", Content: "I'll tell you something interesting about cats"},
+			},
+			expected: `<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+Tell me something interesting.<|im_end|>
+<|im_start|>assistant
+I'll tell you something interesting about cats`,
+		},
+		{
+			name: "complex tool call arguments should remain json encoded",
+			msgs: []api.Message{
+				{Role: "user", Content: "call tool"},
+				{Role: "assistant", ToolCalls: []api.ToolCall{
+					{Function: api.ToolCallFunction{
+						Name: "echo",
+						Arguments: map[string]any{
+							"payload": map[string]any{"foo": "bar"},
+						},
+					}},
+				}},
+				{Role: "tool", Content: "{\"payload\": {\"foo\": \"bar\"}}", ToolName: "echo"},
+			},
+			expected: `<|im_start|>user
+call tool<|im_end|>
+<|im_start|>assistant
+
+<tool_call>
+<function=echo>
+<parameter=payload>
+{"foo":"bar"}
+</parameter>
+</function>
+</tool_call><|im_end|>
+<|im_start|>user
+<tool_response>
+{"payload": {"foo": "bar"}}
+</tool_response>
+<|im_end|>
+<|im_start|>assistant
+`,
+		},
+	}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			rendered, err := Qwen3CoderRenderer(tt.msgs, tt.tools, nil)
+			if err != nil {
+				t.Fatal(err)
+			}
+			if diff := cmp.Diff(rendered, tt.expected); diff != "" {
+				t.Errorf("mismatch (-got +want):\n%s", diff)
+			}
+		})
+	}
+}
+
+func TestFormatToolCallArgument(t *testing.T) {
+	tests := []struct {
+		name     string
+		arg      any
+		expected string
+	}{
+		{
+			name: "string",
+			arg:  "foo",
+			// notice no quotes around the string
+			expected: "foo",
+		},
+		{
+			name:     "map",
+			arg:      map[string]any{"foo": "bar"},
+			expected: "{\"foo\":\"bar\"}",
+		},
+		{
+			name:     "number",
+			arg:      1,
+			expected: "1",
+		},
+		{
+			name:     "boolean",
+			arg:      true,
+			expected: "true",
+		},
+	}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			got := formatToolCallArgument(tt.arg)
+			if got != tt.expected {
+				t.Errorf("formatToolCallArgument(%v) = %v, want %v", tt.arg, got, tt.expected)
+			}
+		})
+	}
+}

model/renderers/renderer.go

@@ -0,0 +1,26 @@
+package renderers
+
+import (
+	"fmt"
+
+	"github.com/ollama/ollama/api"
+)
+
+type rendererFunc func([]api.Message, []api.Tool, *api.ThinkValue) (string, error)
+
+func RenderWithRenderer(name string, msgs []api.Message, tools []api.Tool, think *api.ThinkValue) (string, error) {
+	renderer := rendererForName(name)
+	if renderer == nil {
+		return "", fmt.Errorf("unknown renderer %q", name)
+	}
+	return renderer(msgs, tools, think)
+}
+
+func rendererForName(name string) rendererFunc {
+	switch name {
+	case "qwen3-coder":
+		return Qwen3CoderRenderer
+	default:
+		return nil
+	}
+}

model/sentencepiece.go

@@ -2,7 +2,6 @@ package model
 
 import (
 	"container/heap"
-	"context"
 	"fmt"
 	"log/slog"
 	"strconv"
@@ -13,19 +12,19 @@ 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 {
-	slog.Log(context.TODO(), logutil.LevelTrace, "Tokens", "num tokens", len(vocab.Values), "vals", vocab.Values[:5], "scores", vocab.Scores[:5], "types", vocab.Types[:5])
+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{}
 	var maxTokenLen int
@@ -39,21 +38,21 @@ func NewSentencePieceModel(vocab *Vocabulary) SentencePieceModel {
 		}
 	}
 
-	slog.Log(context.TODO(), logutil.LevelTrace, "Token counts", "normal", counter[TOKEN_TYPE_NORMAL], "unknown", counter[TOKEN_TYPE_UNKNOWN], "control", counter[TOKEN_TYPE_CONTROL],
+	logutil.Trace("Token counts", "normal", counter[TOKEN_TYPE_NORMAL], "unknown", counter[TOKEN_TYPE_UNKNOWN], "control", counter[TOKEN_TYPE_CONTROL],
 		"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)
@@ -182,12 +181,11 @@ func (spm SentencePieceModel) Encode(s string, addSpecial bool) ([]int32, error)
 		}
 	}
 
-	slog.Log(context.TODO(), logutil.LevelTrace, "encoded", "string", s, "ids", ids)
-
 	if addSpecial && len(ids) > 0 {
 		ids = spm.vocab.addSpecials(ids)
 	}
 
+	logutil.Trace("encoded", "string", s, "ids", ids)
 	return ids, nil
 }
 
@@ -220,7 +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)
@@ -246,6 +244,6 @@ func (spm SentencePieceModel) Decode(ids []int32) (string, error) {
 		}
 	}
 
-	slog.Log(context.TODO(), logutil.LevelTrace, "decoded", "ids", ids, "string", sb.String())
+	logutil.Trace("decoded", "ids", ids, "string", sb.String())
 	return sb.String(), nil
 }

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/vocabulary.go

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

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 {
@@ -104,16 +105,18 @@ type ChatCompletionRequest struct {
 	Tools            []api.Tool      `json:"tools"`
 	Reasoning        *Reasoning      `json:"reasoning,omitempty"`
 	ReasoningEffort  *string         `json:"reasoning_effort,omitempty"`
+	DebugRenderOnly  bool            `json:"_debug_render_only"`
 }
 
 type ChatCompletion struct {
-	Id                string   `json:"id"`
-	Object            string   `json:"object"`
-	Created           int64    `json:"created"`
-	Model             string   `json:"model"`
-	SystemFingerprint string   `json:"system_fingerprint"`
-	Choices           []Choice `json:"choices"`
-	Usage             Usage    `json:"usage,omitempty"`
+	Id                string         `json:"id"`
+	Object            string         `json:"object"`
+	Created           int64          `json:"created"`
+	Model             string         `json:"model"`
+	SystemFingerprint string         `json:"system_fingerprint"`
+	Choices           []Choice       `json:"choices"`
+	Usage             Usage          `json:"usage,omitempty"`
+	DebugInfo         *api.DebugInfo `json:"_debug_info,omitempty"`
 }
 
 type ChatCompletionChunk struct {
@@ -140,6 +143,7 @@ type CompletionRequest struct {
 	Temperature      *float32       `json:"temperature"`
 	TopP             float32        `json:"top_p"`
 	Suffix           string         `json:"suffix"`
+	DebugRenderOnly  bool           `json:"_debug_render_only"`
 }
 
 type Completion struct {
@@ -272,8 +276,8 @@ func toChatCompletion(id string, r api.ChatResponse) ChatCompletion {
 				}
 				return nil
 			}(r.DoneReason),
-		}},
-		Usage: toUsage(r),
+		}}, Usage: toUsage(r),
+		DebugInfo: r.DebugInfo,
 	}
 }
 
@@ -567,13 +571,14 @@ func fromChatRequest(r ChatCompletionRequest) (*api.ChatRequest, error) {
 	}
 
 	return &api.ChatRequest{
-		Model:    r.Model,
-		Messages: messages,
-		Format:   format,
-		Options:  options,
-		Stream:   &r.Stream,
-		Tools:    r.Tools,
-		Think:    think,
+		Model:           r.Model,
+		Messages:        messages,
+		Format:          format,
+		Options:         options,
+		Stream:          &r.Stream,
+		Tools:           r.Tools,
+		Think:           think,
+		DebugRenderOnly: r.DebugRenderOnly,
 	}, nil
 }
 
@@ -647,11 +652,12 @@ func fromCompleteRequest(r CompletionRequest) (api.GenerateRequest, error) {
 	}
 
 	return api.GenerateRequest{
-		Model:   r.Model,
-		Prompt:  r.Prompt,
-		Options: options,
-		Stream:  &r.Stream,
-		Suffix:  r.Suffix,
+		Model:           r.Model,
+		Prompt:          r.Prompt,
+		Options:         options,
+		Stream:          &r.Stream,
+		Suffix:          r.Suffix,
+		DebugRenderOnly: r.DebugRenderOnly,
 	}, nil
 }
 
@@ -1005,7 +1011,7 @@ 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

@@ -100,6 +100,10 @@ func (f Modelfile) CreateRequest(relativeDir string) (*api.CreateRequest, error)
 			req.System = c.Args
 		case "license":
 			licenses = append(licenses, c.Args)
+		case "renderer":
+			req.Renderer = c.Args
+		case "parser":
+			req.Parser = c.Args
 		case "message":
 			role, msg, _ := strings.Cut(c.Args, ": ")
 			messages = append(messages, api.Message{Role: role, Content: msg})
@@ -246,7 +250,7 @@ func filesForModel(path string) ([]string, error) {
 		for _, match := range matches {
 			if ct, err := detectContentType(match); err != nil {
 				return nil, err
-			} else if ct != contentType {
+			} else if len(contentType) > 0 && ct != contentType {
 				return nil, fmt.Errorf("invalid content type: expected %s for %s", ct, match)
 			}
 		}
@@ -255,7 +259,8 @@ func filesForModel(path string) ([]string, error) {
 	}
 
 	var files []string
-	if st, _ := glob(filepath.Join(path, "*.safetensors"), "application/octet-stream"); len(st) > 0 {
+	// some safetensors files do not properly match "application/octet-stream", so skip checking their contentType
+	if st, _ := glob(filepath.Join(path, "*.safetensors"), ""); len(st) > 0 {
 		// safetensors files might be unresolved git lfs references; skip if they are
 		// covers model-x-of-y.safetensors, model.fp32-x-of-y.safetensors, model.safetensors
 		files = append(files, st...)
@@ -319,7 +324,7 @@ func (c Command) String() string {
 	switch c.Name {
 	case "model":
 		fmt.Fprintf(&sb, "FROM %s", c.Args)
-	case "license", "template", "system", "adapter":
+	case "license", "template", "system", "adapter", "renderer", "parser":
 		fmt.Fprintf(&sb, "%s %s", strings.ToUpper(c.Name), quote(c.Args))
 	case "message":
 		role, message, _ := strings.Cut(c.Args, ": ")
@@ -345,7 +350,7 @@ const (
 var (
 	errMissingFrom        = errors.New("no FROM line")
 	errInvalidMessageRole = errors.New("message role must be one of \"system\", \"user\", or \"assistant\"")
-	errInvalidCommand     = errors.New("command must be one of \"from\", \"license\", \"template\", \"system\", \"adapter\", \"parameter\", or \"message\"")
+	errInvalidCommand     = errors.New("command must be one of \"from\", \"license\", \"template\", \"system\", \"adapter\", \"renderer\", \"parser\", \"parameter\", or \"message\"")
 )
 
 type ParserError struct {
@@ -605,7 +610,7 @@ func isValidMessageRole(role string) bool {
 
 func isValidCommand(cmd string) bool {
 	switch strings.ToLower(cmd) {
-	case "from", "license", "template", "system", "adapter", "parameter", "message":
+	case "from", "license", "template", "system", "adapter", "renderer", "parser", "parameter", "message":
 		return true
 	default:
 		return false

parser/parser_test.go

@@ -198,6 +198,34 @@ BADCOMMAND param1 value1
 	}
 }
 
+func TestParseFileRenderer(t *testing.T) {
+	input := `
+FROM foo
+RENDERER renderer1
+`
+
+	reader := strings.NewReader(input)
+
+	modelfile, err := ParseFile(reader)
+	require.NoError(t, err)
+
+	assert.Equal(t, []Command{{Name: "model", Args: "foo"}, {Name: "renderer", Args: "renderer1"}}, modelfile.Commands)
+}
+
+func TestParseFileParser(t *testing.T) {
+	input := `
+FROM foo
+PARSER parser1
+`
+
+	reader := strings.NewReader(input)
+
+	modelfile, err := ParseFile(reader)
+	require.NoError(t, err)
+
+	assert.Equal(t, []Command{{Name: "model", Args: "foo"}, {Name: "parser", Args: "parser1"}}, modelfile.Commands)
+}
+
 func TestParseFileMessages(t *testing.T) {
 	cases := []struct {
 		input    string

runner/llamarunner/cache.go

@@ -204,13 +204,8 @@ func (c *InputCache) ShiftDiscard(inputLen int, numKeep int) int {
 	targetFree = max(targetFree, 1)
 
 	currentFree := c.numCtx - inputLen
-	discard := targetFree - currentFree
 
-	if discard < 0 {
-		discard = 0
-	}
-
-	return discard
+	return max(targetFree-currentFree, 0)
 }
 
 type ErrReprocessInputs struct {

runner/ollamarunner/cache.go

@@ -34,8 +34,8 @@ type InputCache struct {
 func NewInputCache(model model.Model, kvCacheType string, kvSize int32, numSlots int, batchSize int, multiUserCache bool) (*InputCache, error) {
 	numCtx := kvSize / int32(numSlots)
 
-	if numCtx < 1 {
-		return nil, fmt.Errorf("must have at least one kv cache entry per parallel sequence (kv: %v parallel: %v)", kvSize, numSlots)
+	if int(numCtx) < batchSize {
+		return nil, fmt.Errorf("kv size must be at least as large as batch size * parallel (kv: %v batch: %v parallel: %v)", kvSize, batchSize, numSlots)
 	}
 
 	slots := make([]InputCacheSlot, numSlots)
@@ -70,11 +70,9 @@ func kvCacheTypeFromStr(s string) ml.DType {
 }
 
 func (c *InputCache) Close() {
-	if c == nil {
-		return
+	if c != nil && c.cache != nil {
+		c.cache.Close()
 	}
-
-	c.cache.Close()
 }
 
 // Locking: Operations on InputCacheSlot (including finding one
@@ -95,7 +93,7 @@ type InputCacheSlot struct {
 	lastUsed time.Time
 }
 
-func (c *InputCache) LoadCacheSlot(prompt []*input.Input) (*InputCacheSlot, []*input.Input, error) {
+func (c *InputCache) LoadCacheSlot(prompt []*input.Input, cachePrompt bool) (*InputCacheSlot, []*input.Input, error) {
 	var slot *InputCacheSlot
 	var numPast int32
 	var err error
@@ -113,6 +111,10 @@ func (c *InputCache) LoadCacheSlot(prompt []*input.Input) (*InputCacheSlot, []*i
 		return nil, nil, err
 	}
 
+	if !cachePrompt {
+		numPast = 0
+	}
+
 	slot.InUse = true
 	slot.lastUsed = time.Now()
 
@@ -240,13 +242,8 @@ func (c *InputCache) ShiftDiscard(inputLen int32, numKeep int32) int32 {
 	targetFree = max(targetFree, 1)
 
 	currentFree := c.numCtx - inputLen
-	discard := targetFree - currentFree
 
-	if discard < 0 {
-		discard = 0
-	}
-
-	return discard
+	return max(targetFree-currentFree, 0)
 }
 
 type ErrReprocessInputs struct {

runner/ollamarunner/cache_test.go

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

runner/ollamarunner/runner.go

@@ -17,7 +17,6 @@ import (
 	"reflect"
 	"regexp"
 	"runtime"
-	"runtime/debug"
 	"strconv"
 	"strings"
 	"sync"
@@ -32,6 +31,7 @@ import (
 	"github.com/ollama/ollama/llm"
 	"github.com/ollama/ollama/logutil"
 	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn/pooling"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/model/input"
 	"github.com/ollama/ollama/runner/common"
@@ -405,6 +405,8 @@ func (s *Server) removeSequence(seqIndex int, reason llm.DoneReason) {
 func (s *Server) run(ctx context.Context) {
 	s.ready.Wait()
 
+	supportsAsync := pooling.Type(s.model.Backend().Config().Uint("pooling_type")) == pooling.TypeNone
+
 	var activeBatch batchState
 	for {
 		select {
@@ -418,7 +420,12 @@ func (s *Server) run(ctx context.Context) {
 			if err != nil {
 				panic(err)
 			}
-			go s.computeBatch(activeBatch)
+
+			if supportsAsync {
+				go s.computeBatch(activeBatch)
+			} else {
+				s.computeBatch(activeBatch)
+			}
 		}
 	}
 }
@@ -429,12 +436,12 @@ func (s *Server) forwardBatch(pendingBatch batchState) (nextBatch batchState, er
 	// before setting up the next batch so the seqs inputs are ready to receive their
 	// token values and we get the correct input pointers for the batchInputs
 	if pendingBatch.ctx != nil {
-		slog.Log(context.TODO(), logutil.LevelTrace, "forwardBatch waiting for compute to start", "pendingBatch.id", pendingBatch.id)
+		logutil.Trace("forwardBatch waiting for compute to start", "pendingBatch.id", pendingBatch.id)
 		<-pendingBatch.computeStartedCh
-		slog.Log(context.TODO(), logutil.LevelTrace, "forwardBatch compute started, setting up next batch", "pendingBatch.id", pendingBatch.id, "id", s.batchID)
+		logutil.Trace("forwardBatch compute started, setting up next batch", "pendingBatch.id", pendingBatch.id, "id", s.batchID)
 		nextBatch.inputsReadyCh = pendingBatch.outputsReadyCh // Chain the ouputs from the pending batch to the next inputs batch
 	} else {
-		slog.Log(context.TODO(), logutil.LevelTrace, "forwardBatch no pending batch detected", "batchID", s.batchID)
+		logutil.Trace("forwardBatch no pending batch detected", "batchID", s.batchID)
 		// No pendingBatch, so the inputs will be ready in the seqs immediately
 		nextBatch.inputsReadyCh = make(chan struct{}, 1)
 		nextBatch.inputsReadyCh <- struct{}{}
@@ -460,6 +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
@@ -542,11 +550,11 @@ 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))
 			}
-			slog.Log(context.TODO(), logutil.LevelTrace, "forwardBatch iBatch", "batchID", s.batchID, "seqIdx", seqIdx, "seq.iBatch", seq.iBatch, "i+1", i+1, "len(seq.inputs)", len(seq.inputs))
+			logutil.Trace("forwardBatch iBatch", "batchID", s.batchID, "seqIdx", seqIdx, "seq.iBatch", seq.iBatch, "i+1", i+1, "len(seq.inputs)", len(seq.inputs))
 			seq.pendingInputs = append(seq.pendingInputs, inp)
 		}
 
@@ -560,7 +568,7 @@ func (s *Server) forwardBatch(pendingBatch batchState) (nextBatch batchState, er
 	}
 
 	if len(batchInputs) == 0 {
-		slog.Log(context.TODO(), logutil.LevelTrace, "forwardBatch no batchInputs, going idle", "batchID", s.batchID)
+		logutil.Trace("forwardBatch no batchInputs, going idle", "batchID", s.batchID)
 		nextBatch.ctx.Close()
 		nextBatch.ctx = nil
 		return
@@ -569,6 +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)
@@ -589,14 +598,14 @@ func (s *Server) computeBatch(activeBatch batchState) {
 	defer activeBatch.ctx.Close()
 
 	// Wait until inputs are ready
-	slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: waiting for inputs to be ready", "batchID", activeBatch.id)
+	logutil.Trace("computeBatch: waiting for inputs to be ready", "batchID", activeBatch.id)
 	<-activeBatch.inputsReadyCh
-	slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: inputs are ready", "batchID", activeBatch.id)
+	logutil.Trace("computeBatch: inputs are ready", "batchID", activeBatch.id)
 
 	// Once we complete, signal the next batch of inputs are ready
 	// This will unblock the next computeBatch, or forwardBatch if new seqs come in
 	defer func() {
-		slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: outputs are ready", "batchID", activeBatch.id)
+		logutil.Trace("computeBatch: outputs are ready", "batchID", activeBatch.id)
 		activeBatch.outputsReadyCh <- struct{}{}
 	}()
 
@@ -626,7 +635,7 @@ func (s *Server) computeBatch(activeBatch batchState) {
 		// Detect if the sequence we're processing has already been completed and replaced
 		// with a new sequence
 		if seq != activeBatch.seqs[i] {
-			slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: sequence replaced, discarding its results", "batchID", activeBatch.id, "seqIdx", i)
+			logutil.Trace("computeBatch: sequence replaced, discarding its results", "batchID", activeBatch.id, "seqIdx", i)
 			continue
 		}
 
@@ -666,18 +675,19 @@ func (s *Server) computeBatch(activeBatch batchState) {
 	activeBatch.batch.Inputs.SetValueFromIntSlice(batchInputs)
 	activeBatch.ctx.ComputeWithNotify(
 		func() {
-			slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: signaling computeStartedCh", "batchID", activeBatch.id)
+			logutil.Trace("computeBatch: signaling computeStartedCh", "batchID", activeBatch.id)
 			activeBatch.computeStartedCh <- struct{}{}
 		},
 		activeBatch.modelOutput)
-	logits := activeBatch.modelOutput.Floats()
 
-	slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: logits ready", "batchID", activeBatch.id)
+	outputs := activeBatch.modelOutput.Floats()
+
+	logutil.Trace("computeBatch: logits ready", "batchID", activeBatch.id)
 
 	s.mu.Lock()
 	defer s.mu.Unlock()
 
-	slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: decoding", "batchID", activeBatch.id)
+	logutil.Trace("computeBatch: decoding", "batchID", activeBatch.id)
 	for i, seq := range s.seqs {
 		if seq == nil || nextBatchTokens[i] == nil {
 			continue
@@ -689,16 +699,15 @@ func (s *Server) computeBatch(activeBatch batchState) {
 
 		// if done processing the prompt, generate an embedding and return
 		if seq.embeddingOnly {
-			// TODO(jessegross): Embedding support
-			slog.Warn("generation of embedding outputs not yet supported", "id", activeBatch.id, "seqIdx", i)
+			seq.embedding <- outputs
 			s.removeSequence(i, llm.DoneReasonStop)
 			continue
 		}
 
 		// sample a token
-		vocabSize := len(logits) / len(activeBatch.batch.Outputs)
-		slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: vocab details", "batchID", activeBatch.id, "seqIdx", i, "len(logits)", len(logits), "len(activeBatch.batch.Outputs)", len(activeBatch.batch.Outputs), "vocabSize", vocabSize, "iBatches", iBatches)
-		token, err := seq.sampler.Sample(logits[iBatches[i]*vocabSize : (iBatches[i]+1)*vocabSize])
+		vocabSize := len(outputs) / 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
@@ -711,7 +720,7 @@ func (s *Server) computeBatch(activeBatch batchState) {
 			// TODO (jmorganca): we should send this back
 			// as it's important for the /api/generate context
 			// seq.responses <- piece
-			slog.Log(context.TODO(), logutil.LevelTrace, "computeBatch: EOS", "batchID", activeBatch.id, "seqIdx", i)
+			logutil.Trace("computeBatch: EOS", "batchID", activeBatch.id, "seqIdx", i)
 			s.removeSequence(i, llm.DoneReasonStop)
 			continue
 		}
@@ -834,7 +843,7 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 	found := false
 	for i, sq := range s.seqs {
 		if sq == nil {
-			seq.cache, seq.inputs, err = s.cache.LoadCacheSlot(seq.inputs)
+			seq.cache, seq.inputs, err = s.cache.LoadCacheSlot(seq.inputs, true)
 			if err != nil {
 				s.mu.Unlock()
 				s.seqsSem.Release(1)
@@ -890,6 +899,67 @@ func (s *Server) completion(w http.ResponseWriter, r *http.Request) {
 	}
 }
 
+func (s *Server) embeddings(w http.ResponseWriter, r *http.Request) {
+	if pooling.Type(s.model.Backend().Config().Uint("pooling_type")) == pooling.TypeNone {
+		http.Error(w, "this model does not support embeddings", http.StatusNotImplemented)
+		return
+	}
+
+	var req llm.EmbeddingRequest
+	if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
+		http.Error(w, fmt.Sprintf("bad request: %s", err), http.StatusBadRequest)
+		return
+	}
+
+	w.Header().Set("Content-Type", "application/json")
+	seq, err := s.NewSequence(req.Content, nil, NewSequenceParams{embedding: true})
+	if err != nil {
+		http.Error(w, fmt.Sprintf("failed to create new sequence: %v", err), http.StatusInternalServerError)
+		return
+	}
+
+	if err := s.seqsSem.Acquire(r.Context(), 1); err != nil {
+		if errors.Is(err, context.Canceled) {
+			slog.Info("aborting embedding request due to client closing the connection")
+		} else {
+			http.Error(w, fmt.Sprintf("failed to acquire semaphore: %v", err), http.StatusInternalServerError)
+		}
+		return
+	}
+
+	s.mu.Lock()
+	found := false
+	for i, sq := range s.seqs {
+		if sq == nil {
+			seq.cache, seq.inputs, err = s.cache.LoadCacheSlot(seq.inputs, false)
+			if err != nil {
+				s.mu.Unlock()
+				s.seqsSem.Release(1)
+				http.Error(w, fmt.Sprintf("failed to load cache: %v", err), http.StatusInternalServerError)
+				return
+			}
+
+			s.seqs[i] = seq
+			s.cond.Signal()
+			found = true
+			break
+		}
+	}
+	s.mu.Unlock()
+
+	if !found {
+		s.seqsSem.Release(1)
+		http.Error(w, "could not find an available sequence", http.StatusInternalServerError)
+		return
+	}
+
+	if err := json.NewEncoder(w).Encode(&llm.EmbeddingResponse{
+		Embedding: <-seq.embedding,
+	}); err != nil {
+		http.Error(w, fmt.Sprintf("failed to encode response: %v", err), http.StatusInternalServerError)
+	}
+}
+
 func (s *Server) health(w http.ResponseWriter, r *http.Request) {
 	w.Header().Set("Content-Type", "application/json")
 	if err := json.NewEncoder(w).Encode(&llm.ServerStatusResponse{
@@ -978,12 +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 {
@@ -1017,9 +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)
 			}
@@ -1206,10 +1276,7 @@ func Execute(args []string) error {
 	mux := http.NewServeMux()
 	// TODO: support embeddings
 	mux.HandleFunc("POST /load", server.load)
-	mux.HandleFunc("POST /embedding", func(w http.ResponseWriter, r *http.Request) {
-		http.Error(w, "this model does not support embeddings", http.StatusNotImplemented)
-	})
-
+	mux.HandleFunc("POST /embedding", server.embeddings)
 	mux.HandleFunc("POST /completion", server.completion)
 	mux.HandleFunc("GET /health", server.health)
 

sample/samplers_test.go

@@ -82,7 +82,6 @@ func modelHelper(t testing.TB) model.BytePairEncoding {
 	merges := make([]string, 0, 1)
 	// Only need vocab for Grammar Test
 	return model.NewBytePairEncoding(
-		``,
 		&model.Vocabulary{
 			Values: tokens,
 			Types:  make([]int32, len(vocab)),

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

scripts/env.sh

@@ -16,6 +16,7 @@ OLLAMA_COMMON_BUILD_ARGS="--build-arg=VERSION \
     --build-arg=OLLAMA_FAST_BUILD \
     --build-arg=CUSTOM_CPU_FLAGS \
     --build-arg=GPU_RUNNER_CPU_FLAGS \
+    --build-arg=PARALLEL \
     --build-arg=AMDGPU_TARGETS"
 
 echo "Building Ollama"